logistic-regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = np.array([\n",
    "    [20,10000],\n",
    "    [23,21000],\n",
    "    [30,25000],\n",
    "    [31,55500],\n",
    "    [22,15000],\n",
    "    [44,70000],\n",
    "    [32,45000],\n",
    "    [12,1000],\n",
    "    [10,1000],\n",
    "    [18,10000],\n",
    "    [40,120000],\n",
    "    [25,80000],\n",
    "    [40,200000],\n",
    "    [46,90000],\n",
    "    [40,40000],\n",
    "    [25,50000],\n",
    "    [28,20000],\n",
    "    [35,67090],\n",
    "    [50,22000]\n",
    "])\n",
    "\n",
    "train_label = np.array([\n",
    "    0,\n",
    "    1,\n",
    "    0,\n",
    "    1,\n",
    "    1,\n",
    "    1,\n",
    "    1,\n",
    "    0,\n",
    "    0,\n",
    "    0,\n",
    "    1,\n",
    "    1,\n",
    "    1,\n",
    "    1,\n",
    "    1,\n",
    "    1,\n",
    "    0,\n",
    "    1,\n",
    "    0\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Yb9MN2rByD8Ns5AzXmMYq4InS669Jek3So5KmpFgn8G5pm6Mp1pmW+8YvaBMR54DTwLRh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\nPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f24bba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = np.nonzero(train_label==1)\n",
    "neg = np.nonzero(train_label==0)\n",
    "\n",
    "plt.scatter(train_data[pos, 0][0], train_data[pos, 1][0], marker=\"+\")\n",
    "plt.scatter(train_data[neg, 0][0], train_data[neg, 1][0], marker=\"*\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   -43.34210526  27572.89473684]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: overflow encountered in exp\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "def sigmoid(z):\n",
    "    return 1 / (1 + np.exp(-z))\n",
    "\n",
    "def predict(x, weights):\n",
    "    return sigmoid(x.dot(weights))\n",
    "\n",
    "def gradient(X, weights, Y):\n",
    "    g = np.zeros(2)\n",
    "    m = len(X)\n",
    "\n",
    "    i = 0\n",
    "    for x in X:\n",
    "        error = sigmoid(weights.dot(x))\n",
    "        g += (error - Y[i]) * x\n",
    "        i += 1\n",
    "    return g / m\n",
    "\n",
    "\n",
    "def gradient_descent(X, Y, alpha, step):\n",
    "    weights = np.zeros(2)\n",
    "\n",
    "    for i in range(0, step):\n",
    "        weights = weights - (alpha * gradient(X, weights, Y))\n",
    "    return weights\n",
    "\n",
    "\n",
    "w = gradient_descent(train_data, train_label, 1, 10)\n",
    "\n",
    "\n",
    "print(w)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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