def _mutation(self, child1_weights: np.ndarray, child2_weights: np.ndarray,
child1_bias: np.ndarray, child2_bias: np.ndarray) -> None:
scale = .2
rand_mutation = random.random()
mutation_bucket = np.digitize(rand_mutation, self._mutation_bins)
mutation_rate = self._mutation_rate
if self.settings['mutation_rate_type'].lower() == 'decaying':
mutation_rate = mutation_rate / sqrt(self.current_generation + 1)
# Gaussian
if mutation_bucket == 0:
# Mutate weights
gaussian_mutation(child1_weights, mutation_rate, scale=scale)
gaussian_mutation(child2_weights, mutation_rate, scale=scale)
# Mutate bias
gaussian_mutation(child1_bias, mutation_rate, scale=scale)
gaussian_mutation(child2_bias, mutation_rate, scale=scale)
# Uniform random
elif mutation_bucket == 1:
# Mutate weights
random_uniform_mutation(child1_weights, mutation_rate, -1, 1)
random_uniform_mutation(child2_weights, mutation_rate, -1, 1)
# Mutate bias
random_uniform_mutation(child1_bias, mutation_rate, -1, 1)
random_uniform_mutation(child2_bias, mutation_rate, -1, 1)
else:
raise Exception('Unable to determine valid mutation based off probabilities.')
def _mutation(self, child1_weights: np.ndarray, child2_weights: np.ndarray,
child1_bias: np.ndarray, child2_bias: np.ndarray) -> None:
mutation_rate = self.config.Mutation.mutation_rate
scale = self.config.Mutation.gaussian_mutation_scale
if self.config.Mutation.mutation_rate_type == 'dynamic':
mutation_rate = mutation_rate / math.sqrt(self.current_generation + 1)
# Mutate weights
gaussian_mutation(child1_weights, mutation_rate, scale=scale)
gaussian_mutation(child2_weights, mutation_rate, scale=scale)
# Mutate bias
gaussian_mutation(child1_bias, mutation_rate, scale=scale)
gaussian_mutation(child2_bias, mutation_rate, scale=scale)
def _mutation(self, chromosome: np.ndarray) -> None:
"""
Randomly decide if we should perform mutation on a gene within the chromosome. This is done in place
"""
rand_mutation = random.random()
mutation_bucket = np.digitize(rand_mutation, self._mutation_bins)
# Gaussian
if mutation_bucket == 0:
mutation_rate = get_ga_constant('mutation_rate')
if get_ga_constant('mutation_rate_type').lower() == 'dynamic':
mutation_rate = mutation_rate / math.sqrt(
self.current_generation + 1)
gaussian_mutation(chromosome,
mutation_rate,
scale=get_ga_constant('gaussian_mutation_scale'))
# Random uniform
elif mutation_bucket == 1:
#@TODO: add to this
pass
else:
raise Exception(
'Unable to determine valid mutation based off probabilities')