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-8from 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, labelMatdef sigmoid(inX):    sig = 1.0/(1 + exp(-inX))    return sigdef 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  weightsdef 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 weightsdef 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  weightsdef classifyVector(inX ,weights):    prob = sigmoid(sum(inX * weights))    if prob >0.5:        return 1.0    else:        return 0.0def 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 errorRatedef 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