ga = Genetic_Algorithm(individuals = 1000,
mutation_rate = 0.1,
# (4 caracteristicas + 1 do bias) * 2 neuronios = 10
# (2 saidas de neuronio + 1 do bias) * 3 neuronios de saida = 9
# total de pesos 10+9 = 19
genotype = 19,
fenotype = fenotype,
genotype_type = Genetic_Algorithm.TYPE_FLOAT)
# ga.print_individuals()
ga.execute(generations = 100, log=True)
# ga.print_individuals()
print "Best Individual for all generations", ga.get_best_individual().get_genotype(), ga.get_best_individual_fenotype()
genotype = ga.get_best_individual().get_genotype()
pos = 0
for i, layer in enumerate(mlp.get_layers()):
for j, neuron in enumerate(layer.get_neurons()):
end_neuron_position = pos + len(neuron.get_weights())
neuron.get_weights()[:] = genotype[pos: end_neuron_position]
neuron.set_bias(genotype[end_neuron_position])
pos = end_neuron_position + 1
mlp.get_weights('pesos_ga_iris.mlp')
mutation_rate=0.1,
# (4 caracteristicas + 1 do bias) * 2 neuronios = 10
# (2 saidas de neuronio + 1 do bias) * 3 neuronios de saida = 9
# total de pesos 10+9 = 19
genotype=19,
fenotype=fenotype,
genotype_type=Genetic_Algorithm.TYPE_FLOAT)
# ga.print_individuals()
ga.execute(generations=100, log=True)
# ga.print_individuals()
print "Best Individual for all generations", ga.get_best_individual(
).get_genotype(), ga.get_best_individual_fenotype()
genotype = ga.get_best_individual().get_genotype()
pos = 0
for i, layer in enumerate(mlp.get_layers()):
for j, neuron in enumerate(layer.get_neurons()):
end_neuron_position = pos + len(neuron.get_weights())
neuron.get_weights()[:] = genotype[pos:end_neuron_position]
neuron.set_bias(genotype[end_neuron_position])
pos = end_neuron_position + 1
mlp.get_weights('pesos_ga_iris.mlp')
