def test_linear_combination_function_in_mechanism(operation, input,
input_ports, scale, offset,
benchmark, mode):
f = pnl.LinearCombination(default_variable=input,
operation=operation,
scale=scale,
offset=offset)
p = pnl.ProcessingMechanism(size=[len(input[0])] * len(input),
function=f,
input_ports=input_ports)
if mode == 'Python':
EX = p.execute
elif mode == 'LLVM':
e = pnlvm.execution.MechExecution(p)
EX = e.execute
elif mode == 'PTX':
e = pnlvm.execution.MechExecution(p)
EX = e.cuda_execute
res = benchmark(EX, input)
scale = 1.0 if scale is None else scale
offset = 0.0 if offset is None else offset
if operation == pnl.SUM:
expected = np.sum(input, axis=0) * scale + offset
if operation == pnl.PRODUCT:
expected = np.product(input, axis=0) * scale + offset
assert np.allclose(res, expected)
def test_lvoc_features_function(self):
m1 = pnl.TransferMechanism(
input_states=["InputState A", "InputState B"])
m2 = pnl.TransferMechanism()
c = pnl.Composition()
c.add_node(m1, required_roles=pnl.NodeRole.INPUT)
c.add_node(m2, required_roles=pnl.NodeRole.INPUT)
c._analyze_graph()
lvoc = pnl.OptimizationControlMechanism(
agent_rep=pnl.RegressionCFA,
features=[
m1.input_states[0], m1.input_states[1], m2.input_state, m2
],
feature_function=pnl.LinearCombination(offset=10.0),
objective_mechanism=pnl.ObjectiveMechanism(monitor=[m1, m2]),
function=pnl.GradientOptimization(max_iterations=1),
control_signals=[(pnl.SLOPE, m1), (pnl.SLOPE, m2)])
c.add_node(lvoc)
input_dict = {m1: [[1], [1]], m2: [1]}
c.run(inputs=input_dict)
assert len(lvoc.input_states) == 5
for i in range(1, 5):
assert lvoc.input_states[i].function.offset == 10.0
def test_linear_combination_function(variable, operation, exponents, weights,
scale, offset, func_mode, benchmark):
if weights == 'V':
weights = [[-1**i] for i, v in enumerate(variable)]
if exponents == 'V':
exponents = [[v[0]] for v in variable]
f = pnl.LinearCombination(default_variable=variable,
operation=operation,
exponents=exponents,
weights=weights,
scale=scale,
offset=offset)
EX = pytest.helpers.get_func_execution(f, func_mode)
res = benchmark(EX, variable)
scale = 1.0 if scale is None else scale
offset = 0.0 if offset is None else offset
exponent = 1.0 if exponents is None else exponents
weights = 1.0 if weights is None else weights
tmp = (variable**exponent) * weights
if operation == pnl.SUM:
expected = np.sum(tmp, axis=0) * scale + offset
if operation == pnl.PRODUCT:
expected = np.product(tmp, axis=0) * scale + offset
assert np.allclose(res, expected)
def test_combine_param_conflicting_fct_operation_spec(self):
with pytest.raises(pnl.InputStateError) as error_text:
t = pnl.TransferMechanism(
input_states=pnl.InputState(function=pnl.LinearCombination(
operation=pnl.SUM),
combine=pnl.PRODUCT))
assert "Specification of 'combine' argument (PRODUCT) conflicts with specification of 'operation' (SUM) " \
"for LinearCombination in 'function' argument for InputState" in str(error_text.value)
def test_combine_param_redundant_fct_constructor_spec(self):
t1 = pnl.TransferMechanism(size=2)
t2 = pnl.TransferMechanism(size=2)
t3 = pnl.TransferMechanism(
size=2,
input_states=pnl.InputState(
function=pnl.LinearCombination(operation=pnl.PRODUCT),
combine=pnl.PRODUCT))
p1 = pnl.Process(pathway=[t1, t3])
p2 = pnl.Process(pathway=[t2, t3])
s = pnl.System(processes=[p1, p2])
input_dict = {t1: [1, 2], t2: [3, 4]}
val = s.run(inputs=input_dict)
assert np.allclose(val, [[3, 8]])
def test_linear_combination_function_in_mechanism(operation, input, size,
input_states, scale, offset,
expected, benchmark):
f = pnl.LinearCombination(default_variable=input,
operation=operation,
scale=scale,
offset=offset)
p = pnl.ProcessingMechanism(size=[size] * len(input_states),
function=f,
input_states=input_states)
benchmark.group = "CombinationFunction " + pnl.LinearCombination.componentName + "in Mechanism"
res = benchmark(f.execute, input)
if expected is None:
if operation == pnl.SUM:
expected = np.sum(input, axis=0) * scale + offset
if operation == pnl.PRODUCT:
expected = np.product(input, axis=0) * scale + offset
assert np.allclose(res, expected)
def test_linear_combination_function_in_mechanism(operation, input,
input_ports, scale, offset,
benchmark, mech_mode):
f = pnl.LinearCombination(default_variable=input,
operation=operation,
scale=scale,
offset=offset)
p = pnl.ProcessingMechanism(size=[len(input[0])] * len(input),
function=f,
input_ports=input_ports)
EX = pytest.helpers.get_mech_execution(p, mech_mode)
res = benchmark(EX, input)
scale = 1.0 if scale is None else scale
offset = 0.0 if offset is None else offset
if operation == pnl.SUM:
expected = np.sum(input, axis=0) * scale + offset
if operation == pnl.PRODUCT:
expected = np.product(input, axis=0) * scale + offset
assert np.allclose(res, expected)
def test_linear_combination_function(variable, operation, exponents, weights,
scale, offset, mode, benchmark):
if weights == 'V':
weights = [[-1**i] for i, v in enumerate(variable)]
if exponents == 'V':
exponents = [[v[0]] for v in variable]
f = pnl.LinearCombination(default_variable=variable,
operation=operation,
exponents=exponents,
weights=weights,
scale=scale,
offset=offset)
if mode == 'Python':
EX = f.function
elif mode == 'LLVM':
e = pnlvm.execution.FuncExecution(f)
EX = e.execute
elif mode == 'PTX':
e = pnlvm.execution.FuncExecution(f)
EX = e.cuda_execute
res = benchmark(EX, variable)
scale = 1.0 if scale is None else scale
offset = 0.0 if offset is None else offset
exponent = 1.0 if exponents is None else exponents
weights = 1.0 if weights is None else weights
tmp = (variable**exponent) * weights
if operation == pnl.SUM:
expected = np.sum(tmp, axis=0) * scale + offset
if operation == pnl.PRODUCT:
expected = np.product(tmp, axis=0) * scale + offset
assert np.allclose(res, expected)
signalSearchRange = pnl.SampleSpec(start=1.0, stop=1.8, step=0.2)
target_rep_control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, Target_Rep)],
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
flanker_rep_control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, Flanker_Rep)],
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
objective_mech = pnl.ObjectiveMechanism(
function=pnl.LinearCombination(operation=pnl.PRODUCT),
monitor=[
reward, (Decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1)
])
# Model Based OCM (formerly controller)
evc_gratton.add_controller(controller=pnl.OptimizationControlMechanism(
agent_rep=evc_gratton,
features=[
target_stim.input_port, flanker_stim.input_port, reward.input_port
],
feature_function=pnl.AdaptiveIntegrator(rate=1.0),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[target_rep_control_signal, flanker_rep_control_signal]))
evc_gratton.show_graph(show_controller=True)
function=pnl.Linear(default_variable=[[0.0, 0.0]]),
default_variable=[[0.0, 0.0]],
)
word_hidden = pnl.ProcessingMechanism(
name="word_hidden",
function=pnl.Logistic(bias=-4.0, default_variable=[[0.0, 0.0]]),
default_variable=[[0.0, 0.0]],
)
task_input = pnl.ProcessingMechanism(
name="task_input",
function=pnl.Linear(default_variable=[[0.0, 0.0]]),
default_variable=[[0.0, 0.0]],
)
TASK = pnl.LCAMechanism(
name="TASK",
combination_function=pnl.LinearCombination(default_variable=[[0.0, 0.0]]),
function=pnl.Logistic(default_variable=[[0.0, 0.0]]),
integrator_function=pnl.LeakyCompetingIntegrator(
name="LeakyCompetingIntegrator_Function_0",
initializer=[[0.5, 0.5]],
rate=0.5,
default_variable=[[0.0, 0.0]],
),
output_ports=["RESULTS"],
termination_comparison_op=">=",
default_variable=[[0.0, 0.0]],
)
DECISION = pnl.DDM(
name="DECISION",
function=pnl.DriftDiffusionAnalytical(default_variable=[[0.0]]),
input_ports=[{
def get_stroop_model(unit_noise_std=.01, dec_noise_std=.1):
# model params
integration_rate = 1
hidden_func = pnl.Logistic(gain=1.0, x_0=4.0)
# input layer, color and word
reward = pnl.TransferMechanism(name='reward')
punish = pnl.TransferMechanism(name='punish')
inp_clr = pnl.TransferMechanism(
size=N_UNITS, function=pnl.Linear, name='COLOR INPUT'
)
inp_wrd = pnl.TransferMechanism(
size=N_UNITS, function=pnl.Linear, name='WORD INPUT'
)
# task layer, represent the task instruction; color naming / word reading
inp_task = pnl.TransferMechanism(
size=N_UNITS, function=pnl.Linear, name='TASK'
)
# hidden layer for color and word
hid_clr = pnl.TransferMechanism(
size=N_UNITS,
function=hidden_func,
integrator_mode=True,
integration_rate=integration_rate,
# noise=pnl.NormalDist(standard_deviation=unit_noise_std).function,
noise=pnl.NormalDist(standard_deviation=unit_noise_std),
name='COLORS HIDDEN'
)
hid_wrd = pnl.TransferMechanism(
size=N_UNITS,
function=hidden_func,
integrator_mode=True,
integration_rate=integration_rate,
# noise=pnl.NormalDist(standard_deviation=unit_noise_std).function,
noise=pnl.NormalDist(standard_deviation=unit_noise_std),
name='WORDS HIDDEN'
)
# output layer
output = pnl.TransferMechanism(
size=N_UNITS,
function=pnl.Logistic,
integrator_mode=True,
integration_rate=integration_rate,
# noise=pnl.NormalDist(standard_deviation=unit_noise_std).function,
noise=pnl.NormalDist(standard_deviation=unit_noise_std),
name='OUTPUT'
)
# decision layer, some accumulator
signalSearchRange = pnl.SampleSpec(start=0.05, stop=5, step=0.05)
decision = pnl.DDM(name='Decision',
input_format=pnl.ARRAY,
function=pnl.DriftDiffusionAnalytical(drift_rate=1,
threshold =1,
noise=1,
starting_point=0,
t0=0.35),
output_ports=[pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD,
pnl.PROBABILITY_LOWER_THRESHOLD]
)
driftrate_control_signal = pnl.ControlSignal(projections=[(pnl.SLOPE, inp_clr)],
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=1),#pnl.Exponential(rate=0.8),#pnl.Exponential(rate=1),
allocation_samples=signalSearchRange)
threshold_control_signal = pnl.ControlSignal(projections=[(pnl.THRESHOLD, decision)],
variable=1.0,
intensity_cost_function=pnl.Linear(slope=0),
allocation_samples=signalSearchRange)
reward_rate = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.PRODUCT,
exponents=[[1],[1],[-1]]),
monitor=[reward,
decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD],
decision.output_ports[pnl.RESPONSE_TIME]])
punish_rate = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.PRODUCT,
exponents=[[1],[1],[-1]]),
monitor=[punish,
decision.output_ports[pnl.PROBABILITY_LOWER_THRESHOLD],
decision.output_ports[pnl.RESPONSE_TIME]])
objective_mech = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.SUM,
weights=[[1],[-1]]),
monitor=[reward_rate, punish_rate])
# objective_mech = pnl.ObjectiveMechanism(function=object_function,
# monitor=[reward,
# punish,
# decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD],
# decision.output_ports[pnl.PROBABILITY_LOWER_THRESHOLD],
# (decision.output_ports[pnl.RESPONSE_TIME])])
# PROJECTIONS, weights copied from cohen et al (1990)
wts_clr_ih = pnl.MappingProjection(
matrix=[[2.2, -2.2], [-2.2, 2.2]], name='COLOR INPUT TO HIDDEN')
wts_wrd_ih = pnl.MappingProjection(
matrix=[[2.6, -2.6], [-2.6, 2.6]], name='WORD INPUT TO HIDDEN')
wts_clr_ho = pnl.MappingProjection(
matrix=[[1.3, -1.3], [-1.3, 1.3]], name='COLOR HIDDEN TO OUTPUT')
wts_wrd_ho = pnl.MappingProjection(
matrix=[[2.5, -2.5], [-2.5, 2.5]], name='WORD HIDDEN TO OUTPUT')
wts_tc = pnl.MappingProjection(
matrix=[[4.0, 4.0], [0, 0]], name='COLOR NAMING')
wts_tw = pnl.MappingProjection(
matrix=[[0, 0], [4.0, 4.0]], name='WORD READING')
# build the model
model = pnl.Composition(name='STROOP model')
model.add_node(decision, required_roles=pnl.NodeRole.OUTPUT)
model.add_node(reward, required_roles=pnl.NodeRole.OUTPUT)
model.add_node(punish, required_roles=pnl.NodeRole.OUTPUT)
model.add_linear_processing_pathway([inp_clr, wts_clr_ih, hid_clr])
model.add_linear_processing_pathway([inp_wrd, wts_wrd_ih, hid_wrd])
model.add_linear_processing_pathway([hid_clr, wts_clr_ho, output])
model.add_linear_processing_pathway([hid_wrd, wts_wrd_ho, output])
model.add_linear_processing_pathway([inp_task, wts_tc, hid_clr])
model.add_linear_processing_pathway([inp_task, wts_tw, hid_wrd])
model.add_linear_processing_pathway([output, pnl.IDENTITY_MATRIX, decision]) # 3/15/20
# model.add_linear_processing_pathway([output, [[1,-1]], (decision, pnl.NodeRole.OUTPUT)]) # 3/15/20
# model.add_linear_processing_pathway([output, [[1],[-1]], decision]) # 3/15/20
model.add_nodes([reward_rate, punish_rate])
controller = pnl.OptimizationControlMechanism(agent_rep=model,
features=[inp_clr.input_port,
inp_wrd.input_port,
inp_task.input_port,
reward.input_port,
punish.input_port],
feature_function=pnl.AdaptiveIntegrator(rate=0.1),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[driftrate_control_signal,
threshold_control_signal])
model.add_controller(controller=controller)
# collect the node handles
nodes = [inp_clr, inp_wrd, inp_task, hid_clr, hid_wrd, output, decision, reward, punish,controller]
metadata = [integration_rate, dec_noise_std, unit_noise_std]
return model, nodes, metadata
def test_evc_gratton(self):
# Stimulus Mechanisms
target_stim = pnl.TransferMechanism(name='Target Stimulus',
function=pnl.Linear(slope=0.3324))
flanker_stim = pnl.TransferMechanism(
name='Flanker Stimulus', function=pnl.Linear(slope=0.3545221843))
# Processing Mechanisms (Control)
Target_Rep = pnl.TransferMechanism(name='Target Representation')
Flanker_Rep = pnl.TransferMechanism(name='Flanker Representation')
# Processing Mechanism (Automatic)
Automatic_Component = pnl.TransferMechanism(name='Automatic Component')
# Decision Mechanism
Decision = pnl.DDM(name='Decision',
function=pnl.DriftDiffusionAnalytical(
drift_rate=(1.0),
threshold=(0.2645),
noise=(0.5),
starting_point=(0),
t0=0.15),
output_states=[
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD
])
# Outcome Mechanism
reward = pnl.TransferMechanism(name='reward')
# Pathways
target_control_pathway = [target_stim, Target_Rep, Decision]
flanker_control_pathway = [flanker_stim, Flanker_Rep, Decision]
target_automatic_pathway = [target_stim, Automatic_Component, Decision]
flanker_automatic_pathway = [
flanker_stim, Automatic_Component, Decision
]
pathways = [
target_control_pathway, flanker_control_pathway,
target_automatic_pathway, flanker_automatic_pathway
]
# Composition
evc_gratton = pnl.Composition(name="EVCGratton")
evc_gratton.add_node(Decision, required_roles=pnl.NodeRole.OUTPUT)
for path in pathways:
evc_gratton.add_linear_processing_pathway(path)
evc_gratton.add_node(reward, required_roles=pnl.NodeRole.OUTPUT)
# Control Signals
signalSearchRange = pnl.SampleSpec(start=1.0, stop=1.8, step=0.2)
target_rep_control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, Target_Rep)],
function=pnl.Linear,
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
flanker_rep_control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, Flanker_Rep)],
function=pnl.Linear,
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
objective_mech = pnl.ObjectiveMechanism(
function=pnl.LinearCombination(operation=pnl.PRODUCT),
monitor=[
reward,
(Decision.output_states[pnl.PROBABILITY_UPPER_THRESHOLD], 1,
-1)
])
# Model Based OCM (formerly controller)
evc_gratton.add_controller(controller=pnl.OptimizationControlMechanism(
agent_rep=evc_gratton,
features=[
target_stim.input_state, flanker_stim.input_state,
reward.input_state
],
feature_function=pnl.AdaptiveIntegrator(rate=1.0),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[
target_rep_control_signal, flanker_rep_control_signal
]))
evc_gratton.enable_controller = True
targetFeatures = [1, 1, 1]
flankerFeatures = [1, -1, 1]
rewardValues = [100, 100, 100]
stim_list_dict = {
target_stim: targetFeatures,
flanker_stim: flankerFeatures,
reward: rewardValues
}
evc_gratton.run(inputs=stim_list_dict)
expected_results_array = [[[0.32257752863413636], [0.9481940753514433],
[100.]],
[[0.42963678062444666],
[0.47661180945923376], [100.]],
[[0.300291026852769], [0.97089165101931],
[100.]]]
expected_sim_results_array = [
[[0.32257753], [0.94819408], [100.]],
[[0.31663196], [0.95508757], [100.]],
[[0.31093566], [0.96110142], [100.]],
[[0.30548947], [0.96633839], [100.]],
[[0.30029103], [0.97089165], [100.]],
[[0.3169957], [0.95468427], [100.]],
[[0.31128378], [0.9607499], [100.]],
[[0.30582202], [0.96603252], [100.]],
[[0.30060824], [0.9706259], [100.]],
[[0.29563774], [0.97461444], [100.]],
[[0.31163288], [0.96039533], [100.]],
[[0.30615555], [0.96572397], [100.]],
[[0.30092641], [0.97035779], [100.]],
[[0.2959409], [0.97438178], [100.]],
[[0.29119255], [0.97787196], [100.]],
[[0.30649004], [0.96541272], [100.]],
[[0.30124552], [0.97008732], [100.]],
[[0.29624499], [0.97414704], [100.]],
[[0.29148205], [0.97766847], [100.]],
[[0.28694892], [0.98071974], [100.]],
[[0.30156558], [0.96981445], [100.]],
[[0.29654999], [0.97391021], [100.]],
[[0.29177245], [0.97746315], [100.]],
[[0.28722523], [0.98054192], [100.]],
[[0.28289958], [0.98320731], [100.]],
[[0.42963678], [0.47661181], [100.]],
[[0.42846471], [0.43938586], [100.]],
[[0.42628176], [0.40282965], [100.]],
[[0.42314468], [0.36732207], [100.]],
[[0.41913221], [0.333198], [100.]],
[[0.42978939], [0.51176048], [100.]],
[[0.42959394], [0.47427693], [100.]],
[[0.4283576], [0.43708106], [100.]],
[[0.4261132], [0.40057958], [100.]],
[[0.422919], [0.36514906], [100.]],
[[0.42902209], [0.54679323], [100.]],
[[0.42980788], [0.50942101], [100.]],
[[0.42954704], [0.47194318], [100.]],
[[0.42824656], [0.43477897], [100.]],
[[0.42594094], [0.3983337], [100.]],
[[0.42735293], [0.58136855], [100.]],
[[0.42910149], [0.54447221], [100.]],
[[0.42982229], [0.50708112], [100.]],
[[0.42949608], [0.46961065], [100.]],
[[0.42813159], [0.43247968], [100.]],
[[0.42482049], [0.61516258], [100.]],
[[0.42749136], [0.57908829], [100.]],
[[0.42917687], [0.54214925], [100.]],
[[0.42983261], [0.50474093], [100.]],
[[0.42944107], [0.46727945], [100.]],
[[0.32257753], [0.94819408], [100.]],
[[0.31663196], [0.95508757], [100.]],
[[0.31093566], [0.96110142], [100.]],
[[0.30548947], [0.96633839], [100.]],
[[0.30029103], [0.97089165], [100.]],
[[0.3169957], [0.95468427], [100.]],
[[0.31128378], [0.9607499], [100.]],
[[0.30582202], [0.96603252], [100.]],
[[0.30060824], [0.9706259], [100.]],
[[0.29563774], [0.97461444], [100.]],
[[0.31163288], [0.96039533], [100.]],
[[0.30615555], [0.96572397], [100.]],
[[0.30092641], [0.97035779], [100.]],
[[0.2959409], [0.97438178], [100.]],
[[0.29119255], [0.97787196], [100.]],
[[0.30649004], [0.96541272], [100.]],
[[0.30124552], [0.97008732], [100.]],
[[0.29624499], [0.97414704], [100.]],
[[0.29148205], [0.97766847], [100.]],
[[0.28694892], [0.98071974], [100.]],
[[0.30156558], [0.96981445], [100.]],
[[0.29654999], [0.97391021], [100.]],
[[0.29177245], [0.97746315], [100.]],
[[0.28722523], [0.98054192], [100.]],
[[0.28289958], [0.98320731], [100.]],
]
for trial in range(len(evc_gratton.results)):
assert np.allclose(
expected_results_array[trial],
# Note: Skip decision variable OutputState
evc_gratton.results[trial][1:])
for simulation in range(len(evc_gratton.simulation_results)):
assert np.allclose(
expected_sim_results_array[simulation],
# Note: Skip decision variable OutputState
evc_gratton.simulation_results[simulation][1:])
def test_evc(self):
# Mechanisms
Input = pnl.TransferMechanism(name='Input')
reward = pnl.TransferMechanism(
output_states=[pnl.RESULT, pnl.OUTPUT_MEAN, pnl.OUTPUT_VARIANCE],
name='reward')
Decision = pnl.DDM(function=pnl.DriftDiffusionAnalytical(
drift_rate=(1.0,
pnl.ControlProjection(function=pnl.Linear,
control_signal_params={
pnl.ALLOCATION_SAMPLES:
np.arange(0.1, 1.01, 0.3)
})),
threshold=(1.0,
pnl.ControlProjection(function=pnl.Linear,
control_signal_params={
pnl.ALLOCATION_SAMPLES:
np.arange(0.1, 1.01, 0.3)
})),
noise=0.5,
starting_point=0,
t0=0.45),
output_states=[
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD
],
name='Decision')
comp = pnl.Composition(name="evc")
comp.add_node(reward, required_roles=[pnl.NodeRole.OUTPUT])
comp.add_node(Decision, required_roles=[pnl.NodeRole.OUTPUT])
task_execution_pathway = [Input, pnl.IDENTITY_MATRIX, Decision]
comp.add_linear_processing_pathway(task_execution_pathway)
comp.add_controller(controller=pnl.OptimizationControlMechanism(
agent_rep=comp,
features=[Input.input_state, reward.input_state],
feature_function=pnl.AdaptiveIntegrator(rate=0.5),
objective_mechanism=pnl.ObjectiveMechanism(
function=pnl.LinearCombination(operation=pnl.PRODUCT),
monitor=[
reward, Decision.output_states[
pnl.PROBABILITY_UPPER_THRESHOLD],
(Decision.output_states[pnl.RESPONSE_TIME], -1, 1)
]),
function=pnl.GridSearch(),
control_signals=[("drift_rate", Decision), ("threshold",
Decision)]))
comp.enable_controller = True
comp._analyze_graph()
stim_list_dict = {Input: [0.5, 0.123], reward: [20, 20]}
comp.run(inputs=stim_list_dict, retain_old_simulation_data=True)
# Note: Removed decision variable OutputState from simulation results because sign is chosen randomly
expected_sim_results_array = [[[10.], [10.0], [0.0], [0.48999867],
[0.50499983]],
[[10.], [10.0], [0.0], [1.08965888],
[0.51998934]],
[[10.], [10.0], [0.0], [2.40680493],
[0.53494295]],
[[10.], [10.0], [0.0], [4.43671978],
[0.549834]],
[[10.], [10.0], [0.0], [0.48997868],
[0.51998934]],
[[10.], [10.0], [0.0], [1.08459402],
[0.57932425]],
[[10.], [10.0], [0.0], [2.36033556],
[0.63645254]],
[[10.], [10.0], [0.0], [4.24948962],
[0.68997448]],
[[10.], [10.0], [0.0], [0.48993479],
[0.53494295]],
[[10.], [10.0], [0.0], [1.07378304],
[0.63645254]],
[[10.], [10.0], [0.0], [2.26686573],
[0.72710822]],
[[10.], [10.0], [0.0], [3.90353015],
[0.80218389]],
[[10.], [10.0], [0.0], [0.4898672],
[0.549834]],
[[10.], [10.0], [0.0], [1.05791834],
[0.68997448]],
[[10.], [10.0], [0.0], [2.14222978],
[0.80218389]],
[[10.], [10.0], [0.0], [3.49637662],
[0.88079708]],
[[15.], [15.0], [0.0], [0.48999926],
[0.50372993]],
[[15.], [15.0], [0.0], [1.08981011],
[0.51491557]],
[[15.], [15.0], [0.0], [2.40822035],
[0.52608629]],
[[15.], [15.0], [0.0], [4.44259627],
[0.53723096]],
[[15.], [15.0], [0.0], [0.48998813],
[0.51491557]],
[[15.], [15.0], [0.0], [1.0869779],
[0.55939819]],
[[15.], [15.0], [0.0], [2.38198336],
[0.60294711]],
[[15.], [15.0], [0.0], [4.33535807],
[0.64492386]],
[[15.], [15.0], [0.0], [0.48996368],
[0.52608629]],
[[15.], [15.0], [0.0], [1.08085171],
[0.60294711]],
[[15.], [15.0], [0.0], [2.32712843],
[0.67504223]],
[[15.], [15.0], [0.0], [4.1221271],
[0.7396981]],
[[15.], [15.0], [0.0], [0.48992596],
[0.53723096]],
[[15.], [15.0], [0.0], [1.07165729],
[0.64492386]],
[[15.], [15.0], [0.0], [2.24934228],
[0.7396981]],
[[15.], [15.0], [0.0], [3.84279648],
[0.81637827]]]
for simulation in range(len(expected_sim_results_array)):
assert np.allclose(
expected_sim_results_array[simulation],
# Note: Skip decision variable OutputState
comp.simulation_results[simulation][0:3] +
comp.simulation_results[simulation][4:6])
expected_results_array = [[[20.0], [20.0], [0.0], [1.0],
[2.378055160151634], [0.9820137900379085]],
[[20.0], [20.0], [0.0], [0.1],
[0.48999967725112503],
[0.5024599801509442]]]
for trial in range(len(expected_results_array)):
np.testing.assert_allclose(
comp.results[trial],
expected_results_array[trial],
atol=1e-08,
err_msg='Failed on expected_output[{0}]'.format(trial))