def test_scheduled_contrastive_hebbian(self):
o = pnl.TransferMechanism()
m = pnl.ContrastiveHebbianMechanism(
input_size=2,
hidden_size=0,
target_size=2,
separated=False,
mode=pnl.SIMPLE_HEBBIAN,
integrator_mode=True,
enable_learning=False,
matrix=[[0, -1], [-1, 0]],
# auto=0,
# hetero=-1,
)
# set max passes to ensure failure if no convergence instead of infinite loop
m.max_passes = 1000
s = pnl.sys(m, o)
ms = pnl.Scheduler(system=s)
ms.add_condition(o, pnl.WhenFinished(m))
s.scheduler_processing = ms
# m.reinitialize_when=pnl.Never()
print('matrix:\n', m.afferents[1].matrix)
results = s.run(inputs=[2, 2], num_trials=4)
print(results)
np.testing.assert_allclose(results,
[[np.array([2.])], [np.array([2.])],
[np.array([2.])], [np.array([2.])]])
def test_configure_learning(self):
o = pnl.TransferMechanism()
m = pnl.ContrastiveHebbianMechanism(input_size=2,
hidden_size=0,
target_size=2,
mode=pnl.SIMPLE_HEBBIAN,
separated=False,
matrix=[[0, -.5], [-.5, 0]])
with pytest.warns(UserWarning) as record:
m.learning_enabled = True
correct_message_found = False
for warning in record:
if ("Learning cannot be enabled" in str(warning.message)
and "because it has no LearningMechanism" in str(
warning.message)):
correct_message_found = True
break
assert correct_message_found
m.configure_learning()
m.reinitialize_when = pnl.Never()
s = pnl.sys(m, o)
ms = pnl.Scheduler(system=s)
ms.add_condition(o, pnl.WhenFinished(m))
s.scheduler_processing = ms
results = s.run(inputs=[2, 2], num_trials=4)
np.testing.assert_allclose(
results,
[[[2.671875]], [[2.84093837]], [[3.0510183]], [[3.35234623]]])
def test_prediction_mechanism_assignment():
'''Tests prediction mechanism assignment and more tests for ObjectiveMechanism and ControlSignal assignments'''
T1 = pnl.TransferMechanism(name='T1')
T2 = pnl.TransferMechanism(name='T2')
T3 = pnl.TransferMechanism(name='T3')
S = pnl.sys([T1, T2, T3],
controller=pnl.EVCControlMechanism(name='EVC',
prediction_mechanisms=(pnl.PredictionMechanism,
{pnl.FUNCTION: pnl.INPUT_SEQUENCE,
pnl.RATE: 1,
pnl.WINDOW_SIZE: 3,
}),
monitor_for_control=[T1],
objective_mechanism=[T2]
),
control_signals=pnl.ControlSignal(allocation_samples=[1, 5, 10],
projections=(pnl.SLOPE, T1)),
monitor_for_control=T3,
enable_controller=True
)
assert len(S.controller.objective_mechanism.input_states)==3
S.recordSimulationPref = True
input_dict = {T1:[1,2,3,4]}
results = S.run(inputs=input_dict)
assert results == [[[1.]], [[2.]], [[15.]], [[20.]]]
assert S.simulation_results == [[[1.]], [[5.]], [[10.]],
[[1.]], [[2.]], [[5.]], [[10.]], [[10.]], [[20.]],
[[1.]], [[2.]], [[3.]], [[5.]], [[10.]], [[15.]], [[10.]], [[20.]], [[30.]],
[[2.]], [[3.]], [[4.]], [[10.]], [[15.]], [[20.]], [[20.]], [[30.]], [[40.]]]
def test_prediction_mechanism_filter_function():
'''Tests prediction mechanism assignment and more tests for ObjectiveMechanism and ControlSignal assignments'''
f = lambda x: [x[0]*7]
T = pnl.TransferMechanism(name='T')
S = pnl.sys(T,
controller=pnl.EVCControlMechanism(name='EVC',
prediction_mechanisms=(pnl.PredictionMechanism,
{pnl.FUNCTION: pnl.INPUT_SEQUENCE,
pnl.RATE: 1,
pnl.WINDOW_SIZE: 3,
pnl.FILTER_FUNCTION: f
}),
objective_mechanism=[T]
),
control_signals=pnl.ControlSignal(allocation_samples=[1, 5, 10],
projections=(pnl.SLOPE, T)),
enable_controller=True
)
S.recordSimulationPref = True
input_dict = {T: [1, 2, 3, 4]}
results = S.run(inputs=input_dict)
expected_results = [[[1.0]], [[2.0]], [[3.0]], [[4.0]]]
expected_sim_results = [[[1.]], [[5.]], [[10.]], # before EVC | [1]
[[7.]], [[35.]], [[70.]], # [1, 2]
[[7.]], [[35.]], [[70.]], # [1, 2, 3]
[[14.]], [[70.]], [[140.]]] # [2, 3, 4]
np.testing.assert_allclose(results, expected_results, atol=1e-08, err_msg='Failed on results')
np.testing.assert_allclose(S.simulation_results, expected_sim_results, atol=1e-08, err_msg='Failed on results')
def test_control_mechanism_assignment_additional():
'''Tests "free-standing" specifications of monitor_for_control and ControlSignal (i.e., outside of a list)'''
T_1 = pnl.TransferMechanism(name='T_1')
T_2 = pnl.TransferMechanism(name='T_2')
S = pnl.sys([T_1,T_2],
controller=pnl.EVCControlMechanism(control_signals=(pnl.SLOPE, T_1)),
monitor_for_control=T_1,
control_signals=(pnl.SLOPE, T_2),
enable_controller=True)
assert S.controller.objective_mechanism.input_state.path_afferents[0].sender.owner == T_1
assert T_1.parameter_states[pnl.SLOPE].mod_afferents[0].sender.owner == S.controller
assert T_2.parameter_states[pnl.SLOPE].mod_afferents[0].sender.owner == S.controller