RegularisedLogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1760,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1761,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test1</th>\n",
       "      <th>Test2</th>\n",
       "      <th>Accepted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.69956</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.68494</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.69225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.50219</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.513250</td>\n",
       "      <td>0.46564</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Test1    Test2  Accepted\n",
       "0  0.051267  0.69956         1\n",
       "1 -0.092742  0.68494         1\n",
       "2 -0.213710  0.69225         1\n",
       "3 -0.375000  0.50219         1\n",
       "4 -0.513250  0.46564         1"
      ]
     },
     "execution_count": 1761,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(os.getcwd()+'/data/ex2data2.txt',header=None,names=['Test1','Test2','Accepted'])\n",
    "testdata=data['Test2']\n",
    "positive=data[data['Accepted'].isin([1])]\n",
    "negative=data[data['Accepted'].isin([0])]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1762,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9b59015910>"
      ]
     },
     "execution_count": 1762,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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DqOq4Uxh2ZnVhYX9mTNLERNifharOTOZ1fJqGKwAA8pRFm3KUSbLo/tLS3sK5KiyaK1Or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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b59560250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(12,8))\n",
    "ax.scatter(positive['Test1'], positive['Test2'], s=50, c='b', marker='o', label='Accepted')  \n",
    "ax.scatter(negative['Test1'], negative['Test2'], s=50, c='r', marker='x', label='Rejected')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1763,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accepted</th>\n",
       "      <th>Ones</th>\n",
       "      <th>F10</th>\n",
       "      <th>F20</th>\n",
       "      <th>F21</th>\n",
       "      <th>F30</th>\n",
       "      <th>F31</th>\n",
       "      <th>F32</th>\n",
       "      <th>F40</th>\n",
       "      <th>F41</th>\n",
       "      <th>...</th>\n",
       "      <th>F63</th>\n",
       "      <th>F64</th>\n",
       "      <th>F65</th>\n",
       "      <th>F70</th>\n",
       "      <th>F71</th>\n",
       "      <th>F72</th>\n",
       "      <th>F73</th>\n",
       "      <th>F74</th>\n",
       "      <th>F75</th>\n",
       "      <th>F76</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.002628</td>\n",
       "      <td>0.035864</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.001839</td>\n",
       "      <td>0.025089</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000629</td>\n",
       "      <td>0.008589</td>\n",
       "      <td>9.308192e-10</td>\n",
       "      <td>1.270142e-08</td>\n",
       "      <td>1.733163e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.006009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.008601</td>\n",
       "      <td>-0.063523</td>\n",
       "      <td>-0.000798</td>\n",
       "      <td>0.005891</td>\n",
       "      <td>-0.043509</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>-0.000546</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000256</td>\n",
       "      <td>0.001893</td>\n",
       "      <td>-0.013981</td>\n",
       "      <td>-5.901130e-08</td>\n",
       "      <td>4.358241e-07</td>\n",
       "      <td>-3.218751e-06</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>-0.000176</td>\n",
       "      <td>0.001297</td>\n",
       "      <td>-0.009576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.045672</td>\n",
       "      <td>-0.147941</td>\n",
       "      <td>-0.009761</td>\n",
       "      <td>0.031616</td>\n",
       "      <td>-0.102412</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>-0.006757</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003238</td>\n",
       "      <td>0.010488</td>\n",
       "      <td>-0.033973</td>\n",
       "      <td>-2.035982e-05</td>\n",
       "      <td>6.594958e-05</td>\n",
       "      <td>-2.136241e-04</td>\n",
       "      <td>0.000692</td>\n",
       "      <td>-0.002241</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>-0.023518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>-0.188321</td>\n",
       "      <td>-0.052734</td>\n",
       "      <td>0.070620</td>\n",
       "      <td>-0.094573</td>\n",
       "      <td>0.019775</td>\n",
       "      <td>-0.026483</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006679</td>\n",
       "      <td>0.008944</td>\n",
       "      <td>-0.011978</td>\n",
       "      <td>-1.042843e-03</td>\n",
       "      <td>1.396547e-03</td>\n",
       "      <td>-1.870219e-03</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>-0.003354</td>\n",
       "      <td>0.004492</td>\n",
       "      <td>-0.006015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.513250</td>\n",
       "      <td>0.263426</td>\n",
       "      <td>-0.238990</td>\n",
       "      <td>-0.135203</td>\n",
       "      <td>0.122661</td>\n",
       "      <td>-0.111283</td>\n",
       "      <td>0.069393</td>\n",
       "      <td>-0.062956</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013650</td>\n",
       "      <td>0.012384</td>\n",
       "      <td>-0.011235</td>\n",
       "      <td>-9.382157e-03</td>\n",
       "      <td>8.511851e-03</td>\n",
       "      <td>-7.722277e-03</td>\n",
       "      <td>0.007006</td>\n",
       "      <td>-0.006356</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>-0.005232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accepted  Ones       F10       F20       F21       F30       F31       F32  \\\n",
       "0         1     1  0.051267  0.002628  0.035864  0.000135  0.001839  0.025089   \n",
       "1         1     1 -0.092742  0.008601 -0.063523 -0.000798  0.005891 -0.043509   \n",
       "2         1     1 -0.213710  0.045672 -0.147941 -0.009761  0.031616 -0.102412   \n",
       "3         1     1 -0.375000  0.140625 -0.188321 -0.052734  0.070620 -0.094573   \n",
       "4         1     1 -0.513250  0.263426 -0.238990 -0.135203  0.122661 -0.111283   \n",
       "\n",
       "        F40       F41    ...          F63       F64       F65           F70  \\\n",
       "0  0.000007  0.000094    ...     0.000046  0.000629  0.008589  9.308192e-10   \n",
       "1  0.000074 -0.000546    ...    -0.000256  0.001893 -0.013981 -5.901130e-08   \n",
       "2  0.002086 -0.006757    ...    -0.003238  0.010488 -0.033973 -2.035982e-05   \n",
       "3  0.019775 -0.026483    ...    -0.006679  0.008944 -0.011978 -1.042843e-03   \n",
       "4  0.069393 -0.062956    ...    -0.013650  0.012384 -0.011235 -9.382157e-03   \n",
       "\n",
       "            F71           F72       F73       F74       F75       F76  \n",
       "0  1.270142e-08  1.733163e-07  0.000002  0.000032  0.000440  0.006009  \n",
       "1  4.358241e-07 -3.218751e-06  0.000024 -0.000176  0.001297 -0.009576  \n",
       "2  6.594958e-05 -2.136241e-04  0.000692 -0.002241  0.007260 -0.023518  \n",
       "3  1.396547e-03 -1.870219e-03  0.002505 -0.003354  0.004492 -0.006015  \n",
       "4  8.511851e-03 -7.722277e-03  0.007006 -0.006356  0.005766 -0.005232  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 1763,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degree = 8\n",
    "data2=data\n",
    "x1 = data2['Test1']  \n",
    "x2 = data2['Test2']\n",
    "\n",
    "data2.insert(3, 'Ones', 1)\n",
    "\n",
    "for i in range(1, degree):  \n",
    "    for j in range(0, i):\n",
    "        data2['F' + str(i) + str(j)] = np.power(x1, i-j) * np.power(x2, j)\n",
    "\n",
    "data2.drop('Test1', axis=1, inplace=True)  \n",
    "data2.drop('Test2', axis=1, inplace=True)\n",
    "\n",
    "data2.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1764,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ones</th>\n",
       "      <th>F10</th>\n",
       "      <th>Test2</th>\n",
       "      <th>F20</th>\n",
       "      <th>F21</th>\n",
       "      <th>F30</th>\n",
       "      <th>F31</th>\n",
       "      <th>F32</th>\n",
       "      <th>F40</th>\n",
       "      <th>F41</th>\n",
       "      <th>...</th>\n",
       "      <th>F63</th>\n",
       "      <th>F64</th>\n",
       "      <th>F65</th>\n",
       "      <th>F70</th>\n",
       "      <th>F71</th>\n",
       "      <th>F72</th>\n",
       "      <th>F73</th>\n",
       "      <th>F74</th>\n",
       "      <th>F75</th>\n",
       "      <th>F76</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.69956</td>\n",
       "      <td>0.002628</td>\n",
       "      <td>0.035864</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.001839</td>\n",
       "      <td>0.025089</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000629</td>\n",
       "      <td>0.008589</td>\n",
       "      <td>9.308192e-10</td>\n",
       "      <td>1.270142e-08</td>\n",
       "      <td>1.733163e-07</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000440</td>\n",
       "      <td>0.006009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.68494</td>\n",
       "      <td>0.008601</td>\n",
       "      <td>-0.063523</td>\n",
       "      <td>-0.000798</td>\n",
       "      <td>0.005891</td>\n",
       "      <td>-0.043509</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>-0.000546</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000256</td>\n",
       "      <td>0.001893</td>\n",
       "      <td>-0.013981</td>\n",
       "      <td>-5.901130e-08</td>\n",
       "      <td>4.358241e-07</td>\n",
       "      <td>-3.218751e-06</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>-0.000176</td>\n",
       "      <td>0.001297</td>\n",
       "      <td>-0.009576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.69225</td>\n",
       "      <td>0.045672</td>\n",
       "      <td>-0.147941</td>\n",
       "      <td>-0.009761</td>\n",
       "      <td>0.031616</td>\n",
       "      <td>-0.102412</td>\n",
       "      <td>0.002086</td>\n",
       "      <td>-0.006757</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003238</td>\n",
       "      <td>0.010488</td>\n",
       "      <td>-0.033973</td>\n",
       "      <td>-2.035982e-05</td>\n",
       "      <td>6.594958e-05</td>\n",
       "      <td>-2.136241e-04</td>\n",
       "      <td>0.000692</td>\n",
       "      <td>-0.002241</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>-0.023518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.50219</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>-0.188321</td>\n",
       "      <td>-0.052734</td>\n",
       "      <td>0.070620</td>\n",
       "      <td>-0.094573</td>\n",
       "      <td>0.019775</td>\n",
       "      <td>-0.026483</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006679</td>\n",
       "      <td>0.008944</td>\n",
       "      <td>-0.011978</td>\n",
       "      <td>-1.042843e-03</td>\n",
       "      <td>1.396547e-03</td>\n",
       "      <td>-1.870219e-03</td>\n",
       "      <td>0.002505</td>\n",
       "      <td>-0.003354</td>\n",
       "      <td>0.004492</td>\n",
       "      <td>-0.006015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.513250</td>\n",
       "      <td>0.46564</td>\n",
       "      <td>0.263426</td>\n",
       "      <td>-0.238990</td>\n",
       "      <td>-0.135203</td>\n",
       "      <td>0.122661</td>\n",
       "      <td>-0.111283</td>\n",
       "      <td>0.069393</td>\n",
       "      <td>-0.062956</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013650</td>\n",
       "      <td>0.012384</td>\n",
       "      <td>-0.011235</td>\n",
       "      <td>-9.382157e-03</td>\n",
       "      <td>8.511851e-03</td>\n",
       "      <td>-7.722277e-03</td>\n",
       "      <td>0.007006</td>\n",
       "      <td>-0.006356</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>-0.005232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ones       F10    Test2       F20       F21       F30       F31       F32  \\\n",
       "0     1  0.051267  0.69956  0.002628  0.035864  0.000135  0.001839  0.025089   \n",
       "1     1 -0.092742  0.68494  0.008601 -0.063523 -0.000798  0.005891 -0.043509   \n",
       "2     1 -0.213710  0.69225  0.045672 -0.147941 -0.009761  0.031616 -0.102412   \n",
       "3     1 -0.375000  0.50219  0.140625 -0.188321 -0.052734  0.070620 -0.094573   \n",
       "4     1 -0.513250  0.46564  0.263426 -0.238990 -0.135203  0.122661 -0.111283   \n",
       "\n",
       "        F40       F41    ...          F63       F64       F65           F70  \\\n",
       "0  0.000007  0.000094    ...     0.000046  0.000629  0.008589  9.308192e-10   \n",
       "1  0.000074 -0.000546    ...    -0.000256  0.001893 -0.013981 -5.901130e-08   \n",
       "2  0.002086 -0.006757    ...    -0.003238  0.010488 -0.033973 -2.035982e-05   \n",
       "3  0.019775 -0.026483    ...    -0.006679  0.008944 -0.011978 -1.042843e-03   \n",
       "4  0.069393 -0.062956    ...    -0.013650  0.012384 -0.011235 -9.382157e-03   \n",
       "\n",
       "            F71           F72       F73       F74       F75       F76  \n",
       "0  1.270142e-08  1.733163e-07  0.000002  0.000032  0.000440  0.006009  \n",
       "1  4.358241e-07 -3.218751e-06  0.000024 -0.000176  0.001297 -0.009576  \n",
       "2  6.594958e-05 -2.136241e-04  0.000692 -0.002241  0.007260 -0.023518  \n",
       "3  1.396547e-03 -1.870219e-03  0.002505 -0.003354  0.004492 -0.006015  \n",
       "4  8.511851e-03 -7.722277e-03  0.007006 -0.006356  0.005766 -0.005232  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 1764,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=data2.shape[1]\n",
    "train=data2.iloc[:,1:cols]\n",
    "train.insert(2,'Test2',testdata)\n",
    "y=data2.iloc[:,0:1]\n",
    "theta=np.matrix(np.zeros(train.shape[1]))\n",
    "tr=train\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1765,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[  1.00000000e+00,   5.12670000e-02,   6.99560000e-01, ...,\n",
       "           3.22710867e-05,   4.40352691e-04,   6.00879959e-03],\n",
       "        [  1.00000000e+00,  -9.27420000e-02,   6.84940000e-01, ...,\n",
       "          -1.75565626e-04,   1.29662850e-03,  -9.57616530e-03],\n",
       "        [  1.00000000e+00,  -2.13710000e-01,   6.92250000e-01, ...,\n",
       "          -2.24143625e-03,   7.26046625e-03,  -2.35181216e-02],\n",
       "        ..., \n",
       "        [  1.00000000e+00,  -4.84450000e-01,   9.99270000e-01, ...,\n",
       "          -1.13364813e-01,   2.33836427e-01,  -4.82331978e-01],\n",
       "        [  1.00000000e+00,  -6.33640000e-03,   9.99270000e-01, ...,\n",
       "          -2.53664185e-07,   4.00036314e-05,  -6.30869717e-03],\n",
       "        [  1.00000000e+00,   6.32650000e-01,  -3.06120000e-02, ...,\n",
       "           2.22360353e-07,  -1.07593379e-08,   5.20611480e-10]])"
      ]
     },
     "execution_count": 1765,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "train=np.matrix(train.values)\n",
    "y=np.matrix(y.values)\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1766,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    " return 1/(1+(np.exp(-z)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1767,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def computecost(x,y,theta,lam):\n",
    "    hx=sigmoid(x*theta.T)\n",
    "    term1=np.multiply(-y,np.log(hx))\n",
    "    term2=np.multiply((1-y),np.log(1-hx))\n",
    "    \n",
    "    reg=(lam/(2*(len(x))))*(np.sum(np.power(theta[:,1:theta.shape[1]],2)))\n",
    "    jtheta=(np.sum(term1-term2)/len(x))+reg\n",
    "    return jtheta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1768,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6931471805599454"
      ]
     },
     "execution_count": 1768,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computecost(train,y,theta,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1769,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradientdecent(train,y,theta,lam,alpha,iters):\n",
    "    temp=np.matrix(np.zeros(theta.shape[1]))\n",
    "    parameters=int(theta.shape[1])\n",
    "    cost=np.zeros(iters)\n",
    "    for i in range(iters):\n",
    "        error=(sigmoid(train*theta.T)-y)\n",
    "        \n",
    "        for j in range(parameters):\n",
    "            term = np.multiply(error, train[:,j])\n",
    "            if(j==0):\n",
    "                temp[0,j]=temp[0,j]-(alpha/(len(train)))*np.sum(term)\n",
    "            else:\n",
    "                temp[0,j]=temp[0,j]-alpha/len(train)*(np.sum(term)+lam*(theta[:,j]))\n",
    "                \n",
    "       #*(+(np.sum(lam/len(train)*(theta[:,j])))) \n",
    "        theta=temp\n",
    "        cost[i]=computecost(train,y,theta,lam)\n",
    "    return theta , cost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1770,
   "metadata": {},
   "outputs": [],
   "source": [
    "lam=0.001\n",
    "alpha=0.9\n",
    "iters=2000\n",
    "t,c=gradientdecent(train,y,theta,lam,alpha,iters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1771,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48594751969804162"
      ]
     },
     "execution_count": 1771,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computecost(train,y,t,lam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1772,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9b592ad310>]"
      ]
     },
     "execution_count": 1772,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1DC0FgsgI0tzaztItTbyxqYHFmxpYsrmJA23BHMSEgowwHPKZPSGf8QUZ+ogN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\nUCCIiEhIgSAiIoACQUREQgoEEREB4P8DWGbWmuV9UrMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b5948c910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(iters),c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1773,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 89%\n"
     ]
    }
   ],
   "source": [
    "def predict(theta, testmatrix):  \n",
    "    probability = sigmoid( testmatrix* theta.T)\n",
    "    return [1 if x >= 0.5 else 0 for x in probability]\n",
    "\n",
    "theta_min = t\n",
    "predictions = predict(theta_min, train)  \n",
    "correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predictions, y)]  \n",
    "accuracy = (sum(map(int, correct)) % len(correct))  \n",
    "print 'accuracy = {0}%'.format(accuracy)\n",
    "\n",
    "tr.insert(0,'predicted',predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1774,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9b58eb2e50>"
      ]
     },
     "execution_count": 1774,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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orztbqzqy6RRvB6qOAAg3Sh0CrUNQjUhik83ucIoXQNJFaUM7oq0lGxU7jY2K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\nZhV5Um1mZmZmVpEn1WZmZmZmFf0P/5V4WmufA+oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9b5910c3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive1=tr[tr['predicted'].isin([1])]\n",
    "negative1=tr[tr['predicted'].isin([0])]\n",
    "fig,ax=plt.subplots(figsize=(12,8))\n",
    "ax.scatter(positive['Test1'], positive['Test2'], s=50, c='b', marker='o', label='Accepted')  \n",
    "ax.scatter(negative['Test1'], negative['Test2'], s=50, c='r', marker='x', label='Rejected')\n",
    "ax.scatter(positive1['F10'], positive1['Test2'], s=10,c='r',marker='o', label='Admitted(Predicted)')  \n",
    "ax.scatter(negative1['F10'], negative1['Test2'], s=10,c='b',marker='x', label='Not Admitted(Predicted)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}