机器学习实战-KNN算法

import numpyimport osfrom numpy import arrayfrom numpy import tileimport operatorimport matplotlib.pyplot as plt#数据例子def createDataSet():    group=array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])    labels=['A','A','B','B'] #标签与点一一对应    return group,labels'''******************************主要分类函数************************************************************'''#取距离最近的k各点，  返回 k个点中频率最多的类别作为分类def classify0(point,dataArray,labels,k):#（测试[,,...]  比较集array  标签集      OneDimension=dataArray.shape[0]    tmpArray=(tile(point,(OneDimension,1))-dataArray)**2 #point平铺成二维矩与其计算各点距离    sqrtArray=tmpArray.sum(1)    sortedArrayIndex=sqrtArray.argsort()#按索引点排序 -列表    #print(sortedArrayIndex)    classCount={} #空字典    for i in range(k):        lab=labels[sortedArrayIndex[i]]  #取相应索引点的标签        classCount[lab]=classCount.get(lab,0)+1 #字典中有该key则取其映射值（这里为int），否则返回0    sortedClassCount=sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)                                           #指明关键字    return sortedClassCount[0][0]#测试'''group0,labels0=createDataSet()print(group0,labels0)print(classify0([0,0],group0,labels0,3))''''''*********************************************************************''''''***************************约会配对分类*******************************''''''*********************************************************************'''#获取文件数据 返回数据Array 标签listdef file2matrix(filename):    file=open(filename)    fileList=file.readlines()#返回全部行 ，行后有\n---列表    returnMat=numpy.zeros((len(fileList),3))    index=0    labels=[]    for st in fileList:        st=st.strip()#移除字符串头尾指定的字符（默认为空字符）  这里移除\n        strList=st.split('\t')#str.split(sep=None, maxsplit=-1 无限制)        returnMat[index,:]=strList[0:3]        labels.append(int(strList[-1]))        index+=1    return returnMat,labels#数据归一化def Normalize(dataMat): #Array    min_value=dataMat.min(0)    max_value=dataMat.max(0)    range_value=max_value-min_value    normMat=dataMat-tile(min_value,(dataMat.shape[0],1))    normMat=normMat/tile(range_value,(dataMat.shape[0],1))    return normMat,range_value,min_value#测试KNN错误率def datingClassTest():    datingData,datingLabels=file2matrix('datingTestSet2.txt')    datingData,datingRange,datingMinValue=Normalize(datingData)    testnum=int(datingData.shape[0]/10) #100    error_count=0;    for i in range(testnum):        label=classify0(datingData[i],datingData[testnum:datingData.shape[0]],datingLabels[testnum:datingData.shape[0]],3)        if label!=datingLabels[i]:            error_count+=1;    print('错误率:%f'%(error_count/float(testnum)))            #测试'''datingDataMat,datingLabels=file2matrix('datingTestSet2.txt')datingDataMat,datingDataRange,datingDataMin=Normalize(datingDataMat)fg=plt.figure()subfg1=fg.add_subplot(111)subfg1.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*array(datingLabels),15.0*array(datingLabels))#subfg1.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*array(datingLabels),1*array(tile([1],(array(datingLabels).shape[0],1))))plt.xlabel('玩视频耗时百分比')plt.ylabel('周消耗冰激凌公升数')plt.show()datingClassTest()''''''*********************************************************************'''def classifyPerson():    datingData,datingLabels=file2matrix('datingTestSet2.txt')    datingData,datingRange,datingMinValue=Normalize(datingData)    resultClass=['不喜欢','一般','有魅力']    miles=float(input('每年飞行里程数：'))    game=float(input('玩游戏小号百分比:'))    ice=float(input('每周冰淇淋公升：'))    data=array(([miles,game,ice]-datingMinValue)/datingRange)    label=classify0(data,datingData,datingLabels,3)    print('类型是:',resultClass[label-1]) #数据的分类标签1,2,3    #测试  '''classifyPerson()''''''*********************************************************************''''''*****************************手写识别*********************************''''''*********************************************************************'''def img2vector(filename):    file=open(filename)    returnVec=numpy.zeros((1,1024))    for i in range(32):        fileString=file.readline()        for j in range(32):            returnVec[0,i*32+j]=fileString[j]    file.close()    return returnVecdef handWriteClassTest():    trainList=os.listdir('trainingDigits')    DT=len(trainList)    trainArray=numpy.zeros((DT,1024))    labels=[]    for i in range(DT):                                                            filename=trainList[i]        labels.append(int(filename[0]))        trainArray[i,:]=img2vector('trainingDigits/%s'%filename)    testList=os.listdir('trainingDigits')    DS=len(testList)    error_count=0;    for j in range(DS):        filename=testList[j]        label=int(filename[0])        testArray=img2vector('trainingDigits/%s'%filename)        testLabel=classify0(testArray,trainArray,labels,3)        if label!=testLabel:            error_count+=1    error_rate=error_count/DS    print('错误率：%f'%error_rate)        #测试       '''handWriteClassTest()'''