A more flexible version of a markov chain implementation found at http://agiliq.com/blog/2009/06/generating-pseudo-random-text-with-markov-chains-u/
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import random | |
class Markov(object): | |
def __init__(self, open_file, chain_size=3): | |
self.chain_size = chain_size | |
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 words | |
def words_at_position(self, i): | |
"""Uses the chain size to find a list of the words at an index.""" | |
chain = [] | |
for chain_index in range(0, self.chain_size): | |
chain.append(self.words[i + chain_index]) | |
return chain | |
def chains(self): | |
"""Generates chains from the given data string based on passed chain size. | |
So if our string were: | |
"What a lovely day" | |
With a chain size of 3, we'd generate: | |
(What, a, lovely) | |
and | |
(a, lovely, day) | |
""" | |
if len(self.words) < self.chain_size: | |
return | |
for i in range(len(self.words) - self.chain_size - 1): | |
yield tuple(self.words_at_position(i)) | |
def database(self): | |
for chain_set in self.chains(): | |
key = chain_set[:self.chain_size - 1] | |
next_word = chain_set[-1] | |
if key in self.cache: | |
self.cache[key].append(next_word) | |
else: | |
self.cache[key] = [next_word] | |
def generate_markov_text(self, size=25): | |
seed = random.randint(0, self.word_size - 3) | |
gen_words = [] | |
seed_words = self.words_at_position(seed)[:-1] | |
gen_words.extend(seed_words) | |
for i in xrange(size): | |
last_word_len = self.chain_size - 1 | |
last_words = gen_words[-1 * last_word_len:] | |
next_word = random.choice(self.cache[tuple(last_words)]) | |
gen_words.append(next_word) | |
return ' '.join(gen_words) |
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