'''

Created on Sep 16, 2010

kNN: k Nearest Neighbors

Input:      inX: vector to compare to existing dataset (1xN)

            dataSet: size m data set of known vectors (NxM)

            labels: data set labels (1xM vector)

            k: number of neighbors to use for comparison (should be an odd number)

Output:     the most popular class label

@author: pbharrin

'''

from numpy import *

import operator

from os import listdir

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

    dataSetSize = dataSet.shape[0]

    diffMat = tile(inX, (dataSetSize,1)) - dataSet

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)

    distances = sqDistances**0.5

    sortedDistIndicies = distances.argsort()

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

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

    sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

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

def file2matrix(filename):

    fr = open(filename)

    numberOfLines = len(fr.readlines())         #get the number of lines in the file

    returnMat = zeros((numberOfLines,3))        #prepare matrix to return

    classLabelVector = []                       #prepare labels return

    fr = open(filename)

    index = 0

    for line in fr.readlines():

        line = line.strip()

        listFromLine = line.split('\t')

        returnMat[index,:] = listFromLine[0:3]          # 读取前三个属性值

        classLabelVector.append(int(listFromLine[-1]))  # 读取类标签

        index += 1

    return returnMat,classLabelVector

def autoNorm(dataSet):

    minVals = dataSet.min(0)

    maxVals = dataSet.max(0)

    ranges = maxVals - minVals

    normDataSet = zeros(shape(dataSet))

    m = dataSet.shape[0]

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

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

    return normDataSet, ranges, minVals

def datingClassTest():

    hoRatio = 0.50      #hold out 10%

    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')       #load data setfrom file

    normMat, ranges, minVals = autoNorm(datingDataMat)

    m = normMat.shape[0]

    numTestVecs = int(m*hoRatio)

    errorCount = 0.0

    for i in range(numTestVecs):

        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 += 1.0

    print "the total error rate is: %f" % (errorCount/float(numTestVecs))

    print errorCount     

# 解析文本数据

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

# 测试

def handwritingClassTest():

    hwLabels = []

    trainingFileList = listdir('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('trainingDigits/%s' % fileNameStr)

    testFileList = listdir('testDigits')        #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('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))