def test_generate_error(self, training_text):
mm = generate_utils.MarkovModel(state_len=4)
with pytest.raises(RuntimeError):
mm.generate(10)
mm.fit(training_text)
with pytest.raises(ValueError):
mm.generate(10, state="foo")
def test_generate(self, training_text):
mm = generate_utils.MarkovModel(state_len=4)
mm.fit(training_text)
result = mm.generate(10)
assert isinstance(result, str)
assert len(result) == 10
result = mm.generate(10, state="Here")
assert isinstance(result, str)
assert len(result) == 10
assert result.startswith("Here")
def markov_model(self):
"""
:class:`generate_utils.MarkovModel`: Markov model used to generate text in
:meth:`Provider.text_lines()` and :meth:`Provider.text_lines_trailing()`.
It's trained and assigned at the instance- rather than class-level to avoid
having to train the model every time this module is imported. It's fast, but
not *that* fast.
"""
if self._markov_model is None:
self._markov_model = generate_utils.MarkovModel(state_len=4).fit(c.TEXT_SAMPLES)
return self._markov_model
def test_fit(self, training_text):
mm = generate_utils.MarkovModel(state_len=4)
mm.fit(training_text)
assert isinstance(mm.model, dict)
assert len(mm.model) > 1
def test_init(self):
state_len = 4
mm = generate_utils.MarkovModel(state_len=state_len)
assert mm.state_len == state_len
assert mm.model is None