朴素贝叶斯
优点:在数据较少的情况下仍然有效,可以处理多类别问题。
缺点:对输入数据的准备方式较为敏感。
适用数据类型:标称型数据
核心思想:选择高概率对应的类别。
条件概率:
代码:
from numpy import *
def loadDataSet():
postingList=[['my','dog','has','flea',\
'probelms','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] #0正常,1侮辱,对应postingList的属性
return postingList,classVec
def createVocabList(dataSet):
vocabSet=set([]) #创建一个空集
for document in dataSet:
vocabSet=vocabSet|set(document) #创建两个集合的并集
return list(vocabSet)
def setOfWords2Vec(vocabList,inputSet): #vocabList词汇表,inputSet输入的文档
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
def trainNB0(trainMatrix,trainCategory):
numTrainDocs=len(trainMatrix)
numWords=len(trainMatrix[0])
pAbusive=sum(trainCategory)/float(numTrainDocs) #侮辱性文档的概率
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
def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
p1=sum(vec2Classify*p1Vec)+log(pClass1) #计算相乘后的概率
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 postoinDoc in listOPosts:
trainMat.append(setOfWords2Vec(myVocabList,postoinDoc))
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))
if __name__ == '__main__':
testingNB()
# listOPosts,listClasses=loadDataSet()
# myVocabList=createVocabList(listOPosts) #单词列表
# trainMat=[]
# for postoinDoc in listOPosts:
# trainMat.append(setOfWords2Vec(myVocabList,postoinDoc)) #句子分词后的单词在单词列表中出现的矩阵
# # print(trainMat)
# p0V,p1V,pAb=trainNB0(trainMat,listClasses)
# print(p0V)
# print(p1V)
# print(pAb)
# print(myVocabList)
# print(setOfWords2Vec(myVocabList,listOPosts[0]))
# print(setOfWords2Vec(myVocabList, listOPosts[3]))