def generate_random_ftne_configuration(maximum_iterations=100, maximum_number_of_layers=5, maximum_neurons_per_layer=5):
configuration = {}
configuration['stopping_criterion'] = MaxGenerationsCriterion(random.randint(1, maximum_iterations))
#===========================================================================
# configuration['stopping_criterion'] = MaxGenerationsCriterion(DEFAULT_NUMBER_OF_ITERATIONS)
#===========================================================================
configuration['population_size'] = DEFAULT_POPULATION_SIZE
# Jan: [create_network_from_topology(topology) for topology in [[1], [2], [2, 2], [3, 3, 3], [5, 5, 5]]]
number_of_layers = random.randint(1, maximum_number_of_layers)
neurons_per_layer = [random.randint(1, maximum_neurons_per_layer) for i in range(number_of_layers)]
configuration['topology'] = create_network_from_topology(neurons_per_layer)
# Jan: [SelectionOperatorTournament(5)]
tournament_size = random.randint(1, configuration['population_size'])
configuration['selection_operator'] = SelectionOperatorTournament(tournament_size)
# Jan: [MutationOperatorGaussian(0.01), MutationOperatorGaussian(0.1)]
standard_deviation = random.uniform(0.001, 5)
configuration['mutation_operator'] = MutationOperatorGaussian(standard_deviation)
# Jan: [CrossoverOperatorArithmetic()]
configuration['crossover_operator'] = CrossoverOperatorArithmetic()
# Jan: [0.25, 0.5]
configuration['mutation_rate'] = random.uniform(0.001, 1)
# Jan: [0.01, 0.1]
configuration['crossover_rate'] = random.uniform(0.001, 1)
return configuration
def setUp(self):
self.training, self.validation, self.testing = load_samples(
'r_music', 0)
topology = create_network_from_topology([2, 2])
self.sga = SimpleGeneticAlgorithm(100, MaxGenerationsCriterion(200),
topology,
SelectionOperatorTournament(5),
MutationOperatorGaussian(0.05),
CrossoverOperatorArithmetic(), 0.01,
0.5)
def test_benchmark_sga(self):
print('Test BenchmarkSGA()...')
topology = create_network_from_topology([2, 2])
algorithm = BenchmarkSGA(10, MaxGenerationsCriterion(10), topology,
SelectionOperatorTournament(5),
MutationOperatorGaussian(0.1),
CrossoverOperatorArithmetic(), 0.01, 0.25)
X = get_input_variables(self.training).as_matrix()
y = get_target_variable(self.training).as_matrix()
log = algorithm.fit(X, y, RootMeanSquaredError, verbose=True)
self.assertTrue(expr=log)
print()
'node_delete_prob': [0.1],
'weight_mutate_rate': [0.25],
'weight_mutate_power': [0.25]
}
_SGA_PARAMETERS = {
'stopping_criterion':
[MaxGenerationsCriterion(_BASE_PARAMETERS.get('number_generations'))],
'population_size': [_BASE_PARAMETERS.get('population_size')],
'topology': [
create_network_from_topology(topology)
for topology in [[1], [2], [2, 2], [3, 3, 3], [5, 5, 5]]
],
'selection_operator': [SelectionOperatorTournament(5)],
'mutation_operator':
[MutationOperatorGaussian(0.01),
MutationOperatorGaussian(0.1)],
'crossover_operator': [CrossoverOperatorArithmetic()],
'mutation_rate': [0.25, 0.5],
'crossover_rate': [0.01, 0.1]
}
_SVM_PARAMETERS = {
'C': [c / 10 for c in range(1, 11)],
'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
'epsilon': [e / 10 for e in range(1, 6)],
'degree': [d for d in range(1, 5)],
'gamma': [g / 10 for g in range(1, 6)],
'coef0': [co / 10 for co in range(1, 6)],
'probability': [True]
}