def check_add_connection(genome_type, feed_forward):
indexer = InnovationIndexer(0)
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
config.input_nodes = 3
config.output_nodes = 4
config.hidden_nodes = 5
config.feedforward = feed_forward
N = config.input_nodes + config.hidden_nodes + config.output_nodes
connections = {}
for a in range(100):
g = genome_type.create_unconnected(a, config)
g.add_hidden_nodes(config.hidden_nodes)
for b in range(1000):
g.mutate_add_connection(indexer)
for c in g.conn_genes.values():
connections[c.key] = connections.get(c.key, 0) + 1
# TODO: The connections should be returned to the caller and checked
# against the constraints/assumptions particular to the network type.
for i in range(N):
values = []
for j in range(N):
values.append(connections.get((i, j), 0))
print("{0:2d}: {1}".format(i, " ".join("{0:3d}".format(x) for x in values)))
def run():
# load settings file
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'dpole_config'))
# change the number of inputs accordingly to the type
# of experiment: markov (6) or non-markov (3)
# you can also set the configs in dpole_config as long
# as you have two config files for each type of experiment
config.input_nodes = 3
pop = population.Population(config)
pop.epoch(evaluate_population, 200, report=1, save_best=0)
winner = pop.most_fit_genomes[-1]
print('Number of evaluations: {0:d}'.format(winner.ID))
print('Winner fitness: {0:f}'.format(winner.fitness))
# save the winner
with open('winner_chromosome', 'w') as f:
cPickle.dump(winner, f)
# Plots the evolution of the best/average fitness
visualize.plot_stats(pop, ylog=True)
# Visualizes speciation
visualize.plot_species(pop)
# visualize the best topology
visualize.draw_net(winner, view=True)
def run():
# load settings file
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'dpole_config'))
# change the number of inputs accordingly to the type
# of experiment: markov (6) or non-markov (3)
# you can also set the configs in dpole_config as long
# as you have two config files for each type of experiment
config.input_nodes = 3
pop = population.Population(config)
pop.epoch(evaluate_population, 200, report=1, save_best=0)
winner = pop.most_fit_genomes[-1]
print('Number of evaluations: {0:d}'.format(winner.ID))
print('Winner fitness: {0:f}'.format(winner.fitness))
# save the winner
with open('winner_chromosome', 'w') as f:
cPickle.dump(winner, f)
# Plots the evolution of the best/average fitness
visualize.plot_stats(pop, ylog=True)
# Visualizes speciation
visualize.plot_species(pop)
# visualize the best topology
visualize.draw_net(winner, view=True)
