{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用sklearn 模块 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#-*- coding: utf-8 -*-\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler    #引入归一化的包\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "加载数据...\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MDpk+MKN1m3cVeZiOtaqvG/V3us9629UapLEF0L7c4W77eZK+LelDEfFE7eo6mxwzQbbt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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d92208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print u\"加载数据...\\n\"\n",
    "data = np.loadtxt(\"../data/1-linear_regression/data.txt\",delimiter=\",\",dtype=np.float64) #读取数据\n",
    "X = np.array(data[:,0:-1],dtype=np.float64)      # X对应0到倒数第2列                  \n",
    "y = np.array(data[:,-1],dtype=np.float64)        # y对应最后一列  \n",
    "\n",
    "plt.scatter(X[:,0],X[:,1])\n",
    "\n",
    "# 归一化操作\n",
    "scaler = StandardScaler()   \n",
    "scaler.fit(X)\n",
    "\n",
    "\n",
    "x_train = scaler.transform(X)\n",
    "x_test = scaler.transform(np.array([1650.0,3.0]).reshape(1,-1))\n",
    "\n",
    "# 线性模型拟合\n",
    "model = linear_model.LinearRegression()\n",
    "model.fit(x_train, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 109447.79646964   -6578.35485416]\n",
      "340412.659574\n",
      "[ 293081.4643349]\n"
     ]
    }
   ],
   "source": [
    "#预测结果\n",
    "result = model.predict(x_test)\n",
    "print model.coef_       # Coefficient of the features 决策函数中的特征系数\n",
    "print model.intercept_  # 又名bias偏置,若设置为False，则为0\n",
    "print result            # 预测结果"
   ]
  }
 ],
 "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}