1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
|
\name{csi-class}
\docType{class}
\alias{csi-class}
\alias{Q}
\alias{R}
\alias{predgain}
\alias{truegain}
\alias{diagresidues,csi-method}
\alias{maxresiduals,csi-method}
\alias{pivots,csi-method}
\alias{predgain,csi-method}
\alias{truegain,csi-method}
\alias{Q,csi-method}
\alias{R,csi-method}
\title{Class "csi"}
\description{The reduced Cholesky decomposition object}
\section{Objects from the Class}{Objects can be created by calls of the form \code{new("csi", ...)}.
or by calling the \code{csi} function.}
\section{Slots}{
\describe{
\item{\code{.Data}:}{Object of class \code{"matrix"} contains
the decomposed matrix}
\item{\code{pivots}:}{Object of class \code{"vector"} contains
the pivots performed}
\item{\code{diagresidues}:}{Object of class \code{"vector"} contains
the diagonial residues}
\item{\code{maxresiduals}:}{Object of class \code{"vector"} contains
the maximum residues}
\item{predgain}{Object of class \code{"vector"} contains
the predicted gain before adding each column}
\item{truegain}{Object of class \code{"vector"} contains
the actual gain after adding each column}
\item{Q}{Object of class \code{"matrix"} contains
Q from the QR decomposition of the kernel matrix}
\item{R}{Object of class \code{"matrix"} contains
R from the QR decomposition of the kernel matrix}
}
}
\section{Extends}{
Class \code{"matrix"}, directly.
}
\section{Methods}{
\describe{
\item{diagresidues}{\code{signature(object = "csi")}: returns
the diagonial residues}
\item{maxresiduals}{\code{signature(object = "csi")}: returns
the maximum residues}
\item{pivots}{\code{signature(object = "csi")}: returns
the pivots performed}
\item{predgain}{\code{signature(object = "csi")}: returns
the predicted gain before adding each column}
\item{truegain}{\code{signature(object = "csi")}: returns
the actual gain after adding each column}
\item{Q}{\code{signature(object = "csi")}: returns
Q from the QR decomposition of the kernel matrix}
\item{R}{\code{signature(object = "csi")}: returns
R from the QR decomposition of the kernel matrix}
}
}
\author{Alexandros Karatzoglou\cr \email{alexandros.karatzoglou@ci.tuwien.ac.at}}
\seealso{ \code{\link{csi}}, \code{\link{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,]
}
\keyword{classes}
|