MultivariateLinearRegression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 23,
   "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>Size</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2104</td>\n",
       "      <td>3</td>\n",
       "      <td>399900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1600</td>\n",
       "      <td>3</td>\n",
       "      <td>329900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>369000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1416</td>\n",
       "      <td>2</td>\n",
       "      <td>232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3000</td>\n",
       "      <td>4</td>\n",
       "      <td>539900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Size  Bedrooms   Price\n",
       "0  2104         3  399900\n",
       "1  1600         3  329900\n",
       "2  2400         3  369000\n",
       "3  1416         2  232000\n",
       "4  3000         4  539900"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=os.getcwd() +'/data/ex1data2.txt'\n",
    "data=pd.read_csv(path,header=None,names=['Size', 'Bedrooms', 'Price'])\n",
    "data2=data\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Size</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.647532</td>\n",
       "      <td>3.942275</td>\n",
       "      <td>3.198179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.013332</td>\n",
       "      <td>3.942275</td>\n",
       "      <td>2.638358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.019999</td>\n",
       "      <td>3.942275</td>\n",
       "      <td>2.951058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.781799</td>\n",
       "      <td>2.628183</td>\n",
       "      <td>1.855408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.774998</td>\n",
       "      <td>5.256367</td>\n",
       "      <td>4.317822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Size  Bedrooms     Price\n",
       "0  2.647532  3.942275  3.198179\n",
       "1  2.013332  3.942275  2.638358\n",
       "2  3.019999  3.942275  2.951058\n",
       "3  1.781799  2.628183  1.855408\n",
       "4  3.774998  5.256367  4.317822"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=(data)/data.std()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "data.insert(0,'ones',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Size</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.647532</td>\n",
       "      <td>3.942275</td>\n",
       "      <td>3.198179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.013332</td>\n",
       "      <td>3.942275</td>\n",
       "      <td>2.638358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3.019999</td>\n",
       "      <td>3.942275</td>\n",
       "      <td>2.951058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.781799</td>\n",
       "      <td>2.628183</td>\n",
       "      <td>1.855408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3.774998</td>\n",
       "      <td>5.256367</td>\n",
       "      <td>4.317822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ones      Size  Bedrooms     Price\n",
       "0     1  2.647532  3.942275  3.198179\n",
       "1     1  2.013332  3.942275  2.638358\n",
       "2     1  3.019999  3.942275  2.951058\n",
       "3     1  1.781799  2.628183  1.855408\n",
       "4     1  3.774998  5.256367  4.317822"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=data2.Size\n",
    "y=data2.Price\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols=data.shape[1]\n",
    "X=data.iloc[:,0:cols-1]\n",
    "Y=data.iloc[:,cols-1:cols]\n",
    "X=np.matrix(X.values)\n",
    "Y=np.matrix(Y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((47, 1), (47, 1))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta=np.matrix(np.zeros(X.shape[1],dtype=int))\n",
    "X[:,1].shape ,Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def computeCost(X,Y,theta):\n",
    "    inner=np.power((( X * theta.T )- Y ),2)\n",
    "    suminner=np.sum(inner)\n",
    "    return suminner/(2*len(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.1951804759270344"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computeCost(X,Y,theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gradecent(X,Y,theta,alpha,iters):\n",
    "    temp=np.matrix(np.zeros(theta.shape))\n",
    "    parameters=int(theta.shape[1])\n",
    "    cost=np.zeros(iters)\n",
    "    for i in range(iters):\n",
    "        error=X*theta.T-Y\n",
    "        for j in range(parameters):\n",
    "            term=np.multiply(error,X[:,j])\n",
    "            temp[0,j]=theta[0,j]-(alpha/len(X))*np.sum(term)\n",
    "            \n",
    "        theta=temp\n",
    "        cost[i]=computeCost(X,Y,theta)\n",
    "    return theta , cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 0.66220484,  0.88502688, -0.0409523 ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha=0.05\n",
    "iters=1000\n",
    "t,c=gradecent(X,Y,theta,alpha,iters)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13076521845489752"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "computeCost(X,Y,t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f2058386410>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAJCCAYAAADDSQF7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2029dfccd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(12,10))\n",
    "\n",
    "ax.plot(np.arange(iters),c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "py=t[0,0]*X[:,0]+t[0,1]*X[:,1]+t[0,2]*X[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f2029c95190>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAADuCAYAAAAOR30qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKsgUCoUiEP4fg6OkV7dwgw4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2029e042d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "x2=np.linspace(data.Bedrooms.min(),data.Bedrooms.max(),47)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot(data.Size,data.Bedrooms,np.array(py).ravel())\n",
    "ax.scatter(data.Size,data.Bedrooms,data.Price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "err=abs(data.Price-np.array(py).ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc=data.Price-np.array(py).ravel()/data.Price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6897175982363983"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc.mean()\n"
   ]
  },
  {
   "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
}