def run(c, n, length):
"""Run a Markov model given a corpus version and a look-back
Params: c - corpus version
n - lookback
length - number of words to generate
int, int -> string
"""
# set to 100,000 to avoid max recursion depth exceeded error
sys.setrecursionlimit(100000)
words = tokenize("corpus/clean_corpus_{}.txt".format(c))
model = None
# if a model already exists load it
with open("models/trained_model{}_{}.p".format(c, n), "rb") as file:
if len(list(file.peek())) > 0:
model = pickle.load(file)
# if not, train one and pickle it
if not model:
with open("models/trained_model{}_{}.p".format(c, n), "wb") as file:
model = MarkovModel()
model.train(words, n)
pickle.dump(model, file)
sentences = model.random_walk(length)
paragraph = "\""
for sentence in sentences:
paragraph += sentence.lower().capitalize() + " "
return paragraph + "\""
def run(c, n, length):
"""Run a Markov model given a corpus version and a look-back
Params: c - corpus version
n - lookback
length - number of words to generate
int, int -> string
"""
# set to 100,000 to avoid max recursion depth exceeded error
sys.setrecursionlimit(100000)
words = tokenize("corpus/clean_corpus_{}.txt".format(c))
model = None
# if a model already exists load it
with open("models/trained_model{}_{}.p".format(c, n), "rb") as file:
if len(list(file.peek())) > 0:
model = pickle.load(file)
# if not, train one and pickle it
if not model:
with open("models/trained_model{}_{}.p".format(c, n), "wb") as file:
model = MarkovModel()
model.train(words, n)
pickle.dump(model, file)
sentences = model.random_walk(length)
paragraph = "\""
for sentence in sentences:
paragraph += sentence.lower().capitalize() + " "
return paragraph + "\""
from tokenize import tokenize
import pickle
import sys
if __name__ == "__main__":
# set to 100,000 to avoid max recursion depth exceeded error
sys.setrecursionlimit(100000)
# c = Corpus version number
c = 5
words = tokenize("corpus/clean_corpus_{}.txt".format(c))
model = None
# n = lookback
n = 3
# if a model already exists load it
with open("models/trained_model{}_{}.p".format(c, n), "rb") as file:
if len(list(file.peek())) > 0:
model = pickle.load(file)
# if not, train one and pickle it
if not model:
with open("models/trained_model{}_{}.p".format(c, n), "wb") as file:
model = MarkovModel()
model.train(words, n)
pickle.dump(model, file)
inp = input("Len: ")
# keep asking for length
while input != 0:
sentences = model.random_walk(int(inp))
print(sentences)
inp = input("Len: ")
