def get_random_config_slm_ols_edv(init_maximum_layers=5,
maximum_iterations=200,
maximum_neuron_connection_weight=0.5,
maximum_bias_connection_weight=1.0,
mutation_maximum_new_neurons_per_layer=3):
config = {}
config['stopping_criterion'] = ErrorDeviationVariationCriterion(
maximum_iterations=maximum_iterations)
config['population_size'] = 100
config['layers'] = init_maximum_layers
config['learning_step'] = 'optimized'
config['maximum_neuron_connection_weight'] = random.uniform(
0.1, maximum_neuron_connection_weight)
config['maximum_bias_connection_weight'] = random.uniform(
0.1, maximum_bias_connection_weight)
config['mutation_operator'] = Mutation4(
maximum_new_neurons_per_layer=mutation_maximum_new_neurons_per_layer,
maximum_bias_connection_weight=config['maximum_bias_connection_weight']
)
config['random_sampling_technique'] = False
config['random_weighting_technique'] = False
return config
def generate_random_slm_bls_configuration(
option=None,
init_maximum_layers=5,
maximum_iterations=100,
maximum_learning_step=10,
maximum_neuron_connection_weight=0.5,
maximum_bias_connection_weight=1.0,
mutation_maximum_new_neurons_per_layer=3):
configuration = {}
configuration['stopping_criterion'] = MaxGenerationsCriterion(
random.randint(1, maximum_iterations))
#===========================================================================
# configuration['stopping_criterion'] = MaxGenerationsCriterion(DEFAULT_NUMBER_OF_ITERATIONS)
#===========================================================================
configuration['population_size'] = DEFAULT_POPULATION_SIZE
configuration['layers'] = init_maximum_layers
configuration['learning_step'] = random.uniform(0.001,
maximum_learning_step)
configuration['maximum_neuron_connection_weight'] = random.uniform(
0.1, maximum_neuron_connection_weight)
configuration['maximum_bias_connection_weight'] = random.uniform(
0.1, maximum_bias_connection_weight)
configuration['mutation_operator'] = Mutation4(
maximum_new_neurons_per_layer=mutation_maximum_new_neurons_per_layer,
maximum_bias_connection_weight=configuration[
'maximum_bias_connection_weight'])
configuration['random_sampling_technique'] = False
configuration['random_weighting_technique'] = False
configuration['protected_ols'] = False
configuration['bootstrap_ols'] = False
configuration['store_ls_history'] = True
"""
if option == 0: # no RST and no RWT
config['random_sampling_technique'] = False
config['random_weighting_technique'] = False
elif option == 1: # RST
config['random_sampling_technique'] = True
config['random_weighting_technique'] = False
config['subset_ratio'] = random.uniform(0.01, 0.99)
elif option == 2: # RWT
config['random_sampling_technique'] = False
config['random_weighting_technique'] = True
config['weight_range'] = 1
"""
return configuration
def __init__(self,
population_size,
stopping_criterion,
layers,
learning_step,
max_connections=None,
mutation_operator=Mutation4(),
init_minimum_layers=2,
init_maximum_neurons_per_layer=5,
maximum_neuron_connection_weight=0.5,
maximum_bias_connection_weight=1.0,
subset_ratio=1,
weight_range=1.0,
random_sampling_technique=False,
random_weighting_technique=False,
protected_ols=False,
bootstrap_ols=False,
bootstrap_ols_samples=10,
bootstrap_ols_criterion='median',
high_absolute_ls_difference=1,
store_ls_history=False):
super().__init__(population_size, stopping_criterion)
self.layers = layers
self.learning_step = learning_step
self.max_connections = max_connections
self.mutation_operator = mutation_operator
self.init_minimum_layers = init_minimum_layers
self.init_maximum_neurons_per_layer = init_maximum_neurons_per_layer
self.maximum_neuron_connection_weight = maximum_neuron_connection_weight
self.maximum_bias_connection_weight = maximum_bias_connection_weight
self.next_champion = None
self.random_sampling_technique = random_sampling_technique
self.random_weighting_technique = random_weighting_technique
self.subset_ratio = subset_ratio
self.weight_range = weight_range
self.protected_ols = protected_ols
self.bootstrap_ols = bootstrap_ols
self.bootstrap_ols_samples = bootstrap_ols_samples
self.bootstrap_ols_criterion = bootstrap_ols_criterion
if self.bootstrap_ols:
self.high_absolute_ls_difference = high_absolute_ls_difference
self.high_absolute_differences_history = []
self.store_ls_history = store_ls_history
if self.store_ls_history:
self.ls_history = []
self.zero_ls_by_activation_function = {}
self.zero_ls_history = []
self.lr_intercept = None
def get_random_config_slm_fls_grouped(
option=None,
init_maximum_layers=5,
maximum_iterations=200,
maximum_learning_step=1,
maximum_neuron_connection_weight=0.5,
maximum_bias_connection_weight=1.0,
mutation_maximum_new_neurons_per_layer=3):
config = {}
config['stopping_criterion'] = MaxGenerationsCriterion(
random.randint(1, maximum_iterations))
config['population_size'] = 10
config['layers'] = init_maximum_layers
# config['learning_step'] = random.uniform(0.1, maximum_learning_step)
config['learning_step'] = random.uniform(0.001, maximum_learning_step)
config['maximum_neuron_connection_weight'] = random.uniform(
0.1, maximum_neuron_connection_weight)
config['maximum_bias_connection_weight'] = random.uniform(
0.1, maximum_bias_connection_weight)
config['mutation_operator'] = Mutation4(
maximum_new_neurons_per_layer=mutation_maximum_new_neurons_per_layer,
maximum_bias_connection_weight=config['maximum_bias_connection_weight']
)
config['random_sampling_technique'] = False
config['random_weighting_technique'] = False
"""
if option == 0: # no RST and no RWT
config['random_sampling_technique'] = False
config['random_weighting_technique'] = False
elif option == 1: # RST
config['random_sampling_technique'] = True
config['random_weighting_technique'] = False
config['subset_ratio'] = random.uniform(0.01, 0.99)
elif option == 2: # RWT
config['random_sampling_technique'] = False
config['random_weighting_technique'] = True
config['weight_range'] = 1
"""
return config
def get_random_config_slm_fls_tie_edv(
init_maximum_layers=5,
maximum_iterations=200,
maximum_learning_step=1,
maximum_neuron_connection_weight=0.5,
maximum_bias_connection_weight=1.0,
mutation_maximum_new_neurons_per_layer=3):
config = {}
stopping_crit = random.randint(1, 2)
# EDV
if stopping_crit == 1:
config['stopping_criterion'] = ErrorDeviationVariationCriterion(
maximum_iterations=maximum_iterations)
# TIE
else:
config[
'stopping_criterion'] = TrainingImprovementEffectivenessCriterion(
maximum_iterations=maximum_iterations)
config['population_size'] = 100
config['layers'] = init_maximum_layers
# config['learning_step'] = random.uniform(0.1, maximum_learning_step)
config['learning_step'] = random.uniform(0.001, maximum_learning_step)
config['maximum_neuron_connection_weight'] = random.uniform(
0.1, maximum_neuron_connection_weight)
config['maximum_bias_connection_weight'] = random.uniform(
0.1, maximum_bias_connection_weight)
config['mutation_operator'] = Mutation4(
maximum_new_neurons_per_layer=mutation_maximum_new_neurons_per_layer,
maximum_bias_connection_weight=config['maximum_bias_connection_weight']
)
config['random_sampling_technique'] = False
config['random_weighting_technique'] = False
return config