Machine Learning in action –kNN(已勘误)

Machine Learning in action –kNN

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。

talk is cheap show me the code

函数定义代码

#coding=utf-8from numpy import *import operatorimport matplotlibimport matplotlib.pyplot as pltimport osdef classify0(inX, dataset, lables, k):    #shape返回矩阵的[行数， 列数]    #shape[0]就是行数    dataSetSize = dataset.shape[0]    #将 输入的inX转换为和dataSet一样的矩阵形式    matri_temp = tile(inX, (dataSetSize, 1))    #求 inX 到各个 train_data的距离    diffMat = matri_temp - dataset    sqDiffMat = diffMat**2    #平方和    sqDistance = sqDiffMat.sum(axis = 1)#按行累加 ，axis = 1 表示行    #对平方和开根号    distance = sqDistance**0.5    #按照升序排序，返回的是原数组的下标    sortedDistIndicies = distance.argsort()    #创建一个空字典    classCount = {}    #统计前k个最近的样本所属类别包含的样本个数    for i in range(k):        index = sortedDistIndicies[i]        votelable = lables[index]        #classCount.get(votelabel, 0 )返回voteIlabel的值，如果不存在，则返回0        classCount[votelable] = classCount.get(votelable, 0) + 1    #按照类别计数结果降序    sortedClassCount = sorted(classCount.items(), key = operator.itemgetter(1), reverse = True )    return sortedClassCount[0][0]def file2matrix(filename):    fr = open(filename)    arrayOlines = fr.readlines()    numOfLines = len(arrayOlines) #文件的行数    returnMat = zeros((numOfLines, 3)) #创建一个0的矩阵    #returnMat = [[] * 3] * numOfLines    classLabelVector = []    index = 0    for line in arrayOlines:        line = line.strip()        listFromLine = line.split('\t')        returnMat[index,:] = listFromLine[0:3]        #returnMat[index,i] = float(listFromLine[i])        classLabelVector.append(int(listFromLine[-1]))        index += 1    return returnMat, classLabelVector#归一化特征def autoNorm(dataset):    #每列的最小值    minVals = dataset.min(0)    #每列的最大值    maxVals = dataset.max(0)    ranges = maxVals - minVals    normDataSet = zeros(shape(dataset))    m = dataset.shape[0] #行数    normDataSet = dataset - tile(minVals, (m,1))    normDataSet = normDataSet / tile(ranges, (m,1))    return normDataSet, ranges, minValsdef img2vector(filename):    returnVect = zeros((1, 1024))    fr = open(filename)    for i in range(32):        lineStr = fr.readline()        for j in range(32):            returnVect[0, 32*i+j] = int(lineStr[j])    return returnVectdef handwritingClassTest():    hwlabels = []    trainingFileList = os.listdir('trainingDigits')    m = len(trainingFileList)    trainingMat = zeros((m,1024))    for i in range(m):        fileNameStr = trainingFileList[i]        fileStr = fileNameStr.split('.')[0]        classNumStr = int(fileStr.split('_')[0])        hwlabels.append(classNumStr)        trainingMat[i,:] = img2vector('trainingDigits/%s' %fileNameStr)    testFileList = os.listdir('testDigits')    errorCount = 0.0    mTest = len(testFileList)    for i in range(mTest):        fileNameStr = testFileList[i]        fileStr = fileNameStr.split('.')[0]        classNumStr = int(fileStr.split('_')[0])        vectorUnderTest = img2vector('testDigits/%s'%fileNameStr)        classifierResult = classify0(vectorUnderTest, trainingMat, hwlabels,3)        print("the classifier came back with :%d ,the real answer is :%d"\              %(classifierResult,classNumStr))        if(classifierResult != classNumStr):            errorCount += 1    print("\n the total number of error is %d"%errorCount)    print("\n the total number rate is :%f"%(errorCount/float(mTest)))

在撸代码的时候，犯了一个subtle and deadly 的错误，在 file2matrix中，误将 readlines() 写成了readline() ,虽然两者只相差一个 s ，但是确实天壤之别的含义。前者是读入所有的数据，后者是只读入一行数据，就这一点差别，让我debug了好几个小时，以后还得细心啊。

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，直接在代码最后添加

代码块

if __name__ == "__main__":

废话不多少，直接上代码

实验1 -分类

if __name__=="__main__":    #导入数据    dataset, lables = createDataSet()    inX = [0.1, 0.01]    #分类    className = classify0(inX, dataset, lables, 3)    print("the class of test sample is %s" %className)

实验2 ：file2matrix

if __name__ == "__main__":    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')    normData , ranges, minVals = autoNorm(datingDataMat)    print(normData)    print('/n')    print(ranges)    print('/n')    print(minVals)

实验3 ：使用Matplotlib创建散点图

if __name__ == "__main__":    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')    fig = plt.figure()    ax = fig.add_subplot(111)    ax.scatter(datingDataMat[:,1], datingDataMat[:,2],               15.0 * array(datingLabels),               15.0 * array(datingLabels))    plt.show()

实验4 ：归一化特征值

if __name__ == "__main__":    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')    normData , ranges, minVals = autoNorm(datingDataMat)    print(normData)    print('\n')    print(ranges)    print('\n')    print(minVals)

实验5 ：手写识别系统

if __name__ == "__main__":    handwritingClassTest()

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