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欠王迷

http://www.ruanyifeng.com/blog/2013/12/naive_bayes_classifier.html

四九圣尊

thanks

四九圣尊

概念我已经get了

不过自己写貌似太复杂了吧

python有没有类似的代码呢

欠王迷

布吉岛耶······

问问别人吧······

四九圣尊

哪位好心人能给我一个垃圾邮件语料库，最好英文的。中文也ok

清华大学 Dr.潘

如果概念已经get了的话可以试试一下叫sklearn的python包，可以通过

pip install -U scikit-learn

来安装，里面包含有朴素贝叶斯的实现，下面是例子：

http://scikit-learn.org/stable/modules/naive_bayes.html

如果还想看具体的实现，可以看：

https:/喵scikit-learn/scikit-learn/blob/a24c8b46/sklearn/naive_bayes.py#L160

还有什么问题我们也可以继续讨论

清华大学 Dr.潘

下面是几个公开的垃圾邮件数据库：

http://c喵ining.org/index.php/spam-assassin-datasets.html

http://c喵ining.org/index.php/ling-spam-datasets.html

https://plg.uwaterloo.ca/~gvcormac/treccorpus/

http://nlp.cs.aueb.gr/software_and_datasets/Enron-Spam/index.html

如果想自己找的话，可以试试在搜索引擎上输入spam classification dataset这样的关键词来找。

四九圣尊

我要开始优化我的算法了，博士，这里的图没了我学习不了，麻烦您帮我把图补上，用比较简单易懂的算式谢谢

咚当家族编辑部

虽然当当不是太会算法

但给你参考下吧

流程图如下

四九圣尊

急，帮我把这张图的内容写给我，用比较通俗的语言

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import nltk import os from nltk.book import * import numpy from nltk import * from sklearn import * import easygui from nltk.corpus import stopwords english_stopwords = stopwords.words('english') xianyan = 0.5 ku = [] pass133 = os.getcwd() + "\\R" pass134 = os.getcwd() + "\\H" a = os.listdir(pass133) aa = os.listdir(pass134) txt_place= "" txt_text = "" htxt_place = "" htxt_text = "" print("开始学习！") for i in range(len(a)): ku_fu = [] txt_place = os.getcwd() + "\R" + "\\" + a[i] print(txt_place) txt_text = open(txt_place).read() txt_text = txt_text.lower() ku_fu = word_tokenize(txt_text) ku.extend(ku_fu) hku = [] for i in range(len(aa)): hku_fu = [] htxt_place =os.getcwd() + "\H" + "\\" + aa[i] print(htxt_place) htxt_text = open(htxt_place).read() htxt_text = htxt_text.lower() hku_fu = word_tokenize(htxt_text) hku.extend(hku_fu) print(hku) number = ["second","third","fourth","fifth","sixth","seventh","eighth","ninth","tenth"] ku_he = ku ciping = [] zuizhong = [] ku = [w for w in ku if w not in english_stopwords and len(w) >= 5 and w.isalpha() and w not in number] hku = [w for w in hku if w not in english_stopwords and len(w) >= 5 and w.isalpha() and w not in number] print(hku) fdist1 = FreqDist(ku) print(fdist1) zuizhong_fu = list(fdist1) print("INIT SUCCESS") for i in zuizhong_fu: zuizhong.append(i) if fdist1[i]/len(a) >= 1: ciping.append(1) elif fdist1[i]/len(a) < 1: ciping.append(float(fdist1[i]/len(a))) pass10 = input("词汇矩阵构建完成！点击enter开始测试垃圾邮件！") txt = easygui.fileopenbox(default='*.txt') text = open(txt).read() text = text.lower() text_fen = word_tokenize(text) text_fen = list(set(text_fen)) wenti = [] gongyou = [] for i in range(len(text_fen)): if text_fen[i] in zuizhong: gongyou.append(text_fen[i]) hwenti = [] for i in range(len(gongyou)): pass90 = hku.count(gongyou[i]) hwenti.append(pass90) for i in range(len(gongyou)): hwenti[i] = hwenti[i]/len(hku)*100 for i in range(len(gongyou)): wula = zuizhong.index(gongyou[i]) print("抽取：" + str(wula)) wenti.append(float(ciping[wula]*100)) zuihouha = []

就剩最后那个公式了，大佬帮帮我吧！

四九圣尊

大神们快过来给我补公式了

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