def test_should_add_classifier(self):
# given
cfg = acs2.Configuration(
classifier_length=8, number_of_possible_actions=4)
cl_1 = acs2.Classifier(action=1, cfg=cfg)
cl_3 = acs2.Classifier(action=3, cfg=cfg)
cl_4 = acs2.Classifier(action=4, cfg=cfg)
population = acs2.ClassifiersList(*[cl_1, cl_3, cl_4])
match_set = acs2.ClassifiersList()
action_set = acs2.ClassifiersList(*[cl_1])
p0 = Perception('10101010')
cl = acs2.Classifier(
action=1,
condition='1#######',
cfg=cfg)
# when
ga.add_classifier(cl, p0,
population, match_set, action_set,
do_subsumption=True, theta_exp=cfg.theta_exp)
# then
assert acs2.ClassifiersList(*[cl_1, cl_3, cl_4, cl]) == population
assert acs2.ClassifiersList(*[cl]) == match_set
assert acs2.ClassifiersList(*[cl_1, cl]) == action_set
def test_add_ga_classifier_increase_numerosity(self):
# given
cfg = acs2.Configuration(
classifier_length=8, number_of_possible_actions=4)
cl_1 = acs2.Classifier(action=2, condition='1#######', cfg=cfg)
cl_2 = acs2.Classifier(action=3, cfg=cfg)
cl_3 = acs2.Classifier(action=4, cfg=cfg)
population = acs2.ClassifiersList(*[cl_1, cl_2, cl_3])
match_set = acs2.ClassifiersList(*[cl_1])
action_set = acs2.ClassifiersList(*[cl_1])
cl = acs2.Classifier(action=2, condition='1#######', cfg=cfg)
# when
p0 = Perception('10101010')
ga.add_classifier(cl, p0,
population, match_set, action_set,
do_subsumption=True, theta_exp=cfg.theta_exp)
# then
assert cl_1.num == 2
assert acs2.ClassifiersList(*[cl_1, cl_2, cl_3]) == population
assert acs2.ClassifiersList(*[cl_1]) == match_set
assert acs2.ClassifiersList(*[cl_1]) == action_set
def apply_ga(time: int,
population: ClassifierList,
match_set: ClassifierList,
action_set: ClassifierList,
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
mutate(child1, mu)
mutate(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)