perceptron_python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "#程式碼取自 博碩, \"Python 機器學習\", ISBN 978-986-434-140-5, by Sebastian Raschka, 為練習Python函式庫用途\n",
    "#僅供履歷作品集使用\n",
    "print \"hello world\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "class Perceptron(object):\n",
    "    def __init__(self, eta=0.01, n_iter=10):\n",
    "        self.eta = eta\n",
    "        self.n_iter = n_iter\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        self.w_ = np.zeros(1 + X.shape[1])\n",
    "        self.errors_ = []\n",
    "        for _ in range(self.n_iter):\n",
    "            errors = 0\n",
    "            \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",
    "        \n",
    "            self.errors_.append(errors)     \n",
    "        return self\n",
    "\n",
    "    def net_input(self, X):\n",
    "        return np.dot(X, self.w_[1:]) + self.w_[0]\n",
    "    \n",
    "    def predict(self, X):\n",
    "        return np.where(self.net_input(X) >= 0.0, 1 , -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/''machine-learning-databases/iris/iris.data',header=None)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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3R+/sm5uj5SOPPDDph7qtn0i5lPBFYjKDQYWx6bNmRcuzZkXLgwb1Pro0ThNWjU2pibhz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaddf518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "y = df.iloc[0:100, 4].values\n",
    "y = np.where(y == 'Iris-setosa', -1 , 1)\n",
    "X = df.iloc[0:100, [0, 2]].values\n",
    "plt.scatter(X[:50, 0], X[:50, 1], c='red', marker='o', label='setosa')\n",
    "plt.scatter(X[50:100, 0], X[50:100, 1], c='blue', marker='x', label='versicolor')\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('sepal length')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9HlAVv0giyeOUgYdxRl53Hp29jO279b+9tH4NFgUzOzGYInO2mT1mZqPMbKSZ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\nBRERqaWiICIitVQURESkloqCiIjU+v9yIyQcH6Z/IgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa246630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('number of misclassifications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "def plot_decision_regions(X, y, classifier, resolution=0.02):\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\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",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))\n",
    "    \n",
    "    Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    Z = Z.reshape(xx1.shape)\n",
    "    plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\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(x=X[y == cl, 0], y=X[y == cl, 1], alpha=0.8, c=cmap(idx), marker = markers[idx], label=cl)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vJp5+P5Xpd1Pe4UnfmGbAfwE4tGB7briviLuvBlanOI6WM7O17r6o3ePoRPrd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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa1af3c8>"
      ]
     },
     "metadata": {},
     "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",
    "plt.show()"
   ]
  },
  {
   "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}