def test_serialize(self):
q = Queue()
q.enqueue('A')
q.enqueue('B')
items = q.serialize()
assert tuple(['A', 'B']) == items
class Markov(dict):
def __init__(self, word_list, order=2, sentences=1):
super().__init__()
self.order = order # order of the markov chain
self.sentences = sentences
self.memory = Queue(order) # for sampling from markov model
if word_list is not None:
self['START'] = Dictogram()
self._create_chain(word_list)
def _create_chain(self, word_list):
"""Generate the internal markov chain that will be used by the sentence generator."""
for i, message in enumerate(word_list + word_list[:self.order]):
if i < self.order: # to fill the queue initally so that we do not add states that are smaller than order
self.memory.enqueue(message) # enqueue each new item
else:
current_state = self.memory.serialize() # the current state
self.memory.enqueue(message) # create the new state
new_state = self.memory.serialize() # the next state
# TODO: improve how I want to sample start tokens
if re.match('[A-Z]', current_state[0]) is not None:
# TODO: Don't add start tokens
self['START'].add_count(current_state)
if current_state in self: # check to see if the state already exists
# if it does just add the next state to it
self[current_state].add_count(message)
else:
# otherwise create a new dictogram with the new state
self[current_state] = Dictogram([message])
self.memory.clear()
def generate_sentence(self):
"""Generate a sentence from the internal markov chain."""
sentences = [
] # empty list to keep generated sentences so that we can return them :)
for _ in range(self.sentences
): # generate as many sentences as the user wants
# word from starting state
starting_state = self['START'].sample()[0]
sentence_list = list() # empty array to append sentence items to
# the start of the sentence :)
sentence_list.extend(starting_state)
# Count to keep as a failsafe if there is no punctuation.
failsafe = 0
# loop through starting state and add those items to the queue
for item in starting_state:
self.memory.enqueue(item)
while True:
# increase failsafe for each iteration
failsafe += 1
# get the next state by samplying the current state
next_state = self[starting_state].sample()[0] # a word
# enque the word into 'memory'
self.memory.enqueue(next_state)
# add the item to the list
sentence_list.append(next_state)
# check if the word is an end token
if re.search('[\.\?\!]', next_state) is not None:
# clear the memory
self.memory.clear()
# return the sentence as a string without a period because it is added from stop token
sentences.append(' '.join(sentence_list))
break
# set the new 'starting' state to the the tuple that is currently in 'memory'
starting_state = self.memory.serialize()
# if ran 20 times return what we already have and add a period
if failsafe > 20:
# clear memory for next sentence generation
self.memory.clear()
# return the sentence as a string with an appended period because it will not have from stop token
sentences.append(' '.join(sentence_list) + '.')
break
return ' '.join(sentences)
