def test_reset_state_integrator_mechanism(self):
A = pnl.IntegratorMechanism(name='A',
function=pnl.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 - - - - - - - - - - - - - - - - - - - - - - - - -
reset_stateful_functions_to = {}
for attr in A.function.stateful_attributes:
reset_stateful_functions_to[attr] = getattr(A.function, attr)
print(reset_stateful_functions_to)
# 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.reset(**reset_stateful_functions_to)
# 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_DDM_in_composition(benchmark, mode):
M = pnl.DDM(
name='DDM',
function=pnl.DriftDiffusionIntegrator(
rate=1,
noise=0.0,
offset=0.0,
starting_point=0.0,
time_step_size=0.1,
),
)
C = pnl.Composition()
C.add_linear_processing_pathway([M])
inputs = {M: [10]}
val = C.run(inputs, num_trials=2, bin_execute=mode)
# FIXME: Python version returns dtype=object
val = np.asfarray(val)
assert np.allclose(val[0], [2.0])
assert np.allclose(val[1], [0.2])
if benchmark.enabled:
benchmark(C.run, inputs, num_trials=2, bin_execute=mode)