python - 字母尺度和随机文本上的马尔可夫链

Question

我想使用 .txt 文件中一本书的字母频率生成随机文本，以便每个新字符 (string.lowercase + ' ') 都依赖于前一个字符。

我如何使用马尔可夫链来做到这一点？还是对每个字母使用 27 个条件频率数组更简单？

Answer 1

I would like to generate a random text using letter frequencies from a book in a txt file

考虑使用 collections.Counter 在文本文件中一次循环两个字母时建立频率。

How do I use markov chains to do so? Or is it simpler to use 27 arrays with conditional frequencies for each letter?

这两个语句是等价的。马尔可夫链就是您正在做的。具有条件频率的 27 个数组是您如何做的。

这里有一些基于字典的代码可以帮助您入门:

from collections import defaultdict, Counter
from itertools import ifilter
from random import choice, randrange

def pairwise(iterable):
    it = iter(iterable)
    last = next(it)
    for curr in it:
        yield last, curr
        last = curr

valid = set('abcdefghijklmnopqrstuvwxyz ')

def valid_pair((last, curr)):
    return last in valid and curr in valid

def make_markov(text):
    markov = defaultdict(Counter)
    lowercased = (c.lower() for c in text)
    for p, q in ifilter(valid_pair, pairwise(lowercased)):
        markov[p][q] += 1
    return markov

def genrandom(model, n):
    curr = choice(list(model))
    for i in xrange(n):
        yield curr
        if curr not in model:   # handle case where there is no known successor
            curr = choice(list(model))
        d = model[curr]
        target = randrange(sum(d.values()))
        cumulative = 0
        for curr, cnt in d.items():
            cumulative += cnt
            if cumulative > target:
                break

model = make_markov('The qui_.ck brown fox')
print ''.join(genrandom(model, 20))