def test_log_initialization(self):
T = pnl.TransferMechanism(
prefs={
pnl.LOG_PREF:
pnl.PreferenceEntry(pnl.LogCondition.INITIALIZATION,
pnl.PreferenceLevel.INSTANCE)
})
assert T.logged_items == {'value': 'INITIALIZATION'}
def test_delivery_initialization(self):
con_with_rpc_pipeline = pnl.Context(rpc_pipeline=Queue())
pipeline = con_with_rpc_pipeline.rpc_pipeline
T = pnl.TransferMechanism(
prefs={pnl.DELIVERY_PREF: pnl.PreferenceEntry(pnl.LogCondition.EXECUTION, pnl.PreferenceLevel.INSTANCE)}
)
comp = pnl.Composition(name='comp', nodes=[T])
comp.run([1], context=con_with_rpc_pipeline)
actual = []
while not pipeline.empty():
actual.append(pipeline.get())
assert all([
len(actual) == 1,
actual[0].time == '0:0:0:0',
actual[0].value.shape == [1, 1],
actual[0].value.data == [1.0]
])
import numpy as np
import psyneulink as pnl
# Preferences:
mechanism_prefs = pnl.ComponentPreferenceSet(
prefs={
pnl.VERBOSE_PREF: pnl.PreferenceEntry(False,
pnl.PreferenceLevel.INSTANCE),
# pnl.REPORT_OUTPUT_PREF: pnl.PreferenceEntry(True, pnl.PreferenceLevel.INSTANCE)
})
process_prefs = pnl.ComponentPreferenceSet(
reportOutput_pref=pnl.PreferenceEntry(False, pnl.PreferenceLevel.INSTANCE),
verbose_pref=pnl.PreferenceEntry(True, pnl.PreferenceLevel.INSTANCE))
# Control Parameters
signalSearchRange = np.arange(0.8, 2.0, 0.2)
# 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',
function=pnl.Linear(slope=(
1.0,
pnl.ControlProjection(
function=pnl.Linear,
name='Flanker Stimulus',
function=psyneulink.core.components.functions.transferfunctions.Linear(
slope=0.3545))
Flanker_Stim.set_log_conditions('value')
# Processing Mechanisms (Control)
Target_Rep = pnl.TransferMechanism(
name='Target Representation',
function=psyneulink.core.components.functions.transferfunctions.Linear(
slope=(1.0,
pnl.ControlProjection(control_signal_params={
pnl.ALLOCATION_SAMPLES: signalSearchRange
}))),
prefs={
pnl.LOG_PREF:
pnl.PreferenceEntry(pnl.LogCondition.INITIALIZATION,
pnl.PreferenceLevel.INSTANCE)
})
Target_Rep.set_log_conditions('value') # Log Target_Rep
Target_Rep.set_log_conditions('slope') # Log Target_Rep
Target_Rep.loggable_items
#log initialization
Target_Rep.log.LogCondition = 2
Flanker_Rep = pnl.TransferMechanism(
name='Flanker Representation',
function=psyneulink.core.components.functions.transferfunctions.Linear(
slope=(1.0,
pnl.ControlProjection(control_signal_params={
pnl.ALLOCATION_SAMPLES: signalSearchRange
function=psyneulink.core.components.functions.nonstateful.
transferfunctions.Linear(),
default_variable=[0, 0])
myHiddenLayer = pnl.TransferMechanism(
name='Hidden Layer 1',
function=psyneulink.core.components.functions.nonstateful.
transferfunctions.Logistic(gain=1.0, x_0=0),
default_variable=np.zeros((5, )))
myDDM = pnl.DDM(name='My_DDM',
function=psyneulink.core.components.functions.nonstateful.
distributionfunctions.DriftDiffusionAnalytical(
drift_rate=0.5, threshold=1, starting_point=0.0))
comp = pnl.Composition(name='Neural Network DDM Process',
pathways=[[
myInputLayer,
pnl.get_matrix(pnl.RANDOM_CONNECTIVITY_MATRIX, 2,
5), myHiddenLayer,
pnl.FULL_CONNECTIVITY_MATRIX, myDDM
]])
comp.reportOutputPref = True
myInputLayer.reportOutputPref = True
myHiddenLayer.reportOutputPref = True
myDDM.reportOutputPref = pnl.PreferenceEntry(True,
pnl.PreferenceLevel.INSTANCE)
comp.run([[-1, 2], [2, 3], [5, 5]])
test_mech = pnl.TransferMechanism(size=1)
# Stimulus Mechanisms
Target_Stim = pnl.TransferMechanism(name='Target Stimulus', function=pnl.Linear(slope=0.3324))
Target_Stim.set_log_conditions('value')
Flanker_Stim = pnl.TransferMechanism(name='Flanker Stimulus', function=pnl.Linear(slope=0.3545))
Flanker_Stim.set_log_conditions('value')
# Processing Mechanisms (Control)
Target_Rep = pnl.TransferMechanism(name='Target Representation',
function=pnl.Linear(
slope=(1.0, pnl.ControlProjection(
control_signal_params={
pnl.ALLOCATION_SAMPLES: signalSearchRange}))),
prefs = {pnl.LOG_PREF: pnl.PreferenceEntry(pnl.LogCondition.INITIALIZATION, pnl.PreferenceLevel.INSTANCE)})
Target_Rep.set_log_conditions('value') # Log Target_Rep
Target_Rep.set_log_conditions('slope') # Log Target_Rep
Target_Rep.loggable_items
#log initialization
Target_Rep.log.LogCondition =2
Flanker_Rep = pnl.TransferMechanism(name='Flanker Representation',
function=pnl.Linear(
slope=(1.0, pnl.ControlProjection(
control_signal_params={
pnl.ALLOCATION_SAMPLES: signalSearchRange}))))
Flanker_Rep.set_log_conditions('value') # Log Flanker_Rep
Flanker_Rep.set_log_conditions('slope') # Log Flanker_Rep
def test_search_function(controller=None, **kwargs):
result = np.array(controller.allocationPolicy).reshape(
len(controller.allocationPolicy), -1)
return result
def test_outcome_function(**kwargs):
result = np.array([0])
return result
# Preferences:
mechanism_prefs = pnl.ComponentPreferenceSet(
prefs={
pnl.VERBOSE_PREF:
pnl.PreferenceEntry(False, pnl.PreferenceLevel.INSTANCE),
pnl.REPORT_OUTPUT_PREF:
pnl.PreferenceEntry(True, pnl.PreferenceLevel.INSTANCE)
})
process_prefs = pnl.ComponentPreferenceSet(
reportOutput_pref=pnl.PreferenceEntry(False, pnl.PreferenceLevel.INSTANCE),
verbose_pref=pnl.PreferenceEntry(True, pnl.PreferenceLevel.INSTANCE))
# Control Parameters
signalSearchRange = np.arange(0.8, 2.0, 0.2)
# Stimulus Mechanisms
Target_Stim = pnl.TransferMechanism(name='Target Stimulus',
function=pnl.Linear(slope=0.3324))
Flanker_Stim = pnl.TransferMechanism(name='Flanker Stimulus',
