1、首先在jupyter中New一個Untitle.ipynb，然後將它重新命名為kNN.py，接著在kNN.py中輸入一下程式碼(課本程式碼)：

注：以下程式碼中，存在我自己的測試資料檔案的路徑，你們要改為自己測試資料檔案的路徑

from numpy import *

import operator


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  
    sqdistance = sqdiffMat.sum(axis=1)  
    #print(sqdistance)  
    distance = sqdistance**0.5  
    sortedDistIndex = distance.argsort()  
    classCount = {}  
    for i in range(k):  
        voteIlabel = labels[sortedDistIndex[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))
    classLabelVector = []
    index = 0 
    for line in arrayOLines:
        line = line.strip()
        listFromLine = line.split('\t')
        returnMat[index,:] = listFromLine[0:3]
        classLabelVector.append(int(listFromLine[-1]))
        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))
    return normDataSet, ranges, minVals

def datingClassTest():

    hoRatio = 0.1
    datingDataMat,datingLabels = file2matrix('F:\Softwares\Python\datingTestSet2.txt')
    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)))

def classifyPerson():  
    resultList = ["not at all","in small does","in large does"]  
    percentTats = float(input("percentage of time spent playing video games?"))  
    ffMiles = float(input("frequent flier miles earned per year?"))  
    iceCream = float(input("liters of ice cream consumes per year?"))  
    datingDataMat,datingLabels = file2matrix('F:\Softwares\Python\datingTestSet2.txt')  
    normMat,ranges,minVals = autoNorm(datingDataMat)  
    inArr = array([ffMiles,percentTats,iceCream])  
    classifierResult = classify0(((inArr-minVals)/ranges),datingDataMat,datingLabels,3)  
    print("You will probably like this person:",resultList[classifierResult - 1])

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

from os import listdir
def handwritingClassTest():
    hwLabels = []
    trainingFileList = listdir('F:/Softwares/Python/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,:] = img2vector('F:/Softwares/Python/trainingDigits/%s' % fileNameStr)
    testFileList = listdir('F:/Softwares/Python/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('F:/Softwares/Python/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)))
 

2、將測試資料檔案放在和kNN.py同一個目錄下。或者像我一樣，直接在程式碼中修改成自己的測試檔案路徑

注：還有幾處，自己注意修改

3、現在可以開始測試資料，在jupyter中再建立一個檔案，命名為testKNN.ipynb