原始碼如下：

#-*- coding: utf-8 -*-
from numpy import *
import operator
import pdb
import matplotlib
import matplotlib.pyplot as plt

from matplotlib.font_manager import FontProperties
font = FontProperties(fname='/usr/share/fonts/truetype/droid/DroidSansFallbackFull.ttf',
              size=15)

import sys

reload(sys)
sys.setdefaultencoding('utf8')

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):
    #pdb.set_trace()
    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 file2matrix(filename):
#    pdb.set_trace()
    fr = open(filename)
    arrayOfLines = fr.readlines()
    numberOfLines = len(arrayOfLines)
    returnMat = zeros((numberOfLines, 3))
    classLabelVector = []
    index = 0
    for line in arrayOfLines:
        line = line.strip()
        listFromLine = line.split('\t')
        returnMat[index,:] = listFromLine[0:3]
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat, classLabelVector

def conf_zh(font_name):
    from pylab import mpl
    mpl.rcParams['font.sans-serif'] = [font_name]
    mpl.rcParams['axes.unicode_minus'] = False 

def drawPlot():
    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.set_title('你好',fontproperties=font)
    plt.xlabel(u"每年獲取的飛行常客里程數",fontproperties=font)
    plt.ylabel(u"玩視訊遊戲所耗時間百分比", fontproperties=font)
    ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1], 15.0 *
               array(datingLabels), 1.0 * array(datingLabels))

    plt.legend(u"不喜歡", prop=font)
    plt.show()

def autoNorm(dataSet):
    #pdb.set_trace()

    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 = 0.10
    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
    norMat, ranges, minVals = autoNorm(datingDataMat)
    m = norMat.shape[0]
    numTestVecs = int(m * hoRatio)
    errorCount = 0.0
    for i in range(numTestVecs):
        classifierResult = classify0(norMat[i, :], norMat[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 doses', 'in large doses']
    percentTats = float(raw_input("percentage of time spent playing video games?"))
    ffMiles = float(raw_input("frequent flier miles earned per year?"))
    iceCream = float(raw_input("liters of ice cream consumed per year?"))


    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
    norMat, ranges, minVals = autoNorm(datingDataMat)
    inArr = array([ffMiles, percentTats, iceCream])
    classifierResult = classify0((inArr - minVals)/ ranges, norMat,
                                 datingLabels, 3)

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

if __name__ == '__main__':
    #group, labels = createDataSet()
    #print classify0([0,0], group, labels, 3)
    #print group
    #print labels
    
    #drawPlot()
    #datingClassTest()
    classifyPerson()