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
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
key = sort_list[k][1]
if key not in niche_transform.include:
del sort_list[k]
print "length after purge:", len(sort_list)
raw_input()
for k in sort_list[:100]:
print k, niche_names[k[1]]
raw_input()
for _idx in range(0, num_niches):
idx = sort_list[_idx][1]
print niche_names[idx], stuff[0][idx]
imgs, res = evaluate(stuff[1][idx],
debug=True,
save="out/out%d.ply" % _idx)
save_render_plot(imgs,
str(idx) + " " + niche_names[idx][:30],
res_vec=res[:, idx],
save="out/out%d.png" % _idx)
print "done.."
exit()
rng = NEAT.RNG()
#genome generator
def generator():
global rng
g = NEAT.Genome(0, 6, 0, 4, False, NEAT.ActivationFunction.SIGNED_SIGMOID,
for k in range(len(sort_list)-1,-1,-1):
key = sort_list[k][1]
if key not in niche_transform.include:
del sort_list[k]
print "length after purge:",len(sort_list)
raw_input()
for k in sort_list[:100]:
print k,niche_names[k[1]]
raw_input()
for _idx in range(0,num_niches):
idx = sort_list[_idx][1]
print niche_names[idx],stuff[0][idx]
imgs,res = evaluate(stuff[1][idx],debug=True,save="out/out%d.ply"%_idx)
save_render_plot(imgs,str(idx)+" "+niche_names[idx][:30],res_vec=res[:,idx],save="out/out%d.png"%_idx)
print "done.."
exit()
rng=NEAT.RNG()
#genome generator
def generator():
global rng
g= NEAT.Genome(0, 6, 0, 4, False, NEAT.ActivationFunction.SIGNED_SIGMOID, NEAT.ActivationFunction.SIGNED_SIGMOID, 0, params)
g.RandomizeParameters(rng)
return g
def noveltysearch(seed,gens,cpi):
global rng
rng.Seed(seed)