def regression_genetic(data_set, data_set_name, population_size,
crossover_prob, creep_variance, mutation_prob,
tournament_size, convergence_size):
print("Running regression on: {}".format(data_set_name))
network_layouts = get_network_layouts(data_set.num_cols, 1)
folds = data_set.validation_folds(10)
for layer_sizes in network_layouts:
average_error = 0
print("--Testing network layout: {}".format(layer_sizes))
for fold_i, fold in enumerate(folds):
train = fold['train']
test = fold['test']
network = Network(train, test, layer_sizes)
ga = Genetic(network, population_size, crossover_prob,
creep_variance, mutation_prob, tournament_size,
convergence_size)
ga.train()
error = network.get_error(test)
average_error += error / 10
print("----Error of fold {}: {:.2f}".format(fold_i, error))
print("--Final error: {:.2f}".format(average_error))
def test_genetic_wine():
population_size = 20
crossover_prob = 0.5
creep = 1
mutation_prob = 0.05
tournament_k = 2
convergence_size = 100
wine_data = data.get_wine_data("../../data/winequality.data")
training, test = wine_data.partition(.9)
network = Network(training, test, [11, 6, 1])
ga = Genetic(network, population_size, crossover_prob, creep, mutation_prob, tournament_k, convergence_size)
ga.train()
error = network.get_error(test) * 100
print("\n\nError on test set: {}%".format(error))
def regression_diff_evolution(data_set, data_set_name, mutation_f,
recombination_c, pop_size):
print("Running regression on: {}".format(data_set_name))
network_layouts = get_network_layouts(data_set.num_cols, 1)
folds = data_set.validation_folds(10)
for layer_sizes in network_layouts:
average_error = 0
print("--Testing network layout: {}".format(layer_sizes))
for fold_i, fold in enumerate(folds):
train = fold['train']
test = fold['test']
network = Network(train, test, layer_sizes)
diff_evolution = DiffEvolution(network, mutation_f,
recombination_c, pop_size)
diff_evolution.run()
error = network.get_error(test)
average_error += error / 10
print("----Error of fold {}: {:.2f}".format(fold_i, error))
print("--Final error: {:.2f}".format(average_error))
def regression_particle_swarm(data_set, data_set_name, pop_size, cog_factor,
soc_factor, inertia, max_velocity,
convergence_size):
print("Running regression on: {}".format(data_set_name))
network_layouts = get_network_layouts(data_set.num_cols, 1)
folds = data_set.validation_folds(10)
for layer_sizes in network_layouts:
average_error = 0
print("--Testing network layout: {}".format(layer_sizes))
for fold_i, fold in enumerate(folds):
train = fold['train']
test = fold['test']
network = Network(train, test, layer_sizes)
pso = ParticleSwarm(network, pop_size, cog_factor, soc_factor,
inertia, max_velocity, convergence_size)
pso.train()
error = network.get_error(test)
average_error += error / 10
print("----Error of fold {}: {:.2f}".format(fold_i, error))
print("--Final error: {:.2f}".format(average_error))
