def crossover_rows(self, rows1: List[List],
rows2: List[List]) -> List[List]:
num_states = len(rows1) // 2
cross_point = 2 * self._random.randint(0, num_states)
new_rows = copy_lists(rows1[:cross_point])
new_rows += copy_lists(rows2[cross_point:])
return new_rows
def __init__(self,
transitions_C=None,
transitions_D=None,
emission_probabilities=None,
initial_state=0,
initial_action=C) -> None:
super().__init__()
if not transitions_C:
transitions_C = [[1]]
transitions_D = [[1]]
emission_probabilities = [0.5] # Not stochastic
initial_state = 0
self.initial_state = initial_state
self.initial_action = initial_action
self.hmm = SimpleHMM(copy_lists(transitions_C),
copy_lists(transitions_D),
list(emission_probabilities), initial_state)
assert self.hmm.is_well_formed()
self.state = self.hmm.state
self.classifier["stochastic"] = self.is_stochastic()
def crossover_rows(rows1, rows2):
num_states = len(rows1) // 2
cross_point = 2 * randrange(num_states)
new_rows = copy_lists(rows1[:cross_point])
new_rows += copy_lists(rows2[cross_point:])
return new_rows
def test_copy_lists(self):
l1 = [list(range(10)), list(range(20))]
l2 = copy_lists(l1)
self.assertIsNot(l1, l2)