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_node(m)
m.is_finished_flag = False
comp.add_projection(MappingProjection(), A, C)
comp.add_projection(MappingProjection(), B, 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_flag = True
B.is_finished_flag = True
if i == 4:
C.is_finished_flag = 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 test_connect_compositions_with_complicated_states(self, mode):
inner_composition_1 = Composition(name="comp1")
A = TransferMechanism(name="A1",
default_variable=[[0.0], [0.0]],
function=Linear(slope=2.0))
B = TransferMechanism(name="B1",
default_variable=[[0.0], [0.0]],
function=Linear(slope=3.0))
inner_composition_1.add_node(A)
inner_composition_1.add_node(B)
inner_composition_1.add_projection(MappingProjection(sender=A, receiver=B), A, B)
inner_composition_1.add_projection(MappingProjection(sender=A.output_ports[1], receiver=B.input_ports[1]), A,
B)
inner_composition_2 = Composition(name="comp2")
A2 = TransferMechanism(name="A2",
default_variable=[[0.0], [0.0]],
function=Linear(slope=2.0))
B2 = TransferMechanism(name="B2",
default_variable=[[0.0], [0.0]],
function=Linear(slope=3.0))
inner_composition_2.add_node(A2)
inner_composition_2.add_node(B2)
inner_composition_2.add_projection(MappingProjection(sender=A2, receiver=B2), A2, B2)
inner_composition_2.add_projection(MappingProjection(sender=A2.output_ports[1], receiver=B2.input_ports[1]),
A2, B2)
outer_composition = Composition(name="outer_composition")
outer_composition.add_node(inner_composition_1)
outer_composition.add_node(inner_composition_2)
outer_composition.add_projection(projection=MappingProjection(), sender=inner_composition_1,
receiver=inner_composition_2)
outer_composition.add_projection(
projection=MappingProjection(sender=inner_composition_1.output_CIM.output_ports[1],
receiver=inner_composition_2.input_CIM.input_ports[1]),
sender=inner_composition_1, receiver=inner_composition_2)
sched = Scheduler(composition=outer_composition)
output = outer_composition.run(
inputs={inner_composition_1: [[[5.0], [50.0]]]},
scheduler=sched,
bin_execute=mode
)
assert np.allclose(output, [[[180.], [1800.]]])
if mode == 'Python':
assert np.allclose(inner_composition_1.get_output_values(outer_composition), [[30.], [300.]])
assert np.allclose(inner_composition_2.get_output_values(outer_composition), [[180.], [1800.]])
assert np.allclose(outer_composition.get_output_values(outer_composition), [[180.], [1800.]])
def test_connect_outer_composition_to_all_input_nodes_in_inner_comp(self):
inner1 = Composition(name="inner")
A1 = TransferMechanism(name="A1", function=Linear(slope=2.0))
B1 = TransferMechanism(name="B1", function=Linear(slope=3.0))
inner1.add_linear_processing_pathway([A1, B1])
inner2 = Composition(name="inner2")
A2 = TransferMechanism(name="A2")
B2 = TransferMechanism(name="B2")
C2 = TransferMechanism(name="C2")
inner2.add_nodes([A2, B2, C2])
outer1 = Composition(name="outer1")
outer1.add_nodes([inner1, inner2])
# Spec 1: add projection *node in* inner1 --> inner 2 (implies first InputPort -- corresponding to A2)
outer1.add_projection(sender=B1, receiver=inner2)
# Spec 2: add projection *node in* inner1 --> *node in* inner2
outer1.add_projection(sender=B1, receiver=B2)
# Spec 3: add projection inner1 --> *node in* inner2
outer1.add_projection(sender=inner1, receiver=C2)
eid = "eid"
outer1.run(inputs={inner1: [[1.]]}, context=eid)
assert np.allclose(A1.parameters.value.get(eid), [[2.0]])
assert np.allclose(B1.parameters.value.get(eid), [[6.0]])
for node in [A2, B2, C2]:
assert np.allclose(node.parameters.value.get(eid), [[6.0]])
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_node(m)
comp.add_projection(MappingProjection(), A, 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_one_input_port_one_output_port(self):
comp = Composition()
A = TransferMechanism(name="A",
function=Linear(slope=2.0))
B = TransferMechanism(name="B",
function=Linear(slope=3.0))
comp.add_node(A)
comp.add_node(B)
comp.add_projection(MappingProjection(sender=A, receiver=B), A, B)
inputs_dict = {
A: [[5.]],
}
sched = Scheduler(composition=comp)
output = comp.run(
inputs=inputs_dict,
scheduler=sched
)
assert np.allclose([30], output)
def test_invtriangle_1(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_node(m)
comp.add_projection(MappingProjection(), A, C)
comp.add_projection(MappingProjection(), B, 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_triangle_4b(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_node(m)
comp.add_projection(MappingProjection(), A, B)
comp.add_projection(MappingProjection(), A, 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_6_two_trials(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_node(m)
comp.add_projection(MappingProjection(), A, B)
comp.add_projection(MappingProjection(), B, 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: [[0], [1], [2], [3], [4], [5]]},
scheduler_processing=sched,
termination_processing=termination_conds
)
output = sched.execution_list[comp.default_execution_id]
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_two_input_states_two_output_states(self):
comp = Composition()
A = TransferMechanism(name="A",
default_variable=[[0.0], [0.0]],
function=Linear(slope=2.0))
B = TransferMechanism(name="B",
default_variable=[[0.0], [0.0]],
function=Linear(slope=3.0))
comp.add_node(A)
comp.add_node(B)
comp.add_projection(MappingProjection(sender=A, receiver=B), A, B)
comp.add_projection(
MappingProjection(sender=A.output_states[1],
receiver=B.input_states[1]), A, B)
comp._analyze_graph()
inputs_dict = {
A: [[5.], [6.]],
}
sched = Scheduler(composition=comp)
output = comp.run(inputs=inputs_dict, scheduler_processing=sched)
assert np.allclose([[30.], [36.]], output)
def test_checkmark2_1(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')
D = TransferMechanism(function=Linear(intercept=.5), name='scheduler-pytests-D')
for m in [A, B, C, D]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, B)
comp.add_projection(MappingProjection(), A, D)
comp.add_projection(MappingProjection(), B, D)
comp.add_projection(MappingProjection(), C, 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_9(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_node(m)
comp.add_projection(MappingProjection(), A, 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
A.is_finished_flag = False
for step in sched.run(termination_conds=termination_conds):
if i == 3:
A.is_finished_flag = 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_composite_condition_multi(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_node(m)
comp.add_projection(MappingProjection(), A, B)
comp.add_projection(MappingProjection(), B, C)
sched = Scheduler(composition=comp)
sched.add_condition(A, EveryNPasses(1))
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, All(
Any(
AfterPass(6),
AfterNCalls(B, 2)
),
Any(
AfterPass(2),
AfterNCalls(B, 3)
)
)
)
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, B, A, A, B, C, A, C, A, B, C
]
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_flag = False
B = TransferMechanism(function=Linear(intercept=4.0), name='B')
B.is_finished_flag = True
C = TransferMechanism(function=Linear(intercept=1.5), name='C')
for m in [A, B, C]:
comp.add_node(m)
comp.add_projection(MappingProjection(), A, C)
comp.add_projection(MappingProjection(), B, 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_LCAMechanism_length_2(self, benchmark, mode):
# Note: since the LCAMechanism's threshold is not specified in this test, each execution only updates
# the Mechanism once.
T = TransferMechanism(function=Linear(slope=1.0), size=2)
L = LCAMechanism(function=Linear(slope=2.0),
size=2,
self_excitation=3.0,
leak=0.5,
competition=1.0,
time_step_size=0.1)
C = Composition()
C.add_linear_processing_pathway([T,L])
L.reset_stateful_function_when = Never()
# - - - - - - - Equations to be executed - - - - - - -
# new_transfer_input =
# previous_transfer_input
# + (leak * previous_transfer_input_1 + self_excitation * result1 + competition * result2 + outside_input1) * dt
# + noise
# result = new_transfer_input*2.0
# recurrent_matrix = [[3.0]]
# - - - - - - - - - - - - - - - - - - - - - - - - - -
C.run(inputs={T: [1.0, 2.0]}, num_trials=3, bin_execute=mode)
# - - - - - - - TRIAL 1 - - - - - - -
# new_transfer_input_1 = 0.0 + ( 0.5 * 0.0 + 3.0 * 0.0 - 1.0*0.0 + 1.0)*0.1 + 0.0 = 0.1
# f(new_transfer_input_1) = 0.1 * 2.0 = 0.2
# new_transfer_input_2 = 0.0 + ( 0.5 * 0.0 + 3.0 * 0.0 - 1.0*0.0 + 2.0)*0.1 + 0.0 = 0.2
# f(new_transfer_input_2) = 0.2 * 2.0 = 0.4
# - - - - - - - TRIAL 2 - - - - - - -
# new_transfer_input = 0.1 + ( 0.5 * 0.1 + 3.0 * 0.2 - 1.0*0.4 + 1.0)*0.1 + 0.0 = 0.225
# f(new_transfer_input) = 0.265 * 2.0 = 0.45
# new_transfer_input_2 = 0.2 + ( 0.5 * 0.2 + 3.0 * 0.4 - 1.0*0.2 + 2.0)*0.1 + 0.0 = 0.51
# f(new_transfer_input_2) = 0.1 * 2.0 = 1.02
# - - - - - - - TRIAL 3 - - - - - - -
# new_transfer_input = 0.225 + ( 0.5 * 0.225 + 3.0 * 0.45 - 1.0*1.02 + 1.0)*0.1 + 0.0 = 0.36925
# f(new_transfer_input) = 0.36925 * 2.0 = 0.7385
# new_transfer_input_2 = 0.51 + ( 0.5 * 0.51 + 3.0 * 1.02 - 1.0*0.45 + 2.0)*0.1 + 0.0 = 0.9965
# f(new_transfer_input_2) = 0.9965 * 2.0 = 1.463
assert np.allclose(C.results, [[[0.2, 0.4]], [[0.43, 0.98]], [[0.6705, 1.833]]])
if benchmark.enabled:
benchmark(C.run, inputs={T: [1.0, 2.0]}, num_trials=3, bin_execute=mode)
def test_five_ABABCDE(self):
A = TransferMechanism(
name='A',
default_variable=[0],
function=Linear(slope=2.0),
)
B = TransferMechanism(
name='B',
default_variable=[0],
function=Linear(slope=2.0),
)
C = IntegratorMechanism(name='C',
default_variable=[0],
function=SimpleIntegrator(rate=.5))
D = TransferMechanism(
name='D',
default_variable=[0],
function=Linear(slope=1.0),
)
E = TransferMechanism(
name='E',
default_variable=[0],
function=Linear(slope=2.0),
)
c = Composition(pathways=[[A, C, D], [B, C, E]])
term_conds = {TimeScale.TRIAL: AfterNCalls(E, 1)}
stim_list = {A: [[1]], B: [[2]]}
sched = Scheduler(composition=c)
sched.add_condition(C, Any(EveryNCalls(A, 1), EveryNCalls(B, 1)))
sched.add_condition(D, EveryNCalls(C, 1))
sched.add_condition(E, EveryNCalls(C, 1))
c.scheduler = sched
c.run(inputs=stim_list, termination_processing=term_conds)
terminal_mechs = [D, E]
expected_output = [
[
numpy.array([3.]),
],
[
numpy.array([6.]),
],
]
for m in range(len(terminal_mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(
expected_output[m][i],
terminal_mechs[m].get_output_values(c)[i])
def test_LCAMechanism_length_1(self):
T = TransferMechanism(function=Linear(slope=1.0))
L = LCAMechanism(
function=Linear(slope=2.0),
self_excitation=3.0,
leak=0.5,
competition=
1.0, # competition does not matter because we only have one unit
time_step_size=0.1)
P = Process(pathway=[T, L])
S = System(processes=[P])
L.reinitialize_when = Never()
# - - - - - - - Equations to be executed - - - - - - -
# new_transfer_input =
# previous_transfer_input
# + (leak * previous_transfer_input_1 + self_excitation * result1 + competition * result2 + outside_input1) * dt
# + noise
# result = new_transfer_input*2.0
# recurrent_matrix = [[3.0]]
# - - - - - - - - - - - - - - - - - - - - - - - - - -
results = []
def record_execution():
results.append(L.parameters.value.get(S)[0][0])
S.run(inputs={T: [1.0]},
num_trials=3,
call_after_trial=record_execution)
# - - - - - - - TRIAL 1 - - - - - - -
# new_transfer_input = 0.0 + ( 0.5 * 0.0 + 3.0 * 0.0 + 0.0 + 1.0)*0.1 + 0.0 = 0.1
# f(new_transfer_input) = 0.1 * 2.0 = 0.2
# - - - - - - - TRIAL 2 - - - - - - -
# new_transfer_input = 0.1 + ( 0.5 * 0.1 + 3.0 * 0.2 + 0.0 + 1.0)*0.1 + 0.0 = 0.265
# f(new_transfer_input) = 0.265 * 2.0 = 0.53
# - - - - - - - TRIAL 3 - - - - - - -
# new_transfer_input = 0.265 + ( 0.5 * 0.265 + 3.0 * 0.53 + 0.0 + 1.0)*0.1 + 0.0 = 0.53725
# f(new_transfer_input) = 0.53725 * 2.0 = 1.0745
assert np.allclose(results, [0.2, 0.53, 1.0745])
def test_four_ABBCD(self):
A = TransferMechanism(
name='A',
default_variable=[0],
function=Linear(slope=2.0),
)
B = IntegratorMechanism(name='B',
default_variable=[0],
function=SimpleIntegrator(rate=.5))
C = IntegratorMechanism(name='C',
default_variable=[0],
function=SimpleIntegrator(rate=.5))
D = TransferMechanism(
name='D',
default_variable=[0],
function=Linear(slope=1.0),
)
p = Process(default_variable=[0], pathway=[A, B, D], name='p')
q = Process(default_variable=[0], pathway=[A, C, D], name='q')
s = System(processes=[p, q], name='s')
term_conds = {TimeScale.TRIAL: AfterNCalls(D, 1)}
stim_list = {A: [[1]]}
sched = Scheduler(system=s)
sched.add_condition(B, EveryNCalls(A, 1))
sched.add_condition(C, EveryNCalls(A, 2))
sched.add_condition(D, Any(EveryNCalls(B, 3), EveryNCalls(C, 3)))
s.scheduler_processing = sched
s.run(inputs=stim_list, termination_processing=term_conds)
terminal_mechs = [D]
expected_output = [
[
numpy.array([4.]),
],
]
for m in range(len(terminal_mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(
expected_output[m][i],
terminal_mechs[m].get_output_values(s)[i])
def __init__(self,
sender=None,
receiver=None,
function=Linear(params={FUNCTION_OUTPUT_TYPE:FunctionOutputType.RAW_NUMBER}),
weight=None,
exponent=None,
gating_signal_params:tc.optional(dict)=None,
params=None,
name=None,
prefs:is_pref_set=None,
**kwargs
):
# If receiver has not been assigned, defer init to Port.instantiate_projection_to_state()
if sender is None or receiver is None:
# Flag for deferred initialization
self.initialization_status = ContextFlags.DEFERRED_INIT
# Validate sender (as variable) and params, and assign to variable
# Note: pass name of mechanism (to override assignment of componentName in super.__init__)
super().__init__(
sender=sender,
receiver=receiver,
weight=weight,
exponent=exponent,
function=function,
gating_signal_params=gating_signal_params,
params=params,
name=name,
prefs=prefs,
**kwargs
)
def test_two_ABB(self):
A = TransferMechanism(
name='A',
default_variable=[0],
function=Linear(slope=2.0),
)
B = IntegratorMechanism(name='B',
default_variable=[0],
function=SimpleIntegrator(rate=.5))
p = Process(default_variable=[0], pathway=[A, B], name='p')
s = System(processes=[p], name='s')
term_conds = {TimeScale.TRIAL: AfterNCalls(B, 2)}
stim_list = {A: [[1]]}
sched = Scheduler(system=s)
sched.add_condition(A, Any(AtPass(0), AfterNCalls(B, 2)))
sched.add_condition(B, Any(JustRan(A), JustRan(B)))
s.scheduler_processing = sched
s.run(inputs=stim_list, termination_processing=term_conds)
terminal_mech = B
expected_output = [
numpy.array([2.]),
]
for i in range(len(expected_output)):
numpy.testing.assert_allclose(
expected_output[i],
terminal_mech.get_output_values(s)[i])
def test_two_AAB(self):
A = IntegratorMechanism(name='A',
default_variable=[0],
function=SimpleIntegrator(rate=.5))
B = TransferMechanism(
name='B',
default_variable=[0],
function=Linear(slope=2.0),
)
c = Composition(pathways=[A, B])
term_conds = {TimeScale.TRIAL: AfterNCalls(B, 1)}
stim_list = {A: [[1]]}
sched = Scheduler(composition=c)
sched.add_condition(B, EveryNCalls(A, 2))
c.scheduler = sched
c.run(inputs=stim_list, termination_processing=term_conds)
terminal_mech = B
expected_output = [
numpy.array([2.]),
]
for i in range(len(expected_output)):
numpy.testing.assert_allclose(
expected_output[i],
terminal_mech.get_output_values(c)[i])
class Parameters(ModulatoryProjection_Base.Parameters):
"""
Attributes
----------
function
see `function <Projection_Base.function>`
:default value: `Linear`
:type: `Function`
gating_signal
see `gating_signal <GatingProjection.gating_signal>`
:default value: None
:type:
:read only: True
"""
function = Parameter(Linear(params={FUNCTION_OUTPUT_TYPE: FunctionOutputType.RAW_NUMBER}), stateful=False, loggable=False)
gating_signal = Parameter(None, read_only=True, getter=_gating_signal_getter, setter=_gating_signal_setter, pnl_internal=True)
gating_signal_params = Parameter(
None,
stateful=False,
loggable=False,
read_only=True,
user=False,
pnl_internal=True
)
def test_previous_value_stored(self):
G = LCAMechanism(integrator_mode=True,
leak=-1.0,
noise=0.0,
time_step_size=0.02,
function=Linear(slope=2.0),
self_excitation=1.0,
competition=-1.0,
initial_value=np.array([[1.0]]))
P = Process(pathway=[G])
S = System(processes=[P])
G.output_state.value = [0.0]
# - - - - - LCAMechanism integrator functions - - - - -
# X = previous_value + (rate * previous_value + variable) * self.time_step_size + noise
# f(X) = 2.0*X + 0
# - - - - - starting values - - - - -
# variable = G.output_state.value + stimulus = 0.0 + 1.0 = 1.0
# previous_value = initial_value = 1.0
# single_run = S.execute([[1.0]])
# np.testing.assert_allclose(single_run, np.array([[2.0]]))
np.testing.assert_allclose(S.execute([[1.0]]), np.array([[2.0]]))
# X = 1.0 + (-1.0 + 1.0)*0.02 + 0.0
# X = 1.0 + 0.0 + 0.0 = 1.0 <--- previous value 1.0
# f(X) = 2.0*1.0 <--- return 2.0, recurrent projection 2.0
np.testing.assert_allclose(S.execute([[1.0]]), np.array([[2.08]]))
# X = 1.0 + (-1.0 + 3.0)*0.02 + 0.0
# X = 1.0 + 0.04 = 1.04 <--- previous value 1.04
# f(X) = 2.0*1.04 <--- return 2.08
np.testing.assert_allclose(S.execute([[1.0]]), np.array([[2.1616]]))
def test_kwta_linear_slope(self):
K = KWTAMechanism(name='K',
threshold=.5,
size=5,
k_value=2,
function=Linear(slope=2))
val = K.execute(input=[1, 3, 4, 2, 1])
assert np.allclose(val, [[-2, 2, 4, 0, -2]])
def test_AfterCall(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_node(m)
sched = Scheduler(composition=comp)
sched.add_condition(B, AfterCall(A, 3))
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, set([A, B]), set([A, B])]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_three_ABAC_convenience(self):
A = IntegratorMechanism(name='A',
default_variable=[0],
function=SimpleIntegrator(rate=.5))
B = TransferMechanism(
name='B',
default_variable=[0],
function=Linear(slope=2.0),
)
C = TransferMechanism(
name='C',
default_variable=[0],
function=Linear(slope=2.0),
)
p = Process(default_variable=[0], pathway=[A, B], name='p')
q = Process(default_variable=[0], pathway=[A, C], name='q')
s = System(processes=[p, q], name='s')
term_conds = {TimeScale.TRIAL: AfterNCalls(C, 1)}
stim_list = {A: [[1]]}
s.scheduler_processing.add_condition(
B, Any(AtNCalls(A, 1), EveryNCalls(A, 2)))
s.scheduler_processing.add_condition(C, EveryNCalls(A, 2))
s.run(inputs=stim_list, termination_processing=term_conds)
terminal_mechs = [B, C]
expected_output = [
[
numpy.array([1.]),
],
[
numpy.array([2.]),
],
]
for m in range(len(terminal_mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(
expected_output[m][i],
terminal_mechs[m].get_output_values(s)[i])
def test_create_scheduler_from_system_StroopDemo(self):
Color_Input = TransferMechanism(name='Color Input',
function=Linear(slope=0.2995))
Word_Input = TransferMechanism(name='Word Input',
function=Linear(slope=0.2995))
# Processing Mechanisms (Control)
Color_Hidden = TransferMechanism(
name='Colors Hidden',
function=Logistic(gain=(1.0, ControlProjection)),
)
Word_Hidden = TransferMechanism(
name='Words Hidden',
function=Logistic(gain=(1.0, ControlProjection)),
)
Output = TransferMechanism(
name='Output',
function=Logistic(gain=(1.0, ControlProjection)),
)
# Decision Mechanisms
Decision = DDM(
function=DriftDiffusionAnalytical(
drift_rate=(1.0),
threshold=(0.1654),
noise=(0.5),
starting_point=(0),
t0=0.25,
),
name='Decision',
)
# Outcome Mechanism:
Reward = TransferMechanism(name='Reward')
myComposition = Composition(
pathways=[[Color_Input, Color_Hidden, Output, Decision],
[Word_Input, Word_Hidden, Output, Decision], [Reward]])
sched = Scheduler(composition=myComposition)
expected_consideration_queue = [{Color_Input, Word_Input, Reward},
{Color_Hidden, Word_Hidden}, {Output},
{Decision}]
assert sched.consideration_queue == expected_consideration_queue
def test_multisource_1(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_node(m)
comp.add_projection(MappingProjection(), A1, B1)
comp.add_projection(MappingProjection(), A1, B2)
comp.add_projection(MappingProjection(), A2, B1)
comp.add_projection(MappingProjection(), A2, B2)
comp.add_projection(MappingProjection(), A2, B3)
comp.add_projection(MappingProjection(), B1, C1)
comp.add_projection(MappingProjection(), B2, C1)
comp.add_projection(MappingProjection(), B1, C2)
comp.add_projection(MappingProjection(), B3, C2)
sched = Scheduler(composition=comp)
for m in comp.nodes:
sched.add_condition(m, Always())
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([B1, B2, B3]), set([C1, C2])
]
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_LCAMechanism_length_1(self, benchmark, mode):
T = TransferMechanism(function=Linear(slope=1.0))
L = LCAMechanism(
function=Linear(slope=2.0),
self_excitation=3.0,
leak=0.5,
competition=
1.0, # competition does not matter because we only have one unit
time_step_size=0.1)
C = Composition()
C.add_linear_processing_pathway([T, L])
L.reset_stateful_function_when = Never()
# - - - - - - - Equations to be executed - - - - - - -
# new_transfer_input =
# previous_transfer_input
# + (leak * previous_transfer_input_1 + self_excitation * result1 + competition * result2 + outside_input1) * dt
# + noise
# result = new_transfer_input*2.0
# recurrent_matrix = [[3.0]]
# - - - - - - - - - - - - - - - - - - - - - - - - - -
C.run(inputs={T: [1.0]}, num_trials=3, bin_execute=mode)
# - - - - - - - TRIAL 1 - - - - - - -
# new_transfer_input = 0.0 + ( 0.5 * 0.0 + 3.0 * 0.0 + 0.0 + 1.0)*0.1 + 0.0 = 0.1
# f(new_transfer_input) = 0.1 * 2.0 = 0.2
# - - - - - - - TRIAL 2 - - - - - - -
# new_transfer_input = 0.1 + ( 0.5 * 0.1 + 3.0 * 0.2 + 0.0 + 1.0)*0.1 + 0.0 = 0.265
# f(new_transfer_input) = 0.265 * 2.0 = 0.53
# - - - - - - - TRIAL 3 - - - - - - -
# new_transfer_input = 0.265 + ( 0.5 * 0.265 + 3.0 * 0.53 + 0.0 + 1.0)*0.1 + 0.0 = 0.53725
# f(new_transfer_input) = 0.53725 * 2.0 = 1.0745
assert np.allclose(C.results, [[[0.2]], [[0.51]], [[0.9905]]])
if benchmark.enabled:
benchmark(C.run, inputs={T: [1.0]}, num_trials=3, bin_execute=mode)
def test_kwta_function_various_spec(self):
specs = [
Logistic, Linear,
Linear(slope=3),
Logistic(gain=2, offset=-4.2)
]
for s in specs:
K = KWTAMechanism(name='K', size=5, function=s, k_value=4)
K.execute([1, 2, 5, -2, .3])
def test_processing_mechanism_linear_function(self):
PM1 = ProcessingMechanism()
PM1.execute(1.0)
assert np.allclose(PM1.value, 1.0)
PM2 = ProcessingMechanism(function=Linear(slope=2.0, intercept=1.0))
PM2.execute(1.0)
assert np.allclose(PM2.value, 3.0)
