| SparsePls(DoubleMatrix, DoubleMatrix, Int32, Int32, Int32, SparsePLSMode, Int32, Double) Constructor |
Constructs a SparsePls object from the given parameters and performs
the sparse PLS calculation on the given data. The data is first centered and
scaled by standard deviation.
Namespace: CenterSpace.NMath.CoreAssembly: NMath (in NMath.dll) Version: 7.4
Syntax public SparsePls(
DoubleMatrix X,
DoubleMatrix Y,
int numComponents,
int[] keepX = null,
int[] keepY = null,
SparsePLSMode mode = SparsePLSMode.Regression,
int maxIterations = 500,
double tolerance = 1E-06
)
Public Sub New (
X As DoubleMatrix,
Y As DoubleMatrix,
numComponents As Integer,
Optional keepX As Integer() = Nothing,
Optional keepY As Integer() = Nothing,
Optional mode As SparsePLSMode = SparsePLSMode.Regression,
Optional maxIterations As Integer = 500,
Optional tolerance As Double = 1E-06
)
public:
SparsePls(
DoubleMatrix^ X,
DoubleMatrix^ Y,
int numComponents,
array<int>^ keepX = nullptr,
array<int>^ keepY = nullptr,
SparsePLSMode mode = SparsePLSMode::Regression,
int maxIterations = 500,
double tolerance = 1E-06
)
new :
X : DoubleMatrix *
Y : DoubleMatrix *
numComponents : int *
?keepX : int[] *
?keepY : int[] *
?mode : SparsePLSMode *
?maxIterations : int *
?tolerance : float
(* Defaults:
let _keepX = defaultArg keepX null
let _keepY = defaultArg keepY null
let _mode = defaultArg mode SparsePLSMode.Regression
let _maxIterations = defaultArg maxIterations 500
let _tolerance = defaultArg tolerance 1E-06
*)
-> SparsePls
Parameters
- X DoubleMatrix
- Matrix of predictor values.
- Y DoubleMatrix
- Matrix of responses.
- numComponents Int32
- The number of components to include in the model.
- keepX Int32 (Optional)
- Vector with length equal to the number of components, the
number of variables to keep in the X loadings for each component. The
default is to keep all variables.
- keepY Int32 (Optional)
- Vector with length equal to the number of components, the
number of variables to keep in the Y loadings for each component. The
default is to keep all variables.
- mode SparsePLSMode (Optional)
- Use regression on canonical mode.
- maxIterations Int32 (Optional)
- Maximum number of iterations. Default is 500.
- tolerance Double (Optional)
- The tolerance used in the iterative algorithm. Default is 1e-6.
See Also