def test_save_load_phenotypes(self):
m = Manager()
m.create_from_scratch()
phenotypes = list(m.pop.phenotypes)
m.save()
m.close()
m2 = Manager()
m2.load()
self.maxDiff=None
self.assertListEqual(m2.pop.phenotypes, phenotypes)
m2.close()
# phenotypes that can be shown and voted for.
elif event.type == self.MONKEY_TESTING_EVENT:
key = random.choice([pygame.K_y, pygame.K_n])
try:
pygame.event.post(pygame.event.Event(pygame.KEYDOWN, {'key': key}))
except pygame.error as e:
logger.error("Monkey testing error: %s", e)
pygame.time.wait(20) # let the processor chill for a bit
def show_phenotype_image(self, ph):
logger.debug("Showing phenotype %s.", ph)
blitdata = rationalSizer(pygame.image.load(self.renderer.get_image_path(ph)),
self.resolution)
self.main_surface = tran_none(self.main_surface, blitdata)
def show_next(self):
ph = self.manager.get_next_phenotype(self.renderer.check_image_available)
self.show_phenotype_image(ph)
if __name__ == '__main__':
manager = Manager(size=5, min_votes=5, directory=os.getcwd(), vote_history=VoteHistoryManager())
try:
manager.load()
except IOError:
manager.create_from_scratch()
renderer = ImageRenderer(os.path.join(os.getcwd(), "images"))
interface = Interface(manager, renderer)
interface.run(monkey_testing=MONKEY_TESTING)
total_permutations = len(sizes) * len(crossover_probabilities) * len(shared_fitness_sigmas) * \
len(mutation_rates) * len(min_votes_tests)
index = 0
for size in sizes:
for crossover_probability in crossover_probabilities:
for shared_fitness_sigma in shared_fitness_sigmas:
for mutation_rate in mutation_rates:
for min_votes in min_votes_tests:
improvement_results = []
for i in range(TRIES):
m = Manager(size=size, crossover_probability=crossover_probability,
shared_fitness_sigma=shared_fitness_sigma,
mutation_rate=mutation_rate, min_votes=min_votes)
m.create_from_scratch()
runner = SimulationRunner(m, objective_function=get_desirability)
runner.run(ITERATIONS)
improvement = runner.improvement
improvement_results.append(improvement)
mean, variance = compensated_mean_and_variance(improvement_results)
index += 1
str_list.append("{0}\t{1}\t{2}\t{3}\t{4}\t{5}\t{6:f}\t{7:f}".format(
index, size, crossover_probability, shared_fitness_sigma, mutation_rate,
min_votes, mean, variance
))
print("{0:.2%} - PERMUTATION {1}/{2} COMPLETE".format(index / float(
total_permutations), index, total_permutations))
print(str_list[-1])
print("== RESULTS ==")
