def test_markov_chain_sample(transition, initial, n):
instance = MarkovChain(transition, initial)
s = instance.sample(n)
assert len(s) == n
states = list(range(len(instance.initial)))
for state in s:
assert state in states
def __init__(self, sample_size=255):
self.sample_size = sample_size
self.pregen_frame = pandas.DataFrame(
data={"MarkovChain": MarkovChain().sample(sample_size)}
)
self.current = 0
self.model = DataModel(
data=self.pregen_frame[: self.current], name="Markov Chain", debug=False
)
def __call__(self):
self.current += 1
if self.current < len(self.pregen_frame):
# simulate dynamic process by making more precomputed data appear...
self.model(new_data=self.pregen_frame[: self.current])
else:
# resetting data
self.pregen_frame = pandas.DataFrame(
data={"MarkovChain": MarkovChain().sample(self.sample_size)}
)
self.current = 0
self.model(new_data=self.pregen_frame[: self.current])
def test_markov_chain_str_repr(transition, initial):
instance = MarkovChain(transition, initial)
assert isinstance(repr(instance), str)
assert isinstance(str(instance), str)
def test_markov_chain_probability(transition, initial):
with pytest.raises(ValueError):
instance = MarkovChain(transition, initial)