Class "inchol"

The reduced Cholesky decomposition object

Objects from the Class

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

Extends

Class "matrix", directly.

Methods

diagresidues

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

maxresiduals

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

pivots

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

Author

Alexandros Karatzoglou
alexandros.karatzoglou@ci.tuwien.ac.at

See also

inchol, csi-class, csi

Examples

data(iris)
datamatrix <- as.matrix(iris[,-5])
# initialize kernel function
rbf <- rbfdot(sigma=0.1)
rbf
#> new("rbfkernel", .Data = function (x, y = NULL) 
#> {
#>     if (!is(x, "vector")) 
#>         stop("x must be a vector")
#>     if (!is(y, "vector") && !is.null(y)) 
#>         stop("y must a vector")
#>     if (is(x, "vector") && is.null(y)) {
#>         return(1)
#>     }
#>     if (is(x, "vector") && is(y, "vector")) {
#>         if (!length(x) == length(y)) 
#>             stop("number of dimension must be the same on both data points")
#>         return(exp(sigma * (2 * crossprod(x, y) - crossprod(x) - 
#>             crossprod(y))))
#>     }
#> }, kpar = list(sigma = 0.1))
#> <bytecode: 0x564204d155f8>
#> <environment: 0x564207ba34b8>
#> attr(,"kpar")
#> attr(,"kpar")$sigma
#> [1] 0.1
#> 
#> attr(,"class")
#> [1] "rbfkernel"
#> attr(,"class")attr(,"package")
#> [1] "kernlab"
Z <- inchol(datamatrix,kernel=rbf)
#> Warning: NaNs produced
#> Error in while (residue > tol && counter < maxiter) {    if (counter%%BLOCKSIZE == 0) {        Tktmp <- matrix(0, m, dim(Tk)[2] + BLOCKSIZE)        Tktmp[1:m > 0, 1:(dim(Tk)[2] + BLOCKSIZE) <= dim(Tk)[2]] <- Tk        Tk <- Tktmp        Ttmp <- matrix(0, dim(T)[1] + BLOCKSIZE, BLOCKSIZE +             counter)        ind <- 1:(dim(T)[1] + BLOCKSIZE) <= dim(T)[1]        ind2 <- 1:(BLOCKSIZE + counter) <= counter        Ttmp[ind, ind2] <- T        Ttmp[ind == FALSE, ind2 == FALSE] <- diag(1, BLOCKSIZE)        T <- Ttmp        padded.veck.tmp <- matrix(0, dim(padded.veck)[1] + BLOCKSIZE)        padded.veck.tmp[1:(dim(padded.veck)[1] + BLOCKSIZE) <=             dim(padded.veck)[1]] <- padded.veck        padded.veck <- padded.veck.tmp        pivots.tmp <- matrix(0, dim(pivots)[1] + BLOCKSIZE)        pivots.tmp[1:(dim(pivots)[1] + BLOCKSIZE) <= dim(pivots)[1]] <- pivots        pivots <- pivots.tmp        maxresiduals.tmp <- matrix(0, dim(maxresiduals)[1] +             BLOCKSIZE)        maxresiduals.tmp[1:(dim(maxresiduals)[1] + BLOCKSIZE) <=             dim(maxresiduals)[1]] <- maxresiduals        maxresiduals <- maxresiduals.tmp        if (counter == 0)             t <- rep(0, BLOCKSIZE)        else t <- rep(0, length(t) + BLOCKSIZE)    }    veck <- kernelFast(kernel, x, x[index, , drop = FALSE], dota)    if (counter == 0) {        tau <- sqrt(veck[index])        T[1, 1] <- tau        update <- veck/tau    }    else {        padded.veck[1:counter] <- veck[pivots[1:counter]]        t[1:counter] <- backsolve(T, k = counter, padded.veck,             transpose = TRUE)        tau <- as.vector(sqrt(veck[index] - crossprod(t)))        T[1:counter, counter + 1] <- t[1:counter]        T[counter + 1, counter + 1] <- tau        update <- (1/tau) * (veck - Tk %*% t)    }    Tk[, counter + 1] <- update    diag.residues <- diag.residues - update^2    pivots[counter + 1] <- index    maxresiduals[counter + 1] <- residue    residue <- max(diag.residues)    index <- which.max(diag.residues)    counter <- counter + 1    if (counter%%blocksize == 0 && (verbose == TRUE))         cat("counter = ", counter, " ", "residue = ", residue,             "\n")}: missing value where TRUE/FALSE needed
dim(Z)
#> Error: object 'Z' not found
pivots(Z)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'pivots': object 'Z' not found
diagresidues(Z)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'diagresidues': object 'Z' not found
maxresiduals(Z)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'maxresiduals': object 'Z' not found