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
monitor=[
task_decision.output_states[pnl.PROBABILITY_UPPER_THRESHOLD],
task_decision.output_states[pnl.PROBABILITY_LOWER_THRESHOLD],
reward
],
# monitored_output_states=[task_decision, reward],
function=objective_function),
agent_rep=pnl.RegressionCFA(
update_weights=pnl.BayesGLM(
mu_0=-0.17,
sigma_0=9.0909), # -0.17, 9.0909 precision = 0.11; 1/p = v
prediction_terms=[pnl.PV.FC, pnl.PV.COST]),
function=pnl.GradientOptimization(
convergence_criterion=pnl.VALUE,
convergence_threshold=0.001, #0.001
step_size=1, #1
annealing_function=lambda x, y: x / np.sqrt(y)
# direction=pnl.ASCENT
),
control_signals=pnl.ControlSignal(
projections=[(pnl.SLOPE, color_task), ('color_control', word_task)],
# function=pnl.ReLU,
function=pnl.Logistic,
cost_options=[
pnl.ControlSignalCosts.INTENSITY, pnl.ControlSignalCosts.ADJUSTMENT
],
intensity_cost_function=pnl.Exponential(rate=0.25, bias=-3),
adjustment_cost_function=pnl.Exponential(rate=0.25, bias=-3),
# allocation_samples=[i / 2 for i in list(range(0, 50, 1))]
))
function=objective_function),
# posterior weight distribution
agent_rep=pnl.RegressionCFA(
# update_weights=pnl.BayesGLM(mu_0=-0.17, sigma_0=0.11), #sigma_0=math.sqrt(0.11))
update_weights=pnl.BayesGLM(
mu_0=-0.17, sigma_0=0.0000000000000001), #sigma_0=math.sqrt(0.11))
# update_weights=pnl.BayesGLM(mu_0=+0.17, sigma_0=0.11), #sigma_0=math.sqrt(0.11))
prediction_terms=[pnl.PV.C, pnl.PV.FC, pnl.PV.FF, pnl.PV.COST]),
# sample control allocs, and return best
# evaluate() computes outcome (obj mech) - costs given state (features) and sample ctrl alloc
function=pnl.GradientOptimization(
convergence_criterion=pnl.VALUE,
convergence_threshold=0.001,
step_size=2, #1
# Note: Falk used 10 in the denom below, but indexed sample numbers from 1;
# but sample_num passed to _follow_gradient is indexed from 0, so use 11 below
annealing_function=lambda x, y: x / np.sqrt(11 + y),
max_iterations=100
# save_samples=True,
# save_values=True,
# direction=pnl.ASCENT
),
# opt control alloc used to compute ctrl sigs
control_signals=[
pnl.ControlSignal(
default_allocation=default_control_signal,
modulates=[(pnl.SLOPE, color_task), ('color_control', word_task)],
# function=pnl.ReLU,
# function=pnl.Logistic,
cost_options=[
pnl.CostFunctions.INTENSITY, pnl.CostFunctions.ADJUSTMENT
],