def test_generalizing_mutation(self, _mu, _cond1, _cond2):
# given
cfg = acs2.Configuration(
classifier_length=4, number_of_possible_actions=2)
cl = acs2.Classifier(condition=_cond1, cfg=cfg)
# when
ga.generalizing_mutation(cl, mu=_mu)
# then
assert cl.condition == acs.Condition(_cond2)
def apply_ga(time: int,
population: ClassifiersList,
match_set: ClassifiersList,
action_set: ClassifiersList,
p: Perception,
theta_ga: int,
mu: float,
chi: float,
theta_as: int,
do_subsumption: bool,
theta_exp: int) -> None:
if ga.should_apply(action_set, time, theta_ga):
ga.set_timestamps(action_set, time)
# Select parents
parent1, parent2 = ga.roulette_wheel_selection(
action_set, lambda cl: pow(cl.q, 3) * cl.num)
child1 = Classifier.copy_from(parent1, time)
child2 = Classifier.copy_from(parent2, time)
# Execute mutation
ga.generalizing_mutation(child1, mu)
ga.generalizing_mutation(child2, mu)
# Execute cross-over
if random.random() < chi:
if child1.effect == child2.effect:
ga.two_point_crossover(child1, child2)
# Update quality and reward
child1.q = child2.q = float(sum([child1.q, child2.q]) / 2)
child2.r = child2.r = float(sum([child1.r, child2.r]) / 2)
child1.q /= 2
child2.q /= 2
# We are interested only in classifiers with specialized condition
unique_children = {cl for cl in [child1, child2]
if cl.condition.specificity > 0}
ga.delete_classifiers(
population, match_set, action_set,
len(unique_children), theta_as)
# check for subsumers / similar classifiers
for child in unique_children:
ga.add_classifier(child, p,
population, match_set, action_set,
do_subsumption, theta_exp)