def cover(p0: Perception, action: int, p1: Perception, time: int,
cfg: Configuration) -> Classifier:
"""
Covering - creates a classifier that anticipates a change correctly.
The reward of the new classifier is set to 0 to prevent *reward bubbles*
in the environmental model.
Parameters
----------
p0: Perception
previous perception
action: int
chosen action
p1: Perception
current perception
time: int
current epoch
cfg: Configuration
algorithm configuration class
Returns
-------
Classifier
new classifier
"""
# In paper it's advised to set experience and reward of newly generated
# classifier to 0. However in original code these values are initialized
# with defaults 1 and 0.5 correspondingly.
new_cl = Classifier(action=action, experience=0, reward=0, cfg=cfg)
new_cl.tga = time
new_cl.talp = time
new_cl.specialize(p0, p1)
return new_cl
def test_should_specialize(self, _p0, _p1, _init_cond, _init_effect,
_res_cond, _res_effect, cfg):
# given
cls = Classifier(condition=Condition(_init_cond),
effect=Effect(_init_effect),
cfg=cfg)
p0 = Perception(_p0)
p1 = Perception(_p1)
# when
cls.specialize(p0, p1, leave_specialized=False)
# then
assert cls.condition == Condition(_res_cond)
assert cls.effect == Effect(_res_effect)
