//Constructor
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="preprocessorCfg">The configuration of the neural preprocessor.</param>
/// <param name="randomizerSeek">The random number generator initial seek.</param>
public NeuralPreprocessor(NeuralPreprocessorSettings preprocessorCfg, int randomizerSeek)
{
_preprocessorCfg = (NeuralPreprocessorSettings)preprocessorCfg.DeepClone();
TotalNumOfHiddenNeurons = 0;
///////////////////////////////////////////////////////////////////////////////////
//Input encoder
_inputEncoder = new InputEncoder(_preprocessorCfg.InputEncoderCfg);
///////////////////////////////////////////////////////////////////////////////////
//Reservoir instance(s)
BootCycles = 0;
//Random generator used for reservoir structure initialization
Random rand = (randomizerSeek < 0 ? new Random() : new Random(randomizerSeek));
ReservoirCollection = new List <ReservoirInstance>(_preprocessorCfg.ReservoirInstancesCfg.ReservoirInstanceCfgCollection.Count);
int reservoirInstanceID = 0;
int defaultBootCycles = 0;
foreach (ReservoirInstanceSettings reservoirInstanceCfg in _preprocessorCfg.ReservoirInstancesCfg.ReservoirInstanceCfgCollection)
{
ReservoirStructureSettings structCfg = _preprocessorCfg.ReservoirStructuresCfg.GetReservoirStructureCfg(reservoirInstanceCfg.StructureCfgName);
ReservoirInstance reservoir = new ReservoirInstance(reservoirInstanceID++,
structCfg,
reservoirInstanceCfg,
_inputEncoder,
rand
);
ReservoirCollection.Add(reservoir);
TotalNumOfHiddenNeurons += reservoir.Size;
defaultBootCycles = Math.Max(defaultBootCycles, reservoir.GetDefaultBootCycles());
}
//Boot cycles setup
if (_preprocessorCfg.InputEncoderCfg.FeedingCfg.FeedingType == InputEncoder.InputFeedingType.Continuous)
{
FeedingContinuousSettings feedingCfg = (FeedingContinuousSettings)preprocessorCfg.InputEncoderCfg.FeedingCfg;
BootCycles = feedingCfg.BootCycles == FeedingContinuousSettings.AutoBootCyclesNum ? defaultBootCycles : feedingCfg.BootCycles;
}
else
{
BootCycles = 0;
}
//Output features
_totalNumOfReservoirsPredictors = 0;
_predictorsTimePointSlicesPlan = null;
PredictorDescriptorCollection = null;
OutputFeatureGeneralSwitchCollection = null;
NumOfActivePredictors = 0;
return;
}
//Methods
/// <summary>
/// Creates input part of the neural preprocessor's configuration.
/// </summary>
private InputEncoderSettings CreateInputCfg()
{
//Definition of input external fields
//In this example we will use three of available input fields: "High" price, "Low" price and "Adj Close" price.
//We want to route input fields to readout layer together with other predictors
const bool RouteToReadout = true;
//All 3 input fields are real numbers and thus they should be standardly normalized and standardized.
RealFeatureFilterSettings realFeatureFilterCfg = new RealFeatureFilterSettings(true, true);
//Input fields collection
ExternalFieldSettings extFieldHighCfg = new ExternalFieldSettings("High", realFeatureFilterCfg, RouteToReadout);
ExternalFieldSettings extFieldLowCfg = new ExternalFieldSettings("Low", realFeatureFilterCfg, RouteToReadout);
ExternalFieldSettings extFieldAdjCloseCfg = new ExternalFieldSettings("Adj Close", realFeatureFilterCfg, RouteToReadout);
ExternalFieldsSettings externalFieldsCfg = new ExternalFieldsSettings(extFieldHighCfg, extFieldLowCfg, extFieldAdjCloseCfg);
//Definition of the continuous input feeding
//We use FeedingContinuousSettings.AutoBootCyclesNum so necessary number of boot cycles will be automatically determined
//based on neural preprocessor structure
FeedingContinuousSettings feedingContinuousCfg = new FeedingContinuousSettings(FeedingContinuousSettings.AutoBootCyclesNum);
//Create and return input configuration
return(new InputEncoderSettings(feedingContinuousCfg, new VaryingFieldsSettings(externalFieldsCfg, null, null, RouteToReadout)));
}
