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#### Symmetric Sparse Matrices in compressed column-oriented format
setAs("dgCMatrix", "dsCMatrix",
function(from) {
if(!exists(".warn.dsC")) { ## now only warn *once* ..
warning("as(.,\"dsCMatrix\") is deprecated; do use as(., \"symmetricMatrix\")")
assign(".warn.dsC", "DONE", envir = .GlobalEnv)
}
as(from, "symmetricMatrix")
})
## Specific conversions, should they be necessary. Better to convert as
## as(x, "TsparseMatrix") or as(x, "denseMatrix")
## Moved to ./Csparse.R
## setAs("dsCMatrix", "dsTMatrix",
## function(from) .Call(Csparse_to_Tsparse, from, FALSE))
setAs("dsCMatrix", "dgTMatrix", # needed for show(), image()
function(from)
## pre-Cholmod -- FIXME: get rid of
.Call(dsCMatrix_to_dgTMatrix, from))
setAs("dsCMatrix", "dgeMatrix",
function(from) as(as(from, "dgTMatrix"), "dgeMatrix"))
setAs("dsCMatrix", "matrix",
function(from) as(as(from, "generalMatrix"), "matrix"))
setAs("matrix", "dsCMatrix",
function(from)
as(as(as(from, "CsparseMatrix"), "symmetricMatrix"), "dMatrix"))
setAs("dsCMatrix", "lsCMatrix",
function(from) new("lsCMatrix", i = from@i, p = from@p, uplo = from@uplo,
x = as.logical(from@x),
Dim = from@Dim, Dimnames = from@Dimnames))
setAs("dsCMatrix", "nsCMatrix",
function(from) new("nsCMatrix", i = from@i, p = from@p, uplo = from@uplo,
Dim = from@Dim, Dimnames = from@Dimnames))
setAs("dsCMatrix", "dgCMatrix",
function(from) .Call(Csparse_symmetric_to_general, from))
setAs("dsCMatrix", "dsyMatrix",
function(from) as(from, "denseMatrix"))
## have rather tril() and triu() methods than
## setAs("dsCMatrix", "dtCMatrix", ....)
setMethod("tril", "dsCMatrix",
function(x, k = 0, ...) {
if(x@uplo == "L" && k == 0)
## same internal structure (speedup potential !?)
new("dtCMatrix", uplo = x@uplo, i = x@i, p = x@p,
x = x@x, Dim = x@Dim, Dimnames = x@Dimnames)
else tril(as(x, "dgCMatrix"), k = k, ...)
})
setMethod("triu", "dsCMatrix",
function(x, k = 0, ...) {
if(x@uplo == "U" && k == 0)
## same internal structure (speedup potential !?)
new("dtCMatrix", uplo = x@uplo, i = x@i, p = x@p,
x = x@x, Dim = x@Dim, Dimnames = x@Dimnames)
else triu(as(x, "dgCMatrix"), k = k, ...)
})
setMethod("solve", signature(a = "dsCMatrix", b = "ddenseMatrix"),
function(a, b, ...) {
if (class(b) != "dgeMatrix")
b <- .Call(dup_mMatrix_as_dgeMatrix, b)
.Call(dsCMatrix_matrix_solve, a, b)
},
valueClass = "dgeMatrix")
setMethod("solve", signature(a = "dsCMatrix", b = "denseMatrix"),
## only triggers for diagonal*, ldense*.. (but *not* ddense: above)
function(a, b, ...)
.Call(dsCMatrix_matrix_solve, a,
as(.Call(dup_mMatrix_as_geMatrix, b), "dgeMatrix")))
setMethod("solve", signature(a = "dsCMatrix", b = "matrix"),
function(a, b, ...)
.Call(dsCMatrix_matrix_solve, a,
.Call(dup_mMatrix_as_dgeMatrix, b)),
valueClass = "dgeMatrix")
setMethod("solve", signature(a = "dsCMatrix", b = "numeric"),
function(a, b, ...)
.Call(dsCMatrix_matrix_solve, a,
.Call(dup_mMatrix_as_dgeMatrix, b)),
valueClass = "dgeMatrix")
## `` Fully-sparse'' solve() :
setMethod("solve", signature(a = "dsCMatrix", b = "dsparseMatrix"),
function(a, b, ...) {
if (!is(b, "CsparseMatrix"))
b <- as(b, "CsparseMatrix")
if (is(b, "symmetricMatrix")) ## not supported (yet) by cholmod_spsolve
b <- as(b, "dgCMatrix")
.Call(dsCMatrix_Csparse_solve, a, b)
})
setMethod("chol", signature(x = "dsCMatrix"),
function(x, pivot = FALSE, ...) .Call(dsCMatrix_chol, x, pivot),
valueClass = "dtCMatrix")
setMethod("Cholesky", signature(A = "dsCMatrix"),
## signature(): leaving away (perm, LDL,..), but specify below:
## <==> all "ANY"
function(A, perm = TRUE, LDL = !super, super = FALSE, Imult = 0, ...)
.Call(dsCMatrix_Cholesky, A, perm, LDL, super, Imult))
setMethod("t", signature(x = "dsCMatrix"),
function(x) .Call(Csparse_transpose, x, FALSE),
valueClass = "dsCMatrix")
.diag.dsC <- function(x, Chx = Cholesky(x, LDL=TRUE), res.kind = "diag") {
force(Chx)
stopifnot(is.integer(Chx@p), is.double(Chx@x))
.Call(diag_tC, Chx@p, Chx@x, Chx@perm, res.kind)
}
## FIXME: kind = "diagBack" is not yet implemented
## would be much more efficient, but there's no CHOLMOD UI (?)
##
## Note: for det(), permutation is unimportant;
## for diag(), apply *inverse* permutation
## q <- p ; q[q] <- seq_along(q); q
ldet1.dsC <- function(x, ...) .Call(CHMfactor_ldetL2, Cholesky(x, ...))
## these are slightly faster (ca. 3 to 4 %):
ldet2.dsC <- function(x, ...) {
Ch <- Cholesky(x, super = FALSE, ...)
.Call(diag_tC, Ch@p, Ch@x, Ch@perm, "sumLog")
}
## only very slightly ( ~ < 1% ) faster (than "ldet2"):
ldet3.dsC <- function(x, perm = TRUE)
.Call(dsCMatrix_LDL_D, x, perm=perm, "sumLog")
setMethod("determinant", signature(x = "dsCMatrix", logarithm = "missing"),
function(x, logarithm, ...) determinant(x, TRUE))
setMethod("determinant", signature(x = "dsCMatrix", logarithm = "logical"),
function(x, logarithm, ...)
{
if((n <- x@Dim[1]) <= 1)
return(mkDet(x@x, logarithm))
Chx <- tryCatch(suppressWarnings(Cholesky(x, LDL=TRUE)),
error = function(e) NULL)
## or
## ldet <- .Call("CHMfactor_ldetL2", Chx) # which would also work
## when Chx <- Cholesky(x, super=TRUE)
## ldet <- tryCatch(.Call(dsCMatrix_LDL_D, x, perm=TRUE, "sumLog"),
## if(is.null(ldet))
if(is.null(Chx)) ## we do *not* have a positive definite matrix
detSparseLU(x, logarithm)
else {
d <- .Call(diag_tC, Chx@p, Chx@x, Chx@perm, res.kind = "diag")
mkDet(d, logarithm=logarithm)
}
})
## setMethod("writeHB", signature(obj = "dsCMatrix"),
## function(obj, file, ...) {
## .Deprecated("writeMM")
## .Call(Matrix_writeHarwellBoeing,
## if (obj@uplo == "U") t(obj) else obj,
## as.character(file), "DSC")
## })
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