Machine Learning in action --逻辑回归(已勘误)

最近在自学机器学习，应导师要求，先把《Machine Learning with R》动手刷了一遍，感觉R真不能算是一门计算机语言，感觉也就是一个功能复杂的计算器。所以这次就决定使用经典教材《Machine Learning in action》。因为开学得换work station ，怕到时候代码又丢了，所以就索性开个博客，把代码上传上来。

因为书上的原代码有很多错误，并且网上的许多博客的代码也是没有改正的，这次我把修正过的代码po上来

version：python3.5

talk is cheap show me the code

函数定义代码

#coding=utf-8
from numpy import *

def loadDataSet():
    dataMat = []
    labelMat = []
    fr = open("testSet.txt")
    lines = fr.readlines()
    for line in lines :
        lineArr = line.strip().split()
        #第一个特征为固定为 1
        dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])]) 
        labelMat.append(int(lineArr[2]))
    return dataMat, labelMat

def sigmoid(inX):
    sig = 1.0/(1 + exp(-inX))
    return sig

def gradAscent(dataMatIn , classMatIn):
    dataMatrix = mat(dataMatIn)
    labelMat = mat(classMatIn).transpose()
    m, n = shape(dataMatrix)
    alpha = 0.01
    maxCycle = 500
    weights = ones((n, 1))
    for k in range(maxCycle):
        h = sigmoid(dataMatrix * weights)
        error = (labelMat - h)
        weights = weights + alpha * dataMatrix.transpose() * error
    return  weights

def plotBestFit(wei):
    import matplotlib.pyplot as plt
    weights = wei
    #weights = wei.getA()
    dataMat, labelMat = loadDataSet()
    dataArr = array(dataMat)
    n = shape(dataArr)[0]
    xcord1 = []; ycord1 = []
    xcord2 = []; ycord2 = []
    for i in range(n):
        if int(labelMat[i]) == 1:
            xcord1.append(dataArr[i, 1])
            ycord1.append(dataArr[i, 2])
        else:
            xcord2.append(dataArr[i, 1])
            ycord2.append(dataArr[i, 2])
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(xcord1, ycord1, s = 30, c = 'red', marker = 's')
    ax.scatter(xcord2, ycord2, s = 30, c = 'green')
    x = arange(-3.0, 3.0, 0.1)
    #最佳拟合直线
    y = (-weights[0] - weights[1] * x )/weights[2]
    ax.plot(x, y)
    plt.xlabel("X1"); plt.ylabel("X2")
    plt.show()

def stocGradAscent0(dataMatrix, classLabels):
    m, n = shape(dataMatrix)
    alpha = 0.01
    weights = ones(n)
    for i in range(m):
        h = sigmoid(sum(dataMatrix[i] * weights))
        error = classLabels[i] - h
        weights = weights + alpha * error * dataMatrix[i]
    return weights

def stocGradAscent1(dataMatrix, classLabels, numIter = 150 ):
    m, n = shape(dataMatrix)
    weights = ones(n)
    for j in range(numIter):
        dataIndex = list(range(m))
        for i in range(m):
            alpha = 4/(10.+i+j) +0.01
            randIndex = int(random.uniform(0, len(dataIndex)))
            h = sigmoid(sum(dataMatrix[randIndex] * weights))
            #print(type(classLabels[randIndex]))
            error = float(classLabels[randIndex]) - h
            weights = weights + alpha * error *                   dataMatrix[dataIndex[randIndex]]
            del(dataIndex[randIndex])
    return  weights

def classifyVector(inX ,weights):
    prob = sigmoid(sum(inX * weights))
    if prob >0.5:
        return 1.0
    else:
        return 0.0

def colicTest():
    frTrain = open('horseColicTraining.txt')
    frTest = open('horseColicTest.txt')
    trainingSet = [] ; trainingLabels = []
    for line in frTrain.readlines():
        currLine = line.strip().split('\t')
        lineArr = []
        for i in range(21):
            lineArr.append(float(currLine[i]))
        trainingSet.append(lineArr)
        trainingLabels.append(currLine[21])
    trainWeights = stocGradAscent1(array(trainingSet), trainingLabels, 500)
    errorCount = 0
    numTestVec = 0.0
    for line in frTest.readlines():
        numTestVec += 1.0
        currLine = line.strip().split('\t')
        lineArr = []
        for i in range(21):
            lineArr.append(float(currLine[i]))
        if int(classifyVector(array(lineArr), trainWeights)) != int(currLine[21]):
            errorCount += 1
    errorRate =  float(errorCount) / numTestVec
    print("the error rate of this test is: %f" % errorRate)
    return errorRate

def multiTest():
    numTests = 10
    errorSum = 0.0
    for k in range(numTests):
        errorSum += colicTest()
    print("after %d iterations the average error rate is :%f "%(numTests, errorSum / float(numTests)))

上面代码块只是定义了主要的函数，离运行还差一点。由于书原文中，采用了使用 iPython 命令行的运行方式，但是博主比较懒，所以干脆舍弃掉原来的方式。

废话不多少，直接上代码

实验1

if __name__=="__main__":
    dataArr, labelMat = loadDataSet()
    #gradAscent(dataArr, labelMat)
    print(gradAscent(dataArr, labelMat))

实验2 ：

if __name__ == "__main__":
     dataArr, labelMat = loadDataSet()
    plotBestFit(gradAscent(dataArr, labelMat))

实验3 ：

if __name__ == "__main__":
    dataArr, labelMat = loadDataSet()
    weights = stocGradAscent0(array(dataArr), labelMat)
    plotBestFit(weights)

实验4 ：

if __name__ == "__main__":
    dataArr, labelMat = loadDataSet()
    weights = stocGradAscent1(array(dataArr), labelMat)
    plotBestFit(weights)

实验5 ：

if __name__ == "__main__":
    multiTest()

更多请戳github
https://github.com/Edgis/Machine-learning-in-action/blob/master/logRegres.py