similar to: RFC: Proposal to make NROW() and NCOL() slightly more general

I'm hoping that someone with deeper insight into S4 than I, that is to say virtually everyone reading this list, could help resolve the following problem in SparseM. We have setGeneric("backsolve", function(r, x, k = NULL, upper.tri = NULL, transpose = NULL, twice = TRUE, ...) standardGeneric("backsolve"), useAsDefault= function(r, x,

Dear R-developers, I am currently trying to develop a package with some customized container classes and found an issue with the functions NROW and NCOL. I guess that I can simply work around the problem by redefining these functions in my own package (Yet, I do not know whether that will work cleanly everywhere). Nevertheless, I thought that perhaps one might think about to redefine these

Dear all, I just installed the new version of R, 2.10.1, and I am currently using the package sparseM. (I also use a 64 bit windows version) I got a problem that I never had: when I try to multiply with a kronecker product (%x%) two sparse matrixes I get the following message: Error in dim(x) <- length(x) : invalid first argument I never had this problem with previous versions of R. May

I would like to add a class to the SparseM package. I have a class "matrix.csr" that describes a matrix in compressed sparse row format, now I would like a class matrix.diag.csr that describes such objects when they happen to be diagonal. The idea is that matrix.diag.csr objects should behave (later in life) exactly like matrix.csr objects, the distinction is only needed in order to

In the last few days I've received couple of messages pointing out that our SparseM package fails to install on the patched version of 1.7.0. Laurent Gaultier kindly suggested that replacing: setGeneric("as.matrix.csr") by setGeneric("as.matrix.csr", function(x, nrow, ncol, eps) standardGeneric("as.matrix.csr")) was sufficient to fix the problem.

Hello, I am working with a sparse matrix that is approx. 13,900 by 14,100. My goal is to select a row out of the matrix and create a new matrix with that row repeated 13,900 times without having to do any looping. Example: Starting Matrix: exampleMatrix 3 x 4 sparse Matrix of class "dgCMatrix" [1,] 1 . . . [2,] . 1 . 0.5 [3,] . . 1 .. New Matrix:.. newExampleMatrix 3 x 4 sparse

Hi, I have just installed R 2.4.0 and when I try to load SpareseM, I get the following error message library(SparseM) Package SparseM (0.71) loaded. To cite, see citation("SparseM") Error in loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'SparseM' methods specified for export, but none defined: as.matrix.csr, as.matrix.csc,

Hi there, I'm having a problem when trying to create a large matrix (1,000,000 x 1,000,000) of the .csr type (package 'SparseM'). > k <- rep(0,1000000) > tmp <- length(k) > tmp2 <- as.matrix.csr(0,tmp,tmp) Error in if (length(x) == nrow * ncol) x <- matrix(x, nrow, ncol) else { : missing value where TRUE/FALSE needed Warning message: In nrow * ncol : NAs

Dear list, I do not know what would be the 'correct' answer to the following but I think that they should return the same value to avoid potential problems and hard to debug errors. Regards, Simone --------------------------------------- > NCOL(NULL) [1] 1 > NROW(NULL) [1] 0 > sessionInfo() R version 4.3.1 RC (2023-06-08 r84523 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit)

Hi, I'm Sorry for any cross-posting. I've reviewed the archives and could not find an exact answer to my question below. I'm trying to generate very large sparse matrices (< 1% non-zero entries per row). I have a sparse matrix function below which works well until the row/col count exceeds 10,000. This is being run on a machine with 32G memory: sparse_matrix <-

Hi, I am writing some basic smoothers in R for cleaning some spectral data. I wanted to see if I could get close to matlab for speed, so I was trying to compare SparseM with Matrix to see which could do the choleski decomposition the fastest. Here is the function using SparseM difsm <- function(y, lambda, d){ # Smoothing with a finite difference penalty # y: signal to be smoothed #

As kindly pointed out to me (oh my decaying gray matter), is.object() is better suited for this test; $ svn diff src/library/base/R/diag.R Index: src/library/base/R/diag.R =================================================================== --- src/library/base/R/diag.R (revision 68345) +++ src/library/base/R/diag.R (working copy) @@ -23,9 +23,11 @@ stop("'nrow' or

I suggest that LLVM needs intrinsics for add/sub with carry, e.g. declare {T, i1} @llvm.addc.T(T %a, T %b, i1 c) The current multiprecision clang intrinsics example: void foo(unsigned *x, unsigned *y, unsigned *z) { unsigned carryin = 0; unsigned carryout; z[0] = __builtin_addc(x[0], y[0], carryin, &carryout); carryin = carryout; z[1] = __builtin_addc(x[1], y[1],

Hello list, I try to optimize some R code and it turns out that the function as.matrix takes a lot of time in my code. There is only one reason why I need as.matrix: My code should work for matrices with ncol=p and p should be allowed to be 1 or larger. Now I have a matrix x with dim(x)=(n,p), and I need to work with the submatrix y <-x[gv,], (gv a vector of n logicals) and to calculate

Do you have plans to add other intrinsics? I'm curious as to why there is an llvm.sin intrinsic and an llvm.cos intrinsic, but no llvm.atan intrinsic. Why is there an llvm.pow intrinsic but no llvm.log intrinsic? Also, have you thought about integer intrinsics like multiprecision multiply (u32xu32->u64) and multiprecision divide (u64/u32->(dividend:u32,remainder:u32))? Regards,

> On Jun 8, 2018, at 10:37 AM, Herv? Pag?s <hpages at fredhutch.org> wrote: > > Also the TRUEs cause problems if some dimensions are 0: > > > matrix(raw(0), nrow=5, ncol=0)[1:3 , TRUE] > Error in matrix(raw(0), nrow = 5, ncol = 0)[1:3, TRUE] : > (subscript) logical subscript too long OK. But this is easy enough to handle. > > H. > > On

Stephen Canon via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > Why do you think this requires new intrinsics instead of teaching the optimizer what to do with the existing intrinsics? IMO, as a multiprecision math library maker, the "teaching the optimizer what to do with the existing intrinsics" approach is much better as long as it can be made to work. If one is

Dear R developers, The trace of the hat matrix H~(n,n) is computed as follows: tr(H) = tr(BS^-1B') = tr(S^-1B'B) := tr(X) = sum(diag(X)) with B~(n,p), S~(p,p). Since p is of the order 10^3 but S is sparse I would like to employ Taucs linear solver ( http://www.tau.ac.il/~stoledo/taucs/ ) on SX = B'B. (Further improvement by implying a looping over i=1,...,p, calling

Full_Name: David Pleydell Version: 2.2.1 OS: Debian Etch Submission from: (NULL) (193.55.70.206) There appears to be a conflict between the chol functions from the Matrix and the SparseM packages. chol() can only be applied to a matrix of class dspMatrix if SparseM is not in the path. with gratitude David > library(Matrix) > sm <- as(as(Matrix(diag(5) + 1), "dsyMatrix"),

> On Jun 8, 2018, at 11:52 AM, Hadley Wickham <h.wickham at gmail.com> wrote: > > On Fri, Jun 8, 2018 at 11:38 AM, Berry, Charles <ccberry at ucsd.edu> wrote: >> >> >>> On Jun 8, 2018, at 10:37 AM, Herv? Pag?s <hpages at fredhutch.org> wrote: >>> >>> Also the TRUEs cause problems if some dimensions are 0: >>>