ch07-逻辑回归/Z4-前向逐步回归.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 前向逐步回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import pandas as pd\n",
    "from numpy import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDataSet(fileName):                 \n",
    "    \"\"\" 加载数据\n",
    "        解析以tab键分隔的文件中的浮点数\n",
    "    Returns：\n",
    "        dataMat ：  feature 对应的数据集\n",
    "        labelMat ： feature 对应的分类标签，即类别标签\n",
    "\n",
    "    \"\"\"\n",
    "    # 获取样本特征的总数，不算最后的目标变量 \n",
    "    numFeat = len(open(fileName).readline().split('\\t')) - 1 \n",
    "    dataMat = []\n",
    "    labelMat = []\n",
    "    fr = open(fileName)\n",
    "    for line in fr.readlines():\n",
    "        # 读取每一行\n",
    "        lineArr =[]\n",
    "        # 删除一行中以tab分隔的数据前后的空白符号\n",
    "        curLine = line.strip().split('\\t')\n",
    "        # i 从0到2，不包括2 \n",
    "        for i in range(numFeat):\n",
    "            # 将数据添加到lineArr List中，每一行数据测试数据组成一个行向量           \n",
    "            lineArr.append(float(curLine[i]))\n",
    "            # 将测试数据的输入数据部分存储到dataMat 的List中\n",
    "        dataMat.append(lineArr)\n",
    "        # 将每一行的最后一个数据，即类别，或者叫目标变量存储到labelMat List中\n",
    "        labelMat.append(float(curLine[-1]))\n",
    "    return dataMat,labelMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standRegres(xArr,yArr):\n",
    "    # mat()函数将xArr，yArr转换为矩阵 mat().T 代表的是对矩阵进行转置操作\n",
    "    xMat = mat(xArr)\n",
    "    yMat = mat(yArr).T\n",
    "    # 矩阵乘法的条件是左矩阵的列数等于右矩阵的行数\n",
    "    xTx = xMat.T*xMat\n",
    "    # 因为要用到xTx的逆矩阵，所以事先需要确定计算得到的xTx是否可逆，条件是矩阵的行列式不为0\n",
    "    # linalg.det() 函数是用来求得矩阵的行列式的，如果矩阵的行列式为0，则这个矩阵是不可逆的，就无法进行接下来的运算                   \n",
    "    if linalg.det(xTx) == 0.0:\n",
    "        print(\"This matrix is singular, cannot do inverse\")\n",
    "        return\n",
    "    # 最小二乘法，求得w的最优解\n",
    "    ws = xTx.I * (xMat.T*yMat)            \n",
    "    return ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rssError(yArr, yHatArr):\n",
    "    return ((yArr - yHatArr) ** 2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 按列进行规范化\n",
    "def regularize(xMat):\n",
    "    inMat = xMat.copy()\n",
    "    inMeans = mean(inMat, 0)  # 计算平均值然后减去它\n",
    "    inVar = var(inMat, 0)  # 计算除以Xi的方差\n",
    "    inMat = (inMat - inMeans) / inVar\n",
    "    return inMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stageWise(xArr,yArr,eps=0.01,numIt=100):\n",
    "    xMat = mat(xArr); yMat=mat(yArr).T\n",
    "    yMean = mean(yMat,0)\n",
    "    yMat = yMat - yMean     # 也可以规则化ys但会得到更小的coef\n",
    "    xMat = regularize(xMat)\n",
    "    m,n=shape(xMat)\n",
    "    returnMat = zeros((numIt,n)) # 测试代码删除\n",
    "    ws = zeros((n,1)); wsTest = ws.copy(); wsMax = ws.copy()\n",
    "    for i in range(numIt):\n",
    "        print (ws.T)\n",
    "        lowestError = inf; \n",
    "        for j in range(n):\n",
    "            for sign in [-1,1]:\n",
    "                wsTest = ws.copy()\n",
    "                wsTest[j] += eps*sign\n",
    "                yTest = xMat*wsTest\n",
    "                rssE = rssError(yMat.A,yTest.A)\n",
    "                if rssE < lowestError:\n",
    "                    lowestError = rssE\n",
    "                    wsMax = wsTest\n",
    "        ws = wsMax.copy()\n",
    "        returnMat[i,:]=ws.T\n",
    "    return returnMat\n",
    "\n",
    "\n",
    "#test for stageWise\n",
    "def regression4():\n",
    "    xArr,yArr=loadDataSet(\"8.Regression/abalone.txt\")\n",
    "    returnMat = stageWise(xArr,yArr,0.01,200)\n",
    "    \n",
    "    xMat = mat(xArr)\n",
    "    yMat = mat(yArr).T\n",
    "    xMat = regularize(xMat)\n",
    "    yM = mean(yMat,0)\n",
    "    yMat = yMat - yM\n",
    "    weights = standRegres(xMat, yMat.T)\n",
    "    return returnMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(ridgeWeights)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.matrixlib.defmatrix.matrix"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mat(eye(5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat(eye(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(eye(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}