def test_MarkovChain_eq():
states = [{
('a', 'b'): c
} for c in (chain.ProbablityMap('aab'), chain.ProbablityMap('abb'))]
chains = [chain.MarkovChain(order=2, states=states[i]) for i in (0, 1)]
assert chain.MarkovChain(order=2) == chain.MarkovChain(order=2)
assert chains[0] == chains[1]
def test_ProbablityMap_overridden():
prob_map = chain.ProbablityMap('aab')
assert isinstance(prob_map + prob_map, chain.ProbablityMap)
assert isinstance(prob_map - prob_map, chain.ProbablityMap)
assert isinstance(prob_map | prob_map, chain.ProbablityMap)
assert isinstance(prob_map & prob_map, chain.ProbablityMap)
assert isinstance(+prob_map, chain.ProbablityMap)
assert isinstance(-prob_map, chain.ProbablityMap)
def test_MarkovChain_setitem_converts_mapping():
mc = chain.MarkovChain(order=2)
mc[('a', 'b')] = {'a': 4}
assert mc.data == {('a', 'b'): chain.ProbablityMap({'a': 4})}
def test_MarkovChain_iterate_chain():
mc = chain.MarkovChain(order=1, states={('a', ): chain.ProbablityMap('a')})
r = consistent_random().random
mci = mc.iterate_chain(randomizer=r)
assert mci._randomizer is r
def test_MarkovChain_iter():
mc = chain.MarkovChain(order=1, states={('a', ): chain.ProbablityMap('a')})
assert isinstance(iter(mc), chain.MarkovChainIterator)
def test_MarkovChain_init_with_states():
prob_map = chain.ProbablityMap('aab')
my_chain = chain.MarkovChain(order=1, states={('a', ): prob_map})
assert my_chain.data == {('a', ): prob_map}
def test_ProbablityMap_weighted_random():
prob_map = chain.ProbablityMap('aab')
weighter = consistent_random().random
assert prob_map.weighted_choice(randomizer=weighter)() == 'a'
assert prob_map.weighted_choice(randomizer=lambda: 0)() == 'b'