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

 #kNN

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

     dataSetSize = dataSet.shape[0] #行数

     diffMat = tile(inX,(dataSetSize,1)) - dataSet # tile(inX,(dataSetSize,1)) 生成 dataSetSize 行 1 列的 元素为 inX的 数组

     sqDiffMat = diffMat ** 2 #  ** 为 ^

     sqDistances = sqDiffMat.sum(axis=1) # axis=0是按列求和 axis=1 是按行求和

     distance = sqDistances ** 0.5

     sortedDisInd = distance.argsort()# argsort，属于numpy中的函数 返回排序后元素在原对象中的下标

     classCount = {}

     for i in range(k):

         votelabel = labels[sortedDisInd[i]]

         classCount[votelabel] = classCount.get(votelabel,0) + 1 #dict.get(key, default=None) key：key在字典中查找。 default：在key不存在的情况下返回值None。

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

     '''

     要通过student的第三个域排序，可以这么写：

     sorted(students, key=operator.itemgetter(2))

     sorted函数也可以进行多级排序，例如要根据第二个域和第三个域进行排序，可以这么写：

     sorted(students, key=operator.itemgetter(1,2))

     即先跟句第二个域排序，再根据第三个域排序。

     '''

     return sortedClassCount[0][0]

 #加载数据

 def file2matrix(filename):

     fr = open(filename)

     arrayOLines = fr.readlines()  #注意需要加s

     numberOfLines = len(arrayOLines)

     returnMat = zeros((numberOfLines,3))

     classLabelVector = []

     index = 0

     for line in arrayOLines:

         line = line.strip()

         listFormLine = line.split('\t')

         for x in range(0,3):

             returnMat[index,x] = float(listFormLine[x])

         classLabelVector.append(int(listFormLine[-1])) # -1 为最后一个元素

         index += 1

     return returnMat,classLabelVector

 import matplotlib

 import matplotlib.pyplot as plt

 datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt')

 fig = plt.figure() #figure创建一个绘图对象

 ax = fig.add_subplot(111)# 若参数为349，意思是：将画布分割成3行4列，图像画在从左到右从上到下的第9块，

 '''

 matplotlib.pyplot.scatter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, hold=None,**kwargs)

 其中，xy是点的坐标，s点的大小

 maker是形状可以maker=（5，1）5表示形状是5边型，1表示是星型（0表示多边形，2放射型，3圆形）

 alpha表示透明度；facecolor=‘none’表示不填充。

 '''

 ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*array(datingLabels),marker=(5,1),alpha=0.5)

 plt.show()

 #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]

     normDataSet = dataSet - tile(minVals,(m,1))

     normDataSet = normDataSet / tile(ranges,(m,1))

     return normDataSet,ranges,minVals

 def datingClassTest():

     hoRatio = 0.10

     datingDataMat,datingLabels = file2matrix('datingTestSet.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 "back %d ,real %d" % (classifierResult,datingLabels[i])

         if(classifierResult != datingLabels[i]):

              errorCount += 1.0

     print "range is %f" % (errorCount / float(numTestVecs))

 #约会网站测试函数

 def classifyPerson():

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

     percentTats = float(raw_input("per of time spent playing video games?"))

     ffMiles = float(raw_input("fre flier miles earned per year?"))

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

     datingDataMat,datingLabels = file2matrix('datingTestSet.txt')

     normMat,ranges,minVals = autoNorm(datingDataMat)

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

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

     print "You will probably like this person :",

     print resultList[classifierResult-1]