kmeans-clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kos enron nips\n",
      "kos\n"
     ]
    }
   ],
   "source": [
    "print('kos enron nips')\n",
    "base = input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "docs: 3430\n",
      "dictionary_count: 6906\n",
      "words: 353160\n"
     ]
    },
    {
     "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>vocab</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aarp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abandon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abandoned</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abandoning</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>abb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        vocab  count  sum\n",
       "1        aarp    NaN  NaN\n",
       "2     abandon    NaN  NaN\n",
       "3   abandoned    NaN  NaN\n",
       "4  abandoning    NaN  NaN\n",
       "5         abb    NaN  NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Configure to show multiples outputs from a single cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "import pandas as pd\n",
    "from scipy.sparse import csc_matrix\n",
    "\n",
    "doc = open('../docword.{}.txt'.format(base))\n",
    "docs_count = int(doc.readline().replace('\\n',''))\n",
    "dictionary_count = int(doc.readline().replace('\\n',''))\n",
    "word_count = int(doc.readline().replace('\\n',''))\n",
    "trainData = pd.read_csv(doc, delimiter=' ', names=['row', 'col', 'value'])\n",
    "doc.close()\n",
    "# csc = csc_matrix((trainData.value.tolist(), (trainData.row.tolist(), trainData.col.tolist())))\n",
    "\n",
    "print('docs: {}\\ndictionary_count: {}\\nwords: {}'.format(docs_count, dictionary_count, word_count))\n",
    "\n",
    "trainData.head()\n",
    "# Read words of dictionary\n",
    "vocabulary = pd.read_csv('../vocab.{}.txt'.format(base), names=['vocab', 'count', 'sum'])\n",
    "# Set the vocabulary index row begin in 1 instead 0\n",
    "vocabulary.index = vocabulary.index+1\n",
    "\n",
    "\n",
    "vocabulary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3430, 6906) (6906, 3)\n",
      "CPU times: user 4.07 s, sys: 104 ms, total: 4.18 s\n",
      "Wall time: 4.18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "InteractiveShell.ast_node_interactivity = \"last\"\n",
    "pivot = trainData.pivot_table('value', ['row'], 'col').fillna(0)\n",
    "none_df = pd.DataFrame(0, range(1, pivot.shape[0]+1), range(1, vocabulary.shape[0]+1))\n",
    "pivot = pivot.combine_first(none_df)\n",
    "print(pivot.shape, vocabulary.shape)\n",
    "pivot.columns = vocabulary.vocab.values\n",
    "X = pivot.values\n",
    "pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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>aarp</th>\n",
       "      <th>abandon</th>\n",
       "      <th>abandoned</th>\n",
       "      <th>abandoning</th>\n",
       "      <th>abb</th>\n",
       "      <th>abc</th>\n",
       "      <th>abcs</th>\n",
       "      <th>abdullah</th>\n",
       "      <th>ability</th>\n",
       "      <th>aboard</th>\n",
       "      <th>...</th>\n",
       "      <th>yucca</th>\n",
       "      <th>yup</th>\n",
       "      <th>zahn</th>\n",
       "      <th>zarqawi</th>\n",
       "      <th>zell</th>\n",
       "      <th>zimbabwe</th>\n",
       "      <th>zogby</th>\n",
       "      <th>zogbys</th>\n",
       "      <th>zone</th>\n",
       "      <th>zones</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3401</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3402</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3403</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3404</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3405</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3406</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3407</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3408</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3409</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3410</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3411</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3412</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3413</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3414</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3415</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3416</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3417</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3418</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3419</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3420</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3422</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3423</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3424</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3425</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3426</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3427</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3428</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3429</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3430</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3430 rows × 6906 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      aarp  abandon  abandoned  abandoning  abb  abc  abcs  abdullah  ability  \\\n",
       "row                                                                             \n",
       "1      0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "2      0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3      0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "4      0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "5      0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "6      0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "7      0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "8      0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "9      0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "10     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "11     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "12     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "13     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "14     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "15     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "16     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "17     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "18     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "19     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "20     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "21     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "22     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "23     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "24     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "25     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "26     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "27     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "28     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "29     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "30     0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "...    ...      ...        ...         ...  ...  ...   ...       ...      ...   \n",
       "3401   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3402   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3403   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3404   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3405   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      1.0   \n",
       "3406   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3407   0.0      0.0        0.0         0.0  0.0  1.0   0.0       0.0      0.0   \n",
       "3408   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3409   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3410   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3411   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3412   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3413   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3414   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3415   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3416   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3417   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3418   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3419   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3420   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3421   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3422   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3423   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3424   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3425   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3426   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3427   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3428   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3429   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "3430   0.0      0.0        0.0         0.0  0.0  0.0   0.0       0.0      0.0   \n",
       "\n",
       "      aboard  ...    yucca  yup  zahn  zarqawi  zell  zimbabwe  zogby  zogbys  \\\n",
       "row           ...                                                               \n",
       "1        0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "2        0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3        0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "4        0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "5        0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "6        0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "7        0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "8        0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "9        0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "10       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "11       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "12       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "13       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "14       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "15       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "16       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "17       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "18       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "19       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "20       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "21       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "22       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "23       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "24       0.0  ...      0.0  0.0   0.0      0.0   1.0       0.0    0.0     0.0   \n",
       "25       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "26       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "27       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "28       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "29       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "30       0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "...      ...  ...      ...  ...   ...      ...   ...       ...    ...     ...   \n",
       "3401     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3402     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3403     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3404     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3405     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3406     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3407     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3408     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3409     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3410     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3411     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3412     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3413     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3414     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3415     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3416     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3417     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3418     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3419     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3420     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3421     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3422     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3423     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3424     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3425     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3426     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3427     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3428     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3429     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "3430     0.0  ...      0.0  0.0   0.0      0.0   0.0       0.0    0.0     0.0   \n",
       "\n",
       "      zone  zones  \n",
       "row                \n",
       "1      1.0    0.0  \n",
       "2      0.0    0.0  \n",
       "3      0.0    0.0  \n",
       "4      0.0    0.0  \n",
       "5      0.0    0.0  \n",
       "6      0.0    0.0  \n",
       "7      0.0    0.0  \n",
       "8      0.0    0.0  \n",
       "9      0.0    0.0  \n",
       "10     0.0    0.0  \n",
       "11     0.0    0.0  \n",
       "12     0.0    0.0  \n",
       "13     0.0    0.0  \n",
       "14     0.0    0.0  \n",
       "15     0.0    0.0  \n",
       "16     0.0    0.0  \n",
       "17     0.0    0.0  \n",
       "18     0.0    0.0  \n",
       "19     0.0    0.0  \n",
       "20     0.0    0.0  \n",
       "21     0.0    0.0  \n",
       "22     0.0    0.0  \n",
       "23     0.0    0.0  \n",
       "24     0.0    0.0  \n",
       "25     0.0    0.0  \n",
       "26     0.0    0.0  \n",
       "27     0.0    0.0  \n",
       "28     0.0    0.0  \n",
       "29     0.0    0.0  \n",
       "30     0.0    0.0  \n",
       "...    ...    ...  \n",
       "3401   0.0    0.0  \n",
       "3402   0.0    0.0  \n",
       "3403   0.0    0.0  \n",
       "3404   0.0    0.0  \n",
       "3405   0.0    0.0  \n",
       "3406   0.0    0.0  \n",
       "3407   0.0    0.0  \n",
       "3408   0.0    0.0  \n",
       "3409   0.0    0.0  \n",
       "3410   0.0    0.0  \n",
       "3411   0.0    0.0  \n",
       "3412   0.0    0.0  \n",
       "3413   0.0    0.0  \n",
       "3414   0.0    1.0  \n",
       "3415   0.0    0.0  \n",
       "3416   0.0    0.0  \n",
       "3417   0.0    0.0  \n",
       "3418   0.0    0.0  \n",
       "3419   0.0    0.0  \n",
       "3420   0.0    0.0  \n",
       "3421   0.0    0.0  \n",
       "3422   0.0    0.0  \n",
       "3423   0.0    0.0  \n",
       "3424   0.0    0.0  \n",
       "3425   0.0    0.0  \n",
       "3426   0.0    0.0  \n",
       "3427   0.0    0.0  \n",
       "3428   0.0    0.0  \n",
       "3429   0.0    0.0  \n",
       "3430   0.0    0.0  \n",
       "\n",
       "[3430 rows x 6906 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot = pivot.drop(vocabulary[ (vocabulary['sum'] > 100) ].vocab.values, axis=1)\n",
    "X = pivot.values\n",
    "pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train before PCA:\n",
      " [[-0.02382547 -0.08762159 -0.07951749 ..., -0.091445    6.36353143\n",
      "  -0.05925222]\n",
      " [-0.02382547 -0.08762159 -0.07951749 ..., -0.091445   -0.09222509\n",
      "  -0.05925222]\n",
      " [-0.02382547 -0.08762159 -0.07951749 ..., -0.091445   -0.09222509\n",
      "  -0.05925222]\n",
      " ..., \n",
      " [-0.02382547 -0.08762159 -0.07951749 ..., -0.091445   -0.09222509\n",
      "  -0.05925222]\n",
      " [-0.02382547 -0.08762159 -0.07951749 ..., -0.091445   -0.09222509\n",
      "  -0.05925222]\n",
      " [-0.02382547 -0.08762159 -0.07951749 ..., -0.091445   -0.09222509\n",
      "  -0.05925222]]\n"
     ]
    }
   ],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X)\n",
    "\n",
    "print('X_train before PCA:\\n', X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# from sklearn.decomposition import PCA\n",
    "# # PCA Dimensionality Reduction\n",
    "\n",
    "# pca = PCA(n_components=2)\n",
    "# X_train = pca.fit_transform(X_train)\n",
    "# explained_variance = pca.explained_variance_ratio_\n",
    "\n",
    "# explained_variance\n",
    "# print('X_train after PCA to 2 dimensions:\\n', X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "col\n",
       "1    12\n",
       "2    28\n",
       "3    25\n",
       "4    17\n",
       "5    20\n",
       "Name: value, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count = Number of unique documents word was used on \n",
    "# Sums = Number of times word was used throughout the documents\n",
    "\n",
    "x = trainData.groupby(['col'])['value']\n",
    "counts, sums = x.count(), x.sum()\n",
    "\n",
    "counts.head()\n",
    "sums.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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>vocab</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aarp</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abandon</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abandoned</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abandoning</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>abb</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abcs</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>abdullah</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>aboard</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>abortions</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>abraham</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>abrams</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>abroad</td>\n",
       "      <td>38</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>absence</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>absent</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>absentee</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>absolute</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>absolutely</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>abstain</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>absurd</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>abuses</td>\n",
       "      <td>44</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>academy</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>accent</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>accept</td>\n",
       "      <td>63</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>acceptable</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>acceptance</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>accepted</td>\n",
       "      <td>41</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>accepting</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>access</td>\n",
       "      <td>83</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>accident</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>accidentally</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6864</th>\n",
       "      <td>writes</td>\n",
       "      <td>64</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6865</th>\n",
       "      <td>writing</td>\n",
       "      <td>84</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6868</th>\n",
       "      <td>wrongdoing</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6870</th>\n",
       "      <td>wsj</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6871</th>\n",
       "      <td>wtc</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6872</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6873</th>\n",
       "      <td>yadda</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6875</th>\n",
       "      <td>yale</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6876</th>\n",
       "      <td>yankees</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6877</th>\n",
       "      <td>yanks</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6878</th>\n",
       "      <td>yard</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6882</th>\n",
       "      <td>yell</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6883</th>\n",
       "      <td>yellin</td>\n",
       "      <td>37</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6885</th>\n",
       "      <td>yesterdays</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6886</th>\n",
       "      <td>yield</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6888</th>\n",
       "      <td>yorker</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6889</th>\n",
       "      <td>yorkers</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6890</th>\n",
       "      <td>youd</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6891</th>\n",
       "      <td>youll</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6893</th>\n",
       "      <td>younger</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6895</th>\n",
       "      <td>youth</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6897</th>\n",
       "      <td>yucca</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6898</th>\n",
       "      <td>yup</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6899</th>\n",
       "      <td>zahn</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6900</th>\n",
       "      <td>zarqawi</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6901</th>\n",
       "      <td>zell</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6902</th>\n",
       "      <td>zimbabwe</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6904</th>\n",
       "      <td>zogbys</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6905</th>\n",
       "      <td>zone</td>\n",
       "      <td>39</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6906</th>\n",
       "      <td>zones</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5889 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             vocab  count  sum\n",
       "1             aarp      3   12\n",
       "2          abandon     27   28\n",
       "3        abandoned     23   25\n",
       "4       abandoning     17   17\n",
       "5              abb     11   20\n",
       "7             abcs     17   18\n",
       "8         abdullah      9   10\n",
       "10          aboard     19   19\n",
       "12       abortions     12   16\n",
       "13         abraham     15   17\n",
       "14          abrams      5   17\n",
       "15          abroad     38   45\n",
       "16         absence     21   23\n",
       "17          absent     16   16\n",
       "18        absentee     17   29\n",
       "19        absolute     26   27\n",
       "20      absolutely     36   37\n",
       "21         abstain     85   85\n",
       "22          absurd     10   10\n",
       "25          abuses     44   71\n",
       "26         academy     14   18\n",
       "27          accent      6   11\n",
       "28          accept     63   74\n",
       "29      acceptable     19   21\n",
       "30      acceptance     30   40\n",
       "31        accepted     41   43\n",
       "32       accepting     10   10\n",
       "33          access     83   97\n",
       "34        accident     16   18\n",
       "35    accidentally      9    9\n",
       "...            ...    ...  ...\n",
       "6864        writes     64   74\n",
       "6865       writing     84   91\n",
       "6868    wrongdoing     11   12\n",
       "6870           wsj     30   31\n",
       "6871           wtc      7   12\n",
       "6872       wyoming     14   17\n",
       "6873         yadda      9   24\n",
       "6875          yale     11   12\n",
       "6876       yankees     12   18\n",
       "6877         yanks     10   13\n",
       "6878          yard     10   13\n",
       "6882          yell     12   12\n",
       "6883        yellin     37   52\n",
       "6885    yesterdays     33   33\n",
       "6886         yield     15   16\n",
       "6888        yorker     14   14\n",
       "6889       yorkers      6   10\n",
       "6890          youd     24   26\n",
       "6891         youll     60   75\n",
       "6893       younger     25   30\n",
       "6895         youth     12   13\n",
       "6897         yucca      3   11\n",
       "6898           yup      9   10\n",
       "6899          zahn      4    6\n",
       "6900       zarqawi      9   25\n",
       "6901          zell     41   64\n",
       "6902      zimbabwe      2    9\n",
       "6904        zogbys     30   32\n",
       "6905          zone     39   49\n",
       "6906         zones     12   12\n",
       "\n",
       "[5889 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocabulary['count'] = counts\n",
    "vocabulary['sum'] = sums\n",
    "vocabulary[ (vocabulary['count'] < 170) & (vocabulary['sum'] < 100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "docId = trainData.iloc[:, 0]\n",
    "vocabularyId = trainData.iloc[:, 1]\n",
    "words = trainData.iloc[:, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3gAAAG5CAYAAADcRZZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3XucHHWd7//3p3tuZCYJk8vkAoYE\nERAJCWwgECP4i7sLSlbFCxHdgD4gnN85x9WNt/UGklVZ1mU3btbbAaIC3sLirheQqEfUgIlAgMGE\nAArEIEkwF5qEGTLJzPT3/NEXenr6Ut1d1V1d83ryyGPo6uqqb32/36rub1d1vc05JwAAAABA84s1\nugAAAAAAAH8wwAMAAACAiGCABwAAAAARwQAPAAAAACKCAR4AAAAARAQDPAAAAACICAZ4AABPzOwa\nM/tWDa9/nZk94WeZwsjMppnZBjN70cz+1cw+aWY3NbpcfjOz95rZvY0uBwBgJAZ4ANCEzOwTZnZX\n3rQ/FJn2rjqU5/Vm9myB6b8ysyskyTl3j3PuJA/LqmkgGQJXStonaYJz7sPOuWudc1dUs6Dc+isx\nzw1m9oSZJc3svQWeX2lmz5nZQTP7upm15zw328x+aWYvmdnjZvaX1ZQTABAeDPAAoDltkLTIzOKS\nZGYzJLVKOj1v2gnpeT2zlEi+P5hZSx1Wc5ykbc45V6fyPCLpf0l6qMDyz5f0cUlvSJfreEmrcmb5\nrqSHJU2W9ClJt5vZVB/KBABokEi+gQPAGPCAUgO6+enHr5P0S0lP5E17yjm3S5LMbJGZPWBmB9J/\nF2UWlj5T9Hkz+42klyQdb2ZzzOzX6UsNfy5pSi0Fzj/LZ2b/YGY708t/wszeYGYXSPqkpGVm1mdm\nj6TnnWlmPzKz583sSTNbkbOco8zsZjNLmNljZvaxvPX8Mb2u30nqN7MWM/u4mT2VXvc2M7soZ/73\nmtlvzGy1mb1gZk+n6+69ZvYnM9tjZpcV2cZvSrpM0sfS5f/L3DOS6TNmzswuN7NnJN1tZh1m9i0z\n259e3wPpyzw/n27DL6WX9aVC63TOfdk59wtJAwWevkzSWufco865hKTPSnpvuiwnSjpD0mecc4ec\nc9+XtEXS24ts2+R0Gxw0s/slvTLv+VL9a5KZfcPMdqXb6Qfp6VPM7I70dj9vZvdE9csFAKiXenyT\nCQDwmXPuiJndJ+lcSQ+m/94jaVfetA1S6gO2pDslfUCpszbvlHSnmZ3gnNufXuxySW9UapBo6ddu\nkvTXkhamX/9DP8pvZidJer+kM51zu8xstqS4c+4pM7tW0gnOub/Necn3JG2VNFPSyZJ+bmZPOefu\nlvQZSbOVOjvVKeknBVZ5iaQLJe1zzg2Z2VNKDZ6eS9fFt9J1sTs9/0JJNyl1ZmtVev0/VuqM6HmS\nvm9m33fO9eWuxDn3XjOTpGedc59Ob+viAuU5T9KrJSWVGoRNlPQKSYeVGqAfcs59ysxeK+lbzrlq\nf8P3Go1ss0ckTTOzyennnnbOvZj3/GuKLOvLSg0iZ0iaI+mnkrZLnvrXrZL60svuk5QZ/H1Y0rOS\nMmcNz5ZU9swnAKA4viUDgOb1a6UGcVJqsHJP+l/utF+n//9CSX9wzt3qnBtyzn1X0uOS/iZned9M\nn+kZUupD/JmSrnLOHXbObVBqgFPKzPSZmOw/SYUGN5I0LKld0ilm1uqc+6Nz7qlCM5rZKyS9VtI/\nOOcGnHO9Sg2+Lk3PcrGka51zCefcs5LWFFjMGufcn5xzhyTJOfefzrldzrmkc26dpD9IOitn/u3O\nuW8454YlrVNq8PWP6br4maQjSg32qnWNc64/XZ5BpQaSJzjnhp1zDzrnDtaw7Fxdkg7kPM78//gC\nz2WeH5+/kPRlv2+XdHW63Fsl3ZwzS9H+lb5U+I2S/v90Gw065zL9clCpvnZcevo9Xi5tBQAUxwAP\nAJrXBkmL02dPpjrn/iBpo1K/zZsk6VS9/Pu7mZJ25L1+h6Rjch7/Kef/Z0pKOOf68+YvZZdz7ujc\nf5IK3mXROfekpL+XdI2kPWb2PTObWWS5MyU9n3emKbfsM/PKnvv/BaeZ2aVm1pszED1VIy9B/XPO\n/2cGhfnTuoqU14vc8tyq1Nmw76UvYfyCmbXWsOxcfZIm5DzO/P+LBZ7LPP+iRpuq1FU/ueXO7Q+l\n+tcrlGq/RIHl/oukJyX9LH0p7MeLbwoAwAsGeADQvDYpdWnfCkm/kaT0mZ9d6Wm7nHPb0/PuUuom\nG7lmSdqZ8zj3zMluSd1m1pk3v2+cc99xzi1Ol8tJ+ucC5ZBSZZ9kZrlnlnLLvlvSsTnPvaLQ6jL/\nY2bHSbpRqUtEJ6cHoluVuiy1XrLlSZ+5WuWcO0WpSxeX6uWzk7WezXpU0rycx/Mk/Tl92eSjSv3W\ncnze848WWM5eSUMaWbe5/aFU//qTUu13dP5CnXMvpu80erykN0v6kJm9wdOWAQAKYoAHAE0qfXnf\nZkkfUurSzIx709Ny7575E0knmtm70zcZWSbpFEl3FFn2jvSyV5lZW/p3ZH9TaN5qmNlJZrbEUrfs\nH1DqjFgy/fSfJc3O3GzDOfcnpc5M/lP6hiSnSbpcUiZK4TZJnzCzbjM7RqmBWymdSg2c9qbL8j6l\nzuA1hJn9f2Y2N30Z5EGlLlvMrYvjy7y+zcw6lBqgtqbrKPP+fouky83slPQA69OSvilJzrnfS+qV\n9Jn0ay6SdJqk7+evI32p6n9JusbMxpnZKUr9djCjaP9K/67xLklfSbdRq5mdmy77UjM7wVI/XDyg\n1KW7SQEAqsYADwCa268l9WjkpZD3pKdlB3jpMzZLlbqpxX5JH5O01Dm3r8Sy363UzUaeV+pGJrf4\nWO52SdcplRf3XLq8n0g/95/pv/vNLHPr/0uUupHKLkn/rdSdH/9v+rl/VOpGHdsl/V9Jtyt1s5KC\nnHPbJP2rUmdA/yxprtJnQBtkulJlPijpMaXa9Nb0c/8u6R3pO08W+m2hJP1MqQHyIkk3pP//XEly\nzq2X9AWl7rD6jFKXTX4m57XvkrRAUkKp9niHc25vkfW8X6nLUp9TapD4jcwTHvrXcqUGro9L2qPU\n5bmS9Cql2qxPqfb4inPul0XWDwDwwPgtMwAgSszsf0p6l3PuvEaXBQCAeuMMHgCgqZnZDDN7rZnF\n0vELH1bqLB8AAGMOOXgAgGbXJun/KJXN9oJSmXVfaWiJAABoEC7RBAAAAICI4BJNAAAAAIiIprhE\nc8qUKW727NmNLgYAAAAANMSDDz64zzk3tdx8TTHAmz17tjZv3tzoYgAAAABAQ5jZDi/zcYkmAAAA\nAEQEAzwAAAAAiAgGeAAAAAAQEU3xGzwAAAAA9TU4OKhnn31WAwMDjS7KmNLR0aFjjz1Wra2tVb2e\nAR4AAACAUZ599lmNHz9es2fPlpk1ujhjgnNO+/fv17PPPqs5c+ZUtQwu0QQAAAAwysDAgCZPnszg\nro7MTJMnT67prCkDPAAAAAAFMbirv1rrnAEeAAAAAEQEAzwAAAAAobNy5Up98YtfzD4+//zzdcUV\nV2Qff/jDH9a//du/Vb38a665Rtdff33B52655Radeuqpmjt3rk4//fSi89Xi2muv9X2ZEgM8AAAA\nACH02te+Vhs3bpQkJZNJ7du3T48++mj2+Y0bN2rRokWeljU0NOR5vXfddZe++MUv6mc/+5m2bNmi\n3/72t5o4cWJlhfeAAR4AAACAUEv0J7Rt1zYl+hM1L2vRokXatGmTJOnRRx/VqaeeqvHjxyuRSOjw\n4cN67LHHdMYZZ8g5p49+9KPZM27r1q2TJP3qV7/S6173Or35zW/WKaecIkn6/Oc/rxNPPFGLFy/W\nE088UXC9//RP/6Trr79eM2fOlCS1t7drxYoVkqTe3l6dffbZOu2003TRRRcpkUht5+tf/3pt3rxZ\nkrRv3z7Nnj1bkvTNb35Tb3vb23TBBRfoVa96lT72sY9Jkj7+8Y/r0KFDmj9/vt7znvfUXFe5iEkA\nAAAAULP1W9dr5bqV2cerl63WBadeUPXyZs6cqZaWFj3zzDPauHGjzjnnHO3cuVObNm3SxIkTNXfu\nXLW1ten73/++ent79cgjj2jfvn0688wzde6550qSHnroIW3dulVz5szRgw8+qO9973vq7e3V0NCQ\nzjjjDP3FX/zFqPVu3bq14HRJuvTSS/Uf//EfOu+883T11Vdr1apVIy4jLaS3t1cPP/yw2tvbddJJ\nJ+nv/u7vdN111+lLX/qSent7q66fYjiDBwAAAKAmif6EVq5bqXgsrs72TsVjca1ct7LmM3mLFi3S\nxo0bswO8c845J/v4ta99rSTp3nvv1SWXXKJ4PK5p06bpvPPO0wMPPCBJOuuss7J5cvfcc48uuugi\njRs3ThMmTNCb3/zmispy4MABvfDCCzrvvPMkSZdddpk2bNhQ9nVveMMbNHHiRHV0dOiUU07Rjh07\nKlpvpRjgAQAAFODnpWZA1O0+sFuS1NHakf3r5LLTq5X5Hd6WLVt06qmn6uyzz9amTZs8//6us7Oz\n4nW+5jWv0YMPPljRa1paWpRMJiVpVIZde3t79v/j8XhFvwesBgM8AACAPOu3rtei6xbp7V99uxZd\nt0jrt65vdJGAUJsxcYYkaWBwIPvXZNnp1Vq0aJHuuOMOTZo0SfF4XJMmTdILL7ygTZs2ZQd4r3vd\n67Ru3ToNDw9r79692rBhg84666xRyzr33HP1gx/8QIcOHdKLL76oH//4xwXX+YlPfEIf/ehH9dxz\nz0mSjhw5optuukkTJ05Ud3e37rnnHknSrbfemj2bN3v27Oyg8Pbbb/e0ba2trRocHKysQjxggAcA\nAJAjqEvNgCjr7uzW6mWrNZwcVt/hPg0nh7V62Wp1d3bXtNy5c+dq3759Ovvss0dMmzhxoqZMmSJJ\nuuiii3Taaadp3rx5WrJkib7whS9o+vTpo5Z1xhlnaNmyZZo3b57e+MY36swzzyy4zje96U16//vf\nr7/8y7/Ua17zGp1xxhk6ePCgJOnmm2/WRz/6UZ122mnq7e3V1VdfLUn6yEc+oq9+9as6/fTTtW/f\nPk/bduWVV+q0007z/SYr5pzzdYFBWLBggcvclQYAACBI23Zt09u/+nZ1tr98aVff4T791//8L50y\n85QGlgyor8cee0yvfvWrK3pNoj+h3Qd2a8bEGTUP7sayQnVvZg865xaUey130QQAAMiRe6lZR2uH\nb5eaAWNBd2c3A7sG4xJNAACAHEFdagYA9cAZPAAAgDwXnHqBFs5ZyKVmGPOcczKzRhdjTKn1J3QM\n8AAAAArgUjOMdR0dHdq/f78mT57MIK9OnHPav3+/Ojo6ql4GAzwAAAAAoxx77LF69tlntXfvXt+W\nmUwmNeyGFbe4YjF+LVZIR0eHjj322KpfzwAPAAAAwCitra2aM2eOb8tbv3W9Vq5bmX28etlqXXDq\nBb4tHykMmwEAAAAEinzJ+mGABwAAACBQuw/sliR1tHZk/zq57HT4hwEeAAAAgEDl5ktm/pIvGQwG\neAAAAAACRb5k/XCTFQAAAACBI1+yPhjgAQAAAKgL8iWDxyWaAAAAABARDPAAAAAAICIY4AEAAABA\nRDDAAwAAAICIYIAHAAAAABHBAA8AAAAAIoIBHgAAAABEBAM8AAAAAIiIwAd4ZhY3s4fN7I704zlm\ndp+ZPWlm68ysLegyAAAAAMBYUI8zeB+U9FjO43+WtNo5d4KkhKTL61AGAAAAAIi8QAd4ZnaspAsl\n3ZR+bJKWSLo9PcvNkt4aZBkAAAAAYKwI+gzeFyV9TFIy/XiypBecc0Ppx89KOqbQC83sSjPbbGab\n9+7dG3AxAQAAAKD5BTbAM7OlkvY45x6s5vXOuRuccwuccwumTp3qc+kAAAAAIHpaAlz2ayW92cze\nJKlD0gRJ/y7paDNrSZ/FO1bSzgDLAAAAAABjRmBn8Jxzn3DOHeucmy3pXZLuds69R9IvJb0jPdtl\nkn4YVBkAAAAAYCxpRA7eP0j6kJk9qdRv8tY2oAwAAAAAEDlBXqKZ5Zz7laRfpf//aUln1WO9AAAA\nADCWNOIMHgAAAAAgAAzwAAAAACAiGOABAAAAQEQwwAMAAACAiGCABwAAAAARwQAPAAAAACKCAR4A\nAAAARAQDPAAAAACICAZ4AAAAABARDPAAAAAAICIY4AEAAABARDDAAwAAAICIYIAHAAAAABHBAA8A\nAAAAIoIBHgAAAABEBAM8AAAAAIgIBngAAAAAEBEM8AAAAAAgIhjgAQAAAEBEMMADAAAAgIhggAcA\nAAAAEcEADwAAAAAiggEeAAAAAEQEAzwAAAAAiAgGeAAAAAAQEQzwAAAAACAiGOABAAAAQEQwwAMA\nAACAiGCABwAAAAARwQAPAAAAACKCAR4AAAAARAQDPAAAAACICAZ4AAAAABARDPAAAAAAICIY4AEA\nAABARDDAAwAAAICIYIAHAAAAABHBAA8AAAAAIoIBHgAAAABEBAM8AAAAAIgIBngAAAAAEBEM8FCV\nRH9C23ZtU6I/0eiiAAAAAEhraXQB0HzWb12vletWZh+vXrZaF5x6QQNLBAAAAEDiDB4qlOhPaOW6\nlYrH4ups71Q8FtfKdSs5kwcAAACEAAM8VGT3gd2SpI7WjuxfJ5edDgAAAKBxGOChIjMmzpAkDQwO\nZP+aLDsdAAAAQOMwwENFuju7tXrZag0nh9V3uE/DyWGtXrZa3Z3djS4aAAAAMOZxkxVU7IJTL9DC\nOQu1+8BuzZg4g8EdAAAAEBIM8FCV7s5uBnYAAABAyHCJJgAAAABEBAM8AAAAAIgIBngAAAAAEBEM\n8AAAAAAgIhjgAQAAAEBEMMADAAAAgIhggAcAAAAAEcEADwAAAAAiggEeAAAAAEQEAzwAAAAAiAgG\neAAAAAAQEQzwAAAAACAiGOABAAAAQEQwwAMAAACAiGCABwAAAAARwQAPAAAAACKCAR4AAAAARAQD\nPAAAAACICAZ4AAAAABARDPAAAAAAICIY4AEAAABARDDAAwAAAICIYIAHAAAAABHBAA8AAAAAIoIB\nHgAAAABERGADPDPrMLP7zewRM3vUzFalp88xs/vM7EkzW2dmbUGVAQAAAADGkiDP4B2WtMQ5N0/S\nfEkXmNnZkv5Z0mrn3AmSEpIuD7AMAAAAADBmBDbAcyl96Yet6X9O0hJJt6en3yzprUGVAQAAAADG\nkkB/g2dmcTPrlbRH0s8lPSXpBefcUHqWZyUdU+S1V5rZZjPbvHfv3iCLCQAAAACREOgAzzk37Jyb\nL+lYSWdJOrmC197gnFvgnFswderUwMoIAAAAAFFRl7toOudekPRLSedIOtrMWtJPHStpZz3KAAAA\nAABRF+RdNKea2dHp/z9K0l9Jekypgd470rNdJumHQZUBAAAAAMaSlvKzVG2GpJvNLK7UQPI259wd\nZrZN0vfM7HOSHpa0NsAyAAAAAMCYEdgAzzn3O0mnF5j+tFK/x0MTS/QntPvAbs2YOEPdnd2NLg4A\nZHF8AgCMZUGewUNErd+6XivXrcw+Xr1stS449YIGlggAUjg+AQDGurrcZAXRkehPaOW6lYrH4ups\n71Q8FtfKdSuV6E80umgAxjiOTwAAMMBDhXYf2C1J6mjtyP51ctnpANAoHJ8AAGCAhwrNmDhDkjQw\nOJD9a7LsdABoFI5PAAAwwEOFuju7tXrZag0nh9V3uE/DyWGtXraaGxkAaDiOTwAASOaca3QZylqw\nYIHbvHlzo4uBHNylDkBYcXwCAESRmT3onFtQbj7uoomqdHd288EJQChxfAIAjGVcogkl+hPatmub\npzvNVTIvAAAAgPriDN4YV0lmFPlSAAAAQLhxBm8MqyQzinwpAAAAIPwY4I1hlWRGkS8FAAAAhB8D\nvDGskswo8qUAAACA8GOAN4ZVkhlFvhQAAAAQfuTgoaLMKPKlAAAAgPojBw+eVZIZRb4UAAAAEF5c\nohlB9ciqIw8veOQTAgAAoFKcwYuYemTVkYcXPPIJAQAAUA1+gxchif6EFl23SPFYXB2tHRoYHNBw\nclgbP77Rt8sq67GOsa6SOqY9AAAAxgavv8HjEs0IqUdWHXl4wSOfEAAAANVigBch9ciqIw8veOQT\nAgAAoFoM8CKkHll15OEFj3xCAAAAVIvf4EVQPbLqyMMLHvmEAAAAyCAHbwyrR1YdeXjBI58QAAAA\nleISTQAAAACICAZ4QEgRXo5y6CPwG30KAJofl2gCIUR4Ocqhj8Bv9CkAiAbO4AEhk+hPaOW6lYrH\n4ups71Q8FtfKdSv5Rh1Z9BH4jT4FANHBAA8IGcLLUQ59BH6jTwFAdDDAA0KG8HKUQx+B3+hTABAd\nDPCAkCG8HOXQR+A3+hQARAdB50BIEV6Ocugj8Bt9CgDCi6BzoMkRXo5y6CPwG30KAJpf2Us0zeyd\nXqZhbCErKXjNUseVlDNM2xSmsgBoHI4FAKLGyxm8T0j6Tw/TMEaQlRS8ZqnjSsoZpm0KU1kANA7H\nAgBRVPQ3eGb2RklvknSxpHU5T02QdIpz7qzgi5fCb/DCI9Gf0KLrFikei6ujtUMDgwMaTg5r48c3\nclmPT5qljispZ5i2KUxlAdA4HAsANBuvv8ErdYnmLkmbJQ1IejDn348kne9HIdF8yEoKXrPUcSXl\nDNM2haksABqHYwGAqCp6iaZz7hFJj5jZd5xzg3UsE0IsNysp840nWUn+apY6rqScYdqmMJUFQONw\nLAAQVV5y8M4ys5+b2e/N7Gkz225mTwdeMoQSWUnBa5Y6rqScYdqmMJUFQONwLAAQVWVz8MzscUkr\nlbo8czgz3Tm3P9iivYzf4IUPWUnBa5Y6rqScYdqmMJUFQONwLADQLLz+Bs/LAO8+59xC30pWBQZ4\nAAAAAMYyP4POf2lm/yLpvyQdzkx0zj1UQ/nQ5Cr9xrPQ/HxrCgAAAPjLywAvc/Yud7ToJC3xvzho\nBpXmBhWaXxLZQwAAAIDPyl6iGQZcohkeleYGFZr/yNARSVJbSxvZQwAAAIAHvl2iaWZXF5runPvH\nagqG5lYoN6jvcJ92H9hdcHBWaP7+I/0ymSa0TvC0DAAAAADeeIlJ6M/5NyzpjZJmB1gmhFhublDm\nb6ncoELzt8Za1RJr8bwMAAAAAN6UHeA55/4159/nJb1e0vGBlwyhVGluUKH511yyRmsuWUP2EAAA\nAOCzin+DZ2bdkh5wzp0QTJFG4zd44cNdNAEAAID68fM3eFuUumumJMUlTZXE7+/GuO7O7ooGZYXm\nr3QZAAAAAErzEpOwNOf/hyT92Tk3FFB5AHjEGVAgGOxbYwvtDSBqyg7wnHM7zGyepNelJ22Q9LtA\nSwWgpEqzCAF4w741ttDeAKKo7E1WzOyDkr4tqSf979tm9ndBFwxAYYn+hFauW6l4LK7O9k7FY3Gt\nXLdSif5Eo4sGNDX2rbGF9gYQVV5iEi6XtNA5d7Vz7mpJZ0taEWyxABRTKFvQyWWnA6gO+9bYQnsD\niCovAzxTKv8uYzg9DUADVJpFCMAb9q2xhfYGEFVeBnjfkHSfmV1jZtdI+q2ktYGWCkBRlWYRAvCG\nfWtsob0BRJWnHDwzO0PS4vTDe5xzDwdaqjzk4AGjcec3IBjsW2ML7Q2gWfiZg3e2pEedcw+lH08w\ns4XOuft8KCeAKpEjCASDfWtsob0BRI2XSzS/Kqkv53FfehoAAAAAIEQ83WTF5VzH6ZxLyltAOiIs\n0Z/Qtl3buJ10CNAWgL+aaZ9qprJGRRTqPArbAIRBWPclLwO1p83sA3r5rN3/kvR0cEVC2BEMGx60\nBeCvZtqnmqmsURGFOo/CNgBhEOZ9qexNVsysR9IaSUskOUm/kPT3zrk9wRcvhZushEeiP6FF1y1S\nPBZXR2uHBgYHNJwc1saPb+Q3DHVGWwD+aqZ9qpnKGhVRqPMobAMQBo3al7zeZKXsJZrOuT3OuXc5\n53qcc9Occ++u5+AO4UIwbHjQFoC/mmmfaqayRkUU6jwK2wCEQdj3JS+/wQOyCIYND9oC8Fcz7VPN\nVNaoiEKdR2EbgDAI+77EAA8VIRg2PGgLwF/NtE81U1mjIgp1HoVtAMIg7PuSp6DzRuM3eOFDMGx4\n0BaAv5ppn2qmskZFFOo8CtsAhEG99yWvv8ErOsAzsw+VeqFz7t+qLFvFGOABAAAAGMu8DvBKxSSM\nT/89SdKZkn6Ufvw3ku6vrXgAACBonKkBgLGn6ADPObdKksxsg6QznHMvph9fI+nOupQOAABUJcwZ\nTQCA4Hi5yco0SUdyHh9JTwMAACGU6E9o5bqVisfi6mzvVDwW18p1K5XoTzS6aACAgJW6RDPjFkn3\nm9l/px+/VdI3AysRAACoSaGMpr7Dfdp9YDeXagJAxJUd4DnnPm9md0l6XXrS+5xzDwdbLAAAUK3c\njKaO1o7QZTQBAIJT8hJNM4ub2ePOuYecc/+e/sfgDgCAEAt7RhMAIDglz+A554bN7Akzm+Wce6Ze\nhQIAALW54NQLtHDOQu6iCQBjjJff4HVLetTM7pfUn5nonHtzYKUCAAA16+7sZmAHAGOMlwHeVdUs\n2MxeodQNWqZJcpJucM79u5lNkrRO0mxJf5R0sXOO23rlIbsIwFgTheNeFLYBjUUfApqH1/213vu1\nl5us/NrMpikVdi5J9zvn9nhY9pCkDzvnHjKz8ZIeNLOfS3qvpF84564zs49L+rikf6iu+NFEdhGA\nsSYKx70obAMaiz4ENA+v+2sj9mtzzpWewexiSf8i6VeSTKm7aX7UOXd7RSsy+6GkL6X/vd45t9vM\nZkj6lXPupFKvXbBggdu8eXMlq2taif6EFl23SPFYPHvns+HksDZ+fCPf5AGIpCgc96KwDWgs+hDQ\nPLzur37v12b2oHNuQbn5vASdf0rSmc65y5xzl0o6SxVetmlmsyWdLuk+SdOcc7vTTz2nIqHpZnal\nmW02s8179+6tZHVNrVB2kZPLTgeAqInCcS8K24DGog8BzcPr/tqo/drLAC+Wd0nmfo+vkySZWZek\n70v6e+fcwdznXOr0YcFTiM65G5xzC5xzC6ZOnep1dU0vN7so85fsIgBRFoXjXhS2AY1FHwKah9f9\ntVH7tZeB2noz+6mZvdfM3it3Zv+eAAAgAElEQVTpTkk/8bJwM2tVanD3befcf6Un/zl9aabSf738\nnm/MILsIwFgTheNeFLYBjUUfApqH1/21Uft12d/gSZKZvV3Sa9MP73HO/beH15ikmyU975z7+5zp\n/yJpf85NViY55z5Wallj6Td4GdxFC8BYE4XjXhS2AY1FHwKaR73voun1N3hFB3hm9veSNkp6yDk3\nVEUBFku6R9IWScn05E8q9Tu82yTNkrRDqZiE50staywO8AAAAAAgw+sAr1RMwrGSvijpZDPbIuk3\nSg34NpYbkEmSc+5epe66Wcgbyr0e4VbNNxFBfysZtW89t+/dri07t2juMXM1Z+qckvMGte1Rq1PA\niyj1ey/bEqXtBaKAfTI4ftdtWNuq6ADPOfcRSTKzNkkLJC2S9D5JN5jZC865U+pTRIRNNXkeQWeA\nRC076HN3fE6fveOz2cdXLb1Kn1766YLzBrXtUatTwIso9Xsv2xKl7QWigH0yOH7XbZjbyksO3kRJ\n5yj1G7xzJB0taYtz7n3BFy+FSzTDo5o8j6CzfaKWHbR973adfNXJkkktsRYNJYckJz3+2cdHnckL\natujVqeAF1Hq9162JUrbC0QB+2Rw/K7bRrVVzTl4ZnaDmf1G0jqlBnYbJb0zHV1Qt8EdwqWaPI+g\nM0Cilh20ZecWSanBXe7fzPRcQW171OoU8CJK/d7LtkRpe4EoYJ8Mjt91G/a2KhWTMEtSu1Jh5Dsl\nPSvphXoUCuFVTZ5H0BkgUcsOmnvMXElKnbnL+ZuZniuobY9anQJeRKnfe9mWKG0vEAXsk8Hxu27D\n3lZFB3jOuQsknSnp+vSkD0t6wMx+Zmar6lE4hE81eR5BZ4BELTtoztQ5umrpVZKThoZTl2detfSq\ngjdaCWrbo1angBdR6vdetiVK2wtEAftkcPyu27C3ldccvGOV+g3eIklLJU12zh0dcNmy+A1e+HAX\nzeBxF02gMaLU77mLJtB82CeD0+x30fQjB+8DSg3oFkkaVDoiIf1vi3MuWfCFAWCABwAAAGAs8yMH\nb7ak/5S00jkXjl8MIjR6n+nVvU/eq8UnLNb8WfOLzpf7zYYkvpECPEr0J/T4c49Lkk6efjL7DBCQ\nUt/AcyalNs3+GaBR7U+/a36NbsNSOXgfqmdB0DwuXXupbv3trdnHy89erlsuv2XUfLn5IH2H+yQn\ndXV0SQpXVggQNuu3rteKW1Zoz8E9kqSe8T268bIb2WcAn5XKsQpzxlUzaPbPAI1qf/pd8wtDG3r6\nDV6jcYlmePQ+06vTP3u6JClmMSXTV+o+fNXDI87k5eaDtMZbtW3XNsmkV09/tYaSQ+S6AEUk+hNa\neO1C7di/Q7FY6j5YSZfUcZOO032fvI99BvBJqRwrSeSR1aDZPwM0KuOMHLzmF3Qb1pyDBxRy75P3\nSkoN7nL/ZqZn5OaDDA4PKmYxmUyDw4OhywoBwmT3gd0aSg4pZrHsP5NpMDnIPgP4qFSOVdgzrsKu\n2T8DNKr96XfNLyxtyAAPFVl8wmJJyp65y/zNTM/IzQdpjbcq6ZJycmqNt4YuKwQIkxkTZ6gl1qKk\nS2b/OTm1xlrZZwAflcqxCnvGVdg1+2eARrU//a75haUNGeChIvNnzdfys5dLenlwt/zs5aNutJKb\nD3Jo8JB6JvSop6tHA0MDocsKAcKku7Nbay5Zo54JPUomk0omk+rp6tGaS9awzwA+KpVjFfaMq7Br\n9s8AjWp/+l3zC0sb8hs8VIW7aALB4i6aQH1wF83gNPtnAO6iiWoF1YY15+CFCQM8AAAAAGMZN1kB\nAAAAgDGGAV7EJPoT2rZrmxL9iUisZyzbvne7ftT7I23fu73u625k+9K3KkN9+a/3mV596e4vqfeZ\n3lHPUd9Addh3guGlXqn7sado0DmaT72CFcMQ4Bh1n7vjc/rsHZ/NPr5q6VX69NJP12XdjWxf+lZl\nqC//Xbr2Ut3621uzj5efvVy3XH6LJOobqBb7TjC81Ct1PzbxG7yIqFc4JiGcwdu+d7tOvupkyaSW\nWIuGkkOSkx7/7OOaM3VOoOtuZPvStypDffmv95lenf7Z0yWlMj4zdwp++KqHddzk46hvoAocq4Lh\npV6p++jhN3hjTL2CFcMS4BhlW3ZukZQa3OX+zUwPUiPbl75VGerLf/c+ea+k1OAu9++9T95LfQNV\nYt8Jhpd6pe7HLgZ4EVGvYMWwBDhG2dxj5kpS6sxdzt/M9CA1sn3pW5Whvvy3+ITFkl7O+Mz8XXzC\nYuobqBL7TjC81Ct1P3YxwIuIegUrhiXAMcrmTJ2jq5ZeJTlpaDh1eeZVS68K/PJMqbHtS9+qDPXl\nv/mz5mv52cslvTy4W372cs2fNZ/6BqrEvhMML/VK3Y9d/AYvYuoVjkkIZ/C2792uLTu3aO4xc+sy\nuMvVyPalb1WG+vJf7zO9uvfJe7X4hMWaP2v+iOeob6A67DvB8FKv1H10EHQOAAAAABHBTVZQs1K5\nKRue2KBP/fentOGJDWXn9XO9qC+/24K2rY4f9VYoV3Gst0ex7W9kBmVQvLZ1qQzAMAiiz5Zaptf1\njfV9KUyqbYuwtWGzfa4KW/3VS63bnehP6KeP/lTfue87vr7nkIOHgkrlprzh+jfo7ifuliRd+5Nr\nNe+YeTo8fLjgvH6uF/Xld1vQttXxo94K5SoumL1gTLdHsXptZAZlULz2oVIZgGEQxDGk1DK9ro9j\nW3hU2xZha0M/y1OPbQtb/dVLrdu9fut6/e2Nf6v9L+2XlLpr+mf+5jO+vOdwiSZGKZWbsuXZLTrv\n+vNGvWb2pNmaPH5yTRkr5LWEh99tQdtWx496K5Sr6JzTK7pfoaPajhqT7VGsXr+74rtaeO3ChmRQ\nBsVrHyqVAZj/O8RGCOIYUmqZkjytj2NbeFTbFmFrQz/LU49tC1v91Uut253oT2jB5xbo6X1PS5LM\nTM45tcZb9cRnnyj6nsMlmqhaqdyUn277acHXDAwNjJrXz/WivvxuC9q2On7UW6FcReecDg8dHrPt\nUaxeMzl4jcigDIrXPlQqAzAMgjiGlFqm1/VxbAuPatsibG3oZ3nqsW1hq796qXW7dx/YrcNDh2Uy\nxSwmS//nnPPlPYcBHkYplZty/innF3xNR0vHqHn9XC/qy++2oG2r40e9FcpVNDO1t7SP2fYoVq+Z\nHLxGZFAGxWsfKpUBGAZBHENKLdPr+ji2hUe1bRG2NvSzPPXYtrDVX73Uut0zJs5Qe0u7nJySLimX\n/s/MfHnPYYCHUUrlppx70rlactKSEfPPO2aeOto6as5YIa8lPPxuC9q2On7UW6FcxauXXq0vv+fL\nY7Y9itXr/FnzG5ZBGRSvfahUBmAYBHEMKbVMr+vj2BYe1bZF2NrQz/LUY9vCVn/1Uut2d3d268vv\n+bImj5ssSXLOqSXWoquXXu3Lew6/wUNRpXJTNjyxQT/d9lOdf8r5Ovekc33NWCGvJTz8bgvatjp+\n1FuhXMWx3h7Ftr+RGZRB8drWpTIAwyCIPltqmV7XN9b3pTCpti3C1obN9rkqbPVXL7Vud6I/ofv/\neL/29+3XOcefU/Y9hxw8AAAAAIgIrwM8YhJCqppvBOr57Un+GTz4L4rfHEfhzEhufUsKrO7LtWum\nLmdNmqW2lramaP96qfWMTKnjm1/7WzPtt4UUK38zblepMof9jGa1mrGd/FZJHWTmPar1KB0aPBR4\nvVWyvjC1ZZjKUougtyPRn9Djzz0uSTp5+smBrIMzeCFUTa5GPTNIcnPwJGnJSUv0i4/8IpB1jVVR\nzF+KQr5Ybn33He6TnNTV0SXJ37ov166Zuky6pIaSQ5rcOVlTx08NdfvXS625ZqWOb37tb8203xZS\nrPzNuF2lyhz2XMBqNWM7+a2SOsjM23e4T3sO7lHP+B51dXQFVm+VrC9MbRmmstQi6O1Yv3W9Vtyy\nQnsO7pEk9Yzv0Y2X3eh5HVyi2aSqydWoZwbJhic2FMzB+/VHfs2ZPJ94bc9myp4plMXWbPliufXd\nGm/Vtl3bJJNePf3VGkoO+Vb35do1W5eSBocHU3fdkulVPa9SLBYLZfvXS625ZqWOb3OPnevL/tZM\n+20hxcr/kw/8RG9a86am2q5SbbFj/45Q5wJWq9n7nx8qqYPMvCbT9n3blVRSJtOcKan3Lb/rrZL1\nhaktw1SWWgS9HYn+hBZeu1A79u9QLJa6z2XSJXXcpON03yfv87QOcvCaVDW5GvXMICmWg1dsOioX\nxfylQllsudObQW59Dw4PZnNrBocHfa37cu2aqbPMm0Mmr2zYDYe2/eul1lyzUsc3v/a3ZtpvCylW\n/ky/bKbtKtUWYc8FrFaz9z8/VFIHmWmZ423mvStmsUDqrZL1haktw1SWWgS9HbsP7NZQckgxi2X/\nmUyDyUHf64oBXshUk6tRzwySYjl4xaajclHMXyqUxZY7vRnk1ndrvDWbW9Mab/W17su1a6bOksmR\neWVxi4e2/eul1lyzUsc3v/a3ZtpvCylW/ky/bKbtKtUWYc8FrFaz9z8/VFIHmWmZ423mvSvpkoHU\nWyXrC1NbhqkstQh6O2ZMnKGWWIuSLpn95+TUGmv1va4Y4IVMNbka9cwgKZSDt+SkJVye6aMo5i8V\nymJrtnyx3Po+NHhIPRN61NPVo4GhAV/rvly7ZutSL3/LO6lzkmKxWGjbv15qzTUrdXzza39rpv22\nkGLlnzN1TtNtV6m2CHsuYLWavf/5oZI6yMzr5DRl/BTJSVO6pkhSIPVWyfrC1JZhKkstgt6O7s5u\nrblkjXom9CiZTCqZTKqnq0drLlnje13xG7yQ4i6a4C6a4cRdNMOPu2gGj7toNrdmbCe/cRdN/4Wp\nLLUI8100uckKAAAAAEREJG+ysn3vdv2o90favne759ck+hPa9NQmbXpqkxL9Cc+v2bZrm+f5wy6I\n7dnwxAZ96r8/pQ1PbPBtmV5V0w+qVa7uguwrazes1fn/dr7Wblhbcr4g6iN/u6rZzqjtR14U22Y/\n6qJcO1e7jii2U6lt8rK9lRzfKq2/KNZ3riC2L3eZQS+/kudqXbafr2mkZitvvdTr80M9PxONNWHo\n29WWoWnO4L31mrdWnKG1fut6rbh5hfa8mM6amNCjGy8tnTURlRyPjCC2p5E5ePXMUitXd0H2lekr\np+vPfX/OPp7WNU3PrX5u1HxB1Ef+dr1n4Xv07fu+nX0ctlzGsAgyG6xcO1e7jii2U5A5eJWsy4/5\nm00Q2zcie3KgTzKpq92/7Mla+0u1y/bzNY3UbOWtl3p9fohCvmxYhaFvFyrDG+e+MTqXaM6dP9f9\n/uzfV5Shlc2aeH5H9vbGyWRSx00unjURlRyPjCC2p5E5ePXMUitXd0H2lbUb1uqKW68YNf2m5Tfp\n8nMvzz4Ooj7yt6v/cL+e3vu0Xjn1lRrXPi50uYxhEWQ2WLl2rra+o9hOQebgFfotXiX1F8X6zhXE\n9uUusyXWoseee0xy0ikzT9Hg8KCvy6+mv1S77KgcO5utvPVSr88PUciXDasw9O1iZXj884/3uqQ7\nvdzrm+ISzUNHDkmqLEMrkzVhspezJiyVWVUsayIqOR4ZQWxPI3Pw6pmlVq7uguwrt22+zdP0IOoj\nf7uyGVDpuzWGLZcxLILMBivXztXWdxTbKcgcvErWVWnZoiCI7cvPnjRZ9n3c7+Xnl7nW7Ql7pq0f\nmq289VKvzw9RyJcNqzD07WJlUFytXl7fFAO8o9qOklRZhlYma8LJvZw14VKZVcWyJqKS45ERxPY0\nMgevnllq5eouyL5y8YKLPU0Poj7ytyubAZXO5AlbLmNYBJkNVq6dq63vKLZTkDl4layr0rJFQRDb\nl5896eSy7+N+Lz+/zLVuT9gzbf3QbOWtl3p9fohCvmxYhaFvFyuDhjXo5fVNMcBrb2mvOEMrmzXR\nlZM1MaF01kRUcjwygtieRubg1TNLrVzdBdlXLj/3ck3rmjZi2rSuaSMuz5SCqY/87ZJSy3Ryocxl\nDIsgs8HKtXO19R3FdgoyB6+SdVVatigIYvtylzkwNKCerh71TOjRocFDvi+/mv5S7bL9fE0jNVt5\n66Venx+ikC8bVmHo28XKIKdhL69vit/gZWISqsnQqiZrIio5HhlBbE8jc/DqmaVWru6C7CtrN6zV\nbZtv08ULLh41uMsVRH3kb1fYcxnDIshssHLtXO06othOQebgVbIuP+ZvNkFsX9DZk7X2l2qX7edr\nGqnZylsv9fr8EIV82bAKQ9/OLwM5eAAAAAAQEZHMwQMAAAAAFBe/5pprGl2Gsm644YZr3vmed2r7\nvu1qibVkb7pSSKI/4Wm+ZuJ1m7bv3a57/nCPDh05pOf7nw+0Dtb8fI0+dNuH1D/Qr4WvXDiqDG3x\ntlGXqAXZLr3P9Or2B29XW7xN0ydOH/Fctesu9bpyy/Rjezc8sUE33HODWmOtOm7KcaOWf+eWO3XH\n7+5QZ1vnqG32i9/t5nV5+fMF0YZhl+hPqPdPvXo28ayGhoe0+8DuUdsR5PaFoe6KHU9KSfQn9JMt\nPym6b3jZrrUb1uoT3/+Ejgwe0RnHnVHzdtRL7rYNDA7U/bhX6zq8tHcQ/bJUee7aepd2HdilyZ2T\nK15fUHXpRx3Uc/+u97Gk2PrKva8Uel3+Z4tGHBfLrbPU559KlxV0WVG7VatW7b7mmmtuKDdfU1yi\neeJrTnTxt8Wzj4uFDYYhlNBvXrcpE3aZdEkNJYc0edxkTZ0wNZA6OPr9R+vA4QPZxxPbJ+qFL71Q\nNHAz6Ha5dO2luvW3t2YfLz97uW65/BZJwQRA1yPAtFTY8vqt63Xx1y7Wi4dfzD6fu81+8bvdvC7P\nj6D1StYXRuu3rteKm1doz4t7NJwclplp+sTp6mrv8jVAvdT6G1131QT4rt+6Xsv+zzIdHDiYnVbp\n8WD6yun6c9+fs4+ndU3Tc6ufq2lb6mFEIPjhPslJXR2VBYIHGfpdbh1e2jvoIPX88qz68arsnQkn\nj5usb634luf1BVWXftRBPffveh9Liq2v3PtKofeZ79z3nRGfLZactES7Duwatewglau/Up9/Kl1W\n0GWFPyL1G7yjZh7lXrnilSXDBsMQSug3r9uUDbuUNDg8KCcnk+mEnhMUj8V9rYM1P1+jD972wVHT\nr37T1brup9eNCty875P36ZIbLwmsXXqf6dXpn03lPcYslr2t/8NXPazjJh/newC0VDr81o9+WCps\nee6xc3X6P56uHc/vGPX8w1c9rPmz5ntaRzl+709el+dH0HoQ5a+nRH9CC69dqB3P75DJdGToiJyc\n2lvadfyU4+XkfAlQL7X+RtddNQG+if6EzvjsGfrj/j+Oes7r8WDthrW64tYrRr3+puU3lbzRUaPl\ntllrvFXbdm2TTHr19FdrKDkU+HGvmnLmLue7K76rhdcuLNneQQep55fnrGvPSmXvmSnzOen4Kcdr\n86c3l11fUHXpRx3Uc/+u97Gk2Pryj5cvHX5JT+19SsdPPV6d7Z2jHg8MDqhvoC/7Ppv72eKVU1+p\no8cdXZfjYrn6K/X5J/+zQNBtEYb3jbEicr/BKxc2GIZQQr953aZMqGUmjDoTTp1MJn2vg3Wb1xWc\nftuDqRDu/MDNe5+819M2VCuz/Gwgd/rvvU/eG0gAdD0CTEuFLe8+sFuHBg8VfD5TF37we3/yujw/\ngtaDKH897T6wW0PJoWyosySZUh8yY7GYbwHqpdYf1LK9qibAd/eB3XrpyEsFn/N6PLht820FX19s\neljkB4LHLCZTZYHgQYZ+l1tH5thVqr2DDlIfVR6X2u9Mlq3PgaEBT+sLqi79qIN67t/1PpYUW1/+\n8TL/s1L+447WDr00+NKIaabUsfjI8JG6bEup7clML/X5p9JlBV1W1F/TDPDKhQ2GIZTQb163KRNq\nmQmjznyLE4vFfK+DZQuWFZx+8V+kQrjzAzcXn7DY0zZUK7P8bCB3+u/iExYHEgBdjwDTUmHLMybO\n0FGtha9rz9SFH/zen7wuz4+g9SDKX08zJs5QS6wlG+osKXVW3kzJZNK3APVS6w9q2V5VE+A7Y+IM\njWsbV/A5r8eDixdcXPD1xaaHRX4geNKlvtyrJBA8yNDvcuvIHLtKtXfQQeqjymOp/c7JZeuzo6XD\n0/qCqks/6qCe+3e9jyXF1pd/vMz/rJT/eGBwQONax42Y5pQ6FrfF2+qyLaW2JzO91OefSpcVdFlR\nf00xwHtF9yvKhg2GIZTQb163KRt2KSkeS/1WsXtct+KxuO918IG/+oAmtk8cMW1i+0StumhVwcDN\n+bPmB9ou82fN1/Kzl0t6+eC2/Ozlmj9rfiAB0PUIMC0Vttzd2a2vLf+axrePH/F8Zpv94vf+5HV5\nfgStB1H+euru7NaaS9aop6snddbOYmqJtWjq+Klycr4FqJdaf6PrrpoA3+7Obn31b7+qCR0TRkyv\n5Hhw+bmXa1rXtBGvn9Y1LdSXZ0oj2+zQ4CH1TOhRT1ePBoYG6nLcq6acucuZP2t+2fYOOkg9vzxX\nL7069UVL+kuWyeMm68vv+bKn9QVVl37UQT3373ofS4qtL/946eSyn5kKPR5ODutry7826rPFkpOW\nqDXeWrfjYrn6K/X5p9JlBV1W1F9T/AZvwYIF7ue//rmnsMEwhBL6zes2ZcIuZ02apbaWtkDrYM3P\n12jd5nVatmCZPvBXHxhVhvzAzaDbpfeZXt375L1afMLigtee+x0AXY8A01Jhy4n+hH7x+C/09N6n\n9den/LWvg7v89fjZbl6X50fQehDlr6dEf0KPP/e4JGn6hOk6NHgokAD1UutvdN1VE+Cb6E/o7sfv\n1lN7nyq4b3jZrrUb1uq2zbfp4gUXh35wl8uPQPAgQ7/LLcdLewcdpJ5fnk1Pb9Lkrsk6a/ZZFa8v\nqLr0ow7quX/X+1hSbH3l3lcKvS7/s0Ujjovl1lnq80+lywq6rKhdpG6yQtA5AAAAgLHM6wCvaXLw\nrrzyyqpfX0kWTb0yPILOEwraZTddpituvkLbdm7TRWdclC1H30DfiLyuasrnd93klykomdyybbu3\nae+Le4tmlxUrZ/58pXLwcp/fd3Cffvfs77T7hd2a1Dkp1NkzlebgFWq7oPt8NdlrQcvNxOtq72pI\nblY911dtXqKU+jb7mh9eo6//5uvqauvSidNPrHhd+Tl4fvaJMOdElcsQ9PJ6L/ljxdz5yJ26/mfX\nK6bYiHbzI9+vmnJXUvZKlzsWZfLaBo4MqP9Iv+d6CUs9BlUOL8sttQ+E6T2r0LZUU29BbpPfOZJB\nHJNKlTNSOXi1nMGrJIum2rwtP8vUiOVUylbYqGknTz9ZfYf7tOfgHvWM71FXR1dV9el33fQN9GnP\ni3vUM6FnRIaY3zK5ZbsP7NawG1bMYopZTNMnTFdXR/H1FtveUjl40uicvIwpXVN06+W3hjJ7ptIc\nvEJtJynQPl9N9lrQ1m9drxW3rNCeg3skST3je3TjZTfWNTerErWur9q8xEK5VZI075h56r2m1/O6\n3rv2vSNy8Ma1jNOQG8o+rqVPhDknqlyGoJfXe8kfK7bN86+Zr0d2PpJ9nGk3P/L9qil3uedqWe5Y\nlJ/XNr5tvI6ZdEzZeglLPQZVDi/LHbEPDPRJJnW1p/aBM2efqXUPvHx380a+ZxXaFqny9+wg34f9\nzpEM4phUrpxcoqnKsmiqzdvys0x+5AkFnTly2U2X6Zb7Rr/hj28fr8HhQSWVusvfrEmztGP/jorq\n0++6kaTt+7bLySmmmOZMmSMnF0ibLrx2of64/48aGh7K3m1Lktpb2jVnSuq3JF6zG69/x/Va+qWl\no9bz64/8WueedG7RnLyM46cer82fKp/VVE+V5uBJo9suc2e9tpa2QPp8NdlrQctm4u3fkb2Vd9Il\nddyk43TfJ++rS25WpeWtZX3V5iXm51blu+P9d+jCeReWXdf+vv167mDhUPNxbeNq6hNhzokqlyHo\n5Xc9XvLHim3znY/cWfCY970rvqdr7rimpny/asodhry6KMnNa8t1/JTj1dbSVrRewlKPQZXDy3Jz\n52mJteix5x6TnHTKzFP00uGX9Ps9v1dbvE0t8ca+ZxXaliNDqUiJSt6zg3wf9jtHMohjkpdyRi4H\nrxqVZNFUm7flZ5kasZxK3bHljoLTM3c8zOQXZW47XEl9+l03mTbN3HI+kyEWRJtmBh+SRmeXWeH1\nFtveHz7yw4LryeTjFcvJy6zz8ODh0GXPVJqDV6jtBocHNZQcCqzPV5O9FrRM38qcEc7mmyUH65ab\nVWl5a1lftXmJublVhRTapwotI/fsVa5MBlYtfSLMOVHlMgS9vF4qnz9WbJuLHfNuf+j27Ouqzfer\nptxhyKuLkkwfyrw3ZgwODZasl7DUY1Dl8LLc/IzLTD7q4PCght2wpJc/ZzXyPavQtgwmK3/PDvJ9\n2O8cySCOSX6VU4r4AK+SLJpq87b8LFMjllOppXNHf8sqvXy5QGagkz3bUEF9+l03mTbNhEZnMsSC\naNPMQUjS6OwyV3i9xbb3LfPeUnA9mXy8Yjl5mXW2t7aHLnum0hy8Qm3XGm/NXu9eahnVqiZ7LWiZ\nvpV0yew/J6fWWGvdcrMqLW8t66s2LzE3t6qQQvtUoWXkRyxkZM7K19InwpwTVS5D0MvrpfL5Y8W2\nudgx7x1nvCP7umrz/aopdxjy6qIk04fyrxhrbWktWS9hqcegyuFlufkZl5l81NZ4q+KWisXKfM5q\n5HtWoW1pjVX+nh3k+7DfOZJBHJP8KqcU8QFeJVk0UnV5W36WqRHLqdTNV9xccPox3cdoyvgpkkv9\nDiwei1dcn37XjZQqi5w0ZfyUbIZYEG265pI1mjZ+2stngtPZZVO6pkhSRdmNF867sGgOnlQ4Jy9j\nStcUffnd3rKa6qnSHDxpdNutuWSN1lyyJrA+X032WtCymXgTepRMJpVMJtXT1aM1l6ypW25WpeWt\nZX2V9pPc+XJzq3LNOwy3MxcAACAASURBVGbeqMsziy3jG+/7xqgcvHEt49QWb6u5T4Q5J6q7s3SG\noJfXe8kfK7bNF867UPOOmTdi2rxj5mnZwmU15/tVU+4w5NVFSW5eW0ZXW5faWtpK1ktY6jGocnhZ\nbu48A0MD6unqUc+EHh0aPKRYLJat10a/ZxXalmres4N8H/ajHXOXEcQxya9yShH/DV5GJVk09crw\nCDpPKGiX3XSZ7thyh5bOXaqbr7g5W46jWo8akddVTfn8rpv8MgUlk1t2cOCgJnRMKJpdVqyc+fOV\nysHLff60madp2A1rStcUnTnnzFB/iKg0B69Q2wXd56vJXgtabibeydNPbkhuVj3XV21eopT6vc/X\nfvU17enbo8tfe3nBwV25ZeTn4PnZJ8KcE1UuQ9DL673kjxVz5yN36oeP/FBvmfeWEe3mR75fNeWu\npOyVLncsyuS1nXbMaZoyforneglLPQZVDi/LLbUPhOk9q9C2VFNvQW6T3zmSkv/HpFLl5CYrAAAA\nABARkczBK5dZl8lYaYu3VZzfky8suSthkV8f5//r+bry1it1zxP3aPmi5dq+d7vu2nqXHnrmIW3b\nuU1d7V3ZM3jV5ODlZn7VmvlSa4aYl+WXykOpti/1PtOrr/36a3r4mYc1fcL0ktu67r51WvXjVXJJ\np1OPPdVzeWutCz9y7QrNm2mvQlmClWYGFZqn2fbvoHK6KlmP3/PUUoZSx4BEf0Jf/83Xtfrnq/Xn\ng3/Wq3peVXGW3sdu+5j+x63/Qzuf36m/es1flSxXmDJNy2VQFctryq3PF156oeTxNch9y1aYVv14\nlVb9eJU++IYPBtK/Ci3rts236dZNt+qolqOymaOZddz/9P26edPNRfNIyy3767/5ur7yy6+oNdaq\nE6efOOpYWO6YGFRml9djsZ99e2dip+7ffv+ovuX38bjce0gx+Tl9OxM7dffjd2vXgV2a3Dk5sGNv\nueN7uc8v5TJz65nlWO7YnN/3asmQq/aznZcyVpLB+sRzT1SU71hrxl/kcvA+983Plcysmzlx5ohc\nsErye/KFJXclLPLrI3OpWK7s7WzT4hbXuxe+Ww/88YHsNK85eCtuXqE9L6YyvyYcNUEdLR3ZjJFK\nM1/yl9czoUc3Xuo9Q6ycUvk0teS25WcGxRTTqresKritx//D8dr+/Pbs4zmT5ujpf366bHkrKU8t\ny8rm2uXlJBbL+8m0V+ZGK7lZglL5+ixXrmbbv4PK6apkPX7PU0sZSmUkrd+6Xm/+jzdrMDmYfb41\n1qof/d2PPGfpLf33pRrWcHZaXHHd8cE7asp482O7q1mGpJLHpwtOvWBEfWZuSd4ST900Kv/4GuS+\nVShf9a4P3uVr/8q3fut6ve0rb9OhwUPZaUtOWqKPXvBRrVy3Un/c90cNDA2MeC43j7TcsvP74pxJ\nc9Te1p7N+BzfMV4vDrxYNKfV98yudB+Q5OlY7Gff3ntwr/a/tF8tsRbFLJbtW34fj8u9hxRbdv57\nbnu8XYeHD2cfTx43Wd9a8S3fj73lju/lMlDLZebWM8ux3LE5P99WTgWPSV7LVk0+rJcyltve3Pl2\nPr9TLx55MftcuXxHPzL+InWJ5ulnnO4GLhzIZkK8dPglPbX3KR0/9Xh1tncq8VJCT+9NfaCNWSy7\nU3vJ78kXltyVsMivj207t+nQ0KHyL0x71bRXaULHBM85eAuvXagdz+9I3azESQNDA2qLt+nVM16t\nl468pD/s+YPnzJdRy1PqblPHTfaWIVZOqXyaweHBqjJgpOKZQS2xFv3+c78fsa3r7lund930rlHz\nfu+K72nZwmVFy1tr3640r8xk2r5vezYnsVA2YG57mUxHho6k7gqazhIcTqY+dJeqz3Llarb9O6ic\nrkrW47Vuvc5TSxlKZSQdPe5onXr1qdp1cNeoZc+cMFNb/3Frwb6Zu76diZ164dALo17fPa5bM4+e\nWVXGmx91X80yco8/hY5Pw8lhfXfFd7Xw2oWSpd47M3dt62jpUFLJEcfXIPetQoO7jOe/+Lwv/atQ\nnc1bNU9/Svxp1HPHHn2s4vG4duwfnauYySMtt+xiffGYCcdo30v7lHRJDQ0PqTXeqpiNzmn1u19k\n+kBudE+pY7GfmbTDyWE9uefJ7Dpb462SpPs+eZ8uufES347H5d5D8rczo9h7br7jpxyvzZ9O/Vwo\n6DxjSWUzUItl4mb6aNDH41zljs35+bbmLHt34txjkteyVZMP66WMlWSwDieHC57wKJbv6FfGX6Ry\n8AaHU9+AZTPrYi/fqVBKZalIL2esZKZ7ye/JF5bclbDIr49KBnfSy7fv9ZqDl7ktfsxikr2cPzU4\nPDgqW69cPkr+8mIWy+bH+NGepfJpMtOqyW0r1m+dc6O2NZMTla/QdD/7dsW5dnltVigbMLe9CmUJ\nesnUKVeuZtu/g8rpqmQ9fs9TSxlKZSTtPrBb/Uf6Cy6770ifpyy9voG+gq9/ceDFqjPeyvE7mynz\nN/f4U+j45OSyx5qWWMuIW9gnlRx1fG3UvuVX/yq03P7DhfvLS0de0uHBwwWfK5VDOmLZRfpi/2Bq\nejyWusV9Jp81P6c1iMyuTB9wcmWPxbWuO3c52ffuvLzhTP/zq8+Uew8ptuxsTp8Kf9Fg6f8GhgZ8\nPfaWO76Xy0At1hcz04M+Hucqd2yWRubbJpXqE/nHJK9lqyYf1ksZvX6m6WjtUP9A4X28WL5jvbN2\nm2KAl/m2J5tZlz5YZM7Utbakns+8QWWme8nvyReW3JWwyK+Po1oqu0Y6cyD3moOXCbZOutS3x5lv\neFrjraOy9crlo+QvL+mS2fwYP9qzVD5NZlo1uW3F+q2ZjdrWTE5UvkLT/ezbFefa5bVZoWzA3PYq\nlCXoJVOnXLmabf8OKqerkvX4PU8tZSiVkTRj4gx1tnUWXHZXW5enLL3MpeD5xneMrzrjrRy/s5ky\nf3OPP4WOTybLHmuGkkMjQqhjio06vjZq3/KrfxVabmd74f4yrm2c2lvbCz5XKod0xLKL9MXO1tT0\nzBUJmXzW/JzWIDK7Mn3AZGWPxbWuO3c5uWdZMtssvfxe51efKfceUmzZ2Zw+Fb6izaX/62jp8PXY\nW+74Xi4DtVhfzEyvZ5ZjuWOzNDLfNpYefuQfk7yWrZp8WC9l9PqZZmBwQJ0dhffxYvmO9c7abYoB\nXjwWH5EJ4eRSORmS+g73qS3els0Fy3Qgr/k9+cKSuxIW+fVR7EfmuUHfUuo3eMvPXq64xSvKwVtz\nyRr1dKUzv1xSU7qmqGd8KmMkHotXlPkyannJpHomeM8QK6dUPk21GTBS4cygmGL6zN98ZtS2Llu4\nTHMmjZw2Z9KcUZdn5pe31r5daV6ZkxuRkyiNzgbMbS/nXPayosz8XuqzXLmabf8OKqerkvX4PU8t\nZSiVkdTd2a2171ur1ljriOW2xlq19n1ri/bN3PV998rvKq74iPniius7K75TdcabH9tdzTJy95dC\nx6fVy1Zr/qz52fpMJpOpb8MVy37Blnt8DXLfcjcW/mB91wfv8q1/FaqzGy69QUe1jvzScslJS3Tj\nZTfqqNaj1NHSMeq5cpdnZpZdqC/OmTRH48eN15SuKTKZJnVOklQ4p9XvfpHpA9MmTNPU8VPLHov9\n7NvxWFzd41KvzZy5vGrpVZo/a76vx+Ny7yHFll3oPbc9PnKAP3ncZH35PV/29dhb7vheLgO1UCZu\nbh8N+nicq9yxOfOb4Ey+7dQJU9UzfvQxyWvZqsmH9VJGr59phpPDcnIa3zZ+xPOl8h3rnbXbFL/B\ny8QklMusy2SsLD5hcVWDu1xhyV0Ji/z6OP9fz9eGJzfo3BPO1U8//FNt37tdm57epMHhQbXGWnXO\nK8/J/m6jmhy83MwvqbbMl1ozxLwsv1QeSrV9qfeZXv2g9weaeNREvXX+W0tu67r71un2h27XO854\nR8HBXbHy+vE7xFpz7QrNm2mvQlmClWYGFZqn2fbvoHK6KlmP3/PUUoZSx4BEf0Lfuf87uvcP92rx\nqxbr3We921PfzF3fx277WHZ/+sLFXyhZLr/q3+9spkL7i1Q4rym3PiWVPL4GuW/l/hYv97d3fq6j\n0LJ+9MiP9Ltnf6e3zHtL9sNxZh3b927Xxqc3Fs0jLbfs79z/HT2w/QG98y/eqQvnXTjqWFjumBhU\nZpfXY7GfffvI0BE98/wzo/qW38fjcu8hxeTn9B0ZOqJtu7dpctdknTX7rMCOveWO7+U+v5TLzA36\neJyr3LE5v+9J1WfIVfvZzksZvX6mmTFxhnbs31FRvmOtGX+RuskKOXgAAAAAxjKvA7yWcjPUUICv\nS1oqaY9z7tT0tEmS1kmaLemPki52ziX8Wmeto2Kp8jMNfn7rVEv5M99kHBw4qAkdE3w/U5VfL6//\n59dr70t7Na1zmp774nNFv5XM/QbsxJ4TC9ZpuTOx+d/+zZo0K3tb68y3c8Xay8u3137VTbEzy9Uu\n839/+3/r7sfv1pKTl2QvDcl9PvPN1eDQoO5+4m4dfdTResv8t1T1rXu+zBnZzDeXkj91d+cjd+qH\nj/xQb5n3Fl0478KC5fNrXeVs37tdm57apCld/4+9N4+Po7jz/j/dPac0MzpG0uiwLUu+bXxwmGsJ\nmzgEEnyRhAcHbIdkDctmk83x2jzJ/p6AbAeebDbJCxKeOJhAkoUkYFgSsDHEGDBgzGVDkDH4RrKN\nZcuWZN0aSXP0749W1VRXV3X3jORsvJkPf8jM9HRVV33r+/12VXe9yzC/bn7WK67ZtOlony6Q+Yax\nXtljx3FrTysA5zGWrc7GeGHVeKwRDRsbcKD1AL58+Zfxbwv/zXJtTtdx84M3Y/OezVg0exEeuuUh\n2/rz3zn54bO1etzZ34mdR3biaMdR1EZrTf62a6DLNJ7tYtrOIzvR0deBy+ovy2l2m11NGF863mK3\ndrPu7Ape0w+apLYOZOcjnGb67WLvr7b/Co+//ThuuOgGrLpylfDcdnXhfWm29pDL2HdqC7fjgC97\ntE/DyPy/7HqcVv743wGw9S1O4zObFaZs2tFJbp4OkNUDsMaX0fqYsX5Kw66tRhsPRDb5l3iChx0b\nH535SLiCypdj52fc5P/kfFC49wgkOmsreIqiXAmgD8DDzA3ejwCc0XX9h4qi/BuAEl3Xv+t0Ljcr\neGPBlhBxOkRsGvZYotGyW0ZTf8J9Odl9Eik9BY/qQWVR5Zjx3vh2GU4NW46ZXjmdfh8JRtAT74Hf\n40fvUIYPokJFdUm1qU35duR5hgumLcCJ7hOUoUMwGGTnJAAoChShd8jKEjLxf4b6AB10E4WxYp/x\n9ef5jLlyiz7zs89YPidMKJbzI+qLO5femRW7itddm+/C2qfX0heAw/4wioJFo267eWvmYXfLbvr/\nc2vmonFN41+kn3jdtfkurNm0Bind2OigLFSG364yGEjZcnCc6snzlbJldMp8w1jz8Vhe4cmuk8Zu\ne7qxQUFxQTF64j223Cw3OhvjhRXf1gBQ4CnAH776B9csRtGW/eQdMTfMqtbuViTTSWiKhqqiKhOb\n6WwxGLe8vwUrHlyBjv4O+pkKFdXF1Ygn4ugc6KTvp0cLo/jdLb8TxjT2HB7Vg9WLV2fFiOKZXADg\n03wADLu9aOJFUnaVqN2JLxuNj3DiZdnF3spvVeJU3yn6XSwUQ+s9raZz27UJ70uzZanlMvbtlA2T\nlC972fxleGn/SzkzZWX+X9YOgOGL2/va0d7XDk3RoKmalFvGcx5537L8kuW4/5X7pXmSqG0ACDlt\n2bSjk2R96dbmeZ9Hcib+fKOtT7bH8MeK2grAqOKBaGzf9ve32Z5jLHwwOzYSqYRpcx7CIeTLsWM4\nu8n/2fPtX7d/UD+jO+54eFYf0VQUZSKAzcwN3gEAH9d1/aSiKFUAXtZ1fZrTeZxu8MaCLUHYFkCG\n06HCyqZhjx0rdsto6k94IEc6jiCZSoJwZjyaBxOjE0fNe+PbZSgp3joaAPweP3RdRyKVgKZqJvA5\nkU/zYVL5JOjQLRwpGc9wfPF4HO86Lt3hilyvpmi0v9hzezUv9p7YCyjAjMoZSKaTY8I+4+2A5zPm\nyi2q/W6t6caYKOwPY/fq3bjmp9fg6Jmj0NO6CaJLpCkaDv3fQ67YVbya25ox7Y5pxpbaIwk+YNjl\nrOpZObfdM7ufwaKfL7J8vuGWDVizec1Z7SfhNd4+DYl0gm6LrUPHxOhEeDWvI7cwmzZl+Uq5MDpl\nvsGOHwVkz2gi16RAQVNbEwX8KlCoT/FqXiiKmJvlRny79Q/1o6mtCZPKJ6HAXzBqP2rHsioPlaMs\nVObIYrz5wZvx8FvWm+8vXvJF/PTGnzoyq+z8MDB23EJWnf2duOiui9DU3mT5jtoMMn0JZHhebExj\nz0HGvlfz4sCdB1wxomRMLp/mAxRAT+uoKanBia4TFnbVodOHpNf37h3vUlvP1kc48bK6BrqksXfb\nvm245be3WM754MoHserKVY5+QOZL3bLUchn7Tk8cuGWS8mUnUgnj/XrNSzdJyYYpK/P/m7+2GZdP\nvlzKcIQCHD512DSeFCgWbhnPeRxMDJp8y8DQAA6fPgzA2LmQH5/PffM5XHvvtaa2YdMNltNGcgs3\n7egkmQ25zV+OdhwVxpf6snqUFJZk7WPcxLZs4p+dzfFc22zjgXBsp42d0idVTBLmYGORu7NjQ4Ei\nzIk3f20zvv3Et2k5PfEeKcMZgGP+z9f7nbvfietn9AKnuv6ld9GM6bpOwBCtAGKyAxVF+UdFUd5W\nFOXttrY225OOBVtCxOkQsWnYY8eK3TKa+hMeCBGpv4Kx4b3x7eIkMtDYLbdZ6XqmTXmOFM8zJH/J\n45iic5KkRVM1U3+x506kEhlOSpa8FTvxdsDzGXPlFg0MDQi/6x/qx56WPZTzAwkbmOXl5cSy0TPc\nH7bNR9N2G3dvFH5OeH1ns594kbYh16coxrUODA+44hZm06aEr0T5T1kyOmW+wY4flYt/ouNczQCQ\nicgYY+17LJhPPBNrtP1t16Y9gz2uWIyb92wW/n7zns227Ur8MM9iBUDZTGMdN4hOdp+UTryZJnBH\nYOYKFAwmBi0xbSg5BMIMJeOf+BI3dZcxudJ6hqk3MDwgZFfZibX1bH0E6RcZL8su9j7+9uPCc5LP\nndqE96W07V2y1HIZ+3Zixzh7PtE44MsmN3Xk+GyZsjL/v3H3RmE7EIYjz88j9eBtkuc88r6F/GWv\nAcjkSeR62bYhkyE8p010rKwdnSSzAbf5Cx9fiP8huVS2PsbNOM/Gj9nZHM8JzjYeiMY2kSwHGwsf\nzI4NcpPKi9g7KceO4ewm/+frLV3t4PTfhknQjcgjraSu67/Udf0iXdcvKi8vtz3XWLAlRJwOEZuG\nPXas2C2jqT/hgRCR+usYG94b3y5OIoYsWxlWlEyb8hwpnmdI/pJtrEXnJMloKp0y9Rd7bq/mzXBS\nsuSt2Im3A57PmCu3qMAvnpgp9Bdids3sTPCRjB6Wl5cTy0bJcH/YNh9N2y2du1T4OeH1nc1+4kXa\nhlyfrhvXWuArcMUtzKZNCV+J8p+yZHTKfIMdPyoX/0TH+cj4YeMHGWOsfY8F84lnYo22v+3aNBKI\nuGIxLpptXWUgn9u1K/HDPIsVAGUzjXXcIKoqqoLfI2a2mSbFdNDxFfAGLDHN7/GDMEPJ+Ce+xE3d\nZUwuVckw9Qp8BUJ2lZ1YW8/WR5B+kfGy7GLvDRfdIDwn+dypTXhfStveJUstl7FvJ3aMs+cTjQO+\nbDaRzYUpK/P/S+cuFbYDYTjy/DxSD94mec4j71vIX/YagEyeRK6XbRv6dAfHaRMdK2tHJ8lswG3+\nwscX4n9ILpWtj3EzzrPxY3Y2x3OCs40HorFNJMvBxsIHs2ODnfhgReydlGPHcHaT//P1Ns3A2ugv\nfYN3auTRTIz8PT0WJx0LtkRJoZXTIWLTsMeOFbtlNPUvKTR4ILFwzDTLFYvExoT3xrcLeZ+C1/TK\n6ZQ1Ew1FoSoqwn4zH0SFiopIBW1TniMl4hkumLYAhYFCytBhV0I8qgce1YOSghIoUEz9xZ47noij\nIlKBipDB0xsr9hlvBzyfMVdu0eP/JJk5/qfHUVdeRzk/slXSNUvWuGZX8aorr0PDogZTshr2h1EZ\nqRxV2y2cuxBza+aaPptbMxfLLll21vuJV115HRoWN0BTNJp8lYXKcN+K+1xxC7NpU5avlAujU+Yb\n7PhRufgn8hsdOsoj5dAU47EnMs6ioSgAOTfLjfh6Aca16NDHxI+KWFaA8Q7ew6sehhsWI7+hCvu5\nXbtSPxyJ0aCvKipi4YwfHuu4QVRSWIJ1y9chWhg1fa5CRSwSQ7QwSlcXAOMdPH7TJv4cuq7Do3rQ\nsKjBNSNKxOSi0oGGxQ24b8V9QnaVjIN359I7TbaerY8g/SLjZdnF3lVXrkIsZH7IKBaK0Y1WnNpE\n5EuzYanlMvbtxI5xp3HAl61AwcpLVyIWjuXElJX5/4VzFwrbgTAcNUWjvoesYom4ZTznETD7Fh06\nGhY3IBYR50kkX2Dbpjws5rSJjs3VL8pswG3+IoovC6YtgM/jy8nHuBnn2fgxO5vjubZ8n+U0tsMV\naFjcAECcg42FD2bHhq7rlnutBdMWYOHchaZy7BjObvJ/Qex0dYP3l34H78cAOphNVkp1Xf+O03nc\nYhLyu2jmd9HM76KZ30XTrfK7aMqvKb+LZn4Xzfwumobyu2jmd9HM76KZ2zH8sfldNMdmF81Z42Y1\n6mld/MI5o7O5i+ajAD4OoAzAKQCrATwF4HEAEwAchYFJOON0rjwHL6+88sorr7zyyiuvvPL6W9Z/\nOwdP1/UbJV998myVmVdeeeWVV1555ZVXXnnl9bcsbc2aNf/ddXDUfevvW3Pl4ivhUT0I+szoh87+\nTjS3N8OjetDa3YpXD70Kn+YzLf+S7/nfitTZ34nGjxpxvPM4Qv6Q6980tzejb7AP+1v3W34rqoPb\nemVTf7buHX0d2Nm8k7bFaOogUvgrYXxv4/fwk2d/gusuuA47m3bioTceQntPO5ram0x9QNTc1oxX\nD72K+HAcZ/rPoG+wDye7T9K/pB7kONE5AOCxtx7D2qfXojfei4A34KoN3fQp249sfdych23LwcQg\nGj9qxM7mnXjt8Gto72tHtDCKwcSgqb3tyuvs78Tap9fiX//rX/HyvpfRNdCF0sJSU1/ubNqJ+1+5\nH1v2bMEvt/8S77e8j7qyOssjHk59LLONxo8a8VbzW2j8qJFygHK1IdKnp7pPYffx3dQGcrG9sRa5\nhpbOFuxs3imtm11/OdlsrnUi9iTrH2KLsmNyHd+51JOM3T+9/yec6D6BaGHUdbnkdwdOHUB3vJuO\nL9k1iPqCbYPv/td3cevDt+JI2xF8Zs5nTOfZ1bwLv3z1l/CqXtSW1Qqv4wvrv4BbHroFjccacf1F\n1wvHvl3d7PrG6ZpGEyf2ndiHp997Gj7Nh8qiStq2rx56FYdPHcbWvVsxODyI/uF+aTkHWg/g4dcf\nRmt3K6qKqoS2ZVeHls4WbNu3DSe7TqK0sFQYs7cf2o6dzTsR8oekcUI0lpx8eeOxRjzxzhOm6xfV\nkbeX1u5Wod129nfi5y/9HD/e8mMouoLzxp0nvX6ZxsI3jEW+IBqrono5+Z5c6kf67aV9L+G5vc+h\n0Fdo6R87+xeNc1G+1Xis0WK7djFbNKb5z8bat4vE50ZsG2w/sN3ir3ixxxztOGp7vFOek6tI2+07\nsQ+ne08Lc2A3ZTrl0051yCXfyfYcgLnN3295Hz/Z+hOoUDG1cqqrXJIvw42dkd/94p5fnF6zes39\nTu1xVt/BGysFq4P6xFUTAchhvm29beiOd9MXaAlUNRugIQuQBtyBPFmIY2t3K3Td2LKfwFQBKzhZ\n9NloYcos8DGZSiKNNN0kYdn8Zdh1ZFdOdRBJBKQ1fQ+DmSUCOab1NJLpJMK+MIZSQwgHwugdzEDK\n7WCQAFD/3Xo0n2mm/+9VvZhUMcm2Dd30qRO41A6Wy8NWebAwYIWFE6AngcOzkHYAQtC5AgUrLl2B\nXUd24Uj7EQwmB4XtL4IDA+6BpQBw68O3oqWzhW7MoEJFaajU9DI54M6GSN8TuyQqC5WhLFR21mDm\nbkSuv623DR39HZQlFC2MojxcbgXbCvrr7SNvO0JKc6kTYIX30v5hbDoSjCDgCZgguEDu4zuXegIG\nyPXRnY9Koc4y8TBoBQpqimukwFpRX0AHbSfyPgar6ZXTAcAybhZMW4D//en/bboO0e/HFY8z+ZDb\nrpTXjfUTYX8YQX9QCl8WQY3J50B2caKls8XEz1x56UpMjU3FnZvvxHBq2PSbsD+MmpIaSzn8OQKe\nAMrCZRbQs6wOBEpNVBYqw29X/dYUs1c8sAIdA2KYuh3w1wlYzgOfV166Eg+vyjANZeOqva8dZ/rO\nUN9EQPAAsPBnC00+q660Dk3/YeUNyuQGYOwkt3mA3XFuoctOvieXPIXE4BPdJ0wxke0fO/sXjXPo\noO8Iq4qKinAFpsamYtuBbfQcQW8QBd4Cas9s7BflBbddeRvu336/yb4+Mf0TtvnIWIjYSCqdQkpP\nmeLij7f82HRNBKLN6pM/+aTpGFb88WMJaGdF2rO1pxXJdNKAyEcqTTmwKHZK87DBPkv/smNdVofR\nAtrdjjW7Nq8rrYPf55der6gMNznEXx3ofKwUrA7qs/55lhTmqygK3m953zjWG0RKT0FP6xgfHY+g\nN+gKaEihiWeO0ptEJ5AnC3Fsam+iHCGfxwcdOsYVj4OmaiZwMgF4jiVMmQU+KlBMkGKP6kEincCU\n2BREApGs6iBS+Cth9CX7HPvMqxpQZBPIEQZLjdw0eBQPUnoKXs0LVVFRXVKNprYmIQyyrrwOj731\nGL7w4BcsZY0vGY9Cf6GwDd30KduPInDps19/1oCLC2C5BJBKYKt7T+5FIiXmOhFY+FByCE1tTZgY\nnWgAy6FDhYq6nnQMHQAAIABJREFUsjok00l09HXgzID81dSaohq0dLdIv1eh4p073nGE4YpsbDg5\njFQ6hWMdx5DUraD682rOg67rrm2IQEF13QplV6BgcmwyNEUbc5i5G5HrT6fTOHT6kAkNoEDBlIop\nUFWVQmcBYwWT7a+h5BA+6vwIiqJIIaW51EkE702kErR/jncdN2xaBwaTg/BpPsyoMiC4oxnfudQz\n4A2gN96Lg6cPGm0ngDrbbeJAYNCsPIoHiqJgcsVkE/RW1BeKnsE6hANhdPR3WMopLihGRbgCB08d\ntHw3rngcigqKEPAGsP/kfvQP91uOUaDA7zVQBGSr9knlZpgu7yd0XcdQcghezYtZ1bMs8GUR1DjX\nOJFIJvBh+4eWentUj7E7m2AsTyqbRP2Zz+NDWk9j38l9wnOcV3MeBT3L6pDSUzh0KgMsJ7iN+vJ6\nvP094x16O5g6IAf+FhcU2wLLZcDnd+94l246IRpXkyomCa+5NlqL+FAcp/usG31vuGUDll2yzPI5\nLxmoPBvf4DYPsDsOgHCs8nGWBaiLfE8ueQqJwUc6jgjj4rt3vIvaaK0UdK6pmmWc67oORVUwnBym\n+VZaT0vjrt/jp3im2qgRs6/56TWmvCCVStF9CckuuKl0ColUQpqPjIVofISOZCoDYJ8cm4yhxBCO\nnTlm+c0r336FbuSx/cB2/P1P/t62DHK8U56Ta3xg88/h1DAd9z7Nh3ElRg7M9iOJnTp0YT4CGL6d\nz6fJWLfLxUcDaHc71ty0eU1RDdr72y3XK/L18eG4Yw7B1+2vFXSek0TQQhb8Fx+Og4JZoVMu3FBi\nyDXQkIJqobgGecrgwAScPJgctICTebjjWMCUWeCjrphv2BXVqBPhb2RTB5Hc3NwBGQ4jC3LkIeik\nbqT9CJxTBIMEMlBsS51GtkEWtaGbPnUCwBK4uAiWywNJncRCWFN6ipZHQNKJVIJuGSxTf8KagLLS\nobuC4coAs0PJISmCIT4cN45Lu7MhChVXzecjYyWdTru2vbEWKZP0A7XLkb8pPUX7H8j4Iba/BhOD\ngG4PKc2lTiJ4L2l3FkgNJdOW7DG5ju9c6gkwbQgx1FkmFgYtEg+9FfUFWWFRFAVdA13C8/QN9qEn\n3iP8bmB4gF7HwPCA8BgdusmH6LpuiUu8nyBxQAZfFkGNyV+3fUj+n1+hIzac1tOWsUc0nBo2ldM3\nKPc7dlBx8v88a4zY5lBiiMZsO5i6HfDXCVjOA5/JX/K5bFz1DfVl4jZTr/hwXOqHZXGIlxuAsZPc\n5gF2x8nGKh/veKA873tyyVNIv8kWEnYc3iGOQyN2KRrnUMzb07MTSiIpimKK/WScsnkBlJGxojCf\njZxSlo+Mhci5WLQKYIwlmf09t/c54b9lIsdkA7rPRqSPAVCbIX1DcmBR7JTlI6qiWvqXHeuyOgCj\nA7S7HWtu2pzkaPz1inz9YNI5hzjnQOfZSAQtZMF/QV8QFMwKhRqb3+t3DTSkoFrorkGeMjgwAScH\nPAELOJmHO44FTJkFPiq6OZjr6ZFH7EYGdTZ1ECnkCTkeA2QSNhbkyEPQSd1I+xE4pwgGCWSg2JY6\n+UPSNnTTp04AWAIXF8FyeSCpk1gIq6ZotDwCkvZqXvpIjEyF3kLb7xUormC4MsCs3+OXBsygL2gc\np7qzIQoVT5vPRx/9VFXXtjfWImWSfqB2OfJXUzTa/0DGD7H9FfAGAMUeUppLnUTwXtLuLJAaeqYt\n2WNyHd+51BNg2hBiqLNMLAxaJB56K+oLdSSM6bqO4oJi4XlCgRAiwYjwuwJfAb2OAp94UlSBYvIh\niqJY4hLvJ0gckMGXRVBj8tdtH5L/5/mkxIZVRbWMPSKf5jOVQx7xFckOKk7+n8SYTCWMfvV7/TRm\n28HU7YC/TsByHvhM/pLPZeMq5A9l4jZTr6AvKPXDsjjEyw3A2Elu8wC742RjlY93PFCe9z255Cmk\n32QThldMvkIch0bsUjTOoY/c1DH5luz85Hs29pNxyuYF0DM3FvSzkVPK8pGxEDkXeSqAXK+qqlL7\nu2bmNcJ/y0SOyQZ0n41IHwOgNkP6huTAotgpy0fIjTafT5OxLqsDMDpAu9ux5qbNSY7GX6/I1wc8\nzjnEuQI6z1l2MN9EKoFoYRQe1WMMlBGo6rqb1rkGGlJoYqjCNciThTiWh8vpO2+6rqMiVIF1y9dZ\nwMk83HEsYMos8JEsCQOgj6+uvHQlNEXLug4i9d7X63gMmcWxgByRmakK+UJQVRWlhaUADKi8T/NJ\nYZAAsOySZagrNT8a4VE9KPQXStvQTZ86AWApXFwAy2WBpIPJQcTCGbAwKxYWDhjXpaqqUY4OCmm/\n98Z78ftbfw+RCGw2HAwbTkGitUvXuoLhygCz65avQ2VxpcmHqDDqSh7VcWtDpO/ZWT2iaCgKTdHO\nCszcjcj1s3ZI+q20sBSqqpqgswAs/bVu+To0LGqwhZTmUicRvJftH2rTehploTJUhDMQ3NGM71zq\n2TfUB1VVsfLSlVKos0wsDJpIgYLKoko0LG4AD70V9UV5JAMkLguXCcupjFRCURTLuFkwbQEeuPkB\neh3jS8cLf19TXEPHfiwSM/ocsNSN9RO6riNaEEUsEhPCl0VQ41zjhNfjRdgfNn238tKVWL14tfXG\nC4Y/8nq8pnLSetpyjoAngMqiShPoWVYHTdGo3wSMZK8sVIZ1N2XA3uuWr0O0QAxTtwP+CqHGDLBc\nBHxeeelKypyUjSsyflQmFYoWRrF+xXo8tOoh0+eA8X6Nm8czATmoPBvf4DYPsDtONlYBOUBd5Hty\nyVNIv8XCMUtMJP0ji0P33ngvAOs4jxXFUB4y8i1N1aDrOmLhGBZMW2A6f9AbRLQgaty0MbGfjlMm\nL4gVGWM6FsmA3GPhmG0+Mhai8XFkNRHIxMX7V95vuaYF0xaYOGtXTrvScozseKc8J9f4QMdmuILe\nOHtUDyrCmRwYsMZOWT5CjmX7lx3rsjqMFtDudqw5tXldaR3CwbDwekW+3k0OwdcNfw2g87HS+Rec\nr/9+8+8dYb5dA11CqOpYAlFlv+HhwOxv7QCeYwlTZuse9AZNYNDR1EEk8i5eoVaInQ070dzWjNeb\nXsec6jkI+oO2IMcJpRPg8/gsUHQ3MEjA2EXziT8/gU/P+jQum3yZqzZ006dO4FK78/BA0v2t+3Gi\n6wTO9J/BhOgEISzcCf75H1v+A396/0+YXjEdV828ClfNvMrUl81tzdi2fxvS6TSOnjmKWdWz8A9X\n/EPWMFyZbexv3Y+WrhYMJ4dxWf1lKC4oztmGSJ8WB4vRFe+iNnC2YeZuxMN0ZXWz6y83kNJc6iQD\n3PK2KDvmbAPjRSBXN0BtXuR3Po8PNcU1jsBaUV8AmTZY/dRq/PHdP+Jz538O9y6/13SePcf3WKC0\nfDlfWP8FbPlgCz4969PY8E8bsoLpOvWN0zWNJk6097bjvZb3KPCYtO2elj3QFA3NHc2YUzMHZeEy\naTlHO45i6wdbMal8EhbMWGCpv1MdhpPD2HtiL8pCZZhfN18Ys+1g6nZjycmX88BnWR15e5GB4Dv7\nO/Hgjgfx2qHXcOP8G13f3Lm9Hrcai3xBNFadAOpAdn1vB5be37ofh1oPobW3FVfPvNrSP3b2Lxrn\nonyr8VijxXbtYrZoTPOfjbVvF4nPjdg22H5guxCizYo9BoDt8U55Tq4ibdcb70U4GBbmwG7KdMqn\nneowWkC727HGtnnvYC827t6IpXOXYuHcha5ySb6Mcwp0PpbKg87zyiuvvPLKK6+88sorr79luQWd\nnxMcvHv+3z1rLrzmQiRTSVuGRuOxRjz8xsP0zljGsCCsnObTzXj54MsWZtBDrz+ELR9sQXGwWMjS\nYZUNdyab3ze3NeOJt5/An4/+mfLPnOpAOF4s+67xo0bsPbkXbb1tpvY70HoAT7zzBAaHB3Gi+4Qr\nRhzLhyn6ahEanm7AD57+Ae5Ycgd+tf1X+Maj38CmdzfhhX0voLO/E7XRWiFfyIn3tKt5F+7ddi9O\ndp9EbWmtkCNFrqvpdBNO9542XRvPvmntbsVDrz2Ep959Cl3xLottuOWesBLxlkwcqP3bhEwltzyz\nls4WrHtxHe7Zeg/2te5DaUEp5eOwx2zbvw3bD23HI28+gpcPGLbMMq54ppFblhe5PsLMYnkum97d\nhLVPr4We1lHoL7RlVvE8JcLp2rZ3G3724s+gp3VXbCmeETMaNlE2jBo7jpJTGSLWl9O5RazI/a37\n8fjOx3H/K/fDp/kwtXKqpSxRHWWfi67TDXfKzfggPrjxo0YcPn0YqXQKze3Nrtigsn6y860sK4n4\ngL7BPvzrhn/F1zd8Hfta9oHs6Eb6oPFYI+5/5X68e+xdVEYqhfZTeFshbt90O37w9A+w4rIV6Bro\nwhNvP4Hn9j6Ht4+8jZ7BHmiKJmRz2cWozv5OPPves9i8ezMK/YXwe/xZcU5zaTMSS149+CpaOlvQ\nHe+2jaXP7H7GxHRyKp+1s2QqiZ1HdmLb/m1obm/Gy/tfxoZdGxD0BBEJRmgbvbj/RWx+bzMKfdY2\n4OM4b3/Nbc1Sf56NL2d9yNb3t2Lt02vR2tWKU72n4NN86Browj1b78Gdz9yJZ3c/i1gkJuWQsWXz\nY+drv/0avvK7r2DHwR24pP4SV/6KZb0OJYdc+x87nhrfV81tzXj6vactXET2GJ6nO1p2nsy2nMTH\nIzdjjnDKjrQdwesfvg6f5oPf47f1ic1tzfjTnj9hx6EdeOnASyj0FeKV/a/g9qdux/HO45hRNcPC\nTzwbjDxSj5NdJ9E72GvqA5GIvbiNp6zsxnU2nztdj4iB7BR7s/V3butsxxZkbRywsn+JvvP4d3Db\nb29Dy5kWlIfLhfxNGZdTFntlfFAyJrft34atH2zFC4+90LWmYc0vnNr9nFjBU8tVXfuc8SxuVVGV\nkNvBM3DCvjAe/8rjFoYFfxwRYQbdsfEOy+csS4dVNtyZbJgcd22+C2s2rTHtTPf9pd8X8ld4jhfL\nvnvpwEto7W6lL3pqqobKokr0xHvQM5jZUU5RFHhVr5Q1wjNj+B3bZFKR2Z0KMN5t+OZV37RwoIAM\nh+lI2xEMpjKcKq/iRaw4ZuJI3b/9fnpdpBxVVekAig/H0TtovCvo1/zoHTa/N8jaRjasQSIRb+mm\nS24y+qGnjTKegAwLjL1G9rpFzKzBxKCJJUXPRW4WdeMa2XJYRQIRVBdXm5hbfUN9pnapCFdIOWP8\n9RX4CpBMJVERrsCJrhMmLhSQ2eCBZ1bxPCXe7oic2FI8S+qKyVfQHd/4cp3khnfI1nswOUh3X3TD\n4iFliFhfLJdT1B+EuURZkf4wBoYHqB8gmlszF41rGmlZIs4jAFv+oy13imkbAK5YWIDct6qKSt/J\nkLFBs+EVidijgOHHiguKhWPHp/lwx6I7cPDUQUsdCTeSyInzSa8LBtoFAN1xz47ztOX9Lbhh/Q0m\nzhwZq7J2cNMedse8feRtUywBQHcJrIxUWmLpvDXzsLtlNz2W2JqsfNb+0noaqXRKumFOQDOYese7\njps+Z9uguqjaxJcKaAGURTIcvvkT51v6j/hzwD37kfUponjGbprGSsQhA8z8LspsA3C887jlWN7e\nePGsV8CwXycuL8+ajRZEUR4pF/ZVIp0wbaQV9oVRU1pD4+vpntOUy6Ypxjb3Mr/rlp0nsy0nWXI7\nfxhFBUW2Y07GKSOxDLD6cz7vkmlcyTiE/CEhe3ksGHmiepA+EJXB20s2rEa7cZ3N507XY2Ig+8MY\nSg7Z8uHcMBj5urhljPo1v8kO2THN+oVUOoWSwhLTe4qkDp5bPUhBbCfknkHG5ZTFXhkflPA/T3Qx\nHMmnAL1NdwxU58YNXoWq60uNevo0H+rL6wFkuB2NxxopA4dVbWkt3m141zRzKzqOlgPVkrwCGZYO\nq2y4M9kwOR699VFc/IOLhVyog//3oOVZeZ7jpUCBR/MgkUrQTWfYQOXVvMLt/H0eH20znhHHsuTo\nLj5ZiA2WHtWDyRWTKT+K5T0NJ4dx6PQhy+89igfnjTsPQ4khHG47DF3XhYmEz+MzeGupBN2tjZyf\nV21pLV769ksWJokTL4y1IZa3VButhU/z4fDpw5Z61ZbWwufxWXiIKT2F42eOm5hZlIMiEdmRzAnJ\nUBetQ0tXC+rL6xHwBvDBiQ9ouxAmkK7rFs7Y/SvuFzJevJoXuq4L60ZsBwyzimdO6WkrB4+VjC3F\ns6SGk8NIppPwal54NW9WbCInDhDLqPGoHuw7uQ/DqWG6yxXL3bJ7vl/E+vKoHkyITkDQG4RX81r6\nI5lK0usiPCQ7bf7aZlw++XIh53Fc6ThAR4aVBzP/EbD6JzJOPKrH1DZkhzU7Fhbg7FuJjYjYoNnw\nikjdWfYoIE/IWWnQhEHZo3pw8C7DtxbeVoiBtBiVIBLZOZduIKRAyHnq7O/E+Xeej6MdRy3nmFE1\nA6qi2nJOc4kn8eE4jp05JvUnfo/fxMB6/fDrWPTzRZbjNtyyAWs2r7GUT7l/Z0b4qyN9YSdZP82o\nmoHh5DA+bLOy/DRomD1+NgaGBihrkde44nEI+oKu2I+sT4HufsKSiOWQAVZ+F4nDiWRCmE8oUPDh\nDz6Uvl8uYr16NS8UKFIuL70mZFizCgyOpKZqpr4CIIyLtaW1ONF9AoAR29j+1BSNMmtZv+uWnSez\nrc1f24yFcxdaG3lEMr9CJrIBWMbcT67/ibAsIhoDGX/eNdCFabdPs41RbFtMrZxK+YmEvew2Dtmp\nua1ZWA+S1ylQTGXI7MUNq1HmV0SsNrvP7fIlkV0Chj1riibkw7lhMPJ1HxgawIdtH6K+vN7CJmXr\n3NHbgSNnjljq+cq3X8H40vHUL2iKRjfGIuxfUod/f+bf8ePnf2zbthtu2UD7hc0TX/n2K7jtd7dZ\nYu9wcpiOTZYPuvP/7MQN99+AI+1HzDbh8gbvnNlFk+Xc8dwOdlaJ1UBiwMSwIMfJdhgVOWPZ+bPh\nzmTD5NhxeEfmLp0Tz1/hOV48A4iI5fzIzk24IzxrhGfJ5SQlUyeeH8Uy+Xrj4h06dRg3beRGSLQl\nMuEpkX+Ldm1kNZAYEDJJnHgwMt5SfDhOmW60TiN1GEgMWHmI6QSGEkMWZpaTFEWBID+yiDgnVTHY\neiyTi+1HnslFGC+iushsJ5VOWZhV9DpHeEpOybeMLcWzpEh9Wa4Oe5yd3PAO2XoDI+0wYr9OLB5S\nhoz1Rbictv2hmFmRMm3cvdEyNokdDSWGTHXg+Y9C7tTIOGTbhl0Zs2NhAXIfTETaQcQGzYZXxPYh\nyx4lZeSitJ6mfZ/NzR1g3qKd7B4n4jyd7D6J+HBceI6+oT5HzimQfTwZTAxKcSfEJtlYunH3RuGx\nZGzy5bM8MSd7JZL5gb6hPvmNlmIkh3YrK/3D/a7Zj6xPIdvTZyOeg8Xyu8h5dV2X5hOsr+El84Pp\ndNqWy8uzZtmJHb6vZDYxlBiiCAJeMr/rlp0nsy3Z50Q0Z5PYl2jMOZ0zract/jwbtp2u6+gb7DPl\npGPFyKPcWJjzAR063YGcLUNmL25YjTK/IsuLcsmXLAzkkWsifkfEh3PDYOTrztunrM5kF3Nez+19\nzuQXSGxRoFD2L6mDXduS8skxfJ5IfAcfe+OJuDBn2HF4h8GRdJP0ieqT06/+G8Ry7nhuB+G38Crw\nFpgYFuQ4WWPxWyLzv2OVDXcmGybHFZOvkN5I8fwVnuPFM4CIWM6P7NyEO8KzRniWXE7SM3Xi+VEs\nky8cDAt/rkAxOFIjQU4UoAhPifybZbGIVOAtEDJJnHgwMt5S0BekTDdap5E6FHgLrDxE1Qu/129h\nZjlJ13VXG+QGvUFaP7L6RtqF7UeeyUV24RLVRWY7mqpZmFX0Okd4Sk7Jt4wtxbOkSH1Zrg57nJ3c\n8A7ZegMj7TBiv04sHlKGjPVFuJy2/aGbWZEyLZ27VMp59Hv9pjrw/EcZ/9CjekxtQ8eTAwsLkPtg\nItIOIjZoNrwitg/5iYOcg6Ci0r4vUMUcPJlYf6RAkXKe7N4JJ+/eyto213gS8AZsE2Pii8m5ls5d\nKjyWjE2+fJYn5vZJIJkfCPlDFpYfU1k64y9Toa/QNfuR9Skkac5GPAeL5XeR8yqKIs0nWF/DS+YH\nyeSmjMvLs2ZZnhrfVzKb8Hv9FCLOS+Z33bLzZLYl+5yI5mw2ExX8mHM6J1lRYf15Nmw7RVEQCoRM\nOelYMfIoNxbmfECBQicj2DJk9uKG1SjzK7K8KJd8ycJAHrkm4ndEfDg3DEa+7rx9yuosw0tdM/Ma\nk18gsUWHTtm/pA52bUvKJ8fweSLxHXzsDXqDwpzhislXGE9t5Th5eW7c4I1Q3jVFQ3m4HICZ28Ey\ncIjCvjDWr1xvWjoWHUe08tKVWLt0rfBz0XbL2XBnsmFyzJswDw2LGkzBTIGC1UtWW5b+yTlMHK8R\nh77y0pWoLKqkAUyBAo/qQSwSQyRgBv6SVSERa4QyTkaYMdIgLJAKlc7UAMY7ZKsXrwYAIe9JVVUE\nNPMA9CpeVBYbHCYdOhoWNZiui5RD3vGJRQwOna4biXnYZ71pJLYhYpI48WBkvKX1K9ZDUzWUFJh/\nGy2IYv3K9UIe4rqb1lmYWZVFlSaWlOlchcb3sXAMpQWl0jpGAhH4vX7KHown4qZ2SafTqAhXCDlj\nV0670jJGCnwFUKAgFomJkxYdFmYVz1OKFVntjsiOLcWzpFRFxYJpC4ygmiWbyIkDxDMNK8IVKAuV\nGTdJHHfLrgwR62v14tWUyynqj8pIJVZeuhLkXVnAeNdElNTOrZmLhXMXWsYmYT2tu2mdmZXH8R/t\nuFN828TCMUcWFmDvW1VFpf5FxAbNhlfE9iFhj2qKRt/zk40dn+bDmqVrhHVcvTjjW/vv75f2reW6\noCIWiSEWiaEiXGHEJgnnqaSwBOtXrLdw5iKBCH13zY5zmks8Wbd8HVYvXm2xIb6tyLkWzl2IuTVz\nTcfOrZmLZZcsE5bP8sSIndslIgEtQN+1E7WBV/Na+FIBLYCqkirEE3ETv41V2BfGAzc/4Jr9yPoU\nmWTXwXPIACu/CzpQHi7PvJ/J6ftLvy/1VyLWK6mPHZdXxJotKSiBpmqWvoJglS7kMzYcISw4Xdep\n3ZD3v0R+1y07T2Zbdo9nApLczh9GZVGldMwtnLtQyikr8BVkYiDjz+vK69CwuMF2EoGoqrgKaT1t\nYS+PBSNPVg+yssOXIbIXt6xGmV+R5UW55EusXZJVtrA/DAWKlA/nhsHI112HTsuxq3PAF7DYIRnT\nrF9IpVOU60keESV1+NENP4IGuZ2svHQlll2yTJgnXjntSmHsXb9yvZAPOm/CPIMjWWTlSLrROfEO\n3ozZM/RfP/VrVEYqbRkajccasXXvVtSX1+OT0z9p+x7VjsM7UF5Yjrb+NgszaGPjRnTFu3DdvOuE\nN3essuHOZPP75rZmvLD3BQCg/DOnOhCOF8u+29+6Hz2DPYgEIqb2O9pxFDsO78CcmjnweozVCidG\nHMuHiX0zhgQS8MKL4QeG8avtv8J/vv6fiBZGURYuw8emfAxL5i0R8oWceE97ju/Bxt0bMXvcbDob\nx3OkyHUpuoJwMGy6NsDMvuka6MLGdzeitacVF9VdZLGNXHhhIt6SiQN1cq+QqeSWZzacHMYf3v4D\nGj9qxLzaefj8BZ+nfBz2mL0n96JzoBOHWg8h4A3g2tnXmhhXIj4f29eyayfXR5hZLM9l6/tb8cSf\nn8D1F1yPi+svtmVW8Twlwunqi/fhnWPv4PoLrncVjHhGzGjYRNkwakRt5rYMEevL6dwiVmRrTyte\nP/Q63mt5DzdcdIMlKZKxwZyYYdlyp9yMD+KDC/2FKCkowcyqmfRxYSc2qKgNnXwry0oiPiDoDeLO\nTXfixQMv4pqZ12DRvEUmO2k81oiNjRsRCUZw3bzrhPZD3sXzwosDPzgAAHhh7wto629D0BvEzOqZ\nmFoxVcjmsotRnf2d2LZvGz5s+xBXz7oatdFa122ba5uRWNI/1I/xpeNRXVJtG0uf2f2MienkVD5r\nZ5WRShw8fRBHO46itLAUp7tP48P2D7F07lLMHjebttE7x95BU1sTrp5pbQM+jpO2ZfltMn+ejS9n\nfcjOpp144s9P4GOTP4aJ5RPpbP5vdvwGOz40coWvLviqlEPGls2PnW9v+Db+9MGfcEndJbj7C3e7\n8lcs63VmzUwA7vyPHU+N1JH0VSKZwHst71m4iOwxPE93tOw8mW05iY9HbsYc4ZTVR+sRT8ZxxeQr\nUButtfWJzW3NeOPDN9Ad70bvUC+unnk1Dpw8gEd3PYorplyBVVesMl2fiL08FiL1KAuVoSJSYeoD\nkYi9uI2nrLLhctp97nQ9IgayU+zN1t+5rbMdW5C1cRH7l+g7j3+HtvlNl94k5G/KuJyy2Cvjg5Ix\nefDUQZzqOYXvLv7uXj2hz3Jq93PiBi/Pwcsrr7zyyiuvvPLKK6+8/pblloPn+UtUZqxEZvYmlU/C\ngukL6CpUXbQOKT2F+FAcO4/sxJxxc3Dl1CulM5R2MxDNbc34xcu/wPvH38eXLv+SdDaE3FH3xnuh\nK8ZNciQQMa3CyGYeRLNg/MwFeyfPzyLIZi3ae9vxZvObtH1IPUSzJfzMgttZGXLcrNWZyQP9AZ3O\nZnxs8scwv34+nWkjq4oTSifQmfwzfWfwetPrmFM9B0F/EMXBYnTFu2h78LMr/OzPG4ffwJYPtuD6\nC67H1eddbZnJJ6t2bBs3HmvEozsfhQIFX7j4C7Yrs3arV3arI2QG5mjHUdRGa5FMJvF60+vCWSK+\nPdl+J79/5I1H8OTuJ1FVVIW7/9fddMaTlLNp9ya8f/x9qFBxuO0wCvwF+M7V38HnLvxc1jNsvJ7Z\n/Qz+653/Ql15HaZUTMG44nHoindhQukEnO45jfa+dlw2yWqbMtsmbbXlvS3Y9N4mVIQqcLrvNJbM\nWYJllyw7Hp0KAAAgAElEQVSzzMgeOnUI7X3tmFk9k+6wuvPITihQcNWMq4TjRjQ2nOqT7aqS7Pfs\njOv8uvmuVpycypat7rLX9kbTGzh0+hCa25rxvy78XxYbIbbEriTbXQtgXVV0Mx6I/vE//xFPNj6J\nj0/9OH558y9d+RLZSr7db0m/8r6DfLfygZV49/i7mF09G2s/uxaxcIzOgrM2S66Xfxqg/v/U07Ka\nftCE7ng3bYOmjibsOb4HS+cuNY1r1k+JVi3J9/GhOJ7f9zw0RcPV512NBdMXuJqhBoBdzbtonwa8\nAboCM5gYxNGOo/B5ffBqXuEM8KbGTXjv+HtYOs9YTRP1e89gD17e9zLeOvIWll+83ORLAGDDzg14\ns+lNXHvetZg9frbFFprbmvGbHb/Bux+9i/PHn4+rZl5F+4dtd9Y/iz4XrUDxca4yXIkplVMcn+xx\nI34FhIyLQ62H8MCrD+DD9g+x4uIV+MonvkKfSCEruHbjobO/E//yu3/B5g82o6aoBj+6/kd0jNo9\ncfTDZ36Ix95+DEvmLMGn53zaYksyZfNE0b3P34uH3ngI59eej+9d+z3h6hC/KkmewOCfXHHyNWTl\nbmh4CO+1vIePTf4YplZNtTw9xfrqzv5OvLj/RTS1NWFW1Syk9JR0ZZL8tq2nDdsPbcel9ZfiskmX\nmXIPWa62q3kXjWcA8MaHb8Dv9SMUCCESiGBvy15s+WALPj3r07hs8mVCH32i6wSGEkO4bNJl0jgD\nWPNC0dNXxQXFpievEskE3mx+U7qiDQBf//3X8cd3/4jPnf85fOvqb1likWxlDICpLJmNiX7PPzFG\n+kEUB0lbtHS14Ez/GdRGa+n4IefRFA0fnPwA9eX1uHDChTQHEOUZbL3YpwfIOOTzP7b+XQNd+M2O\n3+Bg20GsvGSl7UqyXYz6/qbv4/l9z2PR7EW4dva1Fv8hyr1ZH/Zey3v03sVpBZhtv+HkMKDB1btS\n58wK3syvzDQxJZy2kweMrZN5zo8dx+OuzXdZOHgipgjhybT2tJrqoCkaioPFCPqDUn4Hz/S6Y9Ed\nFj5Wd383ZbeRc5ZHjHcPedYH+f+WzhYTX6nAV2C8p6XAwvt65K1HTG25YNoCuj2yqM7sdX/rsW/R\nAeUkr+pFIp2g+AlVUaWbiZCNVGqKaixMl5buFspQ4eVRPaiMVNKkWVVU+D1+452NkWeWZ1TOMHFP\nADnfkGeXBLQA0kibmC2AmV9HmHI/feGn6OgXs+lE/CSe+0IeC5Fpbs1c/PD6H2LFgyuk5RCRgOKG\nU8OL5xURsdv9AkafRUNR+i7P/Inz8diux+j3xLZJW9ltRz6ueBzdtvxE9wlTOQFPwLT7lQoVa5eu\nNY0bwDo2nOoDICs2GyDmzn1i2ifwyFuP0F3+ykJl+O2q39py2zRVsy0b4BiJgTCC3iBCgQwP7NGd\nj1rGhMxGooVR/O6W31m4bOy1hAPG+2GUzRepwNSKqSaeFMtE5H2riB/3p2/8ydaX8Nfspg+ID02m\nk/Rlfa/mpe9g8D6ciOx0VxQoQnmknHIOO/s7TTsRZ7OZFBnXpE48O4ywy94+8jbu3Hynaatwokgg\ngsdue8yW89Q32IeeQTFHUnatLEfps+s+axpDXtVL38OKBCPQdd0AoHP2RIDUfYN9ONVzyrRVtwoV\nHs1DfaOIUQdk3h8qKTSYUm09bege7KZtTfqDcMUAWDhugGEbJ7pOmBmuMHaeqyquErIH3YjniMVC\nMXg9XosfEl1XSUEJegd7hezDLe9vwWd+9hnL7+bWzMWc8XMsbLfH/8lgsxZ+pRADSfNurm44eNlw\neY+1H7OUwfP5eLafR/NgYDjzG8J/dfI1snjCnmdqbKopL1o2fxme+vNTFoYtYPjXslCZiRt85+Y7\nhfGF2Bg/rj2qB0XBIiRSCddjCjCQTZNjkzM++qFbcbL7JPUfmqJhzZI1ljhDGLdsXgjAxM4l/ino\nDQr5p4ARC8tCZTQO3LPsHlz7s2ulG0yVhcrwjU9+I8PDZfhyhMXaNdCFlG7shF1ZVGmxMRGfLj4c\nN3GXie9l+X0kDgKwctxg+I9Cf6FtzkOOKykooTmwjMEJjNzkDcepfwEMW9p1ZBcAWDjFgJzHaBej\n7HLgWCiGklCJhbUry9WBDKdVxrq2tN+T0PV23fGlvHPiBm/mnJn6vkv2Zf07wlwD4Min6xrowtTb\npwpvIlimCOXCdRyVJqxezYtZ1bMs/A6e6ZVMJ6GndYyPjrfwsQAzM2hW9Syk0ik0tTVhUvkkFPgL\n0D/Uj6a2JlQVVwn5Sl7Va9xUKcZOT3VldYgPxylvg3V6k8onobigWMo2IdwRtzd3uUi2nb6I58d/\nT3Y+8nq8lPET9AaRTCelzDiebyhj7rDMlmQ6aeLXAcYOTiSxsxPLT2I5Lh7Vg70n9rpi8FSEK2hC\nbqfxJeMRCUYcOTW8ntn9jC1DSKTzas7DUHIIh04dMnGS9LSOmpIanOg64dg+ZBMgRVFccak8qgcT\nSicg6Asi4A3QsUDGRm+8FwdPH4RP88GjeSz1oX2np12x2QArExIwXsY2IRVg7BRWX16Pt79nPFbO\nc9sUKAazEbqwbBkj0at5MbNqJgaGB4S8SKLKcCVae1stn9eX1ePt29+mM4nstZAygAwnKpVKCW3S\nqxl+hWWoffe/vosHXnvAcmxRsAjN/94s9CUiDp9THxAfStAprD1AB5K6fNKP9S/TK6ejqa0Jw6lh\nqd9xqw23bMAXf/NFS538Hj90XUdVcRVOdJ0AdEjH+MToRPz5jj+bZolN/uHkXkf2JS/CUVr888VC\n4Dbxa7Ktw4nGFY1Da2+rMDaqUOHz+FBTXIMP260MO15TY1Nx8JTBsiP9zH9O+kOBQuM3YIxVEScP\nMODF9eX10KFn5e9kHDHCfXMjn+aj44GUDwB1/18dvWF1o9rSWtx86c34/rPft3xHbqZlHLxsuLzH\nO47jVN8pSxkKMnw+nu2X1tNC+xtXPM4CrgcyvkbGv+OlQYPmMWw9kUrY2roCBZNjk6EpGh699VFc\n8oNLpBPAbpTt+B9XNA5BfxCpdAofdX4kZBaPKx1H4wz136oXs2qMvJDlEBJ2rtt6eBQPZtXMQjKd\nxEdnPpLeoJLzeTUvJpVPgt/jp3y5qZVTcaD1gKnuCgzW3sToRGpjvB/a17qP5jukzmTXy3Q6jZSe\nMsXB2mgtNFXDR2es7ZStZlUbT42JGJzDyWF6raScoNfoo+HUMKZUTIHP48MHJz4QnpvnMdrFqFM9\npxwnBaqLqnGm/wzlyU4onYCjHUelubpH8UBVVQtHkcTpIx1HzO33P4mD1zfUl9PveM6PHU9oT8se\nqUNnuReEPQXId9qS8Tt4phf5y/Kx6DmgmLbDHxwezDA1OObHUEIMmE3D2HKV5X2RGTjK2xophCTV\nMraJHetkrDQajhV1KsyEhZOz5Nlddswdwmzh+XV0ZyMX8YHlJ7G2mI3j6xt0Nxa6BrpccWp4OTGE\neBFODNmmmOzgRmxuYHjA1aqIDp3CxN0oracxlBzKcHC4sUFXZDjmHakP5cPBHZsNsDIh2V2tCLeH\n8O2GEkO23DZynKhsO0ZiIpWgbS2TLPgMJgZNXDb2WlibJ2w+p/HjhqHWN9gn9SUiDp9TH1CuErej\nGOlnO7Fso/5B97tlOonEB9GW+4pijA/o9v5oYNjMbM3VP9BykeEoDQyJ2X4EueLkd8/Ez0jHJcHr\nOE3KkHbviffQfxP2Kv85YOa4JdKGbdiVkUbawh50Iyl3LotVXFIuW/7J7pNZ29jA8AAef+dxcX1g\nz8Gzy23472QrJiw3jPotGVZppJ9k5yK+Jpt4Qny0HbqClEv4fk78TVdyMQZY9SX6kEglTCB4vn5s\nnKH+e4TnSHx8Mp2kvpzPx5xEzmNnY8SnkxVMli8XH46bfD7LX2VtjPdDdOxCN8XctJ42dmfl4mB8\nOC7NT92IbY/B4UFbBifxKfTfI2MSMOxlcFg+kcXbqV2McpODkXsWYtOkn2VtoajGNYhY17lOXADn\nyA0eWdbOVjznx44nNLtmtnSLXJZ7QdhTgDxgy/gdPNOL/GX5WPQcRkZAFfAFMkwNjvnh9/qF9VBh\nJLAs76vAV2D6LbkGgj+QsU3sWCdjpdFwrMhvTYmqg7Pk2V12zB3CbOH5dTQRcOGXWX4Sa4tsvzuJ\nPJrhJLIa68Sp4eXEEOJFODH0xmqE1UNsrsBXQAOMnRQYqA63WwGTR3EpB4cbG5QNyTHvSH0oHw7u\n2GyAlQnJJoGUvTjyCLLf6zf5HJ7bRo4TlW3HSPRqXsebGRmOIuAN0Prw12KaGBlh8zmNHzcMtVAg\nJPUlIg6fUx9QrhKXgDvd9AKZoK9DR2Gg0PF4tyLxQQTN1nVjfDglkQU+M7M1V/9Ay0WGo1TgF7P9\nCB7Hye+WBkul45LcpDnhc0i7R4IR+m9yc8h/Dpg5bl7VsA27MlSoFvagG0m5c1lsSU7KZcuvKqrK\n2sYKfAW44cIbxPWBPQfPLrfhv+NRHUQsN4z6LcmNLukn2bmIr8kmnhAfbQefJ+USHJQTf9OVXIwB\nViFvCF7NC7/HmneR87BxhvrvEZ4j8fEe1WN6koT9vZPIeexsjPh0MsHK8uWCvqDJ57P8VdbGeD/E\nsv/YmKsqqsFQ5OJg0BeU5qduxLZHwBewZXASn0L/PTImAcNeAj4xAw+w5j12McpNDkbuWYhNk36W\ntYWeNq5BxLom9xu56Jy4wSvwFVhYKG4uOhaJAchwfux4QnXldZTRxopnipQUjrCnwhWWOmiKhmhB\nFLFITMjv4Jle0IGGxQ0WPhZht+nQ6TmJodyx6A4Qdhn5/6A3aHG0Bb4CxCIx47llhve1fuV6C59j\nwbQF8GpeW7YJaTvybpcbeVUjMSHsNLJiIhJZ6hcxXVRFlSa1hO3nUT3GzJ9uBB3yWKcCxcI9AcR8\nQxFzx6/5wTJbeH4dYcqtWbIG0cKotC14fhJri4PJQaPfJcGSaG7NXDz0Dw/ZlkNU4C9wxanhJeIV\nEfFJjwKFcmI0RaMcN9a271txHyoiFY5A4ZriGsSKYigPl1vK4eGkKlSDK7d8HR3LgHlssMwsUX1o\n37lkswFWJmQ6nUYsEsPKS1dCUzQa/MpCZVh30zqTz2G5beSRSFnZQkZiYRSxcAyDyUFoqtHWIh84\nt2YufvMPv7HYSLQwinXL19Fr4q+FlMGy+WJFMQtPijAReYbaL7/0S2G/bvjHDVJfIuLwOfUB8aFk\n5RHIvIe1eslq3Ln0TshEHjOKFkShQ6ecQ9besmUNLZi2AMsuWUbrxLLDdF1HRaQC61esR8OiBtNK\nLKtIIIL7VtxnulaLfwjLOZIisRylB774gGUMeVUvTdLKQmWU6cVLhYrCYCEqI5XUn7PfeTQPysJl\n8Hq8thxEwpRSFAXRAqOsdDpN+0NRFFoHnuNGbMOrea0M15E2L4+Ug2cPupGIIxYLxVBVVOVoC6qi\n0nHGsw9LCkuw4R83CH83t2aukO22fuV6rP3sWhR4rDfk5F1HGQfPLrfhvysJlQjLYPl85DeAkTso\nUOjkMNHKS1figZsfsPU1dvGEPc+apWtoXqTA4PiKGLYAEA1FoSka5QbfsegOaV/RVSbue4/qQbQw\nikggktXNnUfxIBQMGT56+TrEwjHTwoCmaFi9ZLUpzhDGbawokxcSm2bZucQ/yfingBELK4sqMZg0\nHsF97LbHpDkViUUNixoAwMSXS6QSlMVKfZaqIRaJmWyM90MVoQpUFlWauMsA0LCoAWuWrLHEwfUr\n1mPdTesMhi7/1AVUx5yHHEdyYBmDk7RdRbiC+hcyUbDy0pXQVI2+18tLxGO0i1EilierWCiGSDBi\n4slqqibN1Uk7ijiKJE5b2s+l0Z4T7+ARTEJ+F838Lpr5XTTzu2jmd9HM76KZ30Uzv4tmfhdNe3vJ\n76KZ30Uzv4vm/8xdNJf/3fI9elKfI/3BiM6pG7y88sorr7zyyiuvvPLKK6+/Rf2P4uB1DnRi7ca1\nCAVCSOkpXD3zauEKzOonV+N3b/0OF028CF/9xFfpzCGZfSWzRY3HGvFU41Pwal5Mr5yO6uJqevc/\nnBzG9578Ht45+g6WzFmCb179TccVnMpIJQ6ePoiOvg66q5TP4wPZbIGd1QHMPL8LJlxAy917Yi/8\nXr+pPmQ14+iZoygpLEFNcY10Nmj7ge146PWH4NW8mD1+NkoKSjCzaiae3/c8tr6/FdNi07Bk3hJM\niU2h9Z1ZZayQOF1j0BvEsTPHjFXSoztx9/N30+tp+kETrr33WhxqPYRYOIbKkkpcXHsxLqi9AN2D\n3Whub8b8uvmoj9bjzeY3jfcl9DQCngDeaH4D1cXVuGbmNZgSm4J4Io7mtmb88d0/wqN68KlZn8KF\nEy401e2yuy7DrqO7MLN6Jr7xyW9g03ubMKtqFiaWT6QziGR1d07NHHg9Xhw4dQCNRxrh9/lx48U3\nojZaa2k/fqWQXX0ks4ZkZohfOXCzEiISWYWJhqK4eOLF2HN8D57b+xwur78cN//6ZnQMdECBgiVz\nluArC75Cr23rB1uhKRr6hvrw3AfPYffx3Zgam4rffPk3wrEhminkZ4zY2arXD7+Ojbs34pPTPmmZ\nqedn39mZJd6e+JnfLe9twRvNb6C+tB4+nw+L5yzGkvOXmFa2gt4gdh3ZBR06Lp54sakv2DL5thXN\nmonGid1xsplURVfQO9iLoaQxnrvj3dj6wVbEIsbjXC8feBlvNr2J6+Zdh6tmXWWM55N76Uw/u2pH\nymLbiIxHdsV+z0d78Pg7j2M4YdTpVN8pTCmfgs9f+HksnbcUO5t24qE3HkI4EEYoEMLHpn4M88bN\nw96TezGcGsauI7vQ3NaMmy+92bQLMLuSSGa1++J9GEoN0f5jZxzZtu4a6LKsYLDHzrx9Jlr7WlET\nrsGv/+HXONpxFKWFpaguqbb4LVHfNLc14/l9z6O9tx3loXLMr5svXMkhq1G1pbU41XcK18y8BuNL\nx2NPyx4caTuCbzz+DQBAQAngZyt+hkQqgf6hflw982oAwH++9p/oHerFzZfdbFnN6hroMq3g3X3D\n3bhwwoXYf2o/FCiYFptGfXxNcQ2C3iCefe9ZNLU3oSJUgaHUEF0l41eX97fux47DO/DnI3/GFVOv\nwLXnXYvWnlb0DPagb7APQ4khumotmokW+Y1YOEbHiwYNrx5+FfPr5uPvJv0dtcGLJ16Mre9vxSO7\nHkF9tB5VxVW4epbRFjsO70B5YTna+ttw8ORBPP3e0ygLl2HN4jW4fPLlpvH95uE3seX9LZhRNQPj\nSsfh4rqLsWTeEgDAIzsfwabGTWhua0ZHfwdm1czC39X/HU72nMRnZn2G+pGDpw9i0+5NOHDyAD4z\n6zOYXzcfz+x5BoOJQVw55UpcUHsBtry/BW82vYkbLrrBNMPeeKwRP/rTj9Dc0YxbrrgFq65cZfJf\noqcqRCv8/Pj859/+M15reg3XnnctNvzTBnrc+pfW476X70MSScwfPx/P/etzlhUE8rRNUaAIu47u\nQnGwGEvnLaX9df7q89F4ohEBLYD/+Px/YOXlKy0rKLwvW/3kajz+zuO44cIb8MmZn8TG3RtRX16P\nyRWThSst/AqL6Jz8qvfelr34w5//gPJQOb664KuoK6+z+ER2dauuvE74RERzWzM2Nm5E10AXrjv/\nOtRGay2x8MX9L+Ltprfxuzd/hxM9JzC3ei5+f9vvpXGFnJs8zUOe9iHvKL2w7wX0DfVhQukEVBdX\n0+vcfmA7basp5VPoinw2T01seGsDntnzDMrD5Vg4dyH2HNuDJxufxIIZC3BZ3WV4avdTmFQxCav+\nbpV0ZYnvl+a2Zmw7sA2zx83G0rlLXa3gAUAimcCze55F10AXplVNw3XzrrOszO9v3Y8vrP8CjnUd\nw8TSifj5TT/H602v4/L6y1FXXif1tXYi9XrhgxfwVONT+MT0T+BrC76GroEuPL/veShQUF1UjQ9O\nfGDksbUX0Fh27wv34sV9L+Kz53/W9HTO/tb9OHDqAE73nDbl8OQJEeKDyKpWcbA4k0vrCoZSQ8In\nE9zEfP7avr/p+3hh/wtYfvFy/NvCf7P0Bes3AeDBHQ/itUOv4cb5N9IVugu/fyGazzRjRsUMTK+a\njuf3PY9PzfgU/vi1P5r6nTzFNXvcbKx9ei227duGj0/9OKZWTTU96UbidiKRwISyCab4SvLC+mg9\n4sk44EHQsRNxjqzgKeWKjuvMn/EcM/9tfgynzbtsEf4aq7rSOhPzhsijelDgKxDuQEcYNCYOGsPf\ncNrlhrBRbl90u4WzpioqCn1mFogKY+el4oJidA50WthjpQWlFqbKj7f82MSrshO/HW/YH8ZQcsjE\ntgIyHC43CICxkKYYz1EPpaw7DY0rGYeQP+QK0+DTfI67uoV9YdSU1gAw2o9wqkh/EvYaeUE5Wmi8\nJ9LZ30k351AVFR7Vg2Xzl+Gl/S9RfAHhSlGe2AjvjOcX3bX5Lqx9em3mRVyBvfLyKl4kdPtd9fix\nQXgubb1tFnYN4a6wzJcPT31oKoMyvcIVlBumqRpS6ZTBsRnsFdpT2B9G71AvVEV13Iqd3W2KbwPi\nvDVFg6IoCHqDdAczwoYCYGI2VUQqjB/rMDGD6HEMowYj/L14IsP20XUdhf5C9A/1j4ntkzaPBCPG\n+1AjfMqwP4wzA2cymweMsLV6Bnoc+zkb1ZXW4Rcrf2Hi8YnaGgDljy2/ZDnu334/ZTn5PX70D/dn\nNsCAiuriasr4kfHnyHWVFpYi4AkgFAiZeExAZgyu3rTasrEDz0PjmW5EueIOWCac3+N35DK5VcAT\nwMSyiQBA21KEKxCN+7JQGWXTkXcvyHjl/cbZlFf1IqWnbHeV9KpepNIpR99F3sV2Oo4X4VTxsRMw\n3ndpvaeVsqJ4NqmIGdve1472vnbbMqdXTpfGGsJ23PL+Fqx4YIWFq0V059I7pWNieuV09A32GY8O\nMtzJB774AJb+v6WWXIaVpmioKqqiMcXEKRvqQ3woTm3YDYeRyK/56bupAEzcRU3R8PdT/960ayVh\nn/FjlrBjAYOt2T/UL/X/JLcaTGTiCjn3S/tfMuU05F1b3h+riorqompEC6MW3p4KY/MPsqOlE3t0\n6c+XusL0EEULoygPm/lsRKRfjrQdwWAqc/0BTwBPfvVJWgc2LpO4M5gYpJxP/nrWLl1L+Za3PnSr\nEFPBllUWKrP4WjtWJKn3oVOHrG0tyVFIXioaC+NKxlFuHivyHqqInSnz5Tzf003M5/uE5wYWeArw\nh6/+Ad967FsW30ByGFaxUEyIGWE1vXI6jnYcpY/q24nwokVtUFNcg/a+duv4cYlJOGdv8IAMx2z1\nk6uF7BjTORRFuDuiWwU8gQwHLZ3C8a7jxgqdYKtckbyqF0/+85NZM8ak52NYe90D3baD3G39VNVg\n+ZAbuuNnjkNR3F/j2ZRX80KDZnKUo9X0yunQVA3x4Tg+6vwIChTbACirVyKVgFf1QtOMTV7IYCQ8\nsbSeRm2pmV/U3NaMaXdMM7YeHqVtikTGBuG5pNNpHDp9iO4uRXbm23/nfhQXFFPmS3w4jqb2Jsv5\nNEWDpmmUBcPDx3mxm9xkm9DJxDp9n+YztmdOpzGudBy9CW1ubzbt8qUoCmZUzjC2WR+pu0f1GGwn\nZHa/A4wdr3LdydWN/B4/5dmpMHZ+ZZOJXG9Q3KoqUoXigmLK47NTfVk9Pur8CIDx8n1aT9P24/uh\nNlpry+Vj5dN8mBKbYhyvAzOrZyKRSiA+HMexM8ekNy1TKqYAAAaGBtDS3eKqrGzkVbxQVHcMxmw0\nNTYVqqLiw7YPKScqWwW9QeN3OvD8t57HVfdcRbcsP5v28tekn93wM7oqK/ru5y//HEc7jlp2851c\nPtnEjB1fOh5NbU2O7aZBQwrivvKrfuy7ax+uuvsqoa90o8pwJToGOpBIJTJxIp1Gob8QnQOdjr/3\nal5MjE7Ec998Dtfeey00VTM4ui0fIJE2n7O6uBotnS3QoTtOCpDJDh06tTEgs6cDyznldyjmz6Oq\nqqvcgTyhAWRsPZ3OjmtHWLgyKXBmj/YP9uNEzwnXZRKdV3MedF03cTtJ3GW5w6yqI9UoDBRCgULj\nMiuW5ya61l3f24Ub7r/Bld8lk4vE19qxcUm9e+I9ONGdfVuI5GbiOhsRvueND9wIALYxn++TabdP\nQ1tfm+Wc5aFyFBcW4/Cpw2PiU4NaEPGU882dk6R+6H8SB08mMpskY8eYpOfOWTN+znDQRmDFIl6a\nSKTcbBljtudkWHv9w6NnOhEOh6qYWW9/LdJ1fUxv7gCgf7DfcPDJQWOLftW6SyQg5uKJRLYKZn9H\ndg5NpM38oj0tezLcmFHYpUxkbJAyKReO48XtadljYr70DMgBnixjhtwcyUTazA2fLCeNzMoqivEY\ndDKdpNdGZkLJroU8e4hl45HjANBV2bMpNmE6a20jUe9gr4XHJ1MimTDsk7FhkciOm9locHiQ+k/S\nN4PJQdtJDsJDy5WJ6iRd0V2hTrJVT7zHsKkst2InIjdxZIX7ub3PjTqWnYt67O3HbL8j459nk/LM\nWLc38LKbOwAYSg9hT8ueTB6QQ190xbsAZLhhxJeR1Tw3SqQTlJtFOWUjPo89J2XfumFFKrpp63ko\n5vjH+1jpyq7iPm6yPERi69lwCAF33EJb9mgqgZ4he3i1TPHhuIXbSf7KuGd9w33GDQgXl2l8sPGF\nhG/p9gaYxDgRm5kX+Xws/azTzV0244ew7kh+Y7JHWGM+3ycyRmzPYE/WccxOY3FzB2T/tAOvv54M\nPgcRBoqMHWOSkjtnzfg5w0Hz+M1O0EGk3GwZY7bnZFh7hb7RM50IhyOtm1lvfy1SFAUBTc4xyUWF\nAeNRCPLIHD/AWWaZGxHYJ/s78jinVzXzi2bXzM5wY87CLDwZG6RMyoXjeHGza2abmC+RAvlW7KYb\nIdkVdzUAACAASURBVNiDsEmbjaXTNBcAyhjye/2mpCCZTtJVUR26hT3Ezjyzq6cs2+dsibQZu3L4\nl1I4ELbw+GTyeryGfTI2LJKiKFnfqAZ8Aeo/Sd8EPAHbhJDw0HJlojpJ0RWchWGISDBi2FSWMGUi\ncjNOkrlrZl4z6lh2LmrZReLdrMl3ZPzzbFKeGevE6yPSIMe6+FU/ZtfMzuQBOfRFcbAYQIYbRnxZ\nOOC8bTyRV/XSd9Iop0zXLeek7Fs3rEg9c4M4UkEhL434TumEmO4+brI8RGLr2U60uTnelj2qeRHx\nu8eQsAr6ghZuJ/kr456FfCEDCcLFZRofbHwh4Vu65aORGCdiM/Min4+ln1UdbjOyGT/kJo7kNyZ7\nhDXm830iQ81EApExnXANaq5ekXOUU9s5//4cFcsxW/vZtfCpVsfNNg5xNjzzhsijeqSd79N8Zg7a\n8nWUv+FmkGmKhobFDVg4d6GFf6MqVhYIYftEC6NC9hjP2nvg5gcsvCo78YlG2B+GomTYViyHS9d1\nKZNlrKUpGvya2CHGIjFMLJ/o6jxugnjYF4YO47GKdcvXUVYMeQeAcKNI+0cLowbPRNFo+5GVjZWX\nrkQsEjPeaWK4UmR1oyJk5RfVldehYVEDnQkF3A1mr+IMPWbHBuG5qKpqYdcQ7grLfPF6vJYyyOOP\n5aFy2gZpPQ1N0RAJRIT2RN4l82geC4NLJI/qMR4lEbRB0Gs4S/LOIxkv6XQaFZEKrLvJYMkBI7xH\nHSgPlyMWiaEiVEGZQYRjo0M3MWpiRTHEIjHaPpqq0WsbK9sn/VwWKkMsPMKnBFBaUGpe1Rtha3mU\nsd3/qq60Dr/+8q9h4vFJ7K20oBQ+jw8NixqoXUPP9Ct9XxAqysPl0FTNlj8HGH1XFipDRbgCyXSS\n8piID1u3fB1WL14tTNRKC0opD+3BLz0otadcV7W8qpc+weCGy+RW7E1rw6IGVBWLkypRP7BsulTa\neDzzjkV34MppV2b8xl/gJs+reh2TZ6/qdeW71JH/stXcmrn4+qe+LuTsxUIxfP1TXzeYWAzfMhaO\noWFRA3hmrFfzIhpy5ohOqZwi/e6pf3kKdeV1WLd8nZCrRWQ3JooKixALx8xxIlKBR259RJjLsNIU\nDbGwwSurK6+jvjueiCNWFDM4j8w571txHxoWN9iuxAMjzFdFMXznCHeR3HhpioYF0xaA5ZyuXrxa\nOGYDWoDeVEQLo7b+36f5KI9SUzRq66sXr7bkNCxnkpWqqKiMVAp5eyqMFV1N1RzZo7/68q9cTwAQ\nRQuj9LFHlsFIYmrQF7TkNAFPAL/68q9w7433muIyG3dYzid/PasXr8a8CfNw7433YlzxONv6BTwB\nVEYqTb7WjhVJ6h0JimOfbPySd/BEqi6pFrJ7V166UsrOlNkpy/dkOY2ymM/3ycOrHracu8BTgIdX\nPWzk3JxvEMWDWCgmrBur2vJamrc4ieeLEilQUFlc6Sp/kumceAdv0sxJ+hf//Yv5XTTzu2jmd9HM\n76KZ30Uzv4tmfhfN/C6a+V0087to5nfRzO+i+Te5i+a/XPMve/WEPstycZzOiRu8PAcvr7zyyiuv\nvPLKK6+88vpbllsO3jn7iGZeeeWVV1555ZVXXnnllVdeZp0ToPOh5BA2NW5CW08bffzkpotvwp7j\ne7Bx90a67P35dZ/H9kPbUVlUiW986huoKa6hj0wOp4bh03yYWTUTjccbsat5F2LhGAaGBxAJRFBS\nWIJTPacQ8ofwyFuP4NDpQ1gwbQF+euNPTY+ayZZ9yTJzXbQO3YPdSCQT8Hq8pkeuyG+b25px99a7\n0Xi8EV+67EtYdeUqy+OQP3z2h3jtw9dwef3l+OH1PwQAbHx3I460H0FFUQWqS6rh03y4rP4y/P/t\n3Xl0VNed6PvvrkFVJalKQ5XmCQmBQAwSYDHZxjbYODG2seMBD+HFHXf7trtzk3556XRysxJnvCu3\n857T8Uq6E8cZnNiOE88YDxiMm8nYzBIgkBASmud5KA1V2u+PGiwZARIYJBW/z1osdE6dqtpV+9Q+\nZ5+9z+8HBKcmvXv8XTLjM1masZQB7wAZzgw8Hg9vH3ub5Jhk1s1fh3vITWljKY2djcxLnucrrz/U\nf2pMKjXtNVjMFiKtkcHpIIEpj4HP9/AzDwc/+45v7GDN/7sGD55g+fNT83nkukfo7uumvLWcRHsi\ntR21tPe2MytpFvlp+fT191HeWk5nbyflLeU8tPQh1s5fyy+3/5JNRzaREZvBA8sfwOvx8vbxt6lt\nq6WkoQSzwUx7fzt3LryTVbNX8fy+58lJyCEhKoH5KfNZM2dNcLpHemw6e07v4ZWDr9Dd1018VDyL\n0hexatYqCjILRk1fGTlVMiYihrcK3+Klgy8xP3k+1866lkRHIrtKd7GjdAeNnY3Ud9bzpZVfYuPK\njZxsOEldRx0DQwNUtVbx3L7naOxsxOP1cGPOjTy54clgPY2c1vn7Xb/n3ePvkp+ezz/d+E9Ut1UH\nh+KfeOMJart8IeGXZizlgWUPkJucS0dPB68Xvs6wHqazt5M9ZXvoGfLdZ7ImZw1r568NToEITAfO\nistiSfoSXj34KjvLdvJQwUPBaXsBI6c7/OL9X7C5cDN2qx1XpIsHCh4g1ZkanIbc4e4gPTadxu7G\n4NTCkVNV69rrONl0kq6+LgoyC+js7eTNojfZWbqTdne7734Kg5HrZ17P9m9uD75/YBrHkaojWIwW\nHlz2IPnp+WdNDTpSdcQ3RTIqgdkJs885bXmszzdy2sSqnFW097bz/sn3KW8uZ3nmcspbyzlac5T1\neetJi00LTkkZOTXPbrXjsDpGTe0urivGFemiILOA9469x7N7nyUrPoulGUtp622jb6gPW5iN3OTc\n4LTNwBTJxo5GGrobyIjJoLm3mYUpCylvLWdX6S5sZhtlTWWcaT3DNTOu4ebcm+no7aCsqYzC6kI6\n3Z3YrXa+uvqrLM1ayt7TezlWe4wdp3YQFxHH19d+3dd+FL5BlC0KPayZlzqP7LhsiuuLae9rp6q1\niqSoJBanL6amo8aXE0tp2nrbqGiqoLCmkNVzVnPPknuCUwrrO+upbK5kXuo81sxZAxCcthJtjeax\nGx5j1axVlDb52pqCGQXE2eMoqi0KTk0JTKkJTHPLTcrFPeTmVMMpGrobmJc0j053Z/B7Bd9UnzcP\nv8lHFb4psTnJObT2tGLESFFdETWtNfzp40/yQKbHppObkEtCdAKfn/d5ShtLeXrX0/T093B3/t18\n9ZavUlxXjMfroa23DYD/+6VPcmTds/geZsfNpqSphNzkXGbEzmDbyW3BaVqZcZk8tfUpnv/4eRId\niaDgzrw7MZqMwf3IYXPw4r4X0WhWzVrF3oq9FNcVkxGTQfdAN4nRieSn5eOwOM6aVjbkGQp+Z2aT\n736NREci7xx7h30V+7hvyX2kxKTwXvF7RIZFMuAdoK2nje7+buxWO5WtlZyoO4Hb46amo4bUqFRu\nzLmROHscN825CbfHze7S3ZxpPUNFcwWHqw+jtOKeJfdw64Jbqeuoo76jnvX569lcuJnnP36erLgs\nvnbL14L1/v7J99lfsZ+egR6KqouoaKkgzZmGI8zBoapD5KXmsXbBWpZnLsdld/HDTT/k7WNvM9M1\nk8dueIwoWxSpMb52ZXfpbt49/i5r5q7he3d8b9TUyr1le/nu69+lubuZpOgkvrH2G7S723HYHNyU\ncxNlzWWUN5ePeRtH4Hd+rPYYmc5MZifODt56sO34Nrae2Mr9S+7nB3f/wHc8OL2XX27/JXsr9gKQ\nE5fD3u/sHdW2jGyXAsf5Qc8g+87sQ6FwD7hHpXdYPmM5G1duxGQ0EWYKC56TBKbkBvbvO5+6k5a+\nFhIiErh5/s3sr9jP6jmruWvxXSit0MqXZ3XQMzjqHOPTU+IDt6WMLO/e03uxmC38ywv/Qk1XDQ6r\ng8dXPc4Dyx4Y1dYalZEDZw4QZYti/aL1wSma24q30TvYS2psavA2lJbuFnaV7eLexfeSk5Qz6pYY\ngKe2PsVfD/yVoqoiejw9xIXH8czfPUN6bHowV9iQZ4iPKj4iKy4ruF+9sO8F9lfs574l97Eub53v\n/U9sQ6NJiUqhorWChSkL2X9mPztLd3Jn3p1ERUSxv2I/JQ0lnGo+xfzE+eSl52ELsxEbEcugZ5B0\nZzo1rTW8cvgVMuMyWZ2zmkhrJPWd9bxV+BZlTWVcl30dX1/7dQp+VECruxWrwcq/rfs3VuesxmV3\n0dLdwkcVHwVzJI+8RSEw5TIwzXDQO8igZzB4C8mnp9e+UfjGqGPOL//7lxysOEi6Mx2jwciQZ4i1\nuWu5I/+O4D4Cn0wpn/uduQzoASKMEez73r5Rtzx8elpqoK0913THpKik4G0gA0MDNHQ1MC9lHjPj\nZ1LeVM6xumMUZBYEbwcYOVW9uL6Y042nOVxzmCxXFgtSF2A2mvmo7CO2ntjK0hlLMZl899EvSFlA\neWs5JkwU1hbS1NlEeWs5yzKWcWPujSgU0RHRwd9IYD8ZeQtM4JyyuK6YwppC5ifNp9/bz/yk+RiM\nhlHTPUfeUvPU1qd4ft/zrJi5gg0FG0a1t4E2YeR52sj6WZWziormCrL/VzbDDGPBgtlkpsfTgwUL\nT218Cme4k+SYZKpaqni98HVSolOY6ZrJ60Wvc7rxNClRKbT2tRIbEcushFmsnbuWA1UHOFZ7jEdW\nPMLa+WtH/Z7/473/YFPhJmbFz2LNvDWhlejcEGfQ+q7JK6czwkl3f/eoROAjkyeOlYA1wICB2MjY\nYACTghkFZ20bZYnCbrPT1N2Ex+uZUGjUQAjuzzLPyEgmg+9AFAi1PB2YldkX9pyxc/QEuCJd/PnR\nP3PgzIFRiYOd4U4cVgcVbRXjfs9AQvTzboMBk9F3TWVDwQZe3PfimAkuPyuZsZnn/QyZsZmU/x9f\nHqdg0tCBnjGTMV+IyWDiwaUP8sHJD6jrqJvw/vjO197hH/70D2O+d15KHicaTgSX5ybOHZXQNnCD\nt81iO2cy18Dn+3Ty0byUPMqby+kenHhyawMGDAYD4WHho8Ivjzc/2Xj2malorHJbjdYJpzGxW+xE\n2aLodHeOK7m4w+rAbDCfM7H0ZAkzhJ03MfVEBYMYGYxjtl8Xyvl1JQUCAJwvJ+al+tH6H/H8x89T\n2lA6oXZl4/KN/OlRX0f/3WPvcv9/3T+u3/m5EmoHBBKd/3jzj/nRZl8glWE9TJQ1CuCi9k9XpAur\n2Yp7wD3h5xuVkWhbNLYwG03dTZiMplHH68D38OPNP+b7m75/3lyMgbZ2rHQSq3NW80HJBxMK7rNx\n+UY2Hd5E50DnObcZsz0xWRn0DI6q74TIBJp7mi/buc5EnC+3W+Ck/Fz5bZ0RTp77++eCServ/uXd\n4247LSYLkWGRwfbSYrKM2XYalTEY5TSQ7B7gi7/9YnD/GitpOEBtW+1FHQ8vt5FB1pRB0dbbNqHj\nZ/D8S3NWex34vgJReAO5AzcUbOClAy+Nat8udF71WTAbzCQ4Emjqbho7tcvVkOj8SjIbfdHEMl2+\nKx6B5IlHqo6w6EeLLvj8+SnzGfAMcKpx7MSUgeTTn3WiXXF+6bHp1HXUnRUy/3Kxmqx4tfecSUyv\ntBf//kXWzl/Lyp+uRKFGdaTGa2SHxqiMF5XM2Wqw4sFzzhPXQILdIe/QOb87s9HMvOR5ZyVzDSRv\n7R/q50zrmQmXTVw+n3USXBGakuxJ1HePnbvrfA5/9zAZzgwW/XDRmAmnL4YJE/u/u59l/3sZKF+b\nN/Ki0cVQKEwG0yVd8Asz+hJ5j9U+bv7KZu7+z7vH9foX24aL8ctyZbHt69tY9e+rqOmY+AVVi8kC\nGga8508kbzVZGdbDpMam4vV6g7+BkakTAknDjQZfFNPA6OBUdL4E8NP5vc71/h6vZ+wLKldDovMr\nKRA+PxAmPJA8MZBw8UIhut2D7vPmodFcnkS74vx6BnpGJRy/3ImuhxkO7ktTwcuHXg7uy5eSB+ZS\nEy/3D1/6CMC5krkG/r+cowzi0lxtibvFxPQMXlzi5d1lu6nvrL/kDthIHjzB434wwfIl7r+fRT7U\nsS5OBsr1RuEbo5avhMt9LJ3O+of6OVp79KJnRimlgrOUzrmNL/kmSvmm0bqH3MFznGBi+RFJw61m\nK739vRdVnlA02fuvd9h7yW2C/ALHyTvsu6I1rIdHJU8MJFy8UEXYwmznPYFWXJ5Eu+L8Ii2RoxKO\nX+4pcwYMwX1pKrh38b3BfflSEm9fakNkNVx6EvtzJXMN/H8p+WTE5XW1Je4WExMZdnGJl6/Lvo6k\nqKRx56QaDxOm4HE/mGD5EvffwAn3Jb3GGMmxA+Van7d+1PKVMB2nn18pVrOVBSkLgknoJ0prjbrA\nAI5GBxPOW8wWbGZb8BwnmFh+RNLw/qF+IqwRF1WeUDTZ+6/RYLzkNmFadPAm++quM8KJ4pNE4COT\nJ+an558zWSP4TuhdkS6GvEMYlXHMbaMsUaREpwS3nwiDurjkseNlMpguuhGaLGZlDibOPh9XpIvf\nbPwNT9zxxKiE485wJ5mxmed97qeN52pPIP9fIDn6uRJcflYu9BkyYzPZsGxDMLmpRpMac/7EqWPR\naEwGExuXbyQpKumi9sfX/udrJEYljvlYXkpeMMGuQp2V0FahcIY7SXAkjJnMNfD5rGbrWSd6eSl5\n2MMuLrm1Ad89AQ6r46zyjOv50/QK91jlthon3nm2W+wkRyePO7m4w+o4b2LpyXKhxNQTZVTG4JS9\nsVyoXbuSrCbrZb9w8qP1PyIqImrC7crG5RvJT88nJiKGX2/89bh/5+dKqB3w5tfeJD89n+/e/l3Q\nvou/JoMJZ7jznMmeL8QZ6QwmKZ8oozLiDHcSZ49Doc46Xm9cvpF1eev43h3fO+/nAl97eK4ZJoFE\n5xOxcflGoixR591mzPbEZD2rvhMiEy7ruc5EnK8cgWPMWB1u8J1P/urhX5EZl8lvv/TbCbWdFpMl\nmMheo8/ZdhqVEZPBxLD2Jbv/1UO/4tcbfx3cv7TWZyUND4wYXezx8HILjDw6w524Il0TPn4qFGHG\nsDHb68D3FXhNk8EUPE/7dPs20XPDi2E2mIm3x2M2Xto54rS4B29B/gL9kz/+RKJoShRNiaIpUTQl\niqZE0ZQomhJFU6JoShRNiaIpUTSvyiiaP7jnB5LoXAghhBBCCCFCwXgTnU+duR7n0enu5LE/PUaW\nM4um3iZSHCk4bA4qmivYV7mPuYlzuSPvDh56+iFa3b4QsPcuupdb5t+Cx+Ohsq2S4eFhDAYDkWGR\n1HfXM9M5E6PRSLglnDkJcyhpKAEgJzGHu391N23uNmJsMRz87kGK64p57qPnyI7P5pbcW4KjF4Er\nNS8fepn9FftJjk4myhbFkaojJEUlsWTGEo7XHqelp4Ubc25kfup8HFYHbT1t/Ozdn1HaXIoz3Em8\n3T8iZwojITKBwzWHOVx5mO7+bmbGz2Rl9kriIuNoc7dxtOooAAvTFpIdn83c5Lkk2BOoaqvilQOv\n8EbhGzgjnXxx6Rfx4GFl1kpeOfgKm49txmrwBZ9w2Bzkp+ZT21mLxWwhNSaV1KhU9lXtIzsum3Rn\nOoXVhTgjnNyz+B7MJjMljSWcrDuJx+uhtaeVP370x2D9bP7KZm7/5e2j6syMmYKZBeQm5hJmDqO+\ns56c+Bza+tro6O0gMjyS1q5WPF4P7iE3tR212K12Pjf/czR2NrLl+BZyk3Nx2p0MDA1gMVvQXs3J\nxpMcrT7KEENYlIVVOasY9A6S6EjkQOUBEuwJPLziYWpaa9heup0FyQvo6O1ge+l2vF4vRpOR67Ku\n49rZ17I8czlmkzk4itLd3017TztvFr7puwKrFR39HazIXMEDyx/gVMMp6rvqSXIk8drB16hsr+Sa\n9Gv4xue/wenm02TEZHC4+jD7KvdxtMp3A3VMeAxfWf0VFs1YRGVrJbERsaREpwSvvP3k7Z+w5dgW\nwsPC+eat36R3oJdn9z7LvJR5o9JpFKQX8ODyB1FacbLxJDtKdtDj7qHf209Lb8uo7/7pLz7Nzbk3\nB68+Ba7KAfzsnZ/x/sn3uXvR3SzOWMyBygMMege5K/+uUVdvd5fu5tXDr9Lp7qRvsI/UmFSy47KJ\nsEZw28LbWJ+3nlcPvspvd/+WTFcmj9/wOGaTma7+ruCo74dlH/Lcx88RHhZOu7ud2tZaCqsKGdCf\nRP5y2pysy1vH8qzlzEuexwclH7Dt+DaO1R8jLSaNx298nKz4LMqayjhUeYjk6GQcNgdbj26lobuB\nREciRoORR1Y+wtKspRytPRq8auqMdGIz2SiqLSLTmUldRx1FtUWUNpYSafXd01PXXodCMcM5g1h7\nLJ19nTR1NlHTXsO6hevITsymsLqQqtYqPMMeGjobiImIYdg7jHvIzarZq3h4+cP8ZsdveK/4PW7I\nvoENyzfwn9v+k20l24iyRvHIykewhdnYcWoHma5McpNzSY9JJ9Lmu2Lc1NVES1cLbx17C0eYg7z0\nPBakLaCiuYLXDr1GbVstHu3Bi5e58XPZsHwDByoOsKNkx6hw1jnxOXz7tm+TFJXEd177DoerDmMx\n+n4j12Rcg9PuJCYihqPVR6lorWBF1gri7HHsLd9LY0cjGa4MVmSt4MPyD2nrbWPFzBXEO+J54/Ab\n7CnbgzPSyZq5vtHx6pZqwm3h3L7wdu7MuxPwjZ7Oe+KTi4rLM5dz7+J7yYjLCF7dfnbPs2w5voUb\nZt/AmnlrRo1AvH/yfXaV7qKlu4XG7ka01mS6MjEZfdOn053pFGQUEO+I5webfsDuU7sxKANDw0Nk\nujK5c9GdMAybizZzoOqTC4JGZSQjJoPrZ1+PQnGw6iBH63ztaFx4HF9e9WX2nt6L1Wwlzh5Hfmo+\n//rKvwaf77A6WJm5krquOhakLQAP7K3Yy9rctTy47EF+/d+/9v0eB3vweDz0efuwGC3cNPsmMhMy\nWZC8gKLqIt478R7x9vjgiFh0eDSLMxaTEpPCFxZ9gT2n9vD+yfd5eNnDwVkBhysPc7z+OLnJuSit\n2FGyA7PJzPzk+ew5vQejMjIjbgYn609iM9lYNGORb1qWPZ6Ovg4auxrpG+ijta+VXSW7aOnztRVx\nEXEMDA+wIGkBN8+7mbToNIobinnn6DvBSLp2q52f3/dzoiKi2F26m86+Tt459g6NPY1E26J59LpH\nuWXeLcyOn83BqoNsLd5KdVs1TZ1NtPe1kxqdSnlzOS19LSxIWkCsPZaSxhLMykx5SzkazbIZy9i4\nciPbS7Zj0AZWzVnFe0Xv8d+n/ps4exyP3/g4Ga4Malpr6PX0suPEDj44+QEePDjCHNyZfydLMpaQ\n5kyjoqWCt4++jXvQzcK0hayevTq4L6/JXeMbcagrptPdSWN3I3aLnerWalDw9AdP49Zuwgjj+X98\nHpvJxjO7nuH1wteD+4FC8YdH/oBC8cHJD6hsqyQjNoOlWUsxGUwU1xWzu2w36a507si7g2RHMk9s\neoKPKj4Kvka0NZoX//FFHFYHxbXF/GbnbxgeHuaW3FtYkLYgOKqi/uGT6X3hxnD6vH2YMZOXnsey\nrGUsSlvEM7ufocPdweOrHmdVzqrgTI01c9bw6sFX+duBv7FuwToKsgrodndzqvkUxXXFNHU2UdJQ\nQllDGW7txoSJjLgMFiYtZGbCTA5UHKBnsIcVWSsobSpFo8mIyeB4/XGy47LpG+rj+uzrmRE3I3ge\n9MNNP2RL8RaWpC3h9vzb0WjSY9KDoxhPvPEE1R3V9A19ElTk87mfJzEqEa/2ctv823j5wMt8UPoB\nBRkFfOGaL5DuTKekroTXC18nJz6HzPjM4MhsRXMFL+x/gbr2Om6bfxv9Q/3sOrWLHSU7KG0qxWQw\nBaOSZ7uy8WgPFqOFvIw8XBEucpNyqWytZPPRzfT09xBpieT+a+4nOjyan275KTWtNVhMFpbPXM6W\n4i3BMl+bda1v/0pfyN+t/DsqWirYV7GPMFMY7X3tXJ99PTaLLTiqVVxbzKbCTTgjnHi1l/uvuZ91\neev4tJ++9VNe2PcC16Rfw4pZKzhYeZB3it7B4/WQ5cqirKWMvsE+7sq/i3uuuYf0WN852s5TO4kO\nj+bJrU8GX+ulf3yJI9VHONNyhgRHArERsTR0NlDRUkF+ej4Lkhaw89ROBrwDDAwNMDdxLrctvG3U\nKPL2k9s5WnOUl/a/REljCVERUazMWsncxLnsO7OPGFsMf7/q7zEbzeyv3M+phlP0DvUSZYvio9Mf\nUd1ezeyE2aRGp1LaVEr/QD+NPY0kOhLJiM0gwZHAzISZpMek47A6ePPom7x24DX6vH0k25P5zu3f\n4Xd7fkd5czlZriyiw6M5WnuUghkF/OTunwRnebx77F2O1x2nd6gXj8dDUnQSrkgXqdGptPe3c0P2\nDTyw7AGO1hxlS/EWVmatxGQy8eXffZm67jrSotPY8c0dAOw9vZfTzac5Un0Eg9GASZlIi06jtLEU\nPaxp6Gnghlk3kO5KJ9OZedY5b8D/uP5/UN1eTUxEDMmOZLaXbCchMgGX3cVLB17C7XX7ooZrD+nR\n6eQk55CXlseR6iN8ePpDIiwR3LvoXuzhdkyY2HZyG/vK9+HBQ7Q1mi8s+QKEkzDmm3/KtBjBmwpp\nEsZiNVklMt8091ncID9RBgxEWCLGlfvrYhmVkYeWPcT+M/sB6Onvobaj9ryf9Xw5kCbCZDAxPDw8\nKeHvJ6M+r3ZhxjDi7fEXFe4bfPuqAQNDemqkDhFiskylHIdTVV5KHkW1RdO2nc9LyePI948ElyMe\nj6DPM/l5hgO5GFt7Wj/TqLPiMpA8eEKIWfGzCA8L51jtsXHlNpIOkhBCCHH5bP7KZtblreOnb/2U\nb7/+7ckuTpBcYJgmJA+eEGJ4eJgh79C4Q/5+FuG+hRBCCDG2QF7Cvx746ySXZLTpMOAjxk86mEJt\nJgAAFLBJREFUeEKEMIPBgNloHndI4c8iYa8QQgghxhbIS7jhmg0X2PLKOldqBzE9SQfvEkji5Olv\nMkacDBjGnfvrYgVyLhoNRvo9/SRFJV3ws+al5F1y3hXwTfOYrHxFMoJ45YUZw0iNnnj+xACjMmJW\nlzcnpBDTwVTKcThVBfKiTld5KXnBQCvfWvctwk1TI8+wK9JFYlTiWblixfQ1LVqT7Phsbrr+Jomi\nKVE0JYqmRNGUKJoSRVOiaEoUTYmiKVE0QyKKZu9/9UoUTYmiOaEomr9/4ffjimg2LYKsSB48IYQQ\nQgghxNVsvHnwpkUHLy4zTrsedlHXWUdXfxdmzESGR2Iz2mjubUZpRWxkLA3dDcHnGDBgUAbi7fHE\n2+M53Xya3sFeHOEOBocG8Xq8mEwmrGYrabFpdLo76R3oZXb8bHaX7w6+zvdu+x4Hqw8yrIeZmzSX\n8LBwTjeepqajhsSoROYmzOUvB/5CVXPVqJEJALvZzoB3AKUVKbEpbLhmA3FRcfxi6y+obK8cta0Z\nM+G2cOxmO009TQwO+0LVpzhSWJi6kBP1J+gb6sM77MVsMBNm9oUmn5s0F601A94B3jj0BoN6ECNG\nUqJS8OJlzZw1vHf8PRp6fN9NTHgMyzOX093fTVFNETG2GOIccfQP9ePRHuIi4piXPI+DlQep66jD\nEe5gdvxsBjwDvhEBswXvsJfaztpPym4wMzR8dohzm9mGPcxOu7sdAwaiI6LpG+gDDQajgb7+PoYY\nCtbXvKR5rJ67mj/v/TNt7jYAHBYHACajiQHPAL2DvaPeI8IUQa+nN/garkgXS9KXYAmzUN9ZT2RY\nJPvP7KdroCv4nPgI34ip0+7khuwbsJgtZMRm8NLBlzhcdZiK1orgvWQKRbg5nKz4LKJsUaREp3C8\n9jinGk4xpIdIdCRyw5wbqG6rprCqcNSISkB6dDpr56/lWM0xWntbMRgNJNoTSYpO4uOyj6lorwAg\n2ZFMz2APXf1dGDCclWYgIyaDZVnLON14mmP1xxjwDpz1XuCbOpwRm8GKmSs41XgKjSbMGEZHfweV\nLZX0DvRSkFnA7Qtu570T75GXmue74u/uYF7SPBKiE/jKc1+hrKXsk7o02bCarSgUC1MXEm+Pp9Pd\nSXNPM/GOeB4seJCFaQt58r0n2XlqJ3MS5nBt9rX84cM/UNNegxpWDDJ2+oWbZt/E2ty1WMIsvLj3\nRfZX70ejUShunXsruWm5bD2+lbaeNvJT88lOyGZ32W6KaorwDPuuzGbGZ3Jr7q209LRQ21lLdUs1\nDpuD+5bcR6QtkkxnJq8dfo2/7f9bsI6c4U7MRjNuj5tZrlk47U4OVR6iubc5WPf2MDu9g71oNDaT\nDa/2YjQYUSiUUqTEpGAymChtKGVQDxJtjWb1nNW8VfgWA3oAq7KSnZBNcUMxCoVJmfBoD8MMY8RI\nhiuD/LR8dp/aTVNPExrt+63YohkYGgju2yPlxOcw0zWTLcVb8DI6Muri1MW09bZR1V4V3H+SHcmk\nO9Pp7u/GarJS015Dh7sDq9mKURnxai9x9jiWz1zOW4Vv0e5uByDGFoPBaGBgcICewR7CzeHEhsfS\n1d8FGtJcaazIXEFDVwM17TXkxOfw10OfBA2wW+zMiptFU1cT7f3tJNoTcVgdnGw6iREjKAgzhbEm\ndw3zk+ez7fg2TtSfoHugG6UVXuXFYrQAMDt+NrctvI3YyFiKqov4y76/jNr/I82RWMOsdLm7gm3n\np4Ubw8mMy+REw4lRv61Zrlm09bXR7e5mSA/hsDro7O8c9dwwFYZJmXypHDSEmcNYkbWCtNg0Xj/y\nOp3uTozKOCrVQ0ZMBvWd9bgiXSQ7kjlQM/oipUJht9rpH+pnTuIcLAYLpU2lvlkNHjfOCCfdfd30\n634izBGsyF6BQRlo722nsauRqvYqAGxGG26vO/g9uIfcGJWR7IRsBj2D1HXUodG4PecPfT5Wm+Ow\nOlBaoQyKKFsUlW2jj1txEXGsXbCWvoE+bEYbO0t30tjVyDDDOMIdtPe1B7dNcaQwzDCJ9kQO1x7+\npF5M4fR5+jBiJMISMaqt3rBkA/900z/x7N5naelqYfPRzaPKGG4Kx+P1YDKaiImIQWlF12AXqdGp\nrM1dS9dAF+HmcMIt4QwODbL1xFYGPYPMjp/N0fqjvmORMnCm7UzwNRelLmJu0lw2F20eVRaAZRnL\nqGyvxGa2MTA4gNvrJswYhlEZaepqwoMHI0ZumHMDX7/563z/9e+fVe85CTk4I5109nRS2VaJwWgg\nPzWfRemLuH729RRVFfHDt384Zh2ZMOHBg91ix+PxMOgdJCUmhfuW3Ef/UD+Hqg/hHfZysv4kXQNd\nuMJdfPm6L5MUncTOkp1sKd6Ce8g95r3SceFxLEhbwKHKQ/QN9hEdEU1zd3OwTUp3ptPl7qKtry34\nXdgsNiqaK4LnM/YwO3abb5/OjM1kQfoCjlUdO+s7AEiMTMQSZqG3v5ek6CSO1R1Dowk3hvPvG/6d\nQc8gT255kprOGqxYsdlsWE1WTEYTM+Nm4hn24PF6MBvNFDcU4x707fefPgaHG8KJDI+kx91DmDmM\nMJNvSnn/UD+nW05jNpqZkzQHh8XBoapDdPR3BJ9rM9hwD5/9uzFjJt2VTv9Qv28f1xAfFc+t825l\nT9keTtadxGQ0EW2Lpm/I910uTV9Kc28zM1wz+PK1X2b/mf28evhV4iPi2X5yO12DXTjCHCzPWs7W\nk1vHrKNIcyQpsSnMT5rPmZYzFNUU4cU76jexfuF6tEFT1lCGxWQhJSYFW5iNls4WjjceZ8gzRP9Q\nPxFhEXi1l7TYNG7OvZk5CXNwWB0U1RVxoPwAH5Z/SM9gzyf1ZU8kwhxBbWctYWbfd3im9QxWs5Xc\npFx6+nsY1sMcrTsaPH4vzliMzWxjT9meUZ/HgO8cqHfId2ztG+jDoz+J3jkvcR4lDSV48GDAgNlg\nZmB4gFRHKvkZ+cxOmk2UNYo3j7xJY1cj9R31ePA932KwEBMeQ99QHwUzCliXt45dJbs4WHWQKFsU\ndpudD09/GHyvR697lOtmXUd7bzt9Q3209bax7/Q+WnpasJltlLeUjzoeRNmiiA2PpaK14qz6CVAo\nEh2JOMOdwWN/siOZ6q7qUdtFGCMYZBBXuMs3W8qfLsOECZfdhQEDDd0NZ6ecepk+3a4jzlmAQDmm\nQwdP0iQIIYQQQgghrmqSJkEIIYQQQgghri7SwRNCCCGEEEKIECEdPCGEEEIIIYQIEdLBE0IIIYQQ\nQogQMS3y4LkiXbgSJIqmRNGUKJoSRVOiaEoUTYmiKVE0P3muRNGUKJoSRVOiaF5VUTQ99DEO0yKK\npuTBE0IIIYQQQlzNQioPnjHeqIfXD194w2nAarDSP9w/2cUQYhQjxuBoUJgxjEHv2KMg52JWZrTW\nwatoU4kRI7Yw26irkUKIqW+sUcVQ5rA66O3vPWtkfqpSqDFHmsT5uSJctPS2THYxrkopjhRqu2ov\nvOFU9ioe3arNF9psWnTwJA+eEEIIIYQQ4qomefCEEEIIIYQQ4uoiHTwhhBBCCCGECBHSwRNCCCGE\nEEKIECEdPCGEEEIIIYQIEdOig2dQ06KY42I1WCe7CEKcxYgx+HeYMWzCzzcrM6YpmlbTiJHIsMjJ\nLoYQYoIM0+MU5TPjsDpGtcVTneKCcR7EGFwRrskuwlUrxZEy2UW4dMPjC1c+LaJoSh48IYQQQggh\nxNUspPLgSZoEIYQQQgghxFXtNbRu0Rec3nB1zX8QQgghhBBCiOlonHOjpYMnhBBCCCGEECFCOnhC\nCCGEEEIIESKkgyeEEEIIIYQQIUI6eEIIIYQQQggRIqSDJ4QQQgghhBBTnWZc6Q+mRZoEyYMnhBBC\nCCGEuJqNNw+ejOAJIYQQQgghRIiYlA6eUupzSqkSpVSZUupbk1EGIYQQQgghhAg1V7yDp5QyAr8C\nPg/kAg8qpXKvdDmEEEIIIYQQItRMxgjeUqBMa12utR4EXgTWT0I5hBBCCCGEECKkTEYHLwWoHrFc\n4183ilLqMaXUAaXUgebm5itWOCGEEEIIIYSYrqZskBWt9dNa62u01tfExcVNdnGEEEIIIYQQYsqb\njA5eLZA2YjnVv04IIYQQQgghxCWYjA7efmCWUipTKRUGPABsmoRyCCGEEEIIIURIMV3pN9Rae5RS\nXwG2AEbg91rr41e6HEIIIYQQQggRaq54Bw9Aa/028PZkvLcQQgghhBBChKopG2RFCCGEEEIIIcTE\nSAdPCCGEEEIIIUKEdPCEEEIIIYQQIkQorfVkl+GClFLdQMlkl0NcVi6gZbILIS4rqePQJ3V8dZB6\nDn1Sx6FP6nh6ytBaXzBB+KQEWbkIJVrraya7EOLyUUodkDoObVLHoU/q+Oog9Rz6pI5Dn9RxaJMp\nmkIIIYQQQggRIqSDJ4QQQgghhBAhYrp08J6e7AKIy07qOPRJHYc+qeOrg9Rz6JM6Dn1SxyFsWgRZ\nEUIIIYQQQghxYdNlBE8IIYQQQgghxAVIB08IIYQQQgghQsSU7uAppT6nlCpRSpUppb412eURE6OU\n+r1SqkkpdWzEulil1Fal1Cn//zH+9Uop9ZS/rouUUotHPOdL/u1PKaW+NBmfRZxNKZWmlPpAKVWs\nlDqulPqaf73UcQhRSlmVUvuUUoX+ev6Bf32mUupjf33+VSkV5l9v8S+X+R+fMeK1vu1fX6KUunVy\nPpEYi1LKqJQ6rJTa7F+W+g0xSqkzSqmjSqkjSqkD/nXSXocQpVS0UuplpdRJpdQJpdQKqeOr05Tt\n4CmljMCvgM8DucCDSqncyS2VmKA/Ap/71LpvAe9rrWcB7/uXwVfPs/z/HgP+C3wHH+AJYBmwFHgi\n0DiJSecB/h+tdS6wHPhn/29U6ji0DACrtdZ5QD7wOaXUcuD/AD/XWmcD7cCj/u0fBdr963/u3w7/\nvvEAMA9fu/Cf/nZeTA1fA06MWJb6DU03aa3zR+Q/k/Y6tPwCeFdrPQfIw/ebljq+Ck3ZDh6+napM\na12utR4EXgTWT3KZxARorXcCbZ9avR541v/3s8BdI9b/Sft8BEQrpZKAW4GtWus2rXU7sJWzO41i\nEmit67XWh/x/d+M7kKQgdRxS/PXV4180+/9pYDXwsn/9p+s5UP8vA2uUUsq//kWt9YDWugIow9fO\ni0mmlEoF1gHP+JcVUr9XC2mvQ4RSKgpYBfwOQGs9qLXuQOr4qjSVO3gpQPWI5Rr/OjG9JWit6/1/\nNwAJ/r/PVd+yH0wD/mlai4CPkToOOf7pe0eAJnwH+9NAh9ba499kZJ0F69P/eCfgROp5KvsP4JvA\nsH/ZidRvKNLAe0qpg0qpx/zrpL0OHZlAM/AH/3TrZ5RSEUgdX5WmcgdPhDjty9EheTqmOaVUJPAK\n8C9a666Rj0kdhwattVdrnQ+k4huVmTPJRRKfEaXU7UCT1vrgZJdFXHbXaa0X45ua989KqVUjH5T2\netozAYuB/9JaLwJ6+WQ6JiB1fDWZyh28WiBtxHKqf52Y3hr9UwDw/9/kX3+u+pb9YApTSpnxde6e\n11q/6l8tdRyi/NN9PgBW4JvOY/I/NLLOgvXpfzwKaEXqeaq6FrhTKXUG360Qq/HdxyP1G2K01rX+\n/5uA1/BdrJH2OnTUADVa64/9yy/j6/BJHV+FpnIHbz8wyx/JKwzfzdubJrlM4tJtAgIRmb4EvDFi\n/f/lj+q0HOj0TynYAqxVSsX4b/Jd618nJpn/vpvfASe01k+OeEjqOIQopeKUUtH+v23ALfjut/wA\nuNe/2afrOVD/9wLb/VeNNwEP+KMwZuK7sX/flfkU4ly01t/WWqdqrWfgO85u11o/jNRvSFFKRSil\n7IG/8bWzx5D2OmRorRuAaqVUjn/VGqAYqeOrkunCm0wOrbVHKfUVfDuVEfi91vr4JBdLTIBS6i/A\njYBLKVWDLyrTT4G/KaUeBSqB+/2bvw3chu/G/D7g7wC01m1KqR/h6/AD/FBr/enALWJyXAtsBI76\n788C+F9IHYeaJOBZf0REA/A3rfVmpVQx8KJS6sfAYfw39vv//7NSqgxfkKUHALTWx5VSf8N3wuEB\n/llr7b3Cn0WM378h9RtKEoDXfNflMAEvaK3fVUrtR9rrUPI/gef9AyPl+OrNgNTxVUf5LrwJIYQQ\nQgghhJjupvIUTSGEEEIIIYQQEyAdPCGEEEIIIYQIEdLBE0IIIYQQQogQIR08IYQQQgghhAgR0sET\nQgghhBBCiBAhHTwhhBAhRyn1c6XUv4xY3qKUembE8v+nlPr6Jbz+95VS3zjfeqXUH5VSFUqpQqVU\nqVLqT0qp1It9TyGEEGI8pIMnhBAiFO0BVgIopQyAC5g34vGVwIfjeSGl1KXkjP1XrXUekIMvn9x2\nf44qIYQQ4rKQDp4QQohQ9CGwwv/3POAY0K2UilFKWYC5wCHl8zOl1DGl1FGl1AYApdSNSqldSqlN\n+JJ3o5T6jn8kbje+Dtu4aZ+fAw3A5z+bjyiEEEKc7VKuSgohhBBTkta6TinlUUql4xut2wuk4Ov0\ndQJHtdaDSql7gHwgD98o336l1E7/yywG5mutK5RSS4AH/NuagEPAwYso2iFgDvDGxX86IYQQ4tyk\ngyeEECJUfYivc7cSeBJfB28lvg7eHv821wF/0Vp7gUal1A6gAOgC9mmtK/zbXQ+8prXuA/CP7F0M\ndZHPE0IIIcZFpmgKIYQIVYH78Bbgm6L5Eb4RvPHef9d7Gcq0CDhxGV5XCCGEAKSDJ4QQInR9CNwO\ntGmtvVrrNiAaXycv0MHbBWxQShmVUnHAKmDfGK+1E7hLKWVTStmBOyZSEP+9fl8FkoB3L+7jCCGE\nEBcmHTwhhBCh6ii+++o++tS6Tq11i3/5NaAIKAS2A9/UWjd8+oW01oeAv/q3ewfYP84y/EwpVQiU\n4pv6eZPWevAiPosQQggxLkprPdllEEIIIYQQQgjxGZARPCGEEEIIIYQIEdLBE0IIIYQQQogQIR08\nIYQQQgghhAgR0sETQgghhBBCiBAhHTwhhBBCCCGECBHSwRNCCCGEEEKIECEdPCGEEEIIIYQIEf8/\n9GnGlHMkFiAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fce3411f160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.26 s, sys: 588 ms, total: 3.85 s\n",
      "Wall time: 3.17 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import numpy as np\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=[15, 7])\n",
    "plt.scatter(vocabularyId, words, c='darkgreen', marker='o', s=20, alpha=0.8, label='Word Count')\n",
    "plt.ylabel('Word count')\n",
    "plt.xlabel('Word ID')\n",
    "plt.xlim(0, dictionary_count+1)\n",
    "plt.legend()\n",
    "plt.title('Word Histogram first 100 docs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# pivot = pivot.drop(vocabulary[ (vocabulary['sum'] > 100) ].vocab.values, axis=1)\n",
    "# X = pivot.values\n",
    "# pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "account\n",
      "admin\n",
      "administration\n",
      "ads\n",
      "advertising\n",
      "air\n",
      "altsite\n",
      "america\n",
      "american\n",
      "americans\n",
      "apparently\n",
      "apr\n",
      "asap\n",
      "asked\n",
      "attack\n",
      "attacks\n",
      "attention\n",
      "aug\n",
      "bad\n",
      "based\n",
      "bill\n",
      "bit\n",
      "blades\n",
      "blast\n",
      "bob\n",
      "boxblogroll\n",
      "boxfeed_listing\n",
      "boxrdf_feeds\n",
      "bush\n",
      "bushs\n",
      "bushsux\n",
      "calculator\n",
      "calistan\n",
      "call\n",
      "called\n",
      "campaign\n",
      "campaigns\n",
      "candidate\n",
      "candidates\n",
      "carolina\n",
      "challenge\n",
      "challenger\n",
      "challengers\n",
      "challenging\n",
      "chance\n",
      "change\n",
      "check\n",
      "chemical\n",
      "cheney\n",
      "cities\n",
      "city\n",
      "clark\n",
      "clear\n",
      "close\n",
      "cnn\n",
      "coming\n",
      "committee\n",
      "congress\n",
      "congressional\n",
      "conservative\n",
      "consistent\n",
      "contact\n",
      "continue\n",
      "convention\n",
      "country\n",
      "create\n",
      "culture\n",
      "current\n",
      "deal\n",
      "dean\n",
      "debate\n",
      "decision\n",
      "defense\n",
      "dem\n",
      "democrat\n",
      "democratic\n",
      "democrats\n",
      "dems\n",
      "dhinmi\n",
      "district\n",
      "dkos\n",
      "dkosopedia\n",
      "dryfly\n",
      "early\n",
      "economy\n",
      "edwards\n",
      "effort\n",
      "efforts\n",
      "election\n",
      "elections\n",
      "electoral\n",
      "end\n",
      "endspan\n",
      "exit\n",
      "experience\n",
      "face\n",
      "fact\n",
      "faq\n",
      "feb\n",
      "federal\n",
      "final\n",
      "find\n",
      "florida\n",
      "force\n",
      "found\n",
      "friday\n",
      "friends\n",
      "full\n",
      "function\n",
      "gave\n",
      "general\n",
      "george\n",
      "gephardt\n",
      "gop\n",
      "gore\n",
      "government\n",
      "governor\n",
      "ground\n",
      "group\n",
      "guy\n",
      "guys\n",
      "hand\n",
      "hard\n",
      "heres\n",
      "high\n",
      "homepage\n",
      "hope\n",
      "house\n",
      "hstewart\n",
      "ill\n",
      "important\n",
      "including\n",
      "information\n",
      "iowa\n",
      "iraq\n",
      "iraqi\n",
      "isnt\n",
      "issue\n",
      "issues\n",
      "ive\n",
      "jan\n",
      "job\n",
      "john\n",
      "jul\n",
      "jun\n",
      "kerry\n",
      "kerrys\n",
      "key\n",
      "kossacks\n",
      "labor\n",
      "large\n",
      "late\n",
      "latest\n",
      "law\n",
      "lead\n",
      "leader\n",
      "left\n",
      "lets\n",
      "lieberman\n",
      "light\n",
      "line\n",
      "local\n",
      "locations\n",
      "login\n",
      "long\n",
      "lost\n",
      "lot\n",
      "major\n",
      "majority\n",
      "makes\n",
      "making\n",
      "man\n",
      "mar\n",
      "materiel\n",
      "matter\n",
      "means\n",
      "media\n",
      "members\n",
      "menu\n",
      "message\n",
      "meteor\n",
      "meter\n",
      "midday\n",
      "military\n",
      "million\n",
      "mind\n",
      "monday\n",
      "money\n",
      "montclair\n",
      "month\n",
      "morning\n",
      "move\n",
      "nader\n",
      "nation\n",
      "national\n",
      "needed\n",
      "news\n",
      "newwindow\n",
      "night\n",
      "north\n",
      "note\n",
      "nov\n",
      "november\n",
      "number\n",
      "numbers\n",
      "oct\n",
      "office\n",
      "official\n",
      "officials\n",
      "ohio\n",
      "openhttpwwwedwardsforprezcomdailykoshtml\n",
      "ourcongressorg\n",
      "party\n",
      "password\n",
      "past\n",
      "people\n",
      "percent\n",
      "philly\n",
      "place\n",
      "plan\n",
      "planned\n",
      "policy\n",
      "political\n",
      "politics\n",
      "poll\n",
      "polling\n",
      "polls\n",
      "power\n",
      "powered\n",
      "president\n",
      "presidential\n",
      "press\n",
      "primary\n",
      "problem\n",
      "profile\n",
      "public\n",
      "put\n",
      "qaqaa\n",
      "question\n",
      "race\n",
      "races\n",
      "racine\n",
      "raised\n",
      "read\n",
      "real\n",
      "reason\n",
      "recommended\n",
      "record\n",
      "remember\n",
      "rep\n",
      "report\n",
      "reports\n",
      "republican\n",
      "republicans\n",
      "republicansforkerry\n",
      "research\n",
      "rest\n",
      "results\n",
      "richard\n",
      "running\n",
      "schaller\n",
      "scoop\n",
      "seat\n",
      "section\n",
      "security\n",
      "select\n",
      "sen\n",
      "senate\n",
      "senator\n",
      "sep\n",
      "set\n",
      "shows\n",
      "side\n",
      "small\n",
      "soldier\n",
      "soldiers\n",
      "south\n",
      "speech\n",
      "split\n",
      "start\n",
      "startspan\n",
      "state\n",
      "states\n",
      "stay\n",
      "steal\n",
      "store\n",
      "straight\n",
      "strong\n",
      "sun\n",
      "sunzoo\n",
      "support\n",
      "supporters\n",
      "takes\n",
      "taking\n",
      "talking\n",
      "theyre\n",
      "thought\n",
      "ticket\n",
      "time\n",
      "times\n",
      "tom\n",
      "top\n",
      "troops\n",
      "trouble\n",
      "true\n",
      "tuesday\n",
      "turn\n",
      "turnout\n",
      "united\n",
      "updated\n",
      "username\n",
      "var\n",
      "victory\n",
      "view\n",
      "vote\n",
      "voted\n",
      "voter\n",
      "voters\n",
      "votes\n",
      "voting\n",
      "war\n",
      "washington\n",
      "watch\n",
      "watchers\n",
      "weeks\n",
      "west\n",
      "weve\n",
      "whats\n",
      "white\n",
      "win\n",
      "won\n",
      "wont\n",
      "working\n",
      "wrong\n",
      "year\n",
      "years\n",
      "yesterday\n",
      "york\n",
      "zogby\n"
     ]
    },
    {
     "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>vocab</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aarp</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abandon</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abandoned</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>abandoning</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>abb</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>abc</td>\n",
       "      <td>75</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>abcs</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>abdullah</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ability</td>\n",
       "      <td>106</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>aboard</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>abortion</td>\n",
       "      <td>51</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>abortions</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>abraham</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>abrams</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>abroad</td>\n",
       "      <td>38</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>absence</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>absent</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>absentee</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>absolute</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>absolutely</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>abstain</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>absurd</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>abu</td>\n",
       "      <td>85</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>abuse</td>\n",
       "      <td>59</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>abuses</td>\n",
       "      <td>44</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>academy</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>accent</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>accept</td>\n",
       "      <td>63</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>acceptable</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>acceptance</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6537</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6538</th>\n",
       "      <td>yadda</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6539</th>\n",
       "      <td>yahoo</td>\n",
       "      <td>110</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6540</th>\n",
       "      <td>yale</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6541</th>\n",
       "      <td>yankees</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6542</th>\n",
       "      <td>yanks</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6543</th>\n",
       "      <td>yard</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6544</th>\n",
       "      <td>yeah</td>\n",
       "      <td>102</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6545</th>\n",
       "      <td>yell</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6546</th>\n",
       "      <td>yellin</td>\n",
       "      <td>37</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6547</th>\n",
       "      <td>yesterdays</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6548</th>\n",
       "      <td>yield</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6549</th>\n",
       "      <td>yorker</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6550</th>\n",
       "      <td>yorkers</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6551</th>\n",
       "      <td>youd</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6552</th>\n",
       "      <td>youll</td>\n",
       "      <td>60</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6553</th>\n",
       "      <td>young</td>\n",
       "      <td>113</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6554</th>\n",
       "      <td>younger</td>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6555</th>\n",
       "      <td>youre</td>\n",
       "      <td>181</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6556</th>\n",
       "      <td>youth</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6557</th>\n",
       "      <td>youve</td>\n",
       "      <td>102</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6558</th>\n",
       "      <td>yucca</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6559</th>\n",
       "      <td>yup</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6560</th>\n",
       "      <td>zahn</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6561</th>\n",
       "      <td>zarqawi</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6562</th>\n",
       "      <td>zell</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6563</th>\n",
       "      <td>zimbabwe</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6564</th>\n",
       "      <td>zogbys</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6565</th>\n",
       "      <td>zone</td>\n",
       "      <td>39</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6566</th>\n",
       "      <td>zones</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6567 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           vocab  count  sum\n",
       "0           aarp      3   12\n",
       "1        abandon     27   28\n",
       "2      abandoned     23   25\n",
       "3     abandoning     17   17\n",
       "4            abb     11   20\n",
       "5            abc     75  101\n",
       "6           abcs     17   18\n",
       "7       abdullah      9   10\n",
       "8        ability    106  111\n",
       "9         aboard     19   19\n",
       "10      abortion     51  105\n",
       "11     abortions     12   16\n",
       "12       abraham     15   17\n",
       "13        abrams      5   17\n",
       "14        abroad     38   45\n",
       "15       absence     21   23\n",
       "16        absent     16   16\n",
       "17      absentee     17   29\n",
       "18      absolute     26   27\n",
       "19    absolutely     36   37\n",
       "20       abstain     85   85\n",
       "21        absurd     10   10\n",
       "22           abu     85  182\n",
       "23         abuse     59  101\n",
       "24        abuses     44   71\n",
       "25       academy     14   18\n",
       "26        accent      6   11\n",
       "27        accept     63   74\n",
       "28    acceptable     19   21\n",
       "29    acceptance     30   40\n",
       "...          ...    ...  ...\n",
       "6537     wyoming     14   17\n",
       "6538       yadda      9   24\n",
       "6539       yahoo    110  115\n",
       "6540        yale     11   12\n",
       "6541     yankees     12   18\n",
       "6542       yanks     10   13\n",
       "6543        yard     10   13\n",
       "6544        yeah    102  109\n",
       "6545        yell     12   12\n",
       "6546      yellin     37   52\n",
       "6547  yesterdays     33   33\n",
       "6548       yield     15   16\n",
       "6549      yorker     14   14\n",
       "6550     yorkers      6   10\n",
       "6551        youd     24   26\n",
       "6552       youll     60   75\n",
       "6553       young    113  145\n",
       "6554     younger     25   30\n",
       "6555       youre    181  230\n",
       "6556       youth     12   13\n",
       "6557       youve    102  115\n",
       "6558       yucca      3   11\n",
       "6559         yup      9   10\n",
       "6560        zahn      4    6\n",
       "6561     zarqawi      9   25\n",
       "6562        zell     41   64\n",
       "6563    zimbabwe      2    9\n",
       "6564      zogbys     30   32\n",
       "6565        zone     39   49\n",
       "6566       zones     12   12\n",
       "\n",
       "[6567 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df = []\n",
    "max_value = 200\n",
    "min_value = 50\n",
    "\n",
    "for index, row in vocabulary.iterrows():\n",
    "    if((row['count'] > 200 and row['sum'] > 100) or (row['count'] < 50 and row['sum'] > 200)):\n",
    "        print(row['vocab'])\n",
    "    else:\n",
    "        filtered_df.append((row['vocab'], row['count'], row['sum']))\n",
    "\n",
    "filtered_df = pd.DataFrame(filtered_df, columns=['vocab', 'count', 'sum'])\n",
    "filtered_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# K Means\n",
    "## Como escolher o número de clusters?\n",
    "\n",
    "### Método Elbow\n",
    "\n",
    "Escolhemos o número de *clusters* de acordo com o primeiro ponto do gráfico que\n",
    "possui menor diferença se comparado com seus vizinhos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3sAAAFNCAYAAAC5cXZ6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAl7AAAAA/T/A4dduHjEsepgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fce89721e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 25.9 s, sys: 9.49 s, total: 35.3 s\n",
      "Wall time: 2min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "wcss = []\n",
    "for i in range(1, 50):\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=100, n_init=1, random_state=0, n_jobs=-1)\n",
    "    kmeans.fit(X_train)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "\n",
    "plt.figure(figsize=[15, 5])\n",
    "plt.plot(range(1,50), wcss)\n",
    "plt.scatter(3, 79000, c='black', s=100, alpha=1.0)\n",
    "plt.title('Método Elbow')\n",
    "plt.xlabel('Número de clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Aplicando o melhor número de clusters:\n",
    "O número de *clusters* determina diretamente o quão complexo o modelo ficara.\n",
    "Determinar a quantidade de *clusters* a se utilizar evita que o modelo produzido\n",
    "caia em:\n",
    "\n",
    "* **Underfitting**: Pode acontecer quando o modelo é produzido com menos\n",
    "*clusters* que o necessário, o que torna o modelo altamente generalizado.\n",
    "\n",
    "* **Overfitting**: Pode acontecer quando o modelo é produzido com mais\n",
    "*clusters* que o necessário, o que torna o modelo altamente complexo e\n",
    "específico para a base de treinamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "K = 3\n",
    "kmeans = KMeans(n_clusters=K, init='k-means++', max_iter=300, n_init=10, random_state=0, n_jobs=-1)\n",
    "y_kmeans = kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Validando o modelo\n",
    "\n",
    "Para avaliar a performance do modelo, deve-se fazer uma análise dos dados e a\n",
    "relação dos clusters gerados com os pontos disponíveis. Para tal, deve-se fazer\n",
    "uma análise dos dados dentro de cada cluster (intra cluster) e a relação entre\n",
    "os diferentes clusters (inter cluster)[1]. Outra métrica possível para realizar\n",
    "uma medição dos clusters é avaliar o _silhouette coeficient_ [2].\n",
    "\n",
    "## Intra e Inter\n",
    "\n",
    "A biblioteca do scikit learn fornece uma métrica chamada\n",
    "[inertia\\_](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMe\n",
    "ans.html#sklearn.cluster.KMeans) que calcula a soma do quadrado da distância dos\n",
    "dados para o cluster mais próximo. Isso quer dizer que quanto menor este valor,\n",
    "mais próximo dos centroids do cluster estão as amostras. Essa métrica é\n",
    "utilizada para escolher o melhor K no método [elbow](#Método-Elbow).\n",
    "\n",
    "Os cálculos de inter e intra cluster são descritos em [1].\n",
    "\\begin{equation*}\n",
    "inertia = \\sum_{i=1}^K \\sum_{x \\in C_i}^n ||x - C_i||^2 \\\\\n",
    "inter = \\frac{intertia}{N} \\\\\n",
    "inter = min(||z_i - z_j||^2) \\quad onde \\quad \\big\\{ i = 1 ... ( K - 1 ) \\ e\\  j\n",
    "= ( i + 1 ) ... K\n",
    "\\end{equation*}\n",
    "\n",
    "Nas equações acima, __K__ é o número de clusters, __C_i__ é um centroid cluster\n",
    "qualquer, __x__ é uma amostra dos dados, __z__ é a representação dos centroids\n",
    "de\n",
    "um cluser e __N__ é o total de clusters.\n",
    "\n",
    "## Silhouette Coefficient\n",
    "\n",
    "É uma métrica que calcula junto os valores de coesão inter e intra cluster.  O\n",
    "cálculo para esta métrica é destrito como\n",
    "\\begin{equation*}\n",
    "    silhouette = \\frac{(b - a)}{max(a,b)}\n",
    "\\end{equation*}\n",
    "\n",
    "em que o valor de __a__ é o valor da média da distância intra-cluster e __b__ é\n",
    "a média do cluster mais próximo a amostra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6.7486667821054658, 113732.94707772927) 16852.6541241\n",
      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
      "Wall time: 2.71 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from scipy.spatial import distance_matrix\n",
    "inter_distances = None\n",
    "def inter_cluster(kmeans_model):\n",
    "    clusters = kmeans_model.cluster_centers_\n",
    "    inter_distances = distance_matrix(clusters, clusters)\n",
    "    inter_distances[ inter_distances == 0 ] = np.inf\n",
    "    return inter_distances.min()\n",
    "\n",
    "max_intra_cluster = kmeans.inertia_ / K\n",
    "min_inter_cluster = inter_cluster(kmeans)\n",
    "print((min_inter_cluster, max_intra_cluster), max_intra_cluster / min_inter_cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Resultado\n",
    "\n",
    "Neste problema de clusterização, não é possível determinar se o modelo fez o\n",
    "trabalho correto em agrupar os dados ou não. Basea-se então na modelagem\n",
    "matemática da análise inter cluster e intra cluster para trazer mais confiança.\n",
    "Como é possível observar no valor da célula anterior da relação intra/inter. Em\n",
    "que busca-se minimizar o valor de intra e maximizar o valor do inter.\n",
    "\n",
    "A seguir, mostraremos um gráfico representativo de cada cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAGfCAYAAAAu6yGIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhUKhWGQoRU2hUCgUCoVCoVAoFhn/F280lNtHNeTXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fce346445f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "InteractiveShell.ast_node_interactivity = 'last'\n",
    "plt.figure(figsize=[15, 7])\n",
    "\n",
    "for cluster in kmeans.cluster_centers_:\n",
    "    plt.plot(np.arange(1, len(cluster) + 1), cluster)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Palavras representativas\n",
    "\n",
    "Com a caracterização dos clusters vistos acima, é perceptível que há algumas\n",
    "palavras que mais caracterizam um determinado cluster. Então, para tentar\n",
    "averiguar se a clusterização foi realizada com uma boa acurácia, iremos tentar\n",
    "identificar se as palavras que aparecem irão ter algum sentido lógico para\n",
    "representar o cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import collections\n",
    "clusters_amount = collections.Counter(kmeans.labels_)\n",
    "clusters = pd.DataFrame(kmeans.cluster_centers_)\n",
    "\n",
    "# indexes = clusters.iloc[0, :].sort_values(ascending=False).index\n",
    "# vocabulary.iloc[indexes]\n",
    "\n",
    "#for cluster in clusters.iterrows():\n",
    " #   idxs = cluster[1].sort_values(ascending=False)[:10].index\n",
    " #   cluster_size = clusters_amount[cluster[0]]\n",
    " #   top10_words = vocabulary.iloc[idxs].vocab.str.cat(sep=', ')\n",
    " #   print(\"cluster {} ({}): {}\".format(cluster[0], cluster_size, top10_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'wordcloud'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-4554a8913b22>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#from os import path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mwordcloud\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mWordCloud\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m#d = path.dirname(\"../\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named 'wordcloud'"
     ]
    }
   ],
   "source": [
    "indexes = clusters.iloc[3, :].sort_values(ascending=False).index\n",
    "vocab = vocabulary.iloc[indexes]\n",
    "vocab.count().vocab\n",
    "\n",
    "#from os import path\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "#d = path.dirname(\"../\")\n",
    "\n",
    "# Read the whole text.\n",
    "# = open('../Unsupervised-Clustering/test.txt').read()\n",
    "\n",
    "indexes = clusters.iloc[0, :].sort_values(ascending=False).index\n",
    "vocabulary.iloc[indexes]\n",
    "text = \"\"\n",
    "for i in range(vocab.count().vocab):\n",
    "    for j in range(int(vocab.iloc[i][\"sum\"]/8)):\n",
    "        text+= (str(vocab.iloc[i][\"vocab\"])+(str(\" \")))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from random import shuffle\n",
    "l = text.split()\n",
    "\n",
    "shuffle(l)\n",
    "\n",
    "text = ' '.join(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# Generate a word cloud image\n",
    "wordcloud = WordCloud().generate(text)\n",
    "\n",
    "# Display the generated image:\n",
    "# the matplotlib way:\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# lower max_font_size\n",
    "wordcloud = WordCloud(width = 1000, height = 500, max_font_size=120).generate(text)\n",
    "plt.figure(figsize=(18, 16), dpi=80)\n",
    "plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#indexes = clusters.iloc[0, :].sort_values(ascending=False).index\n",
    "#vocabulary.iloc[indexes]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Referências\n",
    "\n",
    "[1] [Determination of Number of Clusters in K-Means Clustering and Application\n",
    "in Colour Image Segmentation](http://citeseerx.ist.psu.edu/viewdoc/download?doi=\n",
    "10.1.1.587.3517&rep=rep1&type=pdf)\n",
    "\n",
    "[2] [Silhouette](https://cs.fit.edu/~pkc/classes/ml-internet/silhouette.pdf)"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}