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 apply_alp(population: ClassifiersList, match_set: ClassifiersList,
action_set: ClassifiersList, p0: Perception, action: int,
p1: Perception, time: int, theta_exp: int,
cfg: Configuration) -> None:
"""
The Anticipatory Learning Process. Handles all updates by the ALP,
insertion of new classifiers in pop and possibly matchSet, and
deletion of inadequate classifiers in pop and possibly matchSet.
Parameters
----------
population
match_set
action_set
p0: Perception
action: int
p1: Perception
time: int
theta_exp
cfg: Configuration
Returns
-------
"""
new_list = ClassifiersList()
new_cl: Optional[Classifier] = None
was_expected_case = False
delete_count = 0
for cl in action_set:
cl.increase_experience()
cl.update_application_average(time)
if cl.does_anticipate_correctly(p0, p1):
new_cl = alp_acs2.expected_case(cl, p0, time)
was_expected_case = True
else:
new_cl = alp_acs2.unexpected_case(cl, p0, p1, time)
if cl.is_inadequate():
# Removes classifier from population, match set
# and current list
delete_count += 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_acs2.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 matching(cl.condition, p1)
]
match_set.extend(new_matching)
def test_should_insert_alp_offspring_2(self):
# given
cfg = acs2.Configuration(8, 8)
population = acs2.ClassifiersList()
new_list = acs2.ClassifiersList()
child = acs2.Classifier(
condition='#1O##O##',
action=0,
reward=18.206,
immediate_reward=0,
experience=1,
tga=747,
talp=747,
tav=22.0755,
cfg=cfg
)
c1 = acs2.Classifier(
condition='#1O#O###',
action=0,
quality=0.650831,
reward=14.8323,
immediate_reward=0,
experience=5,
tga=531,
talp=747,
tav=48.3562,
cfg=cfg
)
c2 = acs2.Classifier(
condition='##O#O###',
action=0,
quality=0.79094,
reward=9.97782,
immediate_reward=0,
experience=10,
tga=330,
talp=747,
tav=43.7171,
cfg=cfg
)
c3 = acs2.Classifier(
condition='#1O###1O',
action=0,
effect='#O1####1',
quality=0.515369,
reward=8.3284,
immediate_reward=0,
experience=8,
tga=316,
talp=747,
tav=57.8883,
cfg=cfg
)
c3.mark[0].update(['1'])
c3.mark[3].update(['0'])
c3.mark[4].update(['0'])
c3.mark[5].update(['0'])
c4 = acs2.Classifier(
condition='####O###',
action=0,
quality=0.903144,
reward=14.8722,
immediate_reward=0,
experience=25,
tga=187,
talp=747,
tav=23.0038,
cfg=cfg
)
c5 = acs2.Classifier(
condition='#1O####O',
action=0,
effect='#O1####1',
quality=0.647915,
reward=9.24712,
immediate_reward=0,
experience=14,
tga=154,
talp=747,
tav=44.5457,
cfg=cfg
)
c5.mark[0].update(['1'])
c5.mark[3].update(['0', '1'])
c5.mark[4].update(['0', '1'])
c5.mark[5].update(['0', '1'])
c5.mark[6].update(['0', '1'])
c6 = acs2.Classifier(
condition='#1O#####',
action=0,
quality=0.179243,
reward=18.206,
immediate_reward=0,
experience=29,
tga=104,
talp=747,
tav=22.0755,
cfg=cfg
)
c6.mark[0].update(['1'])
c6.mark[3].update(['1'])
c6.mark[4].update(['1'])
c6.mark[5].update(['1'])
c6.mark[6].update(['0', '1'])
c6.mark[7].update(['0', '1'])
c7 = acs2.Classifier(
condition='##O#####',
action=0,
quality=0.100984,
reward=15.91,
immediate_reward=0,
experience=44,
tga=58,
talp=747,
tav=14.4171,
cfg=cfg
)
c7.mark[0].update(['0', '1'])
c7.mark[1].update(['0', '1'])
c7.mark[3].update(['0', '1'])
c7.mark[5].update(['0', '1'])
c7.mark[6].update(['0', '1'])
c7.mark[7].update(['0', '1'])
# Add classifiers into current ClassifierList
population.extend([c1, c2, c3, c4, c5, c6, c7])
# When
alp.add_classifier(child, population, new_list, cfg.theta_exp)
# Then
assert 7 == len(population)
assert 0 == len(new_list)
assert c1 in population
assert c2 in population
assert c3 in population
assert c4 in population
assert c5 in population
assert c6 in population
assert c7 in population
# `C4` should be subsumer of `child`
assert abs(0.907987 - c4.q) < 0.01
def test_should_insert_alp_offspring_1(self):
# given
cfg = acs2.Configuration(8, 8)
population = acs2.ClassifiersList()
new_list = acs2.ClassifiersList()
child = acs2.Classifier(
condition='1##1#010',
action=0,
effect='0####101',
quality=0.5,
reward=8.96245,
immediate_reward=0,
experience=1,
tga=423,
talp=423,
tav=27.3182,
cfg=cfg
)
c1 = acs2.Classifier(
condition='1##1#010',
action=0,
effect='0####101',
quality=0.571313,
reward=7.67011,
immediate_reward=0,
experience=3,
tga=225,
talp=423,
tav=70.881,
cfg=cfg
)
c2 = acs2.Classifier(
condition='1####010',
action=0,
effect='0####101',
quality=0.462151,
reward=8.96245,
immediate_reward=0,
experience=11,
tga=143,
talp=423,
tav=27.3182,
cfg=cfg
)
c3 = acs2.Classifier(
condition='1####0##',
action=0,
effect='0####1##',
quality=0.31452,
reward=9.04305,
immediate_reward=0,
experience=19,
tga=49,
talp=423,
tav=19.125,
cfg=cfg
)
population.extend([c1, c2, c3])
# when
alp.add_classifier(child, population, new_list, cfg.theta_exp)
# then
assert len(population) == 3
assert c1 in population
assert c2 in population
assert c3 in population
assert abs(0.592747 - c1.q) < 0.01