def demo(n):
print(markov(n, tokenize_text(get_example_text())))
def print_example_model(n):
print(repr(NgramModel(n, tokenize_text(get_example_text())).nexttokens))
def test_markov(self):
chain = ex2_markov.markov(1, ex2.tokenize_text(ex2.get_example_text()))
self.assertEqual(54, len(chain.split(' ')))
self.assertTrue(chain.startswith('E m m a '))
import random
from ex2 import get_example_text, tokenize_text
from ex3_markov import NgramModel
from ex3_poem import is_known_word, words_rhyme
MODEL = NgramModel(1, tokenize_text(get_example_text()))
def random_walk_of_indexes(list_):
walk = list(range(len(list_)))
random.shuffle(walk)
return walk
def get_matching_word(words, indexes, predicate=lambda w: True):
while indexes:
i = indexes.pop()
word = words[i]
if is_known_word(word) and predicate(word):
return i, word
raise ValueError("No matching word found")
def gen_two_rhyming_lines():
words = [w.lower() for w in MODEL.generate(1000) if w.isalpha()]
walk = random_walk_of_indexes(words)
i1, last1 = get_matching_word(words, walk)
i2, last2 = get_matching_word(words, walk, lambda w: words_rhyme(last1, w))
n1 = random.randrange(3,5+1)
def test_get_example_text(self):
text = ex2.get_example_text()
first_line = text.splitlines()[0]
self.assertEqual(
first_line, "The Project Gutenberg EBook of Emma, by Jane Austen")