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_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
c = pnl.Composition()
c.add_linear_processing_pathway([m, o])
c.scheduler.add_condition(o, pnl.WhenFinished(m))
c._analyze_graph()
print('matrix:\n', m.afferents[1].matrix)
c.run(inputs={m: [2, 2]}, num_trials=4)
results = c.results
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_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]])
regexp = r"Learning cannot be enabled for .* because it has no LearningMechanism"
with pytest.warns(UserWarning, match=regexp):
m.learning_enabled = True
m.configure_learning()
m.reset_stateful_function_when = pnl.Never()
c = pnl.Composition()
c.add_linear_processing_pathway([m, o])
c.scheduler.add_condition(o, pnl.WhenFinished(m))
c.learn(inputs={m: [2, 2]}, num_trials=4)
results = c.parameters.results.get(c)
np.testing.assert_allclose(
results,
[[[2.671875]], [[2.84093837]], [[3.0510183]], [[3.35234623]]])
