def classification_genetic(data_set, data_set_name, classes, population_size,
crossover_prob, creep_variance, mutation_prob,
tournament_size, convergence_size):
print("Running classification on: {}".format(data_set_name))
network_layouts = get_network_layouts(data_set.num_cols, len(classes))
folds = data_set.validation_folds(10)
for layer_sizes in network_layouts:
average_accuracy = 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, classes)
ga = Genetic(network,
population_size=population_size,
crossover_prob=crossover_prob,
creep_variance=creep_variance,
mutation_prob=mutation_prob,
tournament_size=tournament_size,
convergence_size=convergence_size)
ga.train()
accuracy = network.get_accuracy(test)
average_accuracy += accuracy / 10
print("----Accuracy of fold {}: {:.2f}".format(fold_i, accuracy))
print("--Final accuracy: {:.2f}".format(average_accuracy))
def test_diff_evolution_image():
image_data = data.get_segmentation_data("../../data/segmentation.data")
training_data, test_data = image_data.partition(.8)
network = Network(training_data, test_data, [19, 13, 7], ["BRICKFACE", "SKY", "FOLIAGE",
"CEMENT", "WINDOW", "PATH", "GRASS"])
diff_evo = DiffEvolution(network, mutation_f=.1, recombination_c=.9, pop_size=20)
diff_evo.run()
accuracy = network.get_accuracy(test_data)*100
print("\n\nAccuracy on test set: {}%".format(accuracy))
def test_particle_swarm_image():
image_data = data.get_segmentation_data("../../data/segmentation.data")
training_data, test_data = image_data.partition(.8)
network = Network(training_data, test_data, [19, 13, 7], ["BRICKFACE", "SKY", "FOLIAGE", "CEMENT",
"WINDOW", "PATH", "GRASS"])
pso = ParticleSwarm(network, pop_size=20, cog_factor=1.0, soc_factor=2.0, inertia=0.05,
max_velocity=100000, convergence_size=50)
pso.train()
accuracy = network.get_accuracy(test_data)*100
print("\n\nAccuracy on test set: {}%".format(accuracy))
def test_genetic_car():
population_size = 20
crossover_prob = 0.5
creep = 20
mutation_prob = 0.05
tournament_k = 2
convergence_size = 50
car_data = data.get_car_data("../../data/car.data")
training_data, testing_data = car_data.partition(0.8)
network = Network(training_data, testing_data, [6, 5, 4], ["acc", "unacc", "good", "vgood"])
ga = Genetic(network, population_size, crossover_prob, creep, mutation_prob, tournament_k, convergence_size)
ga.train()
accuracy = network.get_accuracy(testing_data)*100
print("\n\nAccuracy on test set: {}%".format(accuracy))
def test_genetic_image():
population_size = 20
crossover_prob = 0.5
creep = 20
mutation_prob = 0.05
tournament_k = 2
convergence_size = 50
image_data = data.get_segmentation_data("../../data/segmentation.data")
training_data, testing_data = image_data.partition(0.8)
network = Network(training_data, testing_data, [19, 13, 7], ["BRICKFACE", "SKY", "FOLIAGE", "CEMENT",
"WINDOW", "PATH", "GRASS"])
ga = Genetic(network, population_size, crossover_prob, creep, mutation_prob, tournament_k, convergence_size)
ga.train()
accuracy = network.get_accuracy(testing_data)*100
print("\n\nAccuracy on test set: {}%".format(accuracy))
def test_genetic_abalone():
population_size = 20
crossover_prob = 0.5
creep = 1
mutation_prob = 0.05
tournament_k = 2
convergence_size = 100
abalone_data = data.get_abalone_data("../../data/abalone.data")
training_data, testing_data = abalone_data.partition(0.8)
network = Network(training_data, testing_data, [7, 4, 1], [i for i in range(1, 30)])
ga = Genetic(network, population_size, crossover_prob, creep, mutation_prob, tournament_k, convergence_size)
ga.train()
accuracy = network.get_accuracy(testing_data) * 100
print("\n\nAccuracy on test set: {}%".format(accuracy))
def classification_diff_evolution(data_set, data_set_name, classes, mutation_f,
recombination_c, pop_size):
print("Running classification on: {}".format(data_set_name))
network_layouts = get_network_layouts(data_set.num_cols, len(classes))
folds = data_set.validation_folds(10)
for layer_sizes in network_layouts:
average_accuracy = 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, classes)
diff_evolution = DiffEvolution(network, mutation_f,
recombination_c, pop_size)
diff_evolution.run()
accuracy = network.get_accuracy(test)
average_accuracy += accuracy / 10
print("----Accuracy of fold {}: {:.2f}".format(fold_i, accuracy))
print("--Final accuracy: {:.2f}".format(average_accuracy))
def classification_particle_swarm(data_set, data_set_name, classes, pop_size,
cog_factor, soc_factor, inertia,
max_velocity, convergence_size):
print("Running classification on: {}".format(data_set_name))
network_layouts = get_network_layouts(data_set.num_cols, len(classes))
folds = data_set.validation_folds(10)
for layer_sizes in network_layouts:
average_accuracy = 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, classes)
pso = ParticleSwarm(network, pop_size, cog_factor, soc_factor,
inertia, max_velocity, convergence_size)
pso.train()
accuracy = network.get_accuracy(test)
average_accuracy += accuracy / 10
print("----Accuracy of fold {}: {:.2f}".format(fold_i, accuracy))
print("--Final accuracy: {:.2f}".format(average_accuracy))
