def test_2_item_tuple_from_parameter_state_to_control_signals(self):
C = pnl.ControlMechanism(control_signals=['a','b'])
D = pnl.DDM(name='D3',
function=pnl.BogaczEtAl(drift_rate=(3,C),
threshold=(2,C.control_signals['b']))
)
assert D.parameter_states[pnl.DRIFT_RATE].mod_afferents[0].sender==C.control_signals[0]
assert D.parameter_states[pnl.THRESHOLD].mod_afferents[0].sender==C.control_signals[1]
pnl.ControlProjection(
function=pnl.Linear,
control_signal_params={pnl.ALLOCATION_SAMPLES: signalSearchRange})
)),
prefs=mechanism_prefs)
# Processing Mechanism (Automatic)
Automatic_Component = pnl.TransferMechanism(name='Automatic Component',
function=pnl.Linear(slope=(1.0)),
prefs=mechanism_prefs)
# Decision Mechanisms
Decision = pnl.DDM(
function=pnl.BogaczEtAl(drift_rate=(1.0),
threshold=(0.2645),
noise=(0.5),
starting_point=(0),
t0=0.15),
prefs=mechanism_prefs,
name='Decision',
output_states=[
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD, {
pnl.NAME: 'OFFSET RT',
pnl.VARIABLE: (pnl.OWNER_VALUE, 2),
pnl.FUNCTION: pnl.Linear(0, slope=0.3, intercept=1)
}
],
)
# Outcome Mechanisms:
Flanker_Rep.set_log_conditions('value') # Log Flanker_Rep
Flanker_Rep.set_log_conditions('slope') # Log Flanker_Rep
Flanker_Rep.loggable_items
Target_Rep.log.LogCondition =2
# Processing Mechanism (Automatic)
Automatic_Component = pnl.TransferMechanism(name='Automatic Component',function=pnl.Linear)
Automatic_Component.loggable_items
Automatic_Component.set_log_conditions('value')
# Decision Mechanisms
Decision = pnl.DDM(function=pnl.BogaczEtAl(
drift_rate=1.0,
threshold=0.2645,
# noise=(0.5),
starting_point=0,
t0=0.15
),name='Decision',
output_states=[
pnl.DECISION_VARIABLE,
pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD,
{
pnl.NAME: 'OFFSET_RT',
pnl.VARIABLE: (pnl.OWNER_VALUE, 1),
pnl.FUNCTION: pnl.Linear(0, slope=0.0, intercept=1).function
}
],) #drift_rate=(1.0),threshold=(0.2645),noise=(0.5),starting_point=(0), t0=0.15
Decision.set_log_conditions('DECISION_VARIABLE')
Decision.set_log_conditions('value')
# CONSTRUCTION:
input_layer = pnl.TransferMechanism(size=2, name='Input Layer')
# Takes sum of input layer elements as external component of drift rate
# Notes:
# - drift_rate parameter in constructor for DDM is the "internally modulated" component of the drift_rate;
# - arguments to DDM's function (BogaczEtAl) are specified as CONTROL, so that their values will be determined
# by the EVCControlMechanism of the System to which the action_selection Mechanism is assigned (see below)
# - the input_format argument specifies that the input to the DDM should be one-hot encoded two-element array
# - the output_states argument specifies use of the DECISION_VARIABLE_ARRAY OutputState, which encodes the
# response in the same format as the ARRAY input_format/.
action_selection = pnl.DDM(input_format=pnl.ARRAY,
function=pnl.BogaczEtAl(
drift_rate=pnl.CONTROL,
threshold=pnl.CONTROL,
starting_point=pnl.CONTROL,
noise=pnl.CONTROL,
),
output_states=[pnl.SELECTED_INPUT_ARRAY],
name='DDM')
# Construct Process
# Notes:
# The np.array specifies the matrix used as the Mapping Projection from input_layer to action_selection,
# which insures the left element of the input favors the left action (positive value of DDM decision variable),
# and the right element favors the right action (negative value of DDM decision variable)
# The learning argument specifies Reinforcement as the learning function for the Projection
p = pnl.Process(
default_variable=[0, 0],
# pathway=[input_layer, np.array([[1],[-1]]), action_selection],
pathway=[input_layer, pnl.IDENTITY_MATRIX, action_selection],
import numpy as np
import psyneulink as pnl
myInputLayer = pnl.TransferMechanism(name='Input Layer',
function=pnl.Linear(),
default_variable=[0, 0])
myHiddenLayer = pnl.TransferMechanism(name='Hidden Layer 1',
function=pnl.Logistic(gain=1.0, bias=0),
default_variable=np.zeros((5, )))
myDDM = pnl.DDM(name='My_DDM',
function=pnl.BogaczEtAl(drift_rate=0.5,
threshold=1,
starting_point=0.0))
myProcess = pnl.Process(name='Neural Network DDM Process',
default_variable=[0, 0],
pathway=[
myInputLayer,
pnl.get_matrix(pnl.RANDOM_CONNECTIVITY_MATRIX, 2,
5), myHiddenLayer,
pnl.FULL_CONNECTIVITY_MATRIX, myDDM
])
myProcess.reportOutputPref = True
myInputLayer.reportOutputPref = True
myHiddenLayer.reportOutputPref = True
myDDM.reportOutputPref = pnl.PreferenceEntry(True,
pnl.PreferenceLevel.INSTANCE)