Ejemplo n.º 1
0
def main():

    #read dataset and preprocess it
    dataset = PreProcessing("seeds_dataset.txt", separator='\s+')
    dataset.normalize()
    dataset.normalize_class()

    #divide dataset into training and test sets
    train, test = training.holdout(0.7, dataset.normalized_dataframe)

    nn = Rbf(7, 3)

    nn.train(train, eta=0.5, max_iterations=500)

    print("RBF:", training.accuracy(nn, test, 3))

    mm = Mlp(7, 3, 3)

    mm.backpropagation(train.values.tolist(), max_iterations=500)
    print("MLP:", training.accuracy(mm, test, 3))
Ejemplo n.º 2
0
def seed_test():
    # Carregando e Normalizando os dados da base de vinhos
    dataset = PreProcessing("seeds_dataset.txt", separator='\s+')
    dataset.normalize()
    dataset.normalize_class()

    # Atributos a serem variados nos testes
    n_layers = [1, 2]
    hidden_layer = [3, [6, 6]]
    momentums = [0.3, 0.5]
    max_iterations = [100, 250, 500]
    etas = [0.3, 0.5]
    ps = [0.7, 0.9]

    rbf_accuracy = 0
    mlp_accuracy = 0
    tests = 0

    # Teste
    for layer in n_layers:
        for momentum in momentums:
            for eta in etas:
                for max_iteration in max_iterations:
                    for p in ps:
                        tests += 1

                        print("Test number", tests)

                        train, test = training.holdout(
                            p, dataset.normalized_dataframe)
                        print("INPUT NEURONS = 7 HIDDEN NEURONS = " +
                              str(int(6 / layer)) +
                              " OUTPUT NEURONS = 3 HIDDEN LAYER = " +
                              str(layer) + " ETA = " + str(eta) +
                              " MAX ITERATIONS = " + str(max_iteration) +
                              " MOMENTUM = " + str(momentum) + " P = " +
                              str(p))
                        print()
                        print("RBF")

                        nn = Rbf(7, 3)

                        nn.train(train, eta=0.5, max_iterations=max_iteration)
                        ac = training.accuracy(nn, test, 3)
                        rbf_accuracy += ac
                        print("ACCURACY =", ac)

                        print()
                        print("MLP")
                        example = test.values.tolist()

                        mm = Mlp(7,
                                 hidden_layer[layer - 1],
                                 3,
                                 n_hidden_layers=layer)
                        mm.backpropagation(train.values.tolist(),
                                           eta=eta,
                                           max_iterations=max_iteration)
                        ac = training.accuracy(mm, test, n_classes=3)
                        mlp_accuracy += ac
                        print("ACCURACY =", ac)
                        print()

                        print("Rbf:")
                        nn.feed_forward(example[15][:(-1 * 3)])
                        print(example[15])
                        print("Result 1")
                        nn.show_class()
                        print()

                        print("Mlp")
                        print(example[15])
                        nn.feed_forward(example[15][:(-1 * 3)])
                        print("Result 2")
                        mm.show_class()
                        print()
                        print(
                            "******************************************************//******************************************************"
                        )
                        print()

    print(tests, " tests executed. Rbf accuracy:", rbf_accuracy / tests,
          " Mlp accuracy:", mlp_accuracy / tests)