Python实现的朴素贝叶斯算法经典示例【测试可用】

#encoding:utf-8'''''Created on 2015年9月6日@author: ZHOUMEIXU204朴素贝叶斯实现过程'''#在该算法中类标签为1和0，如果是多标签稍微改动代码既可import numpy as nppath=u"D://Users//zhoumeixu204/Desktop//python语言机器学习//机器学习实战代码  python//机器学习实战代码//machinelearninginaction//Ch04//"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,classVecdef createVocabList(dataset):  vocabSet=set([])  for document in dataset:    vocabSet=vocabSet|set(document)  return list(vocabSet)def setOfWordseVec(vocabList,inputSet):  returnVec=[0]*len(vocabList)  for word in inputSet:    if word in vocabList:      returnVec[vocabList.index(word)]=1  #vocabList.index() 函数获取vocabList列表某个元素的位置，这段代码得到一个只包含0和1的列表    else:      print("the word :%s is not in my Vocabulary!"%word)  return returnVeclistOPosts,listClasses=loadDataSet()myVocabList=createVocabList(listOPosts)print(len(myVocabList))print(myVocabList)print(setOfWordseVec(myVocabList, listOPosts[0]))print(setOfWordseVec(myVocabList, listOPosts[3]))#上述代码是将文本转化为向量的形式，如果出现则在向量中为1，若不出现 ，则为0def trainNB0(trainMatrix,trainCategory):  #创建朴素贝叶斯分类器函数  numTrainDocs=len(trainMatrix)  numWords=len(trainMatrix[0])  pAbusive=sum(trainCategory)/float(numTrainDocs)  p0Num=np.ones(numWords);p1Num=np.ones(numWords)  p0Deom=2.0;p1Deom=2.0  for i in range(numTrainDocs):    if trainCategory[i]==1:      p1Num+=trainMatrix[i]      p1Deom+=sum(trainMatrix[i])    else:      p0Num+=trainMatrix[i]      p0Deom+=sum(trainMatrix[i])  p1vect=np.log(p1Num/p1Deom)  #change to log  p0vect=np.log(p0Num/p0Deom)  #change to log  return p0vect,p1vect,pAbusivelistOPosts,listClasses=loadDataSet()myVocabList=createVocabList(listOPosts)trainMat=[]for postinDoc in listOPosts:  trainMat.append(setOfWordseVec(myVocabList, postinDoc))p0V,p1V,pAb=trainNB0(trainMat, listClasses)if __name__!='__main__':  print("p0的概况")  print (p0V)  print("p1的概率")  print (p1V)  print("pAb的概率")  print (pAb)

# -*- coding:utf-8 -*-#!python2#构建样本分类器testEntry=['love','my','dalmation'] testEntry=['stupid','garbage']到底属于哪个类别import numpy as npdef 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,classVecdef createVocabList(dataset):  vocabSet=set([])  for document in dataset:    vocabSet=vocabSet|set(document)  return list(vocabSet)def setOfWordseVec(vocabList,inputSet):  returnVec=[0]*len(vocabList)  for word in inputSet:    if word in vocabList:      returnVec[vocabList.index(word)]=1  #vocabList.index() 函数获取vocabList列表某个元素的位置，这段代码得到一个只包含0和1的列表    else:      print("the word :%s is not in my Vocabulary!"%word)  return returnVecdef trainNB0(trainMatrix,trainCategory):  #创建朴素贝叶斯分类器函数  numTrainDocs=len(trainMatrix)  numWords=len(trainMatrix[0])  pAbusive=sum(trainCategory)/float(numTrainDocs)  p0Num=np.ones(numWords);p1Num=np.ones(numWords)  p0Deom=2.0;p1Deom=2.0  for i in range(numTrainDocs):    if trainCategory[i]==1:      p1Num+=trainMatrix[i]      p1Deom+=sum(trainMatrix[i])    else:      p0Num+=trainMatrix[i]      p0Deom+=sum(trainMatrix[i])  p1vect=np.log(p1Num/p1Deom)  #change to log  p0vect=np.log(p0Num/p0Deom)  #change to log  return p0vect,p1vect,pAbusivedef  classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):  p1=sum(vec2Classify*p1Vec)+np.log(pClass1)  p0=sum(vec2Classify*p0Vec)+np.log(1.0-pClass1)  if p1>p0:    return 1  else:    return 0def testingNB():  listOPosts,listClasses=loadDataSet()  myVocabList=createVocabList(listOPosts)  trainMat=[]  for postinDoc in listOPosts:    trainMat.append(setOfWordseVec(myVocabList, postinDoc))  p0V,p1V,pAb=trainNB0(np.array(trainMat),np.array(listClasses))  print("p0V={0}".format(p0V))  print("p1V={0}".format(p1V))  print("pAb={0}".format(pAb))  testEntry=['love','my','dalmation']  thisDoc=np.array(setOfWordseVec(myVocabList, testEntry))  print(thisDoc)  print("vec2Classify*p0Vec={0}".format(thisDoc*p0V))  print(testEntry,'classified as :',classifyNB(thisDoc, p0V, p1V, pAb))  testEntry=['stupid','garbage']  thisDoc=np.array(setOfWordseVec(myVocabList, testEntry))  print(thisDoc)  print(testEntry,'classified as :',classifyNB(thisDoc, p0V, p1V, pAb))if __name__=='__main__':  testingNB()

# -*- coding:utf-8 -*-#! python2#使用朴素贝叶斯过滤垃圾邮件# 1.收集数据：提供文本文件# 2.准备数据：讲文本文件见习成词条向量# 3.分析数据：检查词条确保解析的正确性# 4.训练算法：使用我们之前简历的trainNB0()函数# 5.测试算法：使用classifyNB()，并且对建一个新的测试函数来计算文档集的错误率# 6.使用算法，构建一个完整的程序对一组文档进行分类，将错分的文档输出到屏幕上# import re# mySent='this book is the best book on python or M.L. I hvae ever laid eyes upon.'# print(mySent.split())# regEx=re.compile('//W*')# print(regEx.split(mySent))# emailText=open(path+"email//ham//6.txt").read()import numpy as nppath=u"C://py//jb51PyDemo//src//Demo//Ch04//"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,classVecdef createVocabList(dataset):  vocabSet=set([])  for document in dataset:    vocabSet=vocabSet|set(document)  return list(vocabSet)def setOfWordseVec(vocabList,inputSet):  returnVec=[0]*len(vocabList)  for word in inputSet:    if word in vocabList:      returnVec[vocabList.index(word)]=1  #vocabList.index() 函数获取vocabList列表某个元素的位置，这段代码得到一个只包含0和1的列表    else:      print("the word :%s is not in my Vocabulary!"%word)  return returnVecdef trainNB0(trainMatrix,trainCategory):  #创建朴素贝叶斯分类器函数  numTrainDocs=len(trainMatrix)  numWords=len(trainMatrix[0])  pAbusive=sum(trainCategory)/float(numTrainDocs)  p0Num=np.ones(numWords);p1Num=np.ones(numWords)  p0Deom=2.0;p1Deom=2.0  for i in range(numTrainDocs):    if trainCategory[i]==1:      p1Num+=trainMatrix[i]      p1Deom+=sum(trainMatrix[i])    else:      p0Num+=trainMatrix[i]      p0Deom+=sum(trainMatrix[i])  p1vect=np.log(p1Num/p1Deom)  #change to log  p0vect=np.log(p0Num/p0Deom)  #change to log  return p0vect,p1vect,pAbusivedef  classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):  p1=sum(vec2Classify*p1Vec)+np.log(pClass1)  p0=sum(vec2Classify*p0Vec)+np.log(1.0-pClass1)  if p1>p0:    return 1  else:    return 0def textParse(bigString):  import re  listOfTokens=re.split(r'/W*',bigString)  return [tok.lower() for tok in listOfTokens if len(tok)>2]def spamTest():  docList=[];classList=[];fullText=[]  for i in range(1,26):    wordList=textParse(open(path+"email//spam//%d.txt"%i).read())    docList.append(wordList)    fullText.extend(wordList)    classList.append(1)    wordList=textParse(open(path+"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(np.random.uniform(0,len(trainingSet)))    testSet.append(trainingSet[randIndex])    del (trainingSet[randIndex])  trainMat=[];trainClasses=[]  for  docIndex in trainingSet:    trainMat.append(setOfWordseVec(vocabList, docList[docIndex]))    trainClasses.append(classList[docIndex])  p0V,p1V,pSpam=trainNB0(np.array(trainMat),np.array(trainClasses))  errorCount=0  for  docIndex in testSet:    wordVector=setOfWordseVec(vocabList, docList[docIndex])    if classifyNB(np.array(wordVector), p0V, p1V, pSpam)!=classList[docIndex]:      errorCount+=1  print 'the error rate is :',float(errorCount)/len(testSet)if __name__=='__main__':  spamTest()