KNN近隣アルゴリズム(python 3)手書き数字の識別

import numpy as np
import operator
from os import listdir
def img2vector(filename):
    returnVect=np.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

def classify0(inX, dataSet, labels, k):
    #         
    dataSetSize = dataSet.shape[0]
    #     ，                     
    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet
    # sqDistances          
    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 handwritingClassTest():
    #           
    hwLabels = []

    #         
    trainingFileList = listdir(r'F:\learning\lab3_0930\digits\trainingDigits')
    m = len(trainingFileList)

    #          （M*1024）
    trainingMat = np.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,:] = img2vector(r'F:\learning\lab3_0930\digits\trainingDigits\%s' % fileNameStr)

    #         
    testFileList = listdir(r'F:\learning\lab3_0930\digits\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 = img2vector(r'F:\learning\lab3_0930\digits\testDigits\%s' % fileNameStr)

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

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

        #   KNN        
        if (classifierResult != classNumStr): errorCount += 1.0

    #      
    print ("
the total number of errors is: %d" % errorCount)
    print ("
the total error rate is: %f" % (errorCount/float(mTest)))


handwritingClassTest()