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_four_integrators_mixed(self):
A = IntegratorMechanism(name='A',
default_variable=[0],
function=SimpleIntegrator(rate=1))
B = IntegratorMechanism(name='B',
default_variable=[0],
function=SimpleIntegrator(rate=1))
C = IntegratorMechanism(name='C',
default_variable=[0],
function=SimpleIntegrator(rate=1))
D = IntegratorMechanism(name='D',
default_variable=[0],
function=SimpleIntegrator(rate=1))
p = Process(default_variable=[0], pathway=[A, C], name='p')
p1 = Process(default_variable=[0], pathway=[A, D], name='p1')
q = Process(default_variable=[0], pathway=[B, C], name='q')
q1 = Process(default_variable=[0], pathway=[B, D], name='q1')
s = System(processes=[p, p1, q, q1], name='s')
term_conds = {
TimeScale.TRIAL: All(AfterNCalls(C, 1), AfterNCalls(D, 1))
}
stim_list = {A: [[1]], B: [[1]]}
sched = Scheduler(system=s)
sched.add_condition(B, EveryNCalls(A, 2))
sched.add_condition(C, EveryNCalls(A, 1))
sched.add_condition(D, EveryNCalls(B, 1))
s.scheduler_processing = sched
s.run(inputs=stim_list, termination_processing=term_conds)
mechs = [A, B, C, D]
expected_output = [
[
numpy.array([2.]),
],
[
numpy.array([1.]),
],
[
numpy.array([4.]),
],
[
numpy.array([3.]),
],
]
for m in range(len(mechs)):
for i in range(len(expected_output[m])):
numpy.testing.assert_allclose(expected_output[m][i],
mechs[m].get_output_values(s)[i])
def test_kwta_size_10_k_3_threshold_1(self):
K = KWTAMechanism(
name='K',
size=10,
k_value=3,
threshold=1,
)
p = Process(pathway=[K], prefs=TestKWTALongTerm.simple_prefs)
s = System(processes=[p], prefs=TestKWTALongTerm.simple_prefs)
kwta_input = {K: [[-1, -.5, 0, 0, 0, 1, 1, 2, 3, 3]]}
print("")
for i in range(20):
s.run(inputs=kwta_input)
print('\ntrial number', i)
print('K.parameters.value.get(s): ', K.parameters.value.get(s))
assert np.allclose(K.parameters.value.get(s), [[0.012938850123312412, 0.022127587008877226, 0.039010157367582114,
0.039010157367582114, 0.039010157367582114, 0.19055156271846602,
0.19055156271846602, 0.969124504436019, 0.9895271824560731, 0.9895271824560731]])
kwta_input2 = {K: [0] * 10}
print('\n\nturning to zero-inputs now:')
for i in range(20):
s.run(inputs=kwta_input2)
print('\ntrial number', i)
print('K.parameters.value.get(s): ', K.parameters.value.get(s))
assert np.allclose(K.parameters.value.get(s), [[0.13127237999481228, 0.13130057846907178, 0.1313653354768465, 0.1313653354768465,
0.1313653354768465, 0.5863768938723602, 0.5863768938723602, 0.8390251365605804,
0.8390251603214743, 0.8390251603214743]])
def test_not_all_output_port_values_have_label(self):
input_labels_dict = {
"red": [1.0, 0.0],
"green": [0.0, 1.0],
"blue": [2.0, 2.0]
}
output_labels_dict = {"red": [1.0, 0.0], "green": [0.0, 1.0]}
M = ProcessingMechanism(size=2,
params={
INPUT_LABELS_DICT: input_labels_dict,
OUTPUT_LABELS_DICT: output_labels_dict
})
P = Process(pathway=[M])
S = System(processes=[P])
store_output_labels = []
def call_after_trial():
store_output_labels.append(M.get_output_labels(S))
S.run(inputs=['red', 'blue', 'green', 'blue'],
call_after_trial=call_after_trial)
assert np.allclose(
S.results,
[[[1.0, 0.0]], [[2.0, 2.0]], [[0.0, 1.0]], [[2.0, 2.0]]])
assert store_output_labels[0] == ['red']
assert np.allclose(store_output_labels[1], [[2.0, 2.0]])
assert store_output_labels[2] == ['green']
assert np.allclose(store_output_labels[3], [[2.0, 2.0]])
def test_system_run_with_sticky_condition(self):
# Construction
T = TransferMechanism()
P = Process(pathway=[T])
S = System(processes=[P])
assert T.noise == 0.0
assert T.parameter_states['noise'].value == 0.0
# Runtime param used for noise
# ONLY mechanism value should reflect runtime param -- attr should be changed back by the time we inspect it
S.run(inputs={T: 2.0},
runtime_params={T: {
"noise": (10.0, AfterTrial(1))
}},
num_trials=4)
# Runtime param NOT used for noise
S.run(inputs={T: 2.0})
assert np.allclose(
S.results,
[
[np.array([2.])], # Trial 0 - condition not satisfied yet
[np.array([2.])], # Trial 1 - condition not satisfied yet
[np.array([12.])], # Trial 2 - condition satisfied
[np.array([12.])], # Trial 3 - condition satisfied (sticky)
[np.array([2.])]
]) # New run (runtime param no longer applies)
def test_recurrent_transfer_origin(self):
R = RecurrentTransferMechanism(has_recurrent_input_state=True)
P = Process(pathway=[R])
S = System(processes=[P])
S.run(inputs={R: [[1.0], [2.0], [3.0]]})
print(S.results)
def test_convergent(self):
a = TransferMechanism(name='a', default_variable=[0, 0])
b = TransferMechanism(name='b')
c = TransferMechanism(name='c')
c = TransferMechanism(name='c', default_variable=[0])
d = TransferMechanism(name='d')
e = TransferMechanism(name='e')
p1 = Process(pathway=[a, b, e], name='p1')
p2 = Process(pathway=[c, d, e], name='p2')
s = System(
processes=[p1, p2],
name='Convergent System',
initial_values={a: [1, 1]},
)
inputs = {a: [[2, 2]], c: [[0]]}
s.run(inputs=inputs)
assert set([a, c]) == set(s.origin_mechanisms.mechanisms)
assert [e] == s.terminal_mechanisms.mechanisms
assert a.systems[s] == ORIGIN
assert b.systems[s] == INTERNAL
assert c.systems[s] == ORIGIN
assert d.systems[s] == INTERNAL
assert e.systems[s] == TERMINAL
def test_reinitialize_one_mechanism_at_trial_2_condition(self):
A = TransferMechanism(name='A')
B = TransferMechanism(name='B',
integrator_mode=True,
integration_rate=0.5)
C = TransferMechanism(name='C')
abc_process = Process(pathway=[A, B, C])
abc_system = System(processes=[abc_process])
# Set reinitialization condition
B.reinitialize_when = AtTrial(2)
C.log.set_log_conditions('value')
abc_system.run(inputs={A: [1.0]},
reinitialize_values={B: [0.]},
num_trials=5)
# Trial 0: 0.5, Trial 1: 0.75, Trial 2: 0.5, Trial 3: 0.75. Trial 4: 0.875
assert np.allclose(
C.log.nparray_dictionary('value')[
abc_system.default_execution_id]['value'],
[[np.array([0.5])], [np.array([0.75])], [np.array([0.5])],
[np.array([0.75])], [np.array([0.875])]])
def cyclic_extended_loop(self):
a = TransferMechanism(name='a', default_variable=[0, 0])
b = TransferMechanism(name='b')
c = TransferMechanism(name='c')
d = TransferMechanism(name='d')
e = TransferMechanism(name='e', default_variable=[0])
f = TransferMechanism(name='f')
p1 = Process(pathway=[a, b, c, d], name='p1')
p2 = Process(pathway=[e, c, f, b, d], name='p2')
s = System(
processes=[p1, p2],
name='Cyclic System with Extended Loop',
initial_values={a: [1, 1]},
)
inputs = {a: [2, 2], e: [0]}
s.run(inputs=inputs)
assert set([a, c]) == set(s.origin_mechanisms.mechanisms)
assert [d] == s.terminal_mechanisms.mechanisms
assert a.systems[s] == ORIGIN
assert b.systems[s] == CYCLE
assert c.systems[s] == INTERNAL
assert d.systems[s] == TERMINAL
assert e.systems[s] == ORIGIN
assert f.systems[s] == INITIALIZE_CYCLE
def test_initialize_mechanisms(self):
A = TransferMechanism(name='A')
B = TransferMechanism(name='B')
C = RecurrentTransferMechanism(name='C', auto=1.0)
abc_process = Process(pathway=[A, B, C])
abc_system = System(processes=[abc_process])
C.log.set_log_conditions('value')
abc_system.run(inputs={A: [1.0, 2.0, 3.0]},
initial_values={
A: 1.0,
B: 1.5,
C: 2.0
},
initialize=True)
abc_system.run(inputs={A: [1.0, 2.0, 3.0]},
initial_values={
A: 1.0,
B: 1.5,
C: 2.0
},
initialize=False)
# Run 1 --> Execution 1: 1 + 2 = 3 | Execution 2: 3 + 2 = 5 | Execution 3: 5 + 3 = 8
# Run 2 --> Execution 1: 8 + 1 = 9 | Execution 2: 9 + 2 = 11 | Execution 3: 11 + 3 = 14
assert np.allclose(
C.log.nparray_dictionary('value')[abc_system.default_execution_id]
['value'], [[[3]], [[5]], [[8]], [[9]], [[11]], [[14]]])
def test_recurrent_mech_change_learning_rate(self):
R = RecurrentTransferMechanism(size=4,
function=Linear,
enable_learning=True,
learning_rate=0.1
)
p = Process(pathway=[R])
s = System(processes=[p])
assert R.learning_rate == 0.1
assert R.learning_mechanism.learning_rate == 0.1
# assert R.learning_mechanism.function.learning_rate == 0.1
s.run(inputs=[[1.0, 1.0, 1.0, 1.0]])
matrix_1 = [[0., 1.1, 1.1, 1.1],
[1.1, 0., 1.1, 1.1],
[1.1, 1.1, 0., 1.1],
[1.1, 1.1, 1.1, 0.]]
assert np.allclose(R.recurrent_projection.mod_matrix, matrix_1)
print(R.recurrent_projection.mod_matrix)
R.learning_rate = 0.9
assert R.learning_rate == 0.9
assert R.learning_mechanism.learning_rate == 0.9
# assert R.learning_mechanism.function.learning_rate == 0.9
s.run(inputs=[[1.0, 1.0, 1.0, 1.0]])
matrix_2 = [[0., 1.911125, 1.911125, 1.911125],
[1.911125, 0., 1.911125, 1.911125],
[1.911125, 1.911125, 0., 1.911125],
[1.911125, 1.911125, 1.911125, 0.]]
# assert np.allclose(R.recurrent_projection.mod_matrix, matrix_2)
print(R.recurrent_projection.mod_matrix)
def test_dict_of_arrays(self):
input_labels_dict = {"red": [1.0, 0.0], "green": [0.0, 1.0]}
output_labels_dict = {"red": [1.0, 0.0], "green": [0.0, 1.0]}
M = ProcessingMechanism(size=2,
params={
INPUT_LABELS_DICT: input_labels_dict,
OUTPUT_LABELS_DICT: output_labels_dict
})
P = Process(pathway=[M])
S = System(processes=[P])
store_output_labels = []
def call_after_trial():
store_output_labels.append(M.get_output_labels(S))
S.run(inputs=['red', 'green', 'green', 'red'],
call_after_trial=call_after_trial)
assert np.allclose(
S.results,
[[[1.0, 0.0]], [[0.0, 1.0]], [[0.0, 1.0]], [[1.0, 0.0]]])
assert store_output_labels == [['red'], ['green'], ['green'], ['red']]
store_output_labels = []
S.run(inputs=[[1.0, 0.0], 'green', [0.0, 1.0], 'red'],
call_after_trial=call_after_trial)
assert np.allclose(
S.results,
[[[1.0, 0.0]], [[0.0, 1.0]], [[0.0, 1.0]], [[1.0, 0.0]],
[[1.0, 0.0]], [[0.0, 1.0]], [[0.0, 1.0]], [[1.0, 0.0]]])
assert store_output_labels == [['red'], ['green'], ['green'], ['red']]
def test_system_run_with_combined_condition(self):
# Construction
T = TransferMechanism()
P = Process(pathway=[T])
S = System(processes=[P])
# Runtime param used for noise
# ONLY mechanism value should reflect runtime param -- attr should be changed back by the time we inspect it
S.run(inputs={T: 2.0},
runtime_params={
T: {
"noise": (10.0, Any(AtTrial(1), AfterTrial(2)))
}
},
num_trials=5)
# Runtime param NOT used for noise
S.run(inputs={T: 2.0})
assert np.allclose(
S.results,
[
[np.array([2.])], # Trial 0 - NOT condition 0, NOT condition 1
[np.array([12.])], # Trial 1 - condition 0, NOT condition 1
[np.array([2.])], # Trial 2 - NOT condition 0, NOT condition 1
[np.array([12.])], # Trial 3 - NOT condition 0, condition 1
[np.array([12.])], # Trial 4 - NOT condition 0, condition 1
[np.array([2.])]
]) # New run (runtime param no longer applies)
def test_dict_of_subdicts(self):
input_labels_dict_M1 = {"red": [1, 1], "green": [0, 0]}
output_labels_dict_M2 = {0: {"red": [0, 0], "green": [1, 1]}}
M1 = ProcessingMechanism(
size=2, params={INPUT_LABELS_DICT: input_labels_dict_M1})
M2 = ProcessingMechanism(
size=2, params={OUTPUT_LABELS_DICT: output_labels_dict_M2})
P = Process(pathway=[M1, M2], learning=ENABLED, learning_rate=0.25)
S = System(processes=[P])
learned_matrix = []
count = []
def record_matrix_after_trial():
learned_matrix.append(M2.path_afferents[0].get_mod_matrix(S))
count.append(1)
S.run(inputs=['red', 'green', 'green', 'red'],
targets=['red', 'green', 'green', 'red'],
call_after_trial=record_matrix_after_trial)
assert np.allclose(S.results,
[[[1, 1]], [[0., 0.]], [[0., 0.]], [[0.5, 0.5]]])
assert np.allclose(learned_matrix, [
np.array([[0.75, -0.25], [-0.25, 0.75]]),
np.array([[0.75, -0.25], [-0.25, 0.75]]),
np.array([[0.75, -0.25], [-0.25, 0.75]]),
np.array([[0.625, -0.375], [-0.375, 0.625]])
])
def test_3_input_ports_2_label_dicts(self):
input_labels_dict = {
0: {
"red": [1, 0],
"green": [0, 1]
},
2: {
"red": [0, 1],
"green": [1, 0]
}
}
M = TransferMechanism(default_variable=[[0, 0], [0, 0], [0, 0]],
params={INPUT_LABELS_DICT: input_labels_dict})
P = Process(pathway=[M])
S = System(processes=[P])
S.run(inputs=[['red', [0, 0], 'green'], ['green', [1, 1], 'red'],
['green', [2, 2], 'green']])
assert np.allclose(S.results,
[[[1, 0], [0, 0], [1, 0]], [[0, 1], [1, 1], [0, 1]],
[[0, 1], [2, 2], [1, 0]]])
S.run(inputs=[['red', [0, 0], [1, 0]], ['green', [1, 1], 'red'],
[[0, 1], [2, 2], 'green']])
assert np.allclose(
S.results, [[[1, 0], [0, 0], [1, 0]], [[0, 1], [1, 1], [0, 1]],
[[0, 1], [2, 2], [1, 0]], [[1, 0], [0, 0], [1, 0]],
[[0, 1], [1, 1], [0, 1]], [[0, 1], [2, 2], [1, 0]]])
def test_change_termination_condition(self):
D = DDM(function=DriftDiffusionIntegrator(threshold=10))
P = Process(pathway=[D])
S = System(processes=[P])
D.set_log_conditions(VALUE)
def change_termination_processing():
if S.termination_processing is None:
S.scheduler_processing.termination_conds = {TimeScale.TRIAL: WhenFinished(D)}
S.termination_processing = {TimeScale.TRIAL: WhenFinished(D)}
elif isinstance(S.termination_processing[TimeScale.TRIAL], AllHaveRun):
S.scheduler_processing.termination_conds = {TimeScale.TRIAL: WhenFinished(D)}
S.termination_processing = {TimeScale.TRIAL: WhenFinished(D)}
else:
S.scheduler_processing.termination_conds = {TimeScale.TRIAL: AllHaveRun()}
S.termination_processing = {TimeScale.TRIAL: AllHaveRun()}
change_termination_processing()
S.run(inputs={D: [[1.0], [2.0]]},
# termination_processing={TimeScale.TRIAL: WhenFinished(D)},
call_after_trial=change_termination_processing,
num_trials=4)
# Trial 0:
# input = 1.0, termination condition = WhenFinished
# 10 passes (value = 1.0, 2.0 ... 9.0, 10.0)
# Trial 1:
# input = 2.0, termination condition = AllHaveRun
# 1 pass (value = 2.0)
expected_results = [[np.array([[10.]]), np.array([[10.]])],
[np.array([[2.]]), np.array([[1.]])],
[np.array([[10.]]), np.array([[10.]])],
[np.array([[2.]]), np.array([[1.]])]]
assert np.allclose(expected_results, S.results)
def test_bypass(self):
a = TransferMechanism(name='a', default_variable=[0, 0])
b = TransferMechanism(name='b', default_variable=[0, 0])
c = TransferMechanism(name='c')
d = TransferMechanism(name='d')
p1 = Process(pathway=[a, b, c, d], name='p1')
p2 = Process(pathway=[a, b, d], name='p2')
s = System(
processes=[p1, p2],
name='Bypass System',
initial_values={a: [1, 1]},
)
inputs = {a: [[2, 2], [0, 0]]}
s.run(inputs=inputs)
assert [a] == s.origin_mechanisms.mechanisms
assert [d] == s.terminal_mechanisms.mechanisms
assert a.systems[s] == ORIGIN
assert b.systems[s] == INTERNAL
assert c.systems[s] == INTERNAL
assert d.systems[s] == TERMINAL
def test_2_target_mechanisms_fn_spec(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B")
C = TransferMechanism(name="learning-process-mech-C")
LP = Process(name="learning-process", pathway=[A, B], learning=ENABLED)
LP2 = Process(name="learning-process2",
pathway=[A, C],
learning=ENABLED)
S = System(
name="learning-system",
processes=[LP, LP2],
)
def target_function():
val_1 = NormalDist(mean=3.0)()
val_2 = NormalDist(mean=3.0)()
return [val_1, val_2]
with pytest.raises(RunError) as error_text:
S.run(inputs={A: [[[1.0]]]}, targets=target_function)
assert 'Target values for' in str(error_text.value) and \
'must be specified in a dictionary' in str(error_text.value)
def test_reinitialize_run(self):
L = LCAMechanism(name="L",
function=Linear,
initial_value=0.5,
integrator_mode=True,
leak=0.1,
competition=0,
self_excitation=1.0,
time_step_size=1.0,
noise=0.0)
P = Process(name="P", pathway=[L])
S = System(name="S", processes=[P])
L.reinitialize_when = Never()
assert np.allclose(L.integrator_function.previous_value, 0.5)
assert np.allclose(L.initial_value, 0.5)
assert np.allclose(L.integrator_function.initializer, 0.5)
S.run(inputs={L: 1.0},
num_trials=2,
initialize=True,
initial_values={L: 0.0})
# IntegratorFunction fn: previous_value + (rate*previous_value + new_value)*time_step_size + noise*(time_step_size**0.5)
# Trial 1 | variable = 1.0 + 0.0
# integration: 0.5 + (0.1*0.5 + 1.0)*1.0 + 0.0 = 1.55
# linear fn: 1.55*1.0 = 1.55
# Trial 2 | variable = 1.0 + 1.55
# integration: 1.55 + (0.1*1.55 + 2.55)*1.0 + 0.0 = 4.255
# linear fn: 4.255*1.0 = 4.255
assert np.allclose(
L.integrator_function.parameters.previous_value.get(S), 4.255)
L.integrator_function.reinitialize(0.9, execution_context=S)
assert np.allclose(
L.integrator_function.parameters.previous_value.get(S), 0.9)
assert np.allclose(L.parameters.value.get(S), 4.255)
L.reinitialize(0.5, execution_context=S)
assert np.allclose(
L.integrator_function.parameters.previous_value.get(S), 0.5)
assert np.allclose(L.parameters.value.get(S), 0.5)
S.run(inputs={L: 1.0}, num_trials=2)
# Trial 3 | variable = 1.0 + 0.5
# integration: 0.5 + (0.1*0.5 + 1.5)*1.0 + 0.0 = 2.05
# linear fn: 2.05*1.0 = 2.05
# Trial 4 | variable = 1.0 + 2.05
# integration: 2.05 + (0.1*2.05 + 3.05)*1.0 + 0.0 = 5.305
# linear fn: 5.305*1.0 = 5.305
assert np.allclose(
L.integrator_function.parameters.previous_value.get(S), 5.305)
assert np.allclose(L.initial_value, 0.5)
assert np.allclose(L.integrator_function.initializer, 0.5)
def test_udf_system_terminal(self):
def myFunction(variable, params, context):
return [variable[0][2], variable[0][0]]
myMech = ProcessingMechanism(function=myFunction, size=3, name='myMech')
T2 = TransferMechanism(size=3, function=Linear)
p2 = Process(pathway=[T2, myMech])
s2 = System(processes=[p2])
s2.run(inputs = {T2: [[1, 2, 3]]})
assert(np.allclose(s2.results[0][0], [3, 1]))
def test_udf_system_origin(self):
def myFunction(variable, params, context):
return [variable[0][1], variable[0][0]]
myMech = ProcessingMechanism(function=myFunction, size=3, name='myMech')
T = TransferMechanism(size=2, function=Linear)
p = Process(pathway=[myMech, T])
s = System(processes=[p])
s.run(inputs = {myMech: [[1, 3, 5]]})
assert np.allclose(s.results[0][0], [3, 1])
def run_twice_in_system(mech, input1, input2=None):
if input2 is None:
input2 = input1
simple_prefs = {REPORT_OUTPUT_PREF: False, VERBOSE_PREF: False}
simple_process = Process(size=mech.size[0], pathway=[mech], name='simple_process')
simple_system = System(processes=[simple_process], name='simple_system', prefs=simple_prefs)
first_output = simple_system.run(inputs={mech: [input1]})
second_output = simple_system.run(inputs={mech: [input2]})
return second_output[1][0]
def test_kwta_threshold_float(self):
K = KWTAMechanism(name='K', size=4, threshold=0.5)
p = Process(pathway=[K], prefs=TestKWTARatio.simple_prefs)
s = System(processes=[p], prefs=TestKWTARatio.simple_prefs)
s.run(inputs={K: [1, 2, 3, 3]})
assert np.allclose(
K.parameters.value.get(s),
[[0.2689414213699951, 0.5, 0.7310585786300049, 0.7310585786300049]
])
def test_kwta_threshold_int(self):
K = KWTAMechanism(name='K', size=4, threshold=-1)
p = Process(pathway=[K], prefs=TestKWTAThreshold.simple_prefs)
s = System(processes=[p], prefs=TestKWTAThreshold.simple_prefs)
s.run(inputs={K: [1, 2, 3, 4]})
assert np.allclose(K.parameters.value.get(s), [[
0.07585818002124355, 0.18242552380635635, 0.3775406687981454,
0.6224593312018546
]])
def test_is_finished_stops_system(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=10.0))
P = Process(pathway=[D])
S = System(processes=[P], reinitialize_mechanisms_when=Never())
S.run(inputs={D: 2.0},
termination_processing={TimeScale.TRIAL: WhenFinished(D)})
# decision variable's value should match threshold
assert D.parameters.value.get(S)[0] == 10.0
# it should have taken 5 executions (and time_step_size = 1.0)
assert D.parameters.value.get(S)[1] == 5.0
def test_kwta_k_value_empty_size_4(self):
K = KWTAMechanism(
name='K',
size=4
)
assert K.k_value == 0.5
p = Process(pathway=[K], prefs=TestKWTARatio.simple_prefs)
s = System(processes=[p], prefs=TestKWTARatio.simple_prefs)
s.run(inputs={K: [1, 2, 3, 4]})
assert np.allclose(K.parameters.value.get(s), [[0.18242552380635635, 0.3775406687981454, 0.6224593312018546, 0.8175744761936437]])
def test_heterogeneous_variables(self):
# from psyneulink.core.components.mechanisms.processing.objectivemechanism import ObjectiveMechanism
a = TransferMechanism(name='a', default_variable=[[0.0], [0.0, 0.0]])
p1 = Process(pathway=[a])
s = System(processes=[p1])
inputs = {a: [[[1.1], [2.1, 2.1]], [[1.2], [2.2, 2.2]]]}
s.run(inputs)
def test_kwta_average_k_1(self):
K = KWTAMechanism(name='K',
size=4,
k_value=1,
threshold=0,
function=Linear,
average_based=True)
p = Process(pathway=[K], prefs=TestKWTAAverageBased.simple_prefs)
s = System(processes=[p], prefs=TestKWTAAverageBased.simple_prefs)
kwta_input = {K: [[1, 2, 3, 4]]}
s.run(inputs=kwta_input)
assert np.allclose(K.parameters.value.get(s), [[-2, -1, 0, 1]])
def test_kwta_ratio_empty(self):
K = KWTAMechanism(
name='K',
size=4
)
p = Process(pathway = [K], prefs = TestKWTARatio.simple_prefs)
s = System(processes=[p], prefs = TestKWTARatio.simple_prefs)
s.run(inputs = {K: [2, 4, 1, 6]})
assert np.allclose(K.parameters.value.get(s), [[0.2689414213699951, 0.7310585786300049, 0.11920292202211755, 0.9525741268224334]])
s.run(inputs = {K: [1, 2, 3, 4]})
assert np.allclose(K.parameters.value.get(s), [[0.09271329298112314, 0.7368459299092773, 0.2631540700907225, 0.9842837170829899]])
def test_dict_target_spec_length2(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B",
default_variable=[[0.0, 0.0]])
LP = Process(name="learning-process", pathway=[A, B], learning=ENABLED)
S = System(name="learning-system", processes=[LP])
S.run(inputs={A: 1.0}, targets={B: [2.0, 3.0]})
S.run(inputs={A: 1.0}, targets={B: [[2.0, 3.0]]})
