def test_it_without_bp():
pop, stats = main()
stringh = "_without_bp"
fronts = tools.sortNondominated(pop, len(pop))
if len(fronts[0]) < 30:
pareto_front = fronts[0]
else:
pareto_front = random.sample(fronts[0], 30)
print("Pareto Front: ")
for i in range(len(pareto_front)):
print(pareto_front[i].fitness.values)
neter = Neterr(indim, outdim, n_hidden, random)
print("\ntest: test on one with min validation error",
neter.test_err(min(pop, key=lambda x: x.fitness.values[1])))
tup = neter.test_on_pareto_patch(pareto_front)
print("\n test: avg on sampled pareto set", tup[0], "least found avg",
tup[1])
st = str(neter.test_err(min(
pop, key=lambda x: x.fitness.values[1]))) + " " + str(
tup[0]) + " " + str(tup[1])
print(note_this_string(st, stringh))
def test_it_with_bp(play=1, NGEN=100, MU=4 * 25):
pop, stats = main(play=play, NGEN=NGEN, MU=MU)
stringh = "_with_bp_without_clustring" + str(play) + "_" + str(NGEN)
fronts = tools.sortNondominated(pop, len(pop))
if len(fronts[0]) < 30:
pareto_front = fronts[0]
else:
pareto_front = random.sample(fronts[0], 30)
print("Pareto Front: ")
for i in range(len(pareto_front)):
print(pareto_front[i].fitness.values)
neter = Neterr(indim, outdim, n_hidden, random)
print("\ntest: test on one with min validation error",
neter.test_err(min(pop, key=lambda x: x.fitness.values[1])))
tup = neter.test_on_pareto_patch_correctone(pareto_front)
print("\n test: avg on sampled pareto set", tup)
st = str(neter.test_err(min(
pop, key=lambda x: x.fitness.values[1]))) + " " + str(tup)
print(
note_this_string(st, stringh)
) ##################################################################################