在Python上基于Markov链生成伪随机文本的教程

 首先看一下来自Wolfram的定义

    马尔可夫链是随机变量{X_t}的集合（t贯穿0,1,...），给定当前的状态，未来与过去条件独立。

Wikipedia的定义更清楚一点儿

    ...马尔可夫链是具有马尔可夫性质的随机过程...[这意味着]状态改变是概率性的，未来的状态仅仅依赖当前的状态。

马尔可夫链具有多种用途，现在让我看一下如何用它生产看起来像模像样的胡言乱语。

算法如下,

代码如下
 

import randomclass Markov(object):def __init__(self, open_file): self.cache = {} self.open_file = open_file self.words = self.file_to_words() self.word_size = len(self.words) self.database()def file_to_words(self): self.open_file.seek(0) data = self.open_file.read() words = data.split() return wordsdef triples(self): """ Generates triples from the given data string. So if our string were"What a lovely day", we'd generate (What, a, lovely) and then(a, lovely, day). """if len(self.words) < 3:returnfor i in range(len(self.words) - 2):yield (self.words[i], self.words[i+1], self.words[i+2])def database(self): for w1, w2, w3 in self.triples():key = (w1, w2)if key in self.cache:self.cache[key].append(w3)else:self.cache[key] = [w3]def generate_markov_text(self, size=25): seed = random.randint(0, self.word_size-3) seed_word, next_word = self.words[seed], self.words[seed+1] w1, w2 = seed_word, next_word gen_words = [] for i in xrange(size):gen_words.append(w1)w1, w2 = w2, random.choice(self.cache[(w1, w2)]) gen_words.append(w2) return ' '.join(gen_words)

为了看到一个示例结果，我们从古腾堡计划中拿了沃德豪斯的《My man jeeves》作为文本，示例结果如下。
 

In [1]: file_ = open('/home/shabda/jeeves.txt')In [2]: import markovgenIn [3]: markov = markovgen.Markov(file_)In [4]: markov.generate_markov_text()Out[4]: 'Can you put a few years of your twin-brother Alfred,who was apt to rally round a bit. I should strongly advocatethe blue with milk'

[如果想执行这个例子，请下载jeeves.txt和markovgen.py
马尔可夫算法怎样呢？

这是一个示例文本。

复制代码 代码如下:

"The quick brown fox jumps over the brown fox who is slow jumps over the brown fox who is dead."

这个文本对应的语料库像这样，
 

{('The', 'quick'): ['brown'], ('brown', 'fox'): ['jumps', 'who', 'who'], ('fox', 'jumps'): ['over'], ('fox', 'who'): ['is', 'is'], ('is', 'slow'): ['jumps'], ('jumps', 'over'): ['the', 'the'], ('over', 'the'): ['brown', 'brown'], ('quick', 'brown'): ['fox'], ('slow', 'jumps'): ['over'], ('the', 'brown'): ['fox', 'fox'], ('who', 'is'): ['slow', 'dead.']}

现在如果我们从"brown fox"开始，接下来的单词可以是"jumps"或者"who"。如果我们选择"jumps"，然后当前的状态就变成了"fox jumps"，再接下的单词就是"over"，之后依此类推。

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