def add_successful_genome_for_test(self, current_gen, use_this_genome):
"""
This function adds a pre programmed genome which is known to converge for the XOR dataset.
:param current_gen:
:param use_this_genome: Whether this genome should be added to the population or not
:return:
"""
# Wait for current_gen > 1 because if using backprop the first gen skips using backprop.
if current_gen > 1 and use_this_genome:
node_list = [
NodeGene(node_id=0, node_type='source'),
NodeGene(node_id=1, node_type='source'),
NodeGene(node_id=2, node_type='output', bias=0.5),
NodeGene(node_id=3, node_type='hidden', bias=1),
NodeGene(node_id=4, node_type='hidden', bias=1),
NodeGene(node_id=5, node_type='hidden', bias=1),
NodeGene(node_id=6, node_type='hidden', bias=1),
]
connection_list = [ConnectionGene(input_node=0, output_node=3, innovation_number=1, enabled=True,
weight=np.random.randn()),
ConnectionGene(input_node=1, output_node=3, innovation_number=2, enabled=True,
weight=np.random.randn()),
ConnectionGene(input_node=0, output_node=4, innovation_number=3, enabled=True,
weight=np.random.randn()),
ConnectionGene(input_node=1, output_node=4, innovation_number=4, enabled=True,
weight=np.random.randn()),
ConnectionGene(input_node=3, output_node=5, innovation_number=5, enabled=True,
weight=np.random.randn()),
ConnectionGene(input_node=4, output_node=5, innovation_number=6, enabled=True,
weight=np.random.randn()),
ConnectionGene(input_node=3, output_node=6, innovation_number=7, enabled=True,
weight=np.random.randn()),
ConnectionGene(input_node=4, output_node=6, innovation_number=8, enabled=True,
weight=np.random.randn()),
ConnectionGene(input_node=5, output_node=2, innovation_number=9, enabled=True,
weight=np.random.rand()),
ConnectionGene(input_node=6, output_node=2, innovation_number=10, enabled=True,
weight=np.random.randn())
]
test_genome = Genome(connections=connection_list, nodes=node_list, key=1)
test_genome.fitness = -99999999999
self.population[32131231] = test_genome
def pole_evaluate(genome: Genome):
numnodes: int = len(genome.nodes)
# how many visits is allowed in every activation
thresh: int = numnodes * 2
# TODO organism and trait
# cross MAX_STEPS means it's the winner
MAX_STEPS = 100000
# balance the pole
genome.fitness = go_crat(genome, MAX_STEPS, thresh)
# print("genome fitness {}".format(genome.fitness))
if genome.fitness > MAX_STEPS:
genome.winner = True
return True
else:
genome.winner = False
return False
