//Constructor
/// <summary>
/// Creates an initialized instance of hidden neuron having analog activation
/// </summary>
/// <param name="location">Information about a neuron location within the neural preprocessor</param>
/// <param name="analogActivation">Instantiated activation function.</param>
/// <param name="bias">Constant bias to be applied.</param>
/// <param name="firingThreshold">A number between 0 and 1 (LT1). Every time the new activation value is higher than the previous 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="retainmentStrength">Strength of the analog neuron's retainment property.</param>
/// <param name="predictorsCfg">Configuration of neuron's predictors</param>
public HiddenNeuron(NeuronLocation location,
IActivationFunction analogActivation,
double bias,
double firingThreshold,
int thresholdMaxRefDeepness,
double retainmentStrength,
PredictorsSettings predictorsCfg
)
{
Location = location;
Statistics = new NeuronStatistics();
Bias = bias;
//Activation check
if (analogActivation.TypeOfActivation == ActivationType.Spiking)
{
throw new InvalidOperationException($"Called wrong type of hidden neuron constructor for analog activation.");
}
_activation = analogActivation;
_analogFiringThreshold = firingThreshold;
_histActivationsQueue = thresholdMaxRefDeepness < 2 ? null : new SimpleQueue <double>(thresholdMaxRefDeepness);
_analogRetainmentStrength = retainmentStrength;
_predictors = predictorsCfg != null ? new PredictorsProvider(predictorsCfg) : null;
OutputData = new NeuronOutputData();
Reset(false);
return;
}
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="elem">Xml element containing the initialization settings.</param>
public SpikingNeuronGroupSettings(XElement elem)
{
//Validation
XElement settingsElem = Validate(elem, XsdTypeName);
//Parsing
//Name
Name = settingsElem.Attribute("name").Value;
//Relative share
RelShare = double.Parse(settingsElem.Attribute("relShare").Value, CultureInfo.InvariantCulture);
//Activation settings
ActivationCfg = ActivationFactory.LoadSettings(settingsElem.Elements().First());
//Homogenous excitability
XElement homogenousExcitabilityElem = settingsElem.Elements("homogenousExcitability").FirstOrDefault();
HomogenousExcitabilityCfg = homogenousExcitabilityElem == null ? new HomogenousExcitabilitySettings() : new HomogenousExcitabilitySettings(homogenousExcitabilityElem);
//Bias
XElement biasSettingsElem = settingsElem.Elements("bias").FirstOrDefault();
BiasCfg = biasSettingsElem == null ? null : new RandomValueSettings(biasSettingsElem);
//Predictors
XElement predictorsSettingsElem = settingsElem.Elements("predictors").FirstOrDefault();
if (predictorsSettingsElem != null)
{
PredictorsCfg = new PredictorsSettings(predictorsSettingsElem);
}
Check();
return;
}
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="elem">Xml element containing the initialization settings.</param>
public AnalogNeuronGroupSettings(XElement elem)
{
//Validation
XElement settingsElem = Validate(elem, XsdTypeName);
//Parsing
//Name
Name = settingsElem.Attribute("name").Value;
//Relative share
RelShare = double.Parse(settingsElem.Attribute("relShare").Value, CultureInfo.InvariantCulture);
//Activation settings
ActivationCfg = ActivationFactory.LoadSettings(settingsElem.Elements().First());
//Firing threshold
FiringThreshold = double.Parse(settingsElem.Attribute("firingThreshold").Value, CultureInfo.InvariantCulture);
ThresholdMaxRefDeepness = int.Parse(settingsElem.Attribute("thresholdMaxRefDeepness").Value, CultureInfo.InvariantCulture);
//Bias
XElement biasSettingsElem = settingsElem.Elements("bias").FirstOrDefault();
BiasCfg = biasSettingsElem == null ? null : new RandomValueSettings(biasSettingsElem);
//Retainment
XElement retainmentSettingsElem = settingsElem.Elements("retainment").FirstOrDefault();
RetainmentCfg = retainmentSettingsElem == null ? null : new RetainmentSettings(retainmentSettingsElem);
//Predictors
XElement predictorsSettingsElem = settingsElem.Elements("predictors").FirstOrDefault();
if (predictorsSettingsElem != null)
{
PredictorsCfg = new PredictorsSettings(predictorsSettingsElem);
}
Check();
return;
}
/// <summary>
/// Creates reservoir instance configuration
/// </summary>
/// <param name="instName">Name of the reservoir instance</param>
/// <param name="structName">Name of the associated reservoir structure configuration</param>
/// <param name="pool1Name">Name of the first pool</param>
/// <param name="pool2Name">Name of the second pool</param>
/// <param name="inputMaxDelay">Maximum delay of input synapses</param>
/// <param name="internalMaxDelay">Maximum delay of internal synapses</param>
private ReservoirInstanceSettings CreateResInstCfg(string instName,
string structName,
string pool1Name,
string pool2Name,
int inputMaxDelay,
int internalMaxDelay
)
{
//Maximum weight of input connection
const double ConnMaxWeight = 0.6d;
//Create input connections configurations for each input field
//We connect High input field to Pool1
InputConnSettings inpConnHighCfg = CreateInputConnCfg("High", pool1Name);
//We connect Low input field to Pool2
InputConnSettings inpConnLowCfg = CreateInputConnCfg("Low", pool2Name);
//We connect Adj Close input field to both pools
InputConnSettings inpConnAdjCloseP1Cfg = CreateInputConnCfg("Adj Close", pool1Name);
InputConnSettings inpConnAdjCloseP2Cfg = CreateInputConnCfg("Adj Close", pool2Name);
//Synapse general configuration
AnalogSourceSettings asc = new AnalogSourceSettings(new URandomValueSettings(0, ConnMaxWeight));
SynapseATInputSettings synapseATInputSettings = new SynapseATInputSettings(Synapse.SynapticDelayMethod.Random, inputMaxDelay, asc, null);
SynapseATIndifferentSettings synapseATIndifferentSettings = new SynapseATIndifferentSettings(Synapse.SynapticDelayMethod.Random, internalMaxDelay);
SynapseATSettings synapseATCfg = new SynapseATSettings(SynapseATSettings.DefaultSpectralRadiusNum, synapseATInputSettings, synapseATIndifferentSettings);
SynapseSettings synapseCfg = new SynapseSettings(null, synapseATCfg);
//Predictors configuration
//We use constructor accepting array of boolean switches
//Initially we set all switches to false - all available predictors are forbidden
bool[] predictorSwitches = new bool[PredictorsProvider.NumOfSupportedPredictors];
predictorSwitches.Populate(false);
//Now we enable Activation and ActivationMWAvg predictors
predictorSwitches[(int)PredictorsProvider.PredictorID.Activation] = true;
predictorSwitches[(int)PredictorsProvider.PredictorID.ActivationMWAvg] = true;
//We configure activation moving average predictor to use constant weights and last 7 values without the leaks
ActivationMWAvgSettings activationMWAvgCfg = new ActivationMWAvgSettings(7, PredictorsProvider.PredictorMWAvgWeightsType.Constant);
//We create predictors parameters configuration including custom configuration of activation moving average predictor
PredictorsParamsSettings predictorsParamsCfg = new PredictorsParamsSettings(activationMWAvgCfg);
//Create predictors configuration using prepared boolean switches and predictors parameters
PredictorsSettings predictorsCfg = new PredictorsSettings(predictorSwitches, predictorsParamsCfg);
//Create reservoir instance configuration
ReservoirInstanceSettings resInstCfg = new ReservoirInstanceSettings(instName,
structName,
new InputConnsSettings(inpConnHighCfg, inpConnLowCfg, inpConnAdjCloseP1Cfg, inpConnAdjCloseP2Cfg),
synapseCfg,
predictorsCfg
);
return(resInstCfg);
}
//Constructors
/// <summary>
/// Creates an initialized instance
/// </summary>
/// <param name="name">Name of the reservoir structure settings</param>
/// <param name="structureCfgName">Name of the reservoir structure settings</param>
/// <param name="inputConnsCfg">Collection of input connections settings</param>
/// <param name="synapseCfg">Synapse configuration</param>
/// <param name="predictorsCfg">Configuration of the predictors</param>
public ReservoirInstanceSettings(string name,
string structureCfgName,
InputConnsSettings inputConnsCfg,
SynapseSettings synapseCfg = null,
PredictorsSettings predictorsCfg = null
)
{
Name = name;
StructureCfgName = structureCfgName;
InputConnsCfg = (InputConnsSettings)inputConnsCfg.DeepClone();
SynapseCfg = synapseCfg == null ? null : (SynapseSettings)synapseCfg.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="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">Pool name</param>
/// <param name="proportionsCfg">Pool dimensions</param>
/// <param name="neuronGroupsCfg">Settings of the neuron groups in the pool</param>
/// <param name="interconnectionCfg">Configuration of the pool's neurons interconnection</param>
/// <param name="predictorsCfg">Configuration of the predictors</param>
/// <param name="coordinatesCfg">Pool coordinates within the 3D space</param>
public PoolSettings(string name,
ProportionsSettings proportionsCfg,
NeuronGroupsSettings neuronGroupsCfg,
InterconnSettings interconnectionCfg,
PredictorsSettings predictorsCfg = null,
CoordinatesSettings coordinatesCfg = null
)
{
Name = name;
ProportionsCfg = (ProportionsSettings)proportionsCfg.DeepClone();
NeuronGroupsCfg = (NeuronGroupsSettings)neuronGroupsCfg.DeepClone();
InterconnectionCfg = (InterconnSettings)interconnectionCfg.DeepClone();
PredictorsCfg = predictorsCfg == null ? null : (PredictorsSettings)predictorsCfg.DeepClone();
CoordinatesCfg = coordinatesCfg == null ? new CoordinatesSettings() : (CoordinatesSettings)coordinatesCfg.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;
}
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="elem">Xml element containing the initialization settings</param>
public ReservoirInstanceSettings(XElement elem)
{
//Validation
XElement settingsElem = Validate(elem, XsdTypeName);
//Parsing
Name = settingsElem.Attribute("name").Value;
StructureCfgName = settingsElem.Attribute("reservoirStructure").Value;
//Input connections
InputConnsCfg = new InputConnsSettings(settingsElem.Elements("inputConnections").First());
//Synapse
XElement synapseElem = settingsElem.Elements("synapse").FirstOrDefault();
SynapseCfg = synapseElem == null ? new SynapseSettings() : new SynapseSettings(synapseElem);
//Predictors
XElement predictorsElem = settingsElem.Elements("predictors").FirstOrDefault();
if (predictorsElem != null)
{
PredictorsCfg = new PredictorsSettings(predictorsElem);
}
Check();
return;
}
//Constructor
/// <summary>
/// Creates an initialized instance of hidden neuron having spiking activation
/// </summary>
/// <param name="location">Information about a neuron location within the neural preprocessor</param>
/// <param name="spikingActivation">Instantiated activation function.</param>
/// <param name="bias">Constant bias to be applied.</param>
/// <param name="predictorsCfg">Configuration of neuron's predictors</param>
public HiddenNeuron(NeuronLocation location,
IActivationFunction spikingActivation,
double bias,
PredictorsSettings predictorsCfg
)
{
Location = location;
Statistics = new NeuronStatistics();
Bias = bias;
//Activation check
if (spikingActivation.TypeOfActivation == ActivationType.Analog)
{
//Spiking
throw new InvalidOperationException($"Called wrong type of hidden neuron constructor for spiking activation.");
}
_activation = spikingActivation;
_analogFiringThreshold = 0;
_histActivationsQueue = null;
_analogRetainmentStrength = 0;
_predictors = predictorsCfg != null ? new PredictorsProvider(predictorsCfg) : null;
OutputData = new NeuronOutputData();
Reset(false);
return;
}
/// <summary>
/// Creates an initialized instance from the given xml element.
/// </summary>
/// <param name="elem">Xml element containing the initialization settings</param>
public PoolSettings(XElement elem)
{
//Validation
XElement poolSettingsElem = Validate(elem, XsdTypeName);
//Parsing
Name = poolSettingsElem.Attribute("name").Value;
ProportionsCfg = new ProportionsSettings(poolSettingsElem.Elements("proportions").First());
NeuronGroupsCfg = new NeuronGroupsSettings(poolSettingsElem.Elements("neuronGroups").First());
InterconnectionCfg = new InterconnSettings(poolSettingsElem.Elements("interconnection").First());
//Predictors
XElement predictorsElem = poolSettingsElem.Elements("predictors").FirstOrDefault();
if (predictorsElem != null)
{
PredictorsCfg = new PredictorsSettings(predictorsElem);
}
//Coordinates
XElement coordinatesElem = poolSettingsElem.Elements("coordinates").FirstOrDefault();
CoordinatesCfg = coordinatesElem == null ? new CoordinatesSettings() : new CoordinatesSettings(coordinatesElem);
Check();
return;
}
/// <summary>
/// Creates StateMachine configuration following pure LSM design
/// </summary>
/// <param name="proportionsCfg">LSM pool proportions</param>
/// <param name="aFnCfg">Spiking activation function configuration</param>
/// <param name="hes">Homogenous excitability configuration</param>
/// <param name="inputConnectionDensity">Density of the input field connections to hidden neurons</param>
/// <param name="maxInputDelay">Maximum delay of input synapse</param>
/// <param name="interconnectionDensity">Density of the hidden neurons interconnection</param>
/// <param name="maxInternalDelay">Maximum delay of internal synapse</param>
/// <param name="steadyBias">Constant bias (0 means bias is not required)</param>
/// <param name="predictorsParamsCfg">Predictors parameters (use null for defaults)</param>
/// <param name="allowedPredictor">Allowed predictor(s)</param>
public StateMachineSettings CreatePureLSMCfg(ProportionsSettings proportionsCfg,
RCNetBaseSettings aFnCfg,
HomogenousExcitabilitySettings hes,
double inputConnectionDensity,
int maxInputDelay,
double interconnectionDensity,
int maxInternalDelay,
double steadyBias,
PredictorsParamsSettings predictorsParamsCfg,
params PredictorsProvider.PredictorID[] allowedPredictor
)
{
//Activation check
if (ActivationFactory.Create(aFnCfg, new Random()).TypeOfActivation != ActivationType.Spiking)
{
throw new ArgumentException("Specified activation must be spiking.", "aFnCfg");
}
//One neuron group
SpikingNeuronGroupSettings grp = CreateSpikingGroup(aFnCfg, hes, steadyBias);
//Simple spiking pool
PoolSettings poolCfg = new PoolSettings(GetPoolName(ActivationContent.Spiking, 0),
proportionsCfg,
new NeuronGroupsSettings(grp),
new InterconnSettings(new RandomSchemaSettings(interconnectionDensity, 0d, false, false))
);
//Simple reservoir structure
ReservoirStructureSettings resStructCfg = new ReservoirStructureSettings(GetResStructName(ActivationContent.Spiking, 0),
new PoolsSettings(poolCfg)
);
//Input connections configuration
List <InputConnSettings> inputConns = new List <InputConnSettings>(InputCfg.VaryingFieldsCfg.ExternalFieldsCfg.FieldCfgCollection.Count);
foreach (ExternalFieldSettings fieldCfg in InputCfg.VaryingFieldsCfg.ExternalFieldsCfg.FieldCfgCollection)
{
InputConnSettings inputConnCfg = new InputConnSettings(fieldCfg.Name,
poolCfg.Name,
inputConnectionDensity,
0
);
inputConns.Add(inputConnCfg);
}
//Synapse general configuration
SynapseSTInputSettings synapseSTInputSettings = new SynapseSTInputSettings(Synapse.SynapticDelayMethod.Random, maxInputDelay);
SynapseSTExcitatorySettings synapseSTExcitatorySettings = new SynapseSTExcitatorySettings(Synapse.SynapticDelayMethod.Random, maxInternalDelay);
SynapseSTInhibitorySettings synapseSTInhibitorySettings = new SynapseSTInhibitorySettings(Synapse.SynapticDelayMethod.Random, maxInternalDelay);
SynapseSTSettings synapseSTCfg = new SynapseSTSettings(synapseSTInputSettings, synapseSTExcitatorySettings, synapseSTInhibitorySettings);
SynapseSettings synapseCfg = new SynapseSettings(synapseSTCfg, null);
//Initially set all switches to false - all available predictors are forbidden
bool[] predictorSwitches = new bool[PredictorsProvider.NumOfSupportedPredictors];
predictorSwitches.Populate(false);
//Enable specified predictors
foreach (PredictorsProvider.PredictorID predictorID in allowedPredictor)
{
predictorSwitches[(int)predictorID] = true;
}
//Create predictors configuration using default params
PredictorsSettings predictorsCfg = new PredictorsSettings(predictorSwitches, predictorsParamsCfg);
//Create reservoir instance
ReservoirInstanceSettings resInstCfg = new ReservoirInstanceSettings(GetResInstName(ResDesign.PureLSM, 0),
resStructCfg.Name,
new InputConnsSettings(inputConns),
synapseCfg,
predictorsCfg
);
//Build and return SM configuration
return(new StateMachineSettings(new NeuralPreprocessorSettings(InputCfg,
new ReservoirStructuresSettings(resStructCfg),
new ReservoirInstancesSettings(resInstCfg)
),
ReadoutCfg
));
}
/// <summary>
/// Creates StateMachine configuration following pure ESN design
/// </summary>
/// <param name="totalSize">Total number of hidden neurons</param>
/// <param name="inputConnectionDensity">Density of the input field connections to hidden neurons</param>
/// <param name="maxInputDelay">Maximum delay of input synapse</param>
/// <param name="interconnectionDensity">Density of the hidden neurons interconnection</param>
/// <param name="maxInternalDelay">Maximum delay of internal synapse</param>
/// <param name="maxAbsBias">Maximum absolute value of the bias (0 means bias is not required)</param>
/// <param name="maxRetainmentStrength">Maximum retainment strength (0 means retainment property is not required)</param>
/// <param name="predictorsParamsCfg">Predictors parameters (use null for defaults)</param>
/// <param name="allowedPredictor">Allowed predictor(s)</param>
public StateMachineSettings CreatePureESNCfg(int totalSize,
double inputConnectionDensity,
int maxInputDelay,
double interconnectionDensity,
int maxInternalDelay,
double maxAbsBias,
double maxRetainmentStrength,
PredictorsParamsSettings predictorsParamsCfg,
params PredictorsProvider.PredictorID[] allowedPredictor
)
{
const double MaxInputWeightSum = 2.75d;
//Default ESN activation
RCNetBaseSettings aFnCfg = new TanHSettings();
//One neuron group
AnalogNeuronGroupSettings grp = CreateAnalogGroup(aFnCfg, maxAbsBias, maxRetainmentStrength);
//Simple analog pool
PoolSettings poolCfg = new PoolSettings(GetPoolName(ActivationContent.Analog, 0),
new ProportionsSettings(totalSize, 1, 1),
new NeuronGroupsSettings(grp),
new InterconnSettings(new RandomSchemaSettings(interconnectionDensity))
);
//Simple reservoir structure
ReservoirStructureSettings resStructCfg = new ReservoirStructureSettings(GetResStructName(ActivationContent.Analog, 0),
new PoolsSettings(poolCfg)
);
//Input connections configuration
List <InputConnSettings> inputConns = new List <InputConnSettings>(InputCfg.VaryingFieldsCfg.ExternalFieldsCfg.FieldCfgCollection.Count);
double maxInpSynWeight = MaxInputWeightSum / InputCfg.VaryingFieldsCfg.ExternalFieldsCfg.FieldCfgCollection.Count;
foreach (ExternalFieldSettings fieldCfg in InputCfg.VaryingFieldsCfg.ExternalFieldsCfg.FieldCfgCollection)
{
InputConnSettings inputConnCfg = new InputConnSettings(fieldCfg.Name,
poolCfg.Name,
0,
inputConnectionDensity
);
inputConns.Add(inputConnCfg);
}
//Synapse general configuration
AnalogSourceSettings asc = new AnalogSourceSettings(new URandomValueSettings(0, maxInpSynWeight));
SynapseATInputSettings synapseATInputSettings = new SynapseATInputSettings(Synapse.SynapticDelayMethod.Random, maxInputDelay, asc, null);
SynapseATIndifferentSettings synapseATIndifferentSettings = new SynapseATIndifferentSettings(Synapse.SynapticDelayMethod.Random, maxInternalDelay);
SynapseATSettings synapseATCfg = new SynapseATSettings(SynapseATSettings.DefaultSpectralRadiusNum, synapseATInputSettings, synapseATIndifferentSettings);
SynapseSettings synapseCfg = new SynapseSettings(null, synapseATCfg);
//Initially set all switches to false - all available predictors are forbidden
bool[] predictorSwitches = new bool[PredictorsProvider.NumOfSupportedPredictors];
predictorSwitches.Populate(false);
//Enable specified predictors
foreach (PredictorsProvider.PredictorID predictorID in allowedPredictor)
{
predictorSwitches[(int)predictorID] = true;
}
//Create predictors configuration using default params
PredictorsSettings predictorsCfg = new PredictorsSettings(predictorSwitches, predictorsParamsCfg);
//Create reservoir instance
ReservoirInstanceSettings resInstCfg = new ReservoirInstanceSettings(GetResInstName(ResDesign.PureESN, 0),
resStructCfg.Name,
new InputConnsSettings(inputConns),
synapseCfg,
predictorsCfg
);
//Build and return SM configuration
return(new StateMachineSettings(new NeuralPreprocessorSettings(InputCfg,
new ReservoirStructuresSettings(resStructCfg),
new ReservoirInstancesSettings(resInstCfg)
),
ReadoutCfg
));
}
