机器学习实现bayes

代码链接：http://download.csdn.net/detail/edwards_june/9856048

from numpy import *# 词表到向量的转换函数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]  # 文档是否合法:1非法，0合法    return postingList, classVec# 创建单词列表def createVocabList(dataSet):    vocabSet = set([])    for document in dataSet:        vocabSet = vocabSet | set(document)  # 并集    return list(vocabSet)# 对测试词集标记在单词列表上的出现与否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 returnVec# 对每个单词计算其占所在分类的比例def trainNB0(trainMatrix, trainCategory):    numTrainDocs = len(trainMatrix)    numWords = len(trainMatrix[0])    pAbusive = sum(trainCategory) / float(numTrainDocs)    p0Num = ones(numWords)    p1Num = ones(numWords)    p0Denom = 2.0    p1Denom = 2.0    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)    p0Vect = log(p0Num / p0Denom)    return p0Vect, p1Vect, pAbusive# 分类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# 测试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 bagOfWords2VecMN(vocabList, inputSet):    returnVec = [0] * len(vocabList)    for word in vocabList:        returnVec[vocabList.index(word)] += 1    return returnVecdef 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('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 = list(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(setOfWords2Vec(vocabList, docList[docIndex]))        trainClasses.append(classList[docIndex])    p0v, p1v, pSpam = trainNB0(array(trainMat), array(trainClasses))    errorCount = 0    for docIndex in testSet:        wordVector = setOfWords2Vec(vocabList, docList[docIndex])        if classifyNB(array(wordVector), p0v, p1v, pSpam) != classList[docIndex]:            errorCount += 1    print('the error rate is :', float(errorCount) / len(testSet))# 计算出现频率def calcMostFreq(vocabList, fullText):    import operator    freqDict = {}    for token in vocabList:        freqDict[token] = fullText.count(token)    sortedFreq = sorted(freqDict.items(), key=operator.itemgetter(1), reverse=True)    return sortedFreq[:30]def localWords(feed1, feed0):    import feedparser    docList = []    classList = []    fullText = []    minLen = min(len(feed1['entries']), len(feed0['entries']))    for i in range(minLen):        wordList = textParse(feed1['entries'][i]['summary'])        docList.append(wordList)        fullText.extend(wordList)        classList.append(1)        wordList = textParse(feed0['entries'][i]['summary'])        docList.append(wordList)        fullText.extend(wordList)        classList.append(0)    vocabList = createVocabList(docList)    top30Words = calcMostFreq(vocabList, fullText)    for pairW in top30Words:        if pairW[0] in vocabList: vocabList.remove(pairW[0])    trainingSet = list(range(2 * minLen))  # 0~2*minLen    testSet = []  # 存放文档下标    for i in range(20):        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('the error rate is: ', float(errorCount) / len(testSet))    return vocabList, p0V, p1V# 最具表征性的词汇显示函数def getTopWords(ny, sf):    import operator    vocabList, p0V, p1V = localWords(ny, sf)    topNY = []    topSF = []    for i in range(len(p0V)):        if p0V[i] > -6.0:            topSF.append((vocabList[i], p0V[i]))        if p1V[i] > -6.0:            topNY.append((vocabList[i], p1V[i]))    sortedSF = sorted(topSF, key=lambda pair: pair[1], reverse=True)    print("SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**")    for item in sortedSF:        print(item[0])    sortedNY = sorted(topNY, key=lambda pair: pair[1], reverse=True)    print("NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**")    for item in sortedNY:        print(item[0])