def test_threshold_param(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=10.0))
assert D.function_object.threshold == 10.0
D.function_object.threshold = 5.0
assert D.function_object._threshold == 5.0
def test_reset_state_integrator_mechanism(self):
A = IntegratorMechanism(name='A', function=DriftDiffusionIntegrator())
# Execute A twice
# [0] saves decision variable only (not time)
original_output = [A.execute(1.0)[0], A.execute(1.0)[0]]
# SAVING STATE - - - - - - - - - - - - - - - - - - - - - - - - -
reinitialize_values = []
for attr in A.function_object.stateful_attributes:
reinitialize_values.append(getattr(A.function_object, attr))
# Execute A twice AFTER saving the state so that it continues accumulating.
# We expect the next two outputs to repeat once we reset the state b/c we will return it to the current state
output_after_saving_state = [A.execute(1.0)[0], A.execute(1.0)[0]]
# RESETTING STATE - - - - - - - - - - - - - - - - - - - - - - - -
A.reinitialize(*reinitialize_values)
# We expect these results to match the results from immediately after saving the state
output_after_reinitialization = [A.execute(1.0)[0], A.execute(1.0)[0]]
assert np.allclose(output_after_saving_state,
output_after_reinitialization)
assert np.allclose(
original_output,
[np.array([[1.0]]), np.array([[2.0]])])
assert np.allclose(
output_after_reinitialization,
[np.array([[3.0]]), np.array([[4.0]])])
def test_save_state_before_simulations(self):
A = TransferMechanism(name='A',
integrator_mode=True,
integration_rate=0.2)
B = IntegratorMechanism(name='B',
function=DriftDiffusionIntegrator(rate=0.1))
C = TransferMechanism(name='C')
P = Process(pathway=[A, B, C])
S = System(processes=[P], reinitialize_mechanisms_when=Never())
S.run(inputs={A: [[1.0], [1.0]]})
run_1_values = [A.value, B.value[0], C.value]
# "Save state" code from EVCaux
# Get any values that need to be reinitialized for each run
reinitialization_values = {}
for mechanism in S.stateful_mechanisms:
# "save" the current state of each stateful mechanism by storing the values of each of its stateful
# attributes in the reinitialization_values dictionary; this gets passed into run and used to call
# the reinitialize method on each stateful mechanism.
reinitialization_value = []
if isinstance(mechanism.function_object, Integrator):
for attr in mechanism.function_object.stateful_attributes:
reinitialization_value.append(
getattr(mechanism.function_object, attr))
elif hasattr(mechanism, "integrator_function"):
if isinstance(mechanism.integrator_function, Integrator):
for attr in mechanism.integrator_function.stateful_attributes:
reinitialization_value.append(
getattr(mechanism.integrator_function, attr))
reinitialization_values[mechanism] = reinitialization_value
# Allow values to continue accumulating so that we can set them back to the saved state
S.run(inputs={A: [[1.0], [1.0]]})
run_2_values = [A.value, B.value[0], C.value]
S.run(inputs={A: [[1.0], [1.0]]},
reinitialize_values=reinitialization_values)
run_3_values = [A.value, B.value[0], C.value]
assert np.allclose(run_2_values, run_3_values)
assert np.allclose(
run_1_values,
[np.array([[0.36]]),
np.array([[0.056]]),
np.array([[0.056]])])
assert np.allclose(run_2_values, [
np.array([[0.5904]]),
np.array([[0.16384]]),
np.array([[0.16384]])
])
def test_integrator_type_diffusion_rate_list(self):
I = IntegratorMechanism(
default_variable=[0, 0, 0],
name='IntegratorMechanism',
function=DriftDiffusionIntegrator(rate=[5.0, 5.0, 5.0]))
# P = Process(pathway=[I])
val = list(I.execute([10.0, 10.0, 10.0])[0])
assert val == [50.0, 50.0, 50.0]
def test_integrator_drift_diffusion_noise_val(self):
I = IntegratorMechanism(name='IntegratorMechanism',
function=DriftDiffusionIntegrator(noise=5.0, ),
time_scale=TimeScale.TIME_STEP)
val = float(I.execute(10))
np.testing.assert_allclose(val, 15.010789523731438)
def test_DDM_noise_2_0():
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=2.0,
rate=1.0,
time_step_size=1.0))
val = float(T.execute(stim)[0])
assert val == 12.641125838188323
def test_DDM_Integrator():
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(initializer=20.0),
time_scale=TimeScale.TIME_STEP)
val = float(T.execute(stim)[0])
T.function_object.initializer = 30.0
val2 = float(T.execute(stim)[0])
def test_integrator_drift_diffusion_noise_val(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=DriftDiffusionIntegrator(noise=5.0, ),
)
val = float(I.execute(10))
np.testing.assert_allclose(val, 12.188524664621541)
def test_DDM_size_too_large():
with pytest.raises(DDMError) as error_text:
T = DDM(name='DDM',
size=3.0,
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
assert "must have only a single numeric item" in str(error_text.value)
def test_DDM_size_too_long():
with pytest.raises(DDMError) as error_text:
T = DDM(name='DDM',
size=[1, 1],
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
assert "is greater than 1, implying there are" in str(error_text.value)
def test_DDM_mech_size_negative_one():
with pytest.raises(ComponentError) as error_text:
T = DDM(name='DDM',
size=-1.0,
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
assert "is not a positive number" in str(error_text.value)
def test_DDM_size_int_inputs_():
T = DDM(name='DDM',
size=1.0,
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
val = T.execute([.4]).tolist()
assert val == [[-2.0], [1.0]]
def test_DDM_size_int_check_var():
T = DDM(name='DDM',
size=1,
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
assert len(T.instance_defaults.variable
) == 1 and T.instance_defaults.variable[0][0] == 0
def test_DDM_zero_noise():
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
val = float(T.execute(stim)[0])
assert val == 10
def test_DDM_input_fn():
with pytest.raises(TypeError) as error_text:
stim = NormalDist().function
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
float(T.execute(stim))
assert "not supported for the input types" in str(error_text.value)
def test_DDM_input_rate_negative():
stim = [10]
T = DDM(name='DDM',
default_variable=[0],
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
val = float(T.execute(stim)[0])
assert val == -50
def test_threshold_sets_is_finished(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=5.0))
D.execute(2.0) # 2.0 < 5.0
assert not D.is_finished
D.execute(2.0) # 4.0 < 5.0
assert not D.is_finished
D.execute(2.0) # 5.0 = threshold
assert D.is_finished
def test_DDM_rate_float():
stim = 10
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=5,
time_step_size=1.0),
)
val = float(T.execute(stim)[0])
assert val == 50
def test_integrator_type_diffusion_rate_float(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=DriftDiffusionIntegrator(
rate=5.0
)
)
# P = Process(pathway=[I])
val = I.execute(10.0)
assert np.allclose([[[50.0]], [[1.0]]], val)
def test_DDM_noise_0_5():
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=0.5,
rate=1.0,
time_step_size=1.0))
val = float(T.execute(stim)[0])
assert val == 11.320562919094161
def test_DDM_input_list_len_1():
stim = [10]
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
)
val = float(T.execute(stim)[0])
assert val == 10
def test_integrator_drift_diffusion_noise_val(self):
I = IntegratorMechanism(
name='IntegratorMechanism',
function=DriftDiffusionIntegrator(
noise=5.0,
),
)
val = I.execute(10.0)
assert np.allclose(val, [[[15.01078952]], [[1.]]])
def test_integrator_type_diffusion_rate_list_input_float(self):
with pytest.raises(FunctionError) as error_text:
I = IntegratorMechanism(
name='IntegratorMechanism',
function=DriftDiffusionIntegrator(rate=[5.0, 5.0, 5.0]))
# P = Process(pathway=[I])
float(I.execute(10.0))
assert ("array specified for the rate parameter" in str(error_text)
and "must match the length" in str(error_text)
and "of the default input" in str(error_text))
def test_DDM_noise_fn():
with pytest.raises(FunctionError) as error_text:
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=NormalDist().function,
rate=1.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
float(T.execute(stim)[0])
assert "DriftDiffusionIntegrator requires noise parameter to be a float" in str(
error_text.value)
def test_drift_diffusion_integrator(self):
I = IntegratorMechanism(function=DriftDiffusionIntegrator(
initializer=10.0,
rate=10,
time_step_size=0.5,
offset=10,
))
# P = Process(pathway=[I])
# 10 + 10*0.5 + 0 + 10 = 25
val = I.execute(1)
assert val == 25
def test_is_finished_stops_system(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=10.0))
P = Process(pathway=[D])
S = System(processes=[P])
S.run(inputs={D: 2.0},
termination_processing={TimeScale.TRIAL: WhenFinished(D)})
# decision variable's value should match threshold
assert D.value[0] == 10.0
# it should have taken 5 executions (and time_step_size = 1.0)
assert D.value[1] == 5.0
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),
time_scale=TimeScale.TIME_STEP)
float(T.execute(stim)[0])
assert "incompatible value" in str(error_text.value)
def test_DDM_input_list_len_2():
with pytest.raises(DDMError) as error_text:
stim = [10, 10]
T = DDM(name='DDM',
default_variable=[0, 0],
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
time_scale=TimeScale.TIME_STEP)
float(T.execute(stim)[0])
assert "must have only a single numeric item" in str(error_text.value)
def test_DDM_noise_int():
with pytest.raises(FunctionError) as error_text:
stim = 10
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=2,
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_integrator_diffusion_with_reset_intializer(self):
I = IntegratorMechanism(name='IntegratorMechanism',
function=DriftDiffusionIntegrator(),
time_scale=TimeScale.TIME_STEP)
# val = float(I.execute(10)[0])
# P = Process(pathway=[I])
val = float(I.execute(10))
# testing initializer
I.function_object.reset_initializer = 1.0
val2 = float(I.execute(0))
assert [val, val2] == [10.0, 1.0]