def getbest():
g = NEAT.Genome(0, 3, 0, 1, False,
NEAT.ActivationFunction.UNSIGNED_SIGMOID,
NEAT.ActivationFunction.UNSIGNED_SIGMOID, 0, params)
pop = NEAT.Population(g, params, True, 1.0)
pool = mpc.Pool(processes=4)
generations = 0
for generation in range(1000):
genome_list = NEAT.GetGenomeList(pop)
fitness_list = NEAT.EvaluateGenomeList_Parallel(genome_list, evaluate)
NEAT.ZipFitness(genome_list, fitness_list)
best = max([x.GetLeader().GetFitness() for x in pop.Species])
# print 'Best fitness:', best, 'Species:', len(pop.Species)
# test
net = NEAT.NeuralNetwork()
pop.Species[0].GetLeader().BuildPhenotype(net)
img = np.zeros((250, 250, 3), dtype=np.uint8)
img += 10
NEAT.DrawPhenotype(img, (0, 0, 250, 250), net)
cv2.imshow("nn_win", img)
cv2.waitKey(1)
pop.Epoch()
# print "Generation:", generation
generations = generation
if best > 15.5:
break
return generations
def getbest():
g = NEAT.Genome(0, substrate.GetMinCPPNInputs(), 0,
substrate.GetMinCPPNOutputs(), False,
NEAT.ActivationFunction.SIGNED_GAUSS,
NEAT.ActivationFunction.SIGNED_GAUSS, 0, params)
pop = NEAT.Population(g, params, True, 1.0)
for generation in range(1000):
genome_list = NEAT.GetGenomeList(pop)
fitnesses = NEAT.EvaluateGenomeList_Parallel(genome_list, evaluate)
# fitnesses, elapsed = NEAT.EvaluateGenomeList_Serial(genome_list, evaluate)
[
genome.SetFitness(fitness)
for genome, fitness in zip(genome_list, fitnesses)
]
best = max([x.GetLeader().GetFitness() for x in pop.Species])
# print 'Best fitness:', best
# test
net = NEAT.NeuralNetwork()
pop.Species[0].GetLeader().BuildPhenotype(net)
img = np.zeros((250, 250, 3), dtype=np.uint8)
img += 10
NEAT.DrawPhenotype(img, (0, 0, 250, 250), net)
cv2.imshow("CPPN", img)
net = NEAT.NeuralNetwork()
pop.Species[0].GetLeader().BuildHyperNEATPhenotype(net, substrate)
img = np.zeros((250, 250, 3), dtype=np.uint8)
img += 10
NEAT.DrawPhenotype(img, (0, 0, 250, 250), net, substrate=True)
cv2.imshow("NN", img)
cv2.waitKey(1)
pop.Epoch()
# print "Generation:", generation
generations = generation
if best > 15.5:
break
return generations
