X1,1，X1,2，X1,3，X1,4……X1,36，X1,37，Y1
X2,1，X2,2，X2,3，X2,4……X2,36，X2,37，Y2
…………
X274,1，X274,2，X274,3，X274,4……X274,36，X274,37，Y274

0,0,0,3,1,34,5,0,0,0,11,31,0,0,38,40,0,0,15,8,3,9,14,2,3,0,4,1,1,15,0,0,1,13,0,0,1,1 0,1,0,2,2,24,8,0,0,0,10,37,0,0,44,34,0,0,3,0,4,10,15,5,3,0,0,7,2,13,0,0,2,12,0,0,0,0

#coding:utf-8

from numpy import *
import operator

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

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))  #element wise divide
  return normDataSet, ranges, minVals

def file2matrix(filename):
  fr = open(filename)
  numberOfLines = len(fr.readlines())     #get the number of lines in the file
  returnMat = zeros((numberOfLines,37))    #prepare matrix to return
  classLabelVector = []            #prepare labels return  
  fr = open(filename)
  index = 0
  for line in fr.readlines():
    line = line.strip()
    listFromLine = line.split(',')
    returnMat[index,:] = listFromLine[0:37]
    classLabelVector.append(int(listFromLine[-1]))
    index += 1
  fr.close()
  return returnMat,classLabelVector

def genderClassTest():
  hoRatio = 0.10   #hold out 10%
  datingDataMat,datingLabels = file2matrix('doubanMovieDataSet.txt')    #load data setfrom file
  normMat,ranges,minVals=autoNorm(datingDataMat)
  m = normMat.shape[0]
  numTestVecs = int(m*hoRatio)
  testMat=normMat[0:numTestVecs,:]
  trainMat=normMat[numTestVecs:m,:]
  trainLabels=datingLabels[numTestVecs:m]
  k=3
  errorCount = 0.0
  for i in range(numTestVecs):
    classifierResult = classify0(testMat[i,:],trainMat,trainLabels,k)
    print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i])
    if (classifierResult != datingLabels[i]):
      errorCount += 1.0
  print "Total errors:%d" %errorCount
  print "The total accuracy rate is %f" %(1.0-errorCount/float(numTestVecs))