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)
def test_should_handle_unexpected_case_2(self, cfg):
# given
p0 = Perception([.5, .5], oktypes=(float, ))
p1 = Perception([.5, .5], oktypes=(float, ))
# Effect is not specializable
effect = Effect([UBR(0, 15), UBR(2, 4)], cfg=cfg)
quality = random.random()
time = random.randint(0, 1000)
cl = Classifier(effect=effect, quality=quality, cfg=cfg)
# when
child = unexpected_case(cl, p0, p1, time)
# then
assert cl.q < quality
assert cl.is_marked() is True
# We cannot generate child from non specializable parent
assert child is None
def test_should_handle_unexpected_case_3(self, cfg):
# given
p0 = Perception([.5, .5], oktypes=(float, ))
p1 = Perception([.5, .8], oktypes=(float, ))
# Second effect attribute is specializable
effect = Effect([UBR(0, 15), UBR(10, 14)], cfg=cfg)
quality = 0.4
time = random.randint(0, 1000)
cl = Classifier(effect=effect, quality=quality, cfg=cfg)
# when
child = unexpected_case(cl, p0, p1, time)
# then
assert cl.q < quality
assert cl.is_marked() is True
assert child is not None
assert child.is_marked() is False
assert child.q == .5
assert child.condition == Condition([UBR(0, 15), UBR(0, 15)], cfg=cfg)
assert child.effect == Effect([UBR(0, 15), UBR(10, 14)], cfg=cfg)
def test_should_handle_unexpected_case_1(self, cfg):
# given
p0 = Perception([.5, .5], oktypes=(float, ))
p1 = Perception([.5, .5], oktypes=(float, ))
# Effect is all pass-through. Can be specialized.
effect = Effect([UBR(0, 15), UBR(0, 15)], cfg=cfg)
quality = .4
time = random.randint(0, 1000)
cl = Classifier(effect=effect, quality=quality, cfg=cfg)
# when
child = unexpected_case(cl, p0, p1, time)
# then
assert cl.q < quality
assert cl.is_marked() is True
assert child
assert child.q == .5
assert child.talp == time
# There is no change in perception so the child condition
# and effect should stay the same.
assert child.condition == Condition([UBR(0, 15), UBR(0, 15)], cfg=cfg)
assert child.effect == Effect([UBR(0, 15), UBR(0, 15)], cfg=cfg)