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, cfg):
# given
p0 = Perception([random.random()] * 2, oktypes=(float, ))
p1 = Perception([random.random()] * 2, oktypes=(float, ))
cl = Classifier(cfg=cfg)
# when
cl.specialize(p0, p1)
# then
for condition_ubr, effect_ubr in zip(cl.condition, cl.effect):
assert condition_ubr.lower_bound == condition_ubr.upper_bound
assert effect_ubr.lower_bound == effect_ubr.upper_bound
def test_should_specialize(self, cfg):
# given
p0 = Perception(np.random.random(2), oktypes=(float, ))
p1 = Perception(np.random.random(2), oktypes=(float, ))
cl = Classifier(cfg=cfg)
# when
cl.specialize(p0, p1)
# then
enc_p0 = list(map(cfg.encoder.encode, p0))
enc_p1 = list(map(cfg.encoder.encode, p1))
for i, (c_ubr, e_ubr) in enumerate(zip(cl.condition, cl.effect)):
assert c_ubr.lower_bound <= enc_p0[i] <= c_ubr.upper_bound
assert e_ubr.lower_bound <= enc_p1[i] <= e_ubr.upper_bound
