#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

#創建一個帶有所有單詞的列表
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

#另一種模型???
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)
??? #防止多個概率的成績當中的一個為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)#處于精度的考慮，否則很可能到限歸零
??? 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()