def __call__(self, rated_pop: Sequence[Tuple[Number, np.ndarray]]) -> Tuple[np.ndarray, ...]:
couples = ga.roulette_wheel_cost_selection(rated_pop)
offspring = (ga.single_point_crossover(*couple) for couple in couples)
children = tuple(child for pair in offspring for child in pair)
for i, j in it.product(range(len(children)), range(len(children[0]))):
if self._rand.random() < self._mutation_prob:
children[i][j] = 1 - children[i][j]
return children
def next_transformation_gen(transforms: Tuple[Tuple[int, np.ndarray], ...])\
-> Tuple[np.ndarray, ...]:
# couples = ga.roulette_wheel_rank_selection(transforms)
couples = ga.roulette_wheel_cost_selection(transforms)
# couples = ga.tournament_selection(transforms, 2)
# couples = ga.uniform_random_pairing_selection(transforms)
offspring = (ga.single_point_crossover(*couple) for couple in couples)
children = tuple(child for pair in offspring for child in pair)
mutate(children, .1)
return children
def __call__(
self,
rated_pop: Sequence[Tuple[Number,
np.ndarray]]) -> Tuple[np.ndarray, ...]:
couples = ga.roulette_wheel_cost_selection(rated_pop)
offspring = (ga.single_point_crossover(*couple) for couple in couples)
children = tuple(it.chain(*offspring))
for i, j in it.product(range(len(children)), range(len(children[0]))):
if self._rand.random() < .01:
children[i][j] = self._rand.choice(self._labels)
return children