def test_should_detect_correctable_classifier(self, _c, _e, _p0, _p1,
_result, cfg):
cl = Classifier(condition=_c, effect=_e, cfg=cfg)
p0 = Perception(_p0)
p1 = Perception(_p1)
assert cl.can_be_corrected(p0, p1) is _result
def apply(p0: Perception, p1: Perception, cl: Classifier,
population: ClassifiersList):
if not _perception_changed(p0, p1):
handle_useless_case(cl)
elif cl.does_anticipate_correctly(p0, p1):
handle_expected_case(cl)
elif not cl.does_anticipate_correctly(p0, p1) and _perception_changed(
p0, p1) and cl.can_be_corrected(p0, p1):
handle_correctable_case(p0, p1, cl, population)
else:
handle_not_correctable_case(cl, p0)
# Remove inadequate classifiers
for cl in population:
if cl.is_inadequate() and not cl.is_general():
population.remove(cl)
def handle_correctable_case(p0: Perception, p1: Perception, cl: Classifier,
population: ClassifiersList):
new_cl = Classifier.build_corrected(cl, p0, p1)
existing = [cl for cl in population if cl == new_cl]
if len(existing) == 0:
population.append(new_cl)
def test_should_construct_correct_classifier(self, cfg):
cl = Classifier(condition="000#", effect="000#", quality=0.2, cfg=cfg)
p0 = Perception("0000")
p1 = Perception("0001")
assert cl.does_match(p0)
new_cl = Classifier.build_corrected(cl, p0, p1)
assert new_cl is not cl
assert new_cl.condition is not cl.condition
assert new_cl.effect is not cl.effect
assert new_cl.condition == Condition("0000")
assert new_cl.action == cl.action
assert new_cl.effect == Effect("0001")
assert new_cl.q == 0.5
assert new_cl.does_match(p0)
def handle_not_correctable_case(cl: Classifier, p0: Perception):
cl.decrease_quality()
cl.set_mark(p0)
def handle_expected_case(cl: Classifier):
cl.increase_quality()
def handle_useless_case(cl: Classifier):
cl.decrease_quality()
def test_should_increase_quality(self, cfg):
cl = Classifier(cfg=cfg)
cl.increase_quality()
assert cl.q == 0.525
def test_should_decrease_quality(self, cfg):
cl = Classifier(cfg=cfg)
cl.decrease_quality()
assert cl.q == 0.475
def test_distinguish_general_classifier(self, _c, _e, _result, cfg):
cl = Classifier(condition=Condition(_c), effect=Effect(_e), cfg=cfg)
assert cl.is_general() == _result
def test_should_create_general_classifier(self, cfg):
cl = Classifier.general(action=1, cfg=cfg)
assert cl.condition == Condition("####")
assert cl.action == 1
assert cl.condition == Effect("####")
def _initial_population(self):
cls = []
for action in range(0, self.cfg.number_of_possible_actions):
cls.append(Classifier.general(action, cfg=self.cfg))
return ClassifiersList(*cls)