第一篇，不知写啥，就贴代码吧。朴素贝叶斯，垃圾邮件检测。

from spam.spamEmail import spamEmailBayes
import re
#spam类对象
spam=spamEmailBayes()
#保存词频的词典
spamDict={}
normDict={}
testDict={}
#保存每封邮件中出现的词
wordsList=[]
wordsDict={}
#保存预测结果,key为文件名，值为预测类别
testResult={}
#分别获得正常邮件、垃圾邮件及测试文件名称列表
normFileList=spam.get_File_List("./../data/normal")
spamFileList=spam.get_File_List("./../data/spam")
testFileList=spam.get_File_List("./../data/test")
#获取训练集中正常邮件与垃圾邮件的数量
normFilelen=len(normFileList)
spamFilelen=len(spamFileList)
#获得停用词表，用于对停用词过滤
stopList=spam.getStopWords()
#获得正常邮件中的词频
for fileName in normFileList:
    wordsList.clear()
    for line in open("./../data/normal/"+fileName):
        #过滤掉非中文字符
        rule=re.compile(r"[^\u4e00-\u9fa5]")
        line=rule.sub("",line)
        #将每封邮件出现的词保存在wordsList中
        spam.get_word_list(line,wordsList,stopList)
    #统计每个词在所有邮件中出现的次数
    spam.addToDict(wordsList, wordsDict)
normDict=wordsDict.copy()  

#获得垃圾邮件中的词频
wordsDict.clear()
for fileName in spamFileList:
    wordsList.clear()
    for line in open("./../data/spam/"+fileName):
        rule=re.compile(r"[^\u4e00-\u9fa5]")
        line=rule.sub("",line)
        spam.get_word_list(line,wordsList,stopList)
    spam.addToDict(wordsList, wordsDict)
spamDict=wordsDict.copy()

# 测试邮件
for fileName in testFileList:
    testDict.clear( )
    wordsDict.clear()
    wordsList.clear()
    for line in open("./../data/test/"+fileName):
        rule=re.compile(r"[^\u4e00-\u9fa5]")
        line=rule.sub("",line)
        spam.get_word_list(line,wordsList,stopList)
    spam.addToDict(wordsList, wordsDict)
    testDict=wordsDict.copy()
    #通过计算每个文件中p(s|w)来得到对分类影响最大的15个词
    wordProbList=spam.getTestWords(testDict, spamDict,normDict,normFilelen,spamFilelen)
    #对每封邮件得到的15个词计算贝叶斯概率  
    p=spam.calBayes(wordProbList, spamDict, normDict)
    if(p>0.9):
        testResult.setdefault(fileName,1)
    else:
        testResult.setdefault(fileName,0)
#计算分类准确率（测试集中文件名低于1000的为正常邮件）
testAccuracy=spam.calAccuracy(testResult)
for i,ic in testResult.items():
    print(i+"/"+str(ic))
print(testAccuracy)  


import jieba;
import os;
class spamEmailBayes:
    #获得停用词表
    def getStopWords(self):
        stopList=[]
        for line in open("../data/中文停用词表.txt"):
            stopList.append(line[:len(line)-1])
        return stopList;
    #获得词典
    def get_word_list(self,content,wordsList,stopList):
        #分词结果放入res_list
        res_list = list(jieba.cut(content))
        for i in res_list:
            if i not in stopList and i.strip()!='' and i!=None:
                if i not in wordsList:
                    wordsList.append(i)

    #若列表中的词已在词典中，则加1，否则添加进去
    def addToDict(self,wordsList,wordsDict):
        for item in wordsList:
            if item in wordsDict.keys():
                wordsDict[item]+=1
            else:
                wordsDict.setdefault(item,1)

    def get_File_List(self,filePath):
        filenames=os.listdir(filePath)
        return filenames

    #通过计算每个文件中p(s|w)来得到对分类影响最大的15个词
    def getTestWords(self,testDict,spamDict,normDict,normFilelen,spamFilelen):
        wordProbList={}
        for word,num  in testDict.items():
            if word in spamDict.keys() and word in normDict.keys():
                #该文件中包含词个数
                pw_s=spamDict[word]/spamFilelen
                pw_n=normDict[word]/normFilelen
                ps_w=pw_s/(pw_s+pw_n) 
                wordProbList.setdefault(word,ps_w)
            if word in spamDict.keys() and word not in normDict.keys():
                pw_s=spamDict[word]/spamFilelen
                pw_n=0.01
                ps_w=pw_s/(pw_s+pw_n) 
                wordProbList.setdefault(word,ps_w)
            if word not in spamDict.keys() and word in normDict.keys():
                pw_s=0.01
                pw_n=normDict[word]/normFilelen
                ps_w=pw_s/(pw_s+pw_n) 
                wordProbList.setdefault(word,ps_w)
            if word not in spamDict.keys() and word not in normDict.keys():
                #若该词不在脏词词典中，概率设为0.4
                wordProbList.setdefault(word,0.47)
        sorted(wordProbList.items(),key=lambda d:d[1],reverse=True)[0:15]
        return (wordProbList)

    #计算贝叶斯概率
    def calBayes(self,wordList,spamdict,normdict):
        ps_w=1
        ps_n=1

        for word,prob in wordList.items() :
            print(word+"/"+str(prob))
            ps_w*=(prob)
            ps_n*=(1-prob)
        p=ps_w/(ps_w+ps_n)
#         print(str(ps_w)+""+str(ps_n))
        return p        

    #计算预测结果正确率
    def calAccuracy(self,testResult):
        rightCount=0
        errorCount=0
        for name ,catagory in testResult.items():
            if (int(name)<1000 and catagory==0) or(int(name)>1000 and catagory==1):
                rightCount+=1
            else:
                errorCount+=1
        return rightCount/(rightCount+errorCount)