KNN

#_*_ coding:utf-8 _*_from numpy import *import operatorimport matplotlibimport matplotlib.pyplot as pltimport matplotlib.fontconfig_pattern as fontPatternimport os#构造数据集def createDataSet():    group = array([[1.0,1.1],[1.0,1.1],[0,0],[0,0.1]])    labels = ['A','A','B','B']    return group,labels'''    描述：计算inX附近的k个实例中哪一种类别最多，并将类别最多的那个类别作为inX所属类别    k-近邻算法  这个比我自己实现的那个牛逼的感觉    inX:要识别的实例（它到底属于哪个类型）    dataSet: 表示训练集特征集合    lebels: 表示标签集合    k: 表示阈值k'''def classfy0(inX,dataSet,labels,k):    dataSetsize = dataSet.shape[0]  # 计算特征集合的行数，即特征向量的个数    diffMat = tile(inX, (dataSetsize, 1)) - dataSet # 求出inX和dataSet中每个向量对应的距离，并返回一个新的距离二维矩阵    sqDifat = diffMat ** 2 # 计算 (x1 - x2) ^ 2    sqlDistances = sqDifat.sum(axis=1) #(x1 - x2)^2 + (y1 - y2) ^ 2  axis=1 行相加    distances = sqlDistances ** 0.05    sortedDistances = distances.argsort() # 返回由小到大的排序的后的下标    classCount = {}    for i in range(k):        votelabel = labels[sortedDistances[i]] # 取出前k个距离最近的类别        classCount[votelabel] = classCount.get(votelabel,0) + 1 # 非常优雅的写法,如果votelabel不存在返回0+1,否则返回值 +1    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)    return sortedClassCount[0][0]# 解析文件'''    python中的try结构中都变量都作用域相当于try的作用域,因此不用提前定义，后面的代码也可以正常引用。'''def file2matrix(filename):    try:        with open(filename, 'r') as file:            arrayLines = file.readlines()            numberofLines = len(arrayLines) # 获取文件行数            returnMat = zeros((numberofLines, 3)) # 构造一个arrayLines行3列的0矩阵，用于存储训练特征集            classLabelVector = []  # 由于存储标签            labelDict = {"didntLike":1,"smallDoses":2,"largeDoses":3}            index = 0 # 标记第几行            for line in arrayLines:                line = line.strip() # 截取回车字符                listFromLine = line.split('\t')                returnMat[index,:] =  listFromLine[:3]                classLabelVector.append(labelDict[listFromLine[-1]]) # 构建标签集合                index += 1            return returnMat,classLabelVector    except Exception as e:        print("Error reading file:%s" % e)'''    归一化特征值        newData = (oldData-min)/(max-min)    '''def autoNorm(dataSet):    minVal = dataSet.min(0) # 求出每一列的最小值    maxVal = dataSet.max(0) # 求出每一列的最小值    ranges = maxVal - minVal    normData = zeros(shape(dataSet)) # 建立一个跟dataSet一样维度的0矩阵    m = dataSet.shape[0] # 返回第一维的长度    normData = dataSet - tile(minVal,(m,1))    normData = normData / tile(ranges, (m, 1))    return normData,ranges,minVal# 计算错误率def datingClassTest():    hoRatio = 0.10    # 解析文件 ETL    featMat,labelMat = file2matrix(r'D:\machineLearning\src\KNN\MLRealBattle\datingTestSet.txt')    # 归一化    normMat,ranges,minVals = autoNorm(featMat)    # 取出维度,也就是实例个数    m = normMat.shape[0]    # 计算测试集个数    numTestVecs = int(m * hoRatio)    errorCount = 0.0    for i in range(numTestVecs):        '''            normMat[i,:] : 取出二维数组normMat中的第i行数组  --> 测试            normMat[numTestVecs:m,:]: 取出二维数组中第numTestVecs到m-1行的数组        '''        classifierResult = classfy0(normMat[i,:],normMat[numTestVecs:m,:],labelMat[numTestVecs:m],3)        print("the classfier came back with:%d,the real answer is:%d " % (classifierResult,labelMat[i]))        if classifierResult != labelMat[i]:            errorCount += 1.0    print("the total error rate is: %f" % (errorCount/float(numTestVecs)))    return errorCount/float(numTestVecs)# 构建完整的系统def classfyPerson():    resultList = ['not at all','in small doses','in large doses']    percentTats = float(input('percentage of time spent palying video games?'))    ffiles = float(input('frequent flier miles earned per year?'))    iceCream = float(input('liters of ice cream consumed per year?'))    datingDataMat,datingLabels = file2matrix('D:\machineLearning\src\KNN\MLRealBattle\datingTestSet.txt')    normMat,ranges,minVals = autoNorm(datingDataMat)    inArr = array([ffiles,percentTats,iceCream])    classifierResult = classfy0((inArr-minVals)/ranges,normMat,datingLabels,3)    print("you will probably like this person:", resultList[classifierResult - 1])# 手写识别系统像素转换成向量def img2vector(filename):    returnVect = zeros((1,1024))    fr = open(filename)    for i in range(32):        linstr = fr.readline()        for j in range(32):            returnVect[0,32*i + j] = int(linstr[j])    return returnVect# 手写识别系统构建def handwritingClassTest():    hwLabels = []    trainingFileList = os.listdir(r'D:\02_深度学习基础\machinelearninginaction\Ch02\digits\trainingDigits')           #load the training set    m = len(trainingFileList)    trainingMat = zeros((m,1024))    for i in range(m):        fileNameStr = trainingFileList[i]        fileStr = fileNameStr.split('.')[0]     #take off .txt        classNumStr = int(fileStr.split('_')[0])        hwLabels.append(classNumStr)        trainingMat[i,:] = img2vector(r'D:\02_深度学习基础\machinelearninginaction\Ch02\digits\trainingDigits/%s' % fileNameStr)    testFileList = os.listdir(r'D:\02_深度学习基础\machinelearninginaction\Ch02\digits\trainingDigits')        #iterate through the test set    errorCount = 0.0    mTest = len(testFileList)    for i in range(mTest):        fileNameStr = testFileList[i]        fileStr = fileNameStr.split('.')[0]     #take off .txt        classNumStr = int(fileStr.split('_')[0])        vectorUnderTest = img2vector(r'D:\02_深度学习基础\machinelearninginaction\Ch02\digits\trainingDigits/%s' % fileNameStr)        classifierResult = classfy0(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)))# 画图def drawingDataAdvance(datingDataMat,datingLabels):    plt.figure(figsize=(12, 6), dpi=80)    axes = plt.subplot(111)    type1_x = []    type1_y = []    type2_x = []    type2_y = []    type3_x = []    type3_y = []    print    'range(len(datingLabels)):'    print    range(len(datingLabels))    for i in range(len(datingLabels)):        if datingLabels[i] == 1:  # 不喜欢            type1_x.append(datingDataMat[i][0])            type1_y.append(datingDataMat[i][1])        if datingLabels[i] == 2:  # 魅力一般            type2_x.append(datingDataMat[i][0])            type2_y.append(datingDataMat[i][1])        if datingLabels[i] == 3:  # 极具魅力            print            i, '：', datingLabels[i], ':', type(datingLabels[i])            type3_x.append(datingDataMat[i][0])            type3_y.append(datingDataMat[i][1])    type1 = axes.scatter(type1_x, type1_y, s=20, c='red')    type2 = axes.scatter(type2_x, type2_y, s=40, c='green')    type3 = axes.scatter(type3_x, type3_y, s=50, c='blue')    plt.xlabel(u'Frequent Flyier Miles Earned Per Year')    plt.ylabel(u'Percentage of Time Spent Playing Video Games')    axes.legend((type1, type2, type3), (u'didntLike', u'smallDoses', u'largeDoses'), loc=2)    plt.show()if __name__ == '__main__':    handwritingClassTest()