from numpy import *
import operator
from os import listdir
def createDataSet():
group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
labels = ['A','B','C','D']
return group, labels
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.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
#归一化函数
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('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 classjj(inX, dataSet, labels):
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()
voteIlabel = labels[sortedDistIndicies[0]]
return voteIlabel
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?"))
datingDatMat,datingLabels=file2matrix('datingTestSet2.txt')
normMat,ranges,minVals=autoNorm(datingDatMat)
inArr=array([ffMiles,percentTats,iceCream])
classifierResult=classify0((inArr-minVals)/ranges,normMat,datingDatMat,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
def handwritingClassTest():
hwLabels=[]
trainingFileList=listdir('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('trainingDigits/%s'%fileNameStr)
testFileList=listdir('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('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)))
