机器学习基础KNN分类算法

咸鱼跟书学机器学习ing（0.0）然后数据包可以去https://www.manning.com/books/machine-learning-in-action下

#-*-coding:UTF-8-*-import operator  #运算符模块from numpy import *  #科学计算包import matplotlib  #绘图库import matplotlib.pyplot as pltfrom os import listdir      #列出给定目录的文件名#创造训练集def createDataSet():    group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])    labels = ['A', 'A', 'B', 'B']    return group, labels#将文本记录转成numpy的解析函数def file2matrix(filename):    #得到文件行数    fr = open(filename)    array0Lines = fr.readlines()    numberOfLines = len(array0Lines)    #创建返回的numpy矩阵    returnMat = zeros((numberOfLines, 3))  #选取前三个元素作为特征值    classLabelVector = []    index = 0    for line in array0Lines:        line = line.strip()  #默认去掉line前面的空格和回车        listFromLine = line.split('\t')  #将line按照制表符分割开        returnMat[index, :] = listFromLine[0:3]        #选取最后一列元素存储，必须int否则会当做字符串        classLabelVector.append(int(listFromLine[-1]))        index += 1    return returnMat, classLabelVector#绘图，使用矩阵的第二列和第三列数据绘制散点图def show(DataMat, DataLabels):    fig = plt.figure()    ax = fig.add_subplot(111)    #利用分类标记个性化标记散点图上的点    ax.scatter(DataMat[:, 1], DataMat[:, 0], 15.0 * array(DataLabels),               15.0 * array(DataLabels))    plt.show()#归一化特征值，即将各个特征值化成等权重(都化为[0,1]的值)def autoNorm(dataSet):    minVals = dataSet.min(0)  #选取每列最小值    maxVals = dataSet.max(0)  #选取每列最大值    ranges = maxVals - minVals  #可能的取值范围    normDataSet = zeros(shape(dataSet))  #新的返回矩阵    m = dataSet.shape[0]    normDataSet = dataSet - tile(minVals, (m, 1))    normDataSet = normDataSet / tile(ranges, (m, 1))  #特征值相除    return normDataSet, ranges, minVals#分类def classify0(inX, dataSet, labels, k):    #计算欧式距离 d=sqrt((x1-x2)^2+(y1-y2)^2)    dataSetSize = dataSet.shape[0]    diffMat = tile(inX, (dataSetSize, 1)) - dataSet  #将inX重复shape行，1列    sqDiffMat = diffMat**2    sqDistances = sqDiffMat.sum(axis=1)  #axis=0表示列求和，axis=1表示行求和    distances = sqDistances**0.5    #选取距离最小的k个点    sortedDistIndicies = distances.argsort()  #排序索引    classCount = {}  #建立字典存储每个类的数目    for i in range(k):        voteIlabel = labels[sortedDistIndicies[i]]        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1    #排序(逆序)    # key=operator.itemgetter(1)函数表示选取对象第一个域的值进行排序    sortedClassCount = sorted(        classCount.items(), key=operator.itemgetter(1), reverse=True)    return sortedClassCount[0][0]#分类器验证函数def datingClassTest():    hoRatio = 0.10  #选取抽样数据的比例    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')    normMat = autoNorm(datingDataMat)[0]    m = normMat.shape[0]    numTestVecs = int(m * hoRatio)    errorCount = 0.0    for i in range(numTestVecs):        #本来应该随机选取数据，但是这里的数据本身是随机的所以直接选取        classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :],                                     datingLabels[numTestVecs:m], 3)        print("the classifier came back with %d,the real answer is %d" %              (classifierResult, datingLabels[i]))        if classifierResult != datingLabels[i]: errorCount += 1    if numTestVecs != 0:  #考虑到numTestVecs==0时不能除        print("the total error rate is: %f" %              (errorCount / float(numTestVecs)))#输入数据预测函数def classifyPerson():    #类别    resultList = ["not at all", "in small doses", "in large doses"]    percentTats = float(input("Percentage of time spent playing video games?"))    ffMiles = float(input("Frequent flier miles earned per year?"))    iceCream = float(input("liters of ice cream consumed per year?"))    datingDataMat, datingLabels = file2matrix("datingTestSet2.txt")    normMat, ranges, minVals = autoNorm(datingDataMat)    inArr = array([ffMiles, percentTats, iceCream])    classifierResult = classify0((inArr - minVals) / ranges, normMat,                                 datingLabels, 3)    print("You will probably like this person:", resultList[classifierResult                                                            - 1])#将32*32的二进制图像矩阵转化成1*1024的向量def 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 returnVect#手写数字识别def handwritingClassTest():    hwLabels = []    trainingFileList = os.listdir('trainingDigits')  #获取目录内容    m = len(trainingFileList)    trainingMat = zeros((m, 1024))    for i in range(m):  #从文件名解析分类数字        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 += 1.0    print('\nthe total number of errors is: %d' % errorCount)    print('\nthe total error rate is: %f' % (errorCount / float(mTest)))'''group, labels = createDataSet()print(classify0([0, 0], group, labels, 3))datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')show(autoNorm(datingDataMat)[0], datingLabels)datingClassTest()classifyPerson()'''

还有还有，学着python现在敲代码都不爱打分号了0.0