def test_copy_from_and_mutate_does_not_influence_another_condition(
self, cfg):
""" Verify that not just reference to Condition copied (changing which
will change the original - definitily not original C++ code did). """
# given
s = cfg.mu * 0.5 # less then MU
b = 1 - (1 - cfg.mu) * 0.5 # more then MU
operation_time = 123
original_cl = Classifier(condition='1###1011', cfg=cfg)
copied_cl = Classifier.copy_from(original_cl, operation_time)
# when
mutate(copied_cl, cfg.mu, RandomMock([s, b, b, b, b]))
# then
assert Condition('####1011') == copied_cl.condition
assert Condition('1###1011') == original_cl.condition
# when
mutate(original_cl, cfg.mu, RandomMock([b, s, b, b, b]))
# then
assert Condition('1####011') == original_cl.condition
assert Condition('####1011') == copied_cl.condition
def test_mutate_2(self, cfg):
# given
cl = Classifier(Condition('##011###'), cfg=cfg)
s = cfg.mu * 0.5 # less then MU
b = 1 - (1 - cfg.mu) * 0.5 # more then MU
# when
mutate(cl, cfg.mu, randomfunc=RandomMock([b, b, s]))
# then
assert Condition('##01####') == cl.condition
def apply_ga(self,
time: int,
population: ClassifiersList,
match_set: ClassifiersList,
situation: Perception,
randomfunc=random,
samplefunc=sample) -> None:
if self.should_apply_ga(time):
self.set_ga_timestamp(time)
parent1, parent2 = roulette_wheel_parents_selection(
self, randomfunc=randomfunc)
child1 = Classifier.copy_from(parent1, time)
child2 = Classifier.copy_from(parent2, time)
mutate(child1, child1.cfg.mu, randomfunc=randomfunc)
mutate(child2, child2.cfg.mu, randomfunc=randomfunc)
if randomfunc() < self.cfg.chi:
if child1.effect == child2.effect:
two_point_crossover(child1, child2, samplefunc=samplefunc)
# Update quality and reward
# TODO: check if needed
child2.q = float(sum([child1.q, child2.q]) / 2)
child2.r = float(sum([child1.r, child2.r]) / 2)
child1.q /= 2
child2.q /= 2
children = [
child for child in [child1, child2]
if child.condition.specificity > 0
]
# if two classifiers are identical, leave only one
unique_children = set(children)
self.delete_ga_classifiers(population,
match_set,
len(unique_children),
randomfunc=randomfunc)
# check for subsumers / similar classifiers
for child in unique_children:
self.add_ga_classifier(child, match_set, population)