5.贝叶斯算法、单词拼写错误案例

骑猪看日落 2022-10-16 07:23 158阅读 0赞

5.贝叶斯算法  
5.1.单词拼写错误案例

# 5.贝叶斯算法 #

**贝叶斯简介**

*  贝叶斯(约1701-1761) Thomas Bayes，英国数学家。
 *  贝叶斯方法源于他生前解决一个”逆概”问题写的一篇文章。
 *  生不逢时，死后它的作品才被世人认可。

**贝叶斯要解决的问题：**

*  正向概率：假设袋子里面有N个白球，M个黑球，你伸手进去摸一把，摸出黑球的概率是多大
 *  逆向概率：如果我们事先并不知道袋子里面黑白球的比例，而是闭着眼睛摸出一个（或好几个）球，观察这些取出来的球的颜色之后，那么我们可以就此对袋子里面的黑白球的比例作出什么样的推测

**Why贝叶斯？**  
现实世界本身就是不确定的，人类的观察能力是有局限性的。  
我们日常所观察到的只是事物表面上的结果，因此我们需要提供一个猜测。  
![在这里插入图片描述][20210527233825482.png_pic_left]

*  男生总是穿长裤，女生则一半穿长裤一半穿裙子
 *  正向概率：随机选取一个学生，他（她）穿长裤的概率和穿裙子的概率是多大。
 *  逆向概率：迎面走来一个穿长裤的学生，你只看得见他（她）穿的是否长裤，而无法确定他（她）的性别，你能够推断出他（她）是女生的概率是多大吗？

**假设学校里面人的总数是U个**  
**穿长裤的（男生）：U \* P(Boy) \* P(Pants|Boy)**

*  P(Boy)是男生的概率 = 60%
 *  P(Pants|Boy) 是条件概率，即在 Boy 这个条件下穿长裤的  
    **穿长裤的（女生）：U \* P(Girl) \* P(Pants|Girl)**

**与总人数有关吗？**

**化简：**

*  P(Girl|Pants) = P(Girl) \* P(Pants|Girl) / \[P(Boy) \* P(Pants|Boy) + P(Girl) \* P(Pants|Girl)\]
 *  分母其实就是P(Pants)
 *  分子其实就是P(Pants, Girl)  
    即：  
    ![在这里插入图片描述][20210527234041453.png_pic_left]

**由此推导出了贝叶斯公式：**  
![在这里插入图片描述][20210527234125978.png_pic_left]

## 5.1.单词拼写错误案例 ##

import re, collections
    import sys
    
    
    # 把语料库的单词全部抽取出来，转写成小写，并去掉单词中间的特殊符号
    def words(text):
        return re.findall('[a-z]+', text.lower())
    
    
    """ 如果遇到一个语料库中没有的单词怎么办? 假如说一个单词拼写正确，但是语料库中没有包含这个词，从而这个词也永远不会出现现在的训练集中。if 于是我们要返回出现这个词的概率是0 代表这个事件绝对不可能发生而在我们的概率模型中我们期望用一个很小的概率来代表这种情况。lambda:1 """
    def train(features):
        model = collections.defaultdict(lambda: 1)
        for f in features:
            model[f] += 1
        return model
    
    
    NWORDS = train(words(open('./data/big.txt').read()))
    alphabet = 'abcdefghijklmnopqrstuvwxyz'
    
    """ 编辑距离:两个词之间的编辑距离定义为使用了几次插入(在词中插入一个单字母)，交换（交换相邻两个字母）， 替换(把一个字母换成另一个)的操作从一个词变到另一个词 """
    # 返回所以与单词w编辑距离为1的集合
    def editsl(word):
        n = len(word)
        return set([word[0:i] + word[i + 1:] for i in range(n)] +                                  # deletion
                   [word[0:i] + word[i + 1] + word[i] + word[i + 2:] for i in range(n - 1)] +      # transposition
                   [word[0:i] + c + word[i + 1:] for i in range(n) for c in alphabet] +            # alteration
                   [word[0:i] + c + word[i:] for i in range(n + 1) for c in alphabet])             # insertion
    
    
    # 返回所有与单词w编辑距离为2的单词集合
    # 在这些编辑距离小于2的中间，只把那些正确的词作为候选词
    def know_edits2(word):
        return set(e2 for e1 in editsl(word) for e2 in editsl(e1) if e2 in NWORDS)
    
    
    def known(words):
        return set(w for w in words if w in NWORDS)
    
    
    def correct(words):
        candidates = known([words]) or known(editsl(words)) or know_edits2(words) or [words]
        return max(candidates, key=lambda w: NWORDS[w])
    
    
    def main():
        while True:
            str = input("input word:")
            if str == 'break':
                return
            c = correct(str)
            print(c)
    
    
    if __name__ == '__main__':
        main()

在控制台输入内容：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70_pic_left]

**./data/big.txt的内容如下：**

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[20210527233825482.png_pic_left]: /images/20221014/f2ae864bfb464c9393285dd6e479ed19.png
[20210527234041453.png_pic_left]: /images/20221014/e9cc6f63b1774099b24903c7676baebe.png
[20210527234125978.png_pic_left]: /images/20221014/d94da4d44f714c5999d97903e5f762b7.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70_pic_left]: /images/20221014/74c917ec056049b39d1e282f829d63ad.png