Le 26/05/2021 à 16:36, Vincent Carey a écrit :
On this theme, the following proved sufficient to ingest and
convert sparse csv without column headers or row names:
Nice to share your final solution which could be further shorten to smth
like:
#include "RcppArmadillo.h"
using namespace Rcpp;
// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
arma::sp_mat parse_sparse_csv_short(std::string fname) {
arma::sp_mat D;
D.load(fname, arma::csv_ascii);
return D;
}
Best,
Serguei.
#include "RcppArmadillo.h"
using namespace Rcpp;
// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
List parse_sparse_csv_impl(SEXP fname) {
using namespace Rcpp;
std::string v = Rcpp::as<std::string>(fname);
arma::sp_mat D;
D.load(v, arma::csv_ascii);
return Rcpp::List::create(Rcpp::Named("sp")=D);
}
On Mon, May 10, 2021 at 11:19 PM Dirk Eddelbuettel <[email protected]
<mailto:[email protected]>> wrote:
Vincent,
In the broad terms of the question the best answer may be a simple
"sure".
More seriously, there have been many approaches. Consider for
example the
recent Rcpp Gallery post lead by Zach (with some edits by me):
https://gallery.rcpp.org/articles/sparse-matrix-class/
<https://gallery.rcpp.org/articles/sparse-matrix-class/>
It's focus on not copying <i,p,x> again if we already have them as R
vectors,
which is a fair point. If the goal is to get to SuperLU via
(Rcpp)Armadillo
then I do not think you can avoid the (internal) copies. As always, the
answer may be "it depends".
Hope this helps, happy to refine, Dirk
--
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