{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 総合実験（３日目）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter Notebookを使った多変量解析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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",
    "* [当学の演習室での使い方](https://raw.githubusercontent.com/maskot1977/-/master/%E6%BC%94%E7%BF%92%E5%AE%A4.txt)\n",
    "* [個人PCでのインストールと始め方](http://www.task-notes.com/entry/20151129/1448794509)\n",
    "* [小寺研究室](https://github.com/maskot1977/-/blob/master/L1%E3%82%BC%E3%83%9F2015%E5%B0%8F%E5%AF%BA%E7%A0%94%E7%A9%B6%E5%AE%A4.pptx.pdf) では、MacOSX上で右記のようにセットアップしています。> [環境構築](https://sites.google.com/site/masaakikotera/8-python/8-1-huan-jing-gou-zhu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 前回の復習\n",
    "* 前回の内容を忘れてしまった人は[総合実験（２日目）](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E7%B7%8F%E5%90%88%E5%AE%9F%E9%A8%93%EF%BC%92%E6%97%A5%E7%9B%AE.ipynb) を見てください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 本実習スタート\n",
    "* 本実習では多変量解析のうち主成分分析（PCA; Principal Component Analysis）を行ないます。\n",
    "   * 主成分分析を知らない人は、右記のリンク参照→ [10分でわかる主成分分析(PCA)](http://www.slideshare.net/takanoriogata1121/10pca-49324044) ・ [主成分分析](http://www.yasunaga-lab.bio.kyutech.ac.jp/EosJ/index.php/%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90)\n",
    "* 各自の画面中の IPython Notebook のセルに順次入力して（コピペ可）、「Shift + Enter」してください。\n",
    "* 最後に、課題を解いてもらいます。課題の結果を、指定する方法で指定するメールアドレスまで送信してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### まずは基本統計量を計算する関数の作成から"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 平均値を求める関数\n",
    "def average(data):\n",
    "    sum = 0.0\n",
    "    n = 0.0\n",
    "    for x in data:\n",
    "        sum += x\n",
    "        n += 1.0\n",
    "    return sum / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 分散を求める関数\n",
    "def variance(data):\n",
    "    ave = average(data)\n",
    "    accum = 0.0\n",
    "    n = 0.0\n",
    "    for x in data:\n",
    "        accum += (x - ave) ** 2.0\n",
    "        n += 1.0\n",
    "    return accum / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 標準偏差を求める関数\n",
    "import math\n",
    "standard_deviation = lambda data: math.sqrt(variance(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# リストの正規化をする（Z値に変換する）関数\n",
    "def normalize(data):\n",
    "    ave = average(data)\n",
    "    std = standard_deviation(data)\n",
    "    list = []\n",
    "    for x in data:\n",
    "        list.append((x - ave) / (std / float(len(data))))\n",
    "    return list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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(data):\n",
    "    list = []\n",
    "    for x in data:\n",
    "        list.append(normalize(x))\n",
    "    return list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 計算に必要ないろんなライブラリのインポート"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "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\n",
    "from pandas.tools import plotting # 高度なプロットを行うツールのインポート"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np #数値計算用ライブラリ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 実データ解析開始\n",
    "* データをダウンロードし、まずはデータの全体像を把握します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sake_dataJ.txt', <httplib.HTTPMessage instance at 0x1164e9ab8>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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/sake_dataJ.txt'\n",
    "# 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, 'sake_dataJ.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pref</th>\n",
       "      <th>Sake</th>\n",
       "      <th>Shochu</th>\n",
       "      <th>Bear</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Whisky</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hokkaido</td>\n",
       "      <td>46476000</td>\n",
       "      <td>50642000</td>\n",
       "      <td>315300000</td>\n",
       "      <td>10488000</td>\n",
       "      <td>9749000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aomori</td>\n",
       "      <td>17273000</td>\n",
       "      <td>11503000</td>\n",
       "      <td>83164000</td>\n",
       "      <td>1774000</td>\n",
       "      <td>3122000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iwate</td>\n",
       "      <td>17120000</td>\n",
       "      <td>10220000</td>\n",
       "      <td>67803000</td>\n",
       "      <td>1458000</td>\n",
       "      <td>1870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miyagi</td>\n",
       "      <td>27859000</td>\n",
       "      <td>11768000</td>\n",
       "      <td>109850000</td>\n",
       "      <td>2824000</td>\n",
       "      <td>5049000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Akita</td>\n",
       "      <td>24153000</td>\n",
       "      <td>6240000</td>\n",
       "      <td>67894000</td>\n",
       "      <td>1242000</td>\n",
       "      <td>2099000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pref      Sake    Shochu       Bear      Wine   Whisky\n",
       "0  Hokkaido  46476000  50642000  315300000  10488000  9749000\n",
       "1    Aomori  17273000  11503000   83164000   1774000  3122000\n",
       "2     Iwate  17120000  10220000   67803000   1458000  1870000\n",
       "3    Miyagi  27859000  11768000  109850000   2824000  5049000\n",
       "4     Akita  24153000   6240000   67894000   1242000  2099000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('sake_dataJ.txt', sep='\\t', na_values=\".\") # データの読み込み\n",
    "pd.DataFrame(df).head() # 先頭N行を表示する。カラムのタイトルも確認する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V84vQQI6dO52VM3bvhpQYrmOSlwejR8MHH8TuNZKJdUYONotfcFnsgs3iF1z1HsghIn9Q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a148e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 下記の関数にカラム名を入力すれば、Scatter Matrix が表示されます。\n",
    "#plotting.scatter_matrix(df[['Sake', 'Shochu', 'Bear', 'Wine', 'Whisky']], figsize=(10, 10)) \n",
    "plotting.scatter_matrix(df[['Sake', 'Shochu', 'Bear', 'Wine', 'Whisky']], figsize=(10, 10), diagonal='kde') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上図を「図1」と呼ぶことにします。課題1と4で、似たような図を作成してもらいます。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 主成分分析\n",
    "#### 行をエントリとし、列を説明変数とする場合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 表形式のデータを、PCAにかけられる形に整形する。\n",
    "# 行をエントリとし、列を説明変数とする場合。\n",
    "x_labels = []\n",
    "y_labels = []\n",
    "data1 = []\n",
    "for i, line in enumerate(open('sake_dataJ.txt')):\n",
    "    if i == 0:\n",
    "        a = line.strip().split(\"\\t\")\n",
    "        x_labels = []\n",
    "        for j, val in enumerate(a):\n",
    "            if j == 0:\n",
    "                continue\n",
    "            else:\n",
    "                x_labels.append(val)\n",
    "    else:\n",
    "        a = line.strip().split(\"\\t\")\n",
    "        b = []\n",
    "        for j, val in enumerate(a):\n",
    "            if j == 0:\n",
    "                y_labels.append(val)\n",
    "            else:\n",
    "                b.append(float(val))\n",
    "        data1.append(b)\n",
    "data1 = normalize2(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, n_components=None, whiten=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#主成分分析の実行\n",
    "pca = PCA()\n",
    "pca.fit(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sake</th>\n",
       "      <th>Shochu</th>\n",
       "      <th>Bear</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Whisky</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.665066</td>\n",
       "      <td>-0.745030</td>\n",
       "      <td>0.020380</td>\n",
       "      <td>0.015191</td>\n",
       "      <td>0.044392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.595199</td>\n",
       "      <td>0.492514</td>\n",
       "      <td>-0.291951</td>\n",
       "      <td>-0.423517</td>\n",
       "      <td>-0.372246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.033083</td>\n",
       "      <td>-0.000164</td>\n",
       "      <td>-0.030030</td>\n",
       "      <td>-0.674053</td>\n",
       "      <td>0.737331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.048279</td>\n",
       "      <td>0.048577</td>\n",
       "      <td>-0.844658</td>\n",
       "      <td>0.407499</td>\n",
       "      <td>0.340303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.447214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sake    Shochu      Bear      Wine    Whisky\n",
       "0  0.665066 -0.745030  0.020380  0.015191  0.044392\n",
       "1  0.595199  0.492514 -0.291951 -0.423517 -0.372246\n",
       "2 -0.033083 -0.000164 -0.030030 -0.674053  0.737331\n",
       "3  0.048279  0.048577 -0.844658  0.407499  0.340303\n",
       "4  0.447214  0.447214  0.447214  0.447214  0.447214"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因子負荷量の確認。左から順に第一変数、第二変数、、、\n",
    "# 上から順に因子1、因子２、、、\n",
    "pd.DataFrame(list(pca.components_), columns=x_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UQ+lVEO3atVP41mJ6JFECFMAikgoaghCpIWbMmEFubi6FhYVl1t97773k5uby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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b6247d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# それぞれの因子にどの説明変数がどの程度用いられているか図示する\n",
    "plt.figure(figsize=(5, 5))\n",
    "for x, y, name in zip(pca.components_[0], pca.components_[1], x_labels):\n",
    "    plt.text(x, y, name, alpha=0.8, size=15)\n",
    "plt.scatter(pca.components_[0], pca.components_[1])\n",
    "plt.title(\"Factor loadings\")\n",
    "plt.xlabel(\"Factor 1\")\n",
    "plt.ylabel(\"Factor 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.84883499643508986,\n",
       " 0.13109085939363213,\n",
       " 0.013441153902092553,\n",
       " 0.006632990269185365,\n",
       " 1.097158765092042e-31]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 主成分に対する因子の寄与率を確認。左から順に第一主成分、第二主成分、、、\n",
    "list(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc85710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 累積寄与率を図示する\n",
    "import matplotlib.ticker as ticker\n",
    "plt.gca().get_xaxis().set_major_locator(ticker.MaxNLocator(integer=True))\n",
    "plt.plot([0] + list( np.cumsum(pca.explained_variance_ratio_)), \"-o\")\n",
    "plt.xlabel(\"Number of principal components\")\n",
    "plt.ylabel(\"Cumulative contribution ratio\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# データを主成分空間に写像 = 次元圧縮\n",
    "feature = pca.transform(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hokkaido</th>\n",
       "      <td>-1.110409</td>\n",
       "      <td>-0.360847</td>\n",
       "      <td>-0.005956</td>\n",
       "      <td>-0.081387</td>\n",
       "      <td>-1.848328e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aomori</th>\n",
       "      <td>-0.333849</td>\n",
       "      <td>0.009345</td>\n",
       "      <td>0.142182</td>\n",
       "      <td>-0.121610</td>\n",
       "      <td>-3.301751e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iwate</th>\n",
       "      <td>-0.056292</td>\n",
       "      <td>0.503782</td>\n",
       "      <td>0.018778</td>\n",
       "      <td>-0.158425</td>\n",
       "      <td>-6.492096e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miyagi</th>\n",
       "      <td>0.408308</td>\n",
       "      <td>0.103275</td>\n",
       "      <td>0.165782</td>\n",
       "      <td>-0.058181</td>\n",
       "      <td>-6.292635e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Akita</th>\n",
       "      <td>1.443781</td>\n",
       "      <td>0.962598</td>\n",
       "      <td>0.042480</td>\n",
       "      <td>0.048677</td>\n",
       "      <td>-3.417578e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yamagata</th>\n",
       "      <td>1.231049</td>\n",
       "      <td>0.744283</td>\n",
       "      <td>0.052815</td>\n",
       "      <td>0.017058</td>\n",
       "      <td>-1.316482e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fukushima</th>\n",
       "      <td>0.860825</td>\n",
       "      <td>0.674256</td>\n",
       "      <td>0.124998</td>\n",
       "      <td>-0.052539</td>\n",
       "      <td>-2.086859e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ibaraki</th>\n",
       "      <td>0.068851</td>\n",
       "      <td>0.176213</td>\n",
       "      <td>0.015612</td>\n",
       "      <td>-0.105348</td>\n",
       "      <td>-4.502962e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tochigi</th>\n",
       "      <td>-0.042050</td>\n",
       "      <td>0.188377</td>\n",
       "      <td>0.022389</td>\n",
       "      <td>-0.118697</td>\n",
       "      <td>-4.399736e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gunma</th>\n",
       "      <td>-0.635379</td>\n",
       "      <td>0.424675</td>\n",
       "      <td>-0.061466</td>\n",
       "      <td>-0.186559</td>\n",
       "      <td>-5.561106e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saitama</th>\n",
       "      <td>-0.701716</td>\n",
       "      <td>-0.169577</td>\n",
       "      <td>-0.010401</td>\n",
       "      <td>-0.107353</td>\n",
       "      <td>-1.087064e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chiba</th>\n",
       "      <td>-0.615565</td>\n",
       "      <td>-0.209105</td>\n",
       "      <td>-0.018429</td>\n",
       "      <td>-0.096634</td>\n",
       "      <td>-7.202464e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokyo</th>\n",
       "      <td>-0.487767</td>\n",
       "      <td>-0.841145</td>\n",
       "      <td>-0.121137</td>\n",
       "      <td>0.043356</td>\n",
       "      <td>-7.148378e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kanagawa</th>\n",
       "      <td>-0.645375</td>\n",
       "      <td>-0.484661</td>\n",
       "      <td>0.043345</td>\n",
       "      <td>-0.049275</td>\n",
       "      <td>-8.301455e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niigata</th>\n",
       "      <td>2.266970</td>\n",
       "      <td>1.107016</td>\n",
       "      <td>-0.038925</td>\n",
       "      <td>0.271340</td>\n",
       "      <td>-7.217689e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toyama</th>\n",
       "      <td>1.449565</td>\n",
       "      <td>0.205898</td>\n",
       "      <td>0.014282</td>\n",
       "      <td>0.128808</td>\n",
       "      <td>-2.186333e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ishikawa</th>\n",
       "      <td>1.233966</td>\n",
       "      <td>-0.036312</td>\n",
       "      <td>0.013812</td>\n",
       "      <td>0.115745</td>\n",
       "      <td>-4.581391e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fukui</th>\n",
       "      <td>0.949785</td>\n",
       "      <td>-0.276469</td>\n",
       "      <td>0.053900</td>\n",
       "      <td>0.097084</td>\n",
       "      <td>-4.603907e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yamanashi</th>\n",
       "      <td>-0.498276</td>\n",
       "      <td>-0.141691</td>\n",
       "      <td>-0.971316</td>\n",
       "      <td>-0.018632</td>\n",
       "      <td>-5.392369e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nagano</th>\n",
       "      <td>0.663382</td>\n",
       "      <td>0.499552</td>\n",
       "      <td>-0.184689</td>\n",
       "      <td>-0.054813</td>\n",
       "      <td>-4.997498e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gifu</th>\n",
       "      <td>0.831055</td>\n",
       "      <td>-0.194526</td>\n",
       "      <td>0.005190</td>\n",
       "      <td>0.062385</td>\n",
       "      <td>-4.194302e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shizuoka</th>\n",
       "      <td>-0.154502</td>\n",
       "      <td>0.047311</td>\n",
       "      <td>-0.024293</td>\n",
       "      <td>-0.106965</td>\n",
       "      <td>-4.391609e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aichi</th>\n",
       "      <td>0.032203</td>\n",
       "      <td>-0.861350</td>\n",
       "      <td>0.024752</td>\n",
       "      <td>0.083958</td>\n",
       "      <td>-3.969445e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mie</th>\n",
       "      <td>0.688121</td>\n",
       "      <td>-0.134592</td>\n",
       "      <td>0.041857</td>\n",
       "      <td>0.025899</td>\n",
       "      <td>-1.808552e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shiga</th>\n",
       "      <td>0.976152</td>\n",
       "      <td>-0.043429</td>\n",
       "      <td>0.004308</td>\n",
       "      <td>0.064238</td>\n",
       "      <td>-8.465224e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kyoto</th>\n",
       "      <td>0.399057</td>\n",
       "      <td>-0.759579</td>\n",
       "      <td>-0.001051</td>\n",
       "      <td>0.106776</td>\n",
       "      <td>-7.035925e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Osaka</th>\n",
       "      <td>-0.070228</td>\n",
       "      <td>-1.072591</td>\n",
       "      <td>0.053289</td>\n",
       "      <td>0.124451</td>\n",
       "      <td>-5.013381e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyogo</th>\n",
       "      <td>0.285716</td>\n",
       "      <td>-0.694121</td>\n",
       "      <td>0.046409</td>\n",
       "      <td>0.074852</td>\n",
       "      <td>-3.553832e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nara</th>\n",
       "      <td>0.354718</td>\n",
       "      <td>-0.663109</td>\n",
       "      <td>0.020464</td>\n",
       "      <td>0.078267</td>\n",
       "      <td>-9.422215e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wakayama</th>\n",
       "      <td>0.803013</td>\n",
       "      <td>-0.297697</td>\n",
       "      <td>0.065230</td>\n",
       "      <td>0.073463</td>\n",
       "      <td>-6.015682e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tottori</th>\n",
       "      <td>1.122545</td>\n",
       "      <td>0.279755</td>\n",
       "      <td>0.028076</td>\n",
       "      <td>0.045588</td>\n",
       "      <td>-5.874166e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimane</th>\n",
       "      <td>1.079170</td>\n",
       "      <td>0.825695</td>\n",
       "      <td>-0.189601</td>\n",
       "      <td>-0.009561</td>\n",
       "      <td>-3.303743e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Okayama</th>\n",
       "      <td>0.552460</td>\n",
       "      <td>0.067643</td>\n",
       "      <td>0.022446</td>\n",
       "      <td>-0.027014</td>\n",
       "      <td>-5.810430e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hiroshima</th>\n",
       "      <td>0.030436</td>\n",
       "      <td>-0.419868</td>\n",
       "      <td>0.024179</td>\n",
       "      <td>-0.011057</td>\n",
       "      <td>-1.522397e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yamaguchi</th>\n",
       "      <td>-0.260092</td>\n",
       "      <td>-0.137450</td>\n",
       "      <td>0.073770</td>\n",
       "      <td>-0.090442</td>\n",
       "      <td>-1.754969e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tokushima</th>\n",
       "      <td>0.601511</td>\n",
       "      <td>-0.179480</td>\n",
       "      <td>0.040923</td>\n",
       "      <td>0.019526</td>\n",
       "      <td>-7.212218e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kagawa</th>\n",
       "      <td>0.489876</td>\n",
       "      <td>-0.310648</td>\n",
       "      <td>0.031411</td>\n",
       "      <td>0.026189</td>\n",
       "      <td>-1.727164e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ehime</th>\n",
       "      <td>0.243787</td>\n",
       "      <td>-0.197108</td>\n",
       "      <td>0.034806</td>\n",
       "      <td>-0.027773</td>\n",
       "      <td>-4.798676e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kochi</th>\n",
       "      <td>0.419732</td>\n",
       "      <td>-0.297082</td>\n",
       "      <td>0.072960</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>-5.649252e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fukuoka</th>\n",
       "      <td>-0.725140</td>\n",
       "      <td>-0.283917</td>\n",
       "      <td>0.034624</td>\n",
       "      <td>-0.090111</td>\n",
       "      <td>-2.988795e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saga</th>\n",
       "      <td>0.469455</td>\n",
       "      <td>0.381240</td>\n",
       "      <td>-0.003591</td>\n",
       "      <td>-0.080336</td>\n",
       "      <td>-1.830035e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nagasaki</th>\n",
       "      <td>-0.760055</td>\n",
       "      <td>0.067560</td>\n",
       "      <td>0.052436</td>\n",
       "      <td>-0.149444</td>\n",
       "      <td>-4.453860e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kumamoto</th>\n",
       "      <td>-2.273080</td>\n",
       "      <td>-0.047393</td>\n",
       "      <td>0.017080</td>\n",
       "      <td>-0.075858</td>\n",
       "      <td>-1.269966e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oita</th>\n",
       "      <td>-1.734213</td>\n",
       "      <td>0.448740</td>\n",
       "      <td>0.144752</td>\n",
       "      <td>-0.190635</td>\n",
       "      <td>-3.334187e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miyazaki</th>\n",
       "      <td>-4.282241</td>\n",
       "      <td>0.738680</td>\n",
       "      <td>0.026392</td>\n",
       "      <td>0.226628</td>\n",
       "      <td>-3.211087e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kagoshima</th>\n",
       "      <td>-4.569257</td>\n",
       "      <td>0.657853</td>\n",
       "      <td>0.055128</td>\n",
       "      <td>0.322384</td>\n",
       "      <td>1.821382e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1         2         3             4\n",
       "Hokkaido  -1.110409 -0.360847 -0.005956 -0.081387 -1.848328e-16\n",
       "Aomori    -0.333849  0.009345  0.142182 -0.121610 -3.301751e-16\n",
       "Iwate     -0.056292  0.503782  0.018778 -0.158425 -6.492096e-16\n",
       "Miyagi     0.408308  0.103275  0.165782 -0.058181 -6.292635e-16\n",
       "Akita      1.443781  0.962598  0.042480  0.048677 -3.417578e-16\n",
       "Yamagata   1.231049  0.744283  0.052815  0.017058 -1.316482e-16\n",
       "Fukushima  0.860825  0.674256  0.124998 -0.052539 -2.086859e-16\n",
       "Ibaraki    0.068851  0.176213  0.015612 -0.105348 -4.502962e-16\n",
       "Tochigi   -0.042050  0.188377  0.022389 -0.118697 -4.399736e-17\n",
       "Gunma     -0.635379  0.424675 -0.061466 -0.186559 -5.561106e-16\n",
       "Saitama   -0.701716 -0.169577 -0.010401 -0.107353 -1.087064e-16\n",
       "Chiba     -0.615565 -0.209105 -0.018429 -0.096634 -7.202464e-16\n",
       "Tokyo     -0.487767 -0.841145 -0.121137  0.043356 -7.148378e-16\n",
       "Kanagawa  -0.645375 -0.484661  0.043345 -0.049275 -8.301455e-16\n",
       "Niigata    2.266970  1.107016 -0.038925  0.271340 -7.217689e-16\n",
       "Toyama     1.449565  0.205898  0.014282  0.128808 -2.186333e-17\n",
       "Ishikawa   1.233966 -0.036312  0.013812  0.115745 -4.581391e-16\n",
       "Fukui      0.949785 -0.276469  0.053900  0.097084 -4.603907e-16\n",
       "Yamanashi -0.498276 -0.141691 -0.971316 -0.018632 -5.392369e-16\n",
       "Nagano     0.663382  0.499552 -0.184689 -0.054813 -4.997498e-16\n",
       "Gifu       0.831055 -0.194526  0.005190  0.062385 -4.194302e-16\n",
       "Shizuoka  -0.154502  0.047311 -0.024293 -0.106965 -4.391609e-16\n",
       "Aichi      0.032203 -0.861350  0.024752  0.083958 -3.969445e-16\n",
       "Mie        0.688121 -0.134592  0.041857  0.025899 -1.808552e-16\n",
       "Shiga      0.976152 -0.043429  0.004308  0.064238 -8.465224e-16\n",
       "Kyoto      0.399057 -0.759579 -0.001051  0.106776 -7.035925e-16\n",
       "Osaka     -0.070228 -1.072591  0.053289  0.124451 -5.013381e-16\n",
       "Hyogo      0.285716 -0.694121  0.046409  0.074852 -3.553832e-16\n",
       "Nara       0.354718 -0.663109  0.020464  0.078267 -9.422215e-16\n",
       "Wakayama   0.803013 -0.297697  0.065230  0.073463 -6.015682e-16\n",
       "Tottori    1.122545  0.279755  0.028076  0.045588 -5.874166e-16\n",
       "Shimane    1.079170  0.825695 -0.189601 -0.009561 -3.303743e-16\n",
       "Okayama    0.552460  0.067643  0.022446 -0.027014 -5.810430e-16\n",
       "Hiroshima  0.030436 -0.419868  0.024179 -0.011057 -1.522397e-16\n",
       "Yamaguchi -0.260092 -0.137450  0.073770 -0.090442 -1.754969e-16\n",
       "Tokushima  0.601511 -0.179480  0.040923  0.019526 -7.212218e-17\n",
       "Kagawa     0.489876 -0.310648  0.031411  0.026189 -1.727164e-16\n",
       "Ehime      0.243787 -0.197108  0.034806 -0.027773 -4.798676e-16\n",
       "Kochi      0.419732 -0.297082  0.072960  0.011976 -5.649252e-16\n",
       "Fukuoka   -0.725140 -0.283917  0.034624 -0.090111 -2.988795e-16\n",
       "Saga       0.469455  0.381240 -0.003591 -0.080336 -1.830035e-17\n",
       "Nagasaki  -0.760055  0.067560  0.052436 -0.149444 -4.453860e-16\n",
       "Kumamoto  -2.273080 -0.047393  0.017080 -0.075858 -1.269966e-16\n",
       "Oita      -1.734213  0.448740  0.144752 -0.190635 -3.334187e-16\n",
       "Miyazaki  -4.282241  0.738680  0.026392  0.226628 -3.211087e-17\n",
       "Kagoshima -4.569257  0.657853  0.055128  0.322384  1.821382e-16"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 次元圧縮後のデータ。左から順に第一主成分、第二主成分、、、\n",
    "pd.DataFrame(feature, index=y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZO3cuc+fOzfDLB+pRExERkdtaixYtmDNnDh988MF1nZc/f37AMYfM19fXmX727FmXchs2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\n5b8M+wnZ91MkK9MZNZHs5fTpayL8Z8LWmdlMM8sFYGYVzWyGmf1oZgvN7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bcaf610>"
      ]
     },
     "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], y_labels):\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": {},
   "source": [
    "上図を「図2」と呼ぶことにします。課題2と5で、似たような図を作成してもらいます。\n",
    "#### 列をエントリとし、行を説明変数とする場合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 表形式のデータを、PCAにかけられる形に整形する。\n",
    "# 列をエントリとし、行を説明変数とする場合。\n",
    "x_labels = []\n",
    "y_labels = []\n",
    "data2 = []\n",
    "for i, line in enumerate(open('sake_dataJ.txt')):\n",
    "    if i == 0:\n",
    "        a = line.strip().split(\"\\t\")\n",
    "        y_labels = []\n",
    "        for j, val in enumerate(a):\n",
    "            if j == 0:\n",
    "                continue\n",
    "            else:\n",
    "                y_labels.append(val)\n",
    "                data2.append([])\n",
    "    else:\n",
    "        a = line.strip().split(\"\\t\")\n",
    "        b = []\n",
    "        for j, val in enumerate(a):\n",
    "            if j == 0:\n",
    "                x_labels.append(val)\n",
    "            else:\n",
    "                data2[j - 1].append(float(val))\n",
    "data2 = normalize2(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, n_components=None, whiten=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#主成分分析の実行\n",
    "pca = PCA()\n",
    "pca.fit(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hokkaido</th>\n",
       "      <th>Aomori</th>\n",
       "      <th>Iwate</th>\n",
       "      <th>Miyagi</th>\n",
       "      <th>Akita</th>\n",
       "      <th>Yamagata</th>\n",
       "      <th>Fukushima</th>\n",
       "      <th>Ibaraki</th>\n",
       "      <th>Tochigi</th>\n",
       "      <th>Gunma</th>\n",
       "      <th>...</th>\n",
       "      <th>Kagawa</th>\n",
       "      <th>Ehime</th>\n",
       "      <th>Kochi</th>\n",
       "      <th>Fukuoka</th>\n",
       "      <th>Saga</th>\n",
       "      <th>Nagasaki</th>\n",
       "      <th>Kumamoto</th>\n",
       "      <th>Oita</th>\n",
       "      <th>Miyazaki</th>\n",
       "      <th>Kagoshima</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.275489</td>\n",
       "      <td>0.039741</td>\n",
       "      <td>0.037039</td>\n",
       "      <td>-0.039969</td>\n",
       "      <td>-0.059777</td>\n",
       "      <td>-0.040114</td>\n",
       "      <td>-0.055869</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>0.022621</td>\n",
       "      <td>0.091066</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016326</td>\n",
       "      <td>-0.017543</td>\n",
       "      <td>-0.014956</td>\n",
       "      <td>0.128462</td>\n",
       "      <td>0.008461</td>\n",
       "      <td>0.076744</td>\n",
       "      <td>0.233482</td>\n",
       "      <td>0.136635</td>\n",
       "      <td>0.306588</td>\n",
       "      <td>0.432275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.116942</td>\n",
       "      <td>0.040067</td>\n",
       "      <td>0.022673</td>\n",
       "      <td>0.024537</td>\n",
       "      <td>-0.008161</td>\n",
       "      <td>0.062057</td>\n",
       "      <td>-0.066715</td>\n",
       "      <td>-0.093032</td>\n",
       "      <td>-0.003837</td>\n",
       "      <td>-0.024243</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100946</td>\n",
       "      <td>0.024591</td>\n",
       "      <td>0.084554</td>\n",
       "      <td>-0.276205</td>\n",
       "      <td>0.068738</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>-0.053999</td>\n",
       "      <td>0.027990</td>\n",
       "      <td>-0.025145</td>\n",
       "      <td>-0.120296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.046757</td>\n",
       "      <td>-0.028055</td>\n",
       "      <td>0.040584</td>\n",
       "      <td>0.060954</td>\n",
       "      <td>0.147034</td>\n",
       "      <td>0.100469</td>\n",
       "      <td>0.164137</td>\n",
       "      <td>0.094025</td>\n",
       "      <td>0.026149</td>\n",
       "      <td>0.056845</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050275</td>\n",
       "      <td>-0.029234</td>\n",
       "      <td>-0.057383</td>\n",
       "      <td>-0.037363</td>\n",
       "      <td>-0.007293</td>\n",
       "      <td>-0.041365</td>\n",
       "      <td>-0.130533</td>\n",
       "      <td>-0.056618</td>\n",
       "      <td>-0.114217</td>\n",
       "      <td>-0.159604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.149324</td>\n",
       "      <td>0.223728</td>\n",
       "      <td>0.034813</td>\n",
       "      <td>0.428843</td>\n",
       "      <td>0.075589</td>\n",
       "      <td>0.152153</td>\n",
       "      <td>0.254214</td>\n",
       "      <td>-0.019647</td>\n",
       "      <td>-0.001778</td>\n",
       "      <td>-0.096611</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002780</td>\n",
       "      <td>-0.045749</td>\n",
       "      <td>0.013399</td>\n",
       "      <td>-0.059985</td>\n",
       "      <td>-0.036428</td>\n",
       "      <td>-0.002992</td>\n",
       "      <td>-0.148868</td>\n",
       "      <td>0.124259</td>\n",
       "      <td>-0.102680</td>\n",
       "      <td>-0.163846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.172858</td>\n",
       "      <td>-0.207267</td>\n",
       "      <td>-0.054197</td>\n",
       "      <td>-0.083138</td>\n",
       "      <td>-0.030749</td>\n",
       "      <td>0.071060</td>\n",
       "      <td>-0.057463</td>\n",
       "      <td>-0.006176</td>\n",
       "      <td>-0.013600</td>\n",
       "      <td>0.038431</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.129569</td>\n",
       "      <td>0.279367</td>\n",
       "      <td>-0.017207</td>\n",
       "      <td>0.214137</td>\n",
       "      <td>-0.035018</td>\n",
       "      <td>-0.128023</td>\n",
       "      <td>-0.260437</td>\n",
       "      <td>-0.016798</td>\n",
       "      <td>0.032803</td>\n",
       "      <td>0.069383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hokkaido    Aomori     Iwate    Miyagi     Akita  Yamagata  Fukushima  \\\n",
       "0  0.275489  0.039741  0.037039 -0.039969 -0.059777 -0.040114  -0.055869   \n",
       "1 -0.116942  0.040067  0.022673  0.024537 -0.008161  0.062057  -0.066715   \n",
       "2 -0.046757 -0.028055  0.040584  0.060954  0.147034  0.100469   0.164137   \n",
       "3  0.149324  0.223728  0.034813  0.428843  0.075589  0.152153   0.254214   \n",
       "4 -0.172858 -0.207267 -0.054197 -0.083138 -0.030749  0.071060  -0.057463   \n",
       "\n",
       "    Ibaraki   Tochigi     Gunma    ...        Kagawa     Ehime     Kochi  \\\n",
       "0  0.000841  0.022621  0.091066    ...     -0.016326 -0.017543 -0.014956   \n",
       "1 -0.093032 -0.003837 -0.024243    ...      0.100946  0.024591  0.084554   \n",
       "2  0.094025  0.026149  0.056845    ...     -0.050275 -0.029234 -0.057383   \n",
       "3 -0.019647 -0.001778 -0.096611    ...     -0.002780 -0.045749  0.013399   \n",
       "4 -0.006176 -0.013600  0.038431    ...     -0.129569  0.279367 -0.017207   \n",
       "\n",
       "    Fukuoka      Saga  Nagasaki  Kumamoto      Oita  Miyazaki  Kagoshima  \n",
       "0  0.128462  0.008461  0.076744  0.233482  0.136635  0.306588   0.432275  \n",
       "1 -0.276205  0.068738  0.005495 -0.053999  0.027990 -0.025145  -0.120296  \n",
       "2 -0.037363 -0.007293 -0.041365 -0.130533 -0.056618 -0.114217  -0.159604  \n",
       "3 -0.059985 -0.036428 -0.002992 -0.148868  0.124259 -0.102680  -0.163846  \n",
       "4  0.214137 -0.035018 -0.128023 -0.260437 -0.016798  0.032803   0.069383  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因子負荷量の確認。左から順に第一変数、第二変数、、、\n",
    "# 上から順に因子1、因子２、、、\n",
    "pd.DataFrame(pca.components_, columns=x_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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SmZmJwWBg8ODB1KxZk+3btzNv3jw8PDw4fvw4tWrVsi1cMGPGDDZv3kxWVhZ169ZlyJAh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\nkP8HGSM9kxloDwYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c05bd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# それぞれの因子にどの説明変数がどの程度用いられているか図示する\n",
    "plt.figure(figsize=(5, 5))\n",
    "for x, y, name in zip(pca.components_[0], pca.components_[1], x_labels):\n",
    "    plt.text(x, y, name, alpha=0.8, size=10)\n",
    "plt.scatter(pca.components_[0], pca.components_[1])\n",
    "plt.title(\"Factor loadings\")\n",
    "plt.xlabel(\"Factor 1\")\n",
    "plt.ylabel(\"Factor 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.65580254639595692,\n",
       " 0.22004551664752053,\n",
       " 0.089448901881823112,\n",
       " 0.034703035074699393,\n",
       " 1.962690226154014e-32]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 主成分に対する因子の寄与率を確認。左から順に第一主成分、第二主成分、、、\n",
    "list(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YAYsWwbnnJh2JiEhuKopkUFoaxhW0aJF0JCIiuangq4kWLYJevaC8HFq1ykJg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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b5f1f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 累積寄与率を図示する\n",
    "import matplotlib.ticker as ticker\n",
    "plt.gca().get_xaxis().set_major_locator(ticker.MaxNLocator(integer=True))\n",
    "plt.plot([0] + list(np.cumsum(pca.explained_variance_ratio_)), \"-o\")\n",
    "plt.xlabel(\"Number of principal components\")\n",
    "plt.ylabel(\"Cumulative contribution ratio\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# データを主成分空間に写像 = 次元圧縮\n",
    "feature = pca.transform(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sake</th>\n",
       "      <td>-83.478816</td>\n",
       "      <td>-41.240646</td>\n",
       "      <td>38.205369</td>\n",
       "      <td>-3.137163</td>\n",
       "      <td>-9.606462e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shochu</th>\n",
       "      <td>139.596385</td>\n",
       "      <td>-32.110658</td>\n",
       "      <td>6.807571</td>\n",
       "      <td>-1.155559</td>\n",
       "      <td>8.106997e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bear</th>\n",
       "      <td>-42.598445</td>\n",
       "      <td>-23.181311</td>\n",
       "      <td>-46.321043</td>\n",
       "      <td>-14.139350</td>\n",
       "      <td>-1.285136e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wine</th>\n",
       "      <td>5.375823</td>\n",
       "      <td>77.307076</td>\n",
       "      <td>11.970727</td>\n",
       "      <td>-14.610832</td>\n",
       "      <td>-1.034849e-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Whisky</th>\n",
       "      <td>-18.894947</td>\n",
       "      <td>19.225538</td>\n",
       "      <td>-10.662626</td>\n",
       "      <td>33.042904</td>\n",
       "      <td>2.958189e-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0          1          2          3             4\n",
       "Sake    -83.478816 -41.240646  38.205369  -3.137163 -9.606462e-15\n",
       "Shochu  139.596385 -32.110658   6.807571  -1.155559  8.106997e-15\n",
       "Bear    -42.598445 -23.181311 -46.321043 -14.139350 -1.285136e-13\n",
       "Wine      5.375823  77.307076  11.970727 -14.610832 -1.034849e-13\n",
       "Whisky  -18.894947  19.225538 -10.662626  33.042904  2.958189e-13"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 次元圧縮後のデータ。左から順に第一主成分、第二主成分、、、\n",
    "pd.DataFrame(feature, index=y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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16EaI5aPaDfvceOONXHfddTz44IMRb0s9f5HKI/yxpwp+Dfucpqw3fZo7dy5/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\nr/9U4PZIO0EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11af93f90>"
      ]
     },
     "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], y_labels):\n",
    "    plt.text(x, y, name, alpha=0.8, size=15)\n",
    "plt.scatter(feature[:, 0], feature[:, 1], alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上図を「図3」と呼ぶことにします。課題3で、似たような図を作成してもらいます。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "----------\n",
    "## 課題\n",
    "新しいノートを開いて、以下の課題を解いてください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* __課題1__：下記リンクのデータを用い、図1のような Scatter Matrix を描いてください。\n",
    "   * https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/sports_dataJt.txt\n",
    "   \n",
    "   ここでは、以下の列を使います。\n",
    "       * 'Student' : 学生のID番号\n",
    "       * '50mRun' : 50m走\n",
    "       * 'longjump': 走り幅跳び\n",
    "       * 'handball': ハンドボール投げ\n",
    "       * 'chinning': 懸垂\n",
    "       * 'sidestep': 反復横跳び\n",
    "       * 'vertump': 垂直跳び\n",
    "       * 'back': 背筋力\n",
    "       * 'grip': 握力（両手平均）\n",
    "       * 'backward': 上体そらし\n",
    "       * 'forward' : 立位体前屈\n",
    "       * 'stepping': 踏み台昇降"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題２__：上記データの主成分分析を行ない、図2のような散布図を作成し、特徴的な成績を残している学生のIDを述べてください。また、その特徴についても考察してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題３__：同様に、上記データの主成分分析を行ない、図３のような散布図を作成し、各項目（50m走など）間の関係について考察してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題４__：下記リンクのデータを用い、図1のような Scatter Matrix を描いてください。\n",
    "   * https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/sbnote_dataJt.txt\n",
    "   \n",
    "      ここでは、以下の列を使います。\n",
    "       * 'Note' : スイスフラン紙幣のID番号\n",
    "       * 'length' : 横幅長\n",
    "       * 'left': 左縦幅長\n",
    "       * 'right': 右縦幅長\n",
    "       * 'bottom': 下枠内長\n",
    "       * 'top': 上枠内長\n",
    "       * 'diagonal': 対角長"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "* __課題５__：上記データの主成分分析を行ない、図2のような散布図を作成し、分布の特徴について考察してください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上の結果を [自分の氏名].ipynb 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「総合実験3日目」とし、メール本文に学籍番号と氏名を明記のこと。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "お疲れ様でした。もし時間が余ったら、[総合実験４日目](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E7%B7%8F%E5%90%88%E5%AE%9F%E9%A8%93%EF%BC%94%E6%97%A5%E7%9B%AE.ipynb)に進んでもらって結構です。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
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