# time : 2018/11/9 

# -*- coding: UTF-8 -*-

from numpy import *
import operator
from os import listdir

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 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.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]


def file2matrix(filename):
    fr = open(filename)
    arrayOLines = fr.readlines()
    numberOFlines = len(arrayOLines)
    returnMat = zeros((numberOFlines, 3))
    classLabelVetor = []
    index = 0
    for line in arrayOLines:
        line = line.strip()
        listFormLine = line.split('\t')
        returnMat[index, :] = listFormLine[0:3]
        classLabelVetor.append(int(listFormLine[-1]))
        index += 1
    return returnMat,classLabelVetor


# def file2matrix(filename, dim2):
#     fr = open(filename)
#     arrayOLines = fr.readlines()
#     numberOfLines = len(arrayOLines)
#     returnMat = zeros((numberOfLines, dim2))
#     classLabelVector = []
#     index = 0
#     for line in arrayOLines:
#         line = line.strip()
#         listFromLine = line.split('\t')
#         returnMat[index, :] = listFromLine[0:dim2]
#         classLabelVector.append(int(listFromLine[-1]))
#         index += 1
#     return returnMat, classLabelVector


#归一化特征值
def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet = zeros(shape(dataSet))
    m = dataSet.shape[0]
    # minVals与ranges都是1*3的矩阵，通过tile函数被扩展为m*3的矩阵，这就是tile函数的作用
    normDataSet = dataSet - tile(minVals, (m, 1))
    normDataSet = normDataSet/tile(ranges, (m, 1))
    return normDataSet, ranges, minVals


#分类器针对约会网站的测试代码

def datingClassTest():
    hoRatio = 0.10
    datingDataMat,datingLabels = file2matrix("datingTestSet2.txt")
    normMat, ranges, minVlas = autoNorm(datingDataMat)
    m = normMat.shape[0]
    numTestVecs = int(m*hoRatio)
    errorCount = 0.0
    for i in range(numTestVecs):
        # 输入参数:normMat[i,:]为测试样例，表示归一化后的第i行数据
        #       normMat[numTestVecs:m,:]为训练样本数据，样本数量为(m-numTestVecs)个
        #       datingLabels[numTestVecs:m]为训练样本对应的类型标签
        #       k为k-近邻的取值
        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
    print ("the tatal error rate is %f" % (errorCount/float(numTestVecs)))


#约会网站预测函数

def classifyPerson():
    resultList = ['not at all', 'in small does', 'in large does']
    perconTats = float(input("percentage of time spent playing video game"))
    ffMiles = float(input("frequent flier miles earned per year?"))
    icecream = float(input("listers of ice cream consumed per year?"))
    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDataMat)
    inArr = array([ffMiles, perconTats, icecream])
    classifierResult = classify0((inArr - minVals)/ranges, normMat, datingLabels, 3)
    print("You will probably like this persom:", resultList[classifierResult - 1])



def img2Vwctor(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


#手写数字识别系统测试代码
def handwritingClassTest():
    hwLabels = []
    trainingFileList = listdir('trainingDigits')
    m = len(trainingFileList)
    trainingMat = zeros((m, 1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        hwLabels.append(classNumStr)
        trainingMat[i, :] =img2Vwctor(
            'trainingDigits/%s'% fileNameStr)
    testFileList = listdir('testDigits')
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2Vwctor(
            'testDigits/%s' % fileNameStr)
        classifierRusult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
        print("the classify came back whitn %d, the real answer is:%d"%(classifierRusult, classNumStr))
        if (classifierRusult != classNumStr): errorCount +=1.0
    print("\nthe total number of error is %d"% errorCount)
    print("\nthe total erroe rate id %f"% (errorCount/float(mTest)))





