kernlab (version 0.9-24)

csi-class: Class "csi"

Description

The reduced Cholesky decomposition object

Objects from the Class

Objects can be created by calls of the form new("csi", ...). or by calling the csi function.

Slots

.Data:

Object of class "matrix" contains the decomposed matrix

pivots:

Object of class "vector" contains the pivots performed

diagresidues:

Object of class "vector" contains the diagonial residues

maxresiduals:

Object of class "vector" contains the maximum residues

predgain

Object of class "vector" contains the predicted gain before adding each column

truegain

Object of class "vector" contains the actual gain after adding each column

Q

Object of class "matrix" contains Q from the QR decomposition of the kernel matrix

R

Object of class "matrix" contains R from the QR decomposition of the kernel matrix

Extends

Class "matrix", directly.

Methods

diagresidues

signature(object = "csi"): returns the diagonial residues

maxresiduals

signature(object = "csi"): returns the maximum residues

pivots

signature(object = "csi"): returns the pivots performed

predgain

signature(object = "csi"): returns the predicted gain before adding each column

truegain

signature(object = "csi"): returns the actual gain after adding each column

Q

signature(object = "csi"): returns Q from the QR decomposition of the kernel matrix

R

signature(object = "csi"): returns R from the QR decomposition of the kernel matrix

See Also

csi, inchol-class

Examples

data(iris)

## create multidimensional y matrix
yind <- t(matrix(1:3,3,150))
ymat <- matrix(0, 150, 3)
ymat[yind==as.integer(iris[,5])] <- 1

datamatrix <- as.matrix(iris[,-5])
# initialize kernel function
rbf <- rbfdot(sigma=0.1)
rbf
Z <- csi(datamatrix,ymat, kernel=rbf, rank = 30)
dim(Z)
pivots(Z)
# calculate kernel matrix
K <- crossprod(t(Z))
# difference between approximated and real kernel matrix
(K - kernelMatrix(kernel=rbf, datamatrix))[6,]

Run the code above in your browser using DataCamp Workspace