class ElmanSimpleRecurrentTest(unittest.TestCase):
"""
Tests ElmanSimpleRecurrent
"""
def setUp(self):
self.net = NeuralNet()
self.net.init_layers(2, [1], 1)
self.rec_config = ElmanSimpleRecurrent()
def test_class_init_(self):
self.assertEqual('a', self.rec_config.source_type)
self.assertEqual(1.0, self.rec_config.incoming_weight)
self.assertEqual(0.0, self.rec_config.existing_weight)
self.assertEqual('m', self.rec_config.connection_type)
self.assertEqual(1, self.rec_config.copy_levels)
self.assertEqual(0, self.rec_config.copy_nodes_layer)
def test_get_source_nodes(self):
nodes1 = self.net.layers[1].get_nodes(NODE_HIDDEN)
nodes2 = self.rec_config.get_source_nodes(self.net)
# Should be the same
self.assertEqual(len(nodes1), len(nodes2))
self.assertEqual(self.net.layers[1].get_nodes(NODE_HIDDEN),
self.rec_config.get_source_nodes(self.net))
class ElmanSimpleRecurrentTest(unittest.TestCase):
"""
Tests ElmanSimpleRecurrent
"""
def setUp(self):
self.net = NeuralNet()
self.net.init_layers(2, [1], 1)
self.rec_config = ElmanSimpleRecurrent()
def test_class_init_(self):
self.assertEqual('a', self.rec_config.source_type)
self.assertEqual(1.0, self.rec_config.incoming_weight)
self.assertEqual(0.0, self.rec_config.existing_weight)
self.assertEqual('m', self.rec_config.connection_type)
self.assertEqual(1, self.rec_config.copy_levels)
self.assertEqual(0, self.rec_config.copy_nodes_layer)
def test_get_source_nodes(self):
nodes1 = self.net.layers[1].get_nodes(NODE_HIDDEN)
nodes2 = self.rec_config.get_source_nodes(self.net)
# Should be the same
self.assertEqual(len(nodes1), len(nodes2))
self.assertEqual(
self.net.layers[1].get_nodes(NODE_HIDDEN),
self.rec_config.get_source_nodes(self.net))
def setUp(self):
self.net = NeuralNet()
self.net.init_layers(2, [1], 1)
self.rec_config = ElmanSimpleRecurrent()
y_train = np.array(y_train).reshape((len(y_train), 1))
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
fit1 = NeuralNet()
fit1.init_layers(input_nodes, [hidden_nodes], output_nodes,
ElmanSimpleRecurrent())
fit1.randomize_network()
fit1.layers[1].set_activation_type('sigmoid')
fit1.set_learnrate(0.05)
fit1.set_all_inputs(x_input)
fit1.set_all_targets(y_input)
fit1.set_learn_range(0, i)
fit1.set_test_range(i, i + 1)
fit1.learn(epochs=100, show_epoch_results=True, random_testing=False)
mse = fit1.test()
all_mse.append(mse)
print("test set MSE = ", np.round(mse, 6))
target = [item[0][0] for item in fit1.test_targets_activations]
target = scaler_y.inverse_transform(
np.array(target).reshape((len(target), 1)))
pred = [item[1][0] for item in fit1.test_targets_activations]
def setUp(self):
self.net = NeuralNet()
self.net.init_layers(2, [1], 1)
self.rec_config = ElmanSimpleRecurrent()