class NARXRecurrentTest(unittest.TestCase):
"""
Tests NARXRecurrent
"""
def setUp(self):
self.net = NeuralNet()
self.net.init_layers(2, [1], 1)
self.rec_config = NARXRecurrent(output_order=1,
incoming_weight_from_output=.9,
input_order=1,
incoming_weight_from_input=.7)
def test_class_init_(self):
self.assertEqual(0, self.rec_config.existing_weight)
self.assertEqual(None, self.rec_config._node_type)
self.assertEqual([1, .9], self.rec_config.output_values)
self.assertEqual([1, .7], self.rec_config.input_values)
def test_get_source_nodes(self):
self.rec_config._node_type = NODE_OUTPUT
self.assertEqual(self.net.layers[-1].get_nodes(NODE_OUTPUT),
self.rec_config.get_source_nodes(self.net))
self.rec_config._node_type = NODE_INPUT
self.assertEqual(self.net.layers[0].get_nodes(NODE_INPUT),
self.rec_config.get_source_nodes(self.net))
class NARXRecurrentTest(unittest.TestCase):
"""
Tests NARXRecurrent
"""
def setUp(self):
self.net = NeuralNet()
self.net.init_layers(2, [1], 1)
self.rec_config = NARXRecurrent(
output_order=1,
incoming_weight_from_output=.9,
input_order=1,
incoming_weight_from_input=.7)
def test_class_init_(self):
self.assertEqual(0, self.rec_config.existing_weight)
self.assertEqual(None, self.rec_config._node_type)
self.assertEqual([1, .9], self.rec_config.output_values)
self.assertEqual([1, .7], self.rec_config.input_values)
def test_get_source_nodes(self):
self.rec_config._node_type = NODE_OUTPUT
self.assertEqual(
self.net.layers[-1].get_nodes(NODE_OUTPUT),
self.rec_config.get_source_nodes(self.net))
self.rec_config._node_type = NODE_INPUT
self.assertEqual(
self.net.layers[0].get_nodes(NODE_INPUT),
self.rec_config.get_source_nodes(self.net))
def setUp(self):
self.net = NeuralNet()
self.net.init_layers(2, [1], 1)
self.rec_config = NARXRecurrent(output_order=1,
incoming_weight_from_output=.9,
input_order=1,
incoming_weight_from_input=.7)
def setUp(self):
self.net = NeuralNet()
self.net.init_layers(2, [1], 1)
self.rec_config = NARXRecurrent(
output_order=1,
incoming_weight_from_output=.9,
input_order=1,
incoming_weight_from_input=.7)
xs[14], xs[15], xs[16], xs[17], xs[18], xs[19], xs[20]])
ys = np.concatenate([ys[0],ys[1],ys[2],ys[3], ys[4], ys[5], ys[6],
ys[7], ys[8], ys[9], ys[10], ys[11], ys[12], ys[13],
ys[14], ys[15], ys[16], ys[17], ys[18], ys[19], ys[20]])
#createnetwork and run training
for l1, l2 in zip (xs, ys):
output_order = 6
incoming_weight_from_output = 1.
input_order = 0
incoming_weight_from_input = 0.
net = NeuralNet()
net.init_layers(4, [l1,l2], 1, NARXRecurrent(
output_order,
incoming_weight_from_output,
input_order,
incoming_weight_from_input))
net.randomize_network()
net.set_halt_on_extremes(True)
# Set to constrain beginning weights to -.5 to .5
# Just to show we can
net.set_random_constraint(.5)
net.set_learnrate(.1)
net.set_all_inputs(all_inputs)
net.set_all_targets(all_targets)
return population, all_inputs, all_targets
# generate data
population, all_inputs, all_targets = generate_data()
# NARXRecurrent
input_nodes, hidden_nodes, output_nodes = 1, 10, 1
output_order, incoming_weight_from_output = 3, .6
input_order, incoming_weight_from_input = 2, .4
# init neural network
net = NeuralNet()
net.init_layers(
input_nodes, [hidden_nodes], output_nodes,
NARXRecurrent(output_order, incoming_weight_from_output, input_order,
incoming_weight_from_input))
net.randomize_network()
net.set_halt_on_extremes(True)
# set constrains and rates
net.set_random_constraint(.5)
net.set_learnrate(.1)
# set inputs and outputs
net.set_all_inputs(all_inputs)
net.set_all_targets(all_targets)
# set lengths
length = len(all_inputs)
learn_end_point = int(length * .8)
def serNeural(sDay,nAhead,x0,hWeek):
nLin = sDay.shape[0] + nAhead
nFit = sDay.shape[0] if int(x0['obs_time']) <= 14 else int(x0['obs_time'])
predS = getHistory(sDay,nAhead,x0,hWeek)
weekS = [x.isocalendar()[1] for x in sDay.index]
population = [[float(i),sDay['y'][i],float(i%7),weekS[i]] for i in range(sDay.shape[0])]
all_inputs = []
all_targets = []
factorY = sDay['y'].mean()
factorT = 1.0 / float(len(population))*factorY
factorD = 1./7.*factorY
factorW = 1./52.*factorY
factorS = 4.*sDay['y'].std()
factorH = factorY/sDay['hist'].mean()
def population_gen(population):
pop_sort = [item for item in population]
# random.shuffle(pop_sort)
for item in pop_sort:
yield item
for t,y,y1,y2 in population_gen(population):
#all_inputs.append([t*factorT,(.5-random.random())*factorS+factorY,y1*factorD,y2*factorW])
all_inputs.append([y1*factorD,(.5-random.random())*factorS+factorY,y2*factorW])
all_targets.append([y])
if False:
plt.plot([x[0] for x in all_inputs],'-',label='targets0')
plt.plot([x[1] for x in all_inputs],'-',label='targets1')
plt.plot([x[2] for x in all_inputs],'-',label='targets2')
# plt.plot([x[3] for x in all_inputs],'-',label='targets3')
plt.plot([x[0] for x in all_targets],'-',label='actuals')
plt.legend(loc='lower left', numpoints=1)
plt.show()
net = NeuralNet()
net.init_layers(3,[10],1,NARXRecurrent(3,.6,2,.4))
net.randomize_network()
net.set_random_constraint(.5)
net.set_learnrate(.1)
net.set_all_inputs(all_inputs)
net.set_all_targets(all_targets)
#predS['pred'] = [item[0][0] for item in net.test_targets_activations]
length = len(all_inputs)
learn_end_point = int(length * .8)
# random.sample(all_inputs,10)
net.set_learn_range(0, learn_end_point)
net.set_test_range(learn_end_point + 1, length - 1)
net.layers[1].set_activation_type('tanh')
net.learn(epochs=125,show_epoch_results=True,random_testing=False)
mse = net.test()
#net.save(os.environ['LAV_DIR'] + "/out/train/net.txt")
test_positions = [item[0][0] for item in net.get_test_data()]
all_targets1 = [item[0][0] for item in net.test_targets_activations]
all_actuals = [item[1][0] for item in net.test_targets_activations]
# This is quick and dirty, but it will show the results
plt.subplot(3, 1, 1)
plt.plot([i for i in sDay['y']],'-')
plt.title("Population")
plt.grid(True)
plt.subplot(3, 1, 2)
plt.plot(test_positions, all_targets1, 'b-', label='targets')
plt.plot(test_positions, all_actuals, 'r-', label='actuals')
plt.grid(True)
plt.legend(loc='lower left', numpoints=1)
plt.title("Test Target Points vs Actual Points")
plt.subplot(3, 1, 3)
plt.plot(range(1, len(net.accum_mse) + 1, 1), net.accum_mse)
plt.xlabel('epochs')
plt.ylabel('mean squared error')
plt.grid(True)
plt.title("Mean Squared Error by Epoch")
plt.show()
