def performNN(all_extracted_features, all_targets):
from pyneurgen.neuralnet import NeuralNet
#from pyneurgen.nodes import BiasNode, Connection
net = NeuralNet()
net.init_layers(len(all_extracted_features[0]), [2], 1)
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(.001)
net.set_all_inputs(all_extracted_features)
net.set_all_targets(all_targets)
length = len(all_extracted_features)
learn_end_point = int(length * .8)
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=150, show_epoch_results=True, random_testing=True)
mse = net.test()
print mse
def buildIrisNetwork(all_inputs, all_targets):
net = NeuralNet()
net.init_layers(4, [6], 3)
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)
length = len(all_inputs)
learn_end_point = int(length * .5)
net.set_learn_range(0, learn_end_point)
net.set_test_range(learn_end_point + 1, length-1)
net.layers[0].set_activation_type('tanh')
net.layers[1].set_activation_type('tanh')
net.layers[2].set_activation_type('threshold')
return net
def buildIrisNetwork(all_inputs, all_targets):
net = NeuralNet()
net.init_layers(4, [6], 3)
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)
length = len(all_inputs)
learn_end_point = int(length * .5)
net.set_learn_range(0, learn_end_point)
net.set_test_range(learn_end_point + 1, length - 1)
net.layers[0].set_activation_type('tanh')
net.layers[1].set_activation_type('tanh')
net.layers[2].set_activation_type('threshold')
return net
class TestNeuralNet(unittest.TestCase):
"""
Tests NeuralNet
"""
def setUp(self):
self.net = NeuralNet()
layer = Layer(0, 'input')
layer.add_nodes(1, 'input')
self.net.layers.append(layer)
layer = Layer(1, 'hidden')
layer.add_nodes(1, 'hidden')
self.net.layers.append(layer)
layer = Layer(2, 'output')
layer.add_nodes(1, 'output')
self.net.layers.append(layer)
# Specify connections
self.net.layers[1].nodes[0].add_input_connection(
Connection(self.net.layers[0].nodes[0],
self.net.layers[1].nodes[0], 1.00))
self.net.layers[2].nodes[0].add_input_connection(
Connection(self.net.layers[1].nodes[0],
self.net.layers[2].nodes[0], .75))
self.net._epochs = 1
self.net.copy_levels = 0
self.net._allinputs = [[.1], [.2], [.3], [.4], [.5]]
self.net._alltargets = [[.2], [.4], [.6], [.8], [1.0]]
self.net.input_layer = self.net.layers[0]
self.net.output_layer = self.net.layers[-1]
def test_set_halt_on_extremes(self):
self.net._halt_on_extremes = 'fail'
self.net.set_halt_on_extremes(True)
self.assertEqual(True, self.net._halt_on_extremes)
self.net._halt_on_extremes = 'fail'
self.net.set_halt_on_extremes(False)
self.assertEqual(False, self.net._halt_on_extremes)
self.net._halt_on_extremes = 'fail'
self.failUnlessRaises(ValueError, self.net.set_halt_on_extremes, 'a')
self.net._halt_on_extremes = 'fail'
self.failUnlessRaises(ValueError, self.net.set_halt_on_extremes, 3)
def test_get_halt_on_extremes(self):
self.net.set_halt_on_extremes(True)
self.assertEqual(True, self.net.get_halt_on_extremes())
self.net.set_halt_on_extremes(False)
self.assertEqual(False, self.net.get_halt_on_extremes())
def test_set_random_constraint(self):
self.net._random_constraint = 'fail'
self.net.set_random_constraint(.1)
self.assertEqual(.1, self.net._random_constraint)
self.failUnlessRaises(ValueError, self.net.set_random_constraint, 3)
self.failUnlessRaises(ValueError, self.net.set_random_constraint, 1)
self.failUnlessRaises(ValueError, self.net.set_random_constraint, 0.0)
self.failUnlessRaises(ValueError, self.net.set_random_constraint, -.2)
self.failUnlessRaises(ValueError, self.net.set_random_constraint, 'a')
def test_get_random_constraint(self):
self.net.set_random_constraint(.2)
self.assertEqual(.2, self.net.get_random_constraint())
self.net.set_random_constraint(.8)
self.assertEqual(.8, self.net.get_random_constraint())
def test_set_epochs(self):
self.net._epochs = 'fail'
self.net.set_epochs(3)
self.assertEqual(3, self.net._epochs)
self.failUnlessRaises(ValueError, self.net.set_epochs, .3)
self.failUnlessRaises(ValueError, self.net.set_epochs, 0)
self.failUnlessRaises(ValueError, self.net.set_epochs, -3)
self.failUnlessRaises(ValueError, self.net.set_epochs, -.2)
self.failUnlessRaises(ValueError, self.net.set_epochs, 'a')
def test_get_epochs(self):
self.net.set_epochs(3)
self.assertEqual(3, self.net.get_epochs())
def test_set_time_delay(self):
self.net._time_delay = 'fail'
self.net.set_time_delay(3)
self.assertEqual(3, self.net._time_delay)
self.failUnlessRaises(ValueError, self.net.set_time_delay, .3)
self.failUnlessRaises(ValueError, self.net.set_time_delay, -3)
self.failUnlessRaises(ValueError, self.net.set_time_delay, -.2)
self.failUnlessRaises(ValueError, self.net.set_time_delay, 'a')
def test_get_time_delay(self):
self.net.set_time_delay(3)
self.assertEqual(3, self.net.get_time_delay())
def test_set_all_inputs(self):
pass
def test_set_all_targets(self):
pass
def test_set_learnrate(self):
pass
def test_get_learnrate(self):
pass
def test__set_data_range(self):
pass
def test_set_learn_range(self):
pass
def test_get_learn_range(self):
pass
def test__check_time_delay(self):
pass
def test_get_learn_data(self):
pass
def test_get_validation_data(self):
pass
def test_get_test_data(self):
pass
def test__get_data(self):
pass
def test__get_randomized_position(self):
pass
def test__check_positions(self):
pass
def test_set_validation_range(self):
pass
def test_get_validation_range(self):
pass
def test_set_test_range(self):
pass
def test_get_test_range(self):
pass
def test_init_layers(self):
pass
def test__init_connections(self):
pass
def test__connect_layer(self):
pass
def test__build_output_conn(self):
pass
def test_randomize_network(self):
pass
def test_learn(self):
pass
def test_test(self):
pass
def test_calc_mse(self):
self.assertAlmostEqual(10.0 / 2.0, self.net.calc_mse(100.0, 10))
def test_process_sample(self):
pass
def test__feed_forward(self):
# simplify activations
self.net.layers[0].set_activation_type('sigmoid')
self.net.layers[1].set_activation_type('sigmoid')
self.net.layers[2].set_activation_type('sigmoid')
# These values should be replaced
self.net.layers[1].nodes[0].set_value(1000.0)
self.net.layers[2].nodes[0].set_value(1000.0)
self.assertEqual(1000.0, self.net.layers[1].nodes[0].get_value())
self.assertEqual(1000.0, self.net.layers[2].nodes[0].get_value())
self.net.layers[0].load_inputs([.2])
self.net._feed_forward()
self.assertEqual(.2, self.net.layers[0].nodes[0].get_value())
self.assertEqual(
sigmoid(.2) * 1.0, self.net.layers[1].nodes[0].get_value())
self.assertEqual(
sigmoid(sigmoid(.2) * 1.0) * .75,
self.net.layers[2].nodes[0].get_value())
def test__back_propagate(self):
pass
def test__update_error(self):
pass
def test__adjust_weights(self):
"""
This function goes through layers starting with the top hidden layer
and working its way down to the input layer.
At each layer, the weights are adjusted based upon the errors.
"""
halt_on_extremes = True
for layer_no in range(len(self.net.layers) - 2, 0, -1):
layer = self.net.layers[layer_no + 1]
layer.adjust_weights(self.net._learnrate, halt_on_extremes)
def test__zero_errors(self):
for layer in self.net.layers[1:]:
for node in layer.nodes:
node.error = 1000
self.net._zero_errors()
for layer in self.net.layers[1:]:
for node in layer.nodes:
self.failIfEqual(1000, node.error)
def test_calc_output_error(self):
pass
def test_calc_sample_error(self):
pass
def test__copy_levels(self):
pass
def test__parse_inputfile_layer(self):
pass
def test__parse_inputfile_node(self):
pass
def test__parse_inputfile_conn(self):
pass
def test__parse_inputfile_copy(self):
pass
def test__parse_node_id(self):
pass
def test_load(self):
pass
def test_output_values(self):
pass
def test__node_id(self):
pass
def test_save(self):
pass
from pylab import plot, legend, subplot, grid, xlabel, ylabel, show, title from pyneurgen.neuralnet import NeuralNet from pyneurgen.nodes import BiasNode, Connection from pybrain.utilities import percentError from iris import neurgenData from src.utilities import percentError # Build the inputs all_inputs, all_targets = neurgenData() net = NeuralNet() net.init_layers(4, [6], 3) 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) length = len(all_inputs) learn_end_point = int(length * .5) net.set_learn_range(0, learn_end_point) net.set_test_range(learn_end_point + 1, length - 1)
random.shuffle(pop_sort)
for item in pop_sort:
yield item
# Build the inputs
for position, target in population_gen(population):
pos = float(position)
all_inputs.append([random.random(), pos * factor])
all_targets.append([target])
net = NeuralNet()
net.init_layers(2, [10], 1)
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)
length = len(all_inputs)
learn_end_point = int(length * .8)
net.set_learn_range(0, learn_end_point)
net.set_test_range(learn_end_point + 1, length - 1)
class TestNeuralNet(unittest.TestCase):
"""
Tests NeuralNet
"""
def setUp(self):
self.net = NeuralNet()
layer = Layer(0, 'input')
layer.add_nodes(1, 'input')
self.net.layers.append(layer)
layer = Layer(1, 'hidden')
layer.add_nodes(1, 'hidden')
self.net.layers.append(layer)
layer = Layer(2, 'output')
layer.add_nodes(1, 'output')
self.net.layers.append(layer)
# Specify connections
self.net.layers[1].nodes[0].add_input_connection(
Connection(
self.net.layers[0].nodes[0],
self.net.layers[1].nodes[0],
1.00))
self.net.layers[2].nodes[0].add_input_connection(
Connection(
self.net.layers[1].nodes[0],
self.net.layers[2].nodes[0],
.75))
self.net._epochs = 1
self.net.copy_levels = 0
self.net._allinputs = [[.1], [.2], [.3], [.4], [.5]]
self.net._alltargets = [[.2], [.4], [.6], [.8], [1.0]]
self.net.input_layer = self.net.layers[0]
self.net.output_layer = self.net.layers[-1]
def test_set_halt_on_extremes(self):
self.net._halt_on_extremes = 'fail'
self.net.set_halt_on_extremes(True)
self.assertEqual(True, self.net._halt_on_extremes)
self.net._halt_on_extremes = 'fail'
self.net.set_halt_on_extremes(False)
self.assertEqual(False, self.net._halt_on_extremes)
self.net._halt_on_extremes = 'fail'
self.failUnlessRaises(ValueError, self.net.set_halt_on_extremes, 'a')
self.net._halt_on_extremes = 'fail'
self.failUnlessRaises(ValueError, self.net.set_halt_on_extremes, 3)
def test_get_halt_on_extremes(self):
self.net.set_halt_on_extremes(True)
self.assertEqual(True, self.net.get_halt_on_extremes())
self.net.set_halt_on_extremes(False)
self.assertEqual(False, self.net.get_halt_on_extremes())
def test_set_random_constraint(self):
self.net._random_constraint = 'fail'
self.net.set_random_constraint(.1)
self.assertEqual(.1, self.net._random_constraint)
self.failUnlessRaises(ValueError, self.net.set_random_constraint, 3)
self.failUnlessRaises(ValueError, self.net.set_random_constraint, 1)
self.failUnlessRaises(ValueError, self.net.set_random_constraint, 0.0)
self.failUnlessRaises(ValueError, self.net.set_random_constraint, -.2)
self.failUnlessRaises(ValueError, self.net.set_random_constraint, 'a')
def test_get_random_constraint(self):
self.net.set_random_constraint(.2)
self.assertEqual(.2, self.net.get_random_constraint())
self.net.set_random_constraint(.8)
self.assertEqual(.8, self.net.get_random_constraint())
def test_set_epochs(self):
self.net._epochs = 'fail'
self.net.set_epochs(3)
self.assertEqual(3, self.net._epochs)
self.failUnlessRaises(ValueError, self.net.set_epochs, .3)
self.failUnlessRaises(ValueError, self.net.set_epochs, 0)
self.failUnlessRaises(ValueError, self.net.set_epochs, -3)
self.failUnlessRaises(ValueError, self.net.set_epochs, -.2)
self.failUnlessRaises(ValueError, self.net.set_epochs, 'a')
def test_get_epochs(self):
self.net.set_epochs(3)
self.assertEqual(3, self.net.get_epochs())
def test_set_time_delay(self):
self.net._time_delay = 'fail'
self.net.set_time_delay(3)
self.assertEqual(3, self.net._time_delay)
self.failUnlessRaises(ValueError, self.net.set_time_delay, .3)
self.failUnlessRaises(ValueError, self.net.set_time_delay, -3)
self.failUnlessRaises(ValueError, self.net.set_time_delay, -.2)
self.failUnlessRaises(ValueError, self.net.set_time_delay, 'a')
def test_get_time_delay(self):
self.net.set_time_delay(3)
self.assertEqual(3, self.net.get_time_delay())
def test_set_all_inputs(self):
pass
def test_set_all_targets(self):
pass
def test_set_learnrate(self):
pass
def test_get_learnrate(self):
pass
def test__set_data_range(self):
pass
def test_set_learn_range(self):
pass
def test_get_learn_range(self):
pass
def test__check_time_delay(self):
pass
def test_get_learn_data(self):
pass
def test_get_validation_data(self):
pass
def test_get_test_data(self):
pass
def test__get_data(self):
pass
def test__get_randomized_position(self):
pass
def test__check_positions(self):
pass
def test_set_validation_range(self):
pass
def test_get_validation_range(self):
pass
def test_set_test_range(self):
pass
def test_get_test_range(self):
pass
def test_init_layers(self):
pass
def test__init_connections(self):
pass
def test__connect_layer(self):
pass
def test__build_output_conn(self):
pass
def test_randomize_network(self):
pass
def test_learn(self):
pass
def test_test(self):
pass
def test_calc_mse(self):
self.assertAlmostEqual(10.0 / 2.0, self.net.calc_mse(100.0, 10))
def test_process_sample(self):
pass
def test__feed_forward(self):
# simplify activations
self.net.layers[0].set_activation_type('sigmoid')
self.net.layers[1].set_activation_type('sigmoid')
self.net.layers[2].set_activation_type('sigmoid')
# These values should be replaced
self.net.layers[1].nodes[0].set_value(1000.0)
self.net.layers[2].nodes[0].set_value(1000.0)
self.assertEqual(1000.0, self.net.layers[1].nodes[0].get_value())
self.assertEqual(1000.0, self.net.layers[2].nodes[0].get_value())
self.net.layers[0].load_inputs([.2])
self.net._feed_forward()
self.assertEqual(.2, self.net.layers[0].nodes[0].get_value())
self.assertEqual(
sigmoid(.2) * 1.0,
self.net.layers[1].nodes[0].get_value())
self.assertEqual(
sigmoid(sigmoid(.2) * 1.0) * .75,
self.net.layers[2].nodes[0].get_value())
def test__back_propagate(self):
pass
def test__update_error(self):
pass
def test__adjust_weights(self):
"""
This function goes through layers starting with the top hidden layer
and working its way down to the input layer.
At each layer, the weights are adjusted based upon the errors.
"""
halt_on_extremes = True
for layer_no in range(len(self.net.layers) - 2, 0, -1):
layer = self.net.layers[layer_no + 1]
layer.adjust_weights(self.net._learnrate, halt_on_extremes)
def test__zero_errors(self):
for layer in self.net.layers[1:]:
for node in layer.nodes:
node.error = 1000
self.net._zero_errors()
for layer in self.net.layers[1:]:
for node in layer.nodes:
self.failIfEqual(1000, node.error)
def test_calc_output_error(self):
pass
def test_calc_sample_error(self):
pass
def test__copy_levels(self):
pass
def test__parse_inputfile_layer(self):
pass
def test__parse_inputfile_node(self):
pass
def test__parse_inputfile_conn(self):
pass
def test__parse_inputfile_copy(self):
pass
def test__parse_node_id(self):
pass
def test_load(self):
pass
def test_output_values(self):
pass
def test__node_id(self):
pass
def test_save(self):
pass