def test_adjust_population_fitness(self):
msg = 'Adjusted fitness incorrectly!'
e = Ecosystem(threshold=0.5, dc=1.0, ec=1.0, wc=0.4)
# Create genomes
g = Genome(2, 2)
g2 = Genome(2, 2)
g3 = Genome(2, 2)
# Add connections and/or nodes
g.add_connection(0, 2, weight=1.0)
g.add_connection(0, 3, weight=1.0)
g.add_connection(1, 2, weight=1.0)
g2.add_connection(0, 2, weight=0.9)
g2.add_connection(0, 3, weight=1.0)
g2.add_connection(1, 2, weight=1.0)
g2.add_connection(1, 3, weight=1.0)
g3.add_connection(0, 2, weight=0.1)
g3.add_connection(0, 3, weight=1.0)
g3.add_connection(1, 2, weight=1.0)
g3.add_connection(1, 3, weight=1.0)
g3.add_node(0)
g3.add_node(1)
g3.add_node(2)
g3.add_connection(0, 6, weight=1.0)
g3.add_connection(1, 5, weight=1.0)
g4 = copy.copy(g3)
g4.add_node(3)
# Set genome fitness
g.fitness = 10
g2.fitness = 12
g3.fitness = 22
g4.fitness = 24
# Test to make sure it doesn't change if there are no different genomes
e.add_genome(g)
e.adjust_population_fitness()
self.assertEqual(g.fitness, 10, msg)
e.add_genome(g2)
e.adjust_population_fitness()
self.assertEqual(g.fitness, 10, msg)
self.assertEqual(g2.fitness, 12, msg)
# Test to make sure the fitness does change when there are different genomes
e.add_genome(g3)
e.add_genome(g4)
e.adjust_population_fitness()
self.assertEqual(g.fitness, 5, msg)
self.assertEqual(g2.fitness, 6, msg)
self.assertEqual(g3.fitness, 11, msg)
self.assertEqual(g4.fitness, 12, 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)