def test_should_insert_alp_offspring_2(self, cfg):
# given
population = ClassifiersList(cfg=cfg)
new_list = ClassifiersList(cfg=cfg)
child = Classifier(condition='#1O##O##',
action=0,
quality=0.5,
reward=18.206,
intermediate_reward=0,
experience=1,
tga=747,
talp=747,
tav=22.0755,
cfg=cfg)
c1 = Classifier(condition='#1O#O###',
action=0,
quality=0.650831,
reward=14.8323,
intermediate_reward=0,
experience=5,
tga=531,
talp=747,
tav=48.3562,
cfg=cfg)
c2 = Classifier(condition='##O#O###',
action=0,
quality=0.79094,
reward=9.97782,
intermediate_reward=0,
experience=10,
tga=330,
talp=747,
tav=43.7171,
cfg=cfg)
c3 = Classifier(condition='#1O###1O',
action=0,
effect='#O1####1',
quality=0.515369,
reward=8.3284,
intermediate_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 = Classifier(condition='####O###',
action=0,
quality=0.903144,
reward=14.8722,
intermediate_reward=0,
experience=25,
tga=187,
talp=747,
tav=23.0038,
cfg=cfg)
c5 = Classifier(condition='#1O####O',
action=0,
effect='#O1####1',
quality=0.647915,
reward=9.24712,
intermediate_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 = Classifier(condition='#1O#####',
action=0,
quality=0.179243,
reward=18.206,
intermediate_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 = Classifier(condition='##O#####',
action=0,
quality=0.100984,
reward=15.91,
intermediate_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
population.add_alp_classifier(child, new_list)
# 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, cfg):
# given
population = ClassifiersList(cfg=cfg)
new_list = ClassifiersList(cfg=cfg)
child = Classifier(condition='1##1#010',
action=0,
effect='0####101',
quality=0.5,
reward=8.96245,
intermediate_reward=0,
experience=1,
tga=423,
talp=423,
tav=27.3182,
cfg=cfg)
c1 = Classifier(condition='1##1#010',
action=0,
effect='0####101',
quality=0.571313,
reward=7.67011,
intermediate_reward=0,
experience=3,
tga=225,
talp=423,
tav=70.881,
cfg=cfg)
c2 = Classifier(condition='1####010',
action=0,
effect='0####101',
quality=0.462151,
reward=8.96245,
intermediate_reward=0,
experience=11,
tga=143,
talp=423,
tav=27.3182,
cfg=cfg)
c3 = Classifier(condition='1####0##',
action=0,
effect='0####1##',
quality=0.31452,
reward=9.04305,
intermediate_reward=0,
experience=19,
tga=49,
talp=423,
tav=19.125,
cfg=cfg)
# Add classifiers into current ClassifierList
population.extend([c1, c2, c3])
# when
population.add_alp_classifier(child, new_list)
# then
assert 3 == len(population)
assert c1 in population
assert c2 in population
assert c3 in population
assert abs(0.592747 - c1.q) < 0.01
