Logistic Regression(Diag).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Values of THETA after 100 Iteration is = \n",
      "\n",
      "[-0.14112684  6.9058393 ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'y axis')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function for sigmoid\n",
    "def sigmoid(a):\n",
    "    return ((1.0)/(1 + np.exp(-a)))\n",
    "\n",
    "# Gradient descent main Function\n",
    "def grad_desc(X, y, alpha, num_iter, theta, m):\n",
    "    \n",
    "    X_trans = X.T\n",
    "    \n",
    "    for i in range(0, num_iter):\n",
    "        prediction = sigmoid(np.dot(X, theta))\n",
    "        error = sigmoid(prediction) - y\n",
    "        cost = (np.dot(-y, np.log(prediction)) - np.dot((1 - y), np.log(1 - prediction))) / m \n",
    "#         Calculation for gradient descent starts now\n",
    "        gradient = np.dot(X_trans, error) / m\n",
    "        theta = theta - alpha * gradient\n",
    "    \n",
    "    return theta, prediction\n",
    "\n",
    "# DATA PLOTTING STARTS NOW\n",
    "\n",
    "X = np.zeros((100,2))\n",
    "X[:,0] = np.ones(100)\n",
    "X[:,1] = np.linspace(-3, 3, 100)  #it takes values in increasing order from -3 to 3 thats why we use linspace\n",
    "z = (7.2)*(X[:,1])\n",
    "noise = 0.3*np.random.randn(100)\n",
    "y = 1/(1+np.exp(-z))+noise\n",
    "# print(y)\n",
    "plt.scatter(X[:,1],y)\n",
    "\n",
    "\n",
    "y = (y >= 0.5) # returns boolean value to y (false ot true)\n",
    "# y1 = y\n",
    "y = y*1\n",
    "\n",
    "m, n = np.shape(X)\n",
    "theta = np.ones(n)\n",
    "num_iter = 100 # number of iteration u want to perform feel free to change it as much as u want\n",
    "alpha = 0.1 #Alpha or so called learning factor\n",
    "theta, h = grad_desc(X, y, alpha, num_iter, theta, m)\n",
    "print(\"\\nValues of THETA after \"+str(num_iter)+\" Iteration is = \\n\")\n",
    "print(theta)\n",
    "\n",
    "plt.scatter(X[:,1],y)\n",
    "plt.xlabel('x axis')\n",
    "plt.ylabel('y axis')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e9e42daba8>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,1],y)\n",
    "plt.plot(X[:,1],h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(y1)\n",
    "# print(z)"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}