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_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_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_LCA_length_2(self):
T = TransferMechanism(function=Linear(slope=1.0), size=2)
L = LCA(function=Linear(slope=2.0),
size=2,
self_excitation=3.0,
leak=0.5,
competition=1.0,
time_step_size=0.1)
P = Process(pathway=[T, L])
S = System(processes=[P])
# - - - - - - - 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.value[0])
S.run(inputs={T: [1.0, 2.0]},
num_trials=3,
call_after_trial=record_execution)
# - - - - - - - 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(results,
[[0.2, 0.4], [0.45, 1.02], [0.7385, 1.993]])
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_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),
)
p = Process(default_variable=[0], pathway=[A, C, D], name='p')
q = Process(default_variable=[0], pathway=[B, C, E], name='q')
s = System(processes=[p, q], name='s')
term_conds = {TimeScale.TRIAL: AfterNCalls(E, 1)}
stim_list = {A: [[1]], B: [[2]]}
sched = Scheduler(system=s)
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))
s.scheduler_processing = sched
s.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].output_values[i])
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_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_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_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_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_change_scheduler(self):
comp = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='A')
B = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='B')
for m in [A, B]:
comp.add_mechanism(m)
s1 = Scheduler(composition=comp)
s2 = Scheduler(composition=comp)
cs = ConditionSet(s1)
cs.add_condition(A, Always())
cs.add_condition(B, Always())
assert cs.scheduler is s1
for owner, cond in cs.conditions.items():
assert cond.scheduler is s1
cs.scheduler = s2
assert cs.scheduler is s2
for owner, cond in cs.conditions.items():
assert cond.scheduler is s2
def test_LCA_length_1(self):
T = TransferMechanism(function=Linear(slope=1.0))
L = LCA(
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.value[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 __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):
# Assign args to params and functionParams dicts (kwConstants must == arg names)
params = self._assign_args_to_param_dicts(
function=function,
gating_signal_params=gating_signal_params,
params=params)
# If receiver has not been assigned, defer init to State.instantiate_projection_to_state()
if sender is None or receiver is None:
# Flag for deferred initialization
self.context.initialization_status = ContextFlags.DEFERRED_INIT
# Validate sender (as variable) and params, and assign to variable and paramInstanceDefaults
# 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,
params=params,
name=name,
prefs=prefs,
context=ContextFlags.CONSTRUCTOR)
def test_two_compositions_one_scheduler(self):
comp1 = Composition()
comp2 = Composition()
A = TransferMechanism(function=Linear(slope=5.0, intercept=2.0),
name='scheduler-pytests-A')
comp1.add_mechanism(A)
comp2.add_mechanism(A)
sched = Scheduler(composition=comp1)
sched.add_condition(A, BeforeNCalls(A, 5, time_scale=TimeScale.LIFE))
termination_conds = {}
termination_conds[TimeScale.RUN] = AfterNTrials(6)
termination_conds[TimeScale.TRIAL] = AfterNPasses(1)
comp1.run(inputs={A: [[0], [1], [2], [3], [4], [5]]},
scheduler_processing=sched,
termination_processing=termination_conds)
output = sched.execution_list[comp1._execution_id]
expected_output = [A, A, A, A, A, set()]
# pprint.pprint(output)
assert output == pytest.helpers.setify_expected_output(expected_output)
comp2.run(inputs={A: [[0], [1], [2], [3], [4], [5]]},
scheduler_processing=sched,
termination_processing=termination_conds)
output = sched.execution_list[comp2._execution_id]
expected_output = [A, A, A, A, A, set()]
# pprint.pprint(output)
assert output == pytest.helpers.setify_expected_output(expected_output)
def test_previous_value_stored(self):
G = LCA(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]
# - - - - - LCA 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 __init__(self,
default_gating_policy=None,
size=None,
function=Linear(slope=1, intercept=0),
gating_signals: tc.optional(list) = None,
modulation: tc.optional(
_is_modulation_param) = ModulationParam.MULTIPLICATIVE,
params=None,
name=None,
prefs: is_pref_set = None,
context=None):
# self.system = None
# Assign args to params and functionParams dicts (kwConstants must == arg names)
params = self._assign_args_to_param_dicts(
gating_signals=gating_signals, function=function, params=params)
super().__init__(variable=default_gating_policy,
size=size,
modulation=modulation,
params=params,
name=name,
prefs=prefs,
context=self)
def __init__(
self,
system: tc.optional(System_Base) = None,
monitored_output_states=None,
function=Linear(slope=1, intercept=0),
# control_signals:tc.optional(list) = None,
control_signals=None,
modulation: tc.optional(
_is_modulation_param) = ModulationParam.MULTIPLICATIVE,
params=None,
name=None,
prefs: is_pref_set = None):
# Assign args to params and functionParams dicts (kwConstants must == arg names)
params = self._assign_args_to_param_dicts(
function=function, control_signals=control_signals, params=params)
super().__init__(system=system,
objective_mechanism=ObjectiveMechanism(
monitored_output_states=monitored_output_states,
function=AGTUtilityIntegrator),
control_signals=control_signals,
modulation=modulation,
params=params,
name=name,
prefs=prefs,
context=ContextFlags.CONSTRUCTOR)
self.objective_mechanism.name = self.name + '_ObjectiveMechanism'
self.objective_mechanism._role = CONTROL
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.output_values[i])
def test_transfer_mech_2d_variable(self):
from psyneulink.globals.keywords import MEAN
T = TransferMechanism(name='T',
function=Linear(slope=2.0, intercept=1.0),
default_variable=[[0.0, 0.0], [0.0, 0.0]],
output_states=[MEAN])
val = T.execute([[1.0, 2.0], [3.0, 4.0]])
def test_transfer_mech_2d_variable_noise(self):
T = TransferMechanism(
name='T',
function=Linear(slope=2.0, intercept=1.0),
noise=NormalDist(),
default_variable=[[0.0, 0.0], [0.0, 0.0]]
)
val = T.execute([[1.0, 2.0], [3.0, 4.0]])
def test_transfer_mech_time_constant_0_0(self):
T = TransferMechanism(name='T',
default_variable=[0, 0, 0, 0],
function=Linear(),
time_constant=0.0,
integrator_mode=True)
val = T.execute([1, 1, 1, 1])
assert np.allclose(val, [[0.0, 0.0, 0.0, 0.0]])
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].output_values[i])
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)
def test_transfer_mech_time_constant_0(self):
with pytest.raises(TransferError) as error_text:
T = TransferMechanism(name='T',
default_variable=[0, 0, 0, 0],
function=Linear(),
time_constant=0,
integrator_mode=True)
T.execute([1, 1, 1, 1])
assert ("time_constant parameter" in str(error_text.value)
and "must be a float between 0 and 1" in str(error_text.value))
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_kwta_function_various_spec(self):
specs = [Logistic, Linear, Linear(slope=3), Logistic(gain=2, offset=-4.2)]
for s in specs:
K = KWTA(
name='K',
size=5,
function=s,
k_value=4
)
K.execute([1, 2, 5, -2, .3])
def test_transfer_mech_array_var_float_noise(self):
T = TransferMechanism(name='T',
default_variable=[0, 0, 0, 0],
function=Linear(),
noise=5.0,
time_constant=1.0,
integrator_mode=True)
val = T.execute([0, 0, 0, 0])
assert np.allclose(val, [[5.0, 5.0, 5.0, 5.0]])
def test_kwta_linear_slope(self):
K = KWTA(
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]])