def test_reset_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)
C = Composition(pathways=[L])
L.reset_stateful_function_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)
C.run(
inputs={L: 1.0},
num_trials=2,
# reset_stateful_functions_when=AtRunStart(),
# reset_stateful_functions_when=AtTrialStart(),
initialize_cycle_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(C), 3.755)
L.integrator_function.reset(0.9, context=C)
assert np.allclose(
L.integrator_function.parameters.previous_value.get(C), 0.9)
assert np.allclose(L.parameters.value.get(C), 3.755)
L.reset(0.5, context=C)
assert np.allclose(
L.integrator_function.parameters.previous_value.get(C), 0.5)
assert np.allclose(L.parameters.value.get(C), 0.5)
C.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(C), 4.705)
assert np.allclose(L.initial_value, 0.5)
assert np.allclose(L.integrator_function.initializer, 0.5)
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_LCAMechanism_length_1(self, benchmark, comp_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, execution_mode=comp_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,
execution_mode=comp_mode)