def test_formats_for_control_specification_for_mechanism_and_function_params(
self):
control_spec_list = [
pnl.CONTROL, pnl.CONTROL_SIGNAL, pnl.CONTROL_PROJECTION,
pnl.ControlSignal,
pnl.ControlSignal(), pnl.ControlProjection, "CP_OBJECT",
pnl.ControlMechanism,
pnl.ControlMechanism(), (0.3, pnl.CONTROL),
(0.3, pnl.CONTROL_SIGNAL), (0.3, pnl.CONTROL_PROJECTION),
(0.3, pnl.ControlSignal), (0.3, pnl.ControlSignal()),
(0.3, pnl.ControlProjection), (0.3, "CP_OBJECT"),
(0.3, pnl.ControlMechanism), (0.3, pnl.ControlMechanism())
]
for i, ctl_tuple in enumerate(
[j for j in zip(control_spec_list, reversed(control_spec_list))]):
C1, C2 = ctl_tuple
# This shenanigans is to avoid assigning the same instantiated ControlProjection more than once
if C1 is 'CP_OBJECT':
C1 = pnl.ControlProjection()
elif isinstance(C1, tuple) and C1[1] is 'CP_OBJECT':
C1 = (C1[0], pnl.ControlProjection())
if C2 is 'CP_OBJECT':
C2 = pnl.ControlProjection()
elif isinstance(C2, tuple) and C2[1] is 'CP_OBJECT':
C2 = (C2[0], pnl.ControlProjection())
R = pnl.RecurrentTransferMechanism(noise=C1,
function=pnl.Logistic(gain=C2))
assert R.parameter_states[pnl.NOISE].mod_afferents[0].name in \
'ControlProjection for RecurrentTransferMechanism-{}[noise]'.format(i)
assert R.parameter_states[pnl.GAIN].mod_afferents[0].name in \
'ControlProjection for RecurrentTransferMechanism-{}[gain]'.format(i)
def test_deferred_init_default_ModulatoryProjection_names(self):
LP1 = pnl.LearningProjection()
LP2 = pnl.LearningProjection()
assert LP1.name == 'Deferred Init LearningProjection'
assert LP2.name == 'Deferred Init LearningProjection-1'
CP1 = pnl.ControlProjection()
CP2 = pnl.ControlProjection()
assert CP1.name == 'Deferred Init ControlProjection'
assert CP2.name == 'Deferred Init ControlProjection-1'
GP1 = pnl.GatingProjection()
GP2 = pnl.GatingProjection()
assert GP1.name == 'Deferred Init GatingProjection'
assert GP2.name == 'Deferred Init GatingProjection-1'
def test_formats_for_control_specification_for_mechanism_and_function_params(
self, noise, gain):
# This shenanigans is to avoid assigning the same instantiated ControlProjection more than once
if noise == 'CP_OBJECT':
noise = pnl.ControlProjection()
elif isinstance(noise, tuple) and noise[1] == 'CP_OBJECT':
noise = (noise[0], pnl.ControlProjection())
if gain == 'CP_OBJECT':
gain = pnl.ControlProjection()
elif isinstance(gain, tuple) and gain[1] == 'CP_OBJECT':
gain = (gain[0], pnl.ControlProjection())
R = pnl.RecurrentTransferMechanism(
# NOTE: fixed name prevents failures due to registry naming
# for parallel test runs
name='R-CONTROL',
noise=noise,
function=psyneulink.core.components.functions.transferfunctions.
Logistic(gain=gain))
assert R.parameter_ports[pnl.NOISE].mod_afferents[0].name in \
'ControlProjection for R-CONTROL[noise]'
assert R.parameter_ports[pnl.GAIN].mod_afferents[0].name in \
'ControlProjection for R-CONTROL[gain]'
# 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,
control_signal_params={pnl.ALLOCATION_SAMPLES: signalSearchRange})
)),
prefs=mechanism_prefs)
Flanker_Rep = pnl.TransferMechanism(
name='Flanker Representation',
function=pnl.Linear(slope=(
1.0,
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=psyneulink.core.components.functions.transferfunctions.Linear(
slope=0.3324))
Flanker_Stim = pnl.TransferMechanism(
name='Flanker Stimulus',
function=psyneulink.core.components.functions.transferfunctions.Linear(
slope=0.3545221843))
# Processing Mechanisms (Control)
Target_Rep = pnl.TransferMechanism(
name='Target Representation',
function=psyneulink.core.components.functions.transferfunctions.Linear(
slope=(
1.0,
pnl.ControlProjection(function=psyneulink.core.components.
functions.transferfunctions.Linear,
control_signal_params={
pnl.ALLOCATION_SAMPLES: signalSearchRange
}))),
prefs=mechanism_prefs)
Flanker_Rep = pnl.TransferMechanism(
name='Flanker Representation',
function=psyneulink.core.components.functions.transferfunctions.Linear(
slope=(
1.0,
pnl.ControlProjection(function=psyneulink.core.components.
functions.transferfunctions.Linear,
control_signal_params={
pnl.ALLOCATION_SAMPLES: signalSearchRange
}))),
prefs=mechanism_prefs)
function=psyneulink.core.components.functions.transferfunctions.Linear(
slope=0.3324))
Target_Stim.set_log_conditions('value')
Flanker_Stim = pnl.TransferMechanism(
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(
words_input_layer = pnl.TransferMechanism(
size=2,
function=psyneulink.core.components.functions.transferfunctions.Linear,
name='WORDS_INPUT')
# Specify signalSearchRange for control_signal_params (not sure if needed)
#signalSearchRange = np.array([1.0])#np.arange(1.0,2.1,0.5) # why 0.8 to 2.0 in increments of 0.2 )#
# Task layer, tasks: ('name the color', 'read the word')
task_layer = pnl.TransferMechanism(
size=2,
function=psyneulink.core.components.functions.transferfunctions.
Logistic(gain=(
1.0,
pnl.ControlProjection( # receiver= response_layer.output_ports[1],
# 'DECISION_ENERGY'
# modulation=pnl.ModulationParam.OVERRIDE,#what to implement here
))),
name='TASK')
task_layer.set_log_conditions('gain')
task_layer.set_log_conditions('value')
task_layer.loggable_items
# Hidden layer units, colors: ('red','green') words: ('RED','GREEN')
# Logistic activation function, Gain = 1.0, Bias = -4.0
#should be randomly distributed noise to the net input of each unit (except input unit)
#should have tau = integration_rate = 0.1
colors_hidden_layer = pnl.TransferMechanism(
size=2,
function=psyneulink.core.components.functions.transferfunctions.Logistic(
gain=1.0, x_0=4.0),
import numpy as np
from psyneulink import *
# TEST GaussianProcessOptimization in context of EVC-Gratton model
# Mechanisms
Input = pnl.TransferMechanism(name='Input', )
Reward = pnl.TransferMechanism(
output_ports=[pnl.RESULT, pnl.MEAN, pnl.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),
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))
# Linear input units, colors: ('red', 'green'), words: ('RED','GREEN')
colors_input_layer = pnl.TransferMechanism(size=2,
function=pnl.Linear,
name='COLORS_INPUT')
words_input_layer = pnl.TransferMechanism(size=2,
function=pnl.Linear,
name='WORDS_INPUT')
# Specify signalSearchRange for control_signal_params (not sure if needed)
#signalSearchRange = np.array([1.0])#np.arange(1.0,2.1,0.5) # why 0.8 to 2.0 in increments of 0.2 )#
# Task layer, tasks: ('name the color', 'read the word')
task_layer = pnl.TransferMechanism(size=2,
function=pnl.Logistic(gain=(1.0, pnl.ControlProjection(#receiver= response_layer.output_states[1],
#'DECISION_ENERGY'
#modulation=pnl.ModulationParam.OVERRIDE,#what to implement here
))),
name='TASK')
task_layer.set_log_conditions('gain')
task_layer.set_log_conditions('value')
task_layer.loggable_items
# Hidden layer units, colors: ('red','green') words: ('RED','GREEN')
# Logistic activation function, Gain = 1.0, Bias = -4.0
#should be randomly distributed noise to the net input of each unit (except input unit)
#should have tau = integration_rate = 0.1
colors_hidden_layer = pnl.TransferMechanism(size=2,
function=pnl.Logistic(gain=1.0, bias=4.0),
integrator_mode=True,
# noise=pnl.NormalDist(mean=0.0, standard_dev=.005).function,
