Iris-perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc('figure', figsize=(10, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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": 2,
     "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 = pd.read_csv('iris.data', header=None)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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UOvgG0i05X9OIccxjrAnxY4I8gHGLMVSR0Mj0KpcOq1XP4cpDjGMeY00oFpotAEOKMVSR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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6cd615630>"
      ]
     },
     "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], color = 'red', marker = 'o', label = 'setosa')\n",
    "plt.scatter(X[50:100, 0], X[50:100, 1], color = 'blue', marker = 'x', label = 'versicolor')\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('setal length')\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SdefEIi1bt1tvVe4MHQcAOo14P7Ic4O7Vh2+4+26xhgxISd8/f5hyenbV7HkV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\nWfsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6cd606f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from perceptron import Perceptron\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')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Misclassifications')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.4 , -0.68,  1.82])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppn.w_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AohgakrR/6sL1xNqu3dq6+qaqyeuELAAospBXto5L+l/u/pCZdUvabWbfdPfH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6cb4fae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plot_decision_regions import *\n",
    "plot_decision_regions(X, y, classifier = ppn)\n",
    "plt.xlabel('sepal length')\n",
    "plt.ylabel('petal length')\n",
    "plt.legend(loc = 'upper left')\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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}