import psyneulink as pnl
comp = pnl.Composition(name="comp")
A = pnl.TransferMechanism(
name="A",
function=pnl.Linear(intercept=2.0, slope=5.0, default_variable=[[0]]),
termination_measure=pnl.Distance(
metric=pnl.MAX_ABS_DIFF, default_variable=[[[0]], [[0]]]
),
)
B = pnl.TransferMechanism(
name="B",
function=pnl.Logistic(default_variable=[[0]]),
termination_measure=pnl.Distance(
metric=pnl.MAX_ABS_DIFF, default_variable=[[[0]], [[0]]]
),
)
C = pnl.RecurrentTransferMechanism(
name="C",
function=pnl.Linear(default_variable=[[0]]),
initial_value=[[0]],
output_ports=["RESULTS"],
termination_measure=pnl.Distance(
metric=pnl.MAX_ABS_DIFF, default_variable=[[[0]], [[0]]]
),
)
D = pnl.IntegratorMechanism(
name="D",
function=pnl.SimpleIntegrator(initializer=[[0]], default_variable=[[0]]),
)
optimizer=pnl.OptimizationControlMechanism(
agent_rep=Umemoto_comp,
state_features=[
Target_Stim.input_port, Distractor_Stim.input_port,
Reward.input_port
],
state_feature_function=pnl.AdaptiveIntegrator(rate=1.0),
objective_mechanism=pnl.ObjectiveMechanism(monitor_for_control=[
Reward,
(Decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1)
], ),
function=pnl.GridSearch(save_values=True),
control_signals=[
Target_Rep_Control_Signal, Distractor_Rep_Control_Signal
],
compute_reconfiguration_cost=pnl.Distance(
metric=pnl.EUCLIDEAN) # 0.8046),
))
Umemoto_comp.enable_model_based_optimizer = True
# Umemoto_comp.model_based_optimizer.set_log_conditions('value')
print(Target_Rep.loggable_items)
nTrials = 3
targetFeatures = [w_t]
flankerFeatures_inc = [w_d]
reward = [0] #[100]
targetInputList = targetFeatures
flankerInputList = flankerFeatures_inc
rewardList = reward
stim_list_dict = {
#but we do need to specify the size, which will be the size of our input array.
input_layer = pnl.TransferMechanism(size=(3), name='INPUT LAYER')
#Next, we specify our output layer. This is where we do our sigmoid transformation, by simply applying the Logistic function.
#The size we specify for this layer is the number of output nodes we want. In this case, we want the network to return a scalar
#for each example (either a 1 or a zero), so our size is 1
output_layer = pnl.TransferMechanism(size=1,
function=pnl.Logistic,
name='OUTPUT LAYER')
#Now, we put them together into a process.
#Notice, that we did not need to specify a weighting matrix. One will automatically be generated by psyneulink when we create our
#process.
# JDC ADDED:
# Normally, for learning to occur in a process, we would just specify that learning=pnl.ENABLED.
# However, if we want to specify a specific learning function or error_function to be used, then we must
# specify it by construction a default LearningProjection and giving it the parameters we want. In this
# case it is the error_function, that we will set to CROSS_ENTROPY (using PsyNeulink's Distance Function):
net2l = pnl.Process(pathway=[input_layer, output_layer],
learning=pnl.LearningProjection(
error_function=pnl.Distance(metric=pnl.CROSS_ENTROPY)))
#The pathway argument specifies in which order to execute the layers. THis way, the output of one will be mapped to the input of
#the next.
#To run the process, we will put it into a system.
sys2l = pnl.System(processes=[net2l], learning_rate=4)
sys2l.show_graph(show_learning=pnl.ALL)
