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Markov: implement a min_size, which tries to make a chain of at least min_size words.

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note that this isn't guaranteed, if the chain is such that the
current tuple has nowhere to go but to the end of the line, then
it will follow it --- it doesn't try to go back and rebuilt a different
chain or anything.
This commit is contained in:
Brian S. Stephan 2011-01-19 18:44:07 -06:00
parent ac0429569e
commit 7c05f60ffd
1 changed files with 18 additions and 4 deletions
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22
modules/Markov.py
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@ -166,19 +166,26 @@ class Markov(Module):
# cap the end of the chain
self.brain.setdefault((w1, w2), []).append(self.stop)
def _reply(self, max_size=100):
def _reply(self, min_size=15, max_size=100):
"""Generate a totally random string from the chains, of specified limit of words."""
# if the limit is too low, there's nothing to do
if (max_size <= 3):
raise Exception("max_size is too small: %d" % max_size)
# if the min is too large, abort
if (min_size > 20):
raise Exception("min_size is too large: %d" % min_size)
# start with an empty chain, and work from there
gen_words = [self.start1, self.start2]
# walk a chain, randomly, building the list of words
while len(gen_words) < max_size + 2 and gen_words[-1] != self.stop:
gen_words.append(random.choice(self.brain[(gen_words[-2], gen_words[-1])]))
if len(gen_words) < min_size and len(filter(lambda a: a != self.stop, self.brain[(gen_words[-2], gen_words[-1])])) > 0:
gen_words.append(random.choice(filter(lambda a: a != self.stop, self.brain[(gen_words[-2], gen_words[-1])])))
else:
gen_words.append(random.choice(self.brain[(gen_words[-2], gen_words[-1])]))
# chop off the seed data at the start
gen_words = gen_words[2:]
@ -189,13 +196,17 @@ class Markov(Module):
return ' '.join(gen_words)
def _reply_to_line(self, line, max_size=100):
def _reply_to_line(self, line, min_size=15, max_size=100):
"""Reply to a line, using some text in the line as a point in the chain."""
# if the limit is too low, there's nothing to do
if (max_size <= 3):
raise Exception("max_size is too small: %d" % max_size)
# if the min is too large, abort
if (min_size > 20):
raise Exception("min_size is too large: %d" % min_size)
# get a random word from the input
words = line.split()
target_word = words[random.randint(0, len(words)-1)]
@ -211,7 +222,10 @@ class Markov(Module):
# generate new word
target_word = words[random.randint(0, len(words)-1)]
else:
gen_words.append(random.choice(self.brain[(gen_words[-2], gen_words[-1])]))
if len(gen_words) < min_size and len(filter(lambda a: a != self.stop, self.brain[(gen_words[-2], gen_words[-1])])) > 0:
gen_words.append(random.choice(filter(lambda a: a != self.stop, self.brain[(gen_words[-2], gen_words[-1])])))
else:
gen_words.append(random.choice(self.brain[(gen_words[-2], gen_words[-1])]))
# chop off the seed data at the start
gen_words = gen_words[2:]