#!/usr/bin/python"""Created on Jan 13 , 2013Filename: kNN.py@author : libo"""from numpy import *from sys import argvfrom os import listdir#scritp , filename = argvdef importData():group = array([[1.0 , 1.1 ],[1.0 , 1.0],[0,0],[0,0.1]])labels = ['A' , 'A' , 'B' , 'B']return group , labelsdef classify(inX , trainSet , labels , k):dataSize = trainSet.shape[0]diffMat = tile(inX , (dataSize ,1)) - trainSetsqDiff = diffMat**2sqDistances = sqDiff.sum(axis = 1)distances= sqDistances**0.5sortedIndex = argsort(distances)classCount = {}for index in range(k):xlabel = labels[sortedIndex[index]]classCount[xlabel] = classCount.get(xlabel, 0) + 1#print classCountsortedClassCount = sorted(classCount.iteritems() ,key = lambda x:x[1] , reverse = True)#print sortedClassCount#print "result is : " , sortedClassCount[0][0]return sortedClassCount[0][0]def importData_M(filename):fr = open(filename)numOfLines = len(fr.readlines())dataMat = zeros((numOfLines , 3))classLabels = []fr = open(filename)index = 0for line in fr.readlines():line = line.strip()lineList = line.split('\t')dataMat[index , :] = lineList[0:3]classLabels.append(lineList[-1])index = index +1return dataMat , classLabelsdef autoNorm(dataSet):#print dataSetmaxVals = dataSet.max(0)minVals = dataSet.min(0)ranges = maxVals - minVals m = dataSet.shape[0]normData = zeros(shape(dataSet))diffData = dataSet - tile(minVals , (m , 1))#normData = tile(ranges , (m , 1))normedData = diffData/tile(ranges , (m ,1))#print normedDatareturn normedData def classTest():hoRatio = 0.1dataMat , dataLabels = importData_M("datingTestSet.txt")normedMat = autoNorm(dataMat)m = normedMat.shape[0]testNum = int(m * hoRatio)errorCount = 0.0for i in range(testNum):classifyLabel = classify(normedMat[i,:] , normedMat[testNum :m ,:] , dataLabels[testNum : m] , 5)print "the classify come back with %r , the real labels is %r " %(classifyLabel , dataLabels[i])if (classifyLabel != dataLabels[i]) :errorCount += 1.0print "the error rate is: %f" %(errorCount/float(testNum))def imgVector(filename):returnVec = zeros((1,1024))fr = open(filename)for i in range(32):lineStr = fr.readline()for j in range(32):returnVec[0 , 32*i+j] = int(lineStr[j])return returnVecdef handWriteTest():hwLabels = []trainingList = listdir("trainingDigits")m = len(trainingList)trainingMat = zeros((m , 1024))for i in range(m ):filenameStr = trainingList[i]fileStr = filenameStr.split(".")[0]classLabel = int(fileStr.split("_")[0])hwLabels.append(classLabel)trainingMat[i,:] = imgVector("trainingDigits/%s"\%filenameStr)testFileList = listdir("testDigits")errorCount = 0.0mTest = len(testFileList)for i in range(mTest):testFile = testFileList[i]testStr = testFile.split(".")[0]testLabel = int(testStr.split("_")[0])testVector = imgVector("testDigits/%s" \%testFile)classResult = classify(testVector ,trainingMat , \hwLabels , 3)print "classify come back :%d , the real answer is :%d\" %(classResult , testLabel)if ( classResult != testLabel):errorCount +=1print "the total error num is : %d" %errorCountprint "the error rate is : %f" %(errorCount/float(mTest)) if __name__ == '__main__':#group , labels = importData()#group , labels = importData_M("datingTestSet.txt")#group = autoNorm(group)#classify([0,0 , 0] , group , labels , 3)#classTest()handWriteTest()
