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)