def _create_chains(chatbot_brain, seeds, size=200):
u"""Return list of Markov-Chain generated strings where each word
added onto the sentence is selected solely from the probability
of it following the given last two words in the training data."""
print "the seeds are: " + str(seeds)
candidates = []
while len(candidates) < size:
seed = str(chatbot_brain.i_filter_random(seeds))
pair = str(chatbot_brain._pair_seed(seed))
w_1 = pair[0]
w_2 = pair[1]
next_word = ""
word_1, word_2 = w_1, w_2
candidate = [word_1, word_2]
pair = "{} {}".format(word_1, word_2)
done = False
while not done:
try:
next_word = random.choice(chatbot_brain.tri_lexicon[pair])
candidate.append(next_word)
word_1, word_2 = word_2, next_word
pair = "{} {}".format(word_1, word_2)
except KeyError:
candidates.append(" ".join(candidate))
done = True
if next_word in chatbot_brain.stop_puncts:
candidates.append(" ".join(candidate))
done = True
return candidates
def _create_chains(chatbot_brain, seeds, size=200):
u"""Return list of Markov-Chain generated strings where each word
added onto the sentence is selected solely from the probability
of it following the given last two words in the training data."""
print "the seeds are: " + str(seeds)
candidates = []
while len(candidates) < size:
seed = str(chatbot_brain.i_filter_random(seeds))
pair = str(chatbot_brain._pair_seed(seed))
w_1 = pair[0]
w_2 = pair[1]
next_word = ""
word_1, word_2 = w_1, w_2
candidate = [word_1, word_2]
pair = "{} {}".format(word_1, word_2)
done = False
while not done:
try:
next_word = random.choice(chatbot_brain.tri_lexicon[pair])
candidate.append(next_word)
word_1, word_2 = word_2, next_word
pair = "{} {}".format(word_1, word_2)
except KeyError:
candidates.append(" ".join(candidate))
done = True
if next_word in chatbot_brain.stop_puncts:
candidates.append(" ".join(candidate))
done = True
return candidates
def _create_bi_chains(chatbot_brain, seeds, size=200):
u"""Return list of Markov-Chain generated strings where each word
added onto the sentence is selected solely from the probability
of it following the given last word in the training data."""
print "the seeds are: " + str(seeds)
candidates = []
while len(candidates) < size:
seed = str(chatbot_brain.i_filter_random(seeds))
candidate = [seed]
done = False
count = 0
while not done:
count += 1
try:
next_word = random.choice(chatbot_brain.bi_lexicon[seed])
candidate.append(next_word)
seed = next_word
except KeyError:
candidates.append(" ".join(candidate))
done = True
if next_word in chatbot_brain.stop_puncts:
candidates.append(" ".join(candidate))
done = True
if count > 75:
done = True
return candidates
def _create_bi_chains(chatbot_brain, seeds, size=200):
u"""Return list of Markov-Chain generated strings where each word
added onto the sentence is selected solely from the probability
of it following the given last word in the training data."""
print "the seeds are: " + str(seeds)
candidates = []
while len(candidates) < size:
seed = str(chatbot_brain.i_filter_random(seeds))
candidate = [seed]
done = False
count = 0
while not done:
count += 1
try:
next_word = random.choice(chatbot_brain.bi_lexicon[seed])
candidate.append(next_word)
seed = next_word
except KeyError:
candidates.append(" ".join(candidate))
done = True
if next_word in chatbot_brain.stop_puncts:
candidates.append(" ".join(candidate))
done = True
if count > 75:
done = True
return candidates
