アイスクリーム統計学・第１０章.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# アイスクリーム統計学 + Python (第１０章)\n",
    "\n",
    "第１０章は、ストーリー（解析の流れ）を独自に作成しています。\n",
    "\n",
    "* [アイスクリーム統計学・第１章〜第３章（散布図と相関、相関係数、無相関検定）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%82%A2%E3%82%A4%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%BC%E3%83%A0%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%91%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%93%E7%AB%A0.ipynb)\n",
    "* [アイスクリーム統計学・第４章〜第６章（単回帰、偏相関、重回帰）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%82%A2%E3%82%A4%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%BC%E3%83%A0%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%94%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%96%E7%AB%A0.ipynb)\n",
    "* [アイスクリーム統計学・第７章〜第９章（相関行列、因子分析1、因子分析2）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%82%A2%E3%82%A4%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%BC%E3%83%A0%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%97%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%99%E7%AB%A0.ipynb)\n",
    "* [アイスクリーム統計学・第１０章（主成分分析とクラスター分析）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%82%A2%E3%82%A4%E3%82%B9%E3%82%AF%E3%83%AA%E3%83%BC%E3%83%A0%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%91%EF%BC%90%E7%AB%A0.ipynb)\n",
    "* [ハンバーガー統計学・第１章〜第２章（平均と分散、信頼区間）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%83%8F%E3%83%B3%E3%83%8F%E3%82%99%E3%83%BC%E3%82%AB%E3%82%99%E3%83%BC%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%91%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%92%E7%AB%A0.ipynb)\n",
    "* [ハンバーガー統計学・第３章〜第５章（カイ２乗検定、t検定 （対応なし・対応あり））](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%83%8F%E3%83%B3%E3%83%8F%E3%82%99%E3%83%BC%E3%82%AB%E3%82%99%E3%83%BC%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%93%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%95%E7%AB%A0.ipynb)\n",
    "* [ハンバーガー統計学・第６章〜第７章（分散分析 (１要因、２要因) ）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%83%8F%E3%83%B3%E3%83%8F%E3%82%99%E3%83%BC%E3%82%AB%E3%82%99%E3%83%BC%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%96%E7%AB%A0%E3%80%9C%E7%AC%AC%EF%BC%97%E7%AB%A0.ipynb)\n",
    "* [ハンバーガー統計学・第８章 (分布と仮説検定)](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E3%83%8F%E3%83%B3%E3%83%8F%E3%82%99%E3%83%BC%E3%82%AB%E3%82%99%E3%83%BC%E7%B5%B1%E8%A8%88%E5%AD%A6%E3%83%BB%E7%AC%AC%EF%BC%98%E7%AB%A0.ipynb)\n",
    "\n",
    "### Jupyter Notebook (IPython Notebook) とは\n",
    "* Python という名のプログラミング言語が使えるプログラミング環境。計算コードと計算結果を同じ場所に時系列で保存できるので、実験系における実験ノートのように、いつどんな処理を行って何を得たのか記録して再現するのに便利。\n",
    "* [当学演習室での Jupyter Notebook の起動方法](https://raw.githubusercontent.com/maskot1977/-/master/%E6%BC%94%E7%BF%92%E5%AE%A4.txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10.　主成分分析とクラスター分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.1　[主成分分析－因子分析との比較－](http://cogpsy.educ.kyoto-u.ac.jp/personal/Kusumi/datasem13/masuda2.pdf)\n",
    "主成分分析と因子分析は、似て非なるものです。詳しくは上記の資料を参照のこと。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.2　データの読み込み"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__課題10.2__：[アイスクリームアンケート調査結果](https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/icecream_chosa.txt)をダウンロードして、次のコードを実行してください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# URL によるリソースへのアクセスを提供するライブラリをインポートする。\n",
    "import urllib # Python 2 の場合\n",
    "# import urllib.request # Python 3 の場合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ウェブ上のリソースを指定する\n",
    "url = 'https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/icecream_chosa.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('icecream_chosa.txt', <httplib.HTTPMessage instance at 0x10401ae18>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定したURLからリソースをダウンロードし、名前をつける。\n",
    "urllib.urlretrieve(url, 'icecream_chosa.txt') # Python 2 の場合\n",
    "# urllib.request.urlretrieve(url, 'icecream_chosa.txt') # Python 3 の場合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd # データフレームワーク処理のライブラリをインポート"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# バニラ、ストロベリーなどアイスクリームの種類間の関係を見るための matrix を作成してください。\n",
    "matrix = []\n",
    "val_names = []\n",
    "sta = 5\n",
    "for i, line in enumerate(open(\"icecream_chosa.txt\")):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>banana</th>\n",
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       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caramel</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>walnut</th>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cassis</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chocolate_chips</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green_tea</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marron</th>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chocolate_mint</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adzuki-bean</th>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0    1    2    3    4    5    6    7    8    9  ...    70  \\\n",
       "vanilla          7.0  7.0  7.0  9.0  9.0  5.0  9.0  7.0  7.0  5.0 ...   7.0   \n",
       "strawberry       7.0  8.0  4.0  6.0  5.0  7.0  7.0  7.0  7.0  5.0 ...   7.0   \n",
       "milk_tea         8.0  9.0  3.0  6.0  7.0  5.0  6.0  6.0  4.0  9.0 ...   6.0   \n",
       "macadamia_nuts   3.0  9.0  3.0  5.0  5.0  5.0  3.0  8.0  8.0  5.0 ...   6.0   \n",
       "cookie           9.0  9.0  6.0  8.0  6.0  5.0  7.0  7.0  7.0  8.0 ...   8.0   \n",
       "chocolate        9.0  9.0  4.0  6.0  8.0  7.0  9.0  6.0  7.0  8.0 ...   9.0   \n",
       "melon            6.0  2.0  7.0  8.0  4.0  5.0  4.0  5.0  4.0  5.0 ...   6.0   \n",
       "coffee           8.0  7.0  7.0  9.0  4.0  8.0  6.0  8.0  7.0  6.0 ...   5.0   \n",
       "almond           3.0  7.0  6.0  6.0  4.0  5.0  5.0  7.0  6.0  3.0 ...   5.0   \n",
       "rum_raisin       2.0  9.0  3.0  5.0  6.0  8.0  2.0  5.0  3.0  3.0 ...   5.0   \n",
       "mint             1.0  5.0  3.0  4.0  9.0  9.0  2.0  6.0  5.0  3.0 ...   4.0   \n",
       "banana           7.0  4.0  4.0  4.0  6.0  5.0  6.0  5.0  5.0  8.0 ...   5.0   \n",
       "caramel          9.0  7.0  6.0  9.0  6.0  9.0  7.0  6.0  7.0  7.0 ...   7.0   \n",
       "walnut           3.0  8.0  3.0  5.0  6.0  5.0  5.0  7.0  7.0  2.0 ...   5.0   \n",
       "cassis           3.0  9.0  3.0  4.0  5.0  8.0  5.0  9.0  6.0  8.0 ...   5.0   \n",
       "chocolate_chips  9.0  9.0  6.0  8.0  9.0  8.0  9.0  5.0  8.0  8.0 ...   9.0   \n",
       "orange           7.0  4.0  7.0  9.0  6.0  9.0  9.0  6.0  6.0  6.0 ...   7.0   \n",
       "green_tea        8.0  7.0  3.0  1.0  6.0  9.0  3.0  8.0  3.0  6.0 ...   3.0   \n",
       "marron           8.0  7.0  7.0  2.0  6.0  5.0  5.0  5.0  3.0  2.0 ...   7.0   \n",
       "chocolate_mint   1.0  8.0  3.0  1.0  9.0  9.0  2.0  6.0  7.0  1.0 ...   6.0   \n",
       "adzuki-bean      7.0  9.0  3.0  3.0  1.0  1.0  3.0  8.0  3.0  1.0 ...   8.0   \n",
       "\n",
       "                  71   72   73   74   75   76   77   78   79  \n",
       "vanilla          6.0  6.0  8.0  4.0  5.0  7.0  4.0  9.0  8.0  \n",
       "strawberry       7.0  6.0  8.0  6.0  6.0  5.0  8.0  7.0  5.0  \n",
       "milk_tea         5.0  5.0  4.0  2.0  4.0  6.0  8.0  7.0  5.0  \n",
       "macadamia_nuts   6.0  8.0  8.0  3.0  9.0  6.0  9.0  5.0  2.0  \n",
       "cookie           4.0  8.0  9.0  6.0  6.0  8.0  9.0  4.0  5.0  \n",
       "chocolate        8.0  7.0  7.0  6.0  7.0  9.0  9.0  6.0  4.0  \n",
       "melon            8.0  3.0  3.0  8.0  4.0  4.0  4.0  7.0  7.0  \n",
       "coffee           7.0  6.0  6.0  5.0  3.0  4.0  8.0  7.0  1.0  \n",
       "almond           6.0  7.0  6.0  4.0  6.0  4.0  9.0  4.0  5.0  \n",
       "rum_raisin       8.0  7.0  3.0  2.0  5.0  5.0  1.0  5.0  5.0  \n",
       "mint             4.0  8.0  8.0  2.0  4.0  4.0  9.0  8.0  3.0  \n",
       "banana           4.0  6.0  5.0  3.0  5.0  3.0  6.0  9.0  4.0  \n",
       "caramel          2.0  7.0  4.0  2.0  5.0  9.0  8.0  7.0  5.0  \n",
       "walnut           6.0  8.0  8.0  3.0  5.0  5.0  9.0  4.0  5.0  \n",
       "cassis           8.0  7.0  8.0  2.0  5.0  5.0  5.0  5.0  5.0  \n",
       "chocolate_chips  4.0  8.0  9.0  6.0  7.0  9.0  9.0  8.0  5.0  \n",
       "orange           7.0  5.0  8.0  8.0  7.0  7.0  9.0  8.0  7.0  \n",
       "green_tea        8.0  8.0  9.0  1.0  4.0  6.0  9.0  5.0  1.0  \n",
       "marron           3.0  8.0  6.0  1.0  4.0  5.0  4.0  5.0  1.0  \n",
       "chocolate_mint   2.0  8.0  9.0  2.0  4.0  6.0  9.0  8.0  5.0  \n",
       "adzuki-bean      6.0  7.0  4.0  2.0  4.0  7.0  9.0  4.0  5.0  \n",
       "\n",
       "[21 rows x 80 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# できあがったMatrixの中身を確認してください。\n",
    "pd.DataFrame(matrix, index=val_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.3　データの正規化\n",
    "__課題10.3__：主成分分析に用いるデータを正規化するために、以下の関数を定義してください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 平均値を求める関数\n",
    "def average(list):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 分散を求める関数\n",
    "def variance(list):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 標準偏差を求める関数\n",
    "def standard_deviation(list):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# リストの正規化をする（Z値に変換する）関数\n",
    "def normalize(list):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# リストのリストの正規化をする（Z値に変換する）関数\n",
    "def normalize2(list):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.4　主成分分析の実行\n",
    "__課題10.4__：以下のコードを実行し、得られたプロットに対して考察を行なってください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 図やグラフを図示するためのライブラリをインポートする。\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn #機械学習のライブラリ\n",
    "from sklearn.decomposition import PCA #主成分分析器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, n_components=None, whiten=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#主成分分析の実行\n",
    "pca = PCA()\n",
    "pca.fit(normalize2(matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# データを主成分空間に写像 = 次元圧縮\n",
    "feature = pca.transform(normalize2(matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vi9mzZ/PCCy8QFBREhQoVmDp1KitXrmTcuHHUq1ePUqVKZfqaZaft2REZGcmJEycICwsj\nMTGR1157jUmTJjFjxgzXOpcLxU8//TRHjhzh7NmzrnG7pUuXJj4+nrCwMFq2bEmfPn0A+O233zhz\n5ky22ydys1CIFRHxQKdPn2bhwoUEBgYC8Ouvv/Lee+8xfvx47r//fgCstQwZMoSoqCgqVapEWFiY\na/vU1FQaNmxI27Zt2bRpEw888ADWWmbPnk2rVq2YMGGCa91mzZq5HgcHBxMcHOx6XrduXc6fP8/H\nH3/sCoKrVq0iLi6O9957j5IlSwJQtmxZnn76abdjSN9rCnDnnXdy9OhRPvnkE7dlxhgef/xxHnjg\nAddxDR48mPr16/PMM88AEBQUxNdff80333zDww8/nOlrlp22Z0d8fDwzZ850Bf/jx48zffp0kpKS\nKFCgQLbqKF++PEWLFsVaS1BQkKv8p59+Ij4+nueff56CBQsC0LBhw2y3TeRmohArIuKBAgMDXQEW\ncA0JaNCggVuZtZaYmBgqVarE3r17mTt3Lvv37+fUqVOAIyAePHgQcJxuP3bsGA8++GCW+01KSuLd\nd99l9erVHDlyhAsXLrjqSU1NxcvLi3379lGrVi1XgAVHYAwICHCr68yZM8ydO5fNmzdz7NgxUlJS\nAChTpkyG/aYPn2nHmr7Mz88Pf39/YmJirqrt2VG7dm1XgAVcQwaOHTtG+fLls1VHVipUqEChQoUY\nPXo0Dz30EHfddReFCxe+qjpFblQKsSIiHqhIkSJuz729vTOUp5UlJSVx9OhRBg4cSFBQEKNGjaJU\nqVJ4e3szePBgkpKSADh16hTGGLfwebFZs2axatUq+vbty2233UaRIkXYuHEj77zzDklJSfj6+nLi\nxAn8/f0zbHtxWXh4OJGRkTz99NNUqVIFPz8/li9fzubNmy95vJkda1p52rFcaduz4+L9ps2AkJiY\nmK3tL1f3nDlziIiIYOTIkaSkpNC4cWOef/75qw7IIjcahVgRkZvA1q1bXWNTfXx8AEhJSXH1yAIU\nK1YMay3Hjx/Psp6vv/6axx57jB49erjKLg6dJUqUICoqKsO2sbGxrsdJSUn85z//YcSIEXTu3NlV\nbq3N+cFlU3bafi2kvb7Jyclu5WfOnMk03F8sKCiImTNnkpSUxLZt25g2bRpjxozhnXfeueZtFfFk\nutmBiIgHiI2NZd++fW5BMCcSExPx8vIiX758rrK1a9e6TuEDVKpUidKlS19yAv7ExES3uVdTU1NZ\ns2aN2zq1a9fmp59+4tixY66yPXv2cPLkSdfz5ORkUlNTXb2q4Bhrej1CZU7afi2ULl0aay1//vmn\nq+zo0aNuz+HyPccFChQgJCSEjh078vvvv1/zdop4OvXEiojkcatXryEsbDIQCBymQYMKeHllb0qo\nNMHBwaSkpBAeHk6nTp04cOAA77//PkWLFnWtY4xh0KBBjBkzhjFjxnDfffcB8P3339OuXTtq1qxJ\no0aNWL58ORUqVKBo0aIsX77cNbY0TceOHVmwYAGDBw+mb9++JCQkMHfuXLdeSD8/P4KCgnj77bcp\nVKgQxhgWLVpE4cKFiY+Pv+LX6lKy0/Yrlb4HuXTp0tSuXZu33noLHx8fUlNTeffddylWrJjbNpUr\nV2bz5s1s2rSJ0qVLU6pUKfbv38+qVau45557KFu2LDExMaxYsUIXd4lkQj2xIiJ5WGxsLGFhk8mX\nbx5+fu+TL988Vq/+5pI9eOmlTfVUtWpV1xjUsLAw1qxZw5QpUzJcNHTfffcxdepUoqKiGDFiBOHh\n4URFRbkC6PPPP0+9evWYOnUqL7/8MtWqVaN3795udfj4+DBnzhzXBUpvv/02Q4cOpVy5cm7rTZw4\nkfLlyxMeHs60adNo3bo17du3z9FxXa4svey0/Vrte9KkSZQtW5axY8fy5ptv0qdPHypVquS2ziOP\nPELjxo2ZMGECTzzxBB9//DG33HILxhjefPNNBg4cyOzZs2natCljxozJcTtFbnTmeo4/yk3GGHuj\nHpuI3Dz27dtHly6T8PN731V29mx3VqwYTe3atXOxZSJyLRljsNbm7BTLTS5Xe2KNMT7GmO+MMbuM\nMXuNMeOc5f7GmDXGmP3GmK+MMcXSbTPSGPOrMeYnY0zbrGsXEfF8jt7LwyQkOO7elJDwK8ZEZ+jV\nFBG52eR6T6wxppC19pwxJh+wBRgEdAFOWGunGmNeAPyttSOMMbWBJUAwUAFYB1TPrMtVPbEicqNI\nGxNrbTmMiWb69BG0a6fv8NdaamrqJWdHSH9RnMi1pp7YnMv1EJvGGFMI2Aw8A7wH3GOtPWqMKQts\ntNbWNMaMAKy1dopzmy+BcGvtd5nUpxArIjeM2NhYoqMdPbDZmaZJci40NJSdO3dmuswYw7Zt2/7h\nFsnNRCE253J9dgJjjBewA6gKzLHWbjfGlLHWHgWw1h4xxpR2rl4e+G+6zQ85y0REbmj+/v4Kr9fZ\n6NGjOXfuXG43Q0SyKddnJ7DWplpr6+EYHtDQGBMEXNyFqi5VkSvUpk0b5s+ff03r/PTTTwkODiYh\nISHLdcaPH5+t+9F37NiRWbNmXcvmiVyRihUrUrNmzSz/iUjekus9sWmstaeNMRuBdsDRtN5Y53CC\ntJthHwJuSbdZBWdZpsLDw12PW7RoQYsWLa5xq0VuTsaYy05n9PTTT1+T23CKiNyINm7cyMaNG3O7\nGR4tV0OsMaYkkGytPWWMKQjcC0wGVgG9gCnAE8BK5yargCXGmOk4hhFUA7IcpJQ+xIrIP0v3eRcR\nydrFnWvjx4/PvcZ4qNweTlAO2GCM2Q18B3xlrf0CR3i91xizH2iNI9hird0HfAjsA74A+uvqLblZ\n7N27l2HDhtGuXTuaNWtG9+7dWb16tds6O3fu5F//+hdNmzalZ8+e/PDDD27Ld+zYQXBwcKb/jhw5\nAjju7LR8+XK37SIiImjTps0l27d48WKaNm3KN998Azi+RPbs2TPbx7dgwQLatWtH8+bNGTNmTIa7\nNp0+fZqJEydy33330bRpU5588kn27t3rts6SJUt44oknuOeee7jvvvsYOnQof//9t9s6oaGhvPDC\nC3z11Vd07tyZFi1aMHjwYLdbpIqISN6Xqz2x1tq9wF2ZlJ8EMv2Laa19BXjlOjdNJM+Jjo7m9ttv\np0uXLvj4+LBnzx5eeuklvLy8aNu2LcePH2fw4MHcfvvtPPvssxw7dowxY8a4ndKvWbMmCxcudKt3\n+vTp/PXXXxQpUuSS+7/U8IH58+ezePFiateuzfz58/Hy8mL58uUcO3aMIUOGMGHCBOLi4pg4cSKR\nkZFUqVKFsWPHUq1aNQDeeecdzp07h5+fHwALFy4kNjaW2bNnA5CcnEz9+vUBeOyxx9i0aRNffvkl\n+/fvp2vXrnz++ee8/vrrzJgxg7Nnz/LMM89Qr149Fi1aREhICLfccgteXl7Ur1+f+Ph4Dh06xIkT\nJwgLC+Ppp58mNjaWvXv3ki9fPowxtGnThqFDh5I/f54ZcSUiIhfRb2gRD9G2rfu8oHfeeSdHjx7l\nk08+oW3btixduhQfHx9mzJhBgQIFAPD19XW7XWXa/erTLF++nB9//JE5c+a4AmROzZkzh+XLl/PG\nG2+wcuVKtmzZwrx582jYsCF//fUXe/fu5eWXXyY6OprOnTvzxBNPMHv2bEaPHs2yZcsAOHfuHJMn\nT6ZSpUqcO3eO119/nffee48BAwZQq1YtvvjiC86cOUPNmjX5448/CA8Pp0CBAkyZMoXvvvuOhIQE\nwsPDGTVqFBUrVqRUqVKUKVOGKVOmEB8fz/3338/dd9/NvHnz2Lp1K5UrV2bZsmX4+flRrFgxUlJS\n+OWXX/jggw/4888/mT17NuXKlePxxx+/otdERESuP4VYEQ9x5swZ5s6dy+bNmzl27BgpKSkAlClT\nBoDIyEgaNWrkCrAALVu2zLK+3bt3M23aNAYOHOjq5cypadOmsW7dOubMmUNQUBArV67k9OnTLFy4\nkPnz55OamkqDBg147733GDt2LPfffz8A/fr1Y9iwYURFRQGOe8g//PDDgGPC+bp161K+fHk+/PBD\nxo0bx7Zt2yhevDjJycnMnDmT4sWLA9CwYUPWrVtHUlISQ4cOpXjx4sydO5f9+/fz559/cuTIEapX\nr05AQAAtW7YkKCiI22+/HW9vb7fQXqlSJfLnz0+VKlUICQlh9+7dbNiwQSFWRCQPU4gV8RDh4eFE\nRkby9NNPU6VKFfz8/Fi+fDmbN28G4MSJE9SoUcNtGx8fHwoVKpShrpiYGEaMGEGrVq3o3r37FbXH\nWsv69eupVasWtWrVcpUHBgYSGBjoel6hQgUAxowZw0svvYS1luTkZLy9vYmJcUw8kpCQwIABA9i/\nfz+nTp0CHMMX/vjjDwDi4uI4efIkJ06coF27dq66jTEkJydToEABqlatyv/7f/+PoKAgRo0axYoV\nKzh06BDnzp0jKSkJgNKlS1OiRAlOnz7tdiy33347hw4dcg29uPXWW/n555+v6HUREZF/hkKsiAdI\nSkriP//5DyNGjKBz586u8vTXNZYoUYKTJ0+6bZeYmJhh8vbk5GSGDx+Ov7+/21CDNAUKFCA5Odmt\n7MyZMxnWM8YwY8YMhgwZwtixY3n55ZcBMoyt9fb2BhzjXH18fABHiB42bBhJSUkkJibyf//3f3To\n0IFRo0ZRqlQprLU0a9YMX19fAIoVK4a/vz/NmjXjueeec6v/o48+YsOGDXz77bckJCQwbdo0fHx8\nWLlyJZUrV+a///2v2/o+Pj4Zpv66eChF/vz5NT2YiEgepxArkoel3Wq0aNGipKamugIhQHx8PJs3\nb3ZdcBUUFMSqVatITEx0hcX169dnqHPy5MkcPHiQxYsXu0JieqVLl+bPP/90PbfWsn379kzbV61a\nNWbOnEloaChDhgzJtL40t912m1soTXPy5Eni4uKYNGmSq/yzzz4jJSXFNVQiODiYRYsWUbhw4QyT\nzpcqVQpjDElJSXh5ebnub1+yZEk2b97sGnaRJjEx8YrH/4qISN6hECuSR61evYawsMlAIHCYChX8\nefvttylUqBDGGFeoS5uK6l//+hcffvghQ4YMoXv37sTExLBo0SK3YLl69WpWrVrFU089RVxcHHFx\nca5lNWvWJH/+/LRs2ZLly5dTo0YNypcvzyeffMLZs2ezbOdffx3i0KFEdu5cipdXCo0bZ5hw5JJS\nU1Px8vLiueee44knnuDYsWOEh4dTpEgRSpQoAUD79u0ZPny4q/3ly5fn1KlTREZGsmfPHsARdFNT\nUwkPD6dTp07Exsby/fffU6dOHde+YmJiOHHiBLfeemuGdmi2PhERz6IQK5IHxcbGEhY2mXz55uHr\nW52EhF/5449etGhRkfDwcIoVK0a3bt1ISEjgww8/BBw9krNmzeK1117jhRdeoEqVKkyYMIFhw4a5\nemsPHjyIMYZ33nmHd955x22fq1atomzZsvTp04fY2Fjmzp1L/vz56datG1WrVs0wd2z6dvr5LaVi\nxeMcONCFLVu2ERsbm+1j9ff3p2jRohw+fJjnnnuOU6dOceHCBW677TbXOgUKFKBZs2b8+eefRERE\ncPLkSQICAggKCiIwMJCoqCiqVq3KuHHjiIiIYOPGjVSrVo169erx888/s3//ftavX8/8+fPx9fV1\nqzvN5e5AJiIieYtCrEgeFB0dDQTi61sdAF/f6ly4UJmhQ4dSu3Ztt3X79OnjenzXXXexdOlSt+Vr\n1651Pe7bty99+/a95L4LFizIuHHjMpSn365Dhw506NCBffv2pWtndUqVCiU+fhHR0dGuOj777LNM\n95MWGtetW8eXX35JREQE+fPn5+6772bYsGGMGjXKbX1vb28aNWrEK6+4TxMdERHhenz//fe7ZkAA\nOHz4MNOnT2f79u38+OOPNGjQgC1btrguNktrR9WqVdm2Lcub/4mISB5kbtRTaMYY3cxLPFZsbCxN\nmnRx64lNSQll69aP8Pf3z+3muXhKO0VE8jpjDNZanRLKgdy+7ayIZMLf35/p00eQkhLK2bPdSUkJ\nZfr0EXkuGHpKO0VE5MajnliRPCxtdoJy5crl6WDoKe0UEcmr1BObcwqxIiIiIrlMITbnNJxARERE\nRDyOQqzbOyBoAAAgAElEQVSIiIiIeByFWBERERHxOAqxIiIiIuJxFGJFRERExOMoxIqIiIiIx1GI\nFRERERGPoxArIiIiIh5HIVZEREREPI5CrIiIiIh4HIVYEREREfE4CrEiIiIi4nEUYkVERETE4yjE\nioiIiIjHUYgVEREREY+jECsiIiIiHkchVkREJJ3Y2FgiIiI4cuRIbjdFRC5BIVZERCSdkydPMn/+\nfA4fPpzbTRGRS1CIFRGRXJOYmJjbTciUMSa3myAil2GstbndhuvCGGNv1GMTEcmrPvzwQxYvXsyp\nU6do3Lgx3bp1o3///sybN4+77rqL4OBgwsLCOHLkCF9++SVFihRhxYoVAGzatIkFCxZw4MABihQp\nQvv27enfvz/58uVz1X/gwAFmzZrF7t27Abj77rt5/vnnKVGiBAA7duygX79+zJ07l+XLl7N161YC\nAgLo0aMHXbt2vWz7o6Oj6dixI8YY0v6GGGPYtm0bAKdPn+aNN95g8+bNnD17lpo1azJ06FCCgoJc\ndSxZsoQ1a9YQFRWFj48PQUFBDB06lAoVKlybF1luSM7PnL495YBCrIiIXBMbNmxg+PDhdOvWjXvu\nuYfdu3fz2WefceTIEebOnesKsSVLluSuu+7iwQcfJDU1lSZNmrB27VpefPFFunbtyj333MPff//N\nG2+8QefOnRk0aBAAf//9Nz169CAoKIhHH32UlJQU3nrrLQoVKsTChQuB/4XYihUr0r59e+rUqcNX\nX33Fp59+ysKFC6ldu/YljyE5OZn169fz4osvMnLkSG677TYAgoKCSE5Opnfv3pw9e5bQ0FD8/f1Z\nvnw5O3bsYMWKFQQEBAAwffp0qlWrRmBgIOfOneOjjz5i3759fPzxx/j5+V2/N0A8mkJszuXP7QaI\niMiNYeHChTRr1oznn38egIYNGxIbG8tHH33ktl6pUqWYOHGiW9msWbPo0KGD27b58+dn6tSp9OrV\ni6JFixIREUHJkiWZNWuWq3e2WrVqdO3ala1bt9KkSRNXfffddx9PPvkkAPXr12fz5s1s2LDhsiHW\n29ubatWqAVC5cmW3HtYvvviC33//neXLl1O+fHlXOx9++GGWLFnCwIEDAQgLC3Ntk5qaSsOGDWnb\nti2bNm3igQceyOarKSKXozGxIiJy1VJSUti/fz/NmjVzK2/evHmGddOHTYCoqCiOHDlCmzZtSElJ\ncf1r0KABiYmJHDhwAIBt27bRsmVL1/5SUlIIDAykXLly7Nu3z1WfMYZGjRq5nufLl4+KFSsSExNz\nVce4bds2atWqRdmyZV37t9ZSv359t/3v3buXAQMG0KZNGxo1akRISAjnz5/n4MGDV7V/EXGnnlgR\nEblqcXFxpKSk4O/v71bu7+/PxUO70k67p98WYPDgwRnWNcZw9OhR13qLFi1yDR3IbJ00RYoUcXue\nP3/+q76ILC4ujh9++IHGjRtn2H/aeNejR48ycOBAgoKCGDVqFKVKlcLb25vBgweTlJR0VfsXEXcK\nsSIicsViY2OJjo6mdOnS5MuXj9jY2AzLL77S/+LnxYoVA+DFF1+kRo0aGfYRGBjoWq9ly5Z07tw5\nQ9gtXrz4VR/L5RQrVoygoCBGjhyZYf/e3t4AbNmyhYSEBKZNm4aPjw/g6DU+derUdW+fyM1GIVZE\nRK7I6tVrCAubDAQChylfvgibNm2ic+fOrnU2bdp02XoqVapE6dKlOXToEB07dsxyveDgYH7//XfX\nxVbXS1ogvbjnNDg4mNmzZ1OmTJksQ3NSUhJeXl5uMyqsXbuWlJSU69dgkZuUQqyIiORYbGwsYWGT\nyZdvHr6+1UlI+JVffunGyZObefXVV2nevDl79uxhy5YtAHh5ZX0JhjGGIUOGMHbsWM6ePUvTpk3J\nnz8/f//9N5s2bWLq1Kn4+PjQt29fevXqxeDBg+nUqRPFihUjJiaGbdu28eCDD3LXXXcBZOglzamy\nZcvi4+PDZ599hp+fH/nz56dWrVq0b9+eFStW0LdvX3r06EH58uU5deoUkZGRlCxZkn/9618EBweT\nmppKeHg4nTp14sCBA7z//vsULVr0qtokIhkpxIqISI5FR0cDgfj6VgfA17c6Fy7UpkeP+qxfv55V\nq1bRoEEDwsLCeOGFFyhcuDDgCKyZ3Ujg3nvvpXDhwrz77rt8+umneHl5UaFCBZo1a+bqGa1YsSLv\nvvsub731FpMmTSIhIYHSpUsTHBzMLbfc4qorqxsVZPcGBgUKFGDMmDHMnz+fvn37kpKSwrZt2yhQ\noABz585l3rx5REREcPLkSQICAggKCuKee+4BoGrVqowbN46IiAg2btxIjRo1mDJlCqNGjcr2aysi\n2aN5YkVEJMdiY2Np0qSLW09sSkooW7d+5HZx14IFC3j33XdZv349BQoUyMUWi+Rtmic25xRiRUTk\niqSNibW2HMZEM2HCs/z1VxQNGjTA19eXXbt2sXjxYh566CGee+653G6uSJ6mEJtzCrEiInLF0mYn\nKFeuHAUKFGDUqFHs27ePs2fPUrJkSe6//35CQ0PdLnTKbZe6yMrLyyvbww5EriWF2JxTiBURkZtG\ndHR0ljMgGGPo0KEDY8eO/YdbJaIQeyUUYkVE5KZx4cIFfvvttyyXFy9enLJly/6DLRJxUIjNOYVY\nERERkVymEJtzWU/cJyIiIiKSRynEioiIiIjHUYgVEREREY+jECsiIiIiHkchVkREREQ8jkKsiIiI\niHgchVgRERER8TgKsSIiIiLicRRiRURERMTjKMSKiIiIiMdRiBURERERj6MQKyIiIiIeRyFWRERE\nRDyOQqyIiIiIeByFWBERERHxOLkaYo0xFYwx640xkcaYvcaYQc5yf2PMGmPMfmPMV8aYYum2GWmM\n+dUY85Mxpm3utV5EREREcoux1ubezo0pC5S11u42xhQGdgCdgN7ACWvtVGPMC4C/tXaEMaY2sAQI\nBioA64DqNpODMMZkViwiIiKS5xhjsNaa3G6HJ8nVnlhr7RFr7W7n47PATzjCaSdgkXO1RcBDzscd\ngQ+stRestX8CvwIN/9FGi4iIiEiuyzNjYo0xlYE7gW+BMtbao+AIukBp52rlgb/SbXbIWSYiIiIi\nN5H8ud0AAOdQgv8DBltrzxpjLh4HcEXjAsLDw12PW7RoQYsWLa60iSIiItm2Y8cO+vXrx7Jly7j1\n1lsvu350dDQdO3ZkxowZNG3a9B9ooeS2jRs3snHjxtxuhkfL1TGxAMaY/MBnwJfW2pnOsp+AFtba\no85xsxustbWMMSMAa62d4lxvNTDOWvtdJvVqTKyIiOSKc+fO8ccff1C9enUKFChw2fWTk5P55Zdf\nqFy5Mn5+fv9ACyWv0ZjYnMsLIXYxcNxaOzRd2RTgpLV2ShYXdjXCMYxgLbqwS0TkhpOamkpqair5\n8+eJE4YApKSkYIzByyvPjMSTG4hCbM7l9hRbTYHuQCtjzC5jzE5jTDtgCnCvMWY/0BqYDGCt3Qd8\nCOwDvgD6K6nmro4dOzJr1qwsl0dHRxMcHMyWLVv+wVZ5juDgYJYvX+56HhoayogRI1zPIyIiaNOm\nzRXXf/DgQSIiIoiPj7+qdopcb+PHj6dnz55s2rSJRx99lKZNmzJo0KBMP/8X/9x07NiRmTNnsmjR\nItq1a0eLFi2YMWMGAFu2bOHRRx/lnnvu4bnnnuPs2bPZblNoaCgvvPACH3/8MQ899BBNmzbl+PHj\nREVFMXr0aDp06EBISAiPPvoo//73v0n/52jHjh0EBwfz+++/u8pWrlxJt27dCAkJoU2bNoSGhvLH\nH38Amf+uTDuupUuX0r59e1q1asXo0aP18yzilKtfca21W4B8WSzO9C+3tfYV4JXr1ii5pkqWLMnC\nhQupXLlybjclT1q4cCGBgYGXXMeYK/9ifvDgQebPn0/Hjh11ilKuuWs9jvPw4cO88cYb9OnTh4CA\nAKZPn05cXFy2tl2zZg116tQhPDycn376iTfffBNrLbt27aJ///4kJCQwZcoUZs+e7fZF8XL27NnD\noUOHGDRoEL6+vhQuXJioqCgqVqxIu3bt8PPz45dffmHevHkkJSXxxBNPuLZN/7O7c+dOXnnlFfr3\n70+dOnWIj49n7969bqE6s5/1tWvXUqNGDUaPHk1MTAzTpk1jzpw5DB8+PNvHIHKjyjvnaeSG5O3t\nTVBQUG43I8+63q+NtfaqQrDI5VzLz9fp06eZO3cu1apVA+CPP/4gNjY2W9v6+PgwefJkjDE0btyY\nTZs2sWzZMj755BPKli0LwC+//MLnn3+eoxB79uxZPvjgA4oXL+4qCw4OJjg42PW8bt26nD9/no8/\n/tgtxKa3b98+atSoQc+ePV1lzZo1c1snsxOL3t7evPbaa64hDL///jtr1qxRiBUhD02xJdfezp07\n6devH82bN6dFixb069ePX375BYD9+/fzzDPPEBISQqtWrRgzZgwnT5502/7UqVOMGzeONm3aEBIS\nQmhoKD/99NMl9xkTE0PXrl0ZMGAAiYmJWQ4n+OSTT3j00Udp0qQJDz74IIsXL762B/8PSzsVumXL\nFtfpwiFDhnDmzBn++usv+vXrR7NmzejZsye//faba7uLT4tmx6uvvkrr1q2JjIy85Ho7duxg6FDH\nUPMHH3yQ4OBgOnXq5Fp+9OhRRo0aRevWrQkJCWHgwIFERUW51TF79mwee+wxmjdvTvv27RkzZgwn\nTpzIUXvlxnYtR3SVLl3aFWBzqn79+m6BukKFCgQGBroCbFpZbGwsKSkp2a63Vq1abgEWICkpiXnz\n5tG5c2fuvvtuGjduzJtvvsnhw4dJTU3NtJ4aNWqwf/9+pk+fzq5du7hw4UK29t+gQQO3MbhVqlTJ\n8TGI3KgUYm9QO3bsoH///hQoUIDx48czefJk6tWrx7Fjx4iLiyM0NJTk5GQmTZrE8OHD2blzJ88+\n+6zbL8Zhw4bx3XffERYWxuTJk7HW0q9fPw4dOpTpPg8fPkzfvn2pWLEiM2bMwMfHB8jYU7N48WIm\nT55My5YtmTFjBo888ghz587NcZjLa44cOcK8efPo378/L774Inv37uXll19m9OjR3HfffUydOpWU\nlBRGjx59RfVba3n55ZdZu3Ytc+fOvWwvbq1atRgyZAgAr7/+OgsXLuTVV18FHD1eTz31FAcPHmT0\n6NFMmTKF8+fPM2DAAJKSklx1nDx5kt69ezNjxgyGDRvG4cOH6d+//xW1X3JX2hjN9F9CevfuTaNG\njdzGWD722GO89dZbnDhxggkTJvDQQw8REhJCly5deOutty4bvrIzjvPTTz8lODiYhIQEt2337Nnj\nGmMfGhpKdHQ0x48fJzg4mIYNG/L5559nud8iRYq4Pff29s60zFpLcnLyJY8hvYCAgAxls2bNYsmS\nJXTp0oVZs2bx3nvv8dRTTwG4/fyk17BhQ8aNG8euXbvo168fbdq0YerUqSQmJl5y/9fiGERuVBpO\ncIOaPXs2NWvWdLvoqnHjxgC88cYbeHl58cYbb1CwYEEAbrnlFnr16sXXX39N27Zt2bp1Kz/88AMR\nERHceeedgKNHIK3XdOTIkW77+/vvv+nXrx916tRh4sSJ5Mv3v6HO6Xtq4uPjefvtt+nTp4/rl37D\nhg05f/48CxYsoGvXrh57+vv06dNuY1x//fVX3nvvPcaPH8/9998POF6LIUOGEBUVRaVKlbJdd2pq\nKuHh4Wzfvp2IiIhsjTEuVKiQax81atRw65FasmQJCQkJzJ07l8KFCwNwxx138OCDD7Jq1Sq6du0K\nwNixY93aUKdOHdq3b8/u3btdnwvxDHXq1MHb25tdu3bRpk0bEhMT+fnnn/H29mbPnj00adKE06dP\n8/vvvxMWFkZcXBxFihRhyJAhFCtWzHWRYFxcXIaf/4tdbhynMQZjDLGxscTGxlKuXLkMdYwYMYKd\nO3dy4cIFFi5cCDh6Us+cOXPNX5tLyez30ddff81jjz1Gjx49XGWbN2++bF0PPPAADzzwAKdOnWL9\n+vW8/vrr+Pn5MWDAgGvaZpGbhULsDSghIYHIyEief/75TJfv27ePxo0buwIsOMZmBgYGsmfPHtq2\nbcu+ffsICAhwCyq+vr40a9aMPXv2uNX3559/0qdPH4KDgwkPD7/k9DN79+4lISGB1q1bu/X6NmjQ\ngLfffpuYmBjKlClzpYeeqwIDA90u0qpQoQLgOLb0ZdZaYmJish1iU1JSGDVqFD/++CPz58931Xs1\ntm/fTqNGjShYsKDrfShUqBC1atVyGzKydetWFixYwIEDB1w9acYYDh48qBDrYXx8fKhZs6YrxO7d\nu5fChQvTsGFDdu3aRZMmTdi9ezdeXl7cfvvtFCpUyNWTD44vOb6+vkyYMIHhw4e7fVG9WHbGcZ46\ndYoWLR7Dy+sW4DANGrh/rqtUqULRokU5fPgwZcqUoWTJkoDjAq7clpiY6Db1V2pqao7aVaxYMTp3\n7sz69evdZi8QkZxRiL0BnT59GmstJUqUyHT58ePHqVq1aobygIAATp065VrH39//kuuk+eGHHzh9\n+jSdOnW67PyJcXFxWGt55JFHMiwzxnDkyBGPDbGZnfa7uDytLKtTjplJSEjgv//9L61atbomARYc\n78OPP/6Y4Q9v+jkwIyMjGTp0KK1bt6ZXr16u06q9evW67ClQyZvq1avHt99+CzjGzNerV4/69evz\nxRdfALBr1y6qV69OoUKFAFi6dCmffPIJhw8fdr3naT+n5ctnfcfvS43jzJcvH2fPnuXgwWiqVPmI\nQoXqkJDwK6tXt6VAAffxpKVKleLXX39l/Pjx9OjRg0OHDrFixYpcP1vTqFEjli9fToUKFShatCjL\nly/PdJhF+rNQERERnDp1ivr161O8eHF+/vlndu3axcCBA//JpovcUBRib0BFixbFy8uL48ePZ7q8\nZMmSmV7xe/LkSWrXrn3ZdYoVK+ZW1rFjR+Lj4xk2bBhvvvmmq46s2gYwc+bMTMea5eQUe14QGxtL\ndHQ058+fv2778PPz45VXXmHw4MGUKFGCZ5999qrrLFq0KM2bN6dPnz4ZLsxJCzCbNm0iICCAiRMn\nupYdOXLkqvctuadevXosWbKE+Ph4du/eTUhICHXr1uX1118nOTmZXbt2Ua9ePcARYGfNmkWvXr24\n6667KFKkCJGRkVc9jjNfvnzOi0gL4OvruIjL17c61hYhJSXGbbsCBQpw3333cezYMZ577jlq167N\nxIkTM3wJThuecD1kVu/zzz/P5MmTmTp1Kr6+vq6xv+l/Vi7etnbt2vz73/9m7dq1nDt3jrJly9K3\nb18effTRLPd1PY9L5EagEHsDSQtU5cqVo06dOnzxxRd069Ytw3p16tTho48+4vz5864hBZGRkRw+\nfNh1irhOnTpERES4jX1MSEjgP//5D61atcpQ58iRI4mPj2fQoEHMmzcv055e+N8pyWPHjtGkSZNr\ndei5YvXqNYSFTQYCOX58C3XrXtlV1dnRoEEDJk+ezPDhwylUqBBPPvlktrZL6/m9OHQ0bNiQdevW\nUaVKlSxviXnxKVOAL774Qn9UPVjdunUB+P7779m7dy+DBw+matWqFCxYkG3btvHzzz+7poj6+uuv\nad26Nf369XNtf61OfTvOtiRx/vxP+PnVIyHhV0qVKkmJEkUzrHvLLbe4jc0G2LZtm9vzlStXZthu\n3LhxGco6dOhAhw4dst3OefPmZVoeEBDA1KlTM5Snn/2jfv36bu0MCQkhJCQky32VK1cuW8eV02MQ\nuZFpdoIbxOrVa2jSpAtdukyiSZMu1KtXn19++YVBgwaxYcMGvv32WyIiItiyZQvdu3fHWsuzzz7L\n5s2b+fLLL3nhhReoUaOGK6A2btyYO+64g5EjR/L555/zzTffMHjwYJKSknj88cczbcP48eOpW7cu\nAwYM4O+//850ncKFC9OnTx9effVV3nrrLb777jv++9//8sEHH2Q5hjcvio2NJSxsMvnyzcPP732M\neZDt23/M9pyWV6JZs2aMHz+eiIgIPvzww2xtU6lSJay1rFixgsjISA4cOABA9+7dSU5Opl+/fnz1\n1Vfs3LmTdevWMXXqVNcQg0aNGnHkyBGmTZvG9u3bWbBggeu0s3iW2NhY9u3bx4ULF7j11ltZunQp\n+fLlo0aNGoAj3C5evJjU1FTXl9bExMQMX3Cu1ftfrVo1brmlHAkJoZw9252UlFAGD34sw5ctb29v\nDV0RkSypJ/YGkD5Q+fpWJyHhV+bMCWXu3FdYunQpY8eOxdvbm9tuu42WLVtSvHhx5s2bx4wZMxg9\nejTe3t6EhIQQFhbmdrHG66+/zvTp05k2bRqJiYnUqVOHuXPnuo2FS3+6K1++fEyePJmhQ4fSv39/\n3n77bdc66fXs2ZPSpUuzdOlSlixZgo+PDxUrVuTee+/9B16tayM6OhoIxNe3OgD585cgMbEg0dHR\nmY4lTi/9a5bTXs22bduSkJDApEmT8PPzo3379pdcv2zZsoSFhfHBBx+wbNkyypQpw8qVKylWrBgL\nFy7kzTffZPr06Zw5c4aSJUty5513Ur2645iaNGnCwIEDXRPG33HHHcyYMYOHH344R22W3JX+jAEc\npmnTquzcuZMmTZq4Pn/16tVj5syZVKpUyfX5bdSoEcuWLSMoKIgKFSrw5ZdfZjm9Xk4FBQVRvXo1\nihQpwoMPPoC3tzcff/yxa6aMNJUrV+bbb7/l22+/pVixYgQGBmYYznSlUlNTLznH7aUuXBORvMFc\ny4mq8xJjjL1Rj+1i+/bto0uXSfj5ve8qO3u2OytWjL7k+FS5crGxsTRp0sXti0NKSihbt3502RAr\n8k/J7HN6+vQjlCqVj0GDBrmGDkRGRtK7d286duzIiy++CMD58+d57bXX2LRpEwCtWrXinnvucX0p\nuvXWW4mOjqZTp05Mnz7dddvZTp060aZNG7cLlj777DNeeuklNm/ejK+vLwA//fQTU6ZM4cCBA1Sq\nVInhw4czZswYWrduzaBBgwA4dOgQkyZNYt++fcTHxzNu3LjLfnHLrtDQUHbu3JnpMmNMhlP7Iteb\nMQZrrcZr5YBC7A1AgSp3pPVwWVsOY6KZPn0E7dq1ze1mibjoC27WDh48yLlz57JcXrNmzX+wNSIK\nsVdCwwluAP7+/kyfPoKwsFDOnv1foFKAvb7atWtLo0bBrovpcuP1vtStJ728vHQR1k3OcROBwyQk\n/Or6gmtMdKY3F7jZVKxYMbebICJXST2xN5D0sxMowN74duzY4XbleHrGGPr06UOfPn3+4VZJXqMz\nBiKeQT2xOacQK+Khzp8/T1RUVJbLS5UqleUNL+Tmoi+4InmfQmzOKcSKiIiI5DKF2JzTPLEiIiIi\n4nEUYkVERETE4yjEioiIiIjHUYgVEREREY+jECsiIiIiHkchVkREREQ8jkKsiIiIiHgchVgRERER\n8TgKsSIiIiLicRRiRURERMTjKMSKiIiIiMdRiBURERERj6MQKyIiIiIeRyFWRERERDyOQqyIiIiI\neByFWBERERHxOAqxIiIiIuJxFGJFRERExOMoxIqIiIiIx1GIFRERERGPoxArIiIiIh5HIVZERERE\nPI5CrHiEjh07MmvWLNfz8PBwevbs6Xr+6aefEhwcTEJCQm40T0RERP5h+XO7ASLZ8dprr1GsWDHX\nc2MMxpgsn4uIiMiNLVsh1hgTAlS31r5rjCkFFLbW/nF9mybyPzVq1MjtJoiIiEgectnhBMaYccAL\nwEhnkTfw/vVslOR9n332GXfffTfx8fFu5QcOHCA4OJjt27ezZcsWBgwYQNu2bWnRogW9e/fmu+++\nc1s/IiKCNm3asH//fnr37k1ISAg9evRg9+7dbutdPJwgO2bPns1jjz1G8+bNad++PWPGjOHEiRNX\ndsAiIiKSp2RnTGxnoCMQD2CtPQwUuZ6NkryvRYsWeHl5sWHDBrfytWvXUqJECRo0aMChQ4cICQnh\npZdeYurUqdStW5fBgwfzww8/uG2TkJDA+PHj6dKlC6+++ire3t4MHz6cxMTEq2rjyZMn6d27NzNm\nzGDYsGEcPnyY/v37X1WdIiIikjdkZzhBkrXWGmMsgDHG7zq3STxA4cKFady4MWvXrqVDhw6u8nXr\n1tGmTRuMMXTr1s1Vbq2lfv36HDhwgJUrV3LHHXe4liUlJTFs2DDq168PQIkSJejevTu7du2icePG\nV9zGsWPHuh6npqZSp04d2rdvz+7du7nzzjuvuF4RERHJfdkJsR8aY+YBxY0xfYAngfnXt1niCdq2\nbUt4eDinT5+maNGi7N+/n6ioKMaMGQNATEwMc+bMYfv27Rw/fhxrLUCGAOnt7e0KsAC33nqra/ur\nsXXrVhYsWMCBAwdcwx6MMRw8eFAhVkRExMNdNsRaa18zxtwLnAZuA8Zaa9de95ZJnte8eXPy5cvH\n+vXreeihh1i7di1lypShbt26WGsZOnQo58+f55lnnqFChQoULFiQt956i7i4OLd6ChUq5PY8f37H\nx/JqhhPs27ePoUOH0rp1a3r16kVAQAAAvXr1uuphCiIiIpL7LhlijTH5gHXW2paAgqu4KViwICEh\nIaxdu5aHHnqIdevWce+99wLw119/sX//fmbPnk2jRo1c2/xTAXLjxo0EBAQwceJEV9mRI0f+kX2L\niIjI9XfJC7ustf+fvTsPq6ra/zj+XiBOBIhDOFVOKYqzAqZlDmWWV8shG7zXrDTLGU3TbDCtnHIe\nUivLMrNySktNK7XMn2XOgZVXvVmKKAnOoOH6/cHhBMpwUA5w9PN6Hh7PXnvtfb7n3Gt9Wqy1dhJw\n0RgTkFk/uX7ExcURFRVFXFwckDylYOvWrXz33XccOnSIVq1aAf+E1ZRRVYDo6Gh27tyZK3UmJiam\neW+AlStXai9ZERGRa4Qrc2JPA7uNMWtx7FAAYK3t57aqJF9avXoNERFjgLLAYSZNGkrz5ndSuHBh\nXuFn88QAACAASURBVH/9dcqVK0f16tUBqFChAkFBQUyePJmnn36aM2fOMGfOHIKCgnKl1vDwcD76\n6CMmTpzIHXfcwa5du1i1alWuvLeIiIi4nytbbC0BXgS+Bbam+pHrSFxcHBERY/D2no2v73y8vWcT\nETGGs2fP0rRpU/766y/uueceZ38fHx/Gjx+Pt7c3zz33HLNnz+bxxx+nfv36Lr3fpSOm2R1Bbdy4\nMX379uWbb75h0KBBbN++ncmTJ2frHiIiIpJ/mZQV45l2MqYgkPLIpF+ttRfcWlUOMMZYVz6buCYq\nKoqOHV/H1/ef51ycPt2FJUuGU6NGjTysTERExPMZY7DWas5bNrjyxK5mwF5gBjAT+M0Y09TNdUk+\nU6ZMGeAwCQl7AUhI2Isx0Y52ERERkdyV5UisMWYr8Ki19lfHcVXgI2ttg0wvzGMaic15KXNirS2D\nMdFMmjSU1q1b5XVZIiIiHk8jsdnnSojdZa2tnVVbfqMQ6x5xcXFERyePwAYGBuZ1OSIiItcEhdjs\ncyXEzgUuAimTIbsA3tbaJ9xc21VRiBURERFPoRCbfa6E2EJAb+B2R9N3wExrbb5+7JFCrIiIiHgK\nhdjscyXE+gIJjgcfpDzFq5C19mwu1HfFFGJFRETEUyjEZp8r+8R+DRRJdVwE+Mo95YiIiIiIZM2V\nEFvYWns65cDxuqj7ShIRERERyZwrIfaMMcb5mCVjTAPgnPtKEhERERHJnCshdgDwqTHmO2PMRuBj\noE9OFWCMeccYE2OM2ZWqLdAYs8YY86sx5ktjTECqc8OMMXuNMXuMMdqkVEREROQ65OpjZ32Aao7D\nHH3srDHmduA08H7K3rPGmLHAX9baccaY54BAa+1QY0wN4EMgFChP8tzcW9NbwaWFXSIiIuIptLAr\n+1x57OyDJM+L/Rl4APg49fSCq2Wt3QjEXdJ8PzDP8Xqe430B2gELrbV/W2v/R/LjcMNyqhYRERER\n8QyuTCd40Vp7yjFi2hJ4B3jTvWVxo7U2BsBaewS40dFeDvgjVb9DjjYRERERuY64EmKTHH+2Ad6y\n1n4BFHRfSenSvAARERERcSrgQp9DxpjZwN3AWMcTvFwJv1cjxhgTZK2NMcaUBo6m1ALclKpfeUdb\nukaMGOF83axZM5o1a5bzlYqIiIhk0/r161m/fn1el+HRXHliV1GgNbDbWrvXGFMGqGWtXZNjRRhT\nAVhhra3lOB4LHLfWjs1gYVc4ydMI1qKFXSIiIuLhtLAr+1zancCtBRizAGgGlABigJeBZcCnJI+6\n/g50ttbGO/oPA54ELgD9MwrTCrEiIiLiKRRisy/PQ6y7KMSKiIiIp1CIzT53z20VEREREclxruwT\nO9aVNhERERGR3OLKSOzd6bTdm9OFiIiIiIi4KsMttowxzwC9gErGmF2pTvkB37u7MBERERGRjGS4\nsMsYEwAEAqOBoalOnbLWHs+F2q6KFnaJiIiIp9DCruzLcDqBtfaEtfZ/1tpHgD9J3tLKAjcYY27O\nrQJFRK43n3/+OWFhYSQkJOR1KSIi+ZYrDzvoA4wgeQ/Xi45ma62t7d7Sro5GYkXEU504cYI///yT\nkJCQvC5FRHKJRmKzz5UQ+18g3Fr7V+6UlDMUYkVERMRTKMRmX4YLu1L5Azjh7kJERPKTbdu2MWfO\nHKKiovDy8iI4OJiBAwdSokQJZs6cydatW4mNjSUoKIi77rqLHj16UKDAP/9Ifffdd1m+fDlHjx7l\nhhtuIDg4mJdffpnixYuTlJTEtGnT+Oqrrzh+/DgBAQHUqlWL0aNH4+3tzYoVKxg5ciTfffcdhQsX\nzvJ+IiLXI1dC7H5gvTHmCyAxpdFaO9FtVYmI5KGtW7fSu3dvwsLCeOWVVyhSpAg7d+7k2LFjeHt7\n4+fnx4ABAwgICODgwYPMmTOH+Ph4hg0bBsAXX3zBvHnz6NevHxUrVuTEiRNs2bKFc+fOATB37ly+\n/PJL+vbtS9myZfnrr7/4/vvvSUpKwtvbG2MMxvwzIJPV/URErkeuhNiDjp+Cjh8RkWva9OnTCQ4O\nZurUqc62Ro0aOV8PGDDA+bp27doULlyYUaNGMWTIELy9vYmKiiI8PJwOHTo4+zVr1sz5Oioqitat\nW3Pfffc521q2bJlhPVndT0TkepRliLXWvgJgjClqrT3r/pJERPJOQkICkZGRDB48OMM+CxYsYNmy\nZRw+fJjExORfUBljOHLkCOXKlaNq1ap89tlnzJkzhyZNmlC9enW8vP7ZDKZq1aosXryYwMBAGjdu\nTJUqVTKtKav7iYhcj7IMscaY24B3gBuAm40xdYCe1tpe7i5ORCS3nTx5EmstJUqUSPf8ggULmDp1\nKt26daN+/fr4+fkRGRnJuHHjnIG2Xbt2nD17lqVLl/L222/j7+9Px44defrppzHG0L17d7y9vVm8\neDHTp0+nVKlS/Oc//+Hhhx9O9z2zup+IyPXIlekEk4F7gOUA1tqdxpimbq1KRCSP+Pv74+XlRWxs\nbLrnv/76a1q2bMnTTz/tbNu/f3+aPsYYHnnkER555BGOHj3K6tWrmTFjBkFBQXTo0AEfHx+eeuop\nnnrqKf78808WL17MhAkTqFChQpppC67eT0TkeuTS76OstX9c0pTkhlpERPJMXFwcUVFRnDt3jpo1\na7Jy5cp0+yUmJlKwYNrlARn1Bbjxxhvp2rUrN910EwcOHLjsfPny5enfvz8FCxa8LAxfyf1ERK4X\nLm2xZYxpDFhjjA/QH9jj3rJERHLP6tVriIgYA5QFDtO7dycWLJhPv379aN++PUWKFGHXrl3UqFGD\n8PBwFi5cSEhICOXLl2fVqlUcOnQozf1Gjx6Nv78/tWrVwtfXl59++ok//viDfv36ATB48GCqV69O\ntWrVKFSoEF999RVJSUnUr18/3fqyup+IyPXIlRD7NDAFKAccAtYAvd1ZlIhIbomLiyMiYgze3rMp\nXPhWEhL2MmNGT2bNGs2CBQt46aWX8PHxoVq1ajRv3pzu3bsTHx/PrFmzAGjRogWDBw8mIiLCec9a\ntWqxbNkyli5dSmJiIjfddBMvvPACTZsmz8SqU6cOa9eu5YMPPsBaS8WKFRk/fjzBwcHp1pjV/URE\nrkdZPrHLU+mJXSLiiqioKDp2fB1f3/nOttOnu7BkyXBq1KiRh5WJyPVET+zKPld2JygF9AAqpO5v\nrX3CfWWJiOSOMmXKAIdJSNjrHIk1JtrRLiIi+ZUr0wk+A74DvkILukTkGhMYGMikSUOJiOjJ6dNl\nMCaaSZOGEhgYmNeliYhIJrKcTmCM2WGtrZtL9eQYTScQkeyIi4sjOjp5BFYBVkRym6YTZJ8rIfZV\nYJO1NuM9ZPIhhVgRERHxFAqx2edKiD0F+ALngQuOZmut9XdzbVdFIVZEREQ8hUJs9mU5J9Za65cb\nhYiIiIiIuMqVhV0YY9oBKRsSrrfWfu6+kkREREREMufKdIIxQCjwoaPpEeAna+0wN9d2VTSdQERE\nRDyFphNknyshdhdQ11p70XHsDWy31tbOhfqumEKsiIiIeAqF2OzzcrFfsVSvA9xRiIiIiIiIq1yZ\nEzsa2G6MWQcYkufGDnVrVSIiIiIimchyJNZa+xHQCFgCLAJus9Z+7O7CRESuBytWrCA0NJSEhIS8\nLiVL+/btIzQ0lG3btuV1KSIiru1OANwG3A5YxzVL3VaRiMh1xBiDMZ4zDc6TahWRa1uWI7HGmJnA\n08Bu4GegpzFmhrsLExGR/EcLZkUkv3BlYVcL4B5r7bvW2neB+xxtIiKShd27dzNo0CBat27NHXfc\nQZcuXVi9enWG/aOjowkNDWXNmjWMHDmSZs2a0aZNG1atWgXA+++/z7333svdd9/NtGnTLrt+y5Yt\ndOvWjSZNmnDPPfcwduxYzp075zy/detWQkND2bp1K0OHDqVp06Y88MADLFq06LJ7ffrpp/zrX//i\njjvuYNCgQfz111858I2IiOQMV6YT/Be4GfjdcXyTo01ERLIQHR1NrVq16NixI4UKFWLnzp2MHDkS\nLy8vWrVqleF106dP595772XcuHEsX76cESNG8Ouvv3LkyBFefvll9uzZw8yZMwkODubuu+8Gkues\n9uvXj9tuu43x48dz5MgRpk2bxuHDh5kyZUqa+7/++uu0adOGDh068OWXXzJu3Dhq1KhBjRo1ANiw\nYQPjxo3jwQcf5M4772Tbtm2MHDlS0wlEJN9wJcT6AXuMMT86jkOBn4wxywGste3cVZyIiKe7NKjW\nrVuXmJgYli1blmmIDQ0N5ZlnngEgJCSEr776iu+++45FixZhjKFRo0Zs2LCB9evXO0PsO++8Q9my\nZZkwYYIzbPr7+zNs2DB+/vlnatas6bz/PffcwxNPPAFAgwYN+Pbbb1m3bp0zxL777rs0adKEIUOG\nABAeHs7x48f57LPPcuibERG5Oq6E2JfcXoWIyDXq1KlTzJo1i2+//ZZjx46RlJQEQFBQUKbXhYaG\nOl/7+voSGBhI/fr104yEli9fnpiYGOdxVFQULVu2TNOnRYsWeHt7s2PHDmeINcYQHh7u7OPt7c3N\nN9/M0aNHAUhKSuKXX35xBtgUzZs3V4gVkXwjyxBrrd0AYIzxT93fWnvcjXWJiFwTRowYQWRkJN27\nd6dixYr4+vry6aef8u2332Z6nZ+fX5pjHx+fdNvOnz/vPI6NjaVEiRJp+nh5eVGsWDFOnjyZ6f0L\nFChAYmIiAPHx8SQlJVG8ePE0fYoXL66FXSKSb2QZYo0xTwEjgQTgIskPPLBAJfeWJiLimeLi4oiO\njqZEiRJs3LiRoUOH0r59e+d5dwXBkiVLcvx42vGFixcvEh8fj7+/v8v3KVasGN7e3pfd6/jx45oT\nKyL5hiu7EwwGalprK1hrK1lrK1prFWBFRNKxevUaGjfuSMeOr9O0aWfi4uLx8fFxnj9z5kyWo7BX\nKiQkhPXr16cJyd988w0XL16kbt26Lt/H29ubatWqsWHDhjTt33zzTY7VKiJytVyZE7sPOOvuQkRE\nPF1cXBwREWPw9p5N4cK3kpCwlz/+aMqbb75J0aJFMcYwb948brjhBs6cOZPj7//kk0/y73//m0GD\nBtGpUyeOHDnC9OnTue2229Is6nJlJPjxxx9n8ODBjBkzhubNm7Nt2zY2b96c4zWLiFwpV0LsMGCT\nMeYHIDGl0Vrbz21ViYh4oOjoaKAshQvfCkDhwrdSsmQj/P0LM2LECAICAujcuTMJCQl88sknGd4n\nvV/Zu/Jkr0qVKjF16lRmzJjBkCFD8PX15d5776Vv375Z3v/S9mbNmjFkyBDmzZvHF198QYMGDXjp\npZfo06dPpjWIiOQWk9V/kTu21tpI8hO7Lqa0W2vnube0q2OMsVqAICK5KS4ujsaNO6YZiU1K6smm\nTYsJDAzM6/JEJB8zxmCt1aTzbHAlxG631tbLpXpyjEKsiOSF1avXEBExBmvLYEw0kyYNpXXrjPeD\nFREBhdgr4UqIfR34H7CCtNMJ8vUWWwqxIpJXUnYnKFOmjEZgRcQlCrHZ50qIPZBOs83vOxQoxIqI\niIinUIjNvixDrKdSiBURERFPoRCbfa487MAHeAZo6mhaD8y21l5wY10iIiIiIhlyZTrB24APkLIb\nwX+AJGttdzfXdlU0EisiIiKeQiOx2edKiN1pra2TVVt+oxArIiIinkIhNvtceexskjGmcsqBMaYS\nkOS+kkREREREMufKE7sGA+uMMfsBA9wCPO7WqkREREREMuHS7gTGmEJANcfhr9baxMz65weaTiAi\nIiKeQtMJsi/L6QTGmN5AEWvtLmvtLqCoMaaX+0sTEREREUmfKwu7dlhr617Slu8fRauRWBEREfEU\nGonNPlcWdnkbY5xfqjHGGyjovpJERERERDLnysKu1cDHxpjZjuOejjYRERERkTzhynQCL+Ap4C5H\n01rgbWttvt5mS9MJRERExFNoOkH2ubQ7gSdSiBURERFPoRCbfa7MiRURERERyVcUYkVERETE4yjE\nioiIiIjHyXB3AmPMCiDDSaXW2nZuqcgFxpjWwGSSQ/g71tqxeVWLiIiIiOS+DBd2GWPuzOxCa+0G\nt1SUBcduCb8BLYHDwBbgYWvtL5f008IuERER8Qha2JV9GY7E5lVIdUEYsNda+zuAMWYhcD/wS6ZX\niYiIiMg1I8uHHRhjbgVGAzWAwint1tpKbqwrM+WAP1Id/0lysBURERGR64QrC7veBd4E/gaaA+8D\n891ZlIiIiIhIZlx57GwRa+3XJnmS6e/ACGPMVuAlN9eWkUPAzamOyzvaLjNixAjn62bNmtGsWTN3\n1iVyzevZsyeBgYGMGTMmr0sREfFo69evZ/369Xldhkdz5bGzm4DbgUXANyQHxjHW2mruLy/deryB\nX0le2BUN/Ag8Yq3dc0k/LewSyWEKsSIi7qGFXdnnynSC/kBRoB/QAPgP8Jg7i8qMtTYJ6AOsASKB\nhZcGWJFrSWJiYl6XICIiku9kOZ3AWrsFnFtb9bPWnnJ7VVmw1q4G8mQkWCQr27ZtY86cOURFReHl\n5UVwcDADBw6kRIkSzJw5k61btxIbG0tQUBB33XUXPXr0oECB5L+K0dHRtGvXjlGjRvF///d/fPvt\nt9SoUYMZM2awcuVKlixZwoEDB7DWUrVqVfr370/16tWd7/3KK6+wb98+evbsyZQpUzh8+DANGzZk\n1KhRxMfH89prrxEZGUnFihV56aWXqFKlivNaay3z5s3js88+IyYmhjJlyvDEE0/Qpk2bXP8ORURE\nsuLK7gQNSV7c5ec4PgE8Ya3d6ubaRDzO1q1b6d27N2FhYbzyyisUKVKEnTt3cuzYMby9vfHz82PA\ngAEEBARw8OBB5syZQ3x8PMOGDUtznylTptCiRQvGjh2Lt7c3AIcOHeK+++7jpptu4u+//+bLL7+k\nR48efPLJJ5QtW9Z57ZEjR5g9eza9evUiISGB8ePH8+qrrxIdHU379u157LHHmD59OsOHD+fjjz92\nXjdu3DhWrlxJjx49qFatGj/88AMjR46kWLFiNGnSJHe+QBERERe5srBrLtDLWvsdgDHmdpJDbW13\nFibiiaZPn05wcDBTp051tjVq1Mj5esCAAc7XtWvXpnDhwowaNYohQ4Y4w2rKucGDB6e5d48ePZyv\nrbWEhYXx888/s2rVKp588knnuZMnT/Lee+85g+3evXv54IMPeOWVV7j33nud1w8YMIDff/+dW265\nhT///JPFixczYsQI7rvvPgBCQ0M5duwYb731lkKsiIjkO66E2KSUAAtgrd1ojPnbjTWJeKSEhAQi\nIyMvC5+pLViwgGXLlnH48GHnXFdjDEeOHKFcuXLOfumFxgMHDjBjxgx2797N8ePHndcePHgwTb+y\nZcumGZktX748AA0bNkzTZq3l6NGj3HLLLfz44494eXnRrFkzkpKSnP1CQ0NZs2YN1lqM0XoDERHJ\nP1wJsRuMMbOBjwALPASsN8bUB7DWbnNjfSIe4+TJk1hrKVGiRLrnFyxYwNSpU+nWrRv169fHz8+P\nyMhIxo0bd9nireLFi6c5Pnv2LH369KFkyZIMHDiQ0qVLU6hQIUaNGnXZtX5+fmmOfXx8LmtPaTt/\n/jwA8fHxJCUlceedlz9t2hhDbGwspUqVcuVrEBERyRWuhNg6jj9fvqS9HsmhtkWOViTiofz9/fHy\n8iI2Njbd819//TUtW7bk6aefdrbt378/3b6Xjnru3r2bY8eO8eabb3Lzzf9sk3z69OkcqBwCAgIo\nUKAAc+fOTXfE9dJQLSIiktdc2Z2geW4UIuKp4uLiiI6OpkyZMtSsWZOVK1fSuXPny/olJiZSsGDB\nNG0rV6506T1SRltTRlABdu7cyeHDh9PsTnClGjZsyMWLFzl16hRhYXqKs4iI5H8ZhlhjzL+ttfON\nMQPTO2+tnei+skQ8w+rVa4iIGAOUBQ7Tu3cnFiyYT79+/Wjfvj1FihRh165d1KhRg/DwcBYuXEhI\nSAjly5dn1apVHDqU7sPmLlOzZk2KFCnCq6++SteuXYmJiWHOnDkEBQXlyOe45ZZb6NChA88//zxd\nu3alevXqnD9/nn379nHw4EFeeOGFHHkfERGRnJLZSKyv40+/TPqIXLfi4uKIiBiDt/dsChe+lYSE\nvcyY0ZNZs0azYMECXnrpJXx8fKhWrRrNmzene/fuxMfHM2vWLABatGjB4MGDiYiISHPfjH6dP3bs\nWCZPnsygQYO4+eabef7553n//fevuP5L3+e5557jlltuYdmyZcyePRtfX18qVqzI/fffn2V9IiIi\nuS3Lx856Kj12VtwtKiqKjh1fx9d3vrPt9OkuLFkynBo1auRhZSIi4mn02Nnsy/Kxs8aYecaYYqmO\nA40xc91blkj+V6ZMGeAwCQl7ATh5ch1HjqzjySefJCwsjCNHjmCtZezYsdxzzz2EhYXx1ltv5W3R\nIiIi1whXdieoba2NTzmw1sYZY+q5sSYRjxAYGMikSUOJiOjJ6dNliI39lrp1qzFhwhsULlyYkiVL\nsm7dOhYtWsTLL79MxYoVufHGG/O6bBERkWuCKyHWyxgTaK2NAzDGFHfxOpFrXuvWrQgPDyU6Opoh\nQ05w11130aBBA+f5AwcOEBAQwL/+9a88rFJEROTak+WcWGNMV+B54FPAAJ2A16y1H7i/vCunObGS\nXdu2bWPOnDlERUXh5eVFcHAwAwcOpGrVqvz6669MnjyZ3bt3U7BgQZo0aUJERATFixcnOjqadu3a\npcxnAqBBgwZYa9m2bZuz3RjD8uXLKV26NDExMUyZMoUffviBxMRE6tWrx7PPPsstt9zirOf8+fPM\nmjWLNWvWcPz4cSpUqECfPn1o3LhxXn1FIiLiJpoTm30uLewyxoQAKfvFfmOtjXJrVTlAIVayY+vW\nrfTu3ZuwsDDn1lg7d+6kZs2ahISE8MADD1ClShW6du3K2bNnmTZtGgEBAXzwwQdcvHiR3377jUGD\nBhEWFsZDDz2Er2/y5h7z58/nm2++Ydq0aQBUq1aNs2fP8uijj1KsWDGeeOIJChUqxLvvvsuRI0dY\nsmSJcy/ZAQMGsGfPHnr27En58uVZs2YNn3/+OR988AG33nprnn1XIiKS8xRis8/VaQG/AHEp/Y0x\nN1trD2Z+iYjnmD59OsHBwUydOtXZ1qhRIwCmTZuGl5cX06ZNo0iRIgDcdNNNdOvWja+//ppWrVoR\nEhJCwYIFKVmyJCEhIc573HjjjXh7e6dp+/DDD0lISGDWrFnccMMNANSuXZu2bduyfPlyOnXqxI8/\n/simTZuYM2cOdevWBSAsLIyDBw8yd+5cRo8e7fbvREREJD9zZXeCvkAMsBb4HPjC8afINSEhIYHI\nyEjatGmT7vmoqCgaNWrkDLAAISEhlC1blp07d2b7/bZs2UJ4eDhFihQhKSmJpKQkihYtSvXq1dmz\nZ4+zT4kSJahVq5azT1JSEg0bNnT2ERERuZ65MhLbH6hmrf3L3cWI5IWTJ09iraVEiRLpno+NjaVy\n5cqXtRcvXpwTJ05k+/3i4+P5+eefWbNmTZp2Ywze3t7OPrGxsc7R4NQKFNC6ShEREVf+bfgHkP1/\nU4t4CH9/f7y8vIiNjU33fMmSJYmLi7us/fjx41f0UAN/f3+aNm1Kjx49uHTedtGiRZ19goKCmDBh\nwmV9RERExLUQux9Yb4z5AkhMabTWTnRbVSK5IC4ujujoaMqUKUPNmjVZuXIlnTt3vqxfzZo1Wbx4\nMefOnXNOKYiMjOTw4cPO+arZERYWxldffUXFihWdi7jS6/Phhx9SuHDhNDsWiIiISDJXQuxBx09B\nx4+Ix1u9eg0REWOAssBhevfuxIIF8+nXr59zd4Jdu3YREhJCly5dWLRoEX369OGxxx7jzJkzzJgx\ng6pVq9KiRYtsv3eXLl1YtWoVTz/9NA899BClSpXi+PHjbNu2jbp169KqVSvCw8Np1KgRvXv3pmvX\nrlSuXJkzZ87w66+/cuHCBXr16pXj34mIiIgnyTLEWmtfyY1CRHJLXFwcERFj8PaeTeHCt5KQsJcZ\nM3oya9ZoFixYwEsvvYSPjw/VqlWjefPmFCtWjNmzZzN58mSGDx+Oj48Pt99+OxEREc45rJA8p9WY\nrHdHCQgI4L333mPmzJlMmjSJU6dOUbJkSerWrZtm66w33niDuXPnsnDhQo4cOYK/vz/VqlXjoYce\ncsv3IiIi4kky3CfWGDPZWjvAGLMCuKyTtbadu4u7GtonVjISFRVFx46v4+s739l2+nQXliwZfkVz\nXEVERK6W9onNvsxGYlOeyPVGbhQiklvKlCkDHCYhYa9zJNaYaEe7iIiIeIJMn9hljPEG3rfWdsm9\nknKGRmIlMylzYq0tgzHRTJo0lNatW+V1WSIicp3SSGz2ZfnYWWPMRqCFtfZ87pSUMxRiJSupdycI\nDAzM63JEROQ6phCbfa6E2PeB6sBy4ExKe37fYkshVkRERDyFQmz2ubLF1j7Hjxfg595yRERERESy\nluVIrLOjMf6Atdaecm9JOUMjsSIiIuIpNBKbfV5ZdTDGNDTG7AZ2AbuNMTuNMQ3cX5qIiIiISPpc\nmRO7C+htrf3OcXw7MNNaWzsX6rtiGokVERERT6GR2OzLciQWSEoJsADW2o3A3+4rSUREREQkc66M\nxE4GigAfkfzkroeABGA+gLV2m5trvCIaiRURERFPoZHY7HMlxK7L5LS11rbI2ZJyhkKs5xgxYgT7\n9+/n/fffz+tSRERE8oRCbPZlucWWtbZ5bhQi1y9jDMbo762IiIi4zpU5sSIiIiIi+YorDzsQyRUb\nNmxg2rRpREdHU716dYYPH07FihUB+PDDD1mzZg2///47hQoVIiQkhIEDB1K+fHnn9T179qRYsWK0\naNGCWbNmERcXR506dXjhhRcoVaqUs9/06dPZuHEjhw8fxs/Pj/r16zNgwABKlCjh7NOuXTtaRTbB\nLwAAIABJREFUtmxJqVKl+PDDDzl37hy33XYbzz//PL6+vgAkJCQwbdo0fvjhB44cOUKJEiVo0qQJ\nvXv3dvYRERER93D5YQeeRnNiPccrr7zCxo0b8fX1pVevXhQsWJBZs2Zx+vRpli5dio+PD5MmTaJK\nlSqULVuWs2fPsnjxYqKioli6dKkzMPbs2ZNDhw5RunRpunbtSmJiIm+88QbVq1dn8uTJzvcbOXIk\n4eHhlCpVivj4eD788ENOnz7Nxx9/7OzTrl07Ll68SNWqVenUqRNHjx5l4sSJ/Otf/2LIkCEAxMfH\n8+abbxIWFkbx4sWJiYlh7ty5lC5dmqlTp+bulygiIh5Nc2KzL8ORWGNMh8wutNYuyfly5Hp14sQJ\nJk2aRM2aNQEIDg7mgQceYMWKFXTo0IGIiAhn34sXLxIWFkarVq3YsGED9913n/PcmTNnmDJlijPY\nxsbGMmnSJM6fP0/BggUBeOmll9Lcq2bNmrRp04YdO3ZQt25d5zkfHx/eeOMNvLySZ93s37+fNWvW\nOENssWLFGDZsmLN/UlISZcqUoUePHsTExBAUFJTTX5OIiIg4ZDadoK3jzxuBxsA3juPmwCZAIVZy\nTPHixZ0BFqB06dIEBwcTGRlJhw4d2L17N7NmzeLXX3/lxIkTQPJ/tR48eDDNfWrUqJHmV/mVKlUC\n4NixY5QrVw6ATZs28c4777Bv3z7OnDmT5l6pQ2zDhg2dARagYsWKxMXFkZSUhLe3NwArV65kwYIF\nHDx4kHPnzqW5l0KsiIiI+2QYYq21jwMYY9YANay10Y7jMsB7uVKdXDcCAwMvaytevDixsbHExMTQ\nt29fQkJCeP755ylVqhQ+Pj7079+f8+fPp7nGz88vzXGBAsn/F09MTAQgMjKSgQMH0rJlS7p160bx\n4sUB6Natm7NPRvfy8fHBWsuFCxfw9vZm3bp1vPzyy3Tu3JnevXvj7+9PbGwszz777GV1iYiISM5y\nZWHXTSkB1iEGuNlN9ch1Ki4u7rK248ePU7lyZTZt2kRCQgITJ06kUKFCQPKv7lNGZLNjw4YNFC9e\nnNdee83ZduTIkSuq+euvv6ZWrVoMHjzY2bZtW7589oeIiMg1x5Uttr42xnxpjOlmjOkGfAF85d6y\n5FoXFxdHVFSUM7weP36c3bt3O88fOXKEX375hZCQEBITE/Hy8nL+Ch9g7dq1JCUlZft9ExMTnaOz\nKVauXHlF+9QmJibi4+OTpm3VqlXa81ZERCQXuPKwgz6ORV53OJrmWGuXurcsuZatXr2GiIgxQFng\nMA0blicgIIAXX3yRZ555hoIFCzJ79mxKlixJ27ZtOXjwIElJSYwYMYL777+fffv2MX/+fPz9/V16\nv9S7VISHh/PRRx8xceJE7rjjDnbt2sWqVauu6HOEh4czbtw45s6dS82aNfn+++/ZsmXLFd1LRERE\nsselhx1Ya5dYayMcPwqwcsXi4uKIiBiDt/dsfH3n4+09m9Wrv6NkyZIMGDCAOXPm8MILL+Dn58fU\nqVPx8fGhcuXKjBgxgsjISCIiIlizZg1jx47lhhtuuOz+6Y2Cpm5r3Lgxffv25ZtvvmHQoEFs3749\nzfZbqa/JakS1Q4cO/Pvf/+bjjz9myJAhxMTEpJmmICIiIu6T5T6xjlHYsSTvUmAcP9Za69owWB7R\nPrH5U1RUFB07vo6v73xn2+nTXViyZDg1atTIw8pERETyjvaJzT5XRmLHAe2stQHWWn9rrV9+D7CS\nf5UpUwY4TELCXgASEvZiTLSjXURERMQ1roTYGGvtHrdXIteFwMBAJk0aSlJST06f7kJSUk8mTRqa\n7hZbIiIiIhlxZTrBFKA0sAxwbqSZ35/YpekE+VtcXBzR0ckjsAqwIiJyvdN0guxzJcS+m06ztdY+\n4Z6ScoZCrIiIiHgKhdjsyzLEeiqFWBEREfEUCrHZl+WcWGNMeWPMUmPMUcfPYmNM+dwoTsTTLF26\nlA0bNuRpDdHR0YSGhvL999/naR0iIiLu5MrCrneB5STvTF8WWOFoE5FL5IcQC+nvlysiInItcSXE\nlrLWvmut/dvx8x5Qys11iVzTEhMTs+50Fdw5lSaj2t39mURERFJzJcT+ZYz5tzHG2/Hzb+Avdxcm\nkl/t37+ffv360bJlS+644w46d+7Mp59+Ss+ePdmzZw8rVqwgNDSUsLAwvvjiCwDatWvH5MmTeeed\nd2jTpg3NmjUDYPfu3QwaNIjWrVtzxx130KVLF1avXu18r/Pnz9O4cWPWrFnjbJs+fTqhoaF89913\nzrbx48fTvXv3NHWePn2al19+mTvvvJNWrVrx1ltvXfZZ9u3bR//+/bnzzju58847GTp0KH/99c9f\n761btxIaGsrmzZsZNGgQTZs2Zfz48QCEhoayYMECJk6cyN13380jjzzCokWLaNq0KQkJCWneJ+U+\n//3vf6/wWxcREUmrgAt9ngCmAZMAC2wCHndnUSL52cCBA6lUqRKvvvoqPj4+/P7775w5c4Zhw4Yx\nePBgypcv7wyU5cv/M3189erVVK5cmaFDh5KUlAQkz1+tVasWHTt2pFChQuzcuZORI0fi5eVFq1at\nKFiwIDVq1GD79u20atUKgB07dlCoUCF27NjBHXfcAcD27dtp0qRJmjqnTp3K7bffzrhx49i2bRtv\nvfUWgYGBdOrUCYA///yTJ598kpCQEEaNGkVSUhJvvvkmgwYN4r333ktzr1dffZW2bdvy6KOPUrBg\nQWf7Bx98QP369Rk1ahQXL16kdu3aTJ48ma+//po2bdo4+y1fvpzq1atTpUqVHPpfQURErndZhlhr\n7e9Au1yoRSTfO3HiBIcOHWLChAlUrlwZgIYNGzrPFylShMDAQEJCQi671hjDlClTKFDgn792KcE0\nRd26dYmJiWHZsmXOc/Xq1XOOup4/f56oqCjat2/P9u3bgeQR13379tGnT58096pcuTLDhg0DIDw8\nnOPHj/Puu+86Q+ycOXMoWbIkU6dOxdvbG4AqVarQqVMnNm3aROPGjZ33uuuuu+jZs+dln6lUqVK8\n9tpradpatGjB8uXLnSH23LlzrFu3jn79+qX/pYqIiFwBV3YnmGeMKZbqONAYM9e9ZYnkT/7+/gQF\nBTF69GjWrl1LXFycy9eGhYWlCbAAp06dYvz48bRt25ZGjRrRqFEjlixZwsGDB5196tWrx4EDBzh1\n6hS7d++maNGidOrUiT179pCYmMj27dsxxlCnTp00906ZspCiefPmHDt2jJiYGAB+/PFHmjdvDkBS\nUhJJSUmULVuWMmXKEBUVlebaS0d5U6QOuinuv/9+duzYweHDhwFYu3YtFy9e5J577nHhWxIREXGN\nK9MJaltr41MOrLVxxph6bqxJJN8yxjBjxgxmzpzJqFGjSEhIoE6dOgwePJiqVatmem3x4sUvaxsx\nYgSRkZF0796dihUr4uvry6effsq3337r7FOnTh2MMezYsYPffvuNOnXqULFiRW644QZ+/vlnduzY\nQeXKlfH19U1z70ufhFa8eHGstcTGxhIUFER8fDzz5s27bOqAMcYZdFOO06s9o8/UoEEDypYty+ef\nf85TTz3FihUruPPOO/Hz88v0+xEREckOV0KslzEm0FobB2CMKe7idSLXlNSPyh07dixJSUns2LGD\nqVOnMmDAAOciroxcuu3V+fPn2bhxI0OHDqV9+/bO9kt3FvD19aVKlSps376dX3/91Tn6WbduXbZt\n28b27dupW7duuvWmdvz4cYwxlCqVvLlIQEAAzZs3p3379pe9Z7FixdIcZ7RlV0bt7dq1Y9myZbRu\n3ZodO3Ywffr0dPuJiIhcKVd2J5gA/J8xZpQxZhTJC7vGubcskfxl9eo1NG7ckY4dX6dx446sXr0G\nb29vGjRoQJcuXYiNjeX06dP4+Pi4vNXUhQsXuHjxIj4+Ps62M2fOpBmFTVGvXj22bNnCzz//TL16\n9Zxtmzdv5pdffnG2pbZu3bo0x9988w0lS5bkxhtvBJJ3F9i/fz/VqlUjODg4zU/p0qVd/m7S07Zt\nW2JiYhg1ahRBQUGEhYVd1f1EREQu5crCrveNMT8BLRxNHay1UZldI3ItiYuLIyJiDN7eswFDdPQI\nund/lg8+mIK1lnnz5lG1alX8/PyoUKECmzdvZvPmzQQEBFC2bFkCAgLSva+vry8hISG8/fbbFC1a\nFGMM8+bN44YbbuDMmTNp+tarV4+FCxfi6+tLcHCws23SpEkYY9INsfv372f06NG0aNGCbdu2sWLF\nCp599lnn+aeeeopu3brRv39/7r//fgICAjh69Cg//vgjbdu2pX79+sCV7TlbsmRJbrvtNr7//nse\nf/xxPXxBRERynCsjsQDFgTPW2unAMWNMxat9Y2NMJ2PMz8aYJGNM/UvODTPG7DXG7DHGtErVXt8Y\ns8sY85sxZvLV1iDiiujoaKAshQvfSoECJShUqDInTsQxaNAgxo0bR6VKlZgwYQIATzzxBBUqVGDY\nsGE89thjbNy4EUj+tXt6Qe61116jXLlyjBgxgokTJ9KyZcs0W1OlqFu3LsYYateujZdX8l/batWq\nUbRoUcqVK0eJEiXS9DfG0K9fP86cOcNzzz3H0qVL6d69Ow8++KCzz80338y7775LkSJFeP311+nf\nvz9vvfUWBQsW5Kabbkpzr/Rk9JlSpCwsa9u2bYZ9RERErpTJapTFGPMy0BCoZq2taowpC3xqrU1/\nubKrb2xMNeAiMBt41lq7zdFeHVgAhALlga+AW6211hjzA9DHWrvFGLMSmGKt/TKD+1t3PrVIrh9x\ncXE0btwRb+/ZFC58KwkJe0lK6smmTYsvWzwl/xg6dCjHjx9nzpw5eV2KiEi+Z4zBWqtfW2WDKyOx\n7UneJ/YMgLX2MHDVy4yttb9aa/cCl/4Pdj+w0PGI2/8Be4EwY0xpwM9au8XR733ggautQyQrgYGB\nTJo0lKSknpw+3YWkpJ5MmjRUATYD+/btY/ny5axfv55HHnkkr8sREZFrlCu7DJx3jIJaAGOMb1YX\nXKVywP+lOj7kaPsb+DNV+5+OdhG3a926FeHhoc7dCRRgMxYREcGJEyfo3Lmzcx9aERGRnOZKiP3E\nGDMbKGaM6UHyY2gvfwh7Oowxa4Gg1E0kP7p2uLV2RXaLFclLgYGBCq8uWL58eV6XICIi1wFXdid4\nwxhzN3ASqAa8ZK1d68rNrbV3X0FNh4CbUh2Xd7Rl1J6hESNGOF83a9bssicYiYiIiOSF9evXs379\n+rwuw6O5srDLF0iw1iY5FmNVA1ZZay/kSAHGrCN5YddWx3EN4EMgnOTpAmv5Z2HXZqAfsAX4Aphq\nrV2dwX21sEtEREQ8ghZ2ZZ8rC7u+BQoZY8oBq4H/AO9d7RsbYx4wxvwBNAI+N8asAnDsQfsJEAWs\nBHqlSqO9gXeA34C9GQVYEREREbm2uTISu81aW98Y0xcoYq0dZ4zZYa29/DmX+YhGYkVERMRTaCQ2\n+1wZiTXGmNuALiT/Ch/A230liYiIiIhkzpUQ2x8YBiy11kYaYyoB67K4RkRERETEbbKcTuCpNJ1A\nREREPIWmE2SfKyOxIiIiIiL5ikKsiIiIiHgchVgRERER8ThZhlhjTFVjzNfGmJ8dx7WNMS+4vzQR\nERERkfS5MhL7Fsm7E1wAsNbuAh52Z1EiIiIiIplxJcQWtdb+eEnb3+4oRkRERETEFa6E2FhjTGXA\nAhhjOgHRbq1KRERERCQTrjx2thIwB2gMxAEHgH9ba//n9uqugvaJFREREU+hfWKzz+WHHRhjfAEv\na+0p95aUMxRiRURExFMoxGZfgaw6GGMKAR2BCkABY5K/X2vtSLdWJiIiIiKSgSxDLPAZcALYCiS6\ntxwRERERkay5EmLLW2tbu70SEREREREXubI7wSZjTC23VyIiIiIi4qIMF3Y5ntB1keTR2luB/SRP\nJzCAtdbWzq0ir4QWdomIiIin0MKu7MtsOkE5oG5uFSIiIiIi4qrMQuwBa+3vuVaJiIiIiIiLMgux\nNxpjBmZ00lo70Q31iIiIiIhkKbMQ6w3cQPIcWBERERGRfCOzhV3brLX1c7meHKOFXSIiIuIptLAr\n+zLbYktfpIiIiIjkS5mNxBa31h7P5XpyjEZiRURExFNoJDb7Mgyxnk4hVkRERDyFQmz2ufLELhER\nERGRfEUhVkREREQ8jkKsiIiIiHgchVgRERER8TgKsSIiIiLicRRiRURERMTjKMSKiIiIiMdRiBUR\nERERj6MQKyIiIiIeRyFWRERERDyOQqyIiIiIeByFWBERERHxOAqxIiIiIuJxFGJFRERExOMoxIqI\niIiIx1GIFRERERGPoxArIiIiIh5HIVZEREREPI5CrIiIiIh4HIVYEREREfE4CrEiIiIi4nEUYkVE\nRETE4yjEioiIiIjHUYgVEREREY+jECsiIiIiHkchVkREREQ8jkKsiIiIiHgchVgRERER8TgKsSIi\nIiLicRRiRURERMTjKMSKiIiIiMdRiBURERERj6MQKyIiIiIeRyFWRERERDyOQqyIiIiIeByFWBER\nERHxOHkWYo0x44wxe4wxO4wxi40x/qnODTPG7HWcb5Wqvb4xZpcx5jdjzOS8qVxERERE8lpejsSu\nAUKstXWBvcAwAGNMDaAzUB24F5hpjDGOa94EnrTWVgWqGmPuyf2yRURERCSv5VmItdZ+Za296Djc\nDJR3vG4HLLTW/m2t/R/JATfMGFMa8LPWbnH0ex94IDdrFhEREZH8Ib/MiX0CWOl4XQ74I9W5Q462\ncsCfqdr/dLSJiIiIyHWmgDtvboxZCwSlbgIsMNxau8LRZzhwwVr7UU6//4gRI5yvmzVrRrNmzXL6\nLURERESybf369axfvz6vy/Boxlqbd29uTDegB9DCWpvoaBsKWGvtWMfxauBl4HdgnbW2uqP9YeBO\na+0zGdzb5uVnExEREXGVMQZrrcm6p6TIy90JWgODgXYpAdZhOfCwMaagMaYiUAX40Vp7BDhhjAlz\nLPTqCnyW64WLiIiISJ5z63SCLEwDCgJrHZsPbLbW9rLWRhljPgGigAtAr1RDqr2B94DCwEpr7erc\nL1tERERE8lqeTidwJ00nEBEREU+h6QTZl192JxARERERcZlCrIiIiIh4HIVYEREREfE4CrEiIiIi\n4nEUYkVERETE4yjEioiIiIjHUYgVEREREY+jECsiIiIiHkchVkREREQ8jkKsiIiIiHgchVgRERER\n8TgKsSIiIiLicRRiRURERMTjKMSKiIiIiMdRiBURERERj6MQKyIiIiIeRyFWRERERDyOQqyIiIiI\neByFWBERERHxOAqxIiIiIuJxFGJFRERExOMoxIqIiIiIx1GIFRERERGPoxArIiIiIh5HIVZERERE\nPI5CrIiIiIh4HIVYEREREfE4CrEiIiIi4nEUYkVERETE4yjEioiIiIjHUYgVEREREY+jECsiIiIi\nHkchVkREREQ8jkKsiIiIiHgchVgRERER8TgKsSIiIiLicRRiRURERMTjKMSKiIiIiMdRiBURERER\nj6MQKyIiIiIeRyFWRERERDyOQqyIiIiIeByFWBERERHxOAqxIiIiIuJxFGJFRERExOMoxIqIiMh1\nZ8WKFYSGhpKQkJDXpTgZY7YYYx7M5jXhxphH3FXTlTDGNHB8lkrZvC7EGPOUq/0VYkVEROS6Y4zB\nGJPXZeSERsDDeV3EJfYA3YA/s3ldCNDd1c4FsnlzEREREck/8l0St9aeBSLd/T4KsSIiInLN2rZt\nG3PmzCEqKgovLy+Cg4MZOHCg8/yhQ4eYOHEiu3btIigoiN69e9O8efM09/jkk09YuHAhR44cISgo\niAcffJBHH300TZ+9e/cyc+ZMduzYQVJSEpUqVaJXr16EhYUBcPjwYSZMmMDWrVux1tKgQQMGDhxI\n+fLlM6zdGNMEeBS4FSgIHABmWWt/cJx/CugCWGPMFsdlK6y1Ix3n6wFPkzzCmQCsAyY5QmaWjDHL\nga+BeOARoDCwzFo72VFbP6A0sAUYYa097biuATALeMhau9/RtgWYABQH2gMW+AqYaK392xjzL2Dw\nJZ9lq7X26YzqU4gVERGRa9LWrVvp3bs3YWFhvPLKKxQpUoSdO3dy7NgxAKy1vPDCC7Rv357HHnuM\nhQsXMnz4cD777DNKlSoFwNKlSxk/fjz/+c9/aNSoET/99BOTJ0/m77//pmvXrgD8/vvvdO/enQoV\nKjB8+HD8/f3Zs2cPMTExAFy4cIFnnnkGHx8fXnzxRby8vJg9ezY9e/Zk4cKF+Pn5ZfQRygEbgQ+A\ni0BjYIox5ilr7S5gGXAT0BB41nFNPIAxpg4wg+TgOgQIAPoCfsDQbHyNrYCfgRFAdaCXSZ6HUQ+Y\nSXKwfQ7oA4xJdZ1N515dSA68L5AczPsA0Y7PtxGY7+jTzdH/TKaVWWuvyZ/kjyaSO5YvX24bNmxo\nz507l2XfxMTEXKjI2oYNG9pPPvkkW9ds3rzZLliwwE0ViYjkrm7dutnHHnss3XMrVqywDRs2tCtW\nrHC2xcfH2/DwcLt48WJrrbUXL1609957rx05cmSaa8eMGWObNWtmz58/b6219vnnn7dt2rRxHl9q\n0aJFNjw83B4+fNjZFhMTYxs1amTfe+89a621jtC3BXjQpp9rDOANTANeTNXeH/gsnf5vAzMvaWsI\n/AhUSu890rnHcmAJYFK1vQdsBkqnausLrE513ODS93F8ttmX3P8NYG6q487Aj67UZq3Vwi6Rq7V2\n7VrGjx/Pnj176NChA2+++SYXL14E/ln9GhkZSc+ePbn99tuZP38+ANOmTePhhx+madOmtGnThhdf\nfJG//vorzb3btWvHlClTWLBgAW3atKFFixYMHz6cM2fS/sfp3r17eeKJJ2jSpAkPP/wwmzZt4sCB\nAyxatChNv+3btzvruOuuu3jttdc4e/af3ypt3ryZhQsXuuNrEhHJVQkJCURGRtKmTZsM+xhjCA8P\ndx4HBAQQGBjI0aNHATh69CjHjh2jZcuWaa67++67OXPmDP/9738B+Omnn7j77rvx8fFJ932ioqII\nDg6mTJkyzrYbb7yROnXqsGPHjszqu9EY84oxZiXwA8nhsRFwc2af3RhTCKgJfGWM8U75AXYCSUBw\nZtdfYqsjZKf4EzhsrT1ySVug4z0y88Mlx/uBG7NRSxqaTiByFTZv3szzzz9P1apViY2NpVOnTrz9\n9tucOHGCoUOHOle+vvDCCzz44IM89dRTzl8bxcXF8fjjj1OqVCni4+P58MMP6dWrFx9//HGa91i7\ndi1Vq1Zl+PDhHD16lIkTJzJjxgyGDBkCQGJiIn379qVkyZKMHj2ahIQEJk6cyIULF9LcZ+fOnc65\nXuPGjePEiRNMmzaNU6dOMWZM8m+A0v5zSkTEc508eRJrLSVKlMi036W/yvfx8eH8+fMAxMbGYoy5\n7B4lSpTAWsvJkycBOHHiBCVLlszwPWJjY9Oto0SJ/2/vzsOrqs6+j3/vDBCmTMwRgTAIyKiMgnVW\nrAODCJVS0QeRICiKviAColgQRS0zSpCCWpU6IU5FoJanNb6+QCqDoBShQtRgBMKkkEBY7x9753AS\nkpCAEE74fa7rXDlnnT2ssxcn3Fl7rXVXZceOHQXsAf4t+z8BFYDn8QLFg8A9QGyRHwqi8XptRwGP\n5HvP4Y1jLa79+V4fLqTMgEi8ILm4xzoClC9BXfJQECtSAvknCKSnp9O2bVv69u3L5s2bufzyy3n1\n1Vd58sknSUlJ4ZJLLgGgb9++9OnTB/AmCIwaNYodO3bwxRdf0Lt3b2677TZatGjBjTfeyJo1a6hU\nqRKzZ8/ms88+A7xfdBEREfTo0YOtW7fy/vvv8+OPP5KamkpGRgZ79uzh448/pmXLlgCcd955/O1v\nf8tT97Fjx7Jv3z5WrVpFSkoKiYmJ9OnTh9mzZ7N161aWL1/Oq6++ipnRvn17AG6++WbGjRsHeL24\nL7zwAhs2bCAqKoorr7yS4cOHU7FixTNy7UVESiI6OpqwsDB27tx50seoVq0azjl2796dpzz3rllM\nTEzgZ1HnqVatGlu3bj2ufNeuXURHRxe22/lAE+Be50/kgkAv64nsxwtW5wApBbz/UzGOcdbTcAKR\nYkpNTWXIkCGUK1eO8ePH8+STT3L48GGaN28OHJsg8Lvf/Y46depQvnx5XnnlFXJycujSpQtwbILA\nFVdcwYABA/jxxx8ZPHgwjRo14sYbb8TMWL16NQMHDmT37t00btyY3r17c9VVVwUmCJx//vmsWbOG\nb7/9lkcffZQOHToQFRXFqFGj2L/f+yO3efPmREQc+xs1KyuLr7/+mmuvvZbHHnuMSZMm0bJlS2bP\nnh14r0ePHlx//fVUrVqVBQsWsGDBAu666y7gWC9utWrVmDx5Mg899BApKSk88cQTZ7IJREROKDMz\nk40bN3Lw4EFatGjBRx99dNLHqlGjBtWrV2f58uV5ypctW0blypVp2LAhAO3bt2fZsmXH3QHL1bx5\nc7766ivS09MDZRkZGaxbt46LLrqosNPnBqtHcgvMrDbQOt92h8nXm+mcOwSsB+o5574u4LGLs9Nh\nADMreFxGPuqJFSmmmTNn0rRpU6ZPnw7A7t27qVq1Kp07dw6MUe3Xrx/XXXcdc+fOpUePHqxfv579\n+/cTHx+Pc465c+fSrVs3rrnmGgYMGMDVV19Nq1atAj28d999Nx999BFVqlThxRdfpFevXjRr1ow7\n7rgjUI9///vfHD58mGeffZbExEQWL15M165dWbNmDe+8805g2/DwY0OT9u3bR2xsLCkpKXz66aeB\n8u3btxMZGcmOHTuoUaMG1apVo1y5coHAPNeMGTNo06YNEydODJRVr16dIUOGsHXrVho0KFFSFhGR\n02LJkqUMH/4UkAD8wNCht/Laa39h2LBh9OzZkwoVKrBu3brjfscVxswYNGgQkyZNIiaBLKl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OoSWyJn2rEkBnMCubX/85/fk5m5m2HDhtGzZ08qVKjAunXrir1uqZkdl4krNTWVd955h6FDhwZu\nnd99992BTFz9+vUjJiaGTZs2BTJx3Xzzzbz88svcd999eTJxxcfH07NnzwLPXb9+fWrWrMnUqVMZ\nPHgwP//8M8nJydSsWfO47X7tTFzgjRkeOHAgzz//PNnZ2XTp0oXs7GxSUlIYNGjQCYcZnEhcXBwP\nPPAAgwYNIisri5kzZ3LhhRcGep0nTZpEdHQ0LVu2pFKlSqxevZq0tLTABDsRESm7FMTKOSU3iUFU\nVGMAoqIac+TIBTz88C0sXbqUcePGERkZSZMmTbjyyivJzMws8DinIxNXZGQks2fPZsqUKYFMXO3a\ntWP48OF5xpQGnzsyMpJnnnmGp59++oxn4sp15513EhMTw8KFC1m0aBFVqlTh4osvPuE6s/nHrBY0\nhrVVq1Z06NCB5557jj179tCuXTtGjx4deL9ly5a8++67LFq0iKysLM4//3zGjh3LZZddVqy6i4hI\n6LJjc6bKFjNzZfWzycnLzMykc+deeXpic3KS+Oyzt5Wm8CyTlJREXFxcsderFREJZWaGc04zUktA\nY2LlnBIXF8eUKaPIyUniwIF+5OQkMWXKKAWwIiLnsPbt2/Pmm2+WaJ/MzEySk5OLtZKLnB4aTiDn\nnOuvv46OHduTnp5O7dq1FcAW4ejRoxR2R8PMTvuarFomS0TOhAULFpCQkFCifXbv3s3cuXNp164d\ntWrVOk01k6IoiJVzUlxcnILXYujRo0ee1RKCJSQksHjx4tN27jlz5py2Y4uIBCvuRN789Id26dKY\nWBEp1JYtWwpdEiwyMjKwfJiIyNlg/PjxbNmyhaSkJKZNm8YPP/xAu3bt+OMf/8iePXuYOHEiGzZs\nIDExkXHjxtGoUSPAG04wcuRIevfuDXhj8mNjY7nqqqt44YUXyMzMpHXr1owdO5bq1auTnp5Ot27d\ncsexAl5Au3LlypOuu8bElpx6YqXENOHm3KEgVURCzY4dO5gzZw5Dhgzh0KFDPPPMM0yYMIH09HR6\n9uzJHXfcwcyZMxkzZgx//etfCz3Ohg0b2LVrF8OHDycrK4tnn32WiRMnMnXqVKpVq8aECRMYO3Ys\njzzyCE2aNDmDn1ByKYgVERGRMmPfvn15xrhu3ryZV155hfHjx/Pb3/4W8FJ2P/DAA2zbto169eoV\neJyff/6ZadOmUalSJcDLTDhlyhSys7MpV65coBe3fv36Jz0cQU6NVicQERGRMiMhISHPJK06deoA\n0K5duzxlzjkyMjIKPc6FF14YCGABGjRoAMBPP/30a1dZTpKC2DJq/Pjx9O/fn5SUFPr06cOll17K\nAw88wP79+0lLS2Pw4MH85je/oX///nzzzTeB/ZxzLFiwgJ49e9K5c2d69epVrJSiq1at4s4776RL\nly507dqVp59+moMHDwbeT01NpX379qSmpjJq1Cguu+wyevTowVtvvXVaPr+IiJybgpPDAIGsicHl\nuWXZ2dnFPk5ueu/8Kbyl9CiILcOCxwWNHTuW9evXM2HCBMaMGUPXrl2ZPHkyOTk5jBkzJrDP5MmT\nmT9/Pr169WLatGlceeWVPPHEE6SkpBR6ni1btjBs2DDi4+N55plnSEpKYsmSJYwaNeq4bZ988kku\nuOACnn32Wdq2bcvkyZPZuHHjafn8IiIiUnZpTGwZVtJxQeHh4bz99ts8/vjj3HDDDYA3Y/Onn35i\n7ty5dOnSpcDzzJs3j4SEBJ577rnAciPR0dE88sgjfPnll7Ro0SKwbdeuXRkwYAAAbdu25Z///Cf/\n+Mc/uPDCC0/bdRARkbItMzOT9PT0PHcAT7fi9ObK6aUgtgwr6bigtLQ0wsLCuOKKK8jJyQls0759\ne5YuXYpzrsA18TZu3MjVV1+d572rrrqK8PBw1qxZEwhizYyOHTsGtgkPD6du3bpFjkkSEREpypIl\nSxk+/CkggZ07U2jdutFpO1fw0p21atWifPnyfPDBB1SqVImIiAiaNWt22s4tx1MQW4aVdFzQnj17\nyMnJ4fLLLz/uWGbGzp07qV69+nHv7dy5k6pVq+YpCwsLIzY2ln379hVZp4iICI0vEhGRk5KZmcnw\n4U8RHj6HqKjG7No1jFWr3iQzM/OECW3MLND5UlAHzYnKypUrx6OPPsrcuXMZNGgQOTk5p7ROrJSc\ngtgy5lRuqcTExBAREcGf//znAr+88fHxBe5XrVo1du/enafs6NGj7Nmzh+jo6BLXQ0REpDi8jIIJ\nREU1BqBu3ekcOLCL9PT0QBB70003cdNNN+XZr3bt2nkCzvzBZ0EZA9u2bXvcdl27dqVr166/xkeR\nk6Agtgw51Vsq7dq14+jRo+zfv58OHToUe7/mzZuzYsUKhg4dGgh+P/nkE44ePUqbNm1KVAcREZHi\nql27NvADhw5tJiqqMYcObcYs3S+Xsk5BbBlxKrdUctWrV49bbrmF0aNH079/f5o1a0Z2djZbtmxh\n+/btjB07tsD97rrrLv7whz/w0EMPceutt7Jjxw5mzpzJJZdckmdSl9IAi4jIrykuLo4pU0YxfHgS\nBw7UxiydKVNGFfv/PQltCmLLiPy3VCIiqpKVVSHPLZXCBA8dePjhh6lXrx7vvvsuc+bMoVKlSiQm\nJtK9e/dC92nQoAHTp09n1qxZjBw5kkqVKvHb3/6W++67r9B9ilMuIiJyItdffx0dO7YnPd3rgVUA\ne+6wsto7ZmaurH62gmRmZtK5c69AT+yhQ5vJyUnis8/e1hdaRETkLGdmOOfUq1MCSnZQRuTeUsnJ\nSeLAgX7k5CTploqIiIiUWeqJLWNyVyfQLRUREZHQoZ7YklMQKyIiIlLKFMSWnIYTiIiIiEjIURAr\nIiIiIiFHQayIiIiIhBwFsSIiIiISchTEioiIiEjIURArIiIiIiFHQayIiIiIhBwFsSIiIiISchTE\nioiIiEjIURArIiIiIiFHQayIiIiIhBwFsSIiIiISchTEioiIiEjIURArIiIiIiFHQayIiIiIhBwF\nsSIiIiISchTEioiIiEjIURArIiIiIiFHQayIiIiIhBwFsSIiIiISchTEioiIiEjIURArIiIiIiFH\nQayIiIiIhBwFsSIiIiISchTEioiIiEjIURArIiIiIiFHQayIiIiIhBwFsSIiIiISchTEioiIiEjI\nURArIiIiIiFHQayIiIiIhBwFsSIiIiISchTEioiIiEjIURArIiIiIiFHQayIiIiIhBwFsSIiIiIS\nchTEioiIiEjIKfUg1sweMrOjZhYfVPaImW02s6/M7Lqg8ovNbJ2Z/cfMppZOjUVERESktJVqEGtm\ndYBrgW1BZc2APkAz4LfAbDMz/+3ngbuccxcAF5hZ1zNcZTkDVqxYUdpVkFOg9gttar/QpbaTc01p\n98ROAUbkK+sOLHTOHXHOfQtsBjqYWS2ginNulb/dy0CPM1ZTOWP0izi0qf1Cm9ovdKnt5FxTakGs\nmXUD0pxz6/O9dR6QFvT6e7/sPOC7oPLv/DIREREROcdEnM6Dm9kyoGZwEeCAscBovKEEIiIiIiIl\nYs65M39SsxbAcuAXvMC2Dl6PawdgAIBz7il/2yXAY3jjZv/hnGvml98GXO6cu6eQc5z5DyYiIiJy\nkpxzduKtJFepBLHHVcLsv8DFzrlMM7sQeBXoiDdcYBnQ2DnnzOxzYBiwCvgQmO6cW1Ja9RYRERGR\n0nFahxOUgMPrkcU5t9HM3gA2AoeBIe5YpD0UWABEAR8pgBURERE5N50VPbEiIiIiIiVR2kts/WqU\nNCE0mdlkv33WmNnbZhYd9J7aL4SY2fVm9rXfLg+Xdn3keGZWx8w+MbMNZrbezIb55XFmttTMNpnZ\nx2YWE7RPgd9DKR1mFmZm/zaz9/zXarsQYWYxZvam3x4bzKyj2u/UlIkgVkkTQtpSoLlzrg3emsCP\nAPhjo9V+IcLMwoCZQFegOdDXzJqWbq2kAEeAB51zzYFLgKF+O40CljvnmgCfULzvoZSO+/GG2+VS\n24WOaXhDIZsBrYGvUfudkjIRxKKkCSHLObfcOXfUf/k53koVAN1Q+4WSDsBm59w259xhYCHed1DO\nIs65Hc65Nf7zA8BXeN+57sBL/mYvcew7VeD38IxWWgL8DpsbgBeDitV2IcC/y/gb59x8AL9d9qL2\nOyUhH8QqaUKZMgD4yH+u9gst+dtL7XKWM7P6QBu8Px5rOud+BC/QBWr4mxX2PZTSkdthEzyZRW0X\nGhKBnWY23x8OkmxmFVH7nZKzZXWCIilpQmgrov3GOOfe97cZAxx2zr1eClUUOaeYWWXgLeB+59yB\nAtbV1ozfs4yZ3Qj86JxbY2ZXFLGp2u7sFAFcDAx1zq02syl4Qwn03TsFIRHEOucKDFL9pAn1gbX+\nWJE6wL/NrAPeXy11gzbPTajwPXB+AeVymhTWfrnM7E68W2RXBRUX1k5qv7NTYd83OcuYWQReAPuK\nc26xX/yjmdV0zv3oD9nJ8Mv1fTt7dAG6mdkNQAWgipm9AuxQ24WE7/DuGq/2X7+NF8Tqu3cKQno4\ngXPuS+dcLedcA+dcIt4/kouccxnAe8DvzKycmSUCjYCVfnf9XjPr4Ae+/YHFhZ5ETiszux7v9lg3\n51xW0FvvAbep/ULGKqCRmdUzs3LAbXhtKGefPwMbnXPTgsreA+70n9/Bse9Ugd/DM1VROcY5N9o5\nV9c51wDv+/WJc+524H3Udmc9f8hAmpld4BddDWxA371TEhI9sSWgpAmhZwZQDljmT7z83Dk3RO0X\nWpxzOWZ2L95qE2HAPOfcV6VcLcnHzLoA/YD1ZvYF3u/M0cDTwBtmNgBvlZc+cMLfo3J2eAq1XagY\nBrxqZpHAVuB/gHDUfidNyQ5EREREJOSE9HACERERETk3KYgVERERkZCjIFZEREREQo6CWBEREREJ\nOQpiRURERCTkKIgVERERkZCjIFakDDGzeDP7ws/NnW5m3/nPM83sy1M4bjkzW+Yfq7ef97tpCfbv\nXpLtCzlGbX/dxJPZ92YzG3mS+9Yzs/Uns2+oMbNHSrsOIiLFpXViRcooMxsHHHDO/cnM6gHvO+da\nneSxOgFPOOeuO8F2Yc65owWUzwc+cM69fZLnD3fO5ZzMvqfqVK9dKDGz/c65KmfgPKXWniJSdqgn\nVqTssnyvI/we1C/NbImZlQcwswZm9jczW2Vm/xuUFhH//erAK0B7vye2gZn9w8wu9t/fb2bP+hmg\nOpnZJDPbYGZrzGyymV0CdAMm+/sn5jv+fDN73j//135ueMzsDjNbbGZ/B5YH94j6773t13uTmT0d\ndLzrzSzVP/+yoO1nFHK+G/3yemb2TzNb7T86nfACmz1sZuv83u8n/bI2ZvZ//fO/bWYxfvk/zOxP\n/nk3mFk7//1NZvbHoDp8ZWZ/MbONZvaGmUX5713tX7+1Zvain/UHM/uvmT3uf+a1ue1nZhXNbJ6Z\nfe6/d3Mh1+4pv3wSUME/xyv5PmeYf93W+ee43y9vaF4P/Rr/miX65c+Y2Xp/2z5+2eX+9V2Ml24T\nM+tnZv/PP+fzZpb/36yISOGcc3rooUcZfACPAQ/6z+vhpS5s6b/+K/B7//lyoKH/vAPw9wKOdTnw\nXtDrfwAX+8+PAr385/HA10HbRfs/5wO3FFLP+Xjpg8HLD56Gl4r4DmA7EBP0Gdb5z+8AvgEqA+WB\nb4HzgGr+PnX97WKDtp9+gvNFAeWCylflP2++el8PfAqUz3eutcCl/vPxwJ+Crtkk//kw4Hughn/u\nNCDOP9dRoJO/3TzgQf8zbg9qp5eAYf7z/+KlpAS4B0j2n08MauMYYBNQobBr52+3r5A2uhhYWkC7\nfg5085/nXsNbgI/9shp4qTRr4v0b2h/UNk3x8sOH+69nAX8o7e+NHnroEToP9cSKnDu2Oudyx3am\nAvXNrBLQGXjT70mdgxdwlMQR4B3/+V7goN9T2BM4WMxjvAHgnPsG2IIX4AAsc87tLWSfvzvnDjjn\nsvB69uoBnYD/dc5t94+3pwTnKwe8aGbrgDeBZieo8zXAfP/8OOf2mFk0XtD9qafb9l0AAAMeSURB\nVL/NS8BlQfu85/9cD3zpnMtwzmX7dTjff2+7c+5z//lfgEuBJnjtt6WQ4y7yf6YC9f3n1wGj/HZd\n4X++uv57wdduI961K8pWINHMpplZV2C/mVUGEpxz7/mfP9s5d8iv7+t+WYZ/7vb+cVbmtg1wNV5w\nvMqv41VAgxPUQ0QkIKK0KyAiZ0xW0PMcvF6zMCDTOXfxKRz3kHPOATjncsysA16A0hu4139+IsGD\n8y3o9c9F7BP8eY5y7PdZcW5JF3S+4cAO51wrMwun+AF4SeTW+Sh56+8o/Pdxbl2L+ly5x8oh73Xo\n5ZzbHLyhP0wi/7+FIq+dH6C3BroCg/Ha9oET1ClwyqDnP+crf8k5N6YYxxAROY56YkXOHccFHM65\n/cB/zezWwEZmJZ3AFDiu37Mb65xbgncbPPdY+4HoIo7R2zwNgUS8W98n43PgN+ZNxsLM4kpwvhgg\n3X+/PxAetH1Bwdoy4H/MrELuuZxz+4BMM+vib3M78L8l/Ax1zayj//z3wL/8+tUzs9yeytvxejiL\n8jHesAX8+rUpxrmz/QA+DzOrinfbfxEwFm8oyQEgzcy6+9uU86/Fv4Df+eNoqwO/AVYWcK6/A7f6\n22BmcWZWt4DtREQKpCBW5NxR2FIkfwDu8ifnfIk3Caskxwp+XgX4wMzWAv/E690EWAiM8CcY5ZnY\n5duOF+h8CCT5t9hLIrcneCcwCFjk36JeWMj2BZ1vNnCnv98F5O01PO7aOec+xhsesNrM/g085L91\nJ/Csma0BWgNPFHaMQo6/CRhqZhuBWOAF/7b//wBv+dc2B2/oR1HH/SMQ6U/G+jKoHkWdOxlYn39i\nF9544xX+tXkFGOWX9weG+XVKAWr6ge56vLHBy4ER/rCCvCd17iu8gHipv/9SoFYhdRQROY6W2BKR\nUmXe8lvvO+feOeHGIXi+kvB7kD9wzrUs7bqIiJzt1BMrIqXtTP8lfbb/5X62109E5KygnlgRERER\nCTnqiRURERGRkKMgVkRERERCjoJYEREREQk5CmJFREREJOQoiBURERGRkKMgVkRERERCzv8HKT+1\nBBp2WkEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116537710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第一主成分と第二主成分でプロットする\n",
    "plt.figure(figsize=(10, 10))\n",
    "for x, y, name in zip(feature[:, 0], feature[:, 1], val_names):\n",
    "    plt.text(x, y, name, alpha=0.8, size=15)\n",
    "plt.scatter(feature[:, 0], feature[:, 1], alpha=0.8)\n",
    "plt.title(\"Principal Component Analysis\")\n",
    "plt.xlabel(\"The first principal component score\")\n",
    "plt.ylabel(\"The second principal component score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### 10.5　行と列を入れ替えて主成分分析\n",
    "__課題10.5__:　上記の matrix の列と行を入れ替えた matrixT を作成し、主成分分析を行い、第一主成分と第二主成分でプロットしてください。また、得られたプロットに対して考察を行なってください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10.6 クラスター分析\n",
    "* ここでは、クラスター分析の一種である階層的クラスタリングを行ないます。\n",
    "    * [クラスタリング (クラスター分析)](http://www.kamishima.net/jp/clustering/)\n",
    "    * [階層的クラスタリングとは](http://image.slidesharecdn.com/140914-intel-l-141004040412-conversion-gate02/95/-12-638.jpg) \n",
    "\n",
    "まずは以下のコードを実行してください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import linkage, dendrogram #必要なライブラリをインポート\n",
    "# metric は色々あるので、ケースバイケースでどれかひとつ好きなものを選ぶ。\n",
    "# method も色々あるので、ケースバイケースでどれかひとつ好きなものを選ぶ。\n",
    "result1 = linkage(normalize2(matrix), \n",
    "                  metric = 'braycurtis', \n",
    "                  #metric = 'canberra', \n",
    "                  #metric = 'chebyshev', \n",
    "                  #metric = 'cityblock', \n",
    "                  #metric = 'correlation', \n",
    "                  #metric = 'cosine', \n",
    "                  #metric = 'euclidean', \n",
    "                  #metric = 'hamming', \n",
    "                  #metric = 'jaccard', \n",
    "                  #method= 'single')\n",
    "                  method = 'average')\n",
    "                  #method= 'complete')\n",
    "                  #method='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XAJ4n6WOtnoyIr/ShPmZmNiDtAsB0YGVSm7+ZmU0x7QLAAx7sZWY2dbVLA/WZv5nZFNYu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c8a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# デンドログラムの表示\n",
    "dendrogram(result1, labels=val_names, leaf_rotation=90)\n",
    "plt.title(\"Dedrogram\")\n",
    "plt.ylabel(\"Threshold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__課題10.6__：以下の問いに答えてください。\n",
    "* 上記のクラスタリング結果について考察してください。\n",
    "* パラーメーターを変更して、metric は correlation、methodは complete にして階層的クラスタリングを行ない、最初のクラスタリング結果と比較しながら考察してください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__今日の課題提出__：計算結果を.ipynb ファイル（または.jsonファイル）として保存し、指定したアドレスまでメールしてください。メールタイトルは「__アイスクリーム統計学10__」とし、メール本文に学籍番号と氏名を明記のこと。また、感想も書いてくれると喜びます。今後の講義の改善につながるかも知れません。"
   ]
  }
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