def test_kwta_inputs_list_of_strings(self):
with pytest.raises(KWTAError) as error_text:
K = KWTAMechanism(
name='K',
size = 4,
)
K.execute(["one", "two", "three", "four"])
assert("which is not supported for KWTA" in str(error_text.value))
def test_recurrent_mech_inputs_mismatched_with_default_shorter(self):
with pytest.raises(MechanismError) as error_text:
K = KWTAMechanism(
name='K',
size=6
)
K.execute([1, 2, 3, 4, 5])
assert("does not match required length" in str(error_text.value))
def test_kwta_inputs_list_of_ints(self):
K = KWTAMechanism(
name='K',
default_variable=[0, 0, 0, 0]
)
val = K.execute([10, 12, 0, -1])
assert(np.allclose(val, [[0.9933071490757153, 0.9990889488055994, 0.0066928509242848554, 0.0024726231566347743]]))
val = K.execute([1, 2, 3, 0])
assert(np.allclose(val, [[0.3775406687981454, 0.6224593312018546, 0.8175744761936437, 0.18242552380635635]]))
def test_kwta_function_various_spec(self):
specs = [
Logistic, Linear,
Linear(slope=3),
Logistic(gain=2, offset=-4.2)
]
for s in specs:
K = KWTAMechanism(name='K', size=5, function=s, k_value=4)
K.execute([1, 2, 5, -2, .3])
def test_kwta_no_inputs(self):
K = KWTAMechanism(
name='K'
)
assert(K.defaults.variable == [[0]])
val = K.execute([10])
assert(np.allclose(val, [[0.5]]))
def test_kwta_matrix_keyword_spec(self):
for m in MATRIX_KEYWORD_VALUES:
if m != RANDOM_CONNECTIVITY_MATRIX:
K = KWTAMechanism(name='K', size=4, matrix=m)
val = K.execute([10, 10, 10, 10])
assert (np.allclose(val, [[.5, .5, .5, .5]]))
def test_kwta_log_gain_offset(self):
K = KWTAMechanism(name='K',
size=2,
function=Logistic(gain=-.2, offset=4),
k_value=1)
val = K.execute(input=[.1, -4])
assert np.allclose(val, [[0.017636340339722684, 0.039165722796764356]])
def test_kwta_linear_slope(self):
K = KWTAMechanism(name='K',
threshold=.5,
size=5,
k_value=2,
function=Linear(slope=2))
val = K.execute(input=[1, 3, 4, 2, 1])
assert np.allclose(val, [[-2, 2, 4, 0, -2]])
def test_kwta_log_offset(self):
K = KWTAMechanism(
name='K',
size=3,
function=Logistic(offset=-.2),
k_value=2
)
val = K.execute(input=[1, 2, 3])
assert np.allclose(val, [[0.425557483188341, 0.6681877721681662, 0.84553473491646523]])
def test_kwta_log_gain(self):
K = KWTAMechanism(
name='K',
size=3,
function=Logistic(gain=2),
k_value=2
)
val = K.execute(input = [1, 2, 3])
assert np.allclose(val, [[0.2689414213699951, 0.7310585786300049, 0.9525741268224334]])
def test_kwta_linear(self): # inhibition would be positive: so instead it is set to zero
K = KWTAMechanism(
name='K',
threshold=3,
size=3,
k_value=2,
function=Linear
)
val = K.execute(input=[1, 3, 4])
assert np.allclose(val, [[1, 3, 4]])