//Methods
/// <inheritdoc/>
protected override void Check()
{
if (BootCycles != AutoBootCyclesNum && BootCycles <= 0)
{
throw new ArgumentException($"Invalid BootCycles {BootCycles.ToString(CultureInfo.InvariantCulture)}. BootCycles must be equal to {AutoBootCyclesNum.ToString(CultureInfo.InvariantCulture)} for automatic boot cycles or GT 0.", "BootCycles");
}
return;
}
/// <inheritdoc/>
public override XElement GetXml(string rootElemName, bool suppressDefaults)
{
XElement rootElem = new XElement(rootElemName);
if (!suppressDefaults || !IsDefaultBootCycles)
{
rootElem.Add(new XAttribute("bootCycles", BootCycles == AutoBootCyclesNum ? AutoBootCyclesCode : BootCycles.ToString(CultureInfo.InvariantCulture)));
}
Validate(rootElem, XsdTypeName);
return(rootElem);
}
/// <summary>
/// Initializes the preprocessor, preprocess the specified data bundle and returns the predictors together with the ideal values.
/// </summary>
/// <param name="inputBundle">The data bundle to be preprocessed.</param>
/// <param name="preprocessingOverview">The statistics and other important information related to data preprocessing.</param>
public VectorBundle InitializeAndPreprocessBundle(VectorBundle inputBundle, out PreprocessingOverview preprocessingOverview)
{
//Check amount of input data
if (BootCycles > 0 && inputBundle.InputVectorCollection.Count <= BootCycles)
{
throw new InvalidOperationException($"Insufficient number of input data instances. The number of instances must be greater than the number of boot cycles ({BootCycles.ToString(CultureInfo.InvariantCulture)}).");
}
//Reset reservoirs
ResetReservoirs(true);
//Reset input encoder and initialize its feature filters
_inputEncoder.Initialize(inputBundle);
//Initialize output features descriptors
InitPredictorsDescriptors();
//Allocate output bundle
VectorBundle outputBundle = new VectorBundle(inputBundle.InputVectorCollection.Count);
//Process data
//Collect predictors
for (int dataSetIdx = 0; dataSetIdx < inputBundle.InputVectorCollection.Count; dataSetIdx++)
{
bool readyToCollect = dataSetIdx >= BootCycles || _preprocessorCfg.InputEncoderCfg.FeedingCfg.FeedingType == InputEncoder.InputFeedingType.Patterned;
//Push input data into the network
double[] outputFeatures = PushExtInputVector(inputBundle.InputVectorCollection[dataSetIdx], readyToCollect);
//Collect output features?
if (readyToCollect)
{
//Predictors
outputBundle.InputVectorCollection.Add(outputFeatures);
//Desired outputs
outputBundle.OutputVectorCollection.Add(inputBundle.OutputVectorCollection[dataSetIdx]);
}
//Raise informative event
PreprocessingProgressChanged?.Invoke(inputBundle.InputVectorCollection.Count, dataSetIdx + 1, null);
}
//Initialize output features switches
InitOutputFeaturesGeneralSwitches(outputBundle.InputVectorCollection);
//Buld preprocessing overview
preprocessingOverview = new PreprocessingOverview(CollectStatatistics(),
TotalNumOfHiddenNeurons,
PredictorDescriptorCollection.Count,
NumOfSuppressedPredictors,
NumOfActivePredictors
);
//Raise final informative event
PreprocessingProgressChanged(inputBundle.InputVectorCollection.Count, inputBundle.InputVectorCollection.Count, preprocessingOverview);
//Return output
return(outputBundle);
}
