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看了半个礼拜的朴素贝叶斯，依然没有理解，想想还是跳过先看逻辑回归吧。

前面巴拉巴拉的话就不说了，下面直接贴代码。

5.2.2 训练算法

from math import *from numpy import *import osos.chdir('D:\xx\machinelearning\MLiA_SourceCode')def loadDataSet():    dataMat = []; labelMat = []    fr = open('testSet.txt')    for line in fr.readlines():        lineArr = line.strip().split()        dataMat.append([1.0, float(lineArr[0]),float(lineArr[1])])        labelMat.append(int(lineArr[2]))    return dataMat,labelMatdef sigmoid(inX):    return 1.0/(1 + exp(-inX))def gradAscent(dataMatIn,classLabels):    dataMatrix = mat(dataMatIn)    labelMat = mat(classLabels).transpose()    m,n = shape(dataMatrix)    alpha = 0.001    maxCycles = 500    weights = ones((n,1))    for k in range(maxCycles):        h = sigmoid(dataMatrix * weights)        error = (labelMat - h)        weights = weights + alpha * dataMatrix.transpose() * error    return weights

数据依然还是作者给的，用之前需要提前加载。

上面的代码没有什么大问题，就是在gradAscent函数中，作者给出了梯度计算

dataMatrix.transpose() * error

但是作者卖了个关子，原话是“最后还需说明一点，你可能对公式中的前两行觉得陌生。此处略去了一个简单的数学推导，我把它留给有兴趣的读者。” 显然作者高估了我的水平，我倒是挺有兴趣，但是没能力啊。有哪位大佬知道推导过程请联系我，感激不尽，临表涕零。

接着，照葫芦画瓢，测试一下：

5.2.3 画出决策边界

代码直接贴上：

def plotBestFit(weights):    import matplotlib.pyplot as plt    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)    y = (-weights[0] - weights[1] * x)/weights[2]    ax.plot(x,y)    plt.xlabel('X1');plt.ylabel('X2')    plt.show()

测试一下：

reload(logRegres)weights = logRegres.gradAscent(dataArr,labelMat)#注意原书上有有坑，labelMat敲成了LabelMatlogRegres.plotBestFit(weights.getA())

看一下结果：

5.2.4 训练算法：随机梯度上升

代码是：

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

看下结果：

这个分类器错分了三分之一的样本。

接下来进行改进：

def stocGradAscent1(dataMatrix,classLabels,numIter = 150):    m,n = shape(dataMatrix)    weights = ones(n)    for j in range(numIter):        dataIndex = list(range(m))#python3中range不返回数组对象，而是返回range对象，所以注意与原书的区别        for i in range(m):            alpha = 4 / (1.0+j+i) + 0.01            randIndex = int(random.uniform(0,len(dataIndex)))            h = sigmoid(sum(dataMatrix[randIndex] * weights))            error = classLabels[randIndex] - h            weights = weights + alpha * error * dataMatrix[randIndex]            del(dataIndex[randIndex])    return weights

看下结果：

这个结果与gradAscent()差不多的效果，所用计算也得到减少。

5.3 从疝气病症预测病马的死亡率

5.3.1 处理数据中的缺失值

1.使用0来替换缺失值

2.类别标签缺失，将该条数据丢弃

5.3.2测试算法，使用Logistic回归进行分类

这块代码比较简单，直接贴上代码：

def 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(float(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)))

测试结果为：

logRegres.multiTest()the error rate of this test is: 0.283582the error rate of this test is: 0.388060the error rate of this test is: 0.298507the error rate of this test is: 0.388060the error rate of this test is: 0.447761the error rate of this test is: 0.313433the error rate of this test is: 0.283582the error rate of this test is: 0.402985the error rate of this test is: 0.343284the error rate of this test is: 0.417910after 10 iterations the average error rate is:0.356716

这章逻辑回归的内容还是比较简单的，个人感觉数据预处理阶段实际上应该还有很多有技巧的手段，只有数据合适，后续的算法建立才能顺利进行。