pythonde朴素贝叶斯算法

#coding=utf-8
from numpy import *
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] #1 is abusive, 0 not
 return postingList,classVec
#创建一个带有所有单词的列表
def createVocabList(dataSet):
 vocabSet = set([])
 for document in dataSet:
 vocabSet = vocabSet | set(document)
 return list(vocabSet)
 
def setOfWords2Vec(vocabList, inputSet):
 retVocabList = [0] * len(vocabList)
 for word in inputSet:
 if word in vocabList:
 retVocabList[vocabList.index(word)] = 1
 else:
 print 'word ',word ,'not in dict'
 return retVocabList
#另一种模型 
def bagOfWords2VecMN(vocabList, inputSet):
 returnVec = [0]*len(vocabList)
 for word in inputSet:
 if word in vocabList:
 returnVec[vocabList.index(word)] += 1
 return returnVec
def trainNB0(trainMatrix,trainCatergory):
 numTrainDoc = len(trainMatrix)
 numWords = len(trainMatrix[0])
 pAbusive = sum(trainCatergory)/float(numTrainDoc)
 #防止多个概率的成绩当中的一个为0
 p0Num = ones(numWords)
 p1Num = ones(numWords)
 p0Denom = 2.0
 p1Denom = 2.0
 for i in range(numTrainDoc):
 if trainCatergory[i] == 1:
 p1Num +=trainMatrix[i]
 p1Denom += sum(trainMatrix[i])
 else:
 p0Num +=trainMatrix[i]
 p0Denom += sum(trainMatrix[i])
 p1Vect = log(p1Num/p1Denom)#处于精度的考虑，否则很可能到限归零
 p0Vect = log(p0Num/p0Denom)
 return p0Vect,p1Vect,pAbusive
 
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
 p1 = sum(vec2Classify * p1Vec) + log(pClass1) #element-wise mult
 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 main():
 testingNB()
 
if __name__ == '__main__':
 main()