dr.botzo/dr_botzo/markov/lib.py

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import logging
import random
from django.db.models import Sum
from markov.models import MarkovContext, MarkovState, MarkovTarget
log = logging.getLogger('markov.lib')
def generate_line(context, topics=None, min_words=15, max_words=30, max_sentences=3):
"""String multiple sentences together into a coherent sentence."""
tries = 0
sentences = 0
line = []
while tries < 5:
line += generate_longish_sentence(context, topics=topics, max_words=max_words)
sentences += 1
if sentences >= max_sentences:
return line
if len(line) >= min_words:
return line
else:
if line[-1][-1] not in [',', '.', '!']:
line[-1] += random.choice([',', '.', '!'])
tries += 1
# if we got here, we need to give up
return line
def generate_longish_sentence(context, topics=None, min_words=4, max_words=30):
"""Generate a Markov chain, but throw away the short ones unless we get desperate."""
tries = 0
while tries < 5:
sent = generate_sentence(context, topics=topics, max_words=max_words)
if len(sent) >= min_words:
return sent
tries += 1
# if we got here, we need to just give up
return generate_sentence(context)
def generate_sentence(context, topics=None, max_words=30):
"""Generate a Markov chain."""
words = []
# if we have topics, try to work from it and work backwards
if topics:
topic_word = random.choice(topics)
topics.remove(topic_word)
log.debug(u"looking for topic '{0:s}'".format(topic_word))
new_states = MarkovState.objects.filter(context=context, v=topic_word)
if len(new_states) > 0:
log.debug(u"found '{0:s}', starting backwards".format(topic_word))
words.insert(0, topic_word)
while len(words) <= max_words and words[0] != MarkovState._start2:
log.debug(u"looking backwards for '{0:s}'".format(words[0]))
new_states = MarkovState.objects.filter(context=context, v=words[0])
words.insert(0, get_word_out_of_states(new_states, backwards=True))
# if we didn't get topic stuff, we need to start (forwards) here
if len(words) == 0:
words = [MarkovState._start1, MarkovState._start2]
i = len(words)
while len(words) <= max_words and words[-1] != MarkovState._stop:
log.debug(u"looking for '{0:s}','{1:s}'".format(words[i-2], words[i-1]))
new_states = MarkovState.objects.filter(context=context, k1=words[i-2], k2=words[i-1])
log.debug(u"states retrieved")
words.append(get_word_out_of_states(new_states))
i += 1
words = [word for word in words if word not in
(MarkovState._start1, MarkovState._start2, MarkovState._stop)]
return words
def get_or_create_target_context(target_name):
"""Return the context for a provided nick/channel, creating missing ones."""
# find the stuff, or create it
try:
target = MarkovTarget.objects.get(name=target_name)
return target.context
except MarkovTarget.DoesNotExist:
# we need to create a context and a target, and we have to make the context first
# make a context --- lacking a good idea, just create one with this target name until configured otherwise
context, c = MarkovContext.objects.get_or_create(name=target_name)
target, c = MarkovTarget.objects.get_or_create(name=target_name, context=context)
return target.context
except MarkovContext.DoesNotExist:
# make a context --- lacking a good idea, just create one with this target name until configured otherwise
context, c = MarkovContext.objects.get_or_create(name=target_name)
target.context = context
target.save()
return target.context
def get_word_out_of_states(states, backwards=False):
"""Pick one random word out of the given states."""
new_word = ''
running = 0
count_sum = states.aggregate(Sum('count'))['count__sum']
hit = random.randint(0, count_sum)
log.debug(u"sum: {0:d} hit: {1:d}".format(count_sum, hit))
states_itr = states.iterator()
for state in states_itr:
running += state.count
if running >= hit:
if backwards:
new_word = state.k2
else:
new_word = state.v
break
log.debug(u"found '{0:s}'".format(new_word))
return new_word
def learn_line(line, context):
"""Create a bunch of MarkovStates for a given line of text."""
log.debug(u"learning %s...", line[:40])
words = line.split()
words = [MarkovState._start1, MarkovState._start2] + words + [MarkovState._stop]
for word in words:
if len(word) > MarkovState._meta.get_field('k1').max_length:
return
for i, word in enumerate(words):
log.debug(u"'{0:s}','{1:s}' -> '{2:s}'".format(words[i], words[i+1], words[i+2]))
state, created = MarkovState.objects.get_or_create(context=context,
k1=words[i],
k2=words[i+1],
v=words[i+2])
state.count += 1
state.save()
if i > len(words) - 4:
break