主成分分析.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['load_boston', 'load_breast_cancer', 'load_diabetes', 'load_digits', 'load_files', 'load_iris', 'load_lfw_pairs', 'load_lfw_people', 'load_linnerud', 'load_mlcomp', 'load_sample_image', 'load_sample_images', 'load_svmlight_file', 'load_svmlight_files']\n"
     ]
    }
   ],
   "source": [
    "# sklearn が用意しているお試し用のデータセット\n",
    "print ([i for i in dir(datasets) if i.startswith(\"load\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['make_biclusters', 'make_blobs', 'make_checkerboard', 'make_circles', 'make_classification', 'make_friedman1', 'make_friedman2', 'make_friedman3', 'make_gaussian_quantiles', 'make_hastie_10_2', 'make_low_rank_matrix', 'make_moons', 'make_multilabel_classification', 'make_regression', 'make_s_curve', 'make_sparse_coded_signal', 'make_sparse_spd_matrix', 'make_sparse_uncorrelated', 'make_spd_matrix', 'make_swiss_roll']\n"
     ]
    }
   ],
   "source": [
    "# sklearn が用意している人工データセット\n",
    "print ([i for i in dir(datasets) if i.startswith(\"make\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# そのなかで iris をロードして用いる。\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['target_names', 'data', 'target', 'DESCR', 'feature_names']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iris のデータを確認する。\n",
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML iris datasets.\n",
      "http://archive.ics.uci.edu/ml/datasets/Iris\n",
      "\n",
      "The famous Iris database, first used by Sir R.A Fisher\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      "References\n",
      "----------\n",
      "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# iris のデータの説明文\n",
    "print (iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "# iris のデータは dict 型で与えられている\n",
    "print (iris['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "# ドットで呼び出すこともできる。これは特徴量の名前\n",
    "print (iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('data: ', (150, 4))\n"
     ]
    }
   ],
   "source": [
    "# 特徴量のデータ構造を確認する\n",
    "print(\"data: \",iris.data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
       "       [ 5.4,  3.9,  1.7,  0.4],\n",
       "       [ 4.6,  3.4,  1.4,  0.3],\n",
       "       [ 5. ,  3.4,  1.5,  0.2],\n",
       "       [ 4.4,  2.9,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.4,  3.9,  1.3,  0.4],\n",
       "       [ 5.1,  3.5,  1.4,  0.3],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.8,  1.5,  0.3],\n",
       "       [ 5.4,  3.4,  1.7,  0.2],\n",
       "       [ 5.1,  3.7,  1.5,  0.4],\n",
       "       [ 4.6,  3.6,  1. ,  0.2],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 5. ,  3. ,  1.6,  0.2],\n",
       "       [ 5. ,  3.4,  1.6,  0.4],\n",
       "       [ 5.2,  3.5,  1.5,  0.2],\n",
       "       [ 5.2,  3.4,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.6,  0.2],\n",
       "       [ 4.8,  3.1,  1.6,  0.2],\n",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 5. ,  3.5,  1.3,  0.3],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 4.4,  3.2,  1.3,  0.2],\n",
       "       [ 5. ,  3.5,  1.6,  0.6],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 4.8,  3. ,  1.4,  0.3],\n",
       "       [ 5.1,  3.8,  1.6,  0.2],\n",
       "       [ 4.6,  3.2,  1.4,  0.2],\n",
       "       [ 5.3,  3.7,  1.5,  0.2],\n",
       "       [ 5. ,  3.3,  1.4,  0.2],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.4,  3.2,  4.5,  1.5],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 5.5,  2.3,  4. ,  1.3],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 5.7,  2.8,  4.5,  1.3],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 6.6,  2.9,  4.6,  1.3],\n",
       "       [ 5.2,  2.7,  3.9,  1.4],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 5.9,  3. ,  4.2,  1.5],\n",
       "       [ 6. ,  2.2,  4. ,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  2.9,  3.6,  1.3],\n",
       "       [ 6.7,  3.1,  4.4,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 5.8,  2.7,  4.1,  1. ],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.6,  2.5,  3.9,  1.1],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.1,  2.8,  4. ,  1.3],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.1,  2.8,  4.7,  1.2],\n",
       "       [ 6.4,  2.9,  4.3,  1.3],\n",
       "       [ 6.6,  3. ,  4.4,  1.4],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 5.7,  2.6,  3.5,  1. ],\n",
       "       [ 5.5,  2.4,  3.8,  1.1],\n",
       "       [ 5.5,  2.4,  3.7,  1. ],\n",
       "       [ 5.8,  2.7,  3.9,  1.2],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
       "       [ 5.5,  2.5,  4. ,  1.3],\n",
       "       [ 5.5,  2.6,  4.4,  1.2],\n",
       "       [ 6.1,  3. ,  4.6,  1.4],\n",
       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
       "       [ 5.6,  2.7,  4.2,  1.3],\n",
       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 7.7,  2.8,  6.7,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.7,  3.3,  5.7,  2.1],\n",
       "       [ 7.2,  3.2,  6. ,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 6.4,  2.8,  5.6,  2.1],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.4,  2.8,  6.1,  1.9],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.4,  2.8,  5.6,  2.2],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 7.7,  3. ,  6.1,  2.3],\n",
       "       [ 6.3,  3.4,  5.6,  2.4],\n",
       "       [ 6.4,  3.1,  5.5,  1.8],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.4,  2.1],\n",
       "       [ 6.7,  3.1,  5.6,  2.4],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.8,  3.2,  5.9,  2.3],\n",
       "       [ 6.7,  3.3,  5.7,  2.5],\n",
       "       [ 6.7,  3. ,  5.2,  2.3],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#特徴量を確認する\n",
    "iris.data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#特徴量の最初の５レコードを表示する\n",
    "iris.data[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2\n",
       "5                5.4               3.9                1.7               0.4\n",
       "6                4.6               3.4                1.4               0.3\n",
       "7                5.0               3.4                1.5               0.2\n",
       "8                4.4               2.9                1.4               0.2\n",
       "9                4.9               3.1                1.5               0.1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#特徴量の最初の５レコードをpandasを用いてカッコよく表示する\n",
    "pd.DataFrame(iris.data,columns=iris.feature_names).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.054000           3.758667   \n",
       "std             0.828066          0.433594           1.764420   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)  \n",
       "count        150.000000  \n",
       "mean           1.198667  \n",
       "std            0.763161  \n",
       "min            0.100000  \n",
       "25%            0.300000  \n",
       "50%            1.300000  \n",
       "75%            1.800000  \n",
       "max            2.500000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandasを使って特徴量の統計量を確認する\n",
    "pd.DataFrame(iris.data,columns=iris.feature_names).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], \n",
       "      dtype='|S10')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#クラスラベル（回帰の時は被説明変数）\n",
    "iris.target_names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('target: ', (150,))\n"
     ]
    }
   ],
   "source": [
    "# ラベルのデータ構造を確認\n",
    "print(\"target: \",iris.target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ラベルのデータを確認\n",
    "iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x116dbcb90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6xVuEaMxkoRIrNdab513dXPDtW8LJ3F0Ue59Fd+sBHnxrGO4e6s0Zmvl649ot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\nJIgDmoiISIL+H6/OBjtmU75xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114a06510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第一変数と第二変数でプロットする\n",
    "plt.scatter(iris.data[:, 0], iris.data[:, 1], c=iris.target, s=30, cmap=plt.cm.rainbow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11748c690>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tC7fzUD/rdv8SorzIpayotNyDgkm8azYmG3DwLZ8nYG/G3uTAsQPo7tj0ojDX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\nalU7tAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1149b3b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第一変数と第三変数でプロットする\n",
    "plt.scatter(iris.data[:, 0], iris.data[:, 2], c=iris.target, s=30, cmap=plt.cm.rainbow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x117552f10>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o0aatqk07ZDgJZWKODQqiZctWqjZdn/6k3XTKXgEyuna3WDpm7NWbm0u+Ccgr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\nC1HhFhERsRAVbhEREQv5fxPwZWi4HH3wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174db410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第一変数と第四変数でプロットする\n",
    "plt.scatter(iris.data[:, 0], iris.data[:, 3], c=iris.target, s=30, cmap=plt.cm.rainbow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 主成分分析器を用意する。•\n",
    "# n_components='mle' を指定すると最尤推定により主成分の個数を⾃動的に求める\n",
    "pca = PCA(n_components = 'mle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, n_components='mle', whiten=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#主成分分析の実行\n",
    "pca.fit(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 主成分の数の確認\n",
    "pca.n_components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.92461621,  0.05301557,  0.01718514])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 寄与率を確認。左から順に第一主成分、第二主成分、、、\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.92461621,  0.97763178,  0.99481691])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 累積寄与率を確認\n",
    "np.cumsum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.361590</td>\n",
       "      <td>-0.082269</td>\n",
       "      <td>0.856572</td>\n",
       "      <td>0.358844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.656540</td>\n",
       "      <td>-0.729712</td>\n",
       "      <td>0.175767</td>\n",
       "      <td>0.074706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.580997</td>\n",
       "      <td>-0.596418</td>\n",
       "      <td>-0.072524</td>\n",
       "      <td>-0.549061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0           0.361590         -0.082269           0.856572          0.358844\n",
       "1          -0.656540         -0.729712           0.175767          0.074706\n",
       "2           0.580997         -0.596418          -0.072524         -0.549061"
      ]
     },
     "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",
    "# 上から順に第一主成分、第二主成分、、、\n",
    "pd.DataFrame(pca.components_, columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# データを主成分空間に写像 = 次元圧縮\n",
    "x = pca.transform(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.684207</td>\n",
       "      <td>-0.326607</td>\n",
       "      <td>0.021512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.715391</td>\n",
       "      <td>0.169557</td>\n",
       "      <td>0.203521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.889820</td>\n",
       "      <td>0.137346</td>\n",
       "      <td>-0.024709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.746437</td>\n",
       "      <td>0.311124</td>\n",
       "      <td>-0.037672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.728593</td>\n",
       "      <td>-0.333925</td>\n",
       "      <td>-0.096230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-2.279897</td>\n",
       "      <td>-0.747783</td>\n",
       "      <td>-0.174326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-2.820891</td>\n",
       "      <td>0.082105</td>\n",
       "      <td>-0.264251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2.626482</td>\n",
       "      <td>-0.170405</td>\n",
       "      <td>0.015802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-2.887959</td>\n",
       "      <td>0.570798</td>\n",
       "      <td>-0.027335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-2.673845</td>\n",
       "      <td>0.106692</td>\n",
       "      <td>0.191533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-2.506527</td>\n",
       "      <td>-0.651935</td>\n",
       "      <td>0.069275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-2.613143</td>\n",
       "      <td>-0.021521</td>\n",
       "      <td>-0.107650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-2.787434</td>\n",
       "      <td>0.227740</td>\n",
       "      <td>0.200328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-3.225200</td>\n",
       "      <td>0.503280</td>\n",
       "      <td>-0.068414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-2.643543</td>\n",
       "      <td>-1.186195</td>\n",
       "      <td>0.144506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-2.383869</td>\n",
       "      <td>-1.344754</td>\n",
       "      <td>-0.283731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-2.622526</td>\n",
       "      <td>-0.818090</td>\n",
       "      <td>-0.145316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-2.648323</td>\n",
       "      <td>-0.319137</td>\n",
       "      <td>-0.033394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-2.199078</td>\n",
       "      <td>-0.879244</td>\n",
       "      <td>0.114521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-2.587346</td>\n",
       "      <td>-0.520474</td>\n",
       "      <td>-0.219572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-2.310532</td>\n",
       "      <td>-0.397868</td>\n",
       "      <td>0.233696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-2.543235</td>\n",
       "      <td>-0.440032</td>\n",
       "      <td>-0.214836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-3.215858</td>\n",
       "      <td>-0.141616</td>\n",
       "      <td>-0.299619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-2.303129</td>\n",
       "      <td>-0.105523</td>\n",
       "      <td>-0.045680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-2.356171</td>\n",
       "      <td>0.031210</td>\n",
       "      <td>-0.129408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-2.507917</td>\n",
       "      <td>0.139056</td>\n",
       "      <td>0.247116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-2.469056</td>\n",
       "      <td>-0.137887</td>\n",
       "      <td>-0.101263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-2.562391</td>\n",
       "      <td>-0.374685</td>\n",
       "      <td>0.072359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-2.639821</td>\n",
       "      <td>-0.319290</td>\n",
       "      <td>0.139253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-2.632848</td>\n",
       "      <td>0.190076</td>\n",
       "      <td>-0.046466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2.428167</td>\n",
       "      <td>-0.376782</td>\n",
       "      <td>-0.218649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>1.198097</td>\n",
       "      <td>0.605579</td>\n",
       "      <td>-0.512641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>3.499265</td>\n",
       "      <td>-0.456773</td>\n",
       "      <td>0.576910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1.387668</td>\n",
       "      <td>0.204031</td>\n",
       "      <td>0.063511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2.275854</td>\n",
       "      <td>-0.333387</td>\n",
       "      <td>-0.284678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2.614194</td>\n",
       "      <td>-0.558367</td>\n",
       "      <td>0.208423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>1.257625</td>\n",
       "      <td>0.179137</td>\n",
       "      <td>-0.046978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>1.290670</td>\n",
       "      <td>0.116425</td>\n",
       "      <td>-0.231614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2.122854</td>\n",
       "      <td>0.210855</td>\n",
       "      <td>-0.153516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2.387564</td>\n",
       "      <td>-0.462519</td>\n",
       "      <td>0.452024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2.840961</td>\n",
       "      <td>-0.372743</td>\n",
       "      <td>0.501032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>3.232343</td>\n",
       "      <td>-1.370524</td>\n",
       "      <td>0.118449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2.158738</td>\n",
       "      <td>0.218326</td>\n",
       "      <td>-0.208422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>1.443103</td>\n",
       "      <td>0.143801</td>\n",
       "      <td>0.154083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>1.779640</td>\n",
       "      <td>0.501465</td>\n",
       "      <td>0.175811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>3.076522</td>\n",
       "      <td>-0.685764</td>\n",
       "      <td>0.336423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2.144987</td>\n",
       "      <td>-0.138907</td>\n",
       "      <td>-0.734185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1.904863</td>\n",
       "      <td>-0.048048</td>\n",
       "      <td>-0.160471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>1.168853</td>\n",
       "      <td>0.164502</td>\n",
       "      <td>-0.282461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2.107654</td>\n",
       "      <td>-0.371482</td>\n",
       "      <td>-0.027438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2.314303</td>\n",
       "      <td>-0.182609</td>\n",
       "      <td>-0.322860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1.922451</td>\n",
       "      <td>-0.409271</td>\n",
       "      <td>-0.115493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1.414072</td>\n",
       "      <td>0.574925</td>\n",
       "      <td>-0.296398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2.563323</td>\n",
       "      <td>-0.275975</td>\n",
       "      <td>-0.291254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2.419391</td>\n",
       "      <td>-0.303504</td>\n",
       "      <td>-0.504303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.944017</td>\n",
       "      <td>-0.187415</td>\n",
       "      <td>-0.179303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>1.525664</td>\n",
       "      <td>0.375021</td>\n",
       "      <td>0.120636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>1.764046</td>\n",
       "      <td>-0.078519</td>\n",
       "      <td>-0.130784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>1.901629</td>\n",
       "      <td>-0.115877</td>\n",
       "      <td>-0.722874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>1.389666</td>\n",
       "      <td>0.282887</td>\n",
       "      <td>-0.362318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2\n",
       "0   -2.684207 -0.326607  0.021512\n",
       "1   -2.715391  0.169557  0.203521\n",
       "2   -2.889820  0.137346 -0.024709\n",
       "3   -2.746437  0.311124 -0.037672\n",
       "4   -2.728593 -0.333925 -0.096230\n",
       "5   -2.279897 -0.747783 -0.174326\n",
       "6   -2.820891  0.082105 -0.264251\n",
       "7   -2.626482 -0.170405  0.015802\n",
       "8   -2.887959  0.570798 -0.027335\n",
       "9   -2.673845  0.106692  0.191533\n",
       "10  -2.506527 -0.651935  0.069275\n",
       "11  -2.613143 -0.021521 -0.107650\n",
       "12  -2.787434  0.227740  0.200328\n",
       "13  -3.225200  0.503280 -0.068414\n",
       "14  -2.643543 -1.186195  0.144506\n",
       "15  -2.383869 -1.344754 -0.283731\n",
       "16  -2.622526 -0.818090 -0.145316\n",
       "17  -2.648323 -0.319137 -0.033394\n",
       "18  -2.199078 -0.879244  0.114521\n",
       "19  -2.587346 -0.520474 -0.219572\n",
       "20  -2.310532 -0.397868  0.233696\n",
       "21  -2.543235 -0.440032 -0.214836\n",
       "22  -3.215858 -0.141616 -0.299619\n",
       "23  -2.303129 -0.105523 -0.045680\n",
       "24  -2.356171  0.031210 -0.129408\n",
       "25  -2.507917  0.139056  0.247116\n",
       "26  -2.469056 -0.137887 -0.101263\n",
       "27  -2.562391 -0.374685  0.072359\n",
       "28  -2.639821 -0.319290  0.139253\n",
       "29  -2.632848  0.190076 -0.046466\n",
       "..        ...       ...       ...\n",
       "120  2.428167 -0.376782 -0.218649\n",
       "121  1.198097  0.605579 -0.512641\n",
       "122  3.499265 -0.456773  0.576910\n",
       "123  1.387668  0.204031  0.063511\n",
       "124  2.275854 -0.333387 -0.284678\n",
       "125  2.614194 -0.558367  0.208423\n",
       "126  1.257625  0.179137 -0.046978\n",
       "127  1.290670  0.116425 -0.231614\n",
       "128  2.122854  0.210855 -0.153516\n",
       "129  2.387564 -0.462519  0.452024\n",
       "130  2.840961 -0.372743  0.501032\n",
       "131  3.232343 -1.370524  0.118449\n",
       "132  2.158738  0.218326 -0.208422\n",
       "133  1.443103  0.143801  0.154083\n",
       "134  1.779640  0.501465  0.175811\n",
       "135  3.076522 -0.685764  0.336423\n",
       "136  2.144987 -0.138907 -0.734185\n",
       "137  1.904863 -0.048048 -0.160471\n",
       "138  1.168853  0.164502 -0.282461\n",
       "139  2.107654 -0.371482 -0.027438\n",
       "140  2.314303 -0.182609 -0.322860\n",
       "141  1.922451 -0.409271 -0.115493\n",
       "142  1.414072  0.574925 -0.296398\n",
       "143  2.563323 -0.275975 -0.291254\n",
       "144  2.419391 -0.303504 -0.504303\n",
       "145  1.944017 -0.187415 -0.179303\n",
       "146  1.525664  0.375021  0.120636\n",
       "147  1.764046 -0.078519 -0.130784\n",
       "148  1.901629 -0.115877 -0.722874\n",
       "149  1.389666  0.282887 -0.362318\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x117750090>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D4o8ILSgAYOCNG/z6xTRuDx+Jm7uHjaMVonJJoq5ApR1l2uuJTty6tImTS2Mx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\niIhYmAq1iIiIhf0/9/Pz3xk3OVYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11756cf10>"
      ]
     },
     "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.scatter(x[:, 0], x[:, 1], c=iris.target, s=30, cmap=plt.cm.rainbow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}