def test_should_create_new_classifier_with_covering(
self, _p0, _p1, _child_cond, _child_effect, cfg):
# given
cfg.cover_noise = 0.0
p0 = Perception(_p0, oktypes=(float, ))
p1 = Perception(_p1, oktypes=(float, ))
action = random.randint(0, cfg.number_of_possible_actions)
time = random.randint(0, 100)
# when
new_cl = cover(p0, action, p1, time, cfg)
# then
assert new_cl is not None
assert new_cl.condition == Condition(_child_cond, cfg)
assert new_cl.action == action
assert new_cl.effect == Effect(_child_effect, cfg)
assert new_cl.q == .5
assert new_cl.r == 0
assert new_cl.ir == 0
assert new_cl.tav == 0
assert new_cl.tga == time
assert new_cl.talp == time
# assert new_cl.num == 1
assert new_cl.exp == 0
def apply_alp(population: ClassifierList,
match_set: ClassifierList,
action_set: ClassifierList,
p0: Perception,
action: int,
p1: Perception,
time: int,
theta_exp: int,
cfg: Configuration) -> None:
new_list = ClassifierList()
new_cl: Optional[Classifier] = None
was_expected_case = False
delete_counter = 0
for cl in action_set:
cl.increase_experience()
cl.set_alp_timestamp(time)
if cl.does_anticipate_correctly(p0, p1):
new_cl = alp_racs.expected_case(cl, p0, time)
was_expected_case = True
else:
new_cl = alp_racs.unexpected_case(cl, p0, p1, time)
if cl.is_inadequate():
delete_counter += 1
lists = [x for x in [population, match_set, action_set]
if x]
for lst in lists:
lst.safe_remove(cl)
if new_cl is not None:
new_cl.tga = time
alp.add_classifier(new_cl, action_set, new_list, theta_exp)
# No classifier anticipated correctly - generate new one
if not was_expected_case:
new_cl = alp_racs.cover(p0, action, p1, time, cfg)
alp.add_classifier(new_cl, action_set, new_list, theta_exp)
# Merge classifiers from new_list into self and population
action_set.extend(new_list)
population.extend(new_list)
if match_set is not None:
new_matching = [cl for cl in new_list if
cl.condition.does_match(p1)]
match_set.extend(new_matching)