/// <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
));
}
/// <summary>
/// Performs the training of the state machine.
/// </summary>
/// <param name="trainingData">The training data bundle.</param>
/// <param name="controller">The build process controller (optional).</param>
/// <returns>The training results.</returns>
public TrainingResults Train(VectorBundle trainingData, TNRNetBuilder.BuildControllerDelegate controller = null)
{
//StateMachine reset
Reset();
VectorBundle readoutTrainingData;
NeuralPreprocessor.PreprocessingOverview preprocessingOverview = null;
if (NP == null)
{
//Neural preprocessor is bypassed
readoutTrainingData = trainingData;
}
else
{
//Neural preprocessing
readoutTrainingData = NP.InitializeAndPreprocessBundle(trainingData, out preprocessingOverview);
}
//Training of the readout layer
ReadoutLayer.RegressionOverview regressionOverview = RL.Build(readoutTrainingData, BuildPredictorsMapper(), controller, Config.RandomizerSeek);
//Return the training results
return(new TrainingResults(preprocessingOverview, regressionOverview));
}
/// <summary>
/// Performs training of the StateMachine
/// </summary>
/// <param name="vectorBundle">Training data bundle (input vectors and desired output vectors)</param>
/// <param name="regressionController">Optional regression controller.</param>
/// <returns>Output of the regression stage</returns>
public TrainingResults Train(VectorBundle vectorBundle, TrainedNetworkBuilder.RegressionControllerDelegate regressionController = null)
{
//StateMachine reset
Reset();
VectorBundle readoutInput;
NeuralPreprocessor.PreprocessingOverview preprocessingOverview = null;
if (NP == null)
{
//Neural preprocessor is bypassed
readoutInput = vectorBundle;
}
else
{
//Neural preprocessing
readoutInput = NP.InitializeAndPreprocessBundle(vectorBundle, out preprocessingOverview);
}
//Training of the readout layer
ReadoutLayer.RegressionOverview regressionOverview = RL.Build(readoutInput, BuildPredictorsMapper(), regressionController);
//Return compact results
return(new TrainingResults(preprocessingOverview, regressionOverview));
}
