Percerptron learning algorithm for iris data set.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
      "..   ...  ...  ...  ...             ...\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",
      "\n",
      "[150 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "def load_data():\n",
    "    URL_='https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "    data = pd.read_csv(URL_, header = None)\n",
    "    print(data)\n",
    "    \n",
    "    # make the dataset linearly separable\n",
    "    data = data[:100]\n",
    "    data[4] = np.where(data.iloc[:, -1]=='Iris-setosa', 0, 1)\n",
    "    data = np.asmatrix(data, dtype = 'float64')\n",
    "    return data\n",
    "data = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perceptron(data, num_iter):\n",
    "    features = data[:, :-1]\n",
    "    labels = data[:, -1]\n",
    "    \n",
    "    # set weights to zero\n",
    "    w = np.zeros(shape=(1, features.shape[1]+1))\n",
    "    \n",
    "    misclassified_ = [] \n",
    "  \n",
    "    for epoch in range(num_iter):\n",
    "        misclassified = 0\n",
    "        for x, label in zip(features, labels):\n",
    "            x = np.insert(x,0,1)\n",
    "            y = np.dot(w, x.transpose())\n",
    "            target = 1.0 if (y > 0) else 0.0\n",
    "            \n",
    "            delta = (label.item(0,0) - target)\n",
    "            \n",
    "            if(delta): # misclassified\n",
    "                misclassified += 1\n",
    "                w += (delta * x)\n",
    "        \n",
    "        misclassified_.append(misclassified)\n",
    "    return (w, misclassified_)\n",
    "             \n",
    "num_iter = 10\n",
    "w, misclassified_ = perceptron(data, num_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "epochs = np.arange(1, num_iter+1)\n",
    "plt.plot(epochs, misclassified_)\n",
    "plt.xlabel('iterations')\n",
    "plt.ylabel('misclassified')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}