PLS2CrossValidationData Class

Class PLS2CrossValidationData divides Partial Least Squares (PLS) data into training and testing subsets.

Inheritance Hierarchy

SystemObject
CenterSpace.NMath.CorePLS2CrossValidationData

Namespace: CenterSpace.NMath.Core
Assembly: NMath (in NMath.dll) Version: 7.4

Syntax

[SerializableAttribute]
public class PLS2CrossValidationData : ICloneable

<SerializableAttribute>
Public Class PLS2CrossValidationData
	Implements ICloneable

[SerializableAttribute]
public ref class PLS2CrossValidationData : ICloneable

[<SerializableAttribute>]
type PLS2CrossValidationData = 
    class
        interface ICloneable
    end

The PLS2CrossValidationData type exposes the following members.

Constructors

	Name	Description
	PLS2CrossValidationData	Constructs a PLS2CrossValidationData instance.
	PLS2CrossValidationData(DoubleMatrix, DoubleMatrix, Subset)	Constructs a PLS2CrossValidationData instance from the given PLS data and testing subset. The training subset will be the data not contained in the testing subset.

Properties

	Name	Description
	NumTestingSamples	Gets the number of samples that will be used for testing.
	NumTrainingSamples	Gets the number of samples that will be used for training.
	PredictorTestingSet	Gets and sets the matrix of predictor variable values comprising the testing set.
	PredictorTrainingSet	Gets and sets the matrix of predictor variable values comprising the training set.
	ResponseTestingSet	Gets and sets the matrix of response variable values comprising the testing set.
	ResponseTrainingSet	Gets and sets the matrix of response variable values comprising the training set.

Methods

	Name	Description
	Clone	Creates a deep copy of this PLS2CrossValidationData.
	SetData(DoubleMatrix, DoubleMatrix, Subset)	Sets the cross validation data according the given PLS data and testing subset. The training subset will be the data not contained in the testing subset.
	SetData(DoubleMatrix, DoubleMatrix, Subset, Boolean)	Sets the cross validation data according the given PLS data and testing subset. The training subset will be the data not contained in the testing subset.