本代碼實(shí)現(xiàn)了樸素貝葉斯分類器（假設(shè)了條件獨(dú)立的版本），常用于垃圾郵件分類，進(jìn)行了拉普拉斯平滑。

關(guān)于樸素貝葉斯算法原理可以參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):  #訓(xùn)練模型  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()是為了避免概率乘積時(shí)浮點(diǎn)數(shù)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():  #測(cè)試訓(xùn)練結(jié)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): # 長(zhǎng)字符轉(zhuǎn)轉(zhuǎn)單詞列表  import re  listOfTokens = re.split(r'/W*', bigString)  return [tok.lower() for tok in listOfTokens if len(tok) > 2] def spamTest():  #測(cè)試?yán)募?需要數(shù)據(jù)  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()