def _crossover(
self, parent1_weights: np.ndarray, parent2_weights: np.ndarray,
parent1_bias: np.ndarray, parent2_bias: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
rand_crossover = random.random()
crossover_bucket = np.digitize(rand_crossover, self._crossover_bins)
child1_weights, child2_weights = None, None
child1_bias, child2_bias = None, None
# SBX
if crossover_bucket == 0:
child1_weights, child2_weights = SBX(parent1_weights,
parent2_weights,
self._SBX_eta)
child1_bias, child2_bias = SBX(parent1_bias, parent2_bias,
self._SBX_eta)
# Crossover nhị phân một điểm (SPBX)
elif crossover_bucket == 1:
child1_weights, child2_weights = single_point_binary_crossover(
parent1_weights, parent2_weights, major=self._SPBX_type)
child1_bias, child2_bias = single_point_binary_crossover(
parent1_bias, parent2_bias, major=self._SPBX_type)
else:
raise Exception(
'Không thể xác định lai chéo hợp lệ dựa trên xác suất')
return child1_weights, child2_weights, child1_bias, child2_bias
def _crossover(
self, parent1_weights: np.ndarray, parent2_weights: np.ndarray,
parent1_bias: np.ndarray, parent2_bias: np.ndarray
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
rand_crossover = random.random()
crossover_bucket = np.digitize(rand_crossover, self._crossover_bins)
child1_weights, child2_weights = None, None
child1_bias, child2_bias = None, None
# SBX
if crossover_bucket == 0:
child1_weights, child2_weights = SBX(parent1_weights,
parent2_weights,
self._SBX_eta)
child1_bias, child2_bias = SBX(parent1_bias, parent2_bias,
self._SBX_eta)
# Single point binary crossover (SPBX)
elif crossover_bucket == 1:
child1_weights, child2_weights = single_point_binary_crossover(
parent1_weights, parent2_weights, major=self._SPBX_type)
child1_bias, child2_bias = single_point_binary_crossover(
parent1_bias, parent2_bias, major=self._SPBX_type)
else:
raise Exception(
'Unable to determine valid crossover based off probabilities')
return child1_weights, child2_weights, child1_bias, child2_bias
def _crossover(self, parent1_weights: np.ndarray, parent2_weights: np.ndarray,
parent1_bias: np.ndarray, parent2_bias: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
eta = self.config.Crossover.sbx_eta
# SBX weights and bias
child1_weights, child2_weights = SBX(parent1_weights, parent2_weights, eta)
child1_bias, child2_bias = SBX(parent1_bias, parent2_bias, eta)
return child1_weights, child2_weights, child1_bias, child2_bias
def _crossover(self, p1_chromosome: np.ndarray,
p2_chromosome: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
"""
Perform crossover between two parent chromosomes and return TWO child chromosomes
"""
rand_crossover = random.random()
crossover_bucket = np.digitize(rand_crossover, self._crossover_bins)
# SBX
if crossover_bucket == 0:
c1_chromosome, c2_chromosome = SBX(p1_chromosome, p2_chromosome,
get_ga_constant('SBX_eta'))
else:
raise Exception(
'Unable to determine valid crossover based off probabilities')
return c1_chromosome, c2_chromosome