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)
generations = 0
for generation in range(1000):
genome_list = NEAT.GetGenomeList(pop)
fitness_list = NEAT.EvaluateGenomeList_Serial(genome_list,
evaluate,
display=False)
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(i):
g = NEAT.Genome(0, substrate.GetMinCPPNInputs(), 0,
substrate.GetMinCPPNOutputs(), False,
NEAT.ActivationFunction.TANH, NEAT.ActivationFunction.TANH,
0, params)
pop = NEAT.Population(g, params, True, 1.0, i)
pop.RNG.Seed(i)
for generation in range(2000):
genome_list = NEAT.GetGenomeList(pop)
fitnesses = NEAT.EvaluateGenomeList_Serial(genome_list,
evaluate,
display=False)
[
genome.SetFitness(fitness)
for genome, fitness in zip(genome_list, fitnesses)
]
best = max([x.GetLeader().GetFitness() for x in pop.Species])
pop.Epoch()
generations = generation
if best > 15.0:
break
return generations
def getbest(i):
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, i)
pop.RNG.Seed(i)
generations = 0
for generation in range(1000):
genome_list = NEAT.GetGenomeList(pop)
fitness_list = NEAT.EvaluateGenomeList_Serial(genome_list,
evaluate,
display=False)
NEAT.ZipFitness(genome_list, fitness_list)
best = max([x.GetLeader().GetFitness() for x in pop.Species])
pop.Epoch()
generations = generation
if best > 15.0:
break
return generations
def getbest(run):
g = NEAT.Genome(0, 7, 1, True, NEAT.ActivationFunction.SIGNED_SIGMOID,
NEAT.ActivationFunction.SIGNED_SIGMOID, params)
pop = NEAT.Population(g, params, True, 1.0, run)
for generation in range(1000):
#Evaluate genomes
genome_list = NEAT.GetGenomeList(pop)
fitnesses = NEAT.EvaluateGenomeList_Serial(genome_list,
evaluate_xor,
display=False)
[
genome.SetFitness(fitness)
for genome, fitness in zip(genome_list, fitnesses)
]
# Print best fitness
#print("---------------------------")
#print("Generation: ", generation)
#print("max ", max([x.GetLeader().GetFitness() for x in pop.Species]))
# Visualize best network's Genome
'''
net = NEAT.NeuralNetwork()
pop.Species[0].GetLeader().BuildPhenotype(net)
img = np.zeros((500, 500, 3), dtype=np.uint8)
img += 10
NEAT.DrawPhenotype(img, (0, 0, 500, 500), net )
cv2.imshow("CPPN", img)
# Visualize best network's Pheotype
net = NEAT.NeuralNetwork()
pop.Species[0].GetLeader().Build_ES_Phenotype(net, substrate, params)
img = np.zeros((500, 500, 3), dtype=np.uint8)
img += 10
Utilities.DrawPhenotype(img, (0, 0, 500, 500), net, substrate=True )
cv2.imshow("NN", img)
cv2.waitKey(1)
'''
if max([x.GetLeader().GetFitness() for x in pop.Species]) > 15.0:
break
# Epoch
generations = generation
pop.Epoch()
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 = NEAT.EvaluateGenomeList_Serial(genome_list,
evaluate,
display=False)
[
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
def getbest():
g = NEAT.Genome(0, 7, 1, False, NEAT.ActivationFunction.SIGNED_SIGMOID,
NEAT.ActivationFunction.SIGNED_SIGMOID, params)
pop = NEAT.Population(g, params, True, 1.0)
for generation in range(2000):
genome_list = NEAT.GetGenomeList(pop)
# fitnesses = NEAT.EvaluateGenomeList_Parallel(genome_list, evaluate)
fitnesses = NEAT.EvaluateGenomeList_Serial(genome_list,
evaluate_xor,
display=True)
[
genome.SetFitness(fitness)
for genome, fitness in zip(genome_list, fitnesses)
]
best = max([x.GetLeader().GetFitness() for x in pop.Species])
net = NEAT.NeuralNetwork()
pop.Species[0].GetLeader().BuildPhenotype(net)
img = np.zeros((500, 500, 3), dtype=np.uint8)
img += 10
NEAT.DrawPhenotype(img, (0, 0, 500, 500), net)
cv2.imshow("CPPN", img)
net = NEAT.NeuralNetwork()
pop.Species[0].GetLeader().Build_ES_Phenotype(net, substrate, params)
img = np.zeros((500, 500, 3), dtype=np.uint8)
img += 10
utilities.DrawPhenotype(img, (0, 0, 500, 500), net, substrate=True)
cv2.imshow("NN", img)
cv2.waitKey(1)
generations = generation
if best > 15.0:
break
pop.Epoch()
return generations
def objective_driven(seed):
i = seed
g = NEAT.Genome(0, 6, 0, 4, False, NEAT.ActivationFunction.SIGNED_SIGMOID,
NEAT.ActivationFunction.SIGNED_SIGMOID, 0, params)
pop = NEAT.Population(g, params, True, 1.0, i)
#pop.RNG.Seed(i)
generations = 0
for generation in range(250):
genome_list = NEAT.GetGenomeList(pop)
fitness_list = NEAT.EvaluateGenomeList_Serial(genome_list,
evaluate,
display=False)
fitness_list = [k[0] for k in fitness_list]
NEAT.ZipFitness(genome_list, fitness_list)
best_fits = [x.GetLeader().GetFitness() for x in pop.Species]
best = max(best_fits)
idx = best_fits.index(best)
print best, pop.Species[idx].GetLeader().GetFitness()
imgs, res = evaluate(pop.Species[idx].GetLeader(),
debug=True,
save="gen%d.ply" % generation)
plt.ion()
plt.clf()
subfig = 1
t_imgs = len(imgs)
for img in imgs:
plt.subplot(t_imgs, 1, subfig)
plt.title("Confidence: %0.2f%%" %
(res[subfig - 1, target_class] * 100.0))
plt.imshow(img)
subfig += 1
plt.draw()
plt.pause(0.1)
plt.savefig("out%d.png" % generation)
pop.Epoch()
generations = generation
return generations
print(k, '= %3.4f' % v, end=', ')
else:
print(k, '= {0}'.format(v), end=', ')
print()
print('Links:')
for tr in g.GetLinkTraits():
print(tr[0], tr[1], end=': ')
for k, v in tr[2].items():
if isinstance(v, float):
print(k, '= %3.4f' % v, end=', ')
else:
print(k, '= {0}'.format(v), end=', ')
print()
print()
for generation in range(1000):
genome_list = NEAT.GetGenomeList(pop)
fitness_list = NEAT.EvaluateGenomeList_Serial(genome_list,
evaluate,
display=False)
NEAT.ZipFitness(genome_list, fitness_list)
PrintGenomeTraits(pop.GetBestGenome())
print()
print('Fitnesss:', max(fitness_list), 'Generation:', generation)
print()
pop.Epoch()
rng = NEAT.RNG()
#rng.TimeSeed()
rng.TimeSeed()
g = NEAT.Genome(0, substrate.GetMinCPPNInputs(), 0,
substrate.GetMinCPPNOutputs(), False,
NEAT.ActivationFunction.TANH, NEAT.ActivationFunction.TANH, 0,
params)
pop = NEAT.Population(g, params, True, 1.0)
for generation in range(1000):
genome_list = NEAT.GetGenomeList(pop)
# fitnesses, elapsed = NEAT.EvaluateGenomeList_Parallel(genome_list, evaluate, 4)
fitnesses, elapsed = NEAT.EvaluateGenomeList_Serial(genome_list, evaluate)
[
genome.SetFitness(fitness)
for genome, fitness in zip(genome_list, fitnesses)
]
print 'Best fitness:', max(
[x.GetLeader().GetFitness() for x in 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("CPPN", img)