def initialize_population(self, population=None, size=None):
if population is not None:
self.previous_generation = population
self.max_id = len(population.keys())
elif size is not None:
self.max_id = self.config.POPULATION_SIZE
pop_size = self.config.POPULATION_SIZE
input_size = size[0]
output_size = size[1]
for genome_id in range(pop_size):
genome = Genome(genome_id)
node_id = 0
# Input Nodes
for _ in range(input_size):
node_gene = self.random.choice(self.node_classes)(node_id, NodeType.INPUT)
genome.add_node_gene(node_gene)
node_id += 1
# Output Nodes
for _ in range(output_size):
node_gene = self.random.choice(self.node_classes)(node_id, NodeType.OUTPUT)
genome.add_node_gene(node_gene)
node_id += 1
# Connect each input to every other output
for in_id in range(input_size):
for out_id in range(output_size):
connection = ConnectionGene(in_id, out_id + input_size, 1.0, True, self.innovator.next_innovation_number((in_id, out_id)))
genome.add_connection_gene(connection)
self.previous_generation[genome_id] = genome
else:
raise ValueError("Invalid Parameters")
def generate_complete_genome(id, n, r, ir):
g = Genome(id)
for i in range(n):
g.add_node_gene(TestNode(i, r.choice(list(NodeType))))
for i in range(n):
for j in range(i, n):
w = r.random()
a = r.random() < 0.7
inr = ir.next_innovation_number((i, j))
g.add_connection_gene(ConnectionGene(i, j, w, a, inr))
return g
def generate_genome(id, n, r, ir):
g = Genome(id)
max_c = int(n * (n - 1) / 2)
c = r.randint(max_c - 1, max_c)
for i in range(n):
g.add_node_gene(TestNode(i, r.choice(list(NodeType))))
for _ in range(c):
i = r.randint(0, n - 1)
o = r.randint(0, n - 1)
w = r.random()
a = r.random() < 0.7
inr = ir.next_innovation_number((i, o))
g.add_connection_gene(ConnectionGene(i, o, w, a, inr))
return g
