def test_remove_symbol(self):
# given
attr1 = ProbabilityEnhancedAttribute({'0': 0.4, '1': 0.4, '9': 0.2})
attr2 = ProbabilityEnhancedAttribute({'0': 0.5, '1': 0.5})
# when
assert attr1.remove_symbol('9')
# then
assert attr1 == attr2
def test_insert_symbol(self):
# given
attr1 = ProbabilityEnhancedAttribute({'0': 0.5, '1': 0.5})
attr2 = ProbabilityEnhancedAttribute({'0': 0.4, '1': 0.4, '9': 0.2})
# when
attr1.insert_symbol('9', 0.8, 0.2)
# then
assert attr1 == attr2
def test_remove_last_symbol(self):
# given
attr1 = ProbabilityEnhancedAttribute({'0': 1.0})
attr2 = ProbabilityEnhancedAttribute({'0': 1.0})
# when
assert not attr1.remove_symbol('0')
# then
assert attr1 == attr2
def test_get_best_symbol(self):
# given
attr1 = ProbabilityEnhancedAttribute({'0': 0.4, '1': 0.6})
attr2 = ProbabilityEnhancedAttribute({'0': 0.5, '1': 0.3, '9': 0.2})
attr3 = ProbabilityEnhancedAttribute({'0': 0.3, '1': 0.3, '9': 0.4})
# then
assert attr1.get_best_symbol() == '1'
assert attr2.get_best_symbol() == '0'
assert attr3.get_best_symbol() == '9'
def __init__(self, observation):
# Convert dict to ProbabilityEnhancedAttribute
if not all(
isinstance(attr, ProbabilityEnhancedAttribute)
for attr in observation):
observation = (ProbabilityEnhancedAttribute(attr) if isinstance(
attr, dict) else attr for attr in observation)
super().__init__(observation)
def test_is_compact(self):
# given
attr1 = ProbabilityEnhancedAttribute({'0': 0.6, '1': 0.4, '9': 0.0})
attr2 = ProbabilityEnhancedAttribute({'0': 0.6, '1': 0.4})
# then
assert not attr1.is_compact()
assert attr2.is_compact()
def test_is_similar_3(self):
# given
attr1 = ProbabilityEnhancedAttribute({'0': 0.5, '1': 0.4, '9': 0.1})
attr2 = ProbabilityEnhancedAttribute({'0': 0.9, '9': 0.1})
# then
assert not attr1.is_similar(attr2)
assert not attr2.is_similar(attr1)
def test_is_similar_1(self):
# given
attr1 = ProbabilityEnhancedAttribute({'1': 0.8, '0': 0.2})
attr2 = ProbabilityEnhancedAttribute({'1': 0.4, '0': 0.6})
# then
assert attr1.is_similar(attr2)
assert attr2.is_similar(attr1)
def enhanced_effect(cls,
effect1,
effect2,
q1: float = 0.5,
q2: float = 0.5,
perception: ImmutableSequence = None):
"""
Create a new enhanced effect part.
"""
assert perception is not None
result = cls(observation=effect1)
for i, attr2 in enumerate(effect2):
attr1 = effect1[i]
if attr1 == Effect.WILDCARD and attr2 == Effect.WILDCARD:
continue
if attr1 == Effect.WILDCARD:
attr1 = perception[i]
if attr2 == Effect.WILDCARD:
attr2 = perception[i]
result[i] = ProbabilityEnhancedAttribute.merged_attributes(
attr1, attr2, q1, q2)
return result
def test_should_initialize_correctly_dict(self):
attr = ProbabilityEnhancedAttribute({'1': 1.0, '0': 0.0})
assert attr.sum_of_probabilities() == 1.0
assert attr['1'] == 1.0
def test_should_initialize_correctly_str(self):
attr = ProbabilityEnhancedAttribute('1')
assert attr.sum_of_probabilities() == 1.0
assert attr['1'] == 1.0
def test_is_similar_0(self):
# given
attr = ProbabilityEnhancedAttribute({'1': 0.8, '0': 0.2})
# then
assert attr.is_similar(attr)