Passing an Armadillo sparse matrix by reference with Rcpp - r

Question

This question is related to this and this. The difference here is that I'm not passing an Rcpp type like NumericVector or NumericMatrix, but an arma::sp_mat.
Is there any way to pass an sp_mat to C++, modify its values, and have the changes show up in the original object in R?
This can be done with a NumericMatrix, for example:
cppFunction("void frob(NumericMatrix& x)
{
for(NumericMatrix::iterator it = x.begin(); it != x.end(); ++it)
{
if(*it != 0) *it = *it + 5;
}
}")
M <- Matrix(0, 5, 1, sparse=TRUE)
M[1] <- 1.2345
m <- as.matrix(M)
frob(m)
m
#[,1]
#[1,] 6.2345
#[2,] 0.0000
#[3,] 0.0000
#[4,] 0.0000
#[5,] 0.0000
The same technique works for an arma::mat dense matrix. But for a sparse matrix, it doesn't work:
cppFunction("void frob2(arma::sp_mat& x)
{
for(arma::sp_mat::iterator it = x.begin(); it != x.end(); ++it)
{
*it = *it + 5;
}
}", depends="RcppArmadillo")
frob2(M)
M
#5 x 1 sparse Matrix of class "dgCMatrix"
#[1,] 1.2345
#[2,] .
#[3,] .
#[4,] .
#[5,] .

Answer 1

Unfortunately there is no auxiliary memory constructor for sparse matrices in Armadillo.
However you can construct sparse matrix like structure in C++ using pointers to R objects. Here is example:
template< typename T>
class MappedCSC {
public:
MappedCSC();
MappedCSC(std::uint32_t n_rows,
std::uint32_t n_cols,
size_t nnz,
std::uint32_t * row_indices,
std::uint32_t * col_ptrs,
T * values):
n_rows(n_rows), n_cols(n_cols), nnz(nnz), row_indices(row_indices), col_ptrs(col_ptrs), values(values) {};
const std::uint32_t n_rows;
const std::uint32_t n_cols;
const size_t nnz;
const std::uint32_t * row_indices;
const std::uint32_t * col_ptrs;
T * values;
};
using dMappedCSC = MappedCSC<double>;
Here is how you can extract it:
dMappedCSC extract_mapped_csc(Rcpp::S4 input) {
Rcpp::IntegerVector dim = input.slot("Dim");
Rcpp::NumericVector values = input.slot("x");
uint32_t nrows = dim[0];
uint32_t ncols = dim[1];
Rcpp::IntegerVector row_indices = input.slot("i");
Rcpp::IntegerVector col_ptrs = input.slot("p");
return dMappedCSC(nrows, ncols, values.length(), (uint32_t *)row_indices.begin(), (uint32_t *)col_ptrs.begin(), values.begin());
}
And here is example on how to iterate column by column:
Rcpp::NumericMatrix dense_csc_prod(const Rcpp::NumericMatrix &x_r, const Rcpp::S4 &y_csc_r) {
const arma::dmat x = arma::dmat((double *)&x_r[0], x_r.nrow(), x_r.ncol(), false, false);
const dMappedCSC y_csc = extract_mapped_csc(y_csc_r);
Rcpp::NumericMatrix res(x.n_rows, y_csc.n_cols);
arma::dmat res_arma_map = arma::dmat(res.begin(), res.nrow(), res.ncol(), false, false);
for (uint32_t i = 0; i < y_csc.n_cols; i++) {
const uint32_t p1 = y_csc.col_ptrs[i];
const uint32_t p2 = y_csc.col_ptrs[i + 1];
// mapped indices are uint32_t, but arma only allows indices be uvec = vec<uword> = vec<size_t>
// so we need to construct these indices by copying from uint32_t to uword
const arma::Col<uint32_t> idx_temp = arma::Col<uint32_t>(&y_csc.row_indices[p1], p2 - p1);
const arma::uvec idx = arma::conv_to<arma::uvec>::from(idx_temp);
const arma::colvec y_csc_col = arma::colvec(&y_csc.values[p1], p2 - p1, false, false);
res_arma_map.col(i) = x.cols(idx) * y_csc_col;
}
return res;
}