此处将结果输出到result.txt文件中

各词概率保存到wordsProb.txt中

Code:def getProbWord(self, testDict, normalDict, spamDict, numNormal, numSpam):

"""

计算对分类结果影响最大的15个词

:param testDict: 测试数据字典

:param normalDict: 正常邮件字典

:param spamDict: 垃圾邮件字典

:param numNormal: 正常邮件的数量

:param numSpam: 垃圾邮件的数量

:return wordProbList: 对分类结果影响最大的15个词

"""

wordProbList = {}

for word, num in testDict.items():

当词不在垃圾邮件词表中，在正常邮件词表中，计算概率

if word in spamDict.keys() and word in normalDict.keys():

求类先验概率

正常邮件

pw_n = normalDict[word] / numNormal

垃圾邮件

pw_s = spamDict[word] / numSpam

ps_w = pw_s / (pw_s + pw_n)

wordProbList[word] = ps_w

当词在垃圾邮件词表中，不在正常邮件词表中，计算概率

if word in spamDict.keys() and word not in normalDict.keys():

pw_s = spamDict[word] / numSpam

pw_n = 0.01

ps_w = pw_s / (pw_s + pw_n)

wordProbList[word] = ps_w

当词在垃圾邮件词表中，而且在正常邮件词表中，计算概率

if word not in spamDict.keys() and word in normalDict.keys():

pw_s = 0.01

pw_n = normalDict[word] / numNormal

ps_w = pw_s / (pw_s + pw_n)

wordProbList[word] = ps_w

当词不在垃圾邮件词表中，也不在正常邮件词表中，计算概率

if word not in spamDict.keys() and word not in normalDict.keys():

wordProbList[word] = 0.5 # 0.4

sorted(wordProbList.items(), key=lambda d: d[1], reverse=True)[0:15]

return wordProbList

def calBayes(self, wordList, spamDict, normalDict):

"""

计算贝叶斯概率

:param wordList: 词表

:param spamDict: 垃圾邮件词语字典

:param normalDict: 正常邮件词语字典

:return: 概率

"""

ps_w = 1

ps_n = 1

with open('wordsProb.txt', 'a', encoding='utf-8') as f:

for word, prob in wordList.items():

f.write(word + ":" + str(prob) + "

")

ps_w *= prob

ps_n *= 1 - prob

p = ps_w / (ps_w + ps_n)

return p

def calAccuracy(self, testResult):

"""

计算精度

:return:

"""

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)