def test_set_activation(self):
g = Genome(3, 3)
node_ids = range(len(g.get_nodes()))
act = [activations.relu, activations.gaussian, activations.square]
# Test to make sure the activation is set correctly
msg = 'Activation set incorrectly!'
for nid in node_ids:
for a in act:
g.set_activation(nid, a)
self.assertEqual(g.get_node(nid).activation, a)
def test_mutate_add_connection(self):
# Test adding a connection
msg = 'Connection added incorrectly!'
g = Genome(2, 2)
for i in range(30):
g = Genome(2, 2)
g.mutate_add_connection()
self.assertEqual(len(g.get_connections()), 1, msg)
self.assertEqual(g.get_connections()[0].innovation_number, 0, msg)
self.assertTrue(
g.get_connections()[0].in_node
in [n.id for n in g.get_nodes()], msg)
self.assertTrue(
g.get_connections()[0].out_node
in [n.id for n in g.get_nodes()], msg)
self.assertTrue(-1.0 <= g.get_connections()[0].weight <= 1.0, msg)
self.assertTrue(g.get_connections()[0].expressed, msg)
self.assertNotEqual(g.get_connections()[0].in_node,
g.get_connections()[0].out_node)
in_type = g.get_node(g.get_connections()[0].in_node).type
out_type = g.get_node(g.get_connections()[0].out_node).type
self.assertFalse(in_type == out_type != 'hidden')
self.assertFalse((in_type == 'output' and out_type == 'input'))
self.assertFalse((in_type == 'hidden' and out_type == 'input'))
# Test to make sure connections are always added (unless at max)
msg = 'Connection not added!'
for i in range(2, 4):
g.mutate_add_connection()
self.assertEqual(len(g.get_connections()), i, msg)
# Test to make sure it doesn't go above the maximum connections
msg = 'Connections exceeded maximum amount!'
g.mutate_add_connection()
self.assertEqual(len(g.get_connections()), 4,
msg) # Shouldn't go past 4
def test_get_node_max_distance(self):
g = Genome(2, 2)
# Test to make sure empty genomes return the correct values for output nodes
msg = 'Disconnected output node returned invalid value!'
self.assertEqual(g.get_node_max_distance(g.get_node(2).id), -1, msg)
self.assertEqual(g.get_node_max_distance(g.get_node(3).id), -1, msg)
# Add nodes and connections
g.add_connection(0, 2, weight=-0.7)
g.add_connection(0, 3, weight=-0.1)
g.add_connection(1, 2, weight=0.5)
g.add_connection(1, 3, weight=0.9)
g.add_node(0)
g.add_node(2)
g.add_connection(4, 5, 0.5)
msg = 'Incorrect node max distance!'
# Test the values of each node distance to make sure they are correct
correct_distances = [0, 0, 3, 1, 1, 2]
for node, distance in zip(g.get_nodes(), correct_distances):
self.assertEqual(g.get_node_max_distance(node.id), distance, msg)
# Add a node and test again
g.add_node(8)
correct_distances = [0, 0, 4, 1, 1, 3, 2]
for node, distance in zip(g.get_nodes(), correct_distances):
self.assertEqual(g.get_node_max_distance(node.id), distance, msg)
# Add connection and test again
g.add_connection(6, 3)
correct_distances = [0, 0, 4, 3, 1, 3, 2]
for node, distance in zip(g.get_nodes(), correct_distances):
self.assertEqual(g.get_node_max_distance(node.id), distance, msg)
# Test genome with connection loop
msg = 'Genome failed to handle connection loop!'
g2 = Genome(1, 1)
g2.add_connection(0, 1)
g2.add_node(0)
g2.add_node(0)
g2.add_node(1)
g2.add_connection(3, 2)
g2.add_connection(4, 3)
correct_distances = [0, 4, 3, 3, 3]
for node, distance in zip(g2.get_nodes(), correct_distances):
self.assertEqual(g2.get_node_max_distance(node.id), distance, msg)
def test_cross(self):
e = Ecosystem()
# Create genomes
g = Genome(2, 2, ecosystem=e)
g2 = Genome(2, 2, ecosystem=e)
# Cross the genomes
child = e.cross(g, g2)
# Test child connections
msg = 'Child connection doesn\'t exist within either parent!'
for c in child.get_connections():
self.assertTrue(
g.get_connection(c.innovation_number) is not None
or g2.get_connection(c.innovation_number) is not None, msg)
# Test to make sure the child has the same amount of connections as the fitter parent
msg = 'Child missing fitter parent connection(s)!'
self.assertEqual(len(child.get_connections()),
len(g.get_connections()), msg)
# Test child nodes
msg = 'Child node doesn\'t exist within either parent!'
for n in child.get_nodes():
self.assertTrue(
g.get_node(n.id) is not None or g2.get_node(n.id) is not None,
msg)
# Test to make sure the child has the same amount of nodes as the fitter parent
msg = 'Child is missing fitter parent node(s)!'
self.assertEqual(len(child.get_nodes()), len(g.get_nodes()), msg)
# Test preference for fit parents
msg = 'Child connection preferred less fit parent!'
for c in child.get_connections():
in_both = g.get_connection(
c.innovation_number) is not None and g2.get_connection(
c.innovation_number) is not None
in_fit_parent = g.get_connection(
c.innovation_number) is not None and g2.get_connection(
c.innovation_number) is None
self.assertTrue(in_both or in_fit_parent, msg)
# Add connections and nodes
g.add_connection(0, 2)
g.add_connection(0, 3)
g.add_connection(1, 2)
g.add_connection(1, 3)
g.add_node(0)
g.get_connections()[5].weight = 0.4
g2.add_connection(0, 2)
g2.add_connection(0, 3)
g2.add_connection(1, 2)
g2.add_connection(1, 3)
g2.add_node(1)
g.add_node(2)
# Assign fitness to genomes
g.fitness = 10
g2.fitness = 5
# Cross the genomes
child = e.cross(g, g2)
# Test child connections
msg = 'Child connection doesn\'t exist within either parent!'
for c in child.get_connections():
self.assertTrue(
g.get_connection(c.innovation_number) is not None
or g2.get_connection(c.innovation_number) is not None, msg)
# Test to make sure the child has the same amount of connections as the fitter parent
msg = 'Child missing fitter parent connection(s)!'
self.assertEqual(len(child.get_connections()),
len(g.get_connections()), msg)
# Test child nodes
msg = 'Child node doesn\'t exist within either parent!'
for n in child.get_nodes():
self.assertTrue(
g.get_node(n.id) is not None or g2.get_node(n.id) is not None,
msg)
# Test to make sure the child has the same amount of nodes as the fitter parent
msg = 'Child is missing fitter parent node(s)!'
self.assertEqual(len(child.get_nodes()), len(g.get_nodes()), msg)
# Test preference for fit parents
msg = 'Child connection preferred less fit parent!'
for c in child.get_connections():
in_both = g.get_connection(
c.innovation_number) is not None and g2.get_connection(
c.innovation_number) is not None
in_fit_parent = g.get_connection(
c.innovation_number) is not None and g2.get_connection(
c.innovation_number) is None
self.assertTrue(in_both or in_fit_parent, msg)
# Swap the fitness and test again
g.fitness = 5
g2.fitness = 10
# Cross the genomes
child = e.cross(g, g2)
# Test child connections
msg = 'Child connection doesn\'t exist within either parent!'
for c in child.get_connections():
self.assertTrue(
g.get_connection(c.innovation_number) is not None
or g2.get_connection(c.innovation_number) is not None, msg)
# Test to make sure the child has the same amount of connections as the fitter parent
msg = 'Child missing fitter parent connection(s)!'
self.assertEqual(len(child.get_connections()),
len(g2.get_connections()), msg)
# Test child nodes
msg = 'Child node doesn\'t exist within either parent!'
for n in child.get_nodes():
self.assertTrue(
g.get_node(n.id) is not None or g2.get_node(n.id) is not None,
msg)
# Test to make sure the child has the same amount of nodes as the fitter parent
msg = 'Child is missing fitter parent node(s)!'
self.assertEqual(len(child.get_nodes()), len(g2.get_nodes()), msg)
# Test preference for fit parents
msg = 'Child connection preferred less fit parent!'
for c in child.get_connections():
in_both = g.get_connection(
c.innovation_number) is not None and g2.get_connection(
c.innovation_number) is not None
in_fit_parent = g.get_connection(
c.innovation_number) is None and g2.get_connection(
c.innovation_number) is not None
self.assertTrue(in_both or in_fit_parent, msg)