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)