第6部分 机器学习(1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 利用Python机器学习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据导入和可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据包含鸢尾花（iris）数据集，这是一个包含了三种鸢尾花（山鸢尾、维吉尼亚鸢尾和变色鸢尾）的各50个数据样本的多元数据集，每个样本都有四个特征（或者说变量），即花萼（sepal）和花瓣（petal）的长度和宽度。以厘米为单位。\n",
    "\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "iris_X = iris.data\n",
    "iris_y = iris.target\n",
    "\n",
    "np.unique(iris_y)\n",
    "iris_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[118  37  40  46 141  16  97  67  13  18 126  35  34  38 135  12 117  83\n",
      "  27  51  85  74   6  23  71  98 104 103  41  39  36 137 108  52 138  78\n",
      " 146 105  99 125  89  95  94  44   8  77  69  17  48  73   5  65  90  93\n",
      " 136  22 147  63  92 109 119  20 111  11 113 114  29  10  96 148  31   0\n",
      " 123 130 128  58 124  32  19  45  91  66   4  62   9  87 106  55  28  86\n",
      " 120  30 115 112 139  68  50  33  61 144 101   3   7 140  81 121  84  15\n",
      "  54  26 102  43  88 145  75 132  25 133 142 127 116 131  47  72  14 149\n",
      "  76  64 129 134  56  49 107  70  42 122 100  21  80  57   2  53  24  82\n",
      "   1 110  59  79 143  60]\n"
     ]
    }
   ],
   "source": [
    "# Split iris data in train and test data\n",
    "# A random permutation, to split the data randomlynp.random.seed(0)\n",
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "iris = datasets.load_iris()\n",
    "iris_X = iris.data\n",
    "iris_y = iris.target\n",
    "\n",
    "#print iris_X\n",
    "indices = np.random.permutation(len(iris_X))  #permutation(x) 返回一个随机排列\n",
    "print indices\n",
    "iris_X_train = iris_X[indices[:-10]]\n",
    "iris_y_train = iris_y[indices[:-10]]\n",
    "iris_X_test  = iris_X[indices[-10:]]\n",
    "iris_y_test  = iris_y[indices[-10:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "(422L, 10L)\n",
      "[[ 0.03807591  0.05068012  0.06169621 ..., -0.00259226  0.01990842\n",
      "  -0.01764613]\n",
      " [-0.00188202 -0.04464164 -0.05147406 ..., -0.03949338 -0.06832974\n",
      "  -0.09220405]\n",
      " [ 0.08529891  0.05068012  0.04445121 ..., -0.00259226  0.00286377\n",
      "  -0.02593034]\n",
      " ..., \n",
      " [-0.02004471 -0.04464164 -0.0547075  ..., -0.03949338 -0.07408887\n",
      "  -0.0052198 ]\n",
      " [ 0.02354575 -0.04464164 -0.03638469 ...,  0.03430886 -0.03324879\n",
      "   0.06105391]\n",
      " [ 0.03807591  0.05068012  0.0164281  ...,  0.07120998  0.04976866\n",
      "   0.01549073]]\n"
     ]
    }
   ],
   "source": [
    "#糖尿病数据集包含442个患者的10个生理特征（年龄，性别、体重、血压）和一年以后疾病级数指标。\n",
    "\n",
    "diabetes = datasets.load_diabetes()\n",
    "\n",
    "diabetes_X_train = diabetes.data[:-20]\n",
    "diabetes_X_test = diabetes.data[-20:]\n",
    "\n",
    "diabetes_y_train = diabetes.target[:-20]\n",
    "diabetes_y_test = diabetes.target[-20:]\n",
    "\n",
    "print type(diabetes_X_train)\n",
    "print diabetes_X_train.shape\n",
    "\n",
    "print diabetes_X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预测集和测试集 cross_validation.train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yuli\\AppData\\Local\\Continuum\\Anaconda3\\envs\\Python27\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "data": {
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       "array([[ 5.1,  3.5,  1.4,  0.2],\n",
       "       [ 4.9,  3. ,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.3,  0.2],\n",
       "       [ 4.6,  3.1,  1.5,  0.2],\n",
       "       [ 5. ,  3.6,  1.4,  0.2],\n",
       "       [ 5.4,  3.9,  1.7,  0.4],\n",
       "       [ 4.6,  3.4,  1.4,  0.3],\n",
       "       [ 5. ,  3.4,  1.5,  0.2],\n",
       "       [ 4.4,  2.9,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5.4,  3.7,  1.5,  0.2],\n",
       "       [ 4.8,  3.4,  1.6,  0.2],\n",
       "       [ 4.8,  3. ,  1.4,  0.1],\n",
       "       [ 4.3,  3. ,  1.1,  0.1],\n",
       "       [ 5.8,  4. ,  1.2,  0.2],\n",
       "       [ 5.7,  4.4,  1.5,  0.4],\n",
       "       [ 5.4,  3.9,  1.3,  0.4],\n",
       "       [ 5.1,  3.5,  1.4,  0.3],\n",
       "       [ 5.7,  3.8,  1.7,  0.3],\n",
       "       [ 5.1,  3.8,  1.5,  0.3],\n",
       "       [ 5.4,  3.4,  1.7,  0.2],\n",
       "       [ 5.1,  3.7,  1.5,  0.4],\n",
       "       [ 4.6,  3.6,  1. ,  0.2],\n",
       "       [ 5.1,  3.3,  1.7,  0.5],\n",
       "       [ 4.8,  3.4,  1.9,  0.2],\n",
       "       [ 5. ,  3. ,  1.6,  0.2],\n",
       "       [ 5. ,  3.4,  1.6,  0.4],\n",
       "       [ 5.2,  3.5,  1.5,  0.2],\n",
       "       [ 5.2,  3.4,  1.4,  0.2],\n",
       "       [ 4.7,  3.2,  1.6,  0.2],\n",
       "       [ 4.8,  3.1,  1.6,  0.2],\n",
       "       [ 5.4,  3.4,  1.5,  0.4],\n",
       "       [ 5.2,  4.1,  1.5,  0.1],\n",
       "       [ 5.5,  4.2,  1.4,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 5. ,  3.2,  1.2,  0.2],\n",
       "       [ 5.5,  3.5,  1.3,  0.2],\n",
       "       [ 4.9,  3.1,  1.5,  0.1],\n",
       "       [ 4.4,  3. ,  1.3,  0.2],\n",
       "       [ 5.1,  3.4,  1.5,  0.2],\n",
       "       [ 5. ,  3.5,  1.3,  0.3],\n",
       "       [ 4.5,  2.3,  1.3,  0.3],\n",
       "       [ 4.4,  3.2,  1.3,  0.2],\n",
       "       [ 5. ,  3.5,  1.6,  0.6],\n",
       "       [ 5.1,  3.8,  1.9,  0.4],\n",
       "       [ 4.8,  3. ,  1.4,  0.3],\n",
       "       [ 5.1,  3.8,  1.6,  0.2],\n",
       "       [ 4.6,  3.2,  1.4,  0.2],\n",
       "       [ 5.3,  3.7,  1.5,  0.2],\n",
       "       [ 5. ,  3.3,  1.4,  0.2],\n",
       "       [ 7. ,  3.2,  4.7,  1.4],\n",
       "       [ 6.4,  3.2,  4.5,  1.5],\n",
       "       [ 6.9,  3.1,  4.9,  1.5],\n",
       "       [ 5.5,  2.3,  4. ,  1.3],\n",
       "       [ 6.5,  2.8,  4.6,  1.5],\n",
       "       [ 5.7,  2.8,  4.5,  1.3],\n",
       "       [ 6.3,  3.3,  4.7,  1.6],\n",
       "       [ 4.9,  2.4,  3.3,  1. ],\n",
       "       [ 6.6,  2.9,  4.6,  1.3],\n",
       "       [ 5.2,  2.7,  3.9,  1.4],\n",
       "       [ 5. ,  2. ,  3.5,  1. ],\n",
       "       [ 5.9,  3. ,  4.2,  1.5],\n",
       "       [ 6. ,  2.2,  4. ,  1. ],\n",
       "       [ 6.1,  2.9,  4.7,  1.4],\n",
       "       [ 5.6,  2.9,  3.6,  1.3],\n",
       "       [ 6.7,  3.1,  4.4,  1.4],\n",
       "       [ 5.6,  3. ,  4.5,  1.5],\n",
       "       [ 5.8,  2.7,  4.1,  1. ],\n",
       "       [ 6.2,  2.2,  4.5,  1.5],\n",
       "       [ 5.6,  2.5,  3.9,  1.1],\n",
       "       [ 5.9,  3.2,  4.8,  1.8],\n",
       "       [ 6.1,  2.8,  4. ,  1.3],\n",
       "       [ 6.3,  2.5,  4.9,  1.5],\n",
       "       [ 6.1,  2.8,  4.7,  1.2],\n",
       "       [ 6.4,  2.9,  4.3,  1.3],\n",
       "       [ 6.6,  3. ,  4.4,  1.4],\n",
       "       [ 6.8,  2.8,  4.8,  1.4],\n",
       "       [ 6.7,  3. ,  5. ,  1.7],\n",
       "       [ 6. ,  2.9,  4.5,  1.5],\n",
       "       [ 5.7,  2.6,  3.5,  1. ],\n",
       "       [ 5.5,  2.4,  3.8,  1.1],\n",
       "       [ 5.5,  2.4,  3.7,  1. ],\n",
       "       [ 5.8,  2.7,  3.9,  1.2],\n",
       "       [ 6. ,  2.7,  5.1,  1.6],\n",
       "       [ 5.4,  3. ,  4.5,  1.5],\n",
       "       [ 6. ,  3.4,  4.5,  1.6],\n",
       "       [ 6.7,  3.1,  4.7,  1.5],\n",
       "       [ 6.3,  2.3,  4.4,  1.3],\n",
       "       [ 5.6,  3. ,  4.1,  1.3],\n",
       "       [ 5.5,  2.5,  4. ,  1.3],\n",
       "       [ 5.5,  2.6,  4.4,  1.2],\n",
       "       [ 6.1,  3. ,  4.6,  1.4],\n",
       "       [ 5.8,  2.6,  4. ,  1.2],\n",
       "       [ 5. ,  2.3,  3.3,  1. ],\n",
       "       [ 5.6,  2.7,  4.2,  1.3],\n",
       "       [ 5.7,  3. ,  4.2,  1.2],\n",
       "       [ 5.7,  2.9,  4.2,  1.3],\n",
       "       [ 6.2,  2.9,  4.3,  1.3],\n",
       "       [ 5.1,  2.5,  3. ,  1.1],\n",
       "       [ 5.7,  2.8,  4.1,  1.3],\n",
       "       [ 6.3,  3.3,  6. ,  2.5],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 7.1,  3. ,  5.9,  2.1],\n",
       "       [ 6.3,  2.9,  5.6,  1.8],\n",
       "       [ 6.5,  3. ,  5.8,  2.2],\n",
       "       [ 7.6,  3. ,  6.6,  2.1],\n",
       "       [ 4.9,  2.5,  4.5,  1.7],\n",
       "       [ 7.3,  2.9,  6.3,  1.8],\n",
       "       [ 6.7,  2.5,  5.8,  1.8],\n",
       "       [ 7.2,  3.6,  6.1,  2.5],\n",
       "       [ 6.5,  3.2,  5.1,  2. ],\n",
       "       [ 6.4,  2.7,  5.3,  1.9],\n",
       "       [ 6.8,  3. ,  5.5,  2.1],\n",
       "       [ 5.7,  2.5,  5. ,  2. ],\n",
       "       [ 5.8,  2.8,  5.1,  2.4],\n",
       "       [ 6.4,  3.2,  5.3,  2.3],\n",
       "       [ 6.5,  3. ,  5.5,  1.8],\n",
       "       [ 7.7,  3.8,  6.7,  2.2],\n",
       "       [ 7.7,  2.6,  6.9,  2.3],\n",
       "       [ 6. ,  2.2,  5. ,  1.5],\n",
       "       [ 6.9,  3.2,  5.7,  2.3],\n",
       "       [ 5.6,  2.8,  4.9,  2. ],\n",
       "       [ 7.7,  2.8,  6.7,  2. ],\n",
       "       [ 6.3,  2.7,  4.9,  1.8],\n",
       "       [ 6.7,  3.3,  5.7,  2.1],\n",
       "       [ 7.2,  3.2,  6. ,  1.8],\n",
       "       [ 6.2,  2.8,  4.8,  1.8],\n",
       "       [ 6.1,  3. ,  4.9,  1.8],\n",
       "       [ 6.4,  2.8,  5.6,  2.1],\n",
       "       [ 7.2,  3. ,  5.8,  1.6],\n",
       "       [ 7.4,  2.8,  6.1,  1.9],\n",
       "       [ 7.9,  3.8,  6.4,  2. ],\n",
       "       [ 6.4,  2.8,  5.6,  2.2],\n",
       "       [ 6.3,  2.8,  5.1,  1.5],\n",
       "       [ 6.1,  2.6,  5.6,  1.4],\n",
       "       [ 7.7,  3. ,  6.1,  2.3],\n",
       "       [ 6.3,  3.4,  5.6,  2.4],\n",
       "       [ 6.4,  3.1,  5.5,  1.8],\n",
       "       [ 6. ,  3. ,  4.8,  1.8],\n",
       "       [ 6.9,  3.1,  5.4,  2.1],\n",
       "       [ 6.7,  3.1,  5.6,  2.4],\n",
       "       [ 6.9,  3.1,  5.1,  2.3],\n",
       "       [ 5.8,  2.7,  5.1,  1.9],\n",
       "       [ 6.8,  3.2,  5.9,  2.3],\n",
       "       [ 6.7,  3.3,  5.7,  2.5],\n",
       "       [ 6.7,  3. ,  5.2,  2.3],\n",
       "       [ 6.3,  2.5,  5. ,  1.9],\n",
       "       [ 6.5,  3. ,  5.2,  2. ],\n",
       "       [ 6.2,  3.4,  5.4,  2.3],\n",
       "       [ 5.9,  3. ,  5.1,  1.8]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "iris = datasets.load_iris()\n",
    "data= iris.data\n",
    "target= iris.target\n",
    "\n",
    "from sklearn import cross_validation\n",
    "train, test, t_train, t_test = cross_validation.train_test_split(data, target,test_size=0.4, random_state=0)\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "train_test_split是交叉验证中常用的函数，功能是从样本中随机的按比例选取train data和testdata，形式为：\n",
    "\n",
    "X_train,X_test, y_train, y_test =\n",
    "\n",
    "cross_validation.train_test_split(train_data,train_target,test_size=0.4, random_state=0)\n",
    "•参数解释：\n",
    "\n",
    "train_data：所要划分的样本特征集\n",
    "\n",
    "train_target：所要划分的样本结果\n",
    "\n",
    "test_size：样本占比，如果是整数的话就是样本的数量\n",
    "\n",
    "random_state：是随机数的种子。\n",
    "\n",
    "随机数种子：其实就是该组随机数的编号，在需要重复试验的时候，保证得到一组一样的随机数。比如你每次都填1，其他参数一样的情况下你得到的随机数组是一样的。但填0或不填，每次都会不一样。\n",
    "\n",
    "随机数的产生取决于种子，随机数和种子之间的关系遵从以下两个规则：\n",
    "\n",
    "种子不同，产生不同的随机数；种子相同，即使实例不同也产生相同的随机数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## 下面使用的数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.1  3.5  1.4  0.2]\n",
      " [ 4.9  3.   1.4  0.2]\n",
      " [ 4.7  3.2  1.3  0.2]\n",
      " [ 4.6  3.1  1.5  0.2]\n",
      " [ 5.   3.6  1.4  0.2]\n",
      " [ 5.4  3.9  1.7  0.4]\n",
      " [ 4.6  3.4  1.4  0.3]\n",
      " [ 5.   3.4  1.5  0.2]\n",
      " [ 4.4  2.9  1.4  0.2]\n",
      " [ 4.9  3.1  1.5  0.1]\n",
      " [ 5.4  3.7  1.5  0.2]\n",
      " [ 4.8  3.4  1.6  0.2]\n",
      " [ 4.8  3.   1.4  0.1]\n",
      " [ 4.3  3.   1.1  0.1]\n",
      " [ 5.8  4.   1.2  0.2]\n",
      " [ 5.7  4.4  1.5  0.4]\n",
      " [ 5.4  3.9  1.3  0.4]\n",
      " [ 5.1  3.5  1.4  0.3]\n",
      " [ 5.7  3.8  1.7  0.3]\n",
      " [ 5.1  3.8  1.5  0.3]\n",
      " [ 5.4  3.4  1.7  0.2]\n",
      " [ 5.1  3.7  1.5  0.4]\n",
      " [ 4.6  3.6  1.   0.2]\n",
      " [ 5.1  3.3  1.7  0.5]\n",
      " [ 4.8  3.4  1.9  0.2]\n",
      " [ 5.   3.   1.6  0.2]\n",
      " [ 5.   3.4  1.6  0.4]\n",
      " [ 5.2  3.5  1.5  0.2]\n",
      " [ 5.2  3.4  1.4  0.2]\n",
      " [ 4.7  3.2  1.6  0.2]\n",
      " [ 4.8  3.1  1.6  0.2]\n",
      " [ 5.4  3.4  1.5  0.4]\n",
      " [ 5.2  4.1  1.5  0.1]\n",
      " [ 5.5  4.2  1.4  0.2]\n",
      " [ 4.9  3.1  1.5  0.1]\n",
      " [ 5.   3.2  1.2  0.2]\n",
      " [ 5.5  3.5  1.3  0.2]\n",
      " [ 4.9  3.1  1.5  0.1]\n",
      " [ 4.4  3.   1.3  0.2]\n",
      " [ 5.1  3.4  1.5  0.2]\n",
      " [ 5.   3.5  1.3  0.3]\n",
      " [ 4.5  2.3  1.3  0.3]\n",
      " [ 4.4  3.2  1.3  0.2]\n",
      " [ 5.   3.5  1.6  0.6]\n",
      " [ 5.1  3.8  1.9  0.4]\n",
      " [ 4.8  3.   1.4  0.3]\n",
      " [ 5.1  3.8  1.6  0.2]\n",
      " [ 4.6  3.2  1.4  0.2]\n",
      " [ 5.3  3.7  1.5  0.2]\n",
      " [ 5.   3.3  1.4  0.2]\n",
      " [ 7.   3.2  4.7  1.4]\n",
      " [ 6.4  3.2  4.5  1.5]\n",
      " [ 6.9  3.1  4.9  1.5]\n",
      " [ 5.5  2.3  4.   1.3]\n",
      " [ 6.5  2.8  4.6  1.5]\n",
      " [ 5.7  2.8  4.5  1.3]\n",
      " [ 6.3  3.3  4.7  1.6]\n",
      " [ 4.9  2.4  3.3  1. ]\n",
      " [ 6.6  2.9  4.6  1.3]\n",
      " [ 5.2  2.7  3.9  1.4]\n",
      " [ 5.   2.   3.5  1. ]\n",
      " [ 5.9  3.   4.2  1.5]\n",
      " [ 6.   2.2  4.   1. ]\n",
      " [ 6.1  2.9  4.7  1.4]\n",
      " [ 5.6  2.9  3.6  1.3]\n",
      " [ 6.7  3.1  4.4  1.4]\n",
      " [ 5.6  3.   4.5  1.5]\n",
      " [ 5.8  2.7  4.1  1. ]\n",
      " [ 6.2  2.2  4.5  1.5]\n",
      " [ 5.6  2.5  3.9  1.1]\n",
      " [ 5.9  3.2  4.8  1.8]\n",
      " [ 6.1  2.8  4.   1.3]\n",
      " [ 6.3  2.5  4.9  1.5]\n",
      " [ 6.1  2.8  4.7  1.2]\n",
      " [ 6.4  2.9  4.3  1.3]\n",
      " [ 6.6  3.   4.4  1.4]\n",
      " [ 6.8  2.8  4.8  1.4]\n",
      " [ 6.7  3.   5.   1.7]\n",
      " [ 6.   2.9  4.5  1.5]\n",
      " [ 5.7  2.6  3.5  1. ]\n",
      " [ 5.5  2.4  3.8  1.1]\n",
      " [ 5.5  2.4  3.7  1. ]\n",
      " [ 5.8  2.7  3.9  1.2]\n",
      " [ 6.   2.7  5.1  1.6]\n",
      " [ 5.4  3.   4.5  1.5]\n",
      " [ 6.   3.4  4.5  1.6]\n",
      " [ 6.7  3.1  4.7  1.5]\n",
      " [ 6.3  2.3  4.4  1.3]\n",
      " [ 5.6  3.   4.1  1.3]\n",
      " [ 5.5  2.5  4.   1.3]\n",
      " [ 5.5  2.6  4.4  1.2]\n",
      " [ 6.1  3.   4.6  1.4]\n",
      " [ 5.8  2.6  4.   1.2]\n",
      " [ 5.   2.3  3.3  1. ]\n",
      " [ 5.6  2.7  4.2  1.3]\n",
      " [ 5.7  3.   4.2  1.2]\n",
      " [ 5.7  2.9  4.2  1.3]\n",
      " [ 6.2  2.9  4.3  1.3]\n",
      " [ 5.1  2.5  3.   1.1]\n",
      " [ 5.7  2.8  4.1  1.3]\n",
      " [ 6.3  3.3  6.   2.5]\n",
      " [ 5.8  2.7  5.1  1.9]\n",
      " [ 7.1  3.   5.9  2.1]\n",
      " [ 6.3  2.9  5.6  1.8]\n",
      " [ 6.5  3.   5.8  2.2]\n",
      " [ 7.6  3.   6.6  2.1]\n",
      " [ 4.9  2.5  4.5  1.7]\n",
      " [ 7.3  2.9  6.3  1.8]\n",
      " [ 6.7  2.5  5.8  1.8]\n",
      " [ 7.2  3.6  6.1  2.5]\n",
      " [ 6.5  3.2  5.1  2. ]\n",
      " [ 6.4  2.7  5.3  1.9]\n",
      " [ 6.8  3.   5.5  2.1]\n",
      " [ 5.7  2.5  5.   2. ]\n",
      " [ 5.8  2.8  5.1  2.4]\n",
      " [ 6.4  3.2  5.3  2.3]\n",
      " [ 6.5  3.   5.5  1.8]\n",
      " [ 7.7  3.8  6.7  2.2]\n",
      " [ 7.7  2.6  6.9  2.3]\n",
      " [ 6.   2.2  5.   1.5]\n",
      " [ 6.9  3.2  5.7  2.3]\n",
      " [ 5.6  2.8  4.9  2. ]\n",
      " [ 7.7  2.8  6.7  2. ]\n",
      " [ 6.3  2.7  4.9  1.8]\n",
      " [ 6.7  3.3  5.7  2.1]\n",
      " [ 7.2  3.2  6.   1.8]\n",
      " [ 6.2  2.8  4.8  1.8]\n",
      " [ 6.1  3.   4.9  1.8]\n",
      " [ 6.4  2.8  5.6  2.1]\n",
      " [ 7.2  3.   5.8  1.6]\n",
      " [ 7.4  2.8  6.1  1.9]\n",
      " [ 7.9  3.8  6.4  2. ]\n",
      " [ 6.4  2.8  5.6  2.2]\n",
      " [ 6.3  2.8  5.1  1.5]\n",
      " [ 6.1  2.6  5.6  1.4]\n",
      " [ 7.7  3.   6.1  2.3]\n",
      " [ 6.3  3.4  5.6  2.4]\n",
      " [ 6.4  3.1  5.5  1.8]\n",
      " [ 6.   3.   4.8  1.8]\n",
      " [ 6.9  3.1  5.4  2.1]\n",
      " [ 6.7  3.1  5.6  2.4]\n",
      " [ 6.9  3.1  5.1  2.3]\n",
      " [ 5.8  2.7  5.1  1.9]\n",
      " [ 6.8  3.2  5.9  2.3]\n",
      " [ 6.7  3.3  5.7  2.5]\n",
      " [ 6.7  3.   5.2  2.3]\n",
      " [ 6.3  2.5  5.   1.9]\n",
      " [ 6.5  3.   5.2  2. ]\n",
      " [ 6.2  3.4  5.4  2.3]\n",
      " [ 5.9  3.   5.1  1.8]]\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "iris = datasets.load_iris()\n",
    "data= iris.data\n",
    "target= iris.target\n",
    "print data\n",
    "print target\n",
    "from sklearn import cross_validation\n",
    "train, test, t_train, t_test = cross_validation.train_test_split(data, target,test_size=0.4, random_state=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 贝叶斯分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1 0 2 0 2 0 1 1 1 1 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
      " 1 1 1 2 0 2 0 0 1 2 2 1 2 1 2 1 1 2 1 1 2 1 2]\n",
      "0.933333333333\n",
      "0.977777777778\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        16\n",
      "          1       0.85      1.00      0.92        23\n",
      "          2       1.00      0.81      0.89        21\n",
      "\n",
      "avg / total       0.94      0.93      0.93        60\n",
      "\n",
      "[[16  0  0]\n",
      " [ 0 23  0]\n",
      " [ 0  4 17]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "model = GaussianNB()\n",
    "\n",
    "model.fit(train,t_train) # train\n",
    "\n",
    "expected = t_test\n",
    "predicted = model.predict(test)  ###\n",
    "\n",
    "print (model.predict(test))\n",
    "\n",
    "print (model.score(test,t_test)) # test\n",
    "\n",
    "print (model.score(train,t_train)) # test\n",
    "#0.93333333333333335\n",
    "\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  3.03499549e-01  -2.37639315e+02   5.10530605e+02   3.27736980e+02\n",
      "  -8.14131709e+02   4.92814588e+02   1.02848452e+02   1.84606489e+02\n",
      "   7.43519617e+02   7.60951722e+01]\n",
      "{'coefficient': array([  3.03499549e-01,  -2.37639315e+02,   5.10530605e+02,\n",
      "         3.27736980e+02,  -8.14131709e+02,   4.92814588e+02,\n",
      "         1.02848452e+02,   1.84606489e+02,   7.43519617e+02,\n",
      "         7.60951722e+01]), 'intercept': 152.76430691633442}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2004.5676026898211"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "regr = linear_model.LinearRegression()  ########\n",
    "\n",
    "regr.fit(diabetes_X_train, diabetes_y_train)\n",
    "\n",
    "print(regr.coef_)\n",
    "\n",
    "predictions={}\n",
    "predictions['intercept'] = regr.intercept_\n",
    "predictions['coefficient'] = regr.coef_\n",
    "\n",
    "print predictions\n",
    "\n",
    "# The mean square error\n",
    "np.mean((regr.predict(diabetes_X_test)-diabetes_y_test)**2)\n",
    "# Explained variance score: 1 is perfect prediction# and 0 means that there is no linear relationsh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "\n",
    "model.fit(x,y)\n",
    "\n",
    "print (model.coef_)\n",
    "predictions={}\n",
    "predictions['intercept'] = model.intercept_\n",
    "predictions['coefficient'] = model.coef_\n",
    "\n",
    "print (predictions)\n",
    "\n",
    "# The mean square error\n",
    "np.mean((model.predict(x)-y)**2)\n",
    "\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "print mean_squared_error(model.predict(x),y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        16\n",
      "          1       1.00      0.78      0.88        23\n",
      "          2       0.81      1.00      0.89        21\n",
      "\n",
      "avg / total       0.93      0.92      0.92        60\n",
      "\n",
      "[[16  0  0]\n",
      " [ 0 18  5]\n",
      " [ 0  0 21]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "\n",
    "model.fit(train,t_train)\n",
    "\n",
    "expected = t_test\n",
    "predicted = model.predict(test)  ###\n",
    "\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K近邻"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "     setosa       1.00      1.00      1.00        16\n",
      " versicolor       0.92      0.96      0.94        23\n",
      "  virginica       0.95      0.90      0.93        21\n",
      "\n",
      "avg / total       0.95      0.95      0.95        60\n",
      "\n",
      "[[16  0  0]\n",
      " [ 0 22  1]\n",
      " [ 0  2 19]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier  ####\n",
    "\n",
    "# fit a k-nearest neighbor model to the data\n",
    "model = KNeighborsClassifier() ####\n",
    "\n",
    "model.fit(train,t_train)\n",
    "\n",
    "expected = t_test\n",
    "predicted = model.predict(test)  ###\n",
    "\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets, neighbors, linear_model\n",
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data\n",
    "y_digits = digits.target\n",
    "n_samples = len(X_digits)\n",
    "\n",
    "train_n=int(0.9 * n_samples)\n",
    "X_train = X_digits[:train_n]\n",
    "y_train = y_digits[:train_n]\n",
    "X_test = X_digits[train_n:]\n",
    "y_test = y_digits[train_n:]\n",
    "print ('X_train: \\n', X_train)\n",
    "print ('y_train:  \\n ', y_train)\n",
    "\n",
    "\n",
    "knn = neighbors.KNeighborsClassifier()\n",
    "print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))\n",
    "\n",
    "logistic = linear_model.LogisticRegression()\n",
    "print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best')\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        16\n",
      "          1       0.92      0.96      0.94        23\n",
      "          2       0.95      0.90      0.93        21\n",
      "\n",
      "avg / total       0.95      0.95      0.95        60\n",
      "\n",
      "[[16  0  0]\n",
      " [ 0 22  1]\n",
      " [ 0  2 19]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier   ####\n",
    "\n",
    "model = DecisionTreeClassifier()   ####\n",
    "\n",
    "print model\n",
    "\n",
    "model.fit(train,t_train)\n",
    "\n",
    "expected = t_test\n",
    "predicted = model.predict(test)  ###\n",
    "\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 支持向量机"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00        16\n",
      "          1       0.92      0.96      0.94        23\n",
      "          2       0.95      0.90      0.93        21\n",
      "\n",
      "avg / total       0.95      0.95      0.95        60\n",
      "\n",
      "[[16  0  0]\n",
      " [ 0 22  1]\n",
      " [ 0  2 19]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.svm import SVC   ####\n",
    "\n",
    "model = SVC()   ####\n",
    "model.fit(train,t_train)\n",
    "\n",
    "print model\n",
    "\n",
    "model.fit(train,t_train)\n",
    "\n",
    "expected = t_test\n",
    "predicted = model.predict(test)  ###\n",
    "\n",
    "# summarize the fit of the model\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "print(metrics.confusion_matrix(expected, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "iris_X = iris.data\n",
    "indices = np.random.permutation(len(iris_X))\n",
    "iris_X_train = iris_X[indices[:-10]]\n",
    "iris_y_train = iris_y[indices[:-10]]\n",
    "iris_X_test  = iris_X[indices[-10:]]\n",
    "iris_y_test  = iris_y[indices[-10:]]\n",
    "\n",
    "\n",
    "from sklearn import svm\n",
    "\n",
    "svc = svm.SVC(kernel='linear') #kernel='rbf'\n",
    "svc.fit(iris_X_train, iris_y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 2 2 2 2 2 0 0 0 0 0]\n",
      "[0 0 0 0 0 1 1 1 1 1 2 2 2 2 2]\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import cluster, datasets\n",
    "iris = datasets.load_iris()\n",
    "X_iris = iris.data\n",
    "y_iris = iris.target\n",
    "k_means = cluster.KMeans(n_clusters=3)\n",
    "k_means.fit(X_iris) \n",
    "\n",
    "print(k_means.labels_[::10])\n",
    "print(y_iris[::10])\n",
    "\n",
    "print (y_iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 神经网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如何用Python和SciKit Learn 0.18实现神经网络【***】\n",
    "\n",
    "GitHub 链接：https://github.com/Rogerh91/Springboard-Blog-Tutorials/blob/master/Neural%20Networks%20/JMPortilla_SpringBoard_Blog_Neural_Network.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本文的分析主题为葡萄酒。葡萄酒伪劣品是一件非常现实的事情，让我们来看看 Python 的神经网络是否可以帮助解决这个问题。我们将使用 UCI 机器学习库中的葡萄酒数据集。它具有不同葡萄酒的各种化学特征，均在意大利同一地区生长，但数据标签分类为三种不同的品种。我们将尝试建立一个可以根据其化学特征对葡萄酒品种进行分类的神经网络模型。\n",
    "\n",
    "数据集链接：https://archive.ics.uci.edu/ml/datasets/Wine\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "wine = pd.read_csv('wine_data.csv', names = [\"Cultivator\", \"Alchol\", \"Malic_Acid\", \"Ash\", \"Alcalinity_of_Ash\", \"Magnesium\", \"Total_phenols\", \"Falvanoids\", \"Nonflavanoid_phenols\", \"Proanthocyanins\", \"Color_intensity\", \"Hue\", \"OD280\", \"Proline\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Cultivator</th>\n",
       "      <th>Alchol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity_of_Ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total_phenols</th>\n",
       "      <th>Falvanoids</th>\n",
       "      <th>Nonflavanoid_phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color_intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cultivator  Alchol  Malic_Acid   Ash  Alcalinity_of_Ash  Magnesium  \\\n",
       "0           1   14.23        1.71  2.43               15.6        127   \n",
       "1           1   13.20        1.78  2.14               11.2        100   \n",
       "2           1   13.16        2.36  2.67               18.6        101   \n",
       "3           1   14.37        1.95  2.50               16.8        113   \n",
       "4           1   13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total_phenols  Falvanoids  Nonflavanoid_phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color_intensity   Hue  OD280  Proline  \n",
       "0             5.64  1.04   3.92     1065  \n",
       "1             4.38  1.05   3.40     1050  \n",
       "2             5.68  1.03   3.17     1185  \n",
       "3             7.80  0.86   3.45     1480  \n",
       "4             4.32  1.04   2.93      735  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cultivator</th>\n",
       "      <td>178.0</td>\n",
       "      <td>1.938202</td>\n",
       "      <td>0.775035</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alchol</th>\n",
       "      <td>178.0</td>\n",
       "      <td>13.000618</td>\n",
       "      <td>0.811827</td>\n",
       "      <td>11.03</td>\n",
       "      <td>12.3625</td>\n",
       "      <td>13.050</td>\n",
       "      <td>13.6775</td>\n",
       "      <td>14.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malic_Acid</th>\n",
       "      <td>178.0</td>\n",
       "      <td>2.336348</td>\n",
       "      <td>1.117146</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.6025</td>\n",
       "      <td>1.865</td>\n",
       "      <td>3.0825</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ash</th>\n",
       "      <td>178.0</td>\n",
       "      <td>2.366517</td>\n",
       "      <td>0.274344</td>\n",
       "      <td>1.36</td>\n",
       "      <td>2.2100</td>\n",
       "      <td>2.360</td>\n",
       "      <td>2.5575</td>\n",
       "      <td>3.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alcalinity_of_Ash</th>\n",
       "      <td>178.0</td>\n",
       "      <td>19.494944</td>\n",
       "      <td>3.339564</td>\n",
       "      <td>10.60</td>\n",
       "      <td>17.2000</td>\n",
       "      <td>19.500</td>\n",
       "      <td>21.5000</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magnesium</th>\n",
       "      <td>178.0</td>\n",
       "      <td>99.741573</td>\n",
       "      <td>14.282484</td>\n",
       "      <td>70.00</td>\n",
       "      <td>88.0000</td>\n",
       "      <td>98.000</td>\n",
       "      <td>107.0000</td>\n",
       "      <td>162.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_phenols</th>\n",
       "      <td>178.0</td>\n",
       "      <td>2.295112</td>\n",
       "      <td>0.625851</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.7425</td>\n",
       "      <td>2.355</td>\n",
       "      <td>2.8000</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Falvanoids</th>\n",
       "      <td>178.0</td>\n",
       "      <td>2.029270</td>\n",
       "      <td>0.998859</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.2050</td>\n",
       "      <td>2.135</td>\n",
       "      <td>2.8750</td>\n",
       "      <td>5.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nonflavanoid_phenols</th>\n",
       "      <td>178.0</td>\n",
       "      <td>0.361854</td>\n",
       "      <td>0.124453</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.2700</td>\n",
       "      <td>0.340</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <td>178.0</td>\n",
       "      <td>1.590899</td>\n",
       "      <td>0.572359</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>1.555</td>\n",
       "      <td>1.9500</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Color_intensity</th>\n",
       "      <td>178.0</td>\n",
       "      <td>5.058090</td>\n",
       "      <td>2.318286</td>\n",
       "      <td>1.28</td>\n",
       "      <td>3.2200</td>\n",
       "      <td>4.690</td>\n",
       "      <td>6.2000</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hue</th>\n",
       "      <td>178.0</td>\n",
       "      <td>0.957449</td>\n",
       "      <td>0.228572</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.7825</td>\n",
       "      <td>0.965</td>\n",
       "      <td>1.1200</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OD280</th>\n",
       "      <td>178.0</td>\n",
       "      <td>2.611685</td>\n",
       "      <td>0.709990</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.9375</td>\n",
       "      <td>2.780</td>\n",
       "      <td>3.1700</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Proline</th>\n",
       "      <td>178.0</td>\n",
       "      <td>746.893258</td>\n",
       "      <td>314.907474</td>\n",
       "      <td>278.00</td>\n",
       "      <td>500.5000</td>\n",
       "      <td>673.500</td>\n",
       "      <td>985.0000</td>\n",
       "      <td>1680.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count        mean         std     min       25%  \\\n",
       "Cultivator            178.0    1.938202    0.775035    1.00    1.0000   \n",
       "Alchol                178.0   13.000618    0.811827   11.03   12.3625   \n",
       "Malic_Acid            178.0    2.336348    1.117146    0.74    1.6025   \n",
       "Ash                   178.0    2.366517    0.274344    1.36    2.2100   \n",
       "Alcalinity_of_Ash     178.0   19.494944    3.339564   10.60   17.2000   \n",
       "Magnesium             178.0   99.741573   14.282484   70.00   88.0000   \n",
       "Total_phenols         178.0    2.295112    0.625851    0.98    1.7425   \n",
       "Falvanoids            178.0    2.029270    0.998859    0.34    1.2050   \n",
       "Nonflavanoid_phenols  178.0    0.361854    0.124453    0.13    0.2700   \n",
       "Proanthocyanins       178.0    1.590899    0.572359    0.41    1.2500   \n",
       "Color_intensity       178.0    5.058090    2.318286    1.28    3.2200   \n",
       "Hue                   178.0    0.957449    0.228572    0.48    0.7825   \n",
       "OD280                 178.0    2.611685    0.709990    1.27    1.9375   \n",
       "Proline               178.0  746.893258  314.907474  278.00  500.5000   \n",
       "\n",
       "                          50%       75%      max  \n",
       "Cultivator              2.000    3.0000     3.00  \n",
       "Alchol                 13.050   13.6775    14.83  \n",
       "Malic_Acid              1.865    3.0825     5.80  \n",
       "Ash                     2.360    2.5575     3.23  \n",
       "Alcalinity_of_Ash      19.500   21.5000    30.00  \n",
       "Magnesium              98.000  107.0000   162.00  \n",
       "Total_phenols           2.355    2.8000     3.88  \n",
       "Falvanoids              2.135    2.8750     5.08  \n",
       "Nonflavanoid_phenols    0.340    0.4375     0.66  \n",
       "Proanthocyanins         1.555    1.9500     3.58  \n",
       "Color_intensity         4.690    6.2000    13.00  \n",
       "Hue                     0.965    1.1200     1.71  \n",
       "OD280                   2.780    3.1700     4.00  \n",
       "Proline               673.500  985.0000  1680.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 14)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 178 data points with 13 features and 1 label column\n",
    "wine.shape\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 准备训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = wine.drop('Cultivator',axis=1)\n",
    "y = wine['Cultivator']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(X_train)\n",
    "#StandardScaler(copy=True, with_mean=True, with_std=True)\n",
    "\n",
    "X_train = scaler.transform(X_train)\n",
    "\n",
    "X_test = scaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 使用sklearn.preprocessing.StandardScaler类，使用该类的好处在于可以保存训练集中的参数（均值、方差）直接使用其对象转换测试集数据。"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "scaler = StandardScaler().fit(X)\n",
    "\n",
    "scaler\n",
    "\n",
    "scaler.mean_                                      \n",
    "\n",
    "#array([ 1. ...,  0. ...,  0.33...])\n",
    "\n",
    "scaler.std_                                       \n",
    "\n",
    "#array([ 0.81...,  0.81...,  1.24...])\n",
    "\n",
    "scaler.transform(X)                               \n",
    "\n",
    "#array([[ 0.  ..., -1.22...,  1.33...],\n",
    "\n",
    "#       [ 1.22...,  0.  ..., -0.26...],\n",
    "\n",
    "#       [-1.22...,  1.22..., -1.06...]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们来训练模型。SciKit-learn 使用 estimator（估计量）对象。我们将从 SciKit-Learn 的 neural_network 库导入我们的估计量（多层感知器分类器模型/MLP）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们创建一个模型的实例，可以自定义很多参数，我们将只定义 hidden_layer_sizes 参数。此参数传入的是一个元组，表示计划在每个层的神经元数量，其中元组中的第 n 个元素表示 MLP 模型第 n 层中的神经元数量。有很多参数可供选择，但是为了简单起见，我们将选择具有相同数量神经元的 3 层神经网络，每层的神经元数量与数据的特征数相同（13），并将最大迭代次数设置为 500 次。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(5,4,3),max_iter=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.71154722 -0.83293567  1.22720537  0.14464654 -0.37953804  0.755904\n",
      "  0.93287277 -0.61872602  1.66148197 -1.00367782  0.01853102  0.99370121\n",
      " -0.20758636]\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yuli\\AppData\\Local\\Continuum\\Anaconda3\\envs\\Python27\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
      "  % (), ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(5, 4, 3), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=500, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print X_train[0]\n",
    "print type(y_train) #Serries\n",
    "\n",
    "mlp.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = mlp.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12  1  0]\n",
      " [ 1 21  1]\n",
      " [ 0  0  9]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "\n",
    "print(confusion_matrix(y_test,predictions))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.92      0.92      0.92        13\n",
      "          2       0.95      0.91      0.93        23\n",
      "          3       0.90      1.00      0.95         9\n",
      "\n",
      "avg / total       0.93      0.93      0.93        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "结果不错！看来测试集中我们只错误分类了 1 瓶葡萄酒！考虑到我们的 Python 神经网络的代码行很少，这个效果是非常好的。然而，多层感知器模型的缺点在于解释模型本身。例如，模型的权重和偏差与数据特征的关系以及哪些特征最重要将不容易解释。\n",
    "\n",
    "如果想查看 MLP 训练模型得到的权重和偏差，使用 public 属性 coefs_ 和 intercepts_。\n",
    "\n",
    "coefs_ 是权重矩阵的列表，其中索引 i 处的权重矩阵表示层 i 和层 i+1 之间的权重。\n",
    "\n",
    "intercepts_ 是偏差向量的列表，其中索引 i 处的向量表示添加到层 i+1 的偏差值。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 0.3334656 ,  0.1963113 , -0.78004223, -0.69121645, -0.00162402],\n",
      "       [ 0.7181468 , -0.03145861,  0.4238024 ,  0.03938195,  0.37708547],\n",
      "       [-0.18276947,  0.16225809,  0.24203913, -0.79769423, -0.09741635],\n",
      "       [-0.68884032, -0.05278225,  0.27609942,  0.15868726,  0.14975873],\n",
      "       [-0.01138432, -0.22881671, -0.47807436, -0.27305632,  0.3662932 ],\n",
      "       [ 0.30733055, -0.46757901, -0.62469162,  0.26954857,  0.12050697],\n",
      "       [ 1.00843851, -1.00401997,  0.04708207, -0.26119794, -0.45885907],\n",
      "       [-0.00172866, -0.06066618,  0.22152568,  0.15294015,  0.13023803],\n",
      "       [-0.32215151, -0.45139988,  0.36678091,  0.13726917, -0.29302762],\n",
      "       [ 0.43648999,  0.97786165, -0.41163859, -0.67470876,  0.11215247],\n",
      "       [ 0.60453288,  0.11314865,  0.13186717,  0.70532563, -0.56590094],\n",
      "       [ 1.04598987, -0.75187977, -0.791392  ,  0.65833259, -0.40989129],\n",
      "       [ 0.79301039, -0.53388075, -0.19475807,  0.01378644,  0.09423425]]), array([[-0.34629169, -0.32788773,  1.20824451, -0.55023072],\n",
      "       [ 0.5770083 ,  0.55896471, -0.21731978,  0.82345355],\n",
      "       [ 1.16984048,  0.0383347 ,  0.2019638 ,  0.46220823],\n",
      "       [ 1.25812275, -0.42275916, -0.70899322, -0.73752058],\n",
      "       [-0.1173686 , -0.19348006, -0.58655318,  0.67908274]]), array([[ 0.34596632,  0.64662483,  1.22372704],\n",
      "       [-0.30425113, -0.02589355,  0.05331896],\n",
      "       [ 0.41847678, -0.25872919, -0.38249423],\n",
      "       [-0.30262958, -0.70093946,  0.3301845 ]]), array([[ 1.24757866,  0.09431943, -1.35807452],\n",
      "       [-0.76183213,  0.26787287, -1.20908575],\n",
      "       [-0.88523765,  0.3185924 ,  0.7451357 ]])]\n"
     ]
    }
   ],
   "source": [
    "print mlp.coefs_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.3334656   0.1963113  -0.78004223 -0.69121645 -0.00162402]\n",
      " [ 0.7181468  -0.03145861  0.4238024   0.03938195  0.37708547]\n",
      " [-0.18276947  0.16225809  0.24203913 -0.79769423 -0.09741635]\n",
      " [-0.68884032 -0.05278225  0.27609942  0.15868726  0.14975873]\n",
      " [-0.01138432 -0.22881671 -0.47807436 -0.27305632  0.3662932 ]\n",
      " [ 0.30733055 -0.46757901 -0.62469162  0.26954857  0.12050697]\n",
      " [ 1.00843851 -1.00401997  0.04708207 -0.26119794 -0.45885907]\n",
      " [-0.00172866 -0.06066618  0.22152568  0.15294015  0.13023803]\n",
      " [-0.32215151 -0.45139988  0.36678091  0.13726917 -0.29302762]\n",
      " [ 0.43648999  0.97786165 -0.41163859 -0.67470876  0.11215247]\n",
      " [ 0.60453288  0.11314865  0.13186717  0.70532563 -0.56590094]\n",
      " [ 1.04598987 -0.75187977 -0.791392    0.65833259 -0.40989129]\n",
      " [ 0.79301039 -0.53388075 -0.19475807  0.01378644  0.09423425]]\n"
     ]
    }
   ],
   "source": [
    "print mlp.coefs_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.34629169 -0.32788773  1.20824451 -0.55023072]\n",
      " [ 0.5770083   0.55896471 -0.21731978  0.82345355]\n",
      " [ 1.16984048  0.0383347   0.2019638   0.46220823]\n",
      " [ 1.25812275 -0.42275916 -0.70899322 -0.73752058]\n",
      " [-0.1173686  -0.19348006 -0.58655318  0.67908274]]\n"
     ]
    }
   ],
   "source": [
    "print mlp.coefs_[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.34596632  0.64662483  1.22372704]\n",
      " [-0.30425113 -0.02589355  0.05331896]\n",
      " [ 0.41847678 -0.25872919 -0.38249423]\n",
      " [-0.30262958 -0.70093946  0.3301845 ]]\n"
     ]
    }
   ],
   "source": [
    "print mlp.coefs_[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.24757866  0.09431943 -1.35807452]\n",
      " [-0.76183213  0.26787287 -1.20908575]\n",
      " [-0.88523765  0.3185924   0.7451357 ]]\n"
     ]
    }
   ],
   "source": [
    "print mlp.coefs_[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.41274066  0.35195916  0.84022064  0.17062269 -0.65291046]\n"
     ]
    }
   ],
   "source": [
    "print mlp.intercepts_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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