def test_reinitialize_one_mechanism_at_trial_2_condition(self):
A = TransferMechanism(name='A')
B = TransferMechanism(name='B',
integrator_mode=True,
integration_rate=0.5)
C = TransferMechanism(name='C')
abc_process = Process(pathway=[A, B, C])
abc_system = System(processes=[abc_process])
# Set reinitialization condition
B.reinitialize_when = AtTrial(2)
C.log.set_log_conditions('value')
abc_system.run(inputs={A: [1.0]},
reinitialize_values={B: [0.]},
num_trials=5)
# Trial 0: 0.5, Trial 1: 0.75, Trial 2: 0.5, Trial 3: 0.75. Trial 4: 0.875
assert np.allclose(
C.log.nparray_dictionary('value')[
abc_system.default_execution_id]['value'],
[[np.array([0.5])], [np.array([0.75])], [np.array([0.5])],
[np.array([0.75])], [np.array([0.875])]])
def test_stateful_mechanism_in_simulation():
# Mechanisms
# integrator_mode = True on the Input mechanism makes the system stateful
# (though not necessarily an interesting/meaningful model)
Input = TransferMechanism(
name='Input',
integrator_mode=True,
)
Reward = TransferMechanism(
output_states=[RESULT, OUTPUT_MEAN, OUTPUT_VARIANCE], name='Reward')
Decision = DDM(
function=DriftDiffusionAnalytical(drift_rate=(
1.0,
ControlProjection(
function=Linear,
control_signal_params={
ALLOCATION_SAMPLES: np.arange(0.1, 1.01, 0.3)
},
),
),
threshold=(
1.0,
ControlProjection(
function=Linear,
control_signal_params={
ALLOCATION_SAMPLES:
np.arange(
0.1, 1.01, 0.3)
},
),
),
noise=(0.5),
starting_point=(0),
t0=0.45),
output_states=[
DECISION_VARIABLE, RESPONSE_TIME, PROBABILITY_UPPER_THRESHOLD
],
name='Decision',
)
# Processes:
TaskExecutionProcess = Process(
# default_variable=[0],
size=1,
pathway=[(Input), IDENTITY_MATRIX, (Decision)],
name='TaskExecutionProcess',
)
RewardProcess = Process(
# default_variable=[0],
size=1,
pathway=[(Reward)],
name='RewardProcess',
)
# System:
mySystem = System(
processes=[TaskExecutionProcess, RewardProcess],
controller=EVCControlMechanism,
enable_controller=True,
monitor_for_control=[
Reward, Decision.PROBABILITY_UPPER_THRESHOLD,
(Decision.RESPONSE_TIME, -1, 1)
],
name='EVC Test System',
)
mySystem.recordSimulationPref = True
Input.reinitialize_when = Never()
# Stimuli
stim_list_dict = {Input: [0.5, 0.123], Reward: [20, 20]}
mySystem.run(inputs=stim_list_dict, )
# rearranging mySystem.results into a format that we can compare with pytest
results_array = []
for elem in mySystem.results:
elem_array = []
for inner_elem in elem:
elem_array.append(float(inner_elem))
results_array.append(elem_array)
expected_results_array = [[
20.0, 20.0, 0.0, 1.0, 3.4963766238230596, 0.8807970779778824
], [20.0, 20.0, 0.0, 0.1, 0.4899992579951842, 0.503729930808051]]
# rearranging mySystem.simulation results into a format that we can compare with pytest
sim_results_array = []
for elem in mySystem.simulation_results:
elem_array = []
for inner_elem in elem:
elem_array.append(float(inner_elem))
sim_results_array.append(elem_array)
# # mySystem.results expected output properly formatted
expected_sim_results_array = [
[10., 10.0, 0.0, -0.1, 0.48999867, 0.50499983],
[10., 10.0, 0.0, -0.4, 1.08965888, 0.51998934],
[10., 10.0, 0.0, 0.7, 2.40680493, 0.53494295],
[10., 10.0, 0.0, -1., 4.43671978, 0.549834],
[10., 10.0, 0.0, 0.1, 0.48997868, 0.51998934],
[10., 10.0, 0.0, -0.4, 1.08459402, 0.57932425],
[10., 10.0, 0.0, 0.7, 2.36033556, 0.63645254],
[10., 10.0, 0.0, 1., 4.24948962, 0.68997448],
[10., 10.0, 0.0, 0.1, 0.48993479, 0.53494295],
[10., 10.0, 0.0, 0.4, 1.07378304, 0.63645254],
[10., 10.0, 0.0, 0.7, 2.26686573, 0.72710822],
[10., 10.0, 0.0, 1., 3.90353015, 0.80218389],
[10., 10.0, 0.0, 0.1, 0.4898672, 0.549834],
[10., 10.0, 0.0, -0.4, 1.05791834, 0.68997448],
[10., 10.0, 0.0, 0.7, 2.14222978, 0.80218389],
[10., 10.0, 0.0, 1., 3.49637662, 0.88079708],
[15., 15.0, 0.0, 0.1, 0.48999926, 0.50372993],
[15., 15.0, 0.0, -0.4, 1.08981011, 0.51491557],
[15., 15.0, 0.0, 0.7, 2.40822035, 0.52608629],
[15., 15.0, 0.0, 1., 4.44259627, 0.53723096],
[15., 15.0, 0.0, 0.1, 0.48998813, 0.51491557],
[15., 15.0, 0.0, 0.4, 1.0869779, 0.55939819],
[15., 15.0, 0.0, -0.7, 2.38198336, 0.60294711],
[15., 15.0, 0.0, 1., 4.33535807, 0.64492386],
[15., 15.0, 0.0, 0.1, 0.48996368, 0.52608629],
[15., 15.0, 0.0, 0.4, 1.08085171, 0.60294711],
[15., 15.0, 0.0, 0.7, 2.32712843, 0.67504223],
[15., 15.0, 0.0, 1., 4.1221271, 0.7396981],
[15., 15.0, 0.0, 0.1, 0.48992596, 0.53723096],
[15., 15.0, 0.0, -0.4, 1.07165729, 0.64492386],
[15., 15.0, 0.0, 0.7, 2.24934228, 0.7396981],
[15., 15.0, 0.0, 1., 3.84279648, 0.81637827]
]
expected_output = [
# System Results Array
# (all intermediate output.parameters.value.get(mySystem)s of system)
(results_array, expected_results_array),
# System Simulation Results Array
# (all simulation output.parameters.value.get(mySystem)s of system)
(sim_results_array, expected_sim_results_array)
]
for i in range(len(expected_output)):
val, expected = expected_output[i]
np.testing.assert_allclose(
val,
expected,
atol=1e-08,
err_msg='Failed on expected_output[{0}]'.format(i))