注意：条件贝叶斯是保证条件之间独立的（文档分类中是假设一个词汇出现与其他词汇是否出现无关，然而同一主题的词汇一起出现的概率很高，存在关联），所以这个假设过于简单；尽管如此，然而事实表明，朴素贝叶斯的效果还很好。
from numpy import *
#词表到向量的转换函数
def loadDataSet():
postingList=[['my','dog','has','flea', \
'problems','help', 'please'],
['maybe','not','take','him',\
'to','dog','park','stupid'],
['my','dalmation','is','so','cute',\
'I','love','him'],
['stop','posting','stupid','worthless','garbage'],
['mr','licks','ate','my','steak','how',\
'to','stop','him'],
['quit','buying','worthless','dog','food','stupid']]
classVec=[0,1,0,1,0,1]
return postingList,classVec
def createVocabList(dataSet):
vocabSet=set([])
for document in dataSet:
vocabSet=vocabSet|set(document)
return list(vocabSet)
def setOfWords2Vec(vocabList,inputSet):
returnVec=[0]*len(vocabList)
for word in inputSet:
if word in vocabList:
returnVec[vocabList.index(word)]=1
else : print"the word: %s is not in my Vocabulary!" % word
return returnVec
#朴素贝叶斯分类器训练函数
def trainNB0(trainMatrix,trainCategory):
numTrainDocs=len(trainMatrix)
numWords=len(trainMatrix[0])
pAbusive=sum(trainCategory)/float(numTrainDocs)
p0Num=ones(numWords);p1Num=ones(numWords)
p0Denom=2.0;p1Denom=2.0
for i in range(numTrainDocs):
if trainCategory[i]==1:
p1Num+=trainMatrix[i]
p1Denom+=sum(trainMatrix[i])
else:
p0Num+=trainMatrix[i]
p0Denom+=sum(trainMatrix[i])
p1Vect=log(p1Num/p1Denom)
p0Vect=log(p0Num/p0Denom)
return p0Vect,p1Vect,pAbusive
#朴素贝叶斯分类函数
def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
p1=sum(vec2Classify*p1Vec)+log(pClass1)
p0=sum(vec2Classify*p0Vec)+log(1.0-pClass1)
if p1>p0:
return 1
else :
return 0
def testingNB():
listOPosts,listClasses=loadDataSet()
myVocabList=createVocabList(listOPosts)
trainMat=[]
for postinDoc in listOPosts:
trainMat.append(setOfWords2Vec(myVocabList,postinDoc))
p0V,p1V,pAb=trainNB0(array(trainMat),array(listClasses))
testEntry=['love','my','dalmation']
thisDoc=array(setOfWords2Vec(myVocabList,testEntry))
print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)
testEntry=['stupid','garbage']
thisDoc=array(setOfWords2Vec(myVocabList,testEntry))
print testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb)
def bagOfWords2VecMN(vocabList,inputSet):
returnVec=[0]*len(vocabList)
for word in inputSet:
if word in vocabList:
returnVec[vocabList.index(word)]+=1
return returnVec
def textParse(bigString): #input is big string, #output is word list
import re
listOfTokens = re.split(r'\W*', bigString)
return [tok.lower() for tok in listOfTokens if len(tok) > 2]
def spamTest():
docList=[]; classList = []; fullText =[]
for i in range(1,26):
wordList = textParse(open('email/spam/%d.txt' % i).read())
# print wordList
docList.append(wordList)
fullText.extend(wordList)
classList.append(1)
wordList = textParse(open('email/ham/%d.txt' % i).read())
docList.append(wordList)
fullText.extend(wordList)
classList.append(0)
vocabList = createVocabList(docList)#create vocabulary
trainingSet = range(50); testSet=[] #create test set
for i in range(10):
randIndex = int(random.uniform(0,len(trainingSet)))
testSet.append(trainingSet[randIndex])
del(trainingSet[randIndex])
trainMat=[]; trainClasses = []
for docIndex in trainingSet:#train the classifier (get probs) trainNB0
trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
trainClasses.append(classList[docIndex])
p0V,p1V,pSpam = trainNB0(array(trainMat),array(trainClasses))
errorCount = 0
for docIndex in testSet: #classify the remaining items
wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
if classifyNB(array(wordVector),p0V,p1V,pSpam) != classList[docIndex]:
errorCount += 1
print "classification error",docList[docIndex]
print 'the error rate is: ',float(errorCount)/len(testSet)