def test_linear_combination_function_in_mechanism(operation, input, input_ports, scale, offset, benchmark, mode): f = pnl.LinearCombination(default_variable=input, operation=operation, scale=scale, offset=offset) p = pnl.ProcessingMechanism(size=[len(input[0])] * len(input), function=f, input_ports=input_ports) if mode == 'Python': EX = p.execute elif mode == 'LLVM': e = pnlvm.execution.MechExecution(p) EX = e.execute elif mode == 'PTX': e = pnlvm.execution.MechExecution(p) EX = e.cuda_execute res = benchmark(EX, input) scale = 1.0 if scale is None else scale offset = 0.0 if offset is None else offset if operation == pnl.SUM: expected = np.sum(input, axis=0) * scale + offset if operation == pnl.PRODUCT: expected = np.product(input, axis=0) * scale + offset assert np.allclose(res, expected)
def test_lvoc_features_function(self): m1 = pnl.TransferMechanism( input_states=["InputState A", "InputState B"]) m2 = pnl.TransferMechanism() c = pnl.Composition() c.add_node(m1, required_roles=pnl.NodeRole.INPUT) c.add_node(m2, required_roles=pnl.NodeRole.INPUT) c._analyze_graph() lvoc = pnl.OptimizationControlMechanism( agent_rep=pnl.RegressionCFA, features=[ m1.input_states[0], m1.input_states[1], m2.input_state, m2 ], feature_function=pnl.LinearCombination(offset=10.0), objective_mechanism=pnl.ObjectiveMechanism(monitor=[m1, m2]), function=pnl.GradientOptimization(max_iterations=1), control_signals=[(pnl.SLOPE, m1), (pnl.SLOPE, m2)]) c.add_node(lvoc) input_dict = {m1: [[1], [1]], m2: [1]} c.run(inputs=input_dict) assert len(lvoc.input_states) == 5 for i in range(1, 5): assert lvoc.input_states[i].function.offset == 10.0
def test_linear_combination_function(variable, operation, exponents, weights, scale, offset, func_mode, benchmark): if weights == 'V': weights = [[-1**i] for i, v in enumerate(variable)] if exponents == 'V': exponents = [[v[0]] for v in variable] f = pnl.LinearCombination(default_variable=variable, operation=operation, exponents=exponents, weights=weights, scale=scale, offset=offset) EX = pytest.helpers.get_func_execution(f, func_mode) res = benchmark(EX, variable) scale = 1.0 if scale is None else scale offset = 0.0 if offset is None else offset exponent = 1.0 if exponents is None else exponents weights = 1.0 if weights is None else weights tmp = (variable**exponent) * weights if operation == pnl.SUM: expected = np.sum(tmp, axis=0) * scale + offset if operation == pnl.PRODUCT: expected = np.product(tmp, axis=0) * scale + offset assert np.allclose(res, expected)
def test_combine_param_conflicting_fct_operation_spec(self): with pytest.raises(pnl.InputStateError) as error_text: t = pnl.TransferMechanism( input_states=pnl.InputState(function=pnl.LinearCombination( operation=pnl.SUM), combine=pnl.PRODUCT)) assert "Specification of 'combine' argument (PRODUCT) conflicts with specification of 'operation' (SUM) " \ "for LinearCombination in 'function' argument for InputState" in str(error_text.value)
def test_combine_param_redundant_fct_constructor_spec(self): t1 = pnl.TransferMechanism(size=2) t2 = pnl.TransferMechanism(size=2) t3 = pnl.TransferMechanism( size=2, input_states=pnl.InputState( function=pnl.LinearCombination(operation=pnl.PRODUCT), combine=pnl.PRODUCT)) p1 = pnl.Process(pathway=[t1, t3]) p2 = pnl.Process(pathway=[t2, t3]) s = pnl.System(processes=[p1, p2]) input_dict = {t1: [1, 2], t2: [3, 4]} val = s.run(inputs=input_dict) assert np.allclose(val, [[3, 8]])
def test_linear_combination_function_in_mechanism(operation, input, size, input_states, scale, offset, expected, benchmark): f = pnl.LinearCombination(default_variable=input, operation=operation, scale=scale, offset=offset) p = pnl.ProcessingMechanism(size=[size] * len(input_states), function=f, input_states=input_states) benchmark.group = "CombinationFunction " + pnl.LinearCombination.componentName + "in Mechanism" res = benchmark(f.execute, input) if expected is None: if operation == pnl.SUM: expected = np.sum(input, axis=0) * scale + offset if operation == pnl.PRODUCT: expected = np.product(input, axis=0) * scale + offset assert np.allclose(res, expected)
def test_linear_combination_function_in_mechanism(operation, input, input_ports, scale, offset, benchmark, mech_mode): f = pnl.LinearCombination(default_variable=input, operation=operation, scale=scale, offset=offset) p = pnl.ProcessingMechanism(size=[len(input[0])] * len(input), function=f, input_ports=input_ports) EX = pytest.helpers.get_mech_execution(p, mech_mode) res = benchmark(EX, input) scale = 1.0 if scale is None else scale offset = 0.0 if offset is None else offset if operation == pnl.SUM: expected = np.sum(input, axis=0) * scale + offset if operation == pnl.PRODUCT: expected = np.product(input, axis=0) * scale + offset assert np.allclose(res, expected)
def test_linear_combination_function(variable, operation, exponents, weights, scale, offset, mode, benchmark): if weights == 'V': weights = [[-1**i] for i, v in enumerate(variable)] if exponents == 'V': exponents = [[v[0]] for v in variable] f = pnl.LinearCombination(default_variable=variable, operation=operation, exponents=exponents, weights=weights, scale=scale, offset=offset) if mode == 'Python': EX = f.function elif mode == 'LLVM': e = pnlvm.execution.FuncExecution(f) EX = e.execute elif mode == 'PTX': e = pnlvm.execution.FuncExecution(f) EX = e.cuda_execute res = benchmark(EX, variable) scale = 1.0 if scale is None else scale offset = 0.0 if offset is None else offset exponent = 1.0 if exponents is None else exponents weights = 1.0 if weights is None else weights tmp = (variable**exponent) * weights if operation == pnl.SUM: expected = np.sum(tmp, axis=0) * scale + offset if operation == pnl.PRODUCT: expected = np.product(tmp, axis=0) * scale + offset assert np.allclose(res, expected)
signalSearchRange = pnl.SampleSpec(start=1.0, stop=1.8, step=0.2) target_rep_control_signal = pnl.ControlSignal( projections=[(pnl.SLOPE, Target_Rep)], variable=1.0, intensity_cost_function=pnl.Exponential(rate=0.8046), allocation_samples=signalSearchRange) flanker_rep_control_signal = pnl.ControlSignal( projections=[(pnl.SLOPE, Flanker_Rep)], variable=1.0, intensity_cost_function=pnl.Exponential(rate=0.8046), allocation_samples=signalSearchRange) objective_mech = pnl.ObjectiveMechanism( function=pnl.LinearCombination(operation=pnl.PRODUCT), monitor=[ reward, (Decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1) ]) # Model Based OCM (formerly controller) evc_gratton.add_controller(controller=pnl.OptimizationControlMechanism( agent_rep=evc_gratton, features=[ target_stim.input_port, flanker_stim.input_port, reward.input_port ], feature_function=pnl.AdaptiveIntegrator(rate=1.0), objective_mechanism=objective_mech, function=pnl.GridSearch(), control_signals=[target_rep_control_signal, flanker_rep_control_signal])) evc_gratton.show_graph(show_controller=True)
function=pnl.Linear(default_variable=[[0.0, 0.0]]), default_variable=[[0.0, 0.0]], ) word_hidden = pnl.ProcessingMechanism( name="word_hidden", function=pnl.Logistic(bias=-4.0, default_variable=[[0.0, 0.0]]), default_variable=[[0.0, 0.0]], ) task_input = pnl.ProcessingMechanism( name="task_input", function=pnl.Linear(default_variable=[[0.0, 0.0]]), default_variable=[[0.0, 0.0]], ) TASK = pnl.LCAMechanism( name="TASK", combination_function=pnl.LinearCombination(default_variable=[[0.0, 0.0]]), function=pnl.Logistic(default_variable=[[0.0, 0.0]]), integrator_function=pnl.LeakyCompetingIntegrator( name="LeakyCompetingIntegrator_Function_0", initializer=[[0.5, 0.5]], rate=0.5, default_variable=[[0.0, 0.0]], ), output_ports=["RESULTS"], termination_comparison_op=">=", default_variable=[[0.0, 0.0]], ) DECISION = pnl.DDM( name="DECISION", function=pnl.DriftDiffusionAnalytical(default_variable=[[0.0]]), input_ports=[{
def get_stroop_model(unit_noise_std=.01, dec_noise_std=.1): # model params integration_rate = 1 hidden_func = pnl.Logistic(gain=1.0, x_0=4.0) # input layer, color and word reward = pnl.TransferMechanism(name='reward') punish = pnl.TransferMechanism(name='punish') inp_clr = pnl.TransferMechanism( size=N_UNITS, function=pnl.Linear, name='COLOR INPUT' ) inp_wrd = pnl.TransferMechanism( size=N_UNITS, function=pnl.Linear, name='WORD INPUT' ) # task layer, represent the task instruction; color naming / word reading inp_task = pnl.TransferMechanism( size=N_UNITS, function=pnl.Linear, name='TASK' ) # hidden layer for color and word hid_clr = pnl.TransferMechanism( size=N_UNITS, function=hidden_func, integrator_mode=True, integration_rate=integration_rate, # noise=pnl.NormalDist(standard_deviation=unit_noise_std).function, noise=pnl.NormalDist(standard_deviation=unit_noise_std), name='COLORS HIDDEN' ) hid_wrd = pnl.TransferMechanism( size=N_UNITS, function=hidden_func, integrator_mode=True, integration_rate=integration_rate, # noise=pnl.NormalDist(standard_deviation=unit_noise_std).function, noise=pnl.NormalDist(standard_deviation=unit_noise_std), name='WORDS HIDDEN' ) # output layer output = pnl.TransferMechanism( size=N_UNITS, function=pnl.Logistic, integrator_mode=True, integration_rate=integration_rate, # noise=pnl.NormalDist(standard_deviation=unit_noise_std).function, noise=pnl.NormalDist(standard_deviation=unit_noise_std), name='OUTPUT' ) # decision layer, some accumulator signalSearchRange = pnl.SampleSpec(start=0.05, stop=5, step=0.05) decision = pnl.DDM(name='Decision', input_format=pnl.ARRAY, function=pnl.DriftDiffusionAnalytical(drift_rate=1, threshold =1, noise=1, starting_point=0, t0=0.35), output_ports=[pnl.RESPONSE_TIME, pnl.PROBABILITY_UPPER_THRESHOLD, pnl.PROBABILITY_LOWER_THRESHOLD] ) driftrate_control_signal = pnl.ControlSignal(projections=[(pnl.SLOPE, inp_clr)], variable=1.0, intensity_cost_function=pnl.Exponential(rate=1),#pnl.Exponential(rate=0.8),#pnl.Exponential(rate=1), allocation_samples=signalSearchRange) threshold_control_signal = pnl.ControlSignal(projections=[(pnl.THRESHOLD, decision)], variable=1.0, intensity_cost_function=pnl.Linear(slope=0), allocation_samples=signalSearchRange) reward_rate = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.PRODUCT, exponents=[[1],[1],[-1]]), monitor=[reward, decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], decision.output_ports[pnl.RESPONSE_TIME]]) punish_rate = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.PRODUCT, exponents=[[1],[1],[-1]]), monitor=[punish, decision.output_ports[pnl.PROBABILITY_LOWER_THRESHOLD], decision.output_ports[pnl.RESPONSE_TIME]]) objective_mech = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.SUM, weights=[[1],[-1]]), monitor=[reward_rate, punish_rate]) # objective_mech = pnl.ObjectiveMechanism(function=object_function, # monitor=[reward, # punish, # decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], # decision.output_ports[pnl.PROBABILITY_LOWER_THRESHOLD], # (decision.output_ports[pnl.RESPONSE_TIME])]) # PROJECTIONS, weights copied from cohen et al (1990) wts_clr_ih = pnl.MappingProjection( matrix=[[2.2, -2.2], [-2.2, 2.2]], name='COLOR INPUT TO HIDDEN') wts_wrd_ih = pnl.MappingProjection( matrix=[[2.6, -2.6], [-2.6, 2.6]], name='WORD INPUT TO HIDDEN') wts_clr_ho = pnl.MappingProjection( matrix=[[1.3, -1.3], [-1.3, 1.3]], name='COLOR HIDDEN TO OUTPUT') wts_wrd_ho = pnl.MappingProjection( matrix=[[2.5, -2.5], [-2.5, 2.5]], name='WORD HIDDEN TO OUTPUT') wts_tc = pnl.MappingProjection( matrix=[[4.0, 4.0], [0, 0]], name='COLOR NAMING') wts_tw = pnl.MappingProjection( matrix=[[0, 0], [4.0, 4.0]], name='WORD READING') # build the model model = pnl.Composition(name='STROOP model') model.add_node(decision, required_roles=pnl.NodeRole.OUTPUT) model.add_node(reward, required_roles=pnl.NodeRole.OUTPUT) model.add_node(punish, required_roles=pnl.NodeRole.OUTPUT) model.add_linear_processing_pathway([inp_clr, wts_clr_ih, hid_clr]) model.add_linear_processing_pathway([inp_wrd, wts_wrd_ih, hid_wrd]) model.add_linear_processing_pathway([hid_clr, wts_clr_ho, output]) model.add_linear_processing_pathway([hid_wrd, wts_wrd_ho, output]) model.add_linear_processing_pathway([inp_task, wts_tc, hid_clr]) model.add_linear_processing_pathway([inp_task, wts_tw, hid_wrd]) model.add_linear_processing_pathway([output, pnl.IDENTITY_MATRIX, decision]) # 3/15/20 # model.add_linear_processing_pathway([output, [[1,-1]], (decision, pnl.NodeRole.OUTPUT)]) # 3/15/20 # model.add_linear_processing_pathway([output, [[1],[-1]], decision]) # 3/15/20 model.add_nodes([reward_rate, punish_rate]) controller = pnl.OptimizationControlMechanism(agent_rep=model, features=[inp_clr.input_port, inp_wrd.input_port, inp_task.input_port, reward.input_port, punish.input_port], feature_function=pnl.AdaptiveIntegrator(rate=0.1), objective_mechanism=objective_mech, function=pnl.GridSearch(), control_signals=[driftrate_control_signal, threshold_control_signal]) model.add_controller(controller=controller) # collect the node handles nodes = [inp_clr, inp_wrd, inp_task, hid_clr, hid_wrd, output, decision, reward, punish,controller] metadata = [integration_rate, dec_noise_std, unit_noise_std] return model, nodes, metadata
def test_evc_gratton(self): # Stimulus Mechanisms target_stim = pnl.TransferMechanism(name='Target Stimulus', function=pnl.Linear(slope=0.3324)) flanker_stim = pnl.TransferMechanism( name='Flanker Stimulus', function=pnl.Linear(slope=0.3545221843)) # Processing Mechanisms (Control) Target_Rep = pnl.TransferMechanism(name='Target Representation') Flanker_Rep = pnl.TransferMechanism(name='Flanker Representation') # Processing Mechanism (Automatic) Automatic_Component = pnl.TransferMechanism(name='Automatic Component') # Decision Mechanism Decision = pnl.DDM(name='Decision', function=pnl.DriftDiffusionAnalytical( drift_rate=(1.0), threshold=(0.2645), noise=(0.5), starting_point=(0), t0=0.15), output_states=[ pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME, pnl.PROBABILITY_UPPER_THRESHOLD ]) # Outcome Mechanism reward = pnl.TransferMechanism(name='reward') # Pathways target_control_pathway = [target_stim, Target_Rep, Decision] flanker_control_pathway = [flanker_stim, Flanker_Rep, Decision] target_automatic_pathway = [target_stim, Automatic_Component, Decision] flanker_automatic_pathway = [ flanker_stim, Automatic_Component, Decision ] pathways = [ target_control_pathway, flanker_control_pathway, target_automatic_pathway, flanker_automatic_pathway ] # Composition evc_gratton = pnl.Composition(name="EVCGratton") evc_gratton.add_node(Decision, required_roles=pnl.NodeRole.OUTPUT) for path in pathways: evc_gratton.add_linear_processing_pathway(path) evc_gratton.add_node(reward, required_roles=pnl.NodeRole.OUTPUT) # Control Signals signalSearchRange = pnl.SampleSpec(start=1.0, stop=1.8, step=0.2) target_rep_control_signal = pnl.ControlSignal( projections=[(pnl.SLOPE, Target_Rep)], function=pnl.Linear, variable=1.0, intensity_cost_function=pnl.Exponential(rate=0.8046), allocation_samples=signalSearchRange) flanker_rep_control_signal = pnl.ControlSignal( projections=[(pnl.SLOPE, Flanker_Rep)], function=pnl.Linear, variable=1.0, intensity_cost_function=pnl.Exponential(rate=0.8046), allocation_samples=signalSearchRange) objective_mech = pnl.ObjectiveMechanism( function=pnl.LinearCombination(operation=pnl.PRODUCT), monitor=[ reward, (Decision.output_states[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1) ]) # Model Based OCM (formerly controller) evc_gratton.add_controller(controller=pnl.OptimizationControlMechanism( agent_rep=evc_gratton, features=[ target_stim.input_state, flanker_stim.input_state, reward.input_state ], feature_function=pnl.AdaptiveIntegrator(rate=1.0), objective_mechanism=objective_mech, function=pnl.GridSearch(), control_signals=[ target_rep_control_signal, flanker_rep_control_signal ])) evc_gratton.enable_controller = True targetFeatures = [1, 1, 1] flankerFeatures = [1, -1, 1] rewardValues = [100, 100, 100] stim_list_dict = { target_stim: targetFeatures, flanker_stim: flankerFeatures, reward: rewardValues } evc_gratton.run(inputs=stim_list_dict) expected_results_array = [[[0.32257752863413636], [0.9481940753514433], [100.]], [[0.42963678062444666], [0.47661180945923376], [100.]], [[0.300291026852769], [0.97089165101931], [100.]]] expected_sim_results_array = [ [[0.32257753], [0.94819408], [100.]], [[0.31663196], [0.95508757], [100.]], [[0.31093566], [0.96110142], [100.]], [[0.30548947], [0.96633839], [100.]], [[0.30029103], [0.97089165], [100.]], [[0.3169957], [0.95468427], [100.]], [[0.31128378], [0.9607499], [100.]], [[0.30582202], [0.96603252], [100.]], [[0.30060824], [0.9706259], [100.]], [[0.29563774], [0.97461444], [100.]], [[0.31163288], [0.96039533], [100.]], [[0.30615555], [0.96572397], [100.]], [[0.30092641], [0.97035779], [100.]], [[0.2959409], [0.97438178], [100.]], [[0.29119255], [0.97787196], [100.]], [[0.30649004], [0.96541272], [100.]], [[0.30124552], [0.97008732], [100.]], [[0.29624499], [0.97414704], [100.]], [[0.29148205], [0.97766847], [100.]], [[0.28694892], [0.98071974], [100.]], [[0.30156558], [0.96981445], [100.]], [[0.29654999], [0.97391021], [100.]], [[0.29177245], [0.97746315], [100.]], [[0.28722523], [0.98054192], [100.]], [[0.28289958], [0.98320731], [100.]], [[0.42963678], [0.47661181], [100.]], [[0.42846471], [0.43938586], [100.]], [[0.42628176], [0.40282965], [100.]], [[0.42314468], [0.36732207], [100.]], [[0.41913221], [0.333198], [100.]], [[0.42978939], [0.51176048], [100.]], [[0.42959394], [0.47427693], [100.]], [[0.4283576], [0.43708106], [100.]], [[0.4261132], [0.40057958], [100.]], [[0.422919], [0.36514906], [100.]], [[0.42902209], [0.54679323], [100.]], [[0.42980788], [0.50942101], [100.]], [[0.42954704], [0.47194318], [100.]], [[0.42824656], [0.43477897], [100.]], [[0.42594094], [0.3983337], [100.]], [[0.42735293], [0.58136855], [100.]], [[0.42910149], [0.54447221], [100.]], [[0.42982229], [0.50708112], [100.]], [[0.42949608], [0.46961065], [100.]], [[0.42813159], [0.43247968], [100.]], [[0.42482049], [0.61516258], [100.]], [[0.42749136], [0.57908829], [100.]], [[0.42917687], [0.54214925], [100.]], [[0.42983261], [0.50474093], [100.]], [[0.42944107], [0.46727945], [100.]], [[0.32257753], [0.94819408], [100.]], [[0.31663196], [0.95508757], [100.]], [[0.31093566], [0.96110142], [100.]], [[0.30548947], [0.96633839], [100.]], [[0.30029103], [0.97089165], [100.]], [[0.3169957], [0.95468427], [100.]], [[0.31128378], [0.9607499], [100.]], [[0.30582202], [0.96603252], [100.]], [[0.30060824], [0.9706259], [100.]], [[0.29563774], [0.97461444], [100.]], [[0.31163288], [0.96039533], [100.]], [[0.30615555], [0.96572397], [100.]], [[0.30092641], [0.97035779], [100.]], [[0.2959409], [0.97438178], [100.]], [[0.29119255], [0.97787196], [100.]], [[0.30649004], [0.96541272], [100.]], [[0.30124552], [0.97008732], [100.]], [[0.29624499], [0.97414704], [100.]], [[0.29148205], [0.97766847], [100.]], [[0.28694892], [0.98071974], [100.]], [[0.30156558], [0.96981445], [100.]], [[0.29654999], [0.97391021], [100.]], [[0.29177245], [0.97746315], [100.]], [[0.28722523], [0.98054192], [100.]], [[0.28289958], [0.98320731], [100.]], ] for trial in range(len(evc_gratton.results)): assert np.allclose( expected_results_array[trial], # Note: Skip decision variable OutputState evc_gratton.results[trial][1:]) for simulation in range(len(evc_gratton.simulation_results)): assert np.allclose( expected_sim_results_array[simulation], # Note: Skip decision variable OutputState evc_gratton.simulation_results[simulation][1:])
def test_evc(self): # Mechanisms Input = pnl.TransferMechanism(name='Input') reward = pnl.TransferMechanism( output_states=[pnl.RESULT, pnl.OUTPUT_MEAN, pnl.OUTPUT_VARIANCE], name='reward') Decision = pnl.DDM(function=pnl.DriftDiffusionAnalytical( drift_rate=(1.0, pnl.ControlProjection(function=pnl.Linear, control_signal_params={ pnl.ALLOCATION_SAMPLES: np.arange(0.1, 1.01, 0.3) })), threshold=(1.0, pnl.ControlProjection(function=pnl.Linear, control_signal_params={ pnl.ALLOCATION_SAMPLES: np.arange(0.1, 1.01, 0.3) })), noise=0.5, starting_point=0, t0=0.45), output_states=[ pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME, pnl.PROBABILITY_UPPER_THRESHOLD ], name='Decision') comp = pnl.Composition(name="evc") comp.add_node(reward, required_roles=[pnl.NodeRole.OUTPUT]) comp.add_node(Decision, required_roles=[pnl.NodeRole.OUTPUT]) task_execution_pathway = [Input, pnl.IDENTITY_MATRIX, Decision] comp.add_linear_processing_pathway(task_execution_pathway) comp.add_controller(controller=pnl.OptimizationControlMechanism( agent_rep=comp, features=[Input.input_state, reward.input_state], feature_function=pnl.AdaptiveIntegrator(rate=0.5), objective_mechanism=pnl.ObjectiveMechanism( function=pnl.LinearCombination(operation=pnl.PRODUCT), monitor=[ reward, Decision.output_states[ pnl.PROBABILITY_UPPER_THRESHOLD], (Decision.output_states[pnl.RESPONSE_TIME], -1, 1) ]), function=pnl.GridSearch(), control_signals=[("drift_rate", Decision), ("threshold", Decision)])) comp.enable_controller = True comp._analyze_graph() stim_list_dict = {Input: [0.5, 0.123], reward: [20, 20]} comp.run(inputs=stim_list_dict, retain_old_simulation_data=True) # Note: Removed decision variable OutputState from simulation results because sign is chosen randomly expected_sim_results_array = [[[10.], [10.0], [0.0], [0.48999867], [0.50499983]], [[10.], [10.0], [0.0], [1.08965888], [0.51998934]], [[10.], [10.0], [0.0], [2.40680493], [0.53494295]], [[10.], [10.0], [0.0], [4.43671978], [0.549834]], [[10.], [10.0], [0.0], [0.48997868], [0.51998934]], [[10.], [10.0], [0.0], [1.08459402], [0.57932425]], [[10.], [10.0], [0.0], [2.36033556], [0.63645254]], [[10.], [10.0], [0.0], [4.24948962], [0.68997448]], [[10.], [10.0], [0.0], [0.48993479], [0.53494295]], [[10.], [10.0], [0.0], [1.07378304], [0.63645254]], [[10.], [10.0], [0.0], [2.26686573], [0.72710822]], [[10.], [10.0], [0.0], [3.90353015], [0.80218389]], [[10.], [10.0], [0.0], [0.4898672], [0.549834]], [[10.], [10.0], [0.0], [1.05791834], [0.68997448]], [[10.], [10.0], [0.0], [2.14222978], [0.80218389]], [[10.], [10.0], [0.0], [3.49637662], [0.88079708]], [[15.], [15.0], [0.0], [0.48999926], [0.50372993]], [[15.], [15.0], [0.0], [1.08981011], [0.51491557]], [[15.], [15.0], [0.0], [2.40822035], [0.52608629]], [[15.], [15.0], [0.0], [4.44259627], [0.53723096]], [[15.], [15.0], [0.0], [0.48998813], [0.51491557]], [[15.], [15.0], [0.0], [1.0869779], [0.55939819]], [[15.], [15.0], [0.0], [2.38198336], [0.60294711]], [[15.], [15.0], [0.0], [4.33535807], [0.64492386]], [[15.], [15.0], [0.0], [0.48996368], [0.52608629]], [[15.], [15.0], [0.0], [1.08085171], [0.60294711]], [[15.], [15.0], [0.0], [2.32712843], [0.67504223]], [[15.], [15.0], [0.0], [4.1221271], [0.7396981]], [[15.], [15.0], [0.0], [0.48992596], [0.53723096]], [[15.], [15.0], [0.0], [1.07165729], [0.64492386]], [[15.], [15.0], [0.0], [2.24934228], [0.7396981]], [[15.], [15.0], [0.0], [3.84279648], [0.81637827]]] for simulation in range(len(expected_sim_results_array)): assert np.allclose( expected_sim_results_array[simulation], # Note: Skip decision variable OutputState comp.simulation_results[simulation][0:3] + comp.simulation_results[simulation][4:6]) expected_results_array = [[[20.0], [20.0], [0.0], [1.0], [2.378055160151634], [0.9820137900379085]], [[20.0], [20.0], [0.0], [0.1], [0.48999967725112503], [0.5024599801509442]]] for trial in range(len(expected_results_array)): np.testing.assert_allclose( comp.results[trial], expected_results_array[trial], atol=1e-08, err_msg='Failed on expected_output[{0}]'.format(trial))