def genome_simple_shuffle():
config = get_config()
c1 = genome.ConnectionGene(1, 3, 0.5, innov_num=5)
c2 = genome.ConnectionGene(2, 3, 0.5, innov_num=6)
c3 = genome.ConnectionGene(1, 4, 0.1, innov_num=7)
c4 = genome.ConnectionGene(4, 3, 0.8, innov_num=8)
c5 = genome.ConnectionGene(9, 3, 0.25, innov_num=10)
c6 = genome.ConnectionGene(4, 9, 0.333, innov_num=11)
c7 = genome.ConnectionGene(2, 12, 0.22, innov_num=13)
c8 = genome.ConnectionGene(12, 4, 0.22, innov_num=14)
c9 = genome.ConnectionGene(1, 15, 0.1, innov_num=16)
c10 = genome.ConnectionGene(15, 3, 0.1, innov_num=17)
n1 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=1)
n2 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=2)
n3 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='output',
innov_num=3)
n4 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'], innov_num=4)
n5 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'], innov_num=9)
n6 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'], innov_num=12)
n7 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'], innov_num=15)
connections = {
c1.innov_num: c1,
c2.innov_num: c2,
c3.innov_num: c3,
c4.innov_num: c4,
c5.innov_num: c5,
c6.innov_num: c6,
c7.innov_num: c7,
c8.innov_num: c8,
c9.innov_num: c9,
c10.innov_num: c10
}
nodes = {
n1.innov_num: n1,
n2.innov_num: n2,
n3.innov_num: n3,
n4.innov_num: n4,
n5.innov_num: n5,
n6.innov_num: n6,
n7.innov_num: n7
}
g1 = genome.Genome(config=config)
g1.node_genes = nodes
g1.connection_genes = connections
return g1
def create_initial_individual(self, _inputs, _outputs):
_inputs += 1 if self.kBias_node else 0
first_genome = genome.Genome(config=self.config)
innov_num = 1
for input_node in range(_inputs):
first_genome.node_genes[innov_num] = genome.NodeGene(
sigmoid_power=self.kSigmoid_power,
node_type='input',
innov_num=innov_num)
innov_num += 1
for output_node in range(_outputs):
first_genome.node_genes[innov_num] = genome.NodeGene(
sigmoid_power=self.kSigmoid_power,
node_type='output',
innov_num=innov_num)
innov_num += 1
for in_node in first_genome.node_genes.values():
if in_node.node_type == 'input':
for out_node in first_genome.node_genes.values():
if out_node.node_type == 'output':
first_genome.connection_genes[
innov_num] = genome.ConnectionGene(
in_node.innov_num,
out_node.innov_num,
random.gauss(0, 1),
innov_num=innov_num)
innov_num += 1
return Individual(first_genome, self.kBias_node)
def genome_basic():
config = get_config()
c1 = genome.ConnectionGene(1, 2, 0.1, innov_num=3)
n1 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=1)
n2 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='output',
innov_num=2)
connections = {
c1.innov_num: c1,
}
nodes = {
n1.innov_num: n1,
n2.innov_num: n2,
}
g1 = genome.Genome(config=config)
g1.node_genes = nodes
g1.connection_genes = connections
return g1
def genome_isolated_output_node():
config = get_config()
c1 = genome.ConnectionGene(1, 3, 0.1, innov_num=5)
c2 = genome.ConnectionGene(2, 3, 0.25, innov_num=6)
c3 = genome.ConnectionGene(1, 4, 0.1, innov_num=7)
c4 = genome.ConnectionGene(2, 4, -0.6, innov_num=8)
n1 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=1)
n2 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=2)
n3 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='output',
innov_num=3)
n4 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='output',
innov_num=4)
# Isolate output node 4
c3.enabled = False
c4.enabled = False
connections = {
c1.innov_num: c1,
c2.innov_num: c2,
c3.innov_num: c3,
c4.innov_num: c4
}
nodes = {
n1.innov_num: n1,
n2.innov_num: n2,
n3.innov_num: n3,
n4.innov_num: n4,
}
g1 = genome.Genome(config=config)
g1.node_genes = nodes
g1.connection_genes = connections
return g1
def genome_six_nodes():
config = get_config()
c1 = genome.ConnectionGene(1, 3, 0.6, innov_num=5)
c2 = genome.ConnectionGene(2, 3, 0.75, innov_num=6)
c3 = genome.ConnectionGene(1, 4, 0.6, innov_num=7)
c4 = genome.ConnectionGene(4, 3, -0.1, innov_num=8)
c5 = genome.ConnectionGene(1, 9, 0.1, innov_num=10)
c6 = genome.ConnectionGene(9, 3, 0.1, innov_num=11)
c7 = genome.ConnectionGene(1, 12, 0.3, innov_num=13)
c8 = genome.ConnectionGene(12, 4, 0.1, innov_num=14)
n1 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=1)
n2 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=2)
n3 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='output',
innov_num=3)
n4 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'], innov_num=4)
n5 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'], innov_num=9)
n6 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'], innov_num=12)
connections = {
c1.innov_num: c1,
c2.innov_num: c2,
c3.innov_num: c3,
c4.innov_num: c4,
c5.innov_num: c5,
c6.innov_num: c6,
c7.innov_num: c7,
c8.innov_num: c8
}
nodes = {
n1.innov_num: n1,
n2.innov_num: n2,
n3.innov_num: n3,
n4.innov_num: n4,
n5.innov_num: n5,
n6.innov_num: n6
}
g1 = genome.Genome(config=config)
g1.node_genes = nodes
g1.connection_genes = connections
return g1
def genome_four_nodes_recursive():
config = get_config()
c1 = genome.ConnectionGene(1, 3, 0.1, innov_num=5)
c2 = genome.ConnectionGene(2, 3, 0.25, innov_num=6)
c3 = genome.ConnectionGene(1, 4, 0.1, innov_num=7)
c4 = genome.ConnectionGene(4, 3, -0.6, innov_num=8)
c5 = genome.ConnectionGene(4, 4, 0.25, innov_num=9) # Recursive connection
n1 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=1)
n2 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=2)
n3 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='output',
innov_num=3)
n4 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'], innov_num=4)
connections = {
c1.innov_num: c1,
c2.innov_num: c2,
c3.innov_num: c3,
c4.innov_num: c4,
c5.innov_num: c5
}
nodes = {
n1.innov_num: n1,
n2.innov_num: n2,
n3.innov_num: n3,
n4.innov_num: n4,
}
g1 = genome.Genome(config=config)
g1.node_genes = nodes
g1.connection_genes = connections
return g1
def genome_recursive_two_outputs():
config = get_config()
c1 = genome.ConnectionGene(1, 3, 1.0, innov_num=5)
c2 = genome.ConnectionGene(2, 3, 1.0, innov_num=6)
c3 = genome.ConnectionGene(1, 4, -1.0, innov_num=7)
c4 = genome.ConnectionGene(2, 4, -1.0, innov_num=8)
c5 = genome.ConnectionGene(1, 9, 0.0, innov_num=10)
c6 = genome.ConnectionGene(9, 4, 0.5, innov_num=11)
c7 = genome.ConnectionGene(9, 9, 1.0, innov_num=12)
n1 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=1)
n2 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='input',
innov_num=2)
n3 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='output',
innov_num=3)
n4 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'],
node_type='output',
innov_num=4)
n5 = genome.NodeGene(sigmoid_power=config['kSigmoid_power'], innov_num=9)
connections = {
c1.innov_num: c1,
c2.innov_num: c2,
c3.innov_num: c3,
c4.innov_num: c4,
c5.innov_num: c5,
c6.innov_num: c6,
c7.innov_num: c7
}
nodes = {
n1.innov_num: n1,
n2.innov_num: n2,
n3.innov_num: n3,
n4.innov_num: n4,
n5.innov_num: n5
}
g1 = genome.Genome(config=config)
g1.node_genes = nodes
g1.connection_genes = connections
return g1