def setUp(self):
self.net = NeuralNet()
self.net.init_layers(2, [1], 1)
self.rec_config = JordanRecurrent(existing_weight=.8)
y_test = np.array(y_test).reshape((len(y_test), 1))
#transformando os dados para estar no intervalo de 0 a 1
scaler_x = MinMaxScaler()
x_train = scaler_x.fit_transform(x_train)
x_test = scaler_x.transform(x_test)
scaler_y = MinMaxScaler()
y_train = scaler_y.fit_transform(y_train)
y_test = scaler_y.transform(y_test)
x_input = np.concatenate(
(x_train, x_test, np.zeros((1, np.shape(x_train)[1]))))
y_input = np.concatenate((y_train, y_test, np.zeros((1, 1))))
#elaboracao do modelo de rede neural com os parametros definidos
fit2 = NeuralNet()
fit2.init_layers(input_nodes_jordan, [hidden_nodes_jordan],
output_nodes_jordan,
JordanRecurrent(existing_weight_factor))
fit2.randomize_network()
fit2.layers[1].set_activation_type('sigmoid')
fit2.set_learnrate(0.05)
fit2.set_all_inputs(x_input)
fit2.set_all_targets(y_input)
fit2.set_learn_range(0, i)
fit2.set_test_range(i, i + 1)
fit2.learn(epochs=100, show_epoch_results=True, random_testing=False)
mse = fit2.test()
all_mse_jordan.append(mse)
print("test set MSE = ", np.round(mse, 6))
target = [item[0][0] for item in fit2.test_targets_activations]
target = scaler_y.inverse_transform(
np.array(target).reshape((len(target), 1)))
pred = [item[1][0] for item in fit2.test_targets_activations]
