def test_linear_ABBCC(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
comp.add_projection(B, MappingProjection(), C)
sched = Scheduler(composition=comp)
sched.add_condition(A, Any(AtPass(0), EveryNCalls(C, 2)))
sched.add_condition(B, Any(JustRan(A), JustRan(B)))
sched.add_condition(C, Any(EveryNCalls(B, 2), JustRan(C)))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(
C, 4, time_scale=TimeScale.TRIAL)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, B, B, C, C, A, B, B, C, C]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_no_termination_conds(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
comp.add_projection(B, MappingProjection(), C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, EveryNCalls(B, 3))
output = list(sched.run())
expected_output = [
A,
A,
B,
A,
A,
B,
A,
A,
B,
C,
]
# pprint.pprint(output)
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_6_two_trials(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
comp.add_projection(B, MappingProjection(), C)
sched = Scheduler(composition=comp)
sched.add_condition(A, BeforePass(5))
sched.add_condition(B, AfterNCalls(A, 5))
sched.add_condition(C, AfterNCalls(B, 1))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(2)
termination_conds[TimeScale.TRIAL] = AfterNCalls(C, 3)
comp.run(inputs={A: range(6)},
scheduler_processing=sched,
termination_processing=termination_conds)
output = sched.execution_list
expected_output = [
A, A, A, A, A, B, C, B, C, B, C, A, A, A, A, A, B, C, B, C, B, C
]
# pprint.pprint(output)
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_triangle_4b(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
comp.add_projection(A, MappingProjection(), C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, All(WhenFinished(A), AfterNCalls(B, 3)))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(C, 1)
output = []
i = 0
for step in sched.run(termination_conds=termination_conds):
if i == 10:
A.is_finished = True
output.append(step)
i += 1
expected_output = [A, A, B, A, A, B, A, A, B, A, A, set([B, C])]
# pprint.pprint(output)
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_invtriangle_1(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), C)
comp.add_projection(B, MappingProjection(), C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, Any(AfterNCalls(A, 3), AfterNCalls(B, 3)))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(
C, 4, time_scale=TimeScale.TRIAL)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
A, set([A, B]), A, C,
set([A, B]), C, A, C,
set([A, B]), C
]
# pprint.pprint(output)
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_checkmark2_1(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
D = TransferMechanism(function=Linear(intercept=.5), name='D')
for m in [A, B, C, D]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
comp.add_projection(A, MappingProjection(), D)
comp.add_projection(B, MappingProjection(), D)
comp.add_projection(C, MappingProjection(), D)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, EveryNCalls(A, 2))
sched.add_condition(D, All(EveryNCalls(B, 2), EveryNCalls(C, 2)))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(
D, 1, time_scale=TimeScale.TRIAL)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, set([A, C]), B, A, set([A, C]), B, D]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_6(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='scheduler-pytests-A')
B = TransferMechanism(function=Linear(intercept=4.0),
name='scheduler-pytests-B')
C = TransferMechanism(function=Linear(intercept=1.5),
name='scheduler-pytests-C')
for m in [A, B, C]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
comp.add_projection(B, MappingProjection(), C)
sched = Scheduler(composition=comp)
sched.add_condition(A, BeforePass(5))
sched.add_condition(B, AfterNCalls(A, 5))
sched.add_condition(C, AfterNCalls(B, 1))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(C, 3)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, A, A, A, B, C, B, C, B, C]
# pprint.pprint(output)
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_WhenFinishedAll_2(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
A.is_finished = False
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
B.is_finished = True
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), C)
comp.add_projection(B, MappingProjection(), C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNPasses(1))
sched.add_condition(C, WhenFinishedAll(A, B))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(A, 5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
set([A, B]),
set([A, B]),
set([A, B]),
set([A, B]),
set([A, B]),
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_multisource_2(self):
comp = Composition()
A1 = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A1')
A2 = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A2')
B1 = TransferMechanism(function=Linear(intercept=4.0), name='B1')
B2 = TransferMechanism(function=Linear(intercept=4.0), name='B2')
B3 = TransferMechanism(function=Linear(intercept=4.0), name='B3')
C1 = TransferMechanism(function=Linear(intercept=1.5), name='C1')
C2 = TransferMechanism(function=Linear(intercept=.5), name='C2')
for m in [A1, A2, B1, B2, B3, C1, C2]:
comp.add_mechanism(m)
comp.add_projection(A1, MappingProjection(), B1)
comp.add_projection(A1, MappingProjection(), B2)
comp.add_projection(A2, MappingProjection(), B1)
comp.add_projection(A2, MappingProjection(), B2)
comp.add_projection(A2, MappingProjection(), B3)
comp.add_projection(B1, MappingProjection(), C1)
comp.add_projection(B2, MappingProjection(), C1)
comp.add_projection(B1, MappingProjection(), C2)
comp.add_projection(B3, MappingProjection(), C2)
sched = Scheduler(composition=comp)
sched.add_condition_set({
A1:
Always(),
A2:
Always(),
B1:
EveryNCalls(A1, 2),
B3:
EveryNCalls(A2, 2),
B2:
All(EveryNCalls(A1, 4), EveryNCalls(A2, 4)),
C1:
Any(AfterNCalls(B1, 2), AfterNCalls(B2, 2)),
C2:
Any(AfterNCalls(B2, 2), AfterNCalls(B3, 2)),
})
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = All(AfterNCalls(C1, 1),
AfterNCalls(C2, 1))
output = list(sched.run(termination_conds=termination_conds))
expected_output = [
set([A1, A2]),
set([A1, A2]),
set([B1, B3]),
set([A1, A2]),
set([A1, A2]),
set([B1, B2, B3]),
set([C1, C2])
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_leabra_prec_with_train(self):
in_size = 4
out_size = 4
num_hidden = 1
num_trials = 4
train = True
inputs = [[0, 1, .5, -.2]] * num_trials
train_data = [[.2, .5, 1, -.5]] * num_trials
precision = 0.000000001 # how far we accept error between PNL and Leabra output
random_seed = 2 # because Leabra network initializes with small random weights
random.seed(random_seed)
L_spec = LeabraMechanism(input_size=in_size, output_size=out_size, hidden_layers=num_hidden,
training_flag=train)
random.seed(random_seed)
leabra_net = build_leabra_network(in_size, out_size, num_hidden, None, train)
leabra_net2 = copy.deepcopy(leabra_net)
L_net = LeabraMechanism(leabra_net2)
# leabra_net should be identical to the network inside L_net
T1_spec = TransferMechanism(name='T1', size=in_size, function=Linear)
T2_spec = TransferMechanism(name='T2', size=out_size, function=Linear)
T1_net = TransferMechanism(name='T1', size=in_size, function=Linear)
T2_net = TransferMechanism(name='T2', size=out_size, function=Linear)
p1_spec = Process(pathway=[T1_spec, L_spec])
proj_spec = MappingProjection(sender=T2_spec, receiver=L_spec.input_states[1])
p2_spec = Process(pathway=[T2_spec, proj_spec, L_spec])
s_spec = System(processes=[p1_spec, p2_spec])
p1_net = Process(pathway=[T1_net, L_net])
proj_net = MappingProjection(sender=T2_net, receiver=L_net.input_states[1])
p2_net = Process(pathway=[T2_net, proj_net, L_net])
s_net = System(processes=[p1_net, p2_net])
for i in range(num_trials):
out_spec = s_spec.run(inputs={T1_spec: inputs[i], T2_spec: train_data[i]})
pnl_output_spec = out_spec[-1][0]
leabra_output = train_leabra_network(leabra_net, inputs[i], train_data[i])
diffs_spec = np.abs(np.array(pnl_output_spec) - np.array(leabra_output))
out_net = s_net.run(inputs={T1_net: inputs[i], T2_net: train_data[i]})
pnl_output_net = out_net[-1][0]
diffs_net = np.abs(np.array(pnl_output_net) - np.array(leabra_output))
assert all(diffs_spec < precision) and all(diffs_net < precision)
out_spec = s_spec.run(inputs={T1_spec: inputs, T2_spec: train_data})
pnl_output_spec = np.array(out_spec[-1][0])
for i in range(len(inputs)):
leabra_output = np.array(train_leabra_network(leabra_net, inputs[i], train_data[i]))
diffs_spec = np.abs(pnl_output_spec - leabra_output)
out_net = s_net.run(inputs={T1_net: inputs, T2_net: train_data})
pnl_output_net = np.array(out_net[-1][0])
diffs_net = np.abs(pnl_output_net - leabra_output)
assert all(diffs_spec < precision) and all(diffs_net < precision)
def test_projection_no_args_projection_spec_with_default(self):
p = MappingProjection()
T = TransferMechanism(default_variable=[0, 0], input_states=[p])
np.testing.assert_array_equal(T.instance_defaults.variable,
np.array([[0, 0]]))
assert len(T.input_states) == 1
def test_projection_with_sender_and_default(self):
t = TransferMechanism(size=3)
p = MappingProjection(sender=t)
T = TransferMechanism(default_variable=[[0, 0]], input_states=[p])
np.testing.assert_array_equal(T.instance_defaults.variable, np.array([[0, 0]]))
assert len(T.input_states) == 1
def test_9(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
for m in [A, B]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, WhenFinished(A))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(B, 2)
output = []
i = 0
for step in sched.run(termination_conds=termination_conds):
if i == 3:
A.is_finished = True
output.append(step)
i += 1
expected_output = [A, A, A, A, B, A, B]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_termination_conditions_reset(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='scheduler-pytests-A')
B = TransferMechanism(function=Linear(intercept=4.0),
name='scheduler-pytests-B')
for m in [A, B]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
sched = Scheduler(composition=comp)
sched.add_condition(B, EveryNCalls(A, 2))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(B, 2)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, B, A, A, B]
assert output == pytest.helpers.setify_expected_output(expected_output)
# reset the RUN because schedulers run TRIALs
sched.clock._increment_time(TimeScale.RUN)
sched._reset_counts_total(TimeScale.RUN)
output = list(sched.run())
expected_output = [A, A, B]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_projection_with_matrix_and_sender(self):
m = TransferMechanism(size=2)
p = MappingProjection(sender=m, matrix=[[0, 0, 0], [0, 0, 0]])
T = TransferMechanism(input_states=[p])
np.testing.assert_array_equal(T.instance_defaults.variable, np.array([[0, 0, 0]]))
assert len(T.input_states) == 1
def test_outputstate_(self):
with pytest.raises(MechanismError) as error_text:
p = MappingProjection()
TransferMechanism(default_variable=[0, 0],
input_states=[{
VARIABLE: [0, 0, 0],
PROJECTIONS: [p]
}])
assert mismatches_default_variable_error_text in str(error_text.value)
def test_projection_no_args_dict_spec(self):
p = MappingProjection()
T = TransferMechanism(input_states=[{
VARIABLE: [0, 0, 0],
PROJECTIONS: [p]
}])
np.testing.assert_array_equal(T.instance_defaults.variable,
np.array([[0, 0, 0]]))
assert len(T.input_states) == 1
def test_transfer_mech_process_matrix_change(self):
from psyneulink.components.projections.pathway.mappingprojection import MappingProjection
T1 = TransferMechanism(size=4, function=Linear)
proj = MappingProjection(
matrix=[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
T2 = TransferMechanism(size=4, function=Linear)
p = Process(size=4, pathway=[T1, proj, T2])
p.run(inputs={T1: [[1, 2, 3, 4]]})
proj.matrix = [[2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2]]
assert np.allclose(
proj.matrix,
[[2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2]])
# p.run(inputs={T1: [[1, 2, 3, 4]]})
T1.execute([[1, 2, 3, 4]])
proj.execute(context="EXECUTING testing projection")
assert np.allclose(
proj.matrix,
np.array([[2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2], [2, 2, 2, 2]]))
def test_projection_in_tuple(self):
R2 = TransferMechanism(size=3)
P = MappingProjection(sender=R2)
T = TransferMechanism(size=2, input_states=[(R2, None, None, P)])
np.testing.assert_array_equal(T.instance_defaults.variable,
np.array([[0, 0]]))
assert len(T.input_states) == 1
assert len(T.input_state.path_afferents[0].sender.instance_defaults.
variable) == 3
assert len(T.input_state.instance_defaults.variable) == 2
T.execute()
def test_AtPass_underconstrained(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
comp.add_projection(B, MappingProjection(), C)
sched = Scheduler(composition=comp)
sched.add_condition(A, AtPass(0))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(C, 2)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, B, C, B, C]
assert output == pytest.helpers.setify_expected_output(
expected_output)
def test_WhenFinishedAll_noargs(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), C)
comp.add_projection(B, MappingProjection(), C)
sched = Scheduler(composition=comp)
sched.add_condition(A, Always())
sched.add_condition(B, Always())
sched.add_condition(C, Always())
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = WhenFinishedAll()
output = []
i = 0
for step in sched.run(termination_conds=termination_conds):
if i == 3:
A.is_finished = True
B.is_finished = True
if i == 4:
C.is_finished = True
output.append(step)
i += 1
expected_output = [
set([A, B]),
C,
set([A, B]),
C,
set([A, B]),
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def add_linear_processing_pathway(self, pathway):
# First, verify that the pathway begins with a mechanism
if isinstance(pathway[0], Mechanism):
self.add_mechanism(pathway[0])
else:
# 'MappingProjection has no attribute _name' error is thrown when pathway[0] is passed to the error msg
raise CompositionError(
"The first item in a linear processing pathway must be a Mechanism."
)
# Then, add all of the remaining mechanisms in the pathway
for c in range(1, len(pathway)):
# if the current item is a mechanism, add it
if isinstance(pathway[c], Mechanism):
self.add_mechanism(pathway[c])
# Then, loop through and validate that the mechanism-projection relationships make sense
# and add MappingProjections where needed
for c in range(1, len(pathway)):
if isinstance(pathway[c], Mechanism):
if isinstance(pathway[c - 1], Mechanism):
# if the previous item was also a mechanism, add a mapping projection between them
self.add_projection(
pathway[c - 1],
MappingProjection(sender=pathway[c - 1],
receiver=pathway[c]), pathway[c])
# if the current item is a projection
elif isinstance(pathway[c], Projection):
if c == len(pathway) - 1:
raise CompositionError(
"{} is the last item in the pathway. A projection cannot be the last item in"
" a linear processing pathway.".format(pathway[c]))
# confirm that it is between two mechanisms, then add the projection
if isinstance(pathway[c - 1], Mechanism) and isinstance(
pathway[c + 1], Mechanism):
self.add_projection(pathway[c - 1], pathway[c],
pathway[c + 1])
else:
raise CompositionError(
"{} is not between two mechanisms. A Projection in a linear processing pathway must be preceded"
" by a Mechanism and followed by a Mechanism".format(
pathway[c]))
else:
raise CompositionError(
"{} is not a Projection or Mechanism. A linear processing pathway must be made "
"up of Projections and Mechanisms.".format(pathway[c]))
def _create_input_mechanisms(self):
'''
builds a dictionary of { Mechanism : InputMechanism } pairs where each 'ORIGIN' Mechanism has a
corresponding InputMechanism
'''
is_origin = self.get_mechanisms_by_role(MechanismRole.ORIGIN)
has_input_mechanism = self.input_mechanisms.keys()
# consider all of the mechanisms that are only origins OR have input mechanisms
for mech in is_origin.difference(has_input_mechanism):
# If mech IS AN ORIGIN mechanism but it doesn't have an input mechanism, ADD input mechanism
if mech not in has_input_mechanism:
new_input_mech = CompositionInterfaceMechanism()
self.input_mechanisms[mech] = new_input_mech
MappingProjection(sender=new_input_mech, receiver=mech)
# If mech HAS AN INPUT mechanism but isn't an origin, REMOVE the input mechanism
else:
del self.input_mechanisms[mech]
def test_9b(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
A.is_finished = False
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
for m in [A, B]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, WhenFinished(A))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AtPass(5)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, A, A, A]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_NWhen_AfterNCalls(self, n, expected_output):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
for m in [A, B]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
sched = Scheduler(composition=comp)
sched.add_condition(A, Always())
sched.add_condition(B, NWhen(AfterNCalls(A, 3), n))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(1)
termination_conds[TimeScale.TRIAL] = AfterNCalls(A, 6)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A if x == 'A' else B for x in expected_output]
assert output == pytest.helpers.setify_expected_output(
expected_output)
def test_linear_AAB(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
for m in [A, B]:
comp.add_mechanism(m)
comp.add_projection(A, MappingProjection(), B)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNCalls(A, 2))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNCalls(
B, 2, time_scale=TimeScale.RUN)
termination_conds[TimeScale.TRIAL] = AfterNCalls(
B, 2, time_scale=TimeScale.TRIAL)
output = list(sched.run(termination_conds=termination_conds))
expected_output = [A, A, B, A, A, B]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_gating():
Input_Layer = TransferMechanism(name='Input Layer',
function=Logistic,
default_variable=np.zeros((2, )))
Hidden_Layer_1 = TransferMechanism(name='Hidden Layer_1',
function=Logistic(),
default_variable=np.zeros((5, )))
Hidden_Layer_2 = TransferMechanism(name='Hidden Layer_2',
function=Logistic(),
default_variable=[0, 0, 0, 0])
Output_Layer = TransferMechanism(name='Output Layer',
function=Logistic,
default_variable=[0, 0, 0])
Gating_Mechanism = GatingMechanism(
# default_gating_policy=0.0,
size=[1],
gating_signals=[
Hidden_Layer_1,
Hidden_Layer_2,
Output_Layer,
])
Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
# TEST PROCESS.LEARNING WITH:
# CREATION OF FREE STANDING PROJECTIONS THAT HAVE NO LEARNING (Input_Weights, Middle_Weights and Output_Weights)
# INLINE CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and Output_Weights)
# NO EXPLICIT CREATION OF PROJECTIONS (Input_Weights, Middle_Weights and
# Output_Weights)
# This projection will be used by the process below by referencing it in the process' pathway;
# note: sender and receiver args don't need to be specified
Input_Weights = MappingProjection(name='Input Weights',
matrix=Input_Weights_matrix)
# This projection will be used by the process below by assigning its sender and receiver args
# to mechanismss in the pathway
Middle_Weights = MappingProjection(name='Middle Weights',
sender=Hidden_Layer_1,
receiver=Hidden_Layer_2,
matrix={
VALUE: Middle_Weights_matrix,
FUNCTION: ConstantIntegrator,
FUNCTION_PARAMS: {
INITIALIZER:
Middle_Weights_matrix,
RATE: Middle_Weights_matrix
},
})
Output_Weights = MappingProjection(name='Output Weights',
sender=Hidden_Layer_2,
receiver=Output_Layer,
matrix=Output_Weights_matrix)
z = Process(
# default_variable=[0, 0],
size=2,
pathway=[
Input_Layer,
# The following reference to Input_Weights is needed to use it in the pathway
# since it's sender and receiver args are not specified in its
# declaration above
Input_Weights,
Hidden_Layer_1,
# No projection specification is needed here since the sender arg for Middle_Weights
# is Hidden_Layer_1 and its receiver arg is Hidden_Layer_2
# Middle_Weights,
Hidden_Layer_2,
# Output_Weights does not need to be listed for the same reason as Middle_Weights
# If Middle_Weights and/or Output_Weights is not declared above, then the process
# will assign a default for missing projection
# Output_Weights,
Output_Layer
],
clamp_input=SOFT_CLAMP,
learning=LEARNING,
learning_rate=1.0,
target=[0, 0, 1],
prefs={
VERBOSE_PREF: False,
REPORT_OUTPUT_PREF: True
})
g = Process(default_variable=[1.0], pathway=[Gating_Mechanism])
stim_list = {Input_Layer: [[-1, 30]], Gating_Mechanism: [1.0]}
target_list = {Output_Layer: [[0, 0, 1]]}
def print_header():
print("\n\n**** TRIAL: ", CentralClock.trial)
def show_target():
i = s.input
t = s.target_input_states[0].value
print('\nOLD WEIGHTS: \n')
print('- Input Weights: \n', Input_Weights.matrix)
print('- Middle Weights: \n', Middle_Weights.matrix)
print('- Output Weights: \n', Output_Weights.matrix)
print('\nSTIMULI:\n\n- Input: {}\n- Target: {}\n'.format(i, t))
print('ACTIVITY FROM OLD WEIGHTS: \n')
print('- Middle 1: \n', Hidden_Layer_1.value)
print('- Middle 2: \n', Hidden_Layer_2.value)
print('- Output:\n', Output_Layer.value)
s = System(processes=[z, g], targets=[0, 0, 1], learning_rate=1.0)
s.reportOutputPref = True
# s.show_graph(show_learning=True)
results = s.run(
num_trials=10,
inputs=stim_list,
targets=target_list,
call_before_trial=print_header,
call_after_trial=show_target,
)
def test_projection_with_matrix_and_sender_mismatches_default(self):
with pytest.raises(MechanismError) as error_text:
m = TransferMechanism(size=2)
p = MappingProjection(sender=m, matrix=[[0, 0, 0], [0, 0, 0]])
TransferMechanism(default_variable=[0, 0], input_states=[p])
assert mismatches_default_variable_error_text in str(error_text.value)
def test_leabra_prec_half_train(self):
in_size = 4
out_size = 4
num_hidden = 1
num_trials = 2
train = True
inputs = [[0, 1, .5, -.2]] * num_trials
train_data = [[.2, .5, 1, -.5]] * num_trials
precision = 0.000000001 # how far we accept error between PNL and Leabra output
random_seed = 3 # because Leabra network initializes with small random weights
random.seed(random_seed)
L_spec = LeabraMechanism(input_size=in_size, output_size=out_size, hidden_layers=num_hidden, training_flag=train)
random.seed(random_seed)
leabra_net = build_leabra_network(in_size, out_size, num_hidden, None, train)
leabra_net2 = copy.deepcopy(leabra_net)
L_net = LeabraMechanism(leabra_net2)
# leabra_net should be identical to the network inside L_net
T1_spec = TransferMechanism(name='T1', size=in_size, function=Linear)
T2_spec = TransferMechanism(name='T2', size=out_size, function=Linear)
T1_net = TransferMechanism(name='T1', size=in_size, function=Linear)
T2_net = TransferMechanism(name='T2', size=out_size, function=Linear)
p1_spec = Process(pathway=[T1_spec, L_spec])
proj_spec = MappingProjection(sender=T2_spec, receiver=L_spec.input_states[1])
p2_spec = Process(pathway=[T2_spec, proj_spec, L_spec])
s_spec = System(processes=[p1_spec, p2_spec])
p1_net = Process(pathway=[T1_net, L_net])
proj_net = MappingProjection(sender=T2_net, receiver=L_net.input_states[1])
p2_net = Process(pathway=[T2_net, proj_net, L_net])
s_net = System(processes=[p1_net, p2_net])
for i in range(num_trials): # training round
out_spec = s_spec.run(inputs={T1_spec: inputs[i], T2_spec: train_data[i]})
pnl_output_spec = out_spec[-1][0]
leabra_output = train_leabra_network(leabra_net, inputs[i], train_data[i])
diffs_spec = np.abs(np.array(pnl_output_spec) - np.array(leabra_output))
out_net = s_net.run(inputs={T1_net: inputs[i], T2_net: train_data[i]})
pnl_output_net = out_net[-1][0]
diffs_net = np.abs(np.array(pnl_output_net) - np.array(leabra_output))
assert all(diffs_spec < precision) and all(diffs_net < precision)
# assert np.sum(np.abs(pnl_output_spec - np.array(train_data[0]))) < 0.1
# assert np.sum(np.abs(pnl_output_net - np.array(train_data[0]))) < 0.1
# set all learning rules false
for conn in leabra_net.connections:
conn.spec.lrule = None
L_net.training_flag = False
L_spec.training_flag = False
for i in range(num_trials): # non-training round
out_spec = s_spec.run(inputs={T1_spec: inputs[i], T2_spec: train_data[i]})
pnl_output_spec = out_spec[-1][0]
leabra_output = run_leabra_network(leabra_net, inputs[i])
diffs_spec = np.abs(np.array(pnl_output_spec) - np.array(leabra_output))
out_net = s_net.run(inputs={T1_net: inputs[i], T2_net: train_data[i]})
pnl_output_net = out_net[-1][0]
diffs_net = np.abs(np.array(pnl_output_net) - np.array(leabra_output))
assert all(diffs_spec < precision) and all(diffs_net < precision)
# class TestLeabraMechInSystem:
#
# def test_leabra_mech_learning(self):
# T1 = TransferMechanism(size=5, function=Linear)
# T2 = TransferMechanism(size=3, function=Linear)
# L = LeabraMechanism(input_size=5, output_size=3, hidden_layers=2, hidden_sizes=[4, 4])
# train_data_proj = MappingProjection(sender=T2, receiver=L.input_states[1])
# out = TransferMechanism(size=3, function=Logistic(bias=2))
# p1 = Process(pathway=[T1, L, out], learning=LEARNING, learning_rate=1.0, target=[0, .1, .8])
# p2 = Process(pathway=[T2, train_data_proj, L, out])
# s = System(processes=[p1, p2])
# s.run(inputs = {T1: [1, 2, 3, 4, 5], T2: [0, .5, 1]})
def test_stroop_model_learning(self):
process_prefs = {
REPORT_OUTPUT_PREF: True,
VERBOSE_PREF: False,
}
system_prefs = {
REPORT_OUTPUT_PREF: True,
VERBOSE_PREF: False,
}
colors = TransferMechanism(
default_variable=[0, 0],
function=Linear,
name="Colors",
)
words = TransferMechanism(
default_variable=[0, 0],
function=Linear,
name="Words",
)
hidden = TransferMechanism(
default_variable=[0, 0],
function=Logistic,
name="Hidden",
)
response = TransferMechanism(
default_variable=[0, 0],
function=Logistic(),
name="Response",
)
TransferMechanism(
default_variable=[0, 0],
function=Logistic,
name="Output",
)
CH_Weights_matrix = np.arange(4).reshape((2, 2))
WH_Weights_matrix = np.arange(4).reshape((2, 2))
HO_Weights_matrix = np.arange(4).reshape((2, 2))
CH_Weights = MappingProjection(
name='Color-Hidden Weights',
matrix=CH_Weights_matrix,
)
WH_Weights = MappingProjection(
name='Word-Hidden Weights',
matrix=WH_Weights_matrix,
)
HO_Weights = MappingProjection(
name='Hidden-Output Weights',
matrix=HO_Weights_matrix,
)
color_naming_process = Process(
default_variable=[1, 2.5],
pathway=[colors, CH_Weights, hidden, HO_Weights, response],
learning=LEARNING,
target=[2, 2],
name='Color Naming',
prefs=process_prefs,
)
word_reading_process = Process(
default_variable=[.5, 3],
pathway=[words, WH_Weights, hidden],
name='Word Reading',
learning=LEARNING,
target=[3, 3],
prefs=process_prefs,
)
s = System(
processes=[color_naming_process, word_reading_process],
targets=[20, 20],
name='Stroop Model',
prefs=system_prefs,
)
def show_target():
print('\nColor Naming\n\tInput: {}\n\tTarget: {}'.format(
colors.input_states.values_as_lists, s.targets))
print('Wording Reading:\n\tInput: {}\n\tTarget: {}\n'.format(
words.input_states.values_as_lists, s.targets))
print('Response: \n', response.output_values[0])
print('Hidden-Output:')
print(HO_Weights.matrix)
print('Color-Hidden:')
print(CH_Weights.matrix)
print('Word-Hidden:')
print(WH_Weights.matrix)
stim_list_dict = {colors: [[1, 1]], words: [[-2, -2]]}
target_list_dict = {response: [[1, 1]]}
results = s.run(
num_trials=2,
inputs=stim_list_dict,
targets=target_list_dict,
call_after_trial=show_target,
)
results_list = []
for elem in s.results:
for nested_elem in elem:
nested_elem = nested_elem.tolist()
try:
iter(nested_elem)
except TypeError:
nested_elem = [nested_elem]
results_list.extend(nested_elem)
objective_response = s.mechanisms[3]
objective_hidden = s.mechanisms[7]
expected_output = [
(colors.output_states[0].value, np.array([1., 1.])),
(words.output_states[0].value, np.array([-2., -2.])),
(hidden.output_states[0].value, np.array([0.13227553,
0.01990677])),
(response.output_states[0].value, np.array([0.51044657,
0.5483048])),
(objective_response.output_states[0].value,
np.array([0.48955343, 0.4516952])),
(objective_response.output_states[MSE].value,
np.array(0.22184555903789838)),
(objective_hidden.output_states[0].value, np.array([0., 0.])),
(CH_Weights.matrix,
np.array([
[0.02512045, 1.02167245],
[2.02512045, 3.02167245],
])),
(WH_Weights.matrix,
np.array([
[-0.05024091, 0.9566551],
[1.94975909, 2.9566551],
])),
(HO_Weights.matrix,
np.array([
[0.03080958, 1.02830959],
[2.00464242, 3.00426575],
])),
(results, [[np.array([0.50899214, 0.54318254])],
[np.array([0.51044657, 0.5483048])]]),
]
for i in range(len(expected_output)):
val, expected = expected_output[i]
# setting absolute tolerance to be in accordance with reference_output precision
# if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
# which WILL FAIL unless you gather higher precision values to use as reference
np.testing.assert_allclose(
val,
expected,
atol=1e-08,
err_msg='Failed on expected_output[{0}]'.format(i))
