/// <summary>
/// Creates configuration of the group of analog neurons having a TanH activation.
/// </summary>
/// <param name="groupName">The name of the group</param>
/// <param name="relShare">The relative share. It determines how big part of the pool neurons will be occupied by this neuron group.</param>
private AnalogNeuronGroupSettings CreateTanHGroup(string groupName, double relShare)
{
//Each neuron within the group will have its own constant bias
//selected from the range -0.05 to 0.05 using gaussian (normal) distribution
RandomValueSettings biasCfg = new RandomValueSettings(-0.05, 0.05, false, new GaussianDistrSettings(0, 1));
//We want retainment property to be set for every neuron within the group
const double RetainmentDensity = 1d;
//Each neuron will have its own constant retainment strength
//selected from the range 0 to 0.75 using uniform distribution
URandomValueSettings retainmentStrengthCfg = new URandomValueSettings(0, 0.75);
RetainmentSettings retainmentCfg = new RetainmentSettings(RetainmentDensity, retainmentStrengthCfg);
//Predictors configuration
//We will use the Activation and ActivationPower predictors
PredictorsProviderSettings predictorsCfg = new PredictorsProviderSettings(new PredictorActivationSettings(),
new PredictorActivationPowerSettings()
);
//Create the neuron group configuration
AnalogNeuronGroupSettings groupCfg = new AnalogNeuronGroupSettings(groupName,
relShare,
new AFAnalogTanHSettings(),
predictorsCfg,
AnalogNeuronGroupSettings.DefaultFiringThreshold,
AnalogNeuronGroupSettings.DefaultThresholdMaxRefDeepness,
biasCfg,
retainmentCfg
);
return(groupCfg);
}
/// <summary>
/// Creates the 3D pool of analog neurons.
/// </summary>
/// <param name="poolName">The name of the pool.</param>
/// <param name="dimX">Size on X dimension.</param>
/// <param name="dimY">Size on Y dimension.</param>
/// <param name="dimZ">Size on Z dimension.</param>
/// <param name="randomInterconnectionDensity">The density of the random interconnection.</param>
private PoolSettings CreateAnalogPoolCfg(string poolName, int dimX, int dimY, int dimZ, double randomInterconnectionDensity)
{
//Create TanH group of neurons
AnalogNeuronGroupSettings grpCfg = CreateTanHGroup("Exc-TanH-Grp", 1);
//We use two interconnection schemas
//Chain schema (circle shaped). We use ratio 1 so all the neurons within the pool will be connected into the circle shaped chain.
ChainSchemaSettings chainSchemaCfg = new ChainSchemaSettings(1d, true);
//Random schema
RandomSchemaSettings randomSchemaCfg = new RandomSchemaSettings(randomInterconnectionDensity);
//Create pool configuration
PoolSettings poolCfg = new PoolSettings(poolName,
new ProportionsSettings(dimX, dimY, dimZ),
new NeuronGroupsSettings(grpCfg),
new InterconnSettings(chainSchemaCfg, randomSchemaCfg)
);
return(poolCfg);
}
/// <summary>
/// Creates configuration of group of analog neurons having specified analog activation.
/// </summary>
/// <param name="activationCfg">Activation function configuration</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>
private AnalogNeuronGroupSettings CreateAnalogGroup(RCNetBaseSettings activationCfg,
double maxAbsBias = 0d,
double maxRetainmentStrength = 0d
)
{
//Bias configuration
RandomValueSettings biasCfg = maxAbsBias == 0 ? null : new RandomValueSettings(-maxAbsBias, maxAbsBias);
//Retainment configuration
const double RetainmentDensity = 1d;
RetainmentSettings retainmentCfg = maxRetainmentStrength == 0 ? null : new RetainmentSettings(RetainmentDensity, new URandomValueSettings(0, maxRetainmentStrength));
//Create neuron group configuration
AnalogNeuronGroupSettings groupCfg = new AnalogNeuronGroupSettings(GetNeuronGroupName(activationCfg),
1d,
activationCfg,
AnalogNeuronGroupSettings.DefaultFiringThreshold,
AnalogNeuronGroupSettings.DefaultThresholdMaxRefDeepness,
biasCfg,
retainmentCfg,
null
);
return(groupCfg);
}
/// <summary>
/// Creates the simplified configuration of the state machine following the pure ESN design.
/// </summary>
/// <param name="totalSize">The total number of hidden neurons.</param>
/// <param name="maxInputStrength">The max sum of weights of input synapses per an analog hidden neuron (see the constant DefaultAnalogMaxInputStrength).</param>
/// <param name="inputConnectionDensity">The density of the input field connections to hidden neurons.</param>
/// <param name="maxInputDelay">The maximum delay of an input synapse.</param>
/// <param name="interconnectionDensity">The density of the hidden neurons recurrent interconnection.</param>
/// <param name="maxInternalDelay">The maximum delay of an internal synapse.</param>
/// <param name="maxAbsBias">The maximum absolute value of the bias (0 means no bias).</param>
/// <param name="maxRetainmentStrength">The maximum retainment strength (0 means no retainment).</param>
/// <param name="predictorsProviderCfg">The configuration of the predictors provider.</param>
public StateMachineSettings CreatePureESNCfg(int totalSize,
double maxInputStrength,
double inputConnectionDensity,
int maxInputDelay,
double interconnectionDensity,
int maxInternalDelay,
double maxAbsBias,
double maxRetainmentStrength,
PredictorsProviderSettings predictorsProviderCfg
)
{
//Check NP is not bypassed
if (BypassedNP)
{
throw new InvalidOperationException("Neural preprocessor is bypassed thus ESN design can't be created.");
}
//Default ESN activation
IActivationSettings analogActivationCfg = new AFAnalogTanHSettings();
//One neuron group
AnalogNeuronGroupSettings grp = CreateAnalogGroup(analogActivationCfg, predictorsProviderCfg, maxAbsBias, maxRetainmentStrength);
//Simple analog pool
PoolSettings poolCfg = new PoolSettings(BuildPoolName(ActivationContent.Analog, 0),
new ProportionsSettings(totalSize, 1, 1),
new NeuronGroupsSettings(grp),
new InterconnSettings(new RandomSchemaSettings(interconnectionDensity))
);
//Simple reservoir structure
ReservoirStructureSettings resStructCfg = new ReservoirStructureSettings(BuildResStructName(ActivationContent.Analog, 0),
new PoolsSettings(poolCfg)
);
//Input connections configuration
List <InputConnSettings> inputConns = new List <InputConnSettings>(InputEncoderCfg.VaryingFieldsCfg.ExternalFieldsCfg.FieldCfgCollection.Count);
foreach (ExternalFieldSettings fieldCfg in InputEncoderCfg.VaryingFieldsCfg.ExternalFieldsCfg.FieldCfgCollection)
{
InputConnSettings inputConnCfg = new InputConnSettings(fieldCfg.Name,
poolCfg.Name,
0,
inputConnectionDensity
);
inputConns.Add(inputConnCfg);
}
//Synapse general configuration
SynapseATInputSettings synapseATInputSettings = new SynapseATInputSettings(Synapse.SynapticDelayMethod.Random, maxInputDelay, new AnalogSourceSettings(new URandomValueSettings(0d, maxInputStrength)));
SynapseATIndifferentSettings synapseATIndifferentSettings = new SynapseATIndifferentSettings(Synapse.SynapticDelayMethod.Random, maxInternalDelay);
SynapseATSettings synapseATCfg = new SynapseATSettings(SynapseATSettings.DefaultSpectralRadiusNum, synapseATInputSettings, synapseATIndifferentSettings);
SynapseSettings synapseCfg = new SynapseSettings(null, synapseATCfg);
//Create reservoir instance
ReservoirInstanceSettings resInstCfg = new ReservoirInstanceSettings(GetResInstName(ResDesign.PureESN, 0),
resStructCfg.Name,
new InputConnsSettings(inputConns),
synapseCfg
);
//Build and return SM configuration
return(new StateMachineSettings(new NeuralPreprocessorSettings(InputEncoderCfg,
new ReservoirStructuresSettings(resStructCfg),
new ReservoirInstancesSettings(resInstCfg)
),
ReadoutLayerCfg
));
}
/// <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
));
}
