def classify0(inX, dataSet, labels, k):

     '''

     :param inX: 测试样本(arr)

     :param dataSet: 训练数据集(arr)

     :param labels: 类别(list)

     :param k:(int)

     :return: 类别

     '''

     #计算距离

     dataSetSize = dataSet.shape[0]  # 样本数量

     diffMat = tile(inX, (dataSetSize, 1)) - dataSet #tile(inX{数组},(dataSetSize{倍数},1{竖向}))：将数组(inX)竖向(1)复制dataSetSize倍

     sqDiffMat = diffMat ** 2                        #先求平方

     sqDistances = sqDiffMat.sum(axis=1)             #再求平方和

     distances = sqDistances ** 0.5                  #开根号,欧式距离

     sortedDistIndicies = distances.argsort()  #距离从小到大排序的索引

     classCount = {}

     for i in range(k):

         voteIlabel = labels[sortedDistIndicies[i]]  #用索引得到相应的类别

         classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1

     return max(classCount, key=lambda k: classCount[k])  # 返回频数最大的类别

from numpy import *

import re

#从文件导入数据

def file2array(filename):

    '''

    :param filename: 文件名

    :return: 数据集(arr)、类别(list)

    '''

    label={

        'didntLike':0,

        'smallDoses':1,

        'largeDoses':2

    }

    with open(filename) as fr:

        lines =fr.readlines()

    tempLine =re.split('\\s+',lines[0].strip())         #'\\s+'表示tab或多个空格  #strip()除去换行符

    returnArr = zeros((len(lines),len(tempLine)-1))     #初始化数组（存放数据集）

    classLabelVector = []                               #存放类别

    for index,line in enumerate(lines):

        listFromLine = re.split('\\s+',line.strip())    #空格或tab都行

        returnArr[index,:] = listFromLine[0:-1]

        classLabelVector.append(label[listFromLine[-1]])

    return returnArr,classLabelVector

#数据归一化

def Norm(dataSet):

    minVals = dataSet.min(0)    #0：列(特征)的最小值；1：行(样本)的最小值

    maxVals = dataSet.max(0)

    ranges = maxVals - minVals

    normDataSet = zeros(dataSet.shape)

    m = dataSet.shape[0]

    normDataSet = dataSet - tile(minVals, (m,1))

    normDataSet /= tile(ranges, (m,1))   #element wise divide

    return normDataSet, ranges, minVals

#约会网站配对例子

def datingClassTest():

    hoRatio = 0.80      #80%作为测试集，20%为训练集

    datingDataMat,datingLabels = file2array('..\data\Ch02\datingTestSet.txt')   #加载数据

    normMat, ranges, minVals = Norm(datingDataMat)                              #数据归一化

    m = normMat.shape[0]    #数据集大小(样本的数目)

    numTestVecs = int(m*hoRatio)

    errorCount = 0.0

    for i in range(numTestVecs):

        classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)

        print("预测类别: %d, 真实类别: %d" % (classifierResult, datingLabels[i]))

        if (classifierResult != datingLabels[i]): errorCount += 1.0     #统计预测错误的次数

    print ("平均错误率是: %f" % (errorCount/float(numTestVecs)))

    print("总测试数目:",numTestVecs,"总错误数目:",errorCount)

#kNN分类器

def classify0(inX, dataSet, labels, k):

    '''

    :param inX: 测试样本(arr)

    :param dataSet: 训练数据集(arr)

    :param labels: 类别(list)

    :param k:(int)

    :return: 类别

    '''

    #计算距离

    dataSetSize = dataSet.shape[0]  # 样本数量

    diffMat = tile(inX, (dataSetSize, 1)) - dataSet #tile(inX{数组},(dataSetSize{倍数},1{竖向}))：将数组(inX)竖向(1)复制dataSetSize倍

    sqDiffMat = diffMat ** 2                        #先求平方

    sqDistances = sqDiffMat.sum(axis=1)             #再求平方和

    distances = sqDistances ** 0.5                  #开根号,欧式距离

    sortedDistIndicies = distances.argsort()  #距离从小到大排序的索引

    classCount = {}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]  #用索引得到相应的类别

        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1

    return max(classCount, key=lambda k: classCount[k])  # 返回频数最大的类别

if __name__ =='__main__':

    datingClassTest()

 from numpy import *

 from os import listdir

 def loadData(dirname):

     listData =listdir(dirname)

     #把32*32文本文件读为1*1024

     def file2arr(filename):

         with open(filename) as f:

             vec =f.read()

         return list(vec.replace('\n',''))

     sizeData =len(listData)         #文件的数量

     cLabel=zeros(sizeData,dtype='int16')          #文件类别

     arrTrain =zeros((sizeData,1024),dtype='int16')  #训练样本数组

     for i,j in enumerate(listData):

         cLabel[i] =int(j[0]) #每个文件对应的类别

         arrTrain[i,:] =file2arr(dirname + '\\' +j)

     return cLabel,arrTrain

 if __name__ =='__main__':

     from knn import classify0

     fTrain = r'..\data\Ch02\digits\trainingDigits'

     fTest = r'..\data\Ch02\digits\testDigits'

     cLabel,arrTrain =loadData(fTrain)

     cLabelTest,arrTest =loadData(fTest)

     err=0

     for j,i in enumerate(arrTest):

         label =classify0(i,arrTrain,cLabel,3)

         if cLabelTest[j] !=label:err+=1

     print('错误率：',err/len(cLabelTest))

     #sklearn库knn对比

     from sklearn.neighbors import KNeighborsClassifier as knn

     model =knn(n_neighbors=3,n_jobs=4,algorithm='auto')

     model.fit(arrTrain,cLabel)

     cLabelPredict =model.predict(arrTest)

     print('错误率',sum(cLabelPredict!=cLabelTest)/len(cLabelTest))