def _parse_inputfile_node(config, node_id):
"""
This function receives a node id, parses it, and returns the node in
the network to which it pertains. It implies that the network
structure must already be in place for it to be functional.
"""
activation_type = config.get(node_id, 'activation_type')
node_type = config.get(node_id, 'node_type')
if node_type == NODE_BIAS:
node = BiasNode()
elif node_type == NODE_COPY:
node = CopyNode()
node._source_type = config.get(node_id, 'source_type')
node._incoming_weight = float(
config.get(node_id, 'incoming_weight'))
node._existing_weight = float(
config.get(node_id, 'existing_weight'))
node.set_activation_type(activation_type)
else:
node = Node()
node.set_activation_type(activation_type)
node.node_type = node_type
return node
def add_nodes(self, number_nodes, node_type, activation_type=None):
"""
This function adds nodes in bulk for initialization.
If an optional activation type is passed through, that will be set for
the nodes. Otherwise, the default activation type for the layer will
be used.
"""
count = 0
while count < number_nodes:
if node_type == 'copy':
node = CopyNode()
else:
node = Node(node_type)
if activation_type:
node.set_activation_type(activation_type)
self.add_node(node)
count += 1
def setUp(self):
self.node = CopyNode()
class CopyNodeTest(unittest.TestCase):
"""
Tests CopyNode
"""
def setUp(self):
self.node = CopyNode()
def test__init__(self):
self.assertEqual(NODE_COPY, self.node.node_type)
def test_set_source_node(self):
source_node = Node()
self.node.set_source_node(source_node)
self.assertEqual(source_node, self.node._source_node)
def test_get_source_node(self):
self.node._source_node = Node()
self.assertEqual(self.node._source_node, self.node.get_source_node())
def test_load_source_value(self):
self.node._value = .25
self.node._existing_weight = .25
self.node._incoming_weight = .5
source_node = Node()
source_node.set_value(.3)
source_node.set_activation_type(ACTIVATION_SIGMOID)
self.node.set_source_node(source_node)
# activate
self.node._source_type = 'a'
self.node.load_source_value()
self.assertAlmostEqual(sigmoid(.3) * .5 + .25 * .25, self.node._value)
# value
self.node._value = .25
self.node._source_type = 'v'
self.node.load_source_value()
self.assertAlmostEqual(.3 * .5 + .25 * .25, self.node._value)
# invalid source type
self.node._source_type = 'f'
self.failUnlessRaises(ValueError, self.node.load_source_value)
def test_get_source_type(self):
self.node._source_type = 'a'
self.assertEqual('a', self.node.get_source_type())
def test_get_incoming_weight(self):
self.node._incoming_weight = .3
self.assertAlmostEqual(.3, self.node.get_incoming_weight())
def test_get_existing_weight(self):
self.node._existing_weight = .3
self.assertAlmostEqual(.3, self.node.get_existing_weight())
def test_source_update_config(self):
self.node.source_update_config('a', .3, .2)
self.assertEqual('a', self.node._source_type)
self.assertAlmostEqual(.3, self.node._incoming_weight)
self.assertAlmostEqual(.2, self.node._existing_weight)
self.failUnlessRaises(ValueError, self.node.source_update_config, 'e',
.3, .2)
self.failUnlessRaises(ValueError, self.node.source_update_config, 'a',
1.3, .2)
self.failUnlessRaises(ValueError, self.node.source_update_config, 'a',
.3, 1.2)
def _apply_config(self, neural_net):
"""
This function actually does the work.
"""
if not isinstance(neural_net, NeuralNet):
raise ValueError("neural_net must be of the NeuralNet class.")
for snode in self.get_source_nodes(neural_net):
prev_copy_node = None
for level in xrange(self.copy_levels):
copy_node = CopyNode()
if level == 0:
copy_node.set_source_node(snode)
else:
copy_node.set_source_node(prev_copy_node)
copy_node.source_update_config(
self.source_type,
self.incoming_weight,
self.existing_weight)
copy_node.set_activation_type(self.activation_type)
if self.connection_type == 'm':
self._fully_connect(
copy_node,
self.get_upper_nodes(neural_net))
elif self.connection_type == 's':
copy_node.add_input_connection(
Connection(copy_node, snode))
else:
raise ValueError("Invalid connection_type")
neural_net.layers[self.copy_nodes_layer].add_node(copy_node)
prev_copy_node = copy_node
def _apply_config(self, neural_net):
"""
This function actually does the work.
"""
if not isinstance(neural_net, NeuralNet):
raise ValueError("neural_net must be of the NeuralNet class.")
for snode in self.get_source_nodes(neural_net):
prev_copy_node = None
for level in range(self.copy_levels):
copy_node = CopyNode()
if level == 0:
copy_node.set_source_node(snode)
else:
copy_node.set_source_node(prev_copy_node)
copy_node.source_update_config(
self.source_type,
self.incoming_weight,
self.existing_weight)
copy_node.set_activation_type(self.activation_type)
if self.connection_type == 'm':
self._fully_connect(
copy_node,
self.get_upper_nodes(neural_net))
elif self.connection_type == 's':
copy_node.add_input_connection(
Connection(copy_node, snode))
else:
raise ValueError("Invalid connection_type")
neural_net.layers[self.copy_nodes_layer].add_node(copy_node)
prev_copy_node = copy_node