/// <summary>
/// Prepares input for regression stage of State Machine training.
/// All input patterns are processed by internal reservoirs and the corresponding network predictors are recorded.
/// </summary>
/// <param name="patternBundle">
/// The bundle containing known sample input patterns and desired output vectors
/// </param>
/// <param name="informativeCallback">
/// Function to be called after each processed input.
/// </param>
/// <param name="userObject">
/// The user object to be passed to informativeCallback.
/// </param>
public RegressionInput PrepareRegressionData(PatternBundle patternBundle,
NeuralPreprocessor.PredictorsCollectionCallbackDelegate informativeCallback = null,
Object userObject = null
)
{
return(new RegressionInput(NP.InitializeAndPreprocessBundle(patternBundle, informativeCallback, userObject),
NP.CollectStatatistics(),
NP.NumOfNeurons,
NP.NumOfInternalSynapses
));
}
/// <summary>
/// Prepares input for regression stage of State Machine training.
/// All input vectors are processed by internal reservoirs and the corresponding network predictors are recorded.
/// </summary>
/// <param name="vectorBundle">
/// The bundle containing known sample input and desired output vectors (in time order)
/// </param>
/// <param name="informativeCallback">
/// Function to be called after each processed input.
/// </param>
/// <param name="userObject">
/// The user object to be passed to informativeCallback.
/// </param>
public RegressionInput PrepareRegressionData(VectorBundle vectorBundle,
NeuralPreprocessor.PredictorsCollectionCallbackDelegate informativeCallback = null,
Object userObject = null
)
{
VectorBundle preprocessedData = NP.InitializeAndPreprocessBundle(vectorBundle, informativeCallback, userObject);
InitPredictorsGeneralSwitches(preprocessedData.InputVectorCollection);
return(new RegressionInput(preprocessedData,
NP.CollectStatatistics(),
NP.NumOfNeurons,
NP.NumOfInternalSynapses,
NumOfUnusedPredictors
));
}
