#coding:utf-8

import numpy as np

import operator

import os

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

    dataSetSize = dataSet.shape[0]

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

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis = 1)

    distances = sqDistances**0.5

    sortedDistanceIndices = distances.argsort()

    classCount = {}

    for i in range(k):

        voteIlabel = labels[sortedDistanceIndices[i]]

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

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

    return sortedClassCount[0][0]

def file2matric(filename):

    fr = open(filename)

    arrayOLine = fr.readlines()

    numberOfLines = len(arrayOLine)

    returnMat = np.zeros((numberOfLines,3))

    classLabelVector = []

    index = 0

    for line in arrayOLine:

        line = line.strip()

        listFromLine = line.split('\t')

        returnMat[index,:] = listFromLine[0:3]

        if listFromLine[-1] == 'largeDoses':

            temp = 3

        if listFromLine[-1] == 'smallDoses':

            temp = 2

        if listFromLine[-1] == 'didntLike':

            temp = 1

        classLabelVector.append(temp)

        index += 1

    return returnMat, classLabelVector

def autoNormal(dataset):

    minVals = dataset.min(0)

    maxVals = dataset.max(0)

    ranges = maxVals - minVals

    normDataset = np.zeros(np.shape(dataset))

    m = dataset.shape[0]

    normDataset = dataset - np.tile(minVals,(m,1))

    normDataset = normDataset/np.tile(ranges,(m,1))

    return normDataset, ranges, minVals

def classifyPersion():

    resultList = ['not at all', 'in small doses', 'in large doses']

    percentTats = float(raw_input("percentage of time spent play video games?"))

    ffMiles = float(raw_input("frequent filer niles earned per year?"))

    iceCream = float(raw_input("liters of ice cream consumed per year?"))

    datingDataMat, datingLabels = file2matric(r".\datingTestSet.txt")

    normalMat , ranges, minVals = autoNormal(datingDataMat)

    inArr = np.array([ffMiles,percentTats,iceCream])

    classifierResult = classify0((inArr - minVals)/ranges, normalMat, datingLabels,3)

    print "You will probably like this persion :" , resultList[classifierResult - 1]

def img2vertor(filename):

    returnVect = np.zeros((1,1024))

    fr = open(filename,'r+')

    for i in range(32):

        lineStr = fr.readline()

        for j in range(32):

            returnVect[0,32*i+j] = int(lineStr[j])

    return returnVect    

def handwritingclasstest():

    hwLabels = []

    traingFileList = os.listdir(r".\digits\trainingDigits")

    m = len(traingFileList)

    trainingMat = np.zeros((m,1024))

    for i in range(m):

        fileNameStr = traingFileList[i]

        fileStr = fileNameStr.split('.')[0]

        classNumStr = int(fileStr.split('_')[0])

        hwLabels.append(classNumStr)

        trainingMat[i,:] = img2vertor(r".\digits\trainingDigits\%s" % fileNameStr)

    testFileList = os.listdir(r".\digits\testDigits")

    mtest = len(testFileList)

    errorCount = 0.0

    for i in range(mtest):

        fileNameStr = testFileList[i]

        fileStr = fileNameStr.split(".")[0]

        classNumStr = fileStr.split("_")[0]

        vectorUnderTest = img2vertor(r".\digits\testDigits\%s" % fileNameStr)

        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)

        print "the classifier came back with: %d ,the real answer is :%s " % (classifierResult,classNumStr)

        if(classifierResult != int(classNumStr)):

            errorCount += 1.0

    print "\n the total number of error is %d" % errorCount

    print "\n the total error rate is %f" % (errorCount/float(mtest))