PerceptronLearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c50c9acd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0    1    2    3                4\n",
      "0    5.1  3.5  1.4  0.2      Iris-setosa\n",
      "1    4.9  3.0  1.4  0.2      Iris-setosa\n",
      "2    4.7  3.2  1.3  0.2      Iris-setosa\n",
      "3    4.6  3.1  1.5  0.2      Iris-setosa\n",
      "4    5.0  3.6  1.4  0.2      Iris-setosa\n",
      "5    5.4  3.9  1.7  0.4      Iris-setosa\n",
      "6    4.6  3.4  1.4  0.3      Iris-setosa\n",
      "7    5.0  3.4  1.5  0.2      Iris-setosa\n",
      "8    4.4  2.9  1.4  0.2      Iris-setosa\n",
      "9    4.9  3.1  1.5  0.1      Iris-setosa\n",
      "10   5.4  3.7  1.5  0.2      Iris-setosa\n",
      "11   4.8  3.4  1.6  0.2      Iris-setosa\n",
      "12   4.8  3.0  1.4  0.1      Iris-setosa\n",
      "13   4.3  3.0  1.1  0.1      Iris-setosa\n",
      "14   5.8  4.0  1.2  0.2      Iris-setosa\n",
      "15   5.7  4.4  1.5  0.4      Iris-setosa\n",
      "16   5.4  3.9  1.3  0.4      Iris-setosa\n",
      "17   5.1  3.5  1.4  0.3      Iris-setosa\n",
      "18   5.7  3.8  1.7  0.3      Iris-setosa\n",
      "19   5.1  3.8  1.5  0.3      Iris-setosa\n",
      "20   5.4  3.4  1.7  0.2      Iris-setosa\n",
      "21   5.1  3.7  1.5  0.4      Iris-setosa\n",
      "22   4.6  3.6  1.0  0.2      Iris-setosa\n",
      "23   5.1  3.3  1.7  0.5      Iris-setosa\n",
      "24   4.8  3.4  1.9  0.2      Iris-setosa\n",
      "25   5.0  3.0  1.6  0.2      Iris-setosa\n",
      "26   5.0  3.4  1.6  0.4      Iris-setosa\n",
      "27   5.2  3.5  1.5  0.2      Iris-setosa\n",
      "28   5.2  3.4  1.4  0.2      Iris-setosa\n",
      "29   4.7  3.2  1.6  0.2      Iris-setosa\n",
      "30   4.8  3.1  1.6  0.2      Iris-setosa\n",
      "31   5.4  3.4  1.5  0.4      Iris-setosa\n",
      "32   5.2  4.1  1.5  0.1      Iris-setosa\n",
      "33   5.5  4.2  1.4  0.2      Iris-setosa\n",
      "34   4.9  3.1  1.5  0.1      Iris-setosa\n",
      "35   5.0  3.2  1.2  0.2      Iris-setosa\n",
      "36   5.5  3.5  1.3  0.2      Iris-setosa\n",
      "37   4.9  3.1  1.5  0.1      Iris-setosa\n",
      "38   4.4  3.0  1.3  0.2      Iris-setosa\n",
      "39   5.1  3.4  1.5  0.2      Iris-setosa\n",
      "40   5.0  3.5  1.3  0.3      Iris-setosa\n",
      "41   4.5  2.3  1.3  0.3      Iris-setosa\n",
      "42   4.4  3.2  1.3  0.2      Iris-setosa\n",
      "43   5.0  3.5  1.6  0.6      Iris-setosa\n",
      "44   5.1  3.8  1.9  0.4      Iris-setosa\n",
      "45   4.8  3.0  1.4  0.3      Iris-setosa\n",
      "46   5.1  3.8  1.6  0.2      Iris-setosa\n",
      "47   4.6  3.2  1.4  0.2      Iris-setosa\n",
      "48   5.3  3.7  1.5  0.2      Iris-setosa\n",
      "49   5.0  3.3  1.4  0.2      Iris-setosa\n",
      "50   7.0  3.2  4.7  1.4  Iris-versicolor\n",
      "51   6.4  3.2  4.5  1.5  Iris-versicolor\n",
      "52   6.9  3.1  4.9  1.5  Iris-versicolor\n",
      "53   5.5  2.3  4.0  1.3  Iris-versicolor\n",
      "54   6.5  2.8  4.6  1.5  Iris-versicolor\n",
      "55   5.7  2.8  4.5  1.3  Iris-versicolor\n",
      "56   6.3  3.3  4.7  1.6  Iris-versicolor\n",
      "57   4.9  2.4  3.3  1.0  Iris-versicolor\n",
      "58   6.6  2.9  4.6  1.3  Iris-versicolor\n",
      "59   5.2  2.7  3.9  1.4  Iris-versicolor\n",
      "60   5.0  2.0  3.5  1.0  Iris-versicolor\n",
      "61   5.9  3.0  4.2  1.5  Iris-versicolor\n",
      "62   6.0  2.2  4.0  1.0  Iris-versicolor\n",
      "63   6.1  2.9  4.7  1.4  Iris-versicolor\n",
      "64   5.6  2.9  3.6  1.3  Iris-versicolor\n",
      "65   6.7  3.1  4.4  1.4  Iris-versicolor\n",
      "66   5.6  3.0  4.5  1.5  Iris-versicolor\n",
      "67   5.8  2.7  4.1  1.0  Iris-versicolor\n",
      "68   6.2  2.2  4.5  1.5  Iris-versicolor\n",
      "69   5.6  2.5  3.9  1.1  Iris-versicolor\n",
      "70   5.9  3.2  4.8  1.8  Iris-versicolor\n",
      "71   6.1  2.8  4.0  1.3  Iris-versicolor\n",
      "72   6.3  2.5  4.9  1.5  Iris-versicolor\n",
      "73   6.1  2.8  4.7  1.2  Iris-versicolor\n",
      "74   6.4  2.9  4.3  1.3  Iris-versicolor\n",
      "75   6.6  3.0  4.4  1.4  Iris-versicolor\n",
      "76   6.8  2.8  4.8  1.4  Iris-versicolor\n",
      "77   6.7  3.0  5.0  1.7  Iris-versicolor\n",
      "78   6.0  2.9  4.5  1.5  Iris-versicolor\n",
      "79   5.7  2.6  3.5  1.0  Iris-versicolor\n",
      "80   5.5  2.4  3.8  1.1  Iris-versicolor\n",
      "81   5.5  2.4  3.7  1.0  Iris-versicolor\n",
      "82   5.8  2.7  3.9  1.2  Iris-versicolor\n",
      "83   6.0  2.7  5.1  1.6  Iris-versicolor\n",
      "84   5.4  3.0  4.5  1.5  Iris-versicolor\n",
      "85   6.0  3.4  4.5  1.6  Iris-versicolor\n",
      "86   6.7  3.1  4.7  1.5  Iris-versicolor\n",
      "87   6.3  2.3  4.4  1.3  Iris-versicolor\n",
      "88   5.6  3.0  4.1  1.3  Iris-versicolor\n",
      "89   5.5  2.5  4.0  1.3  Iris-versicolor\n",
      "90   5.5  2.6  4.4  1.2  Iris-versicolor\n",
      "91   6.1  3.0  4.6  1.4  Iris-versicolor\n",
      "92   5.8  2.6  4.0  1.2  Iris-versicolor\n",
      "93   5.0  2.3  3.3  1.0  Iris-versicolor\n",
      "94   5.6  2.7  4.2  1.3  Iris-versicolor\n",
      "95   5.7  3.0  4.2  1.2  Iris-versicolor\n",
      "96   5.7  2.9  4.2  1.3  Iris-versicolor\n",
      "97   6.2  2.9  4.3  1.3  Iris-versicolor\n",
      "98   5.1  2.5  3.0  1.1  Iris-versicolor\n",
      "99   5.7  2.8  4.1  1.3  Iris-versicolor\n",
      "100  6.3  3.3  6.0  2.5   Iris-virginica\n",
      "101  5.8  2.7  5.1  1.9   Iris-virginica\n",
      "102  7.1  3.0  5.9  2.1   Iris-virginica\n",
      "103  6.3  2.9  5.6  1.8   Iris-virginica\n",
      "104  6.5  3.0  5.8  2.2   Iris-virginica\n",
      "105  7.6  3.0  6.6  2.1   Iris-virginica\n",
      "106  4.9  2.5  4.5  1.7   Iris-virginica\n",
      "107  7.3  2.9  6.3  1.8   Iris-virginica\n",
      "108  6.7  2.5  5.8  1.8   Iris-virginica\n",
      "109  7.2  3.6  6.1  2.5   Iris-virginica\n",
      "110  6.5  3.2  5.1  2.0   Iris-virginica\n",
      "111  6.4  2.7  5.3  1.9   Iris-virginica\n",
      "112  6.8  3.0  5.5  2.1   Iris-virginica\n",
      "113  5.7  2.5  5.0  2.0   Iris-virginica\n",
      "114  5.8  2.8  5.1  2.4   Iris-virginica\n",
      "115  6.4  3.2  5.3  2.3   Iris-virginica\n",
      "116  6.5  3.0  5.5  1.8   Iris-virginica\n",
      "117  7.7  3.8  6.7  2.2   Iris-virginica\n",
      "118  7.7  2.6  6.9  2.3   Iris-virginica\n",
      "119  6.0  2.2  5.0  1.5   Iris-virginica\n",
      "120  6.9  3.2  5.7  2.3   Iris-virginica\n",
      "121  5.6  2.8  4.9  2.0   Iris-virginica\n",
      "122  7.7  2.8  6.7  2.0   Iris-virginica\n",
      "123  6.3  2.7  4.9  1.8   Iris-virginica\n",
      "124  6.7  3.3  5.7  2.1   Iris-virginica\n",
      "125  7.2  3.2  6.0  1.8   Iris-virginica\n",
      "126  6.2  2.8  4.8  1.8   Iris-virginica\n",
      "127  6.1  3.0  4.9  1.8   Iris-virginica\n",
      "128  6.4  2.8  5.6  2.1   Iris-virginica\n",
      "129  7.2  3.0  5.8  1.6   Iris-virginica\n",
      "130  7.4  2.8  6.1  1.9   Iris-virginica\n",
      "131  7.9  3.8  6.4  2.0   Iris-virginica\n",
      "132  6.4  2.8  5.6  2.2   Iris-virginica\n",
      "133  6.3  2.8  5.1  1.5   Iris-virginica\n",
      "134  6.1  2.6  5.6  1.4   Iris-virginica\n",
      "135  7.7  3.0  6.1  2.3   Iris-virginica\n",
      "136  6.3  3.4  5.6  2.4   Iris-virginica\n",
      "137  6.4  3.1  5.5  1.8   Iris-virginica\n",
      "138  6.0  3.0  4.8  1.8   Iris-virginica\n",
      "139  6.9  3.1  5.4  2.1   Iris-virginica\n",
      "140  6.7  3.1  5.6  2.4   Iris-virginica\n",
      "141  6.9  3.1  5.1  2.3   Iris-virginica\n",
      "142  5.8  2.7  5.1  1.9   Iris-virginica\n",
      "143  6.8  3.2  5.9  2.3   Iris-virginica\n",
      "144  6.7  3.3  5.7  2.5   Iris-virginica\n",
      "145  6.7  3.0  5.2  2.3   Iris-virginica\n",
      "146  6.3  2.5  5.0  1.9   Iris-virginica\n",
      "147  6.5  3.0  5.2  2.0   Iris-virginica\n",
      "148  6.2  3.4  5.4  2.3   Iris-virginica\n",
      "149  5.9  3.0  5.1  1.8   Iris-virginica\n"
     ]
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\n",
    "    'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',\n",
    "    header=None\n",
    ")\n",
    "\n",
    "# 全件表示する\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c19b296a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y\n",
      " [-1 -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 -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  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  1\n",
      "  1  1  1  1]\n",
      "X\n",
      " [[5.1 1.4]\n",
      " [4.9 1.4]\n",
      " [4.7 1.3]\n",
      " [4.6 1.5]\n",
      " [5.  1.4]\n",
      " [5.4 1.7]\n",
      " [4.6 1.4]\n",
      " [5.  1.5]\n",
      " [4.4 1.4]\n",
      " [4.9 1.5]\n",
      " [5.4 1.5]\n",
      " [4.8 1.6]\n",
      " [4.8 1.4]\n",
      " [4.3 1.1]\n",
      " [5.8 1.2]\n",
      " [5.7 1.5]\n",
      " [5.4 1.3]\n",
      " [5.1 1.4]\n",
      " [5.7 1.7]\n",
      " [5.1 1.5]\n",
      " [5.4 1.7]\n",
      " [5.1 1.5]\n",
      " [4.6 1. ]\n",
      " [5.1 1.7]\n",
      " [4.8 1.9]\n",
      " [5.  1.6]\n",
      " [5.  1.6]\n",
      " [5.2 1.5]\n",
      " [5.2 1.4]\n",
      " [4.7 1.6]\n",
      " [4.8 1.6]\n",
      " [5.4 1.5]\n",
      " [5.2 1.5]\n",
      " [5.5 1.4]\n",
      " [4.9 1.5]\n",
      " [5.  1.2]\n",
      " [5.5 1.3]\n",
      " [4.9 1.5]\n",
      " [4.4 1.3]\n",
      " [5.1 1.5]\n",
      " [5.  1.3]\n",
      " [4.5 1.3]\n",
      " [4.4 1.3]\n",
      " [5.  1.6]\n",
      " [5.1 1.9]\n",
      " [4.8 1.4]\n",
      " [5.1 1.6]\n",
      " [4.6 1.4]\n",
      " [5.3 1.5]\n",
      " [5.  1.4]\n",
      " [7.  4.7]\n",
      " [6.4 4.5]\n",
      " [6.9 4.9]\n",
      " [5.5 4. ]\n",
      " [6.5 4.6]\n",
      " [5.7 4.5]\n",
      " [6.3 4.7]\n",
      " [4.9 3.3]\n",
      " [6.6 4.6]\n",
      " [5.2 3.9]\n",
      " [5.  3.5]\n",
      " [5.9 4.2]\n",
      " [6.  4. ]\n",
      " [6.1 4.7]\n",
      " [5.6 3.6]\n",
      " [6.7 4.4]\n",
      " [5.6 4.5]\n",
      " [5.8 4.1]\n",
      " [6.2 4.5]\n",
      " [5.6 3.9]\n",
      " [5.9 4.8]\n",
      " [6.1 4. ]\n",
      " [6.3 4.9]\n",
      " [6.1 4.7]\n",
      " [6.4 4.3]\n",
      " [6.6 4.4]\n",
      " [6.8 4.8]\n",
      " [6.7 5. ]\n",
      " [6.  4.5]\n",
      " [5.7 3.5]\n",
      " [5.5 3.8]\n",
      " [5.5 3.7]\n",
      " [5.8 3.9]\n",
      " [6.  5.1]\n",
      " [5.4 4.5]\n",
      " [6.  4.5]\n",
      " [6.7 4.7]\n",
      " [6.3 4.4]\n",
      " [5.6 4.1]\n",
      " [5.5 4. ]\n",
      " [5.5 4.4]\n",
      " [6.1 4.6]\n",
      " [5.8 4. ]\n",
      " [5.  3.3]\n",
      " [5.6 4.2]\n",
      " [5.7 4.2]\n",
      " [5.7 4.2]\n",
      " [6.2 4.3]\n",
      " [5.1 3. ]\n",
      " [5.7 4.1]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# y: 目的変数 Iris-setosa=-1, Iris-versicolor=1\n",
    "# [1, 1, 1, ..., -1, -1, -1...]\n",
    "y = df.iloc[0:100, 4].values\n",
    "y = np.where(y == 'Iris-setosa', -1, 1)\n",
    "\n",
    "# X: トレーニング変数 [がくの長さ, 花弁の長さ]の一覧\n",
    "# [[5.1, 1.4], [4.9, 1.4], [4.7, 1.3], ...]\n",
    "X = df.iloc[0:100, [0, 2]].values\n",
    "\n",
    "print('y\\n', y)\n",
    "print('X\\n', X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d4430ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "グラフ表示(散布図)\n",
    "\"\"\"\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data_count = 50\n",
    "\n",
    "plt.scatter(X[:data_count, 0], X[:data_count, 1], color='red', marker='o', label='setona')\n",
    "plt.scatter(X[50:50 + data_count, 0], X[50:50 + data_count, 1], color='blue', marker='x', label='versicolor')\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.ylabel('petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ef51a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Perceptronの処理クラス\n",
    "fit()で学習し、predict()で予測できるようになる\n",
    "\"\"\"\n",
    "\n",
    "class Perceptron(object):\n",
    "    # コンストラクタ\n",
    "    def __init__(self, eta=0.01, n_iter=10):\n",
    "        # 学習率 0.0〜1.0\n",
    "        self.eta = eta\n",
    "        \n",
    "        # トレーニング回数\n",
    "        self.n_iter = n_iter\n",
    "        \n",
    "    # トレーニング\n",
    "    def fit(self, X, y):\n",
    "        # X: トレーニング変数 今回は、2　x　N\n",
    "        # y: 目的変数\n",
    "\n",
    "        # 重み(トレーニング結果)\n",
    "        self.w_ = np.zeros(1 + X.shape[1])\n",
    "        \n",
    "        # トレーニングN回目のエラー回数（誤分類回数）\n",
    "        self.errors_ = []\n",
    "        \n",
    "        for _ in range(self.n_iter):\n",
    "            errors = 0\n",
    "            for xi, target in zip(X, y):\n",
    "                # 予測値が正解していたら0, 外していたら符号 * 2 * 学習率\n",
    "                update = self.eta * (target - self.predict(xi))\n",
    "                \n",
    "                # 重みの更新\n",
    "                self.w_[1:] += update * xi\n",
    "                self.w_[0] += update\n",
    "                \n",
    "                errors += int(update != 0.0)\n",
    "            self.errors_.append(errors)\n",
    "\n",
    "        return self\n",
    "\n",
    "    # 総入力を計算\n",
    "    def net_input(self, X):\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "    \n",
    "    # 分類の予測\n",
    "    # 特徴量（X)を元に、一致なら1、不一致なら-1を返却する\n",
    "    def predict(self, X):\n",
    "        return np.where(self.net_input(X) >= 0.0, 1, -1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "06192581",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb6a2d895b0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Perceptronの処理実行\n",
    "\"\"\"\n",
    "\n",
    "ppn = Perceptron(eta=0.1, n_iter=10)\n",
    "ppn.fit(X, y)\n",
    "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a315e944",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "グラフ表示関数\n",
    "\"\"\"\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
    "    markers = ('o', 'x')\n",
    "    colors = ('red', 'blue')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "    \n",
    "    print(cmap)\n",
    "    \n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    \n",
    "    xx1, xx2 = np.meshgrid(\n",
    "        np.arange(x1_min, x1_max, resolution),\n",
    "        np.arange(x2_min, x2_max, resolution)\n",
    "    )\n",
    "    Z = classifier.predict(\n",
    "        np.array([\n",
    "            xx1.ravel(),\n",
    "            xx2.ravel(),\n",
    "        ]).T\n",
    "    )\n",
    "    \n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n",
    "    \n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "    \n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(\n",
    "            x=X[y == cl, 0],\n",
    "            y=X[y == cl, 1],\n",
    "            alpha=0.8,c=cmap(idx),\n",
    "            marker=markers[idx],\n",
    "            label=cl\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90d59924",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<matplotlib.colors.ListedColormap object at 0x7fb6906bef70>\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "処理結果のグラフ表示\n",
    "赤背景のデータだとsetosa、青背景のデータだとversicolorとして分類される\n",
    "\"\"\"\n",
    "\n",
    "plot_decision_regions(X, y, classifier=ppn)\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.ylabel('petap length [cm]')\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}