x_train = train[:, :4]
y_train = train[:, 4:]
x_train_val = train_val[:, :4]
y_train_val = train_val[:, 4:]
x_test = test[:, :4]
y_test = test[:, 4:]
validation_alphas = [0.15]
hidden = 3 * np.arange(1, 5)
simple_net = MultiLayerPerceptron(M, C, epochs=10000)
simple_net.fit(x_train, y_train, x_train_val=x_train_val, y_train_val=y_train_val, alphas=validation_alphas, hidden=hidden)
y_out_simple_net = simple_net.predict(x_test, bias=True)
y_test = simple_net.predicao(y_test)
metrics_calculator = metric(y_test, y_out_simple_net, types=['ACCURACY', 'precision', 'recall', 'f1_score'])
metric_results = metrics_calculator.calculate(average='macro')
print(metric_results)
results['cf'].append((metric_results['ACCURACY'], metrics_calculator.confusion_matrix(list(y_test),
y_out_simple_net, labels=[0,1,2])))
results['alphas'].append(simple_net.learning_rate)
results['realization'].append(realization)
for type in ['ACCURACY', 'precision', 'recall', 'f1_score']:
results[type].append(metric_results[type])
y_train_val = train_val[:, 2:]
x_test = test[:, :2]
y_test = test[:, 2:]
validation_alphas = [0.15]
hidden = 2 * np.arange(1, 4)
simple_net = MultiLayerPerceptron(M, C, epochs=10000)
simple_net.fit(x_train,
y_train,
x_train_val=x_train_val,
y_train_val=y_train_val,
alphas=validation_alphas,
hidden=hidden)
y_out = simple_net.predict(x_test, bias=True)
y_test = simple_net.predicao(y_test)
metrics_calculator = metric(
y_test,
y_out,
types=['ACCURACY', 'precision', 'recall', 'f1_score'])
metric_results = metrics_calculator.calculate(average='macro')
print(metric_results)
results['cf'].append(
(metric_results['ACCURACY'],
metrics_calculator.confusion_matrix(y_test, y_out, labels=[0,
1])))
results['alphas'].append(simple_net.learning_rate)