def wine_test():
# Carregando e Normalizando os dados da base de vinhos
dataset = PreProcessing("wine_dataset.txt")
dataset.normalize(ignore_first_column=True)
dataset.switch_first_last_column()
dataset.normalize_class()
# Atributos a serem variados nos testes
n_layers = [1, 2]
hidden_layer = [10, [5, 5]]
momentums = [0.3, 0.5, 0.7]
max_iterations = [100, 250, 500]
etas = [0.3, 0.5, 0.7]
ps = [0.5, 0.7, 0.9]
# Teste
for layer in n_layers:
for momentum in momentums:
for eta in etas:
for max_iteration in max_iterations:
for p in ps:
train, test = training.holdout(
p, dataset.normalized_dataframe)
example = test.values.tolist()
print("INPUT NEURONS = 13 HIDDEN NEURONS = " +
str(int(10 / layer)) +
" OUTPUT NEURONS = 3 HIDDEN LAYER = " +
str(layer) + " ETA = " + str(eta) +
" MAX ITERATIONS = " + str(max_iteration) +
" MOMENTUM = " + str(momentum) + " P = " +
str(p))
print()
nn = Mlp(13,
hidden_layer[layer - 1],
3,
n_hidden_layers=layer)
nn.backpropagation(train.values.tolist(),
eta=eta,
max_iterations=max_iteration)
print("ACCURACY =",
training.accuracy(nn, test, n_classes=3))
print()
print("Input 1")
nn.feed_forward(example[0][:(-1 * 3)])
print(example[0])
print("Result 1")
nn.show_class()
print()
print("Input 2")
print(example[15])
nn.feed_forward(example[15][:(-1 * 3)])
print("Result 2")
nn.show_class()
print()
print(
"******************************************************//******************************************************"
)
print()
def music_test():
# Carregando e normalizando os dados da base de musicas
dataset = PreProcessing("default_features_1059_tracks.txt")
dataset.normalize(ignore_first_column=False)
# Atributos a serem variados no teste
n_layers = [1, 2]
hidden_layer = [20, [10, 10]]
momentums = [0.3, 0.5, 0.7]
max_iterations = [100, 250, 500]
etas = [0.3, 0.5, 0.7]
ps = [0.5, 0.7, 0.9]
# Teste
for layer in n_layers:
for momentum in momentums:
for eta in etas:
for max_iteration in max_iterations:
for p in ps:
train, test = training.holdout(
p, dataset.normalized_dataframe)
example = test.values.tolist()
print("INPUT NEURONS = 68 HIDDEN NEURONS = " +
str(int(10 / layer)) +
" OUTPUT NEURONS = 2 HIDDEN LAYER = " +
str(layer) + " ETA = " + str(eta) +
" MAX ITERATIONS = " + str(max_iteration) +
" MOMENTUM = " + str(momentum) + " P = " +
str(p))
print()
nn = Mlp(68,
hidden_layer[layer - 1],
2,
n_hidden_layers=layer)
nn.backpropagation(train.values.tolist(),
eta=eta,
max_iterations=max_iteration)
print("SQUARED ERROR =",
training.squared_error(nn, test, n_classes=2))
print()
print("Input 1")
nn.feed_forward(example[0][:(-1 * 2)])
print(example[0])
print("Result 1")
nn.show_class()
print()
print("Input 2")
print(example[15])
nn.feed_forward(example[15][:(-1 * 2)])
print("Result 2")
nn.show_class()
print()
print(
"******************************************************//******************************************************"
)
print()
def main():
dataset = PreProcessing("wine_dataset.txt")
dataset.normalize(ignore_first_column=True)
dataset.switch_first_last_column()
dataset.normalize_class()
train, test = training.holdout(0.7, dataset.normalized_dataframe)
nn = Mlp(13, 10, 3, n_hidden_layers=1)
nn.backpropagation(train.values.tolist(), eta=0.5)
example = test.values.tolist()
print(len(example))
input()
#print(example)
#print(example[17])
#feed example
nn.feed_forward(example[0][:(-1 * 3)])
print(example[0])
nn.show_class()
nn.feed_forward(example[40][:(-1 * 3)])
print(example[40])
print(test.iloc[[40]].values.tolist())
input()
nn.show_class()
nn.feed_forward(example[31][:(-1 * 3)])
print(example[31])
nn.show_class()
print(training.accuracy(nn, test, n_classes=3))
"""
