def run():
t0 = time.time()
# 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, 'xor2_config'))
num_workers = 6
pool = Pool(num_workers)
print "Starting with %d workers" % num_workers
def fitness(chromosomes):
return eval_fitness(chromosomes, pool)
pop = population.Population(config)
pop.epoch(fitness, 400)
print "total evolution time %.3f sec" % (time.time() - t0)
print "time per generation %.3f sec" % ((time.time() - t0) / pop.generation)
winner = pop.most_fit_genomes[-1]
print 'Number of evaluations: %d' % winner.ID
# Verify network output against training data.
print '\nBest network output:'
net = nn.create_fast_feedforward_phenotype(winner)
for i, inputs in enumerate(INPUTS):
output = net.sactivate(inputs) # serial activation
print "%1.5f \t %1.5f" % (OUTPUTS[i], output[0])
# Visualize the winner network and plot statistics.
visualize.plot_stats(pop.most_fit_genomes, pop.avg_fitness_scores)
visualize.plot_species(pop.species_log)
visualize.draw_net(winner, view=True)
def parallel_evaluation(chromo):
""" This function will run in parallel """
net = nn.create_fast_feedforward_phenotype(chromo)
error = 0.0
for inputData, outputData in zip(INPUTS, OUTPUTS):
# serial activation
output = net.sactivate(inputData)
error += (output[0] - outputData) ** 2
return 1 - math.sqrt(error / len(OUTPUTS))
def eval_fitness(genomes):
for g in genomes:
net = nn.create_fast_feedforward_phenotype(g)
error = 0.0
for i, inputs in enumerate(INPUTS):
# Serial activation propagates the inputs through the entire network.
output = net.sactivate(inputs)
error += (output[0] - OUTPUTS[i]) ** 2
g.fitness = 1 - math.sqrt(error / len(OUTPUTS))
def run(self):
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, "evolve_config"))
pop = population.Population(config)
pop.epoch(self.eval_fitness, 300)
winner = pop.most_fit_genomes[-1]
self.current_nn = winner
print "Number of evaluations: %d" % winner.ID
# Verify network output against training data.
print "\nBest network output:"
net = nn.create_fast_feedforward_phenotype(winner)
def eval_fitness(self, genomes):
for g in genomes:
while not self.on:
# case when we dont want to be evaluating the genomes
sleep(5)
start_sensors = self.sensor_values
net = nn.create_fast_feedforward_phenotype(g)
self.current_nn = net
# while start_sensors == None:
# print self.sensor_values
# start_sensors = self.sensor_values #this will be the current sensors from the robot
# sleep(5)
print "start sensor_values:", self.sensor_values
print "NEat sleeping"
sleep(20)
print "done sleeping"
print "FINISH sensor_values:", self.sensor_values
finish_sensors = self.sensor_values
g.fitness = self.fit_func(start_sensors, finish_sensors)
def run():
# 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, 'xor2_config'))
pop = population.Population(config)
pop.epoch(eval_fitness, 300)
winner = pop.most_fit_genomes[-1]
print 'Number of evaluations: %d' % winner.ID
# Verify network output against training data.
print '\nBest network output:'
net = nn.create_fast_feedforward_phenotype(winner)
for i, inputs in enumerate(INPUTS):
output = net.sactivate(inputs) # serial activation
print "%1.5f \t %1.5f" % (OUTPUTS[i], output[0])
# Visualize the winner network and plot statistics.
visualize.plot_stats(pop.most_fit_genomes, pop.avg_fitness_scores)
visualize.plot_species(pop.species_log)
visualize.draw_net(winner, view=True)
