Rcpp Matrix row column permutations - r

Question

I am trying to mimic the R function that allows to run column and row matrix permutations based on a vector of indices. Like in the following code:
m=matrix(sample(c(0:9),5*5,T),ncol=5,nrow=5)
diag(m)=0
rand=sample(c(1:5))
m[rand,rand]
I tried the following code in c++:
Library(Rcpp)
cppFunction(‘
NumericMatrix test(NumericMatrix& M, int col, IntegerVector& rand) {
NumericMatrix M2(col,col);
for(int a=0;a<col;a++){
for(int b=a+1;b<col;b++){
M2(b,a)=M(rand(b),rand(a));
M2(a,b)=M(rand(a),rand(b));
}
}
return M2;
}
‘)
But it is very slow:
microbenchmark::microbenchmark(test(m,5,(rand-1)),m2[rand,rand])
Any ideas how I could speed up the process?

Answer 1

Using a simpler loop:
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
NumericMatrix test(NumericMatrix& M, int col, IntegerVector& rand) {
NumericMatrix M2(col,col);
for(int a=0;a<col;a++){
for(int b=a+1;b<col;b++){
M2(b,a)=M(rand(b),rand(a));
M2(a,b)=M(rand(a),rand(b));
}
}
return M2;
}
// [[Rcpp::export]]
NumericMatrix test2(const NumericMatrix& M, const IntegerVector& ind) {
int col = M.ncol();
NumericMatrix M2(col, col);
for (int j = 0; j < col; j++)
for (int i = 0; i < col; i++)
M2(i, j) = M(ind[i], ind[j]);
return M2;
}
/*** R
N <- 500
m <- matrix(sample(c(0:9), N * N, TRUE), ncol = N, nrow = N)
diag(m) <- 0
rand <- sample(N)
all.equal(test(m, ncol(m), rand - 1), m[rand, rand], test2(m, rand - 1))
microbenchmark::microbenchmark(
test(m, ncol(m), rand - 1),
m[rand, rand],
test2(m, rand - 1)
)
*/
For N = 5, the R version is faster, but in terms of nanoseconds..
For example, with N = 500, you get:
Unit: microseconds
expr min lq mean median uq max neval
test(m, ncol(m), rand - 1) 2092.474 2233.020 2843.145 2360.654 2548.050 7412.057 100
m[rand, rand] 1422.352 1506.117 2064.500 1578.129 1718.345 6700.219 100
test2(m, rand - 1) 698.595 769.944 1161.747 838.811 928.535 5379.841 100