def test_should_anticipate_correctly(self, cfg):
# given
cls = Classifier(effect='#1####0#', cfg=cfg)
p0 = Perception('00001111')
p1 = Perception('01001101')
# then
assert cls.does_anticipate_correctly(p0, p1) is True
def test_should_detect_correct_anticipation_1(self, cfg):
# Classifier is not predicting any change, all pass-through effect
# should predict correctly
# given
cls = Classifier(effect=Effect('########'), cfg=cfg)
p0 = Perception('00001111')
p1 = Perception('00001111')
# then
assert cls.does_anticipate_correctly(p0, p1) is True
def test_should_detect_correct_anticipation_6(self, cfg):
# Case when effect predicts situation incorrectly
# given
cls = Classifier(effect=Effect(
['#', '#', '1', '#', '0', '#', '0', '#']),
cfg=cfg)
p0 = Perception(['0', '0', '0', '1', '1', '0', '1', '0'])
p1 = Perception(['0', '0', '1', '1', '0', '0', '0', '0'])
# then
assert cls.does_anticipate_correctly(p0, p1) is True
def test_should_handle_pass_through_symbol(self, cfg):
# A case when there was no change in perception but effect has no
# pass-through symbol
# given
cls = Classifier(effect=Effect(
['#', '0', '#', '#', '#', '#', '#', '#']),
cfg=cfg)
p0 = Perception(['0', '0', '0', '0', '1', '1', '1', '1'])
p1 = Perception(['0', '0', '0', '0', '1', '1', '1', '1'])
# then
assert cls.does_anticipate_correctly(p0, p1) is False
def test_should_detect_correct_anticipation_2(self, cfg):
# Introduce two changes into situation and effect (should
# also predict correctly)
# given
cls = Classifier(effect=Effect(
['#', '1', '#', '#', '#', '#', '0', '#']),
cfg=cfg)
p0 = Perception(['0', '0', '0', '0', '1', '1', '1', '1'])
p1 = Perception(['0', '1', '0', '0', '1', '1', '0', '1'])
# then
assert cls.does_anticipate_correctly(p0, p1) is True
