大手ヘッジファンドX: 金融モデリングチャレンジ 実験記録

解析ログ.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 大手ヘッジファンドX: 金融モデリングチャレンジ 実験記録\n",
    "\n",
    "[大手ヘッジファンドX: 金融モデリングチャレンジ](https://deepanalytics.jp/compe/53)があまりに出来ないので知見を共有する\n",
    "\n",
    "もしかしたらとんでもないミスをしているかもしれないので気づいたら教えてほしい\n",
    "\n",
    "## 概要\n",
    "\n",
    "### 学習データ (train.csv)\n",
    "- data_id: データのID\n",
    "- period: データが所属する時期を区別する識別子。'train1'から'train14'までの14種類\n",
    "- c1〜c88: 説明用のデータ。説明欄にある「我々が独自に導出した様々なデータ」のことだろう。それぞれが何を表すのかは不明\n",
    "- target: ターゲット。0または1\n",
    "\n",
    "### テストデータ (test.csv)\n",
    "- data_id: データのID\n",
    "- c1〜c88: 説明用のデータ。\n",
    "\n",
    "テストデータには`period`がないのが特徴だと思った。\n",
    "\n",
    "### 提出データ (submit.csv)\n",
    "- data_id: データのID\n",
    "- 確信度: データが1を取る確率\n",
    "\n",
    "評価はloglossで行われる。\n",
    "![](https://i.deepanalytics.jp/i/w600h0t4i3532)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可視化\n",
    "各特徴量についてクラスごとのヒストグラムとPCAによる射影を調べた"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c1\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33c71198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c2\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33fd5b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c3\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33c713c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c4\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34331b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c5\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34302400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c6\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34526940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c7\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33809ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c8\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd344bd9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c9\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdca0d695f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c10\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34494160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c11\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd342a9c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c12\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33e626d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c13\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33ce5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c14\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33fe8518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c15\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34349c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c16\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33d2d4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c17\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33d0a278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c18\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdca0d57550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c19\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33a0ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c20\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33bee320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c21\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33f820f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c22\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc9aec5780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c23\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdca0d43358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c24\n"
     ]
    },
    {
     "data": {
      "image/png": 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0pHFMTw8epoZOEit+TyPJZuBTwCnAZ6rqExNuSctope1dHIt7HTpZrOjQSHIK8Gng3cAs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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33b13a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c25\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33ca1240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c26\n"
     ]
    },
    {
     "data": {
      "image/png": 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DqAW1hPqoPmxncBqLt771rYt1WVpW04aFTiFj3T2V5C8xCIzPVtVvt/LX2qkl2vvLrT4L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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33ac3c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c27\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33d4fba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c28\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd342ffe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c29\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33dacfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c30\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33fb56a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c31\n"
     ]
    },
    {
     "data": {
      "image/png": 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OVJ8ZUZckTcjYoZHkJ4D/APyDqvq/R2o6olYLqI/qw6Yke5PsnZ2dna/LkqQFGis0kvwV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\nGRqSpG6GhiSp2/8H4sQT6BzbYGUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd345c8a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c32\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33b8d668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c33\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd344d47f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c34\n"
     ]
    },
    {
     "data": {
      "image/png": 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tjmqUbAQ2tre/TvLEHPd3BvAswCcm7fnk9Mdjoj/ymBzNY3K0V9sx+dueRgs9NPYCy8fe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc9af0d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c35\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd339f74e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c36\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc9aec3898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c37\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34178748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c38\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd338b0550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c39\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34360f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c40\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33d9b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c41\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34515cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c42\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33ba1898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c43\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3445ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c44\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd339e4518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c45\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc9aec7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c46\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd343a3cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c47\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33f9bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c48\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33fd66a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c49\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd339aacf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c50\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3422ba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c51\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33f63748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c52\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33a32128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c53\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33d71fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c54\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33d5de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c55\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33853390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c56\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34387e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c57\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33af29b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c58\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc9ae75240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c59\n"
     ]
    },
    {
     "data": {
      "image/png": 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SpAkZOzSS/ATwO8A/rao/O9zQEbVaQn1UDzuS7Euy78CBA4u1LElaotXjrJzkLzEIjE9X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc9aecb9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c60\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33cbf978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c61\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33ae9e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c62\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd345c4cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c63\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33c0cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33fb1cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c65\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd342349e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c66\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33e905c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c67\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3457fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c68\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd338cd6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c69\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33c4b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c70\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3408e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c71\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33d079b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c72\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3439d860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c73\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3412f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c74\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd657216d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c75\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34112710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c76\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3450a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c77\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33f41048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c78\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdca0d43518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c79\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33e823c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c80\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3432a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c81\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33dd8d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c82\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33e6bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c83\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33ac4e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c84\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd3439bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c85\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd345d3eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c86\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd33e37cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c87\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34424048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ヒストグラム\n",
    "import csv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "train_X = []\n",
    "train_y = []\n",
    "train_period = []\n",
    "cnt = 0\n",
    "with open('train.csv', 'r') as f:\n",
    "    reader = csv.reader(f)\n",
    "    header = next(reader)\n",
    "    for row in reader:\n",
    "        train_X.append([float(r) for r in row[2:89]])\n",
    "        train_y.append(int(row[90]))\n",
    "        cnt += 1\n",
    "\n",
    "train_X = np.array(train_X)\n",
    "train_y = np.array(train_y)\n",
    "train_period = np.array(train_period)\n",
    "\n",
    "negative = train_X[np.where(train_y==0)]\n",
    "positive = train_X[np.where(train_y==1)]\n",
    "\n",
    "for i in range(87):\n",
    "    print(\"c{}\".format(i+1))\n",
    "    plt.hist(positive[:, i], alpha=0.5, color=\"red\", bins=100)\n",
    "    plt.hist(negative[:, i], alpha=0.5, color=\"blue\", bins=100)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3yZ5mywr+eRYYSkB8GauSXHR+7y+DQZG9wjnnSAYNyW0/iRH66Nvtc/nLYJCm\nYm/fnm2YY+drcJXn1h/P5vbLgcFk2PBA5fLguRhMD6gys0cOSqkn7yUzAB6QpZevQVMer/TgeYl8\n33k/ujzvjRvN0kmPJs+r1y0Toz21uQm+Fh774gL2broZ1o0bTxEf21LvuffojOx4bj753XJamSmD\nbzJwyNouu3al6Y27dxuAZ2VDqlQoGrf1UBiIaxhPgosyvZGpBglQTfiJl6693mmUcjVbuBJUB1rb\nEnDE31Z8vvI97mhkBpx0kGjBiJollEN8nNwsI6Uy7MIgTnXUQKnz2+X77CXzoOfKUJlYYl/nSJxj\nCx49iRG6uhKqlnyVOBOolGQNmbx5eU18enSLTNtMpZTzGmY/dGPaYFsOMF/86bp1y3XyvGXHKF43\nuU8diqBA51TKOQm+nuLFUb0CexE87WJ33w28/noa2CEC7rzTBJQ4IPT66yb4uOaVw7j9iWsQTTXw\nM1TBQ1ufwTVDw0kgbNaBGf29OPiZtx0iO8AURYDvw3yn0QDabRBRHBL00PIqOECbAQD9Xz+MZ3AN\nKmighQC/+5vbcWzZj+OMfzGBvsExEyD7+baHAAB5HrxvfQsEE0AkAO/Cf8UOPISd+HUgfr8P09iM\nAwBM8JPXRfw3QISfxeMAgBbKIHjJNjlYKoOtHBRch28iQGRtTwYeof4PABDa1ntn4F8AACvwGnxE\nCOJAqQfAgxe/MmuXYIYaPpcKGsn6bXhoooK/wo9jBV7DpTiKj2AfKphGCREIDdyA38dmHMD78WdY\ngkn4AIBpPIiPw0cbEQI8hv8b/w8eQ+BFaFMJr2I5duLXsHrqW8n5mSNKz9oH4SfxFDY3/gL/37v2\n4+avD5sAM9oIQPDRws2o48ZjvwsPbfjxN/la/f0LrwEAXsXyJPCLKIK3fHlyz0v7pSeG8TutZ/A7\nq+/Gj33naXhEaMGH/8PXrPtPBvLvv98sAHD77dlgqefB3OvXXAM0GqBKBff/zDOA+N3vvBOYmBiG\n98wz8PcfwDW/nAbhXcHXGdvw8OkdNGXrBf3nejldPPZO3oYrqKO1MFi3hYhm72l3+Z72ULhCld9n\nPZdduyjxdJowxTV/cdU4PbjK5HFzEYuzq33svbJXLfnwlhdQ+922CNhhDNJhDCpP2VcerB9z4LbH\nx17qEWxwzgIkxdAWXLj01l2eumshtX2XB65zz+X6n/6I3Sh6L6r0sVLdojmaSK/jNMpW9ankymU6\nZwNBIh77q5uUAAAgAElEQVTW8kpJTryemXz5LPNbHMU66/3vlVb1fD7tmKbZCxP0fnpLLWlU0oJP\nbUVz7NqVCp2tWKGaZXuxnLLD63V63i5zFMq5nr2e6zYWmKGgYubGeone83ryJs/os8w2MNPhexrE\nZc9NLfnLhSCPbkmzS6ZL/XTvtVleVoOBBsEplKgR9CVTYaYwUqDyrWrKCEaulrfP1amc2aIzVppx\nloYEoycxkky5dfeiXsDb4oIdnx/GoHV8nFtuKkMrisP36a5SLaGbmLdPg7q+89jaMZC66CF5PPp6\nuAaWKZTpN/6N+3rowWofTFWpzpufRCWROmjFMYGnrhhPCs0aJXMPcw46yy/vQJ0+udQWINuJGjWf\nDZ2OSK/PEBFZ38/0yY1seQ25ncVS4FQA+xxaN29D3lh8k2fkTw/16LFrbyfHY2dPPA/E5fHK17IP\naV71p1QKlIDGSwse/U5flVqfEpovqFiAZLJE1tFRrKN9GKX3nxXSjT9m88pc0alb1DWBTOUpB1hZ\nd0UDGR9bGx69jFWWB+wCPQ2uf+yPEmCaMMnf8b2lMKm4TQOkFdqPTXQMa6xtNuDTfmzKDIougM/j\nytmDl98x10gWSIGOYMDW3FHXPwLoSYwklaWcOroDddrcp7OL0oGo6Zt1Hh+uJQVou3cbQbNushDs\n8UtHJNpTy2TA6Nf8DMlngGUgMnrt8evZdg873a0A9jmybt6Gy1uQIlkydevh7V0CM3m0i0MSVbYV\nk4D8wAWmo488XqnAeBiDFJXLFAVB5sHkAN62i20BK6ucHrD6buq0OfYwJUAxKP9lkOqcNOElIKG9\nagazZkwbNMX7Ul/F5J7b6YCT6KMaxjOg2cmzb8KjHahTEGQzVXR+ffvDRpgrD5Tl0gasc0zX8RLv\n/W9Eez6mRkzzajNLaCCgGkzO+rTar0mHNEC8qRwmAx4vj2AbPQmRseRlB3IdzG7CoyPlQTp4aZUe\nujFMdHc0RceOwV5Urevz5bNGjZJmfA8/vN20DJTPAgvJMQjPNGtGDwbOwWGBWgHsc2DdOHZOBZQt\nvqRHctN643lwu7eu08QZ0DXSY9cApHPJuSGFfOhfvHg0w3nneWLSs24ioN1vq1v7TBUYDRAdxbpc\nOsIGvizFwK/3Y5PKULGpkMNIZXwlvePyQrUnq2kZztqRKY/TKNEfbq5bgNZAYDUVcXnkeruPn72t\nowcv+XRDsZgMoB1IWwHy76HjFvyXJRaANBOJz5k5/sj3Kep31xjo9NPkWDyTIjkwkE2R5HoBGW9J\nftNKJRGX0xkw+rXrGZO6Mvp7cpCQz+hC99TZCmCfCwvN1NLF8119dZbflh7FfdfZtIOkZuSUUu+v\nF7pGgzqQLUbhhhb8+aFlI5lp+m23ZbVCGDj3Y1MS3Gwg7UzU8gL61Jm1zD5biXedaqNooJD74ErP\nFux8cwY3CRgM5FpnxdV04tZbVUAPfTHHXMrsJ7KOB3QU66w0wEachz7pm8KcSa8/V84373XLL9PE\nknHruDVldRiDCY/Ov52+tvuxiY5ivfPY2/DoMAbp6kpInzk/DX5KL/ylHx1JKoldC8+8WuLNlqia\nlVw9/3Z5AVrplHSc8cYzUTkASEDXksPyvcXGrbPNK7AD+B0APwDw33pZ/7QAdiWWJUFWq9G5+G7Z\n17MBI4qVNxW19lmtdmwGLG9mKfSk5QB2oE7tPTXavi5MvDIJCHvW1KnVInrfEvvBzAM949HZecNc\nNNOEA9Bi7/0Y1lhgqamCwxi0MlnaAP3e0ipdXbHPh73Yl7A2oXNsKsAE/yaWjFu0kuSgmbo5ivW0\nH5vob/21HcG+CZ/uX2morSeuMj1Wb7gkjIOp5lglDdTySvTSBTbHzg1HhhDSQ0GWTpmOq2y5OEsO\nVjoA3W2ZQikB3zyPHiB6/1luMTmzz4o4tnIiQMbXL70nbC36vahSI6iknb0EbZiXiy6dmOiQuw+r\nPDbm3RdjNgzbfAP7JgDvWijAHkVk0SKuCk+XJ8KZJ9yuTQOT1DBnD8QqyugxHdKljMc3v1QkbKnG\nCTtQpycxQnsuqlvHyTrf7DVq6oKpkWffmTbK+PXA5rHzgpWulERJQ2jwmkTFKtbJFvrYHvs+jGYo\nHa0eqflh5umb4nj0sbUBegNGX4V/50e31OlbF4/QU1eM0+PDtaQ4ir3b9s1VGhsj+vly3YpLtGLe\nfCdqmXTMNuxUUsntu7o4yeNtO46/rWdMpVImVdRVMMXLvdeGdPDSKh0qb8qdcfFSw3gyK2S65omr\naklrPanoKPexcSMZkBbP18HLqpkZrtYwYgmB6JB9zIsF1InmGdjN/rDmdAD2nqtIWZ88CHI1MLQn\nopf7rgupvSfb2SgD6kQzToe86y73Pn/qnJDaIyMW2DUQOPVgTLphWpnIVIdLm6QVg+6UZ3pq6sBn\nK/4eg7Uu8Zeyt9KblVrkhzFINYxbYl8ucJbrykCm3HYz9pQ5RjCtjkkfjwbIr2LQumaPblEznovq\nmVnQb59nUjpXrKBYU93e/iPY5vTCXb+TjhO4AF5eV2uG5QX0O31VZzs82a1JZkV9/ONp0P/6tfag\nIgchuf+vVEYSCvKuuwy4X311+mxwg+whmACqpAa5sXXLM8+XuV5+/Nuks4GxMbL690pJaF66euyn\neDXpTKwAdod1S5HKcHaOTjFsLo89D7hbB7NT38yNWK8bGUTfT4W1HEUeRMZjlw8JkAo7saeuwWAf\nRlVHHz+TVmcCliXnZwyoqVdvA8oj2GZ515wuyTMWBmyZMaNVHBmAJRUip/uTiibQBTt6aQgwb4Hp\nIXN+royc9HsBPfYWGxDDs7IxCp0/3kCQ8NguTfkmfNqBOn3vLessz50XzWmbFMYsRcYe/rRjUONM\nnEewTWSrVKgeVJOBPJ0dpfoxg4PpbLO+ppY4BvLYdb/XGsbpiatqlrqk7qCkZwYbNqRUJHv4D90Y\n0mPxwMn727OmnmbFCAmBSd9u4tKVYz8N9F9mYqccsAO4GcALAF5YvXr1CTvxPI98plWkeSBNlOXY\nXdQIf+ehG1kB0L7BBwcp03g58n2j4TI+nswWGOQ5iNtup57V+88K6an3mZz5IYT0lcqIoAay3mAT\nQRwIzVZ7RkDcdo0DltnqyKkYFNsxf85piJxX/dyZI5lpvxzQmApiWmIIId3s1ZPg7LQ6Nj3TkN6m\n9rjbonqzm5drBii31/xtrEkqPTnwyiJccn1uVSe92hZAX186mHjKTbX/FpAUZukU0ghpRkwK7Hbx\nFgdRWU+npcBXx0nsFnpeMhjyb7YPo5l79t3vjrO5KukA0IKX0EQ1jFO0di1F27bldswiotyep/K+\nuOGSkO66K37u9tQScTvyfUPVOHLbH94ednTOMnYaKDbOxE45YJfLifLYZ+KR85JXRZoH7DorRoI8\nl+7LlK77V9oyA9xz0wSbTPVmyy+nmQxBQN9cPZJMPdvwKfL9pA3bo1vq9OAqxaN7pp+m3fS4kohX\ncdUhe8EtL+vpcr60VEyUOefPe6ZAJ/WYAyc4MejJB3gH6iKjwhwDV6JKikXTQFJbnUGFZxN6RnEE\nA7nBRpdXrt9rx/szg5tNgfD1+TNvhP6+vDY59h2oW8erZwuu91l5Ug8sfL55VAyLl3GRFA+y6XZ9\n+g5WZmYImn7SOf+8P9lVCUi5dg7ssuc/CXPPRg4RuQwQ9/UlkgV54mc3XBImche9etadnLfM+4XH\nfnoD+0w8cgnanapIXeDeLY+dKKVwHt5uGjK8qbJVJj07Pcxw0yXz4HrGSzRctk2ptGLKIu2lmfW8\nW7G0LHuUe1E1/LCYFj91xXjyYE7F+2VO+44z6hn+nSs+pYfYgpHAdYHkYQzGGSBlJwcsBwAZZDZU\nje2hpul12eIcuT32hO3enVkvVnrY7OnL0nq+5nI7ZO3XSzx5Xr8RZ9rI68NBUXLs03XN5HnwoGEP\nCL5jlmIflz7PtLCpnNkOX1/eHy8bNhAtX56+lgOMTcUZB8Gkt5oArfWcxQqgfP+4YiU8IOj0R6rX\nZ9xEo1tTjoJjnx2oPwrgFQBNAN8FcFOn9U+Uxz6TKtFun7uKJVzfkfsmooQPlGlfkmPfCVvHug2P\nJr3+BIhTGqBCR89IM1UipAFK7d1q7/LP3zVuAfm915oB5vFhQ9s8dGNIrZur9OWzRi0PVTa4MNWP\nqZa3KWKxwfURbHN7pb6tj66Bjf8ew5rEi3PJ77oGA9f/LDDm0jKfQtmZ5cOf84Agq2eb8OnbSi7A\n5eHza96O5K91dWretdCfcSzEXA8GYDvryFVwpbdzFOuT+1UOylKHh6+BpM/0ogcYGdvgrKJWfA9b\ndEw1bcgtqRidLXbDJWHiEHG6bzOeyfbaRMNV+JQnDXy626ItUHJ55HkVbi6P3jXya3DvdKM8vSWN\n4FMQUHtPLVMhyp1omn6Z/nrpYPJgaW/m4KXVhG5p+iX69WDceig4IKk7zH9z9UgyTW7Cp9pZNTr/\nfLP/7evsxtJphaKXackmtymlBdgj3Y9N9J3SRQm/3YbpZMTn7wJTG5BTTzuvqjJvGzaIrSPA1n/n\nHGwXrfI3uCTRsJHBZL4OPIPpbVCxteZZy0anKuYtLaTqjtMxr9+MZy+dvqevg94PSyQARO/xQ2v2\npVNEWe9exkT4fzkoRJ5H/+W8akZCgu+Hvz9/0GSPhSFRX1/y+ZRIYdX7Sdo5xpRJ5Ilc+VghdWKr\nWX9ia5jEpVwqpxx74kU2z14otiiBvduP26twkM5+0cCe57FHkWnbJXNuOdXxhktM6uPEVnNjH7zU\nFHTI4JP2Zq5fG1opY1z9dxiDFo8t1QgZ8PVDDph2bnnqg214GflXueSBpAa6hgIlub70OuV+GeA0\nSMnUSUMZlSz9FV5qGKcjGMi8p71N6WVKjzWlGlIp4V4GF5ZPkPnnQPdUxbwZBMcPeCaXV1cgr5H+\njLVmli83nHl/f7aTklTj5BnBPow6es4GVlwlAujRLXXBk2fjC9TXZwrsgiA5D7ltnTvPtR/RHlE3\ngixF1vICo0FTqTj5ctdzmhEbWwCUzKIDdpfGhKsYSI/4nbzv2Xj5UWRynpnnZlCPRBXrxFYjVRA5\nsgbYm9Hl3zrdTwak5PcY/KW3vROpx+4CO36YWJBKe4LsWZvZQ2cagIO1kvPt5LXmefZS/CrNMgms\n9Titb8oxkBzGoFGyVJkjGhzl8UqQ58EmDSSbmYAcnOQ5SkojL+tFgrcGaNc1ms6JL8jlEdhaNC34\nVp63FvBiXl0HfrlhNw8ksr4gOT4/3Tbfb8b7Fzeq5xGNjiYduqZL/fT55XY/208urSV574Bxxu67\nTvQxrVToxYtHs+qinpfKb6oMlzzaNKFiFkgQddEBO1GXSrdZ/rizyX3XaWBt4Y2wlCkfi27vBZgb\n/sor7XPQIMUPqJ7eam+dM0seXJVm4+R5gq/gPCfITMXZGjtQz+R/u8A75YazHrBsWyc9TvndfRil\n25faglyPYJsFlvx9KdErj4HPWw6GeR47zwQaKFkgyqqPPFg6PVSxX64elfnn6QBiqCuu8O00I+DB\nlAcTVqzk2IeetfFAojNQblofJhlUk+jLDJqSbspel/R3S97z7CwlBnjrGpdKpl2ib5q5/MKyekI/\nynt9YmtIrU+ZmA/Psm+4xNzD1691Cbl5uR675tSdwL5A0h4XJbATddCmIJrRj6vpGP1ar8s3livq\nf991dnNrLrl+6MbQaljgWnSVqAQuGWhlblaDxnewMhHb4hRI6RHKB5fpDA3anIaX0e6GR8dwkcrw\nQVLSztkjOuNEeq9SeIs9aC7+0XSGi65xpQ3KmQqDnqFzyhm66XWcab3Wgx3TLK4+rXkDm+t1AyWa\nigeQvHNwee3yPpIcvqwolVo4nIEyhJDeFPUTMu89AcoO582zE/keN+rQlMonltWTe6/lBZk0yIEB\nc69zqu8Nl6TeeaPU79R/0TPLfRjtyLGfCKfuVLRFDewd1eR6+HFnI+LPg4mla1Eu09E4OHrfdbaI\nkRQOW7rUlHSz3gY/xO9bYhc3yRRGqz1dBz42ry1aOwblb2MNHcW6JAvG5Y1yRaTkVqdRTvLeZTWp\nPE4eePg9HSBtA4m2ugZ8jhl0AlN7Wx59N87j5oWPgykGme3SCYgl6DKlk8eZT/dwjJ3AexrlTHaR\npntkUxJ9nXSrQpc6JGftuAK6/L+OUTQd68n7pwFTk/Hud9vNW3iG1YCpEtVyGitWED31PlsioB0X\nI8n1rKCt79MTV9W6Pn9dG3rMkmPvJkMyn7Yogb0nTrzLj5u3DS1wlPcdBvcXL7abHuxZU89s+73v\nTftHykGBHw4z/c+v3kuladlj9xSY2E0qOPNFrxcB9F2szGib8MLTe/YY81IEdfs6rUmiJW8ZLDgA\naVIvUxDWYMNg5hKn4v3L43E1pziGiyzwloOMFAXj9Znn10HEbh66ax96UGvCpz+pjFqzk/Q38pP0\nUx3s1NucRjkJMk+iQtevDenea7MBUzmA6sGVqbYnMUJfwojjuiO5z6Sw3YYNKT/OzVuYNnp0izuN\nUjo/jbg9IwvSpbNT4WRUKs7nTtuJ6Kp0qnVqWpTATjQ3P4QLqDlF0RWNl5WnrLfxJGxtEfb8hmBu\nYhZPkjd8ttrQp6mY1nAp8en0tJdUN54f4qyM16jFsDp5wcdwUVJOb1rDlRLAmbLymW3BLs7C4O0x\nR5/XmzOKQUPHByQHLIEoq5BovNqPleoxmJnBhLVN0pkHMl2GOi1fwkiSzdEpx94F3Hnr6c8ZeGWP\n1f3YZBWNuQTF9Hkx3SRBOn3PtPKzZwKwJBNk1pXr/ngJazOpihzHHBszekifXCq1iAJqBBX6/PJq\n5r7le6S9p5b03B1COjtlvSIe+J+7rNqzl5zkxOe8non1mjwxn7ZogZ1oZlOnTuXJEnCjDty8pFUm\ntpob9rfPs0FqSuiZsyTpr/wK0QfOtsFZimAxuGmFPr2wLrsr4yXrIXcGHtub8zLCTxrEmrAbTQ+B\nGzZ41jkwLaKrUVNw8ugpT7fYc4OmXpgHH4KtYe4KUrq8Udd5RTC6MZ1SDu1B0yOtTR8hGxyWgyxv\nh6kSKbuQXl/fGoz0j8/7kEVm5jqn95FLQ4ZnAuzVT6HszC7ihWUU5DKEkH6lZMeIfnWpkOONz9Pl\nlAA2J25n7hi5aVZ+fOjGbM666zXz7LoGRSY69IoLcp1TSf99UQN7r5bn3Uu9F76B8zx2IuMVtNtp\nDjsD+NNXjFP7J0foy8vclMr1a7Pa2K7Mk4eCauah4OPSGQo8ZXflSssSewkMeQHIbsDHi8yp1/nN\nvC6LkE2iYlEuGjw0z6/BVC+cirkPo/QyVlmBWNc2ugG0Bsy8QUAPliwsplMi9XovY1VC77BMMscr\n8oqaTIPs/IGNB0/p4dsZJdlrwAFV/b6+L1gaweXdu3LTr18bUlSyKR+p0rliha1JA2T7F0yX+qn9\nWaNuysqRDM78bPIzy1pNu3Zlew3zoLFxo5EVlt+THc/42c+zbjIk82kFsHexTtMs2Q2pG8e+aZNd\nZCEBvL2nlsiTmgyFlDdNs0yygP8F337g9qKaeOWcj/4eP/tgdQryvRFnpTSRzS/X/LrLe88D2iaQ\nDC6S49YzA8m3c068KwPFCpp12K/cbiMnDdH1nZZjnQhIBLTyBjE9WHQePLIzBQZdmS6Y9hr1cwOb\nXAkrB+e/wVr6VyzJgDJrtgwhdIqQ6UFGZwjJe6OTZMUk+jKtC3nWNoSQmupcplGi+s+FyXOVV0gk\nHRWXNpPMfdeqqtx0W26Xl8FB+3t33ZXGtqSQn4uuLTz20wzYifJ/NO0V5PH08gYZGDCKeLJy9NMf\nCWlw0Hz+6Ba7aIUDWrrLEnPRScWlVzHBqYYtDewaFFxpeXkP90yWTiAfAXQIgxaF0ISfyXRhNUh5\nnlpE7HDc3MLkgffuQXdaLwtKlaQQS37nn7HUeq/T4KY/19uSAdoIJuMk1d+xhdXyBkIGe1k4xPK5\nsqm03L+8DzQN5ZrBMY8v9d2Zr/+Cn600TgdHz2pJKAcuPVtowafWb9WJKI0/6epwIOtl62dSq6jK\nzxPHKrKbewBkdS3T31uxwo51acAuOPbTFNiJ8qdZnfg4rT2jM1s+uTQNCm3YkFb/6QIjKQy2fV1I\n169NMxmmUaa9qNL2dWEm0CoLZniazDeyUTjM9zw7ebS6eOgHWG7lM/e6zcMYjD3v9D2eqXzBG83V\nOP9Skr8eOLerjy9vsHkdyzKfMQ+9H5vomEh5zANt1+Dh6u2qX7cAC8BTnfkg45XzACCBlkGfA9Zy\nNsL3hNTMfxXnxPvxrJmbnvU8gm1xfCN7TTXwt+HRN94xGgcz/bj61nZKfiIIM7M0Vw/a1m/Vk2y0\naI+p6QAMbRntqdFN60ML2DtpM7kcMcBWVdXFgWNj9qCQt+QBdZEVcxoCey/TLP1jP3RjSsnkeRit\nlj1N3LDBLQlw77VhQq0AlMka4epCHgA++vYwI8MaIW3QwN5+nkZ4JzCUS6fG1nnvSUDch1ELtNkj\n1XnYbXj0v2NPWQJw3sDT6Xjk/l10Sy/f1yD+bayhIxigwxiMeW4bLFvw6BFsi0E8fS8VaNPt+Nyg\nakDap8+dm7byc7X84yB0KnNsX0+pE8/7ZtkFjkO4GlM7//b10Rc+VKdPnZkG9j93btXqLLW5L6tT\npDNneCbbKJn6kUap33RLqlSIPI+iipmRSq67kzZTu207OFwA6GogL9vrtVqdgX02CRYnwwpg72K9\nTLP0aN1+TqRkxRVzH/+4fYMMwVTYtZ8LrWwZ/mwvqvS7S6p077XmZm42089cBTwMVNyTVAc/2fvn\nYJzLg5xCiV7BW20A8zw6tmqTpfKXfsezgpAu71R78trzPIr1mXPR7eDyBohOHrH+66Js5HebHdbj\nwKDcz35son0YzWiY68GSy/c520fn4BtPNsich2vwMoNzhT42ENJN60NT3KbOh6/5TtToPX5Iz5w9\nmuHyX8Yq0RgjDcrK+4KbhcvgbXqOAUV8PwTZ3qL6PpeiYTpza8OGtH2jpA0pCChifpKP/ZZq8kx2\nKjJqtdIYkw7ebl8X0vZ16XtRf38yO5AcOy9yli331wtudHp9oq1XYC9hkZrnAeecA9x+OzAxYV5P\nTJjPzjnH/H39deD++83/ExPAn44fwAeogRLaaLYa+KX3HMB/xHCyzR14CA/iVvivRJh+bx/O/sgz\nePHF9POvYhhfxTAwBdx2ATBxF3DllcAQDuMZXIMKpgEAEQAPgA+AAAQAfLRAaMMHgeL32Qge1uGb\n6MM0PPVZAIDQRgMV6/zb5OGPXxnCL+DZZH2K90vxfyS25MXH1UYAAGiigj/D+zGKL8BX3y+hjXX4\nZvIef1++dr0nj12vR+rzFoAIZXwZ78NP4anMNvjvy7gIa/Ad+IjE9UrX9QC04eF7uBAHcDXewDL8\nOP4KJTST4wvi7/I1ADz4IJTRxG/hYwCACB6C+IoFiDCMEGRdQWR+G/neP1wwhH3fG8Ynpn8NHrWT\nfcvjJXh4FcuxLjqKq//5cXgga5s/gu/iR/DdZLtNeHgXXkQZjWR7ZTSxAq/h93EDzsc/4m34Hvre\neQle/9t/wlcbA/iF0m+iTA2gUsGxVZsxOAh87Wvmu0M4jM04gAPYjM04gArMs0AA/gGr8VUMIwiA\nd7cPY/PXzXrAMJ5fshmNqQo8rwG/UgEueBtcds89wPPPA2Nj6bMIABs3Au/858P40/cewI9+fzO+\nj2H80vABVA7z/hv44Ku/j+/3rcbt+Ax+dvg1/PSnN+OO4WH8/Upz/C++CAwMAC+8YJ65F18EVqwA\nPvYx4J//2X7OPc95eLj7boMJvA4RcOedBi/uvtv9nZNmvaD/XC+ngsfO1o1Lz0t7lCleK1YQ1X8u\npIYIXDZjlb0zzsh6O8uX2xH+Ty4VJdYwTRJcOcVSNVF7gq6qSe1hyu014XXUQGfVwyPYEO839QB3\noka3+PUkyJvnVXdrCOE6VrPPAaewl16P9ehn69Fr7921H/0dLiJyZRflXf+82YR83YxnXX/gZZuX\nyO9wM+68bcnzcUkR8HXT4mg8A3n+HduofeEq+trSTQlHDYeH/O9EDvwk+uj6teZZ+JkVWZ4bMEHN\nx4dr9LEBIwAW9fUZKqavjya2hlaKMctvTGxNvxv191MLAb3p9Sc9ftPK61RmeNI3n0vqZuNGszAX\nzzNp1oLvVTLkVAikoqBi5sZ0cLX9nOHY5bTz4x9nfk9kA3hBbmGR5CBXrHBVapYylYIsC8tT6LzW\nbZ2oC82dy8pM1/c44Pm3lXUJHZFqZPclgbUjGCBubt0J4PLAimIgOoY1CUergUeeK69vBMTcjbnz\n9uXaVh7Yu773CLblqly6ttHt3OVrHRtogwfz/AGE19P7fR3LrCYicjGicG45Y7lMo0zbLnZLRDC3\nrlvfPT6cOimS5+ZURF3Ix+DNVIuuBeEgq5S41tLBlm6SZ7fbY/C97TYbfFut7HPeCxac7NTHAtjn\nwFw/pM5xZ4/mvuvYU5El90HS9o6B3FWUtGEDZYpFtFb2PRfW6WdWhPRLfs0pCJX3gLfF9uwG1dmu\nSPrB1g0t8jxxzp/ei2pSDt/qclzspeoc9EewjQBuFN2bpnse2LkqV3sNrOYB6ZMYcUg/eHFlsbu/\nqxyMXNvNm220YJpmvIwLc49HBqX19ZUBXyngpdvj5R1TG7AUMvnzNI891eXZiRrdeivRU1eME8sN\n67z9225zZ6jIzJWnt9gB0OiWKlG1SlGl4pS4BrLFTcyt82cPrjJZanOR2XKyi5UKYD9O61bAlKRY\niZskOpT1IJowbe2iwNx0LiW+wcFsRox8INnzTzQ4hJZGO0510yBsADdI0vu+jTW0H5sSESd+OJnO\n4IddBlM7ZXFIEGAagY9P6shEQKIxo8GHhb/0Njkd0kXHfAcrLS/dBcrNmB7Qwl28uIqxGPSPYr11\n7P6KS6wAACAASURBVPq6ygbcPFh+Ylmd6j8X0q8urcXt+dzHltcwIw/oDYhmNXTSxiAlOoxBqmHc\nSYm1rH2XM122umVPsaa9q9uS1tzZgTp9YpktafH8O7ZZTgxTJDKzBbBzze+5MC3amkI5lQHu66P2\nLdWE8tELJy0MIdV3l2Jjb6CfblofHheFsug8dgAfAPASgGMAdnZb/3QAdqLOkgPSdF67BOGWX070\nqZmeeVMVJQExj1gux/nWRkBJrqO74HBxCfPepoEDks/5gdcP61TcC1RL6nI6nCsVLgVELxEl0yBl\nxKNc3eyN58hl8fpYpGStHEBcKpR/g0vijJUgl7/fj02WdnmnQUCD6BRKGTlk2ZNVtsAbWRbSrnJK\nyd16q1nM72QPjvuxKRlETeOR7PF0qj3g7x7z11qpi/o31Lo+vI02YFU7p7nu9jVoi/W/HdNiUvaY\nBxV9j7BekM6E+v7Zay0H5/BZI4lEgAucP7GMQT1tDZhsz/cp2lMjlVCTLMyhj42lmTN5ld3HC+qL\ngmOHSbz4NoCLAVQAfB3A+k7fOV2Anai39CbXj8wNeB/dUk+abDSDCn3uXAMUjw/bPP1915mAEgex\nHt3SuVNNI9a85uYH0mNjNUWpMCmn1yY1z9bq5oe1gSAp4HF5tNy1SVNHP8C5Ga8yz1uW38vrShQh\nFdb6NtZkQD7vOwxgUlSrmz6MPGYGTJ2XzU0yplCim71UF1/mbe9A3ake2YyvaR5XztLF+hj5/1dx\nTtzlyUuumU2rpUJsO1Cnl7DWAmMetOT9JgXjXNdjytGBiq+PTOPkc9LdliKYvPrpUr8B5fi4Jz27\ncIiPJ69qOvm/XLYoFr1w5amsMrUCrJ5N4cwGiE+FYqX5BPZhAH8uXv8igF/s9J3TCdildQL5PHGi\n3bvJiIZVq0R9fYliHVfc8U19+KyRxONtKu+CF93WTjbN0IFWfsjd4OrFZeRerhhU3gPPwKm/Ix/E\nJrhlne0tOx9Yx2fyfZ0Tr8FZf28aQS7F4DpWvV3O8pCB72z8IwUxVkbkfeouT532KUF0H0ZjGYne\njlerSLbEdji4rX9HPqeULnQHnvmvdADk7MU1aOtr+Y13jNKGDaag728vtu/tx4dr9OmPhEm7RqBT\nk3VQyy/Ro1vqibfOTWnkZebKVQZfHjRkZbdcf7Ze9umSxz4XwH4tgP8kXl8P4D861rsZwAsAXli9\nevU8XIK5tU6jtbyZ+DNuWJ0oQYq2fBK4tSJiEz69KQKuO1Cnz6yUsr42b5zXdIK3vwN1Oor19Cre\n4gQLVhp0PdgutULOgtDpmBrE8hQj87xlue927KlzUDKvF2sUAxxzznmDRTdAd3nQe5EqaroKx/IG\nqk7H4DqmtAjNS3nlnG3mDX6uz6ZQtqQTIkDozJg4DRei5W3zKNZbsxeW+e00A2rBzxQH3Xed6CrW\n3097Lko12ye9ftp2cZq+2EqeAzPAtm+uJpkzGzfaoD4wYF4zp87PJyut6hnv48M1qw3fXFAo8w30\n8wns1zmA/Tc7fed089g78WtSU1rSMPJGpjA0WhmiefUHzpaeSspDPlMeoU9dVM+APecOywyIFnx6\nCWsznpdMP0sHjqwGegTjWe/HJov7ZXqHPbRpBeIM+C7AzAO4TuCngU42mJbgx8vXsS6Z+nNetmzR\nNxMAtAeIlM7i7e5ELdO6rtNA1a1/Kf8/E/ngvGvWabDSEg5TSmcmUsfiPhd79qIdAP27HMFAJsWX\nKRcJtHnct6S0eF1Og3RlpzH9YiUwOJ6/PWvq1msG9+OlUE4GNVNQMXNsuRHxQ3YxBWD3gLQac8RC\nSI8J/lzygK5AKT9IDQRxmXoKqBw4lRknbeVtugSh8hYGM8kfu7I8WKTseIBUg4N8neex8qCjC3Q4\nUMwZLS4vtNuAkicZrCmPpgJCvZ1XcU4G6Ltd+06gLfehz6vTNebrJGcgrCHTiWrT25WKkbpfLF/r\n9LfzrISAsTFKmk9Hdfueny5lEwjkwvrpgNF4f+p9dkxKd0aSnvLTW1RP1ZER6/XTW2pz4qmfjGDq\nfAJ7CcDfAfhRETx9Z6fvzCewz+VUKZPDWq8TlUpEvk9RpWIF06K8ptmx596EWzQJcNMznKImH0Bu\n3cYA21It6oBskKyFtIFCpJ4mloXlPHSZ+WKDXdpuTR7P6ziTvoOV8TS/s2fKvTZZl6WbgiQfo6s1\nXifA1MDYjLMtNEi2wAqIJefx9rK/CMhkhujPZCYKz3zyAF0PprJozbX9L8VKixEQZy6Vk0FYFpXp\nGETe9vi+ewTbrO90GhAkjcUVozJ4yvenltfVy/LlRM1nQ/rdJXZTdF5/+zrjJPGzZT3Xanb8WJzA\nwK8ntoZzArwnI/1xvtMdPwjgW3F2zC93W3++gH0up0oueYGWX848fHzzccWc7rakCzBcQVLePiv9\nPYmRZHoqPXbjMaVNkF/CWqubURokywcOucggoAtc2/F+XDMIplC4SxJ7iGnGjb0t1l4HiGoY7+hF\n6vfyZH1dHrS8VnogcnVbYnnbFrLFUZ328wreSjWMZxpwMxBzQZgW5Poz0aovbxbDx6/jKbIIqwkv\nE4vgOAXfmxJ8j2BDsn4WnEGv4Dzah1FnOmq3QY9lpF0zz5YX0IOr8sXFBgeJ+vpEDrqVVhkk2WSS\nWtFdlogomR3zTJqPZ65AXeKCPP4Fw7HPZpkPYJ/LqZLru09cldOtKAjoiatqybpyythuE91wia1x\n8Vuxl88tVeUiPfcGAvra0k2KY/eo4WgqLRsKc/DLBdYSSDiVjTNZ8jxCSRu58uQZxGRHHa5IZaDR\nMQDXYOKiMMy202Cpi5ZxHXcLSILI3QaPl7A28VA1DdPpe0ex3tkVyuw/rTfgNMy9qNLHSnVnil+n\ngU3/bp3Wl9fQfV2yswUtO5En75u3TzPI+1RbZjd+MfencXxuuMT89itXmtNZupQSL51PUQ8K0mn6\n5FLxWRDQAxekz5trhi4uW26/hePFGF4WlMc+02W+PPa5vPAZ7z8W9W97Ru50Gmnz3dZBo0O9aVN2\nhrBrlwF39mT1FFMurlZ3EtDegMkweO5Mu9mBlIxlwDZeaH4aYJrPnu05Kvf9CLapIhd7IJBdlCZR\nsaRhudkFV2/uRI3+Bpc4vNOs56o/Z89Se8gub5oHpW6gafZpe+rsvcp1/wX9me++grfmpg824+pg\n3j7L/eqm35yGKs/BJfzm2ofrOk2pQV9nD+nvNuEl9Qhy8HDt739jqfWZHmR+IxjPDPxN+HTHGXXa\ntMnw77t2pVrpOn1Rxp6mUKajWGc1LX/Tszn6lSvdvLvr+e/UCq9XW/Ac+2yW4wb20ARkNM3hsrmc\nKmW8ARE4HUJIn19ugo43XBJaJc76h263O1fFyZtbcr4pUAT07KXVJKVMB2DzWu7pnqRGRbKUeNFM\nnUgu2AUYku7RYl3czYdnFC4gYWkDF6+ugUSDsIsbdwG03mcvRUquJtQGXEu5cgCdFult6u0a2iql\nSVLtdLsZC1cV68wkubhAugWf9mE0aclnV6umKYXyeBsIMhlATYdXH6G7PIIekAip9gxnynCl6O7d\nJliq730+ZplayzSPjktpkS9XKz3993gBeEFnxcxmOS5gD0MTkHQFJpXN11RJplgx/WE81bQhhz4m\nFxi7gkn/ThWEsGf1JEasogt5o192mZ06ZnfP4SYMpaQJAwNLIn2aHL9b1OpJjFj71VWTPKvg7WhQ\n1R6wC2QlaB3DRUJCoTSrPHI9YLhmA62cwKI5hjWZ/fTiObfgJ1WrrvXYk+XUSv69OEYh76kj2JA7\niDTBwVhW4UylbKdjGQhujL0Po4lMgva6Gdw5ruMqXuNB29WkJbI+t9+fjiuiWQyMq0MfH67RB99i\n38t83twJKh0cPKcTxAVKRCngctFgs5mdEcwVDizYPPbZLMcF7KLQx0olVDanU6UeZgjRHlvLJfHS\nxDHqY2g/F9L9K2vWA5ynXsfZKgy2MsOAQVw/HEYv3S5o4qbSaVl6Vt+lEYuHMc+sAV5SKVdX3PnN\nLDM8hDATtJTNpHv5KysmtbJlN0CX4Mmft8QxaI+VB0E9eGh+Pu+v3v9+bMoUlemBQ9NrQwjJ87Kz\nOhmsbsGkVh7DRYlmPtMtaW1CkDkuPk/zuXvgbsV0GUBJpanR55cyFjbN1hTnYH9uZnD7scl5X096\n/RnVRl3boZUlr67Y2+FuSJJm4dey74FctN7T6WILF9hn4LHPyVSph/1Jj70hPN6Gw2OXx9RumyyA\nTrSM9MQ1nSKbB8uHw5VRIKfDUr51On5Y2cMznKYdcG3GWibMc+qB6GavTlOORsl7UaUlSziX3gaW\nKZF54mqoLNfl2UyqROllzi0POHnR+vaugUHmbGvtHa3BrgE9L5PFiKNlA9J5sxTOnmHv3UWxMdh1\nkt6NAGp4+VWseYMg/88zuulYjjj9zXxqxNeD4wScScTOAvfq1XSJXnYi1Vlvw6NnL61mPPaov58e\n3VKnr11RpfYt1STwqhdX4wwX+Mv1m83sc3yq28IFdqIZc+ydXnc1xwxBbkMKD92zytzo96yqd+TY\no4isfqj3XmvybiUnPjlJ9B4/q92uKRyXDLDOKJDLFEo05aXSvX/hbaK/e+ug9aAaoSddqCMFqEqZ\nwWUSlQy4PV4aTVLXZPaHBq99GKWjWEdHsMHyIpvw6K/iJh7ZoGY2/Y/3+yrOyXjcr+CtTj5eAzED\nkqGo0oEFoAz/rK8vyyfLNEPOJ88bTFyiaEzjceYR0yIAN6ouOwc2va3vlC/qGDDPGxDNbwRrIOPa\nB+1QMNWj9eldM8+rK9l6jWkvDdY3/bJVC8ISAFxdGkUmf11z64BxkPQz5hoAGNQZ7O+6K11/vgW9\nZmMLG9jn05TH/tCNNlBz662b1ptO7JxbK9t96ZtFa7vfdRfRB862b1j2dDuVXn/07e7KVeNx2mDC\n5d6Sc5frcLNso6desr4r8+D5IWePUQ8u2mPn4+WmD03x0LtoGp1i6AJDBsz92ET/iiUZoNIpe50W\nDXBpEwkhN4tBOoIBq7DJ5bXzcXGmDr+/H5syfDYPBDoV1VTP8r7NQMGZNGluuJ0pZTzssuNaZmcY\n3RqKy/XyqmanEVDdt6/RtFCflPfr0qW20qJ0UlassIXt+LfjOoghGG11lhR46MaQJn17G2NjBtzl\nM+by2OXSbKbP2w2XhBn65lT23Atgn0uLZwgP3RhaEXUOAAGmJVgkPPtoT8qru8x18y1ZQjQ9nW7T\nBdpy/VtvNeuyp70D9aQzvBau4rxyGZSSOeuSopGBMZccKw8EnI+dUgQptcK56jpzRnLvMrVSg6T8\nq9/jClLXcbVEmbtrG9/Gmhgs3aDG0sRSkKqTl+46vmkF1q/gPHqinGakyN6xLKzFuixMN8l983Zc\nGvvN+JyYIpNg3ox/R33t8gY1F9g31WDG25joSxu0uHqrTqJC7/FtmY00nuPTL/m1xIuf9LI6P+z1\n/9Q56TP3wAV2/jrXi8jAaSaO1c5PpdQxrU6gfrJVHdkKYJ9j0162vFEGBkwgtFfun4iSyrg8/pE9\nijvOSFUeXXxl3k2qJXvTytXUy5LgwR57JkYgdL9lOb/Mi55EhR7BtsS7bAjgMh6ZBE4voYtc3HM3\nEDUDRzaN7odYRvuxif5sINs2UC66z+trsbaLBK19GI01eLIxikjsM29QyluaCOhlXEg1jCe/nfxt\nU04byeyA1LGxWFsnEE7X9+kbq+38/W4SBvr1UayzNGKkE8DHwwO8lviV96ir45L8XPYE0F4/05pf\n+FA9aSZPHcS8pNqqnhkD+TPhPLA+FXTY2QpgPwGWN8VLCiN6yZ6JKKNl0Q2s77kwP2tGekJSrpfB\n/UmM0McCO++c5XBNwNOjlhdYwS5Xyp0OsGoKQYJgM06fZP7V9vTLwpPv6woueaCpXxs98lKsO+Nl\nAKwNIxeg0/eO4aIELOX2tKer9zvtkMV1zRDyPOFHYLeN0y3oehno8rafFol5NOn30xEMpDSQ5xF5\nHkWeR2349Lp/NkWlUjzT8TKDBmfBdDoe1v3he0MqQrqlAez7NAvwfUoquEKR51FUqdCjW+qWHHZe\nJSk/kwzKUiFyJh77qdI5ia0AdspedN2ZXFeq9WK6X+NMfuDEk9hTo7aYlj69pWbNAqw2eF5AT/l2\nJemusg3e8mHjKbnMpOH/r/LCZNrPDyzv41NnaH34rEiZ5Oe/iwsyD3cK8n7ijTPI8v/ak0sLZ/o6\nZrpkVSDtbk8aSF3g7BLAkp6sSzuG//8Bzo1jBGnz7rxGJp08d3m9ZHqpzHbi9VxZQqk37iUUl2vw\nk03L82Yw0wjMrC0IKPrwKO1ZU7fiLrYWkYk1yFiB3C/3vdUV1TIriI+7U90G3xeyvaE8t72oJs+b\nzlvP86g1h95qmervXjn2kyEdkGeLHtj1j71pkx0FZ+7t6qt736aLr2NOW99s2uTN8dgWGxDuWWU8\nak7J0uDKU/TI9zMtvnQwzS7p77MeMjsomOYdvym2+YsdArZScVIDlSwg0uX73Cc1TzpBgrwM3PI0\nX/O3U3GgkIOaecCuNejz/idk9cslAE/HWuauAdOVRpn3v+s9qSPTSc4hb3DIP3c/ptJKCTi7vp/8\nAL5P7W3bEmXPFjx6/h3bLCVSHtibftl5Li66T15TFmJLC+Wy90R/vxEB49ca2J99Z9XSYOcEBfkM\nukD64e0hPXFVLUk95qw0nSKZR6/Mt9hXni1qYNc/bquVguaKFbY2BQv2z2Sb/B3u3LJhg7lBerk5\nbr89G0h66n21xIO499qQNmyw+dc3YKRPqVymL3yobuXksm6GS09EFx197twqTZdScNaFRK7caVf+\nvPYiZa45a37LY5CpjXlxAgmY+zCa5ELzwy0bdec1bU69ey8pjOkGgnIQ2IfRpHCHr81+bBJFOX1J\nY2lO+dSFOnrbLm87vTapBMMb6LcGCZmV0oahjP5UDZiuc+P3juEi+kplJGlC0kRAbc/P/V7eYM0V\ny7J5Nks2y3TIZhw7kQF/rU2TdotK95Mnp7FiBdEFF5iUXxlcfv9ZYaLBxM+39Lh5kQFVpj4jFf/q\npOme9+zzUnjscwjsM4lM91qkMBM6RgZlZP76xo0duDfFu0eRne0S9fcT1evUKPVTGz5F5TK1P5sG\nlvaianhRIKMcGUVm30MI6eClVVUJ6Fu85xvopz/cXKf2LVU6tsSuovwuVloAoz1T6alrrRbO/ebj\ndTX24IFDz0JYx0RzzbKM3giY2VSCS6WR//9fSy+gll9OaBgG39RL9JIBT3u47lTPzlklnJOvs5Bc\nSxMpHaSzXngGxe9No5wpkpJxjfR4fHoFKzL75hjHpNdP96wynramZHShl+uvblYtwZgHcVkxCxgZ\njMMYpIa6lnrb7fj4OZgMEJ1xBtH552cHe5dDIKWxtUdtZcrsyWl80yPmFBz7CQT22USm9Y/datmv\nZ8Ox5xUnOUdylQMv+y3yzZrcnH7qDTfijvKZlMBKhR7eHlqdm1gIbOlSd5bFYQyK6W+FoiANhrk8\nNfa85MCivS72YNuwJXj5vOxipDQLJs2a8DMAw4BxFOtyK0QpBhidYSHP5RguEpW1XlJen3rjPrUV\niDLosnecN2i4wGk6/q1kto3eTnrtvGTANY2ibe5ZbpepplbOccmZRqq5bs7DDNQ2COf1tT2CASu9\nsimONQ+MmVKR9JT8261NoWv5+XKd3v1uM/NtPusObsplCGkzG64bkc8V57jLqnCrVeUMrMiKOUHA\nPptR80R47HnHJrdpHYuoWnXptPPxTWwNKSrblZmfWVmjttKfad9SNfs8ZN/U3D9VLjqw162RhQQY\nWRnoylzRFM9O1OgngnRQSXVtUk+Osz6kt6oBTxdVuY6XC3V2wMgVG1okiAccv6MuSyeg7nV9vbC3\nqT12FuRybYs7Vclrq2cGsnrVtbQFCPN3XsGKjJfM10sCu/y9vxo3PNHgvBdVmvL6knPIq2BlVVBZ\nH+EqoEqP8TyneNiTGKGxMRMDk3rrLqqGqcEkAcELUidJZJvJmWde45uZPOedXs+XLShgJ5oZz3Ui\nOPZZHVPssbNOO1etMqhblan1OkWldFp7wyUhtZ+ze0M+dKO5KWVPRwoC+uxFWY7yeQU00uNKASb7\n4O3HpmQb0stmT62BgBpBxUpJu9mzC5S4w7xOeePgqi7N523rnprybxt21yXANGbY3Gdn+nCgULfQ\nI8d29bnLa8A0UbcmJfsw6hxIOadcluLLz7nAS+r2yAYiefvMG5j1b8vXQFb/urJjZKGUpj7+0wpD\nl00sGY+147PXswXP0v/nAiyuh9DxkN9bOU6Tfn9m0NJ57W/AcOLaYx9ZFlIjSDtytT2fGqX+FLgT\n7RmWvjDb4KDpqQLQs7UFB+xEM4tMn4isGH0svcwiokMmGs+aMTq1yhpUwpDae2qW0JHWnLnrrrQL\nE08ttccu6ZAUPE2+el7rNvYSpQbH9WvtqlHunfnH/mhCFaSZEHZgVeYq60wJFo5y8dzy2DSfrx/+\ntPWfXYLfikFSe8F5nrqurNyH0QTkZKWn6/uHMZgcEzcW19vjQKI+HqP8mA7mfC11rr0eTHo5J/am\nJSg++86qFZzdj01W/IRpIp4B8QA1jRL9RjDunFGxxy4DnDtQp0+ekQ4S+7GJfnjmKmqPj1MUEX3x\np+vU9MSAWSollCL/trKhury3NaX0/RXriKpVA9yxIxUFAbV8W7n04GXVxJE6blHAHnWqToTNC7AD\nuA7ANwBEAK7s9Xsn2mOX35E2F3ns0nrl3mZ67K5ceW5OwNu5fq0dUOKZyKc/ks1g+YcVAwmAcDob\nJQ9tqsp3a6VOl16aCittXxfSXwaDydSZAVM+2JwRoz1bFopyBWC1rrocDDgjpuEILvLDrjM07KBx\nCtZ8bt0aZRvNFqP7LXXpOQ9brtvJ03T1+OSBwgxAfuYa8bWX2+HryefgCkK6zqMNE2Tl/UlKbSdq\n9Ae+PVNi/R49M3IOHOUyfW2pXbV7DGuENlEp3n+JfiKQVF7qGLQ83zSAr9WShAACKPI8enw4v46C\nU4pdwE5BkFZ71+sU3VKl5y4z3H8jsJuWsMM06yDoDJRlT5TNF7CvA/AOAAdOJLCfapFpfWydXsv3\nJVB3AnU5CHD16B+9r54JmMql1aKkXV+aweInXrZL7ZG9rV8OzACxuc8AwE+dE9IH3xLSm8huS0/H\nI4COYEMm/3xKTINdKZOuTki8rktugGVjNcDxACK1yvmzlgie6swae0BJUzK1Wqbmzo9ivZXRo9UK\ns5o5Rv/lJay1uGVZEJVXLzCNMt1zYZ3+cHM9ydnXGjlSy0V2wsrLapLXJpslk41/JAOJ71N7nZ2N\n1IKX8PHyfR5UNH0TASblsF4nqgj9oKAvCXY+Ppzl19nR4OdBDm7EA4TvE5VKREGQUjO3VJPPW15A\nT2+puZMXesWPHntBOG2OPP15pWJONLATnVqRaW3dwL1Xj11K+bK4l/Xg+WWKAsPX6zQw/VBEXlqA\nxF6t3ak+5V9dnpKt/eHT358/GA8QbsVAKSBmMj7sh5Pz0yUlIcHgZayyPHzp6eXpmjM4cbVtp5J8\nV3YJr8edfeR1sEva+5IsEfaKU214O9WP6RhO5dPFWnzMcqCRXrY8T1se125o8W2syTTx4FlPOgsr\nOb1yPg5JVTD9pOMf5px9agQVOta3znnt9KxmH0atAS4DxDVTKHTwMjMDu2m9UXHk+o0k3dXvt7qE\nAZxO66fbKpcNyJbLptYjBt1oT1yMJJIMOC1S7mNGWTKz9djn0NNfcMBOdGoGPnRFmx5weubixfsc\n6D20bCQz1c4+8EFSjXrT+jARSWr6Wc10nSa4+212vrwLULjxRtLqD2WS0roSRHkQ+QsvLepxKf9x\nRof8LuddM5ia1EQDyJOoOFQN7QFiGmVnw2oXyGuPV6fuaQ30IYQZ6QLJo7fiXHaZKy4BVL5+CWut\nQdOVeinpA5ckcjOZPWVTCnngzDtvfv+7WGnVOPDAJJui8ED4v5atSgYLva0mfKr7VYtjz5NxJsAA\nsajpmNhqU4orV5r+BI8Pm/fY0eFakUzqYkztPPuecWr6pjqbU4vHxszzyZ4y91jVkh2sxNqTzcbz\nPh5PX9mcATuALwP4b47lw2KdrsAO4GYALwB4YfXq1bM+sVPJUuoj7ZTkAu2us434ZpHfB7IeuyzN\n/9y51UQmuIGAHrigRrt2GZXJJ66Ki0dKttCYDHJK3RbtIeuGwcwP84Ms6QkJAP/TX2UBeANBKocg\n1k1BNc2JzitDZ6pEt4WTfVZdYOsCNAm2vJ1sPnuaE86BQJnZoSkK+du4in20vjw3lpbqmS6ZYfZ8\nNZXCGjm6CIzPxxR0lTLHIQcZORhKLj4I3A3KI3FeTfh0FOuTDCSOD+iCIruoTWgAlcuGIgnDpCI0\nL1+dM8e0LO/E1tAWAhMUZMsvU1SvJ1Xh/N122wwaANF919lpkTetnwEdMxsrPPbTzGo1C1yZJ80T\nErJeH4pH/nrdLmKq22X3Ub1O0cgIPbrFfnj45mRqhlMhicyAMTZmbuC9qNKzl1bp+rXZgifOmrCL\nh2zAZw9WPugPLHVnSGg53ZYCZBuEvEQjXtIfWieev6MpAl0QpME2D4DT4ChXgLpBlf+6aCO97XZ8\n7nJmxX+ZY9dVtlwNy0VTLq/6KNYl14d1dEyA1+bGWZ/eljzItio85pDflXQcL7w/Q6m5OyPlVZ3K\nbXAa5dP+SEKTJINTqZ+iW6pJ2q4rXz1PuTEDwjnPoQR2WUh4223m+eNZAc8GTqgVHPtpZCK9St7c\nXW8SOYKXSmnVqe/HFIrdIYZvSh4w+DXn7sqeqmxRZKah0mu/99owQ7lofQ8Z7GSJX53N8dIFm5L2\neaxp4srVZlBzUQIR3OmLzGnbHruhMJritTmPLN/vAnKeBbAHXMM4sXZ8dnCyt5Gnfe6aFbA3U+14\nYQAAIABJREFUbbf3SyUX7BmT7UE3AXrdP0ftO+0mJGdNnB9vz37sgHVSZBTLGLPujT52np2xt+4K\nBmsp3vRc8kFZg/x0qd9I78ZvtryADl5atWgV3c90ptkq+jnUVeHcbUm/dypQur3afGXF/F8Avgtg\nGsD3Afx5L99bMMBOdp56zzek5NxicS+deysrVTdu7D4dzVgYEo2knlLLCxIPhQFb5jlLYN2LqkV1\nuMCsUyXrc6VNCQjoDJfUW/RywWAH6nGLOJnxYe/jS/FMQL7XVJ7slzCSaTlocvylSFlKLXCDi0Zc\n9WmoL03V+AkNoa8LF1rpwYbpCl1D4BqUXIMHp2Hax5FtNqIH7KVLie48M/Wsp1DJePJ6cNe0iK5K\nHUJIvk9dPXZeBgYMmPIsYBKVBMgf3m4qQtt7aokkACsuzjTrzfUcamBnVUj93ulkC7JAac4sb1o0\nw+nSrNMwNecmAkAtv2wFgJiLt7YVppICTs4u3n7ikXqelUnDWR7srUnPSvOr0nt00RE6y6SlANvN\n2SKzb3t9Owul234jgI71racvLxu1PNkmAqr/XJj03OTiH7kO88yScuDgcZoRY1NIpvm2W4jMdZ2k\n6Ji8VvqceKA5jMFMN6Fs02vb42eKiSs+eWB9cJXtWXNmkv4dftHL98A16D+CbQmNJdv85YE7AzsP\nELK0X8ehuLZkJllvrudOgzoDfUdtp9PACmDPs7xAxiwDHLNOw9SDCE8nOR+3Xk+2l/lOtdo5yl6r\nWZxmy/PpCx+q0/nn6ypQ+wHObVdXLlN7fDxJKWPPdRJ9cbDOboot0xZ/pZTOAvZjEx3DRXQU6y0a\nRi55YlXZDkc2OP5JxW7Oze//Xn/VmcvNAM7BTJnNwlWzuuBInqOmp5iymkRfTH9kZxoa0PXSgk/T\npX66upLy6q78exLb5mAmBzA5MMsg/Ill9eTcp2MZAx1PWb68c3/dPAE33sa0qlmQp3r55UQffIs9\nMGRyx8PQVI9yUJXSZ0lap9fyOXQpr0pA59nvqVILMxNb1MDe8YbISz06jpSkOUnDVIJhMgUrisjK\nyaW+PqJKxTkIMbfe8ssZoALijIBKhcjzqBFU6Pq1nT32CKCoUqGJrSE9vD2kaHSUjvWtS/jqqytp\nFyTZQIH1QmS5vCtwq4E9W+mZBfQGAmr65WQdVy41//3P51Xp/pXZilD2vFPxKtZp8aw4A1dUNsVg\n4qKh2jFdsgN1i8OXzbUjtf5RrLPiBhxMlr+HS6vGRQG5AuHsed9xRirAJvvZujpk5enlczpjnoCb\n9vSXLk2/r9MLd6KWgmkYmnuZV+7rc85An95iFxO5HCgN9Jq+3LiRrEDpqVQL06stWmDvKbVwDj32\nOTMhGDbp99OjW+pJYHRsjOjBVTVqS03pajVJkWTbvdtMe8fGDAfa9IKMtG77uZCoz1AdU15fkhXA\nZduG316naAAzMOjOTxN9dpaHzqKQA4tuqdaGrcnCH2mP/WWsyhbY+D7R4CBFHx6lg5dWafu6bAs1\nBvwPnB0mwbu2pYmSPV4JzgB1DPxqcGVgM162zeEzF92ET3+Hi+gr54wmmSVyW1KXPAVEW4OnBSNN\n7DoGnin9/+1de5BcVZn/ndudngwajWQoAoSIaLRCJSRb4DiDVTISd2SFhVBLqNoF4xIomRKcENQU\nqIS4knFdiwyxQBmwagtqVWSX1YAWpRB3lKQDteACK+iu1PrA14K4PnYT0tN9v/3j9Hfvd8499/ad\nR/p5flVdSffcx+nb9/7Od77H77Mt7+sxYWSfcJXtZW8sJ9ws6e6xUhR/kOc+gMGEr318nOjqq839\nbYld1myiiYm4ehSICpjkcxH299dFvRbpezCnte0yuNqxFmY26Eliz+3zXiAf+0KDU7AiTWtlVpnK\nDu1ULqc2/hgc1Ja5Lcx0PSZo/9o4/70KRfctG4syBfgBti3SI1ikLf3R0egBDAH6ad+bDEs4bspg\n+4PT5XQlmXJ1aqz13pdeURoE0bXhxgzSuq0CdGdBp/INDBDdeXmZbjspdgtJfZm0YKIrL91eEchX\nXJwTGNfOtoRPP11Kz5rXWRLrua81JQp4NWGfQxI7/455s1s+XszObunrI7rtJEs+GvHknKZ3Y782\nrypr2emxMbrz8nLclq6sV5DRJGVb7BNmrwLWv29E6t2KniR2ormJhR1tzMZyCEO3Xsb4uMh9r5dG\n2636pB9RpjXOINCd6JW2rGXPShnE/KfjTKu3irhxcxiSFlmyLEzpumGXwk+xwjiOznAxfdU2Kdnk\nzzICZs40yySY2R9MlmmZGkMo082vjotndAOLZHWmLEpyBUftbe3vwuONrduCQXbvfk2Zbl0eT9x2\njnxVuMz4NYQyfVQlZXVlUJcnhUtPdQcv+TifwxiFpZJhnQ+hTIeVWyL32GMpWsVxJfPhemaNvWJw\nTQrr18cS2fI8r9SDu9xIevISPbb9a8eSqbvlsq5Wja5RPIF1gwU+W/QssRM1N6Up68YKw2RQJwyJ\nrjhNZytEMgT1VK27tsTv7eW06zu4mmtHD5EgXKlTXlUFeuHEwWj5Kx/Ix88wiV3qn3MV4L3nTFGt\nXjDFE4idw343dFNktsbZ925UIDrI3baKq5a/29WTdcvqMl1zjQ7QyXTNpLJkoR7YLCZIms/H6oS2\n/gqvLJ46dSM9smRjImVQErt2xSyKVkq21Rw3o0jqt8ygYFjTkpRtC1xPHvHYDqOU0FWxXy7/+/r1\numMRp8IODBB9/OP6d34Io7TnGK2fHga6qvPL50zRo2vMrlryd5Lnu+aauOrzsyeYcY6atU+m8TU1\npeNO9X05QH/FaeWEH73TfOazRc8SezMt9ix/Pgdv2Irm/29ZLR7woq40ZVmCSqFEtavGaPemZJ/H\ntEIKW+KXl/hSOuCpUzcm9DVcpdzvfo3pvuFqVdaD50mESZ5Fm6SrJM6SCOo6M0Wj0jKbWFXi8xnL\nil282CS64483r+lMUDSCtlJUS7oR5GQiUxRd2/C/310zRnuH3JkyR+oTh5y85KRpZiOZ37NWJ/VX\nhF6Oa0IwpSHMYHAFBbp9xYQzzY9f7Nqp1O89VgkdH48Di9UqJVZmkcSuENfi+0emVrrOuXuTLqKr\n3ZFsmSevT9bzGYZEj661WzQqw6ffqVkus0VPEvuc88oX+FwuQpdWkwxkye4zrht2fDwmst2byokV\ngTw2k0BVBAnZqt28qkxTb4jdOLz8/cqx5kM5usS0fCtYRKFSVCn0RR2g7IdXukA4+4JJ0i6aYa1x\nWXkpSXIGAT3bZxbyZPluAaKPqiTZMoHKnG27QbTeBlHeOOeA2xa7XMHIFQNPVnFlaLpFKv3cdqbL\nM8cM0uNnjCWsadf9YhYTxZLK1UA3QbeJvb9fW878+dmlMt38qgnj/rJ1VcLBQfMgQWAkFbjEu/i1\nbp2eHPi+PRzEshd2fIPdfNEzdCA9xsVV1FLHvar0ZCbP382kTtSjxE7UXHnf1NVBXTedq+nkizME\n2KfJRCHLrWegq0RrtXoQNKhb9MU4W0c+XFyKLbMoqgho/6tHI6vWvunv2qKPK0XC2BfNWiMJf3i9\nU429khgcNAtQZKCO9U0Oo0TDlrXJVr0dbJXZKmyx2ysYeT116XwpMVFwJgpPMmxZ2u3apIjV24My\nbVldpkdeE0sUx371OHf/cxiL8s1dUggVFOgTK6bo6qs1H/JYr8dEorT/20s3RqsOO4/cDHyWDBcT\n6/gcQTHKgWfCtrmZs574XrBdeBGph0RPnbrRmNToHe9IEO6OHRQFrfl36evT592xQ68kZZppBbqS\nulYj+sSKqbiBdj1LxpUckHjeDuh8d+rrizJswgPm/dDNpE7Uw8RO1NyASsKfb2lAu5aouzfFPk1+\neB9dM0aVQl+0TK7t19axtNiM/PpymSrFeIm+e1MyfU5aurYlE+5KpsC5GjIYVuuFGyO3EU9Ke5a7\nyZZJhzsBScVIW+/cbmbxbN96slvGHVLucndeobAWCluD1WARfaaw3UmWcgKUrh4eN0+Unzhpin52\n4iD98nWrjSwU9inLJs6222cGBdo7NEFnnkmJ39/VmAKI4wQ8Tu6SdSWm6CvHSks3ziKSbjc2CNiv\nza4ye0LkTKoEsR+IlUFngnrKZrEYWenynmeLnF18h1R8fdeti6tND6v6pBTEfUcfPicmfHbvPHiW\nlc6bVUeSoobqLfYuJ/ZmwWWxP3hWrDTHxRjy4ZEiRLZv0CZ8gKLO64n8elHQxDowmrCYWMzSfrsN\noN0oO0tDOwQoLBRo/1qxjRAsY0vSztyQvncXgb73TXGqoq0WyW37rsRUPUsoScQ6cBv7XTmTxtUW\nzg7AHlZmN3spmDVT6KNdp+jJpKoKFJZKVCnYjUqSDUWkRr6cSE44gYz2bjJz5hUU6e8Xj9EtF5cj\nK/uyN+qJ/ojq05OWWkTfXrrRmFzYgrfdbrdcHGdMpeWo21265CTJDS64jkJmYMlV70036eytLHVG\nKeXA4yKiqHE71aUzqFw2C/Ay6kjslbgkc+9j98Q+b6TdWLyklFrPts99cDC9As6Z0ePKr5cFTXVr\n6cvnpFvsnBIpz/d3q6bouZWjkfVn6n4zWYIqqki1O6YMtchI0wawtg+cudYy/5jHU63qMX3wg2Z6\nph1cc61E7Myf2JqGQWIu4mIy/+wJsevI1nh5uj8m7bBQoK8cO2YUH0mfvRRUc1nI3DiFqzEHBqQo\nVp9BvJEcszLHY/vmq5C68gE9e/IoXXGa+Z1lJook3s2rzLZwruYTsjo0jTA5iOqSIUisBkLzvmXx\nO5bOiP7GUtYOX7vtZuWOYzt2JJ+jbkVeYi/CY05QCli6FNi6FZic1O8nJ4FtGMY9f9yHU34yje8P\njOCue4cT+zz2WPye/52c1P/fts08z7ZtwO7dwwiG4+MQAWp4GPTIPjz0kWnsKo/gzauAdc/fjxoU\niiBUEeCUV78M/C8wMAA89RRwxhnAk08C110HPL7nIL5TvBaFWgV78Cg2YB82YB+uW3YPLvqfL0CF\nNcygiAeWXYHdL2/GK3cM44ILgNsv2ofF/3gP3v6GX2PtC98AhSEAIABAAIoIQagAACroA3AEIQq4\nGrfh+YFhVH8NfOhDwJ49+t/XvlZ//3uwGe/D3SjhCAoIUUWAYn8J04dHMIJp1BCgWP98AC9jBNNY\nhBmo+nkZBQCLUMFm3IMRTGMaI3gMwxjBNEqooIgagArWLn8ZH/+/G/CRYz6F0qEKApBxnOcPn4g3\nq38HUQW1WgEv/xZ4bNVmHPc74KKXplAAIUANBAUFoIAaLsDXsBR/SNwrv/kNMISDGDmkx3PoxGE8\n9hs9piKqKKIGQgUjmMa16wF1/xGA9HgIgAKgQHgAF+B8fB0BQlSxCAAhRA21oIRvDu3ECW8ZBp6L\nz/ulX43gCpRAqKCqSpimEWxedRCf/9EGlFBB4cEScHAfgGFMYwSV+rYIijhv9c/wkT0HEezR9528\nz4nq9+7wMPb8+T78933xdXbh1lvjex3T00ClAoSh/vDll+MN+R7fsEFvUyoB+/YBw8MgApb+4CCO\n3DeNPb8awdZ7h/H73+v7+uyz4zHxGHseedh/oV/dYLEzGuWxZ21r/y1taemytmXrPW4XNiOW5dJP\nf+ONpisAMJfR3Oh3/fqkSNi33jlhBNpkkIsFwSJ3DcyiJ7Ze3/O6Mi1bFn83dgVwdxx5bLbQHxiO\nm3YbAUSlRbLSyv05E8bVTJuPcaj+2cBAvfhGBHC5Fd8QylTbb1rVR4r99KElsV+d89Xt78/X4EpM\n0d6hCcMXf8gRI5AZIl96p7k6qarAKLiqfkQ3xp7A9kRMYPFi8zfm1FDOfuL6CbkSYWlo9rHvXxtL\n67p6DETVzvtjmdz165PBWvn64AcdFnuay2UsbkBtx5Rk16Nc+e9dCHhXTOfBXmrKAqTMfqm7zEyT\nH506GqVG8ssm0PCqMZopxNrYVNb6HZtX6aCs/QDx63rEMQSbzNj37OrKw+4oOSHt2KEDhLKgiLfn\niehtb9PEsGV17Kv9wAeIHoLZD/ZFHEt349J68ZA7V9p2k2xZXaZXlJnJIsfhmuiYSHkCsuUO+AvU\nhOqhTAG1fdF8DOnCqop4wleOHYvG7HJJ2dkt9uu9byobgWdumcjZJ3dtKSfuJZffXE7I0t3IbsDB\nQV39K8lcji3hjkmT9Ojri3cqlZwxpbz5790IT+wdCvtGdQW67HROttjZ18kWnEubWmYyhH19FF6l\nUxjlpLFldTJrxwiKFpM+6bBQcE4GXOB05+XJzIrJS9yt+tat0xl2MpjMgWe2DG2SmxGFUTymtIpI\nJh5bprgKFaWZMiltXmVainaXn7jc3iw6qgrpAynpkCWJazcR50wTJlW7uflzK0czSd11/OsxkcgX\nl7/LnZfHmSy86pPGRa2mEwTsNEaeqAcHzT6jnCefy+8tFVaV0tY7w1vsROSJvWORpisjH1ZXZgBX\n+MlONC6rWVpk7IKRkgd26zAZdGSiGUI5ksnl18NnbKf7sZGePmYwsiQ53U02+za+264Jo78pN+kY\nHycKt2/Xrh7RJESOa2BAqy++iGMplr5VdUINEmJXzBdyRaADsObEwisdIzBXjrv83HijeW2uxwTt\nOmWKbl8xQdctmYoaWXAT6xmoqMKYJynptrDTP6ULiwvC+Pf+2nlTxuqodkdji911/DQylAkAnz1h\nInLl8f0UBSktyQv5u9r6LbX9+r7kgKirjWOEDDeNrNvgoG8vZMHY8MTegXAVV8nKQJfFftNNZus8\n3sf2eY6P1/30wvKpFPujiYCPaRO7K1uCP+esGAoCevYtJtFXPz9FD55lTiKRlchCZgfKVCkkiVVa\n4wRd0i6LlFiK95CSVbYqstJlRaN8LV6svx8X6bD/+X5o+V/26XMv2dr+svG7zMzEE6adZcO69rbU\nbk0FFNYzP6Sssk2+ruKr6DcTqN0xpSWLN25MTOKcfcP/yuPvWW4SdSNyt+83O8Yjx5x2rL3n1/Ph\n6z5zVuRkTSQnMhRWm1l82K5oCrED+AyAHwJ4BsBXASzNs58n9iRcFkieri9yuzRy5v0jH3e5nMhR\nzqrsjPKblRn4O6L6KFQ67/vHxw8m3AR3Xh43GaZSiWjjRqNBSHigTFecZvrYx8eJnltpuhyqKkgE\nIO9bFssPzyCg54M3Rb7pRs2VefyHLGnk8XG98qkU+40xSquViTMtyKzz92M99hqCqGmK/VvVaqZf\nmlcSrt89grBqeWLmHqE33qjH+Na36n0HB/WLJ7LJS8pUu1lbvFlkmGeFmBbzibY9YDZ7kTEPdt3M\n9TnJet/taBaxjwIo1v//aQCfzrOfJ3Y3XNaSq5G1q3OMS8NFEnoqUVC8tGbr85aLy7RkiUmCTPob\njok147V2u+7EZGepXImpWP+Dy8CFzkdY0EVVckw8Tjsf/25cahR+VaA73If9sergBLYbvumZ75Yz\n3RQPDIsgcD075KabtHvI7qTlur5cOMYrH7b2gWRAVGr8SA19PubxxxOddFJcabp7UzndN211+pKT\nBpE+LgvQcdZRtarHy0FxKU2R5x60V4hZwnfR36z2jDJbK9Md45GJprtiAFwE4It5tvXEng6XtZQV\n7Erbz9W4VxZF8T5hmAyG3bo8tnZZ/8O23meCItXY5RAE9NzKUZrAdnoIozGp87kkGQG6LV8xLt5i\nbZ3dm8oRmYXbtxMFuuHGkWK/dkH0x4JSd20pE01NUTXQOimc8cGZM+y+WrZMy9ByJguvOLgQKLLM\nuShmaiouoLF6zyasWLHyYe0U2/2xZXXSOnaRPBf7MPFyQDuBnJ2+5MSxdStRzYqtyNaLclx5qjnT\n4kDGtgfKdfmJwGh47Ul9fmgFsT8I4LI823pidyPLMsx6kF37sX/WtaQmMmWFhxALjbG75fTTzcwz\nwHQ/6GpTFZXZh0GQyHu2c5fDer/W8KqxyD/MBMvZDrs3aZJ88Cxh8QUBPTBc16uf0L7iMDQ1R2z3\ny4knxuO3mylzZgv70iMyZ5KvN+7mknfpm05YsXVy56pPe/VhT6au346Isnvuuhqf5+j0Je8LIxsq\nY0JIs8g5cJoYd8r5eOVyYMlopmZR1jF7zc2SBwtG7AAeAfB9x+tCsc3H6j52lXGc9wN4AsATK1eu\nbNqF6BSkWUupImAN9gOSmTHSD8rbcJ44W7SbV5WNtEL5ssv4Oa2PM1skucq0NyJKNhMR308GZ2u7\ndJaOK2ebJysODl9xmlsRETCa7iT84bWbJwwJB1t357mVo7GbJgjowJJRI0jI144n3bCg9VXYTy/j\nHEzsDUnqKPXilSuNIcQrjEb7SLAPP0/Qks8nV3fsfskK3PrAaD40zWIH8D4ABwEck3cfb7G74bq5\n2Tea9WDPpbDJZeXbEwFXMrKP/exSmarvHyOzKbXOQmFy3b2pnCl0Zn8/mY7HlmQYEu1fGwt8sfCX\nnVEiC5fSdEokybD/nXPqeVx3bSkbbp6oiCeIM24q0HrnPO6tW/WKgYSfnmMG/Lrl4nLUmCIXSbks\n8SxLvgEa+ctne4yG2jHib8bqzopVuFxTec/R62hW8PRcaHWK42aznyf2dDiXozmW3rb/k62sNCkC\n/kw+9NJ1w2R+7aumDKnea181ZTQfDvviRtlDiAnTlaZpB4GZpK+EbsPGHejtBseyxZx9LNvdJMdu\nF0rxZyzRYPuEpRTsEFgSVwiT1SVsjd+l38ygkefjCk/OupkTSc3RYp8vWdqEbcdrskhd+tgrMGsY\nstww852EegHNIvbnAbwA4Kn66448+3WTHns7wWW5SzJPexD5xWX8chkdsq+57mr59NIJ2nv+FP34\n+EGqXbgx0UxErghcAV0mYyaKK04zfeyTl5QN33maREEYxo2SXda5LVWb9nKRh3QnSMkACoJk0DFF\nG9yu+JxPil9en7qNubo30uop7OvfcD/L/dYIzgC1h4GeLVDqVV/dXJfMbNVzRse6dVYGhSiH5+wU\nacGzBSxdOXY6n03u4+M6vW8IZe3PDuJG2996p/bzRxNLfz+d+9qktMFb30rGedescRNqo3x2F6nb\nAcCZoKjdMvVuPy5Xgl1LcMvF6dWZc/lt5f/t93n3zdrefl7kd3H1UU2zpudqVHmLPR96kth73Vc3\nm4dDToBhGLtN3vY2cxl9ONBkzkp+sppUEuf69bpAhjMoZACXU/9s1wj7sQmIiJNTB+30RJYnsLVC\n1q/XPT1ti50J9eySabGzJrrr+sjrJ9NFh1A21CaNa1rWK4xdp2jJgPBAOYpx5KnOTEXdSpciXTKT\niQO/C2G0ZMVoXCuuhX6mev25nQ16ktiJ/Myftpy1S9Ndlp8tFsZExX9n33NaJsrWrfUu9xSTEHdq\nsl0jUiWyCp0HL89lV8/uHTI7U92+YiLhwwc0ydf2a62TNDeM08cexuOWE16mb7kuz1ATTaXD/jid\nMus8mSibwVzOtrELzexVwlyQRaouUpf7LOQquFdX2rNFzxI7Ue/66lwP5eQl5ci90ajTjOu62eRv\nu0QkWVar8XGZ2MNdE0bp/0MYjYSuKoUShUpXrkqr1rX0lwVF3MTY2bdTTGSDg0TLl5vbDAzolUWj\n65B1TSKIjBVbJjhNQz+Xq0JokkvNdNdrIYwW132T1uw6c9zzRK/GxmaDniX2XrXY5feWkrN2YU6a\nBSndMbZVKJf9aQQjzyutyd2b4gpEWVb+5XOm6DD6tPxvX18iCOta+m9epVUWqVx2ugvSsm74e9rq\ngLxdnmuaZrFzlapsCziEsnOFlMsqdWiSy2wb+7VQ97U9gbks+F54jtodPUnsve6rs3VIXE0iXARl\nW8mNlv07diQVBO3jymPGqYNxds1zK0eNwqtwl2mV2ta3HZiVgdM8bhVuoFxFYGilZAUT89xLLIdw\n7zlTxiqmYTpg2r1paZKHV41lTqhH02LPSpX1aA16ktiJutxXN8t8drv4R7pObDKQgTn5gHPlpEu8\nijW65fbSUpVW4BDiKs0jxX7ae/6U4VqxLXZ76e/KsslDPmFYv26LhNJgEGQWzMhrknUv2deM/88r\nnyxyTyVmkbcur0ujyda1QsiD3BNYlxtFnYKeJXaiLvXVzaJQRT6cdl/MLEuPLe20pb5tCbNPPc1i\nd/n7Hz5nIlY7rDewsDs+ZaVppk0icnyJ6zY6GmuCAzp9sUGJe9oxbZLnyc9F8Fkl95muFDGBy8mF\n1Rr52DzZnX32/IyZrjaGugw9TexdiZyl5TYx5nVb2Ps2Wuon1APFe1fGhu3/d33utL5TCoByuSGE\nD5wt9WqwKLcoVSNkXS/XMeca/7FXTHLilKml83E/dqUx1IXwxN5tyGux1/OqZYDQaPNGbotsLvGJ\n3Nrc5CYiSW5O69uR9jcr8pKTYRBoy90KRC6EfzrP8RJjrldl5slxb/TbNOqL69E98MTejWjkY5f+\n2b4+nTZX3zaP20JahbwNk3Easiy9LDdGLnK1VBdlWX4ud4FDy2UhCXC2Fng02R0oJ7Xl53mu3NfU\no6Phib0X4WhoMVupV1ui1dabmS9mRYYOYraP1RAOV85CZEzx/lyNOyufvcx/n4ViYxp5z9XF49F5\nyEvsATy6ByMjQKkEKKXfEwGVCjA9nWt3IuD3vwf27AG2bdPvr7tOv//d7/T7tP2y3svPt23Tx9u6\nFQhD/a88n4HhYWDfPuCTnwT27YM6a9j4M3/NVBw8qL/7yAjUWcNYulSfb3JS7zs5qd8vXZrjWBaU\nAtb88SC+U9yA8x6/EepdGzB5ycGGx1MK8e9UKECVSvp9A/C1k9i2TV/DWV1Tj95AHvZf6Je32I8i\nyvUeo6Jp9Gws9jm7F3K6SJqWgZESk1jQIOFEXFUr+6M2HBe34Mup2NjIxz6bRhgenQ14V0yPY45S\nr0TzCAjmdG80JQNjtg0q5nK9ZquVPo9uSI0mRJ/V0hvwxO4xJ8zWYm9b/+5sSHQ+7efEJIiIAAAG\nPklEQVRmMyHMoxsSkSdvj/zE7n3sHhFotj5wxL5qCfZhtxSWfx7Dw+nbTk/rWEStNquYRHSeG27I\nPj5D+NaR07cuYV/Tll9jj7ZFsdUD8GgfKAVngBFIDwhSSlCvbch9NoRbqcyJcDMhArjRZCPfe3gc\nBShymWFHGWeeeSY98cQTTT+vRz4QmaRsv5efSwt/cjL5vuXknhc2AS/UMTdsiCeMRisHD48GUEo9\nSURnNtrOW+weCeRd8s/Fwm9b5LXuZwOXi8cTu0cTMC9iV0p9EsCFAEIALwL4ayL65UIMzKMzsHOn\nadEzuXcUqR8tHE0Xj4dHBuYbPP0MEZ1OROsBfB3AjgUYk0eHwQf1TETezbpPnf4mRwDXw2MBMS9i\nJ6I/iLevAuDr3Dx6Gjt3mhlENDSMbS/egJ3f9KTu0TzMO91RKbVLKfUCgEuRYbErpd6vlHpCKfXE\nSy+9NN/Teni0HYi09IJMD+VgcpYkg4fHQqNhVoxS6hEAyx1/+hgR7RXb3QBgMRHd1OikPivGo1sh\nyZzRcRlCHm2LvFkxC5buqJR6PYBvENGaRtt6YvfoZhABgVgLh6EndY+FQV5in5crRim1Sry9AMAP\n53M8D49OR1rBlnfDeDQT8/Wx/61S6vtKqWcAjALYugBj8nDh4EHgU5/S/3qkwibQZhLqXCQZPDyO\nBuaVx05Ef7FQA/HIQDMrGI9GBWaTsHOnDlKyP5uJdulS/bejja4q2PLoaPjK005AsyoYO7gEXmak\nAEl5gzRZhIWGL9jyaAd4Yu8ENKuCsYNL4KV1vGdPTPCtyEjxBVserYYXAesUNMNF0sEWO8NnpHh0\nM7wIWLfhaIhUuc7RwbKybS0h7OHRRHhi9zDRjAnkKCBLQhjw5O7RW/DE7tEVmHNGSgdnAXl4pMH7\n2D26CnmbhADoipiCR2+hKZWnHh7thlllpMyn16mHRxvDE7tH72KezaU9PNoV3sfu0bvo8CwgD480\neGL36G10aBaQh0cWvCvGw8PDo8vgid3Dw8Ojy+CJ3cPDw6PL4Indw8PDo8vgid3Dw8Ojy+CJ3cPD\nw6PL4Indw8PDo8vQEq0YpdRLAH46z8MMAPjNAgynF+CvVT7465QP/jrlx0Jfq9cT0XGNNmoJsS8E\nlFJP5BHD8fDXKi/8dcoHf53yo1XXyrtiPDw8PLoMntg9PDw8ugydTOx3tnoAHQR/rfLBX6d88Ncp\nP1pyrTrWx+7h4eHh4UYnW+weHh4eHg50BbErpT6slCKl1ECrx9KOUEp9Rin1Q6XUM0qpryqllrZ6\nTO0GpdS5Sqn/UEo9r5S6vtXjaUcopU5WSv2LUuoHSqlnlVJbWz2mdoZSqqCU+jel1Nebfe6OJ3al\n1MkA/hTAz1o9ljbGwwDWENHpAP4TwA0tHk9bQSlVAHA7gD8DcBqAv1RKndbaUbUlqgA+RESrAQwB\nuNpfp0xsBfCDVpy444kdwCSA7QB8sCAFRPQtIqrW3z4GYEUrx9OGGATwPBH9FxFVANwL4MIWj6nt\nQES/IqLv1f//R2jSOqm1o2pPKKVWADgPwBdacf6OJnal1AUAfkFET7d6LB2ELQAeavUg2gwnAXhB\nvP85PGFlQil1CoA/AfB4a0fStrgV2uAMW3Hytm+Np5R6BMByx58+BuCjAEabO6L2RNZ1IqK99W0+\nBr2c/mIzx9YBUI7P/AowBUqpVwO4H8C1RPSHVo+n3aCUOh/Ai0T0pFJqpBVjaHtiJ6J3uT5XSq0F\n8AYATyulAO1e+J5SapCIft3EIbYF0q4TQyn1PgDnA9hAPsfVxs8BnCzerwDwyxaNpa2hlFoETepf\nJKJ/bvV42hRvB3CBUuo9ABYDeI1S6h+I6LJmDaBr8tiVUj8BcCYReXEiC0qpcwHsBnA2Eb3U6vG0\nG5RSReig8gYAvwDwrwD+ioiebenA2gxKW1B3A/gtEV3b6vF0AuoW+4eJ6PxmnrejfeweuXEbgCUA\nHlZKPaWUuqPVA2on1APL1wD4JnRA8D5P6k68HcB7AZxTv4+eqlulHm2GrrHYPTw8PDw0vMXu4eHh\n0WXwxO7h4eHRZfDE7uHh4dFl8MTu4eHh0WXwxO7h4eHRZfDE7uHh4dFl8MTu4eHh0WXwxO7h4eHR\nZfh/zNMtzMveULsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdd34fc67b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PCA\n",
    "from sklearn import decomposition\n",
    "\n",
    "train_X = []\n",
    "train_y = []\n",
    "train_period = []\n",
    "with open('train-light.csv', 'r') as f: # 100行ごとに間引いたCSV\n",
    "    reader = csv.reader(f)\n",
    "    header = next(reader)\n",
    "    for row in reader:\n",
    "        train_period.append(row[1])\n",
    "        train_X.append([float(r) for r in row[2:89]])\n",
    "        train_y.append(int(row[90]))\n",
    "\n",
    "train_X = np.array(train_X)\n",
    "train_y = np.array(train_y)\n",
    "train_period = np.array(train_period)\n",
    "\n",
    "pca = decomposition.PCA(n_components=2)\n",
    "pca.fit(train_X)\n",
    "\n",
    "X_pca = pca.transform(train_X)\n",
    "negative = X_pca[np.where(train_y==0)]\n",
    "positive = X_pca[np.where(train_y==1)]\n",
    "\n",
    "plt.scatter(negative[:,0], negative[:,1], marker='x', color='blue')\n",
    "plt.scatter(positive[:,0], positive[:,1], marker='.', color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ヒストグラムにもPCAにも分類できる見込みが全くない。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 機械学習編\n",
    "\n",
    "`train.csv`を訓練データとテストデータに分けて機械学習モデルの性能を調べてみることにする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.860083333333\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "dataset = pd.read_csv('train.csv')\n",
    "\n",
    "dataset_train, dataset_test = train_test_split(dataset, test_size=0.3, random_state=1)\n",
    "X_train, y_train = dataset_train.iloc[:, 2:-1], dataset_train.iloc[:, -1]\n",
    "X_test, y_test = dataset_test.iloc[:, 2:-1], dataset_test.iloc[:, -1]\n",
    "rf = RandomForestClassifier(random_state=1).fit(X_train, y_train)\n",
    "print(rf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "チューニングなしのRandomForestでもそれなりの性能が得られるので全データでRandomForestを学習した結果を投げたのだがサンプルより悪い結果しか得られなかった。\n",
    "`predict_proba`で出たクラス1の確率をそのまま出力する、`1`と予測したら`0.55`にするなどいろいろなパターンを試したがどれも良くならなかった。\n",
    "XGBoostでも同様だった。\n",
    "\n",
    "今のところ無視している`'period'`の情報を使うために`'period'`が同じかどうかで予測精度が変わるかどうかを調べた。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# periodごとにデータを分けて別々のRandomForestを訓練する\n",
    "X_target_train = {}\n",
    "X_target_test = {}\n",
    "y_target_train = {}\n",
    "y_target_test = {}\n",
    "\n",
    "period_list = []\n",
    "for i in range(14):\n",
    "    period = 'train{}'.format(i+1)\n",
    "    period_list.append(period)\n",
    "    subset = dataset_train[dataset_train.period==period]\n",
    "    X, y = subset.iloc[:, 2:-1], subset.iloc[:, -1]\n",
    "    X_target_train[period], X_target_test[period], y_target_train[period], y_target_test[period]\\\n",
    "        = train_test_split(X, y, test_size=0.3, random_state=1)\n",
    "\n",
    "rf_target = {}\n",
    "\n",
    "for p in period_list:\n",
    "    rf_target[p] = RandomForestClassifier(random_state=1).fit(X_target_train[p], y_target_train[p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train1: 0.8507853403141361\n",
      "train2: 0.4988075363701407\n",
      "train3: 0.5011302795954788\n",
      "train4: 0.5087049844979729\n",
      "train5: 0.5112701252236136\n",
      "train6: 0.48523809523809525\n",
      "train7: 0.49357907253269917\n",
      "train8: 0.4949868703747911\n",
      "train9: 0.4994031988541418\n",
      "train10: 0.49898313195358296\n",
      "train11: 0.49672189772320896\n",
      "train12: 0.5054605887939221\n",
      "train13: 0.4917374881964117\n",
      "train14: 0.49567073893962754\n"
     ]
    }
   ],
   "source": [
    "# train1で訓練したRandomForestのスコアを各periodのデータで調べる\n",
    "for p in period_list:\n",
    "    print(\"{}: {}\".format(p, rf_target['train1'].score(X_target_test[p], y_target_test[p])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train1: 0.51142313184198\n",
      "train2: 0.8191032673503458\n",
      "train3: 0.4989886972040452\n",
      "train4: 0.5019079418077749\n",
      "train5: 0.48992248062015503\n",
      "train6: 0.5051190476190476\n",
      "train7: 0.5045184304399525\n",
      "train8: 0.4871090952494629\n",
      "train9: 0.5054905705418954\n",
      "train10: 0.49264266060533557\n",
      "train11: 0.49767552747645727\n",
      "train12: 0.5002374169040835\n",
      "train13: 0.5118035882908404\n",
      "train14: 0.5006523544063575\n"
     ]
    }
   ],
   "source": [
    "# train12訓練したRandomForestのスコアを各periodのデータで調べる\n",
    "for p in period_list:\n",
    "    print(\"{}: {}\".format(p, rf_target['train2'].score(X_target_test[p], y_target_test[p])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "このようにして、同じperiodのデータについては予測精度が高く、異なるperiodのデータについてはランダム並みの予測しかできないことが確認できた。\n",
    "\n",
    "そこでテストデータについて、どの`'period'`からのデータなのかを予測した後で予測された`'period'`の分類器に投げるという方法を考えた。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.994654761905\n"
     ]
    }
   ],
   "source": [
    "# どのperiodからのデータなのかを判定するRandomForest\n",
    "X_train, period_train = dataset_train.iloc[:, 2:-1], dataset_train.iloc[:, 1]\n",
    "X_test,  period_test  = dataset_test.iloc[:, 2:-1],  dataset_test.iloc[:, 1]\n",
    "\n",
    "rf_period = RandomForestClassifier(random_state=1).fit(X_train, period_train)\n",
    "print(rf_period.score(X_test, period_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "scikit-learn氏は99%当てられる分類器ができたと言っている。（そんな精度での分類ができるとは思えないので何か間違えている気がする）\n",
    "\n",
    "とりあえずこれを信用して、periodごとに別の分類器を使う方法を実装した。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84432738095238091"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test, y_test  = dataset_test.iloc[:, 2:-1],  dataset_test.iloc[:, -1]\n",
    "\n",
    "# 各分類器の予測結果\n",
    "period_pred = rf_period.predict(X_test)\n",
    "y_pred_each = {}\n",
    "for p in period_list:\n",
    "    y_pred_each[p] = rf_target[p].predict(X_test)\n",
    "\n",
    "# 予測されたperiodの分類器を使う\n",
    "y_pred = [y_pred_each[p][i] for i, p in enumerate(period_pred)]\n",
    "\n",
    "# scoreを調べる\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "このaccuracyはどの単体の分類器よりも良い予測である。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.52469047619\n",
      "0.527845238095\n",
      "0.528166666667\n",
      "0.527452380952\n",
      "0.527041666667\n",
      "0.527821428571\n",
      "0.52425\n",
      "0.528910714286\n",
      "0.526386904762\n",
      "0.524857142857\n",
      "0.527392857143\n",
      "0.526029761905\n",
      "0.52743452381\n",
      "0.52405952381\n"
     ]
    }
   ],
   "source": [
    "for p in period_list:\n",
    "    print(accuracy_score(y_test, y_pred_each[p]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "仮にこの学習が正しくできているとしても、`test.csv`のデータが`'period1'`〜`'period14'`のどれでもなかったら何の意味もないのだがそれについては何も記述がないので分からない。\n",
    "試しにこの方法で予測した結果が`0`なら`0.45`, `1`なら`0.55`としたファイルを投げたところスコアは`0.69827`であった。これは正解率が`0.4995`のときに出るスコアなのでランダムよりも悪いことになる。\n",
    "おそらくこのテストデータは異なる期間のデータなのだろう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 結論\n",
    "ここまでの調査の結果、\n",
    "- クラス間の分布に差がほとんどない\n",
    "- 訓練データを分割して作ったテストデータで調べた精度と、実際のテストデータに対する精度がかなり異なる\n",
    "\n",
    "という機械学習が非常にやりにくい性質があることが分かった。\n",
    "\n",
    "~~特徴量を作るところからやり直すべきなのでは……~~"
   ]
  }
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解析ログ2.ipynb