Iris-Adaline.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 0x7f541278d6a0>"
      ]
     },
     "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": [],
   "source": [
    "# normalize data\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": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yO7QtAWYB/62WhWm33u7d04zMnz2rwdVIkqSpYNwQl5nrgGdHxPOBE0rN/5mZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5438291e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from perceptron import AdalineGD\n",
    "ppn = AdalineGD(eta = 0.01, n_iter = 20)\n",
    "ppn.fit(X_std, y)\n",
    "plt.plot(range(1, len(ppn.cost_) + 1), ppn.cost_, marker = 'o')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Cost (normalized data)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00187579, -0.16017162,  1.09875738])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppn.w_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4eX+/MpxPAMVCGANQeBTGA6gyasYAFFoziK1ZI51zzsGasTVrkp9TGA+g7LgzBqDQzKQt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f541277ceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plot_decision_regions import *\n",
    "plot_decision_regions(X_std, 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
}