class Parameters(Function.Parameters): """ Attributes ---------- variable see `variable <Function_Base.variable>` :default value: numpy.array([0]) :type: ``numpy.ndarray`` :read only: True enable_output_type_conversion see `enable_output_type_conversion <Function_Base.enable_output_type_conversion>` :default value: False :type: ``bool`` output_type see `output_type <Function_Base.output_type>` :default value: FunctionOutputType.DEFAULT :type: `FunctionOutputType` """ variable = Parameter(np.array([0]), read_only=True, pnl_internal=True, constructor_argument='default_variable') output_type = Parameter( FunctionOutputType.DEFAULT, stateful=False, loggable=False, pnl_internal=True, valid_types=FunctionOutputType ) enable_output_type_conversion = Parameter(False, stateful=False, loggable=False, pnl_internal=True)
class Parameters(ModulatoryProjection_Base.Parameters): """ Attributes ---------- control_signal see `control_signal <ControlProjection.control_signal>` :default value: None :type: :read only: True function see `function <ControlProjection.function>` :default value: `Linear` :type: `Function` """ function = Parameter(Linear, stateful=False, loggable=False) control_signal = Parameter(None, read_only=True, getter=_control_signal_getter, setter=_control_signal_setter, pnl_internal=True) control_signal_params = Parameter( None, stateful=False, loggable=False, read_only=True, user=False, pnl_internal=True )
class Parameters(ObjectiveFunction.Parameters): """ Attributes ---------- matrix see `matrix <Stability.matrix>` :default value: `HOLLOW_MATRIX` :type: ``str`` metric see `metric <Stability.metric>` :default value: `ENERGY` :type: ``str`` metric_fct see `metric_fct <Stability.metric_fct>` :default value: None :type: transfer_fct see `transfer_fct <Stability.transfer_fct>` :default value: None :type: """ matrix = HOLLOW_MATRIX metric = Parameter(ENERGY, stateful=False) metric_fct = Parameter(None, stateful=False, loggable=False) transfer_fct = Parameter(None, stateful=False, loggable=False) normalize = Parameter(False, stateful=False)
class Parameters(ControlMechanism.Parameters): """ Attributes ---------- value see `value <GatingMechanism.value>` :default value: numpy.array([0.5]) :type: ``numpy.ndarray`` gating_allocation see `gating_allocation <GatingMechanism.gating_allocation>` :default value: numpy.array([0.5]) :type: ``numpy.ndarray`` :read only: True """ # This must be a list, as there may be more than one (e.g., one per control_signal) value = Parameter(np.array([defaultGatingAllocation]), aliases='control_allocation', pnl_internal=True) gating_allocation = Parameter(np.array([defaultGatingAllocation]), getter=_gating_allocation_getter, setter=_gating_allocation_setter, read_only=True, pnl_internal=True)
class Parameters(IntegratorMechanism.Parameters): """ Attributes ---------- filter_function see `filter_function <PredictionMechanism.filter_function>` :default value: None :type: rate see `rate <PredictionMechanism.rate>` :default value: 1.0 :type: float window_size see `window_size <PredictionMechanism.window_size>` :default value: 1 :type: int """ window_size = Parameter(1, stateful=False, loggable=False) filter_function = Parameter(None, stateful=False, loggable=False) input_type = None rate = Parameter(1.0, modulable=True)
class Parameters(DistributionFunction.Parameters): """ Attributes ---------- variable see `variable <UniformToNormalDist.variable>` :default value: numpy.array([0]) :type: numpy.ndarray :read only: True mean see `mean <UniformToNormalDist.mean>` :default value: 0.0 :type: float standard_deviation see `standard_deviation <UniformToNormalDist.standard_deviation>` :default value: 1.0 :type: float """ variable = Parameter(np.array([0]), read_only=True, pnl_internal=True, constructor_argument='default_variable') mean = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM]) standard_deviation = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM])
class Parameters(SelectionFunction.Parameters): """ Attributes ---------- mode see `mode <OneHot.mode>` :default value: `MAX_VAL` :type: ``str`` random_state see `random_state <OneHot.random_state>` :default value: None :type: ``numpy.random.RandomState`` """ mode = Parameter(MAX_VAL, stateful=False) random_state = Parameter(None, stateful=True, loggable=False) def _validate_mode(self, mode): options = { MAX_VAL, MAX_ABS_VAL, MAX_INDICATOR, MAX_ABS_INDICATOR, MIN_VAL, MIN_ABS_VAL, MIN_INDICATOR, MIN_ABS_INDICATOR, PROB, PROB_INDICATOR } if mode in options: # returns None indicating no error message (this is a valid assignment) return None else: # returns error message return 'not one of {0}'.format(options)
class Parameters(ControlMechanism.Parameters): """ Attributes ---------- base_level_gain see `base_level_gain <LCControlMechanism.base_level_gain>` :default value: 0.5 :type: ``float`` function see `function <LCControlMechanism_Function>` :default value: `FitzHughNagumoIntegrator` :type: `Function` modulated_mechanisms see `modulated_mechanisms <LCControlMechanism_Modulated_Mechanisms>` :default value: None :type: scaling_factor_gain see `scaling_factor_gain <LCControlMechanism.scaling_factor_gain>` :default value: 3.0 :type: ``float`` """ function = Parameter(FitzHughNagumoIntegrator, stateful=False, loggable=False) base_level_gain = Parameter(0.5, modulable=True) scaling_factor_gain = Parameter(3.0, modulable=True) modulated_mechanisms = Parameter(None, stateful=False, loggable=False)
class Parameters(ProcessingMechanism_Base.Parameters): """ Attributes ---------- variable see `variable <Mechanism_Base.variable>` :default value: [[0]] :type: list """ variable = Parameter([[0]], pnl_internal=True, constructor_argument='default_variable') function = Parameter(ContentAddressableMemory, stateful=False, loggable=False) content_size = 1 assoc_size = 0 input_ports = Parameter( _generate_content_input_port_spec(content_size), stateful=False, loggable=False, read_only=True, structural=True, parse_spec=True, ) output_ports = Parameter( [{NAME: CONTENT_OUTPUT, VARIABLE: (OWNER_VALUE, 0)}], stateful=False, loggable=False, read_only=True, structural=True, )
class Parameters(LearningMechanism.Parameters): """ Attributes ---------- modulation see `modulation <AutoAssociativeLearningMechanism.modulation>` :default value: ADDITIVE :type: str """ function = Parameter(Hebbian, stateful=False, loggable=False) modulation = ADDITIVE input_ports = Parameter( [ACTIVATION_INPUT], stateful=False, loggable=False, read_only=True, structural=True, parse_spec=True, ) output_ports = Parameter( [{ NAME: LEARNING_SIGNAL, # NOTE: This is the default, but is overridden by any LearningSignal arg VARIABLE: (OWNER_VALUE, 0) }], stateful=False, loggable=False, read_only=True, structural=True, )
class Parameters(Function_Base.Parameters): """ Attributes ---------- initializer see `initializer <StatefulFunction.initializer>` :default value: numpy.array([0]) :type: numpy.ndarray noise see `noise <StatefulFunction.noise>` :default value: 0.0 :type: float previous_value see `previous_value <StatefulFunction.previous_value>` :default value: numpy.array([0]) :type: numpy.ndarray rate see `rate <StatefulFunction.rate>` :default value: 1.0 :type: float """ noise = Parameter(0.0, modulable=True) rate = Parameter(1.0, modulable=True) previous_value = Parameter(np.array([0]), pnl_internal=True) initializer = Parameter(np.array([0]), pnl_internal=True)
class Parameters(ComparatorMechanism.Parameters): """ Attributes ---------- variable see `variable <Mechanism_Base.variable>` :default value: None :type: :read only: True function see `function <PredictionErrorMechanism.function>` :default value: `PredictionErrorDeltaFunction` :type: `Function` learning_rate see `learning_rate <PredictionErrorMechanism.learning_rate>` :default value: 0.3 :type: float """ variable = Parameter(None, read_only=True, pnl_internal=True, constructor_argument='default_variable') learning_rate = Parameter(0.3, modulable=True) function = PredictionErrorDeltaFunction sample = None target = None
class Parameters(ModulatorySignal.Parameters): """ Attributes ---------- variable see `variable <ControlSignal.variable>` :default value: numpy.array([1.]) :type: numpy.ndarray value see `value <GatingSignal.value>` :default value: numpy.array([0]) :type: numpy.ndarray :read only: True allocation_samples see `allocation_samples <ControlSignal.allocation_samples>` """ variable = Parameter(np.array(defaultGatingAllocation), aliases='allocation', getter=_output_state_variable_getter) value = Parameter(np.array(defaultGatingAllocation), read_only=True, aliases=['intensity']) allocation_samples = Parameter(np.arange(0.1, 1.01, 0.3), modulable=True)
class Parameters(ModulatoryProjection_Base.Parameters): """ Attributes ---------- function see `function <Projection_Base.function>` :default value: `Linear` :type: `Function` gating_signal see `gating_signal <GatingProjection.gating_signal>` :default value: None :type: :read only: True """ function = Parameter(Linear(params={FUNCTION_OUTPUT_TYPE: FunctionOutputType.RAW_NUMBER}), stateful=False, loggable=False) gating_signal = Parameter(None, read_only=True, getter=_gating_signal_getter, setter=_gating_signal_setter, pnl_internal=True) gating_signal_params = Parameter( None, stateful=False, loggable=False, read_only=True, user=False, pnl_internal=True )
class Parameters(RecurrentTransferMechanism.Parameters): """ Attributes ---------- average_based see `average_based <KWTAMechanism.average_based>` :default value: False :type: ``bool`` function see `function <KWTAMechanism.function>` :default value: `Logistic` :type: `Function` inhibition_only see `inhibition_only <KWTAMechanism.inhibition_only>` :default value: True :type: ``bool`` k_value see `k_value <KWTAMechanism.k_value>` :default value: 0.5 :type: ``float`` ratio see `ratio <KWTAMechanism.ratio>` :default value: 0.5 :type: ``float`` threshold see `threshold <KWTAMechanism.threshold>` :default value: 0.0 :type: ``float`` """ function = Parameter(Logistic, stateful=False, loggable=False, dependencies='integrator_function') k_value = Parameter(0.5, modulable=True) threshold = Parameter(0.0, modulable=True) ratio = Parameter(0.5, modulable=True) output_ports = Parameter( [RESULT], stateful=False, loggable=False, read_only=True, structural=True, ) average_based = False inhibition_only = True
class Parameters(ProcessingMechanism_Base.Parameters): """ Attributes ---------- hidden_layers see `hidden_layers <LeabraMechanism.hidden_layers>` :default value: 0 :type: ``int`` hidden_sizes see `hidden_sizes <LeabraMechanism.hidden_sizes>` :default value: None :type: input_size see `input_size <LeabraMechanism.input_size>` :default value: 1 :type: ``int`` network see `network <LeabraMechanism.network>` :default value: None :type: output_size see `output_size <LeabraMechanism.output_size>` :default value: 1 :type: ``int`` quarter_size see `quarter_size <LeabraMechanism.quarter_size>` :default value: 50 :type: ``int`` training_flag see `training_flag <LeabraMechanism.training_flag>` :default value: None :type: """ input_size = 1 output_size = 1 hidden_layers = 0 hidden_sizes = None quarter_size = 50 function = Parameter(LeabraFunction, stateful=False, loggable=False) network = FunctionParameter(None) training_flag = Parameter(False, setter=_training_flag_setter, dependencies='network')
class Parameters(ProcessingMechanism_Base.Parameters): """ Attributes ---------- hidden_layers see `hidden_layers <LeabraMechanism.hidden_layers>` :default value: 0 :type: int hidden_sizes see `hidden_sizes <LeabraMechanism.hidden_sizes>` :default value: None :type: input_size see `input_size <LeabraMechanism.input_size>` :default value: 1 :type: int network see `network <LeabraMechanism.network>` :default value: None :type: output_size see `output_size <LeabraMechanism.output_size>` :default value: 1 :type: int quarter_size see `quarter_size <LeabraMechanism.quarter_size>` :default value: 50 :type: int training_flag see `training_flag <LeabraMechanism.training_flag>` :default value: None :type: """ input_size = 1 output_size = 1 hidden_layers = 0 hidden_sizes = None quarter_size = 50 network = Parameter(None, getter=_network_getter, setter=_network_setter) training_flag = Parameter(None, setter=_training_flag_setter)
class Parameters(MappingProjection.Parameters): """ Attributes ---------- variable see `variable <AutoAssociativeProjection.variable>` :default value: numpy.array([[0]]) :type: numpy.ndarray :read only: True auto see `auto <AutoAssociativeProjection.auto>` :default value: 1 :type: int function see `function <AutoAssociativeProjection.function>` :default value: `LinearMatrix` :type: `Function` hetero see `hetero <AutoAssociativeProjection.hetero>` :default value: 0 :type: int matrix see `matrix <AutoAssociativeProjection.matrix>` :default value: `AUTO_ASSIGN_MATRIX` :type: str """ variable = Parameter(np.array([[0]]), read_only=True, pnl_internal=True, constructor_argument='default_variable') # function is always LinearMatrix that requires 1D input function = Parameter(LinearMatrix, stateful=False, loggable=False) auto = Parameter(1, getter=_auto_getter, setter=_auto_setter, modulable=True) hetero = Parameter(0, getter=_hetero_getter, setter=_hetero_setter, modulable=True) matrix = Parameter(DEFAULT_MATRIX, function_parameter=True, getter=_matrix_getter, setter=_matrix_setter, modulable=True)
def _instantiate_attributes_before_function(self, function=None, context=None): super()._instantiate_attributes_before_function(function=function, context=context) # create transient Parameters objects for custom function params # done here because they need to be present before _instantiate_value which calls self.function for param_name in self.cust_fct_params: p = Parameter(self.cust_fct_params[param_name], modulable=True) setattr(self.parameters, param_name, p) p._set(p.default_value, context, skip_history=True)
class Parameters(Composition.Parameters): """""" optimizer = None learning_rate = Parameter(.001, fallback_default=True) losses = Parameter([]) trial_losses = Parameter([]) tracked_loss = Parameter(None, pnl_internal=True) tracked_loss_count = Parameter(0, pnl_internal=True) pytorch_representation = None
class Parameters(DistributionFunction.Parameters): """ Attributes ---------- bias see `bias <DriftDiffusionAnalytical.bias>` :default value: 0.5 :type: float :read only: True drift_rate see `drift_rate <DriftDiffusionAnalytical.drift_rate>` :default value: 1.0 :type: float noise see `noise <DriftDiffusionAnalytical.noise>` :default value: 0.5 :type: float starting_point see `starting_point <DriftDiffusionAnalytical.starting_point>` :default value: 0.0 :type: float t0 see `t0 <DriftDiffusionAnalytical.t0>` :default value: 0.2 :type: float threshold see `threshold <DriftDiffusionAnalytical.threshold>` :default value: 1.0 :type: float """ drift_rate = Parameter(1.0, modulable=True, aliases=[MULTIPLICATIVE_PARAM]) starting_point = Parameter(0.0, modulable=True, aliases=[ADDITIVE_PARAM]) threshold = Parameter(1.0, modulable=True) noise = Parameter(0.5, modulable=True) t0 = .200 bias = Parameter(0.5, read_only=True, getter=_DriftDiffusionAnalytical_bias_getter)
class Parameters(ObjectiveMechanism.Parameters): """ Attributes ---------- variable see `variable <ComparatorMechanism.variable>` :default value: numpy.array([[0], [0]]) :type: ``numpy.ndarray`` :read only: True function see `function <ComparatorMechanism.function>` :default value: `LinearCombination`(weights=numpy.array([[-1], [ 1]])) :type: `Function` output_ports see `output_ports <ComparatorMechanism.output_ports>` :default value: [`OUTCOME`] :type: ``list`` :read only: True sample see `sample <ComparatorMechanism.sample>` :default value: None :type: target see `target <ComparatorMechanism.target>` :default value: None :type: """ # By default, ComparatorMechanism compares two 1D np.array input_ports variable = Parameter(np.array([[0], [0]]), read_only=True, pnl_internal=True, constructor_argument='default_variable') function = Parameter(LinearCombination(weights=[[-1], [1]]), stateful=False, loggable=False) sample = None target = None output_ports = Parameter( [OUTCOME], stateful=False, loggable=False, read_only=True, structural=True, )
class Parameters(TransferMechanism.Parameters): """ Attributes ---------- enable_learning see `enable_learning <KohonenMechanism.enable_learning>` :default value: True :type: ``bool`` learning_function see `learning_function <KohonenMechanism.learning_function>` :default value: `Kohonen` :type: `Function` learning_rate see `learning_rate <KohonenMechanism.learning_rate>` :default value: None :type: matrix see `matrix <KohonenMechanism.matrix>` :default value: `AUTO_ASSIGN_MATRIX` :type: ``str`` output_ports see `output_ports <KohonenMechanism.output_ports>` :default value: [`RESULT`, "{name: INPUT_PATTERN, variable: OWNER_VARIABLE}"] :type: ``list`` :read only: True """ learning_function = Parameter(Kohonen(distance_function=GAUSSIAN), stateful=False, loggable=False, reference=True) learning_rate = Parameter(None, modulable=True) enable_learning = True matrix = DEFAULT_MATRIX output_ports = Parameter( [RESULT, { NAME: INPUT_PATTERN, VARIABLE: OWNER_VARIABLE }], stateful=False, loggable=False, read_only=True, structural=True, )
class Parameters(RecurrentTransferMechanism.Parameters): """ Attributes ---------- competition see `competition <LCAMechanism.competition>` :default value: 1.0 :type: float initial_value see `initial_value <LCAMechanism.initial_value>` :default value: None :type: integrator_mode see `integrator_mode <LCAMechanism.integrator_mode>` :default value: True :type: bool leak see `leak <LCAMechanism.leak>` :default value: 0.5 :type: float self_excitation see `self_excitation <LCAMechanism.self_excitation>` :default value: 0.0 :type: float time_step_size see `time_step_size <LCAMechanism.time_step_size>` :default value: 0.1 :type: float """ function = Parameter(Logistic, stateful=False, loggable=False) leak = Parameter(0.5, modulable=True) auto = Parameter(0.0, modulable=True, aliases='self_excitation') hetero = Parameter(-1.0, modulable=True) competition = Parameter(1.0, modulable=True) time_step_size = Parameter(0.1, modulable=True) initial_value = None integrator_mode = Parameter(True, setter=_integrator_mode_setter) integrator_function = Parameter(LeakyCompetingIntegrator, stateful=False, loggable=False) termination_measure = Parameter(max, stateful=False, loggable=False)
class Parameters(LearningMechanism.Parameters): """ Attributes ---------- function see `function <KohonenLearningMechanism.function>` :default value: `Hebbian` :type: `Function` learning_rate see `learning_rate <KohonenLearningMechanism.learning_rate>` :default value: None :type: learning_timing see `learning_timing <KohonenLearningMechanism.learning_timing>` :default value: LearningTiming.EXECUTION_PHASE :type: `LearningTiming` learning_type see `learning_type <KohonenLearningMechanism.learning_type>` :default value: LearningType.UNSUPERVISED :type: `LearningType` matrix see `matrix <KohonenLearningMechanism.matrix>` :default value: None :type: modulation see `modulation <KohonenLearningMechanism.modulation>` :default value: ModulationParam.ADDITIVE :type: `ModulationParam` """ function = Parameter(Hebbian, stateful=False, loggable=False) matrix = Parameter(None, modulable=True) learning_rate = Parameter(None, modulable=True) learning_type = LearningType.UNSUPERVISED learning_timing = LearningTiming.EXECUTION_PHASE modulation = ModulationParam.ADDITIVE
class Parameters(RecurrentTransferMechanism.Parameters): """ Attributes ---------- average_based see `average_based <KWTAMechanism.average_based>` :default value: False :type: bool function see `function <KWTAMechanism.function>` :default value: `Logistic` :type: `Function` inhibition_only see `inhibition_only <KWTAMechanism.inhibition_only>` :default value: True :type: bool k_value see `k_value <KWTAMechanism.k_value>` :default value: 0.5 :type: float ratio see `ratio <KWTAMechanism.ratio>` :default value: 0.5 :type: float threshold see `threshold <KWTAMechanism.threshold>` :default value: 0.0 :type: float """ function = Parameter(Logistic, stateful=False, loggable=False) k_value = Parameter(0.5, modulable=True) threshold = Parameter(0.0, modulable=True) ratio = Parameter(0.5, modulable=True) average_based = False inhibition_only = True
class Parameters(LearningMechanism.Parameters): """ Attributes ---------- function see `function <AutoAssociativeLearningMechanism.function>` :default value: `Hebbian` :type: `Function` input_ports see `input_ports <AutoAssociativeLearningMechanism.input_ports>` :default value: [`ACTIVATION_INPUT`] :type: ``list`` :read only: True output_ports see `output_ports <AutoAssociativeLearningMechanism.output_ports>` :default value: ["{name: LearningSignal, variable: (OWNER_VALUE, 0)}"] :type: ``list`` :read only: True """ function = Parameter(Hebbian, stateful=False, loggable=False) modulation = ADDITIVE input_ports = Parameter( [ACTIVATION_INPUT], stateful=False, loggable=False, read_only=True, structural=True, parse_spec=True, ) output_ports = Parameter( [{ NAME: LEARNING_SIGNAL, # NOTE: This is the default, but is overridden by any LearningSignal arg VARIABLE: (OWNER_VALUE, 0) }], stateful=False, loggable=False, read_only=True, structural=True, ) learning_type = LearningType.UNSUPERVISED learning_timing = LearningTiming.EXECUTION_PHASE
class Parameters(CompositionFunctionApproximator.Parameters): """ Attributes ---------- prediction_vector see `prediction_vector <RegressionCFA.prediction_vector>` :default value: None :type: previous_state see `previous_state <RegressionCFA.previous_state>` :default value: None :type: regression_weights see `regression_weights <RegressionCFA.regression_weights>` :default value: None :type: update_weights see `update_weights <RegressionCFA.update_weights>` :default value: `BayesGLM` :type: `Function` """ update_weights = Parameter(BayesGLM, stateful=False, loggable=False) prediction_vector = None previous_state = None regression_weights = None
class Parameters(Function_Base.Parameters): """ Attributes ---------- filter_function see `filter_function <PredictionMechanism.filter_function>` :default value: None :type: rate see `rate <PredictionMechanism.rate>` :default value: 1.0 :type: float window_size see `window_size <PredictionMechanism.window_size>` :default value: 1 :type: int """ variable = Parameter(None, read_only=True, pnl_internal=True, constructor_argument='default_variable')
class Parameters(InterfaceFunction.Parameters): corresponding_input_port = Parameter( None, structural=True, stateful=False, loggable=False )