//Constructors
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="outputActivationCfg">Configuration of the output layer activation function.</param>
/// <param name="hiddenLayersCfg">The configuration of the hidden layers. Hidden layers are optional.</param>
/// <param name="trainerCfg">The configuration of the associated trainer.</param>
public FeedForwardNetworkSettings(IActivationSettings outputActivationCfg,
HiddenLayersSettings hiddenLayersCfg,
RCNetBaseSettings trainerCfg
)
{
OutputActivationCfg = (IActivationSettings)outputActivationCfg.DeepClone();
HiddenLayersCfg = hiddenLayersCfg == null ? new HiddenLayersSettings() : (HiddenLayersSettings)hiddenLayersCfg.DeepClone();
TrainerCfg = trainerCfg.DeepClone();
Check();
return;
}
//Constructors
/// <summary>
/// Creates an initialized instance
/// </summary>
/// <param name="outputActivationCfg">Output layer activation configuration</param>
/// <param name="hiddenLayersCfg">Hidden layers configuration. Hidden layers are optional.</param>
/// <param name="trainerCfg">Configuration of associated trainer</param>
public FeedForwardNetworkSettings(RCNetBaseSettings outputActivationCfg,
HiddenLayersSettings hiddenLayersCfg,
RCNetBaseSettings trainerCfg
)
{
OutputActivationCfg = ActivationFactory.DeepCloneActivationSettings(outputActivationCfg);
OutputRange = ActivationFactory.GetInfo(OutputActivationCfg, out _, out _);
HiddenLayersCfg = hiddenLayersCfg == null ? new HiddenLayersSettings() : (HiddenLayersSettings)hiddenLayersCfg.DeepClone();
TrainerCfg = trainerCfg.DeepClone();
Check();
return;
}
//Constructors
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="name">The name of the generated field.</param>
/// <param name="generatorCfg">The configuration of an associated generator.</param>
/// <param name="routeToReadout">Specifies whether to route the generated field to the readout layer.</param>
/// <param name="featureFilterCfg">The configuration of the real feature filter.</param>
public GeneratedFieldSettings(string name,
RCNetBaseSettings generatorCfg,
bool routeToReadout = DefaultRouteToReadout,
RealFeatureFilterSettings featureFilterCfg = null
)
{
Name = name;
GeneratorCfg = generatorCfg.DeepClone();
RouteToReadout = routeToReadout;
FeatureFilterCfg = featureFilterCfg == null ? null : (RealFeatureFilterSettings)featureFilterCfg.DeepClone();
Check();
return;
}
//Constructors
/// <summary>
/// Creates an initialized instance
/// </summary>
/// <param name="name">Transformed field name</param>
/// <param name="transformerCfg">Configuration of associated transformer</param>
/// <param name="routeToReadout">Specifies whether to route transformed field to readout layer together with other predictors</param>
/// <param name="featureFilterCfg">Configuration of real feature filter</param>
/// <param name="spikingCodingCfg">Configuration of spiking coding neurons</param>
public TransformedFieldSettings(string name,
RCNetBaseSettings transformerCfg,
bool routeToReadout = DefaultRouteToReadout,
RealFeatureFilterSettings featureFilterCfg = null,
SpikeCodeSettings spikingCodingCfg = null
)
{
Name = name;
TransformerCfg = transformerCfg.DeepClone();
RouteToReadout = routeToReadout;
FeatureFilterCfg = featureFilterCfg == null ? null : (RealFeatureFilterSettings)featureFilterCfg.DeepClone();
SpikingCodingCfg = spikingCodingCfg == null ? null : (SpikeCodeSettings)spikingCodingCfg.DeepClone();
Check();
return;
}
//Constructors
/// <summary>
/// Creates an initialized instance
/// </summary>
/// <param name="name">Name of the neuron group</param>
/// <param name="relShare">Specifies how big relative portion of pool's neurons is formed by this group of the neurons</param>
/// <param name="activationCfg">Common activation function settings of the groupped neurons</param>
/// <param name="homogenousExcitabilityCfg">Configuration of the neuron's homogenous excitability</param>
/// <param name="biasCfg">Each neuron within the group receives constant input bias. Value of the neuron's bias is driven by this random settings</param>
/// <param name="predictorsCfg">Configuration of the predictors</param>
public SpikingNeuronGroupSettings(string name,
double relShare,
RCNetBaseSettings activationCfg,
HomogenousExcitabilitySettings homogenousExcitabilityCfg = null,
RandomValueSettings biasCfg = null,
PredictorsSettings predictorsCfg = null
)
{
Name = name;
RelShare = relShare;
ActivationCfg = activationCfg.DeepClone();
HomogenousExcitabilityCfg = homogenousExcitabilityCfg == null ? new HomogenousExcitabilitySettings() : (HomogenousExcitabilitySettings)homogenousExcitabilityCfg.DeepClone();
BiasCfg = biasCfg == null ? null : (RandomValueSettings)biasCfg.DeepClone();
PredictorsCfg = predictorsCfg == null ? null : (PredictorsSettings)predictorsCfg.DeepClone();
Check();
return;
}
//Constructors
/// <summary>
/// Creates an initialized instance
/// </summary>
/// <param name="name">Name of the neuron group</param>
/// <param name="relShare">Specifies how big relative portion of pool's neurons is formed by this group of the neurons</param>
/// <param name="activationCfg">Common activation function settings of the groupped neurons</param>
/// <param name="firingThreshold">
/// A number between 0 and 1 (LT1). Every time the new normalized activation value is higher than the previous
/// normalized activation value by at least the threshold, it is evaluated as a firing event.
/// </param>
/// <param name="thresholdMaxRefDeepness">Maximum deepness of historical normalized activation value to be compared with current normalized activation value when evaluating firing event.</param>
/// <param name="biasCfg">Each neuron within the group receives constant input bias. Value of the neuron's bias is driven by this random settings</param>
/// <param name="retainmentCfg">Neurons' retainment property configuration</param>
/// <param name="predictorsCfg">Configuration of the predictors</param>
public AnalogNeuronGroupSettings(string name,
double relShare,
RCNetBaseSettings activationCfg,
double firingThreshold = DefaultFiringThreshold,
int thresholdMaxRefDeepness = DefaultThresholdMaxRefDeepness,
RandomValueSettings biasCfg = null,
RetainmentSettings retainmentCfg = null,
PredictorsSettings predictorsCfg = null
)
{
Name = name;
RelShare = relShare;
ActivationCfg = activationCfg.DeepClone();
FiringThreshold = firingThreshold;
ThresholdMaxRefDeepness = thresholdMaxRefDeepness;
BiasCfg = biasCfg == null ? null : (RandomValueSettings)biasCfg.DeepClone();
RetainmentCfg = retainmentCfg == null ? null : (RetainmentSettings)retainmentCfg.DeepClone();
PredictorsCfg = predictorsCfg == null ? null : (PredictorsSettings)predictorsCfg.DeepClone();
Check();
return;
}