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July 13, 2019 05:18
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Markov Chain Text generator
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import random | |
import string | |
class MarkovModel: | |
def __init__(self): | |
self.model = None | |
def learn(self,tokens,n=2): | |
model = {} | |
for i in range(0,len(tokens)-n): | |
gram = tuple(tokens[i:i+n]) | |
token = tokens[i+n] | |
if gram in model: | |
model[gram].append(token) | |
else: | |
model[gram] = [token] | |
final_gram = tuple(tokens[len(tokens) - n:]) | |
if final_gram in model: | |
model[final_gram].append(None) | |
else: | |
model[final_gram] = [None] | |
self.model = model | |
return model | |
def generate(self,n=2,seed=None, max_tokens=100): | |
if seed == None: | |
seed = random.choice(self.model.keys()) | |
output = list(seed) | |
output[0] = output[0].capitalize() | |
current = seed | |
for i in range(n, max_tokens): | |
# get next possible set of words from the seed word | |
if current in self.model: | |
possible_transitions = self.model[current] | |
choice = random.choice(possible_transitions) | |
if choice is None: break | |
# check if choice is period and if so append to previous element | |
if choice == '.': | |
output[-1] = output[-1] + choice | |
else: | |
output.append(choice) | |
current = tuple(output[-n:]) | |
else: | |
# should return ending punctuation of some sort | |
if current not in string.punctuation: | |
output.append('.') | |
return output |
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