def test_accumulator_standalone_noise_function(self):
A = AccumulatorIntegrator(rate = 1.0, noise=NormalDist(standard_deviation=0.1))
A()
A()
A()
val = A()
assert np.allclose([[-0.34591555]], val)
def test_accumulator_standalone_noise_function(self):
np.random.seed(22)
A = AccumulatorIntegrator(rate = 1.0, noise=NormalDist(standard_deviation=0.1))
A()
A()
A()
val = A()
assert np.allclose([[-0.06509346]], val)
def test_buffer_standalone_noise_function(self):
np.random.seed(22)
B = Buffer(history=3, rate = 1.0, noise=NormalDist(standard_deviation=0.1))
B.execute([1,2,3])
B.execute([4,5,6])
B.execute([7,8,9])
val = B.execute([10,11,12])
assert np.allclose(deque(np.atleast_1d([[ 3.8925223, 5.03957263, 6.00262384],
[ 7.00288551, 7.97692328, 9.05877522],
[10, 11, 12]])), val)
def test_accumulator_standalone_noise_function_in_array(self):
A = AccumulatorIntegrator(noise=[10, NormalDist(standard_deviation=0.1), 20])
A()
A()
A()
val = A()
expected_val = [[40.0, 0.2480800486427607, 80.0]]
for i in range(len(val)):
for j in range(len(val[i])):
assert np.allclose(expected_val[i][j], val[i][j])
def test_DDM_input_fn():
with pytest.raises(TypeError) as error_text:
stim = NormalDist()
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
)
float(T.execute(stim))
assert "not supported for the input types" in str(error_text.value)
def test_buffer_standalone_noise_function(self, benchmark):
B = Buffer(history=3, rate = 1.0, noise=NormalDist(standard_deviation=0.1))
B.execute([1, 2, 3])
B.execute([4, 5, 6])
B.execute([7, 8, 9])
val = B.execute([10,11,12])
assert np.allclose(deque(np.atleast_1d([[4.02430687, 4.91927251, 5.95087965],
[7.09586966, 7.91823773, 8.86077491],
[10, 11, 12]])), val)
if benchmark.enabled:
benchmark(B.execute, [1, 2, 3])
def test_buffer_standalone_noise_function_in_array(self):
B = Buffer(history=3, noise=[10, NormalDist(standard_deviation=0.1), 20])
np.random.seed(22)
B.execute([1,2,3])
B.execute([4,5,6])
B.execute([7,8,9])
val = B.execute([10,11,12])
expected_val = [[24, 5.0800314416734444, 46], [17, 8.040015720836722, 29], [10, 11, 12]]
for i in range(len(val)):
for j in range(len(val[i])):
assert np.allclose(expected_val[i][j], val[i][j])
def test_recurrent_mech_normal_fun(self):
with pytest.raises(TransferError) as error_text:
R = RecurrentTransferMechanism(
name='R',
default_variable=[0, 0, 0, 0],
function=NormalDist(),
integration_rate=1.0,
integrator_mode=True
)
R.execute([0, 0, 0, 0])
assert "must be a TRANSFER FUNCTION TYPE" in str(error_text.value)
def test_accumulator_standalone_noise_function_in_array(self):
A = AccumulatorIntegrator(noise=[10, NormalDist(standard_deviation=0.1), 20])
np.random.seed(22)
A()
A()
A()
val = A()
expected_val = [[40.0, 0.16006288334688934, 80.0]]
for i in range(len(val)):
for j in range(len(val[i])):
assert np.allclose(expected_val[i][j], val[i][j])
def test_DDM_rate_fn():
with pytest.raises(typecheck.framework.InputParameterError) as error_text:
stim = [10]
T = DDM(
name='DDM',
default_variable=[0],
function=DriftDiffusionIntegrator(noise=0.0,
rate=NormalDist().function,
time_step_size=1.0),
)
float(T.execute(stim)[0])
assert "incompatible value" in str(error_text.value)
def test_DDM_noise_fn():
with pytest.raises(FunctionError) as error_text:
stim = 10
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=NormalDist(),
rate=1.0,
time_step_size=1.0),
)
float(T.execute(stim)[0])
assert "DriftDiffusionIntegrator requires noise parameter to be a float" in str(
error_text.value)
def test_buffer_standalone_noise_function_in_array(self, benchmark):
B = Buffer(history=3)
# Set noise parameter ouside of a constructor to avoid problems
# with extra copying
B.parameters.noise.set([10, NormalDist(standard_deviation=0.1), 20])
B.execute([1, 2, 3])
B.execute([4, 5, 6])
B.execute([7, 8, 9])
val = B.execute([10, 11, 12])
expected_val = [[24, 4.693117564500052, 46], [17, 7.744647273059847, 29], [10, 11, 12]]
for v_v, v_e in zip(val, expected_val):
for v, e in zip(v_v, v_e):
assert np.allclose(v, e)
if benchmark.enabled:
benchmark(B.execute, [1, 2, 3])
assert np.allclose(val[0][0][0], 8.194383551861414)
benchmark(ex, [10])
# ------------------------------------------------------------------------------------------------
# INVALID NOISE:
# ------------------------------------------------------------------------------------------------
@pytest.mark.ddm_mechanism
@pytest.mark.mechanism
@pytest.mark.benchmark(group="DDM")
@pytest.mark.parametrize("noise", [
2,
NormalDist(),
],
ids=["int", "functions"])
def test_DDM_noise_invalid(noise):
with pytest.raises(FunctionError) as error_text:
stim = 10
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=noise,
rate=1.0,
time_step_size=1.0),
)
float(T.execute(stim)[0])
assert "DriftDiffusionIntegrator requires noise parameter to be a float" in str(
error_text.value)
def target_function():
val_1 = NormalDist(mean=3.0)()
val_2 = NormalDist(mean=3.0)()
target_value = np.array([val_1, val_2])
return target_value
def target_function():
val_1 = NormalDist(mean=3.0)()
val_2 = NormalDist(mean=3.0)()
return [val_1, val_2]
def target_function():
val_1 = NormalDist(mean=3.0)()
return val_1
