code/ch02/ch02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2장. 간단한 분류 알고리즘 훈련"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**아래 링크를 통해 이 노트북을 주피터 노트북 뷰어(nbviewer.jupyter.org)로 보거나 구글 코랩(colab.research.google.com)에서 실행할 수 있습니다.**\n",
    "\n",
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://nbviewer.jupyter.org/github/rickiepark/python-machine-learning-book-2nd-edition/blob/master/code/ch02/ch02.ipynb\"><img src=\"https://jupyter.org/assets/main-logo.svg\" width=\"28\" />주피터 노트북 뷰어로 보기</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/python-machine-learning-book-2nd-edition/blob/master/code/ch02/ch02.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`watermark`는 주피터 노트북에 사용하는 파이썬 패키지를 출력하기 위한 유틸리티입니다. `watermark` 패키지를 설치하려면 다음 셀의 주석을 제거한 뒤 실행하세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last updated: 2020-05-22 \n",
      "\n",
      "numpy 1.18.4\n",
      "pandas 0.24.2\n",
      "matplotlib 3.2.1\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -u -d -p numpy,pandas,matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 파이썬으로 퍼셉트론 학습 알고리즘 구현하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 객체 지향 퍼셉트론 API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "class Perceptron(object):\n",
    "    \"\"\"퍼셉트론 분류기\n",
    "\n",
    "    매개변수\n",
    "    ------------\n",
    "    eta : float\n",
    "      학습률 (0.0과 1.0 사이)\n",
    "    n_iter : int\n",
    "      훈련 데이터셋 반복 횟수\n",
    "    random_state : int\n",
    "      가중치 무작위 초기화를 위한 난수 생성기 시드\n",
    "\n",
    "    속성\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "      학습된 가중치\n",
    "    errors_ : list\n",
    "      에포크마다 누적된 분류 오류\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=50, random_state=1):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "        self.random_state = random_state\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"훈련 데이터 학습\n",
    "\n",
    "        매개변수\n",
    "        ----------\n",
    "        X : {array-like}, shape = [n_samples, n_features]\n",
    "          n_samples개의 샘플과 n_features개의 특성으로 이루어진 훈련 데이터\n",
    "        y : array-like, shape = [n_samples]\n",
    "          타깃값\n",
    "\n",
    "        반환값\n",
    "        -------\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        rgen = np.random.RandomState(self.random_state)\n",
    "        self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1])\n",
    "        self.errors_ = []\n",
    "\n",
    "        for _ in range(self.n_iter):\n",
    "            errors = 0\n",
    "            for xi, target in zip(X, y):\n",
    "                update = self.eta * (target - self.predict(xi))\n",
    "                self.w_[1:] += update * xi\n",
    "                self.w_[0] += update\n",
    "                errors += int(update != 0.0)\n",
    "            self.errors_.append(errors)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        \"\"\"최종 입력 계산\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"단위 계단 함수를 사용하여 클래스 레이블을 반환합니다\"\"\"\n",
    "        return np.where(self.net_input(X) >= 0.0, 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1 = np.array([1, 2, 3])\n",
    "v2 = 0.5 * v1\n",
    "np.arccos(v1.dot(v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 붓꽃 데이터셋에서 퍼셉트론 훈련하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 붓꽃 데이터셋 읽기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3               4\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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/'\n",
    "        'machine-learning-databases/iris/iris.data', header=None)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 노트:\n",
    "\n",
    "이 깃허브에는 붓꽃 데이터셋이 포함되어 있습니다(이 책에서 사용하는 다른 데이터셋도 모두 포함되어 있습니다). 인터넷에 연결되어 있지 않거나 이따금 UCI 서버(https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data)에 접근할 수 없을 때 사용할 수 있습니다. 이런 경우 로컬 디렉터리에서 붓꽃 데이터를 로드하려면 다음 코드를\n",
    "```python\n",
    "    df = pd.read_csv('https://archive.ics.uci.edu/ml/'\n",
    "        'machine-learning-databases/iris/iris.data', header=None)\n",
    "```\n",
    "다음과 같이 바꿉니다.\n",
    "```python \n",
    "    df = pd.read_csv('your/local/path/to/iris.data', header=None)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 붓꽃 데이터 그래프 그리기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# setosa와 versicolor를 선택합니다\n",
    "y = df.iloc[0:100, 4].values\n",
    "y = np.where(y == 'Iris-setosa', -1, 1)\n",
    "\n",
    "# 꽃받침 길이와 꽃잎 길이를 추출합니다\n",
    "X = df.iloc[0:100, [0, 2]].values\n",
    "\n",
    "# 산점도를 그립니다\n",
    "plt.scatter(X[:50, 0], X[:50, 1],\n",
    "            color='red', marker='o', label='setosa')\n",
    "plt.scatter(X[50:100, 0], X[50:100, 1],\n",
    "            color='blue', marker='x', label='versicolor')\n",
    "\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.ylabel('petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 퍼셉트론 모델 훈련하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "ppn = Perceptron(eta=0.1, n_iter=10)\n",
    "\n",
    "ppn.fit(X, y)\n",
    "\n",
    "plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Number of errors')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결정 경계 그래프 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "\n",
    "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
    "\n",
    "    # 마커와 컬러맵을 설정합니다\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "\n",
    "    # 결정 경계를 그립니다\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",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "                           np.arange(x2_min, x2_max, resolution))\n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha=0.3, cmap=cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "\n",
    "    # 샘플의 산점도를 그립니다\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x=X[y == cl, 0], \n",
    "                    y=X[y == cl, 1],\n",
    "                    alpha=0.8, \n",
    "                    c=colors[idx],\n",
    "                    marker=markers[idx], \n",
    "                    label=cl, \n",
    "                    edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "plot_decision_regions(X, y, classifier=ppn)\n",
    "plt.xlabel('sepal length [cm]')\n",
    "plt.ylabel('petal length [cm]')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 적응형 선형 뉴런과 학습의 수렴"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 파이썬으로 아달린 구현하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AdalineGD(object):\n",
    "    \"\"\"적응형 선형 뉴런 분류기\n",
    "\n",
    "    매개변수\n",
    "    ------------\n",
    "    eta : float\n",
    "      학습률 (0.0과 1.0 사이)\n",
    "    n_iter : int\n",
    "      훈련 데이터셋 반복 횟수\n",
    "    random_state : int\n",
    "      가중치 무작위 초기화를 위한 난수 생성기 시드\n",
    "\n",
    "    속성\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "      학습된 가중치\n",
    "    cost_ : list\n",
    "      에포크마다 누적된 비용 함수의 제곱합\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=50, random_state=1):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "        self.random_state = random_state\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        \"\"\"훈련 데이터 학습\n",
    "\n",
    "        매개변수\n",
    "        ----------\n",
    "        X : {array-like}, shape = [n_samples, n_features]\n",
    "          n_samples 개의 샘플과 n_features 개의 특성으로 이루어진 훈련 데이터\n",
    "        y : array-like, shape = [n_samples]\n",
    "          타깃값\n",
    "\n",
    "        반환값\n",
    "        -------\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        rgen = np.random.RandomState(self.random_state)\n",
    "        self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1])\n",
    "        self.cost_ = []\n",
    "\n",
    "        for i in range(self.n_iter):\n",
    "            net_input = self.net_input(X)\n",
    "            # Please note that the \"activation\" method has no effect\n",
    "            # in the code since it is simply an identity function. We\n",
    "            # could write `output = self.net_input(X)` directly instead.\n",
    "            # The purpose of the activation is more conceptual, i.e.,  \n",
    "            # in the case of logistic regression (as we will see later), \n",
    "            # we could change it to\n",
    "            # a sigmoid function to implement a logistic regression classifier.\n",
    "            output = self.activation(net_input)\n",
    "            errors = (y - output)\n",
    "            self.w_[1:] += self.eta * X.T.dot(errors)\n",
    "            self.w_[0] += self.eta * errors.sum()\n",
    "            cost = (errors**2).sum() / 2.0\n",
    "            self.cost_.append(cost)\n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        \"\"\"최종 입력 계산\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def activation(self, X):\n",
    "        \"\"\"선형 활성화 계산\"\"\"\n",
    "        return X\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"단위 계단 함수를 사용하여 클래스 레이블을 반환합니다\"\"\"\n",
    "        return np.where(self.activation(self.net_input(X)) >= 0.0, 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
    "\n",
    "ada1 = AdalineGD(n_iter=10, eta=0.01).fit(X, y)\n",
    "ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')\n",
    "ax[0].set_xlabel('Epochs')\n",
    "ax[0].set_ylabel('log(Sum-squared-error)')\n",
    "ax[0].set_title('Adaline - Learning rate 0.01')\n",
    "\n",
    "ada2 = AdalineGD(n_iter=10, eta=0.0001).fit(X, y)\n",
    "ax[1].plot(range(1, len(ada2.cost_) + 1), ada2.cost_, marker='o')\n",
    "ax[1].set_xlabel('Epochs')\n",
    "ax[1].set_ylabel('Sum-squared-error')\n",
    "ax[1].set_title('Adaline - Learning rate 0.0001')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 특성 스케일을 조정하여 경사 하강법 결과 향상시키기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 특성을 표준화합니다.\n",
    "X_std = np.copy(X)\n",
    "X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()\n",
    "X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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"
    },
    {
     "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": [
    "ada = AdalineGD(n_iter=15, eta=0.01)\n",
    "ada.fit(X_std, y)\n",
    "\n",
    "plot_decision_regions(X_std, y, classifier=ada)\n",
    "plt.title('Adaline - Gradient Descent')\n",
    "plt.xlabel('sepal length [standardized]')\n",
    "plt.ylabel('petal length [standardized]')\n",
    "plt.legend(loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Sum-squared-error')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 대규모 머신 러닝과 확률적 경사 하강법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AdalineSGD(object):\n",
    "    \"\"\"ADAptive LInear NEuron 분류기\n",
    "\n",
    "    Parameters\n",
    "    ------------\n",
    "    eta : float\n",
    "      학습률 (0.0과 1.0 사이)\n",
    "    n_iter : int\n",
    "      훈련 데이터셋 반복 횟수\n",
    "    shuffle : bool (default: True)\n",
    "      True로 설정하면 같은 반복이 되지 않도록 에포크마다 훈련 데이터를 섞습니다\n",
    "    random_state : int\n",
    "      가중치 무작위 초기화를 위한 난수 생성기 시드\n",
    "\n",
    "    Attributes\n",
    "    -----------\n",
    "    w_ : 1d-array\n",
    "      학습된 가중치\n",
    "    cost_ : list\n",
    "      모든 훈련 샘플에 대해 에포크마다 누적된 평균 비용 함수의 제곱합\n",
    "\n",
    "    \"\"\"\n",
    "    def __init__(self, eta=0.01, n_iter=10, shuffle=True, random_state=None):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "        self.w_initialized = False\n",
    "        self.shuffle = shuffle\n",
    "        self.random_state = random_state\n",
    "        \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"훈련 데이터 학습\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : {array-like}, shape = [n_samples, n_features]\n",
    "          n_samples 개의 샘플과 n_features 개의 특성으로 이루어진 훈련 데이터\n",
    "        y : array-like, shape = [n_samples]\n",
    "          타깃 벡터\n",
    "\n",
    "        반환값\n",
    "        -------\n",
    "        self : object\n",
    "\n",
    "        \"\"\"\n",
    "        self._initialize_weights(X.shape[1])\n",
    "        self.cost_ = []\n",
    "        for i in range(self.n_iter):\n",
    "            if self.shuffle:\n",
    "                X, y = self._shuffle(X, y)\n",
    "            cost = []\n",
    "            for xi, target in zip(X, y):\n",
    "                cost.append(self._update_weights(xi, target))\n",
    "            avg_cost = sum(cost) / len(y)\n",
    "            self.cost_.append(avg_cost)\n",
    "        return self\n",
    "\n",
    "    def partial_fit(self, X, y):\n",
    "        \"\"\"가중치를 다시 초기화하지 않고 훈련 데이터를 학습합니다\"\"\"\n",
    "        if not self.w_initialized:\n",
    "            self._initialize_weights(X.shape[1])\n",
    "        if y.ravel().shape[0] > 1:\n",
    "            for xi, target in zip(X, y):\n",
    "                self._update_weights(xi, target)\n",
    "        else:\n",
    "            self._update_weights(X, y)\n",
    "        return self\n",
    "\n",
    "    def _shuffle(self, X, y):\n",
    "        \"\"\"훈련 데이터를 섞습니다\"\"\"\n",
    "        r = self.rgen.permutation(len(y))\n",
    "        return X[r], y[r]\n",
    "    \n",
    "    def _initialize_weights(self, m):\n",
    "        \"\"\"랜덤한 작은 수로 가중치를 초기화합니다\"\"\"\n",
    "        self.rgen = np.random.RandomState(self.random_state)\n",
    "        self.w_ = self.rgen.normal(loc=0.0, scale=0.01, size=1 + m)\n",
    "        self.w_initialized = True\n",
    "        \n",
    "    def _update_weights(self, xi, target):\n",
    "        \"\"\"아달린 학습 규칙을 적용하여 가중치를 업데이트합니다\"\"\"\n",
    "        output = self.activation(self.net_input(xi))\n",
    "        error = (target - output)\n",
    "        self.w_[1:] += self.eta * xi.dot(error)\n",
    "        self.w_[0] += self.eta * error\n",
    "        cost = 0.5 * error**2\n",
    "        return cost\n",
    "    \n",
    "    def net_input(self, X):\n",
    "        \"\"\"최종 입력 계산\"\"\"\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "\n",
    "    def activation(self, X):\n",
    "        \"\"\"선형 활성화 계산\"\"\"\n",
    "        return X\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"단위 계단 함수를 사용하여 클래스 레이블을 반환합니다\"\"\"\n",
    "        return np.where(self.activation(self.net_input(X)) >= 0.0, 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dVklSleQEvZmcfvnL1JqsnHul1DlmDOy/f6ej6w2DXoOKiK83enHONmbWXSofxNWqr0n1gupSZ+XcK6XO55/vrfeik4Z9DSoiPt38cMysk6o/iCtlvcoytK4lVbZSWrNLnWU7v25RrxffbcXPOsAbgD8VPzsDa7U+NDNrNymVsKo/iA8+OC2PGdOaD9Wy9hqsTlIVw0lOZT2/bjBoC6py/UnSx4E9I+KlYvksYPZgrzOz7rb//qvOm9bKllNZew0OVuocyntR5vPrBjlj8W0IjAOeKpbXL9aZWZfJLTW1a960svYaHEqps957Wtbz6xY5N+qeBvxB0kxJ5wO3A19pbVhm1mxlLTU1q5TWTLmlzpz3tIzn1y0aJqiIOA/YDbgM+CmwR6Pu52ZWLmXulVbWXoP7798/kVQSTaWLee57Wtbz6wYNS3ySBLwV2CYiTpL0aknTImJO68Mzs2ZoRalp5UoYNWrw5RyVD+8bboC99+4rpd1wQ3q+zC2NnPd0qL0i3duvv5w/p+8AewDvK5aXAme2LCIza4lmlppOPx1OOSUlJUj/nnJKWj/UmObPh402gunT0/L06Wl5/vzOfjg3o3w3lF6RZS3BdlJOgtotIj4JvAAQEU/jbuZmXadZpaaVK+G55+DOO/uS1CmnpOXnnutLWrkxbbMNPPUUzJqVlmfNSsvbbNO5Mlgzy3eNSoVDOV6vyenFt0LSGkAASJoADOFP0Mw6rZk34I4aBSecAF/+ckpKhx2W1r/+9Wn9UMp8g5XJKuW+4bSgcspkjbZpdvmuUa9I9/arLedP6ZukDhKbSjoFuBH34jPrKs2+Affqq2Hq1P4tialT0/rhxNas0mNOmSy3lNbM8l0O9/ZbVU4vvguB44BTgceAgyPiklYHZmbNlVNqyhGRSnk//jG8/HJafvnltPzcc0MvRzWr9JhTJhtKKa1Z5buhxO/efv3l9OI7F/hWRJxZte7EiDixlYGZlVUze1o1oxw1FLk34NbroRcBd98NL74Ia68Nm28Ojz2Wlu++O/8m1spyM26IrZxLTpksZ5tmlu9ydGoMxLLLKfG9Azhf0oeq1h3UonjMSq2ZPa2aWY5qpkY99EaNgvXWg112SclJSv/usktaX0lkub3gmnVDbGV/jcpkudu0c0zCToyB2A1yEtQTwN7AoZLOlLQm0KNvl/WyZva0anY5qllye+gdfXS65lTdipg6Na3PPb+KZt0QW9m2UZkst5TWzPJdjnYfrxvk9OJTRCwGDpR0InAdML6VQZmVUTN7WjWzHNVMlR56laRU6aG34459PfQqH/A33rhqOap6YNmhxF5vOXdfOWUyGFoprV1jEnbqeGWX04K6ovKguO70VeDBFsVjVmq5Pa0Gfhuv1dppVjmq2SpJqlp19/HcctRQYm/0fjWrLFfZZs89+2+z5561S2k5v0drnZxefF8csPw/EbFf60IyK6+c8lDu9ZJmlqOaqVLWq1Z9TQrybz7NiT3n/Wp2WS6npeKRHTpv0AQl6cbi36WSllT9LJW0pH0hmpXDwBLS6aenf6uvjQx1BIJG+2q0TbNVX3PacUe4+OL0b/U1qYp6H/K5sedeixvK+9Aortzfj0d26Lx6ExbuWfw7tlUHl/Q94J3AExExtVXHMWuGwUpI0L88lHO9JHdfOds006hRsO66/a85Va5Jrbtu/igRg5XSIgYvA9Z7v3L2lRtX7u/HIzt03qAJStJG9V4YEU/Vez7TTODbwPebsC+zlsuZbbayrvKhBrU/1HL21c7ZbSuOOab/fU+VJDXUkcohr5SW+341qwPBUI6Xs521Tr0/uduAW4t/FwLzgD8Vj29rxsEj4gb6Zuo16wqNPiiHct0o9wO80TbNNjAZDWcajWZ1DW92uS339+ORHTqvXolvawBJ5wCXRcTPi+UDgIMHe51ZLxvqiAA5o0Q0Y96loRyvGZrZNbyZ5bbc43lkh3LIuQ9q94j4aGUhIn4haUYLY+pH0lHAUQATJry6XYc1G5bca0uQeoM9//yqH4pjxvT1Ojv99HSDbKXEVunEsO66qRQ3FDnHa6acElnu+9WscttQjtfu63+2qpwE9aikLwAXFMvvBx5tXUj9RcTZwNkAU6bs6sa1lV7OdaPqshX0/4a+1159vdeqR3WovoF2xx2H1pLKOV6zP3QHK5EN5zpb7r5y5F7X68T1P+svJ0G9D/giacqNAG6gb3ZdM6sh5wJ/o7KVNLR5l+qV79rdK22oJbKhdFlvRrkt97peJ67/WZ+637+KiQq/FRGfiYhdIuINEfHZJvXgQ9JFwO+A7SUtkHRkM/Zr1g2uuqqvpQR9j6+6qm+b3HmXmjE9eTPljOrQiX1Zd6nbgoqIlyVtKWmtiFje7INHhFti1pMq5bv/+Z+0vMEG8MwzafnAA/sSTfW8S2us0Tfv0nvf29dCyi3fNbNMlqOZJTKX23pTTolvPvBbSVcAz1ZWRsQZLYvKrMvl9LxrVFaKyJt3qfrG1eryXfWNra3oXZizTTNLZC639Z6cS6x/Bq4sth1b9WNmNTSaTwn6ylYHHZRaT1L696CD+spWufMuQSoLVhIR9CWLSrlwKGWyss5TZb2nYQsqIr7UjkDMRoKB8ynV63n3jnfAZZf1L1tFpPUVRx+dtrnxxr5tpk6Fd72rb5tKie+KYt6BSrnwiitSwqskq2b1LoT29wi03pQz5fsE4DjgdcA6lfUe0dy6SbtuUM2ZT6ly/MqH+t57933I33BD/84MOfMuVfY32HlXa0bvQvA4ddYeOSW+C4E/AlsDXyLNBXVLC2Mya6p2l6MazacE6UN8/nzYaCOYPj0tT5+elufP77u2lDvv0l/+Altv3b9cuPXWaf1wumCXcZ4q6z05CWrjiDgXWBER10fEhwG3nqwrdGLahJz5lCJgm23gqadg1qy0PGtWWt5mm764cuddqiSjZ55Jy88805e0WjFWnceps3bI6cW3ovj3MUn/QBpFou5I52Zl0e4bVAfOp1Rd7qtckxo1avC4KuW+3JtY662rt34wOb39wOPUWXvktKC+LGk8cAxwLPBd4F9bGpVZE7WzHDVqVGq9bLopHH98Wj7++LT8zDOrlvmaEVdOj8Ch7itn2nTfOGutltOL78ri4WLgLa0Nx6z52nmDakS6/+j661MvuoMPTv+OHp3WV3fOaGZcOT0Cc5V1nirrPfUmLPwWaey9miLi0y2JyKyJ2j1twmClu332Gd40E0M5x5zefkM5j3rLuduYrY56Lahbi3/fDOwAXFwsHwrc28qgzJplsHIUtK4cVemRVz01RKWnXivi6sQ5mrWDokG3G0k3A3tGxEvF8mhgdkTs3ob4+pkyZdc444xbG29oNkC77oOC1H39lltSj7xKqW2jjeBNb1p13qVmxtXOczRrpoMO0m0RsevA9TmdJDYExlUtr1+sM+sa7SpHrVyZktOdd6ak9B//kf698860vrqrebPjcsnNRpqcbuanAX+QdC0gYG/gxFYGZdatRo2CXYvvgU89Bccem1oyO+6Y1g9lDiezXpfTi+88Sb8AditWfS4iHm9tWGbd64ADUu+5Y49Ny1LtCQbbPQW7WbfJnDCaNYCFwNPAdpL2bl1IZt2tMipEtcpoEdXbtHuEC7NukzNY7FeBw4B7gEoFvTL1u1nLdGP5K7f7+FBGuOjG98GsGXKuQR0MbB8RL7Y6GLOKbi1/DaXLd+W56u7oA5NTt74PZs2QU+KbD4xudSBmFd1e/soZ4BUaD7ja7e+D2erKaUE9B8yV9GvglVaUR5KwVmn3AK+t0KjLd24psNvfB7OBZszI3zYnQV1R/Ji1TU75q5vllgJH+vtgI8ecOXDdpYvqb7R0CSxezHGTL+q3+sZBNs/pZn5+boBmzdLOAV47JXcK9pH+Plh3mPG5RSnBDGbxYvYdP5eTtzp38G02BvbdF6bt2X/9QbWbVTm9+KYAp5LG46ue8n2bRq81G0y9nmmVD+Ubblh1OnQYWR/O9UqB7R7o1nrTjBnAfQ2GV12+HIDZB5xaf7sPfAA4rilxQV6J7zzgi8B/kqbbOIL8+6fMVtGoZ9pg06HffXffdOi9wIPA2uqYMweu+6+Mcb2XL2f2Tp9KLZt6pk0DPtCM0LLlJKgxEfFrSYqIh4ATJd0G/HuLY7MRqLpnGvRvFey1V1/PtG22SS2mWbPSNpXp0KdO7a37gDzvktUy4/1zs7Y7bvJFHHjIWvU3mjaNZrZ6miknQb0oaRTwJ0n/AjxCGjDWbMhye6blTofeCzwIbG+Z8blFsODhuttMXOtJLj7v+Yy97dl4kxLLSVCfAdYFPg2cTCrzfaiVQdnIltMzzb3XrNvMmVP/+Xnz4NFf5LV8Zh9wanE9p7flJKitIuIWYBnp+hOSDgV+38rAbOTK6Znm3mtWFhdc0HibSuKZuNaTdbc7bvI1HPjVnFaNkxPkJajPA5dkrDNrKKdnGrj3mrVPvQT06E1/eaX7dD3bjYeTv7NxxtG6u+TWboMmKEkHAH8PTJL0zaqnxgEvtTowG5lye6a595qtrqwRC+6Yy77j5zJt7H21nx8LB35nT9INPNZu9VpQjwK3AgcBt1WtXwr8ayuDspEtp2eae6/ZYGZ8rsFoBfBKJ4OBIxYMdOBON8Fxx+GWTTkNmqAi4g7gDkk/jIgVAJI2BLaIiKfbFaCNTDk909x7rbdccEFRUqvn+ef77tupZ2MyE48TU5nlXIO6RtJBxba3AU9Iuiki3IqyUvB8Sd1hxhGNRyuYvdOnYOLE+ts1ebQCK6+cBDU+IpZI+gjw/Yj4oqQ7Wx2YWQ7Pl9R5OfftQHHvzsevr7/RNCce65OToNaUtDnwXuCEFsdjli1nVAq3pIZvxgzgjsz7dj5zaTEiQSM525glOQnqJOAq4MaIuEXSNsCfWhuWWWOeL2l45syB676RmXh2+lRxLacRJx5rvpzpNi6h6p6niJgPHNLKoMxy9dqIE41GKwC4bma6d6ee4yZflHnDqEtu1jn17oM6KiLOrvfinG0avH5/4BvAGsB3I+K04e7LetNIGnGi0YgFjz4K3DG34WgFE4GLP3N9g5Kbe69Z+dVrQf2bpHr/E0Qap29YCUrSGsCZwNuABcAtkq6IiIzx4c26Z76k3BtGJ671JNuNWTDoJtsBJx/wi8wx2lxys+5XL0FdDxzY4PXXrMaxpwEPFCVDJP0ImA44QVmWMsyXdMEF8OhddW4czbxhlMkUJbdGIxZ4jDbrHfVu1D2ixceeBFT3TV0A7DZwI0lHAUcBTJjw6haHZN2mlSNO5I5YcNzkizhw45tqP599w6iZDZTTi6+jimtcZwNMmbJrdDgcK6Ghjjgx4xMNRisAWLw43bfzN1+qv93rJxYlNycgs2brZIJ6BNiianlysc5sWGZ8bhE88df6Gy1fDhTz7TTiEQvMOqqTCeoWYIqkrUmJ6R+Bf+pgPFZiuffuzD7gVNhuu/obTZuGr+WYlV/DBCVpbdJ9T1tVbx8RJ63OgSPipWIK+atI3cy/FxH3rM4+rTvNOOLeV1o29ew7fm7GnDtOPGYjRU4LahawmDRQ7IvNPHhE/Bz4eTP3aeWRO0YbwOwLc7bznDxmvSQnQU2OCA+72WPqjVgwb16a4jrrhtEDzsm8b8fMrL+cBHWTpNdHxF0tj8ZartFoBQCP/jqV3OoloNk7fSlzjDYnJzMbnnpDHd0FRLHNEZLmk0p8AiIidmxPiJZjzhy47roGGz2Yxmjbd3z9zgbbjYGTz2tUTnPvNjNrrXotqHe2LQprqOFNowseZt/xc5k29r7BtxkLBx6+Vua0CGZmnVVvJImHACT9ICI+WP2cpB8AH6z5QhuSGTNILZt6ipGp605z7RELzGyEybkG9brqhWKQ1ze2JpyRY86cYtqDRhYv9jTXZmY11LsG9XngeGCMpCWV1cByhjmC+Ugx44iM8WyXL09jtL3+wcG3qdxQ6mmuzcxWUa/EdypwqqRTI+LzbYypo3KmuZ641pNc/PHrG+9s2p645GZmNjw5Jb5LJL1hwLrFwEMR8VILYmqJCy5I9+7kmH3AqRn37rijgZlZK+UkqO8AbwDuJJX4Xg/cDYyX9PGIuLqF8fXz7LO1byDNGaMNhjCnyiUAABAZSURBVDLNte/dMTPrtJwE9ShwZGWcPEk7ACeRrtj/FGhbgnrhiSXM+69frbJ+3/ELMsZoA5fbzMy6R06C2q56ENeIuFfSayNivto8n/Y2WwcXn/F8jWc8RpuZ2UiTk6DukfRfwI+K5cOAe4tRzle0LDIzM+tpozK2ORx4APhs8TO/WLcCeEurAjMzs97WsAUVEc8Dpxc/Ay1rekRmZmbkTVj4ZuBEYEv6T1i4TevCMjOzXpdzDepc4F9JExa+3NpwzMzMkpwEtTgiftHySMzMzKrkJKhrJX2NdM/TK1O+R8TtLYvKzMx6Xk6C2q34d9eqdQHs1/xwzMzMkpxefO5KbmZmbZfTi28z4CvAxIg4oBjqaI+IOLfl0VlX2e/oo1lSTK5Ybdz48fzmjDNGzDHNrD1ySnwzgfOAE4rlecDFpN59Zq9Ysngxt44fv8r6XWskkG4+ppm1R85IEptExI+BlQDFFBvubm5mZi2Vk6CelbQxqWMEknYnzQdlZmbWMjklvqOBK4BtJf0WmAC8p6VRmZlZz8vpxXe7pH2A7UkTFt4fER7F3MzMWmrQBCXp3YM8tZ0kIuKnLYrJutS48eNrdk4YV6MTQ7M8tmQJkxYtWvWJ0aNbcrxJhx4KK2p8Pxs9mkcuuaTpx3MvRetl9VpQB9Z5LkgjS5i9ohMfmJuPG9feXnwrVvBIjeQ3qVbSagL3UrReNmiCiogj2hmImZlZtZxefGZmZm2X04vPLOtaSDOvz2w8fTqjI1ZZv0Ji0axZrywvePppdn366VW2WzCko5lZGTlBWZasayFNvD4zOoLHpVXWv2pA0lpj5UpubeM1ITNrn+H04gNwLz4rhVGjRnHvy6sObDJqVIuq16NH105+Leo12ImekWZl4V58Pa4T3Zhzy3cra2wz0PKXXqJWW2l51ePc0qO7dJuVi3vx9bhOdGPOLd/ltIEE7DTI+ldklh7bXcbM4W7m1suyrkFJ+gfgdcA6lXURcdJwDyrpUOBE4G+AaRFx63D3ZWZmI1POfFBnAesCbwG+SxqHb85qHvdu4N3Af6/mfnpWs8pR8xcuZNLChausf77GfhtdC3lu5Upe9eKLq2yz6prG5bsXgVcNsr7acvpP9Vy9vuLllSu5t0ZcrRqS36VCs+bIaUH9bUTsKOnOiPiSpNOBX6zOQSPiPgDVKPNYnmaVftYCHskot+V8sK47alR2+atR+W5tyCoDrgXUan4PTG471Ppby7jGNRwuy5k1R06Zv/Jl+jlJE4EVwOatC6k/SUdJulXSrQv9H9zMrGfktKCulLQB8DXgdlIPvu82epGkX1G7SnNCRMyqsb6miDgbOBtg1ylTWvOVtwvllOZyS03PZ7Qkmlm2yinfBXBHjbgGrsnZ1wpgp1q9Bgcs55QxX4ioWcZcUdVCa+bNw+5mbr0sJ0HNiIgXgUslXUnqKPFCoxdFxFtXNzgbXE5pLqfUtMaoUYypUZZbYzg93DJtO2FCw30J2KnG+WlAoskpK26dcTzIK2Nuvckmjd/TJt487GtW1stySny/qzyIiBcjYnH1OjMzs1aoN5LEq4BJwBhJu9B3a8k4Uq++YZP0LuBbpNl5fyZpbkS8Y3X22YtySnMNNXNkhMx95ZStlgM71ji/5QNXZByz3WWyto9ugXsO2shUr8T3DuBwYDJQ/Re+BDh+dQ4aEZcBl63OPnpdbmmukWZOspe7r5wPzDGjRnFnRpks55jt/oDebMMN2aFG8tushZ183HPQRqJ6I0mcD5wv6ZCIuLSNMZmZmWV1kvitpHOBiRFxgKQdgD0i4twWx9aTsks1bS5ttbtM9vKoUexao0z2cgvLZDly3gf3vDNrjpwEdV7xc0KxPA+4GHCCaoHcUk27S1vtLpNN3nDDUpasct4HX/Mxa46cr6ObRMSPgZUAEfESrRslxszMDMhrQT0raWOKeyQl7Q74yusIUdbeXy6TDY3fLxuJchLU0cAVwLaSfkvqGv6elkZlbVPW3l8ukw2N3y8biRomqIi4XdI+wPake6HujwjPp21mZi2VM93GOsAngD1JZb7Zks6KiIbDHdnQuVRjZpbklPi+DywljfwA8E/AD4BDWxVUL3OpxswsyUlQUyNih6rlayXd26qAzMzMIC9B3S5p94i4GUDSbtSeI866kEuKZlZWOQnqjcBNkv63WH41cL+ku4CIiB1bFp21nEuKZlZWOQlq/5ZHYWZmNkBON/OH2hGImZlZtc6OvGlmZjYIJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyslJygzMyuljiQoSV+T9EdJd0q6TNIGnYjDzMzKq1MtqGuAqRGxIzAP+HyH4jAzs5LqSIKKiKsj4qVi8WZgcifiMDOz8irDNagPA7/odBBmZlYua7Zqx5J+BbyqxlMnRMSsYpsTgJeAC+vs5yjgKIBXT5jQgkjNzKyMWpagIuKt9Z6XdDjwTuDvIiLq7Ods4GyAXadMGXQ7MzMbWVqWoOqRtD9wHLBPRDzXiRjMzKzcOnUN6tvAWOAaSXMlndWhOMzMrKQ60oKKiNd04rhmZtY9ytCLz8zMbBVOUGZmVkpOUGZmVkpOUGZmVkpOUGZmVkpOUGZmVkpOUGZmVkpOUGZmVkqqMwxe6UhaCDzU6TiaYBPgyU4H0Ua9dr7Qe+fs8x3ZWn2+W0bEKqOBd1WCGikk3RoRu3Y6jnbptfOF3jtnn+/I1qnzdYnPzMxKyQnKzMxKyQmqM87udABt1mvnC713zj7fka0j5+trUGZmVkpuQZmZWSk5QZmZWSk5QXWIpK9J+qOkOyVdJmmDTsfUSpIOlXSPpJWSRmz3XEn7S7pf0gOS/q3T8bSapO9JekLS3Z2OpR0kbSHpWkn3Fn/Pn+l0TK0kaR1JcyTdUZzvl9p5fCeozrkGmBoROwLzgM93OJ5Wuxt4N3BDpwNpFUlrAGcCBwA7AO+TtENno2q5mcD+nQ6ijV4CjomIHYDdgU+O8N/xi8B+EbETsDOwv6Td23VwJ6gOiYirI+KlYvFmYHIn42m1iLgvIu7vdBwtNg14ICLmR8Ry4EfA9A7H1FIRcQPwVKfjaJeIeCwibi8eLwXuAyZ1NqrWiWRZsTi6+GlbzzonqHL4MPCLTgdhq20S8HDV8gJG8IdXr5O0FbAL8PvORtJaktaQNBd4ArgmItp2vmu260C9SNKvgFfVeOqEiJhVbHMCqWxwYTtja4Wc8zUbCSStD1wKfDYilnQ6nlaKiJeBnYvr5JdJmhoRbbnm6ATVQhHx1nrPSzoceCfwdzECbkhrdL494BFgi6rlycU6G0EkjSYlpwsj4qedjqddIuIZSdeSrjm2JUG5xNchkvYHjgMOiojnOh2PNcUtwBRJW0taC/hH4IoOx2RNJEnAucB9EXFGp+NpNUkTKj2MJY0B3gb8sV3Hd4LqnG8DY4FrJM2VdFanA2olSe+StADYA/iZpKs6HVOzFZ1e/gW4inTx/McRcU9no2otSRcBvwO2l7RA0pGdjqnF3gx8ENiv+H87V9LfdzqoFtocuFbSnaQvYNdExJXtOriHOjIzs1JyC8rMzErJCcrMzErJCcrMzErJCcrMzErJCcrMzErJCcpGPEn7Slqla+xg65twvIOrBxCVdF2jEdyLWBZL+nmD7Y5vVpzF/pY13qru60+UdGzx+CRJq32zdiUmSdsW3bhXK0brXk5QZs13MGk086GaHRGN7qlpaoIaCiWDfmZExL9HxK+adbyI+HNE7Nys/Vn3cYKyjpO0nqSfFXPO3C3psGL9GyVdL+k2SVdJ2rxYf52kbxTfru+WNK1YP03S7yT9QdJNkrYfYgzfK+a++YOk6cX6wyX9VNIvJf1J0oyq1xwpaV7xmnMkfVvS3wIHAV8r4tu22PzQYrt5kvbKiGdzSTdUneNekk4DxhTrLiy2u7x4f+6RdFTV65dJOqV4T2+WtFmxfuviPbpL0pertl9f0q8l3V48Vzn/rZTmt/o+aXibLSSdUJzHjcD2VfuYKek9knatuon1LklRPL9t8T7eJmm2pNfWi8mMiPCPfzr6AxwCnFO1PJ40rP9NwIRi3WHA94rH11W2B/YG7i4ejwPWLB6/Fbi0eLwvcGWN476yHvgK8IHi8QakObrWAw4H5hcxrQM8RBpvbyLwILBREets4NvF62cC76k6znXA6cXjvwd+VS+WYvkY0iC7AGsAY4vHywa8bqPi3zGkBLJxsRzAgcXjGcAXisdXAB8qHn+ysj/SuJzjisebAA8AArYCVgK7F8+9EbgLWLd4vx8Ajq113sW6rwFfKx7/GphSPN4N+E29mKr2sWzg++Wf3vjxYLFWBncBp0v6KulDerakqcBU0lBQkD6kH6t6zUWQ5iOSNE5pvLCxwPmSppA+oEcPIYa3AwdVrqeQktGri8e/jojFAJLuBbYkfYhfHxFPFesvAbars//KoKK3kT70G7kF+J7SwKSXR8TcQbb7tKR3FY+3AKYAi4DlQOX62m2kMdQgDdVzSPH4B8BXi8cCviJpb1JCmgRsVjz3UETcXDzeC7gsivEjJQ061mDREn4D8Hal0b//Frik+H0CrN0gJutxTlDWcRExT9IbSK2LL0v6NXAZcE9E7DHYy2osnwxcGxHvUpqr57ohhCHgkBgwqaKk3Uizila8zPD+31T2kfX6IvHuDfwDMFPSGRHx/QGx7UtqKe4REc9Juo6UWAFWRETlPRp4zFrjm70fmAC8MSJWSHqwal/PNop3oOILxonA3hHxcnHt6pkY/JqSx1yzVfgalHWcpInAcxFxAakk9AbgfmCCpD2KbUZLel3VyyrXqfYEFhctnPH0TW9x+BDDuAr4lIqv95J2abD9LcA+kjaUtCZ9LQCApaTW3LBJ2hL4a0ScA3yX9J4ArChaVZDO9+kiOb2WNAV5I78ljbIOKSlVjAeeKJLTW0itxFpuAA6WNEbSWODAGrFvQGrhfigiFgJEmjPpL5IOLbaRpJ0axGQ9zgnKyuD1wBylWTu/CHw50pTp7wG+KukOYC6pRFTxgqQ/AGcBlRG0ZwCnFuuH2so5mVQSvFPSPcXyoCLiEdJ1qzmkD9gHgcXF0z8C/m/R2WLb2ntoaF/gjuJcDgO+Uaw/u4jxQuCXwJqS7gNOA26utaMBPgN8UtJd9J/t90Jg12L9hxhkSoVI051fDNxBmgX6lhqbTScluHMqnSWK9e8Hjix+n/cU29WLyXqcRzO3rlOUso6NiFs7HMf6EbGsaEFdRurEcdkw97Uv6Zze2cwYRwJJyyJi/U7HYe3nFpTZ8J1YtA7uBv4CXL4a+1oOTFWDG3V7SdEtfS7w107HYp3hFpSZmZWSW1BmZlZKTlBmZlZKTlBmZlZKTlBmZlZKTlBmZlZK/x/wKi/w9E5KBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "ada = AdalineSGD(n_iter=15, eta=0.01, random_state=1)\n",
    "ada.fit(X_std, y)\n",
    "\n",
    "plot_decision_regions(X_std, y, classifier=ada)\n",
    "plt.title('Adaline - Stochastic Gradient Descent')\n",
    "plt.xlabel('sepal length [standardized]')\n",
    "plt.ylabel('petal length [standardized]')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Average Cost')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}