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#### Sparse Matrices in Compressed column-oriented format
### contains = "dsparseMatrix", "CsparseMatrix"
## Specific conversions, should they be necessary. Better to convert as
## as(x, "TsparseMatrix") or as(x, "denseMatrix")
## Moved to ./Csparse.R :
## setAs("dgCMatrix", "dgTMatrix", ....
## setAs("dgCMatrix", "dgeMatrix", ....
## setAs("dgeMatrix", "dgCMatrix", ....
setAs("dgCMatrix", "ngCMatrix", function(from) .C2nC(from, FALSE))
## rather use Csparse* to lsparse* in ./lsparseMatrix.R ,
## but this is for "back-compatibility" (have had tests for it..):
setAs("dgCMatrix", "lgCMatrix",
function(from) { ## FIXME use .Call() too!
r <- new("lgCMatrix")
r@x <- as.logical(from@x)
## and copy the other slots
for(nm in c("i", "p", "Dim", "Dimnames"))
slot(r, nm) <- slot(from, nm)
r
})
setMethod("image", "dgCMatrix", function(x, ...) image(as(x, "dgTMatrix"), ...))
## Group Methods, see ?Arith (e.g.)
## -----
##
## "Arith" is now in ./Ops.R
##
## "Math" and "Math2" in ./Math.R
## "[<-" methods { setReplaceMethod()s } are now in ./Csparse.R
## setMethod("writeHB", signature(obj = "dgCMatrix"),
## function(obj, file, ...) {
## .Deprecated("writeMM")
## .Call(Matrix_writeHarwellBoeing, obj,
## as.character(file), "DGC")
## })
##-> ./colSums.R for colSums,... rowMeans
setMethod("t", signature(x = "dgCMatrix"),
function(x) .Call(Csparse_transpose, x, FALSE),
valueClass = "dgCMatrix")
setMethod("determinant", signature(x = "dgCMatrix", logarithm = "logical"),
detSparseLU) # using mkDet() --> ./Auxiliaries.R
setMethod("qr", signature(x = "dgCMatrix"),
function(x, tol = 1e-07, LAPACK = FALSE, keep.dimnames = TRUE,
verbose = !is.null(v <- getOption("Matrix.verbose")) && v >= 1)
.Call(dgCMatrix_QR, # -> cs_sqr() and cs_qr() >> ../src/dgCMatrix.c
x, ## order =
if(verbose) -1L else TRUE, keep.dimnames))
setMethod("qr", signature(x = "sparseMatrix"),
function(x, ...)
qr(as(as(as(x, "CsparseMatrix"), "dsparseMatrix"), "dgCMatrix"), ...))
LU.dgC <- function(x, errSing = TRUE, order = TRUE, tol = 1.0, keep.dimnames = TRUE, ...) {
chk.s(..., which.call=-2)
.Call(dgCMatrix_LU, x, order, tol, errSing, keep.dimnames) ## ../src/dgCMatrix.c
}
setMethod("lu", signature(x = "dgCMatrix"), LU.dgC)
setMethod("lu", signature(x = "sparseMatrix"),
function(x, ...)
.set.factors(x, "lu",
lu(as(as(as(x, "CsparseMatrix"), "dsparseMatrix"), "dgCMatrix"),
...)))
.solve.dgC.lu <- function(a, b, tol = .Machine$double.eps) {
## @MM: see also solveSparse() in ~/R/MM/Pkg-ex/Matrix/Doran-A.R
lu.a <- LU.dgC(a)
if(tol > 0) {
rU <- range(abs(diag(lu.a@U)))
if(rU[1] / rU[2] < tol)
stop(gettextf("LU computationally singular: ratio of extreme entries in |diag(U)| = %9.4g",
rU[1] / rU[2]),
domain=NA)
}
n <- dim(a)[1L] ## == dim(a)[2], as a[.,.] is square matrix
b.isMat <-
if(missing(b)) {
## default b = Identity = Diagonal(nrow(a)), however more efficiently
b <- .sparseDiagonal(n)
TRUE
} else {
isM <- !is.null(dim(b))
if(isM && nrow(b) != n)
stop("RHS 'b' has wrong number of rows:", nrow(b))
if(!isM && length(b) != n)
stop("RHS 'b' has wrong length", length(b))
isM
}
## bp := P %*% b
bp <- if(b.isMat) b[lu.a@p+1L, ] else b[lu.a@p+1L]
## R:= U^{-1} L^{-1} P b
R <- solve(lu.a@U, solve(lu.a@L, bp))
## result = Q'R = Q' U^{-1} L^{-1} P b = A^{-1} b, as A = P'LUQ
R[invPerm(lu.a@q, zero.p=TRUE), ]
}
## FIXME: workaround, till .Call(dgCMatrix_matrix_solve, a, b, sparse=TRUE) works:
.solve.dgC <- function(a, b, sparse, tol = .Machine$double.eps)
if(sparse) .solve.dgC.lu(a, b, tol=tol) else .Call(dgCMatrix_matrix_solve, a, b, FALSE)
.solve.dgC.mat <- function(a, b, sparse=FALSE, tol = .Machine$double.eps, ...) {
chk.s(..., which.call=-2)
if(sparse) .solve.dgC.lu(a, b, tol=tol) else .Call(dgCMatrix_matrix_solve, a, b, FALSE)
}
## Provide also for pkg MatrixModels
.solve.dgC.chol <- function(x, y)
.Call(dgCMatrix_cholsol, as(x, "CsparseMatrix"), y)
.solve.dgC.qr <- function(x, y, order = 1L) {
cld <- getClass(class(x))
.Call(dgCMatrix_qrsol, # has AS_CSP(): must be dgC or dtC:
if(extends1of(cld, c("dgCMatrix", "dtCMatrix"))) x
else as(x, "dgCMatrix"),
y, order)
}
setMethod("solve", signature(a = "dgCMatrix", b = "matrix"), .solve.dgC.mat)
setMethod("solve", signature(a = "dgCMatrix", b = "ddenseMatrix"), .solve.dgC.mat)
setMethod("solve", signature(a = "dgCMatrix", b = "dsparseMatrix"),
function(a, b, sparse=NA, tol = .Machine$double.eps, ...) {
chk.s(..., which.call=-2)
if(is.na(sparse)) {
if(isSymmetric(a))
## TODO: fast cholmod_symmetric() for Cholesky
return(solve(forceCspSymmetric(a, isTri=FALSE), b, tol=tol))
#-> sparse result
## else
sparse <- FALSE # (old default)
}
## FIXME: be better when sparse=TRUE (?)
.solve.dgC(a, as(b, "denseMatrix"), tol=tol, sparse=sparse)
})
## This is a really dumb method but some people apparently want it
## (MM: a bit less dumb now with possibility of staying sparse)
setMethod("solve", signature(a = "dgCMatrix", b = "missing"),
function(a, b, sparse=NA, tol = .Machine$double.eps, ...) {
chk.s(..., which.call=-2)
if(is.na(sparse)) {
if(isSymmetric(a))
## TODO: fast cholmod_symmetric() for Cholesky
return(solve(forceCspSymmetric(a, isTri=FALSE),
b = Diagonal(nrow(a)))) #-> sparse result
## else
sparse <- FALSE # (old default)
}
if(sparse)
.solve.dgC.lu(a, tol=tol) # -> "smart" diagonal b
else .Call(dgCMatrix_matrix_solve, a, b=diag(nrow(a)), FALSE)
})
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