/// <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 )); }