def test_evolve():
test_values = [random.random() for _ in range(10)]
def evaluate_genome(genomes):
for g in genomes:
net = ctrnn.create_phenotype(g)
fitness = 0.0
for t in test_values:
net.reset()
output = net.serial_activate([t])
expected = t ** 2
error = output[0] - expected
fitness -= error ** 2
g.fitness = fitness
# Load the config file, which is assumed to live in
# the same directory as this script.
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'ctrnn_config'))
config.node_gene_type = ctrnn.CTNodeGene
config.prob_mutate_time_constant = 0.1
config.checkpoint_time_interval = 0.1
config.checkpoint_gen_interval = 1
pop = population.Population(config)
pop.run(evaluate_genome, 10)
# Save the winner.
print('Number of evaluations: {0:d}'.format(pop.total_evaluations))
winner = pop.statistics.best_genome()
with open('winner_genome', 'wb') as f:
pickle.dump(winner, f)
repr(winner)
str(winner)
for g in winner.node_genes:
repr(g)
str(g)
for g in winner.conn_genes:
repr(g)
str(g)
sim.theta) >= sim.angle_limit_radians:
break
fitness += 1.0
fitnesses.append(fitness)
# The genome's fitness is its worst performance across all runs.
return min(fitnesses)
# Load the config file, which is assumed to live in
# the same directory as this script.
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'ctrnn_config'))
config.node_gene_type = ctrnn.CTNodeGene
pop = population.Population(config)
pe = parallel.ParallelEvaluator(4, evaluate_genome)
pop.run(pe.evaluate, 2000)
# Save the winner.
print('Number of evaluations: {0:d}'.format(pop.total_evaluations))
winner = pop.statistics.best_genome()
with open('ctrnn_winner_genome', 'wb') as f:
pickle.dump(winner, f)
print(winner)
# Plot the evolution of the best/average fitness.
visualize.plot_stats(pop.statistics, ylog=True, filename="ctrnn_fitness.svg")
if abs(sim.x) >= sim.position_limit or abs(sim.theta) >= sim.angle_limit_radians:
break
fitness += 1.0
fitnesses.append(fitness)
# The genome's fitness is its worst performance across all runs.
return min(fitnesses)
# Load the config file, which is assumed to live in
# the same directory as this script.
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'ctrnn_config'))
config.node_gene_type = ctrnn.CTNodeGene
pop = population.Population(config)
pe = parallel.ParallelEvaluator(4, evaluate_genome)
pop.run(pe.evaluate, 2000)
# Save the winner.
print('Number of evaluations: {0:d}'.format(pop.total_evaluations))
winner = pop.statistics.best_genome()
with open('ctrnn_winner_genome', 'wb') as f:
pickle.dump(winner, f)
print(winner)
# Plot the evolution of the best/average fitness.
visualize.plot_stats(pop.statistics, ylog=True, filename="ctrnn_fitness.svg")
