LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106678588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10525aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def draw(x1,x2):\n",
    "    ln = plt.plot(x1,x2)\n",
    "\n",
    "def sigmoid(score):\n",
    "    return (1 / (1 + np.exp(-score)))\n",
    "def calculate_error(line_parameters,points,y):\n",
    "    m = points.shape[0]\n",
    "    p = sigmoid(points * line_parameters)\n",
    "    cross_etropy = -(1/m)*(np.log(p).T * y + np.log(1-p).T*(1-y))\n",
    "    return cross_etropy\n",
    "\n",
    "def gradient_descent(line_parameters, points, y, alpha):\n",
    "    n=points.shape[0]\n",
    "    for i in range(2000):\n",
    "        p = sigmoid(points * line_parameters)\n",
    "        gradient= points.T*(p-y)*(alpha/n)\n",
    "        line_parameters = line_parameters - gradient\n",
    "        \n",
    "        w1 =line_parameters.item(0)\n",
    "        w2 =line_parameters.item(1)\n",
    "        b =line_parameters.item(2)\n",
    "        \n",
    "        x1=np.array([points[:,0].min(), points[:,0].max()])\n",
    "        x2= -b/w2 + (x1*(-w1/w2))\n",
    "    draw(x1,x2) \n",
    "        \n",
    "        \n",
    "\n",
    "n_pts = 100\n",
    "np.random.seed(0)\n",
    "\n",
    "top_region = np.array([np.random.normal(10,2,n_pts), np.random.normal(12,2,n_pts)]).T\n",
    "bottom_region = np.array([np.random.normal(5,2,n_pts), np.random.normal(2,2,n_pts)]).T\n",
    "\n",
    "_, ax = plt.subplots(figsize = (4,4))\n",
    "ax.scatter(top_region[:,0],top_region[:,1], color='r')\n",
    "ax.scatter(bottom_region[:,0],bottom_region[:,1], color='b')\n",
    "plt.show()\n",
    "\n",
    "# Apply Sigmoid function every points\n",
    "# W1x1 + W2x2 +b\n",
    "# in this case b=1\n",
    "\n",
    "n_pts = 10\n",
    "np.random.seed(0)\n",
    "bias =np.ones(n_pts)\n",
    "\n",
    "top_region = np.array([np.random.normal(10,2,n_pts), np.random.normal(12,2,n_pts),bias]).T\n",
    "bottom_region = np.array([np.random.normal(5,2,n_pts), np.random.normal(2,2,n_pts),bias]).T\n",
    "\n",
    "all_points = np.vstack((top_region,bottom_region))\n",
    "\n",
    "w1 = -0.2\n",
    "w2 = -0.35\n",
    "b = 3.5\n",
    "\n",
    "line_parameters = np.matrix(np.zeros(3)).T\n",
    "\n",
    "#x1 = bottom_region[:,0].min()\n",
    "# w1x1+w2x2+b = 0\n",
    "# x2 = -b / w2 +x1 *(-w1/w2)\n",
    "#x2 = -b / w2 +x1 *(-w1/w2)\n",
    "y = np.array([np.zeros(n_pts), np.ones(n_pts)]).reshape(n_pts*2, 1)\n",
    "\n",
    "\n",
    "\n",
    "_, ax = plt.subplots(figsize = (4,4))\n",
    "ax.scatter(top_region[:,0],top_region[:,1], color='r')\n",
    "ax.scatter(bottom_region[:,0],bottom_region[:,1], color='b')\n",
    "ax.scatter(bottom_region[:,0],bottom_region[:,1], color='b')\n",
    "gradient_descent(line_parameters, all_points, y , 0.06)\n",
    "plt.show()\n"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}