利用大量邮件先验数据，使用朴素贝叶斯分类算法来自动识别垃圾邮件。
python实现：
#过滤垃圾邮件 def textParse(bigString): #正则表达式进行文本解析 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): #导入并解析文本文件 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): #随机构建训练集 randIndex = int(random.uniform(0,len(trainingSet))) testSet.append(trainingSet[randIndex]) #随机挑选一个文档索引号放入测试集 del(trainingSet[randIndex]) #将该文档索引号从训练集中剔除 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: #对测试集进行分类 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)
