本代码实现了朴素贝叶斯分类器（假设了条件独立的版本），常用于垃圾邮件分类，进行了拉普拉斯平滑。

关于朴素贝叶斯算法原理可以参考博客中原理部分的博文。

#!/usr/bin/python# -*- coding: utf-8 -*-from math import logfrom numpy import*import operatorimport matplotlibimport matplotlib.pyplot as pltfrom os import listdirdef 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,classVecdef createVocabList(dataSet): vocabSet = set([]) #create empty set for document in dataSet: vocabSet = vocabSet | set(document) #union of the two sets 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 returnVecdef trainNB0(trainMatrix,trainCategory): #训练模型 numTrainDocs = len(trainMatrix) numWords = len(trainMatrix[0]) pAbusive = sum(trainCategory)/float(numTrainDocs) p0Num = ones(numWords); p1Num = ones(numWords) #拉普拉斯平滑 p0Denom = 0.0+2.0; p1Denom = 0.0 +2.0 #拉普拉斯平滑 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) #用log()是为了避免概率乘积时浮点数下溢 p0Vect = log(p0Num/p0Denom) return p0Vect,p1Vect,pAbusive 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 bagOfWords2VecMN(vocabList, inputSet): returnVec = [0] * len(vocabList) for word in inputSet: if word in vocabList: returnVec[vocabList.index(word)] += 1 return returnVec def testingNB(): #测试训练结果 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 textParse(bigString): # 长字符转转单词列表 import re listOfTokens = re.split(r'\W*', bigString) return [tok.lower() for tok in listOfTokens if len(tok) > 2] def spamTest(): #测试垃圾文件 需要数据 docList = []; classList = []; fullText = [] for i in range(1, 26): wordList = textParse(open('email/spam/%d.txt' % i).read()) 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) trainingSet = range(50); testSet = [] 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: trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex])) trainClasses.append(classList[docIndex]) p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses)) errorCount = 0 for docIndex in testSet: 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) listOPosts,listClasses=loadDataSet()myVocabList=createVocabList(listOPosts)print myVocabList,'\n'# print setOfWords2Vec(myVocabList,listOPosts[0]),'\n'trainMat=[]for postinDoc in listOPosts: trainMat.append(setOfWords2Vec(myVocabList,postinDoc))print trainMatp0V,p1V,pAb=trainNB0(trainMat,listClasses)print pAbprint p0V,'\n',p1VtestingNB()

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