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]]})