Rcpp Armadillo中的样本

Question

I am currently struggeling with the sample() command provided in RcppArmadillo . When I try to run the code below I get the error no matching function for call to sample and I already add the extra Rcpp:: namespace in front since this worked out well in another post .

I also tried several other container classes, but I am always stuck with this error. Below is some code, which produces the error.

Any help would be greatly appreciated :)

#include <RcppArmadillo.h>
// [[Rcpp::depends(RcppArmadillo)]]
#include <RcppArmadilloExtensions/sample.h>
using namespace Rcpp;

// [[Rcpp::export]]
NumericMatrix example(arma::mat fprob,
                      int K) {
  int t = fprob.n_rows;
  IntegerVector choice_set = seq_len(K);
  arma::mat states(t,1); states.fill(0);
  arma::rowvec p0(K);
  arma::rowvec alph(K);
  double fit;

  p0 = fprob.row(t-1);
  fit = accu(p0);
  alph = p0/fit;
  states(t-1,1) = Rcpp::RcppArmadillo::sample(choice_set, 1, false, alph)[0];

  return wrap(states);
}

Answer 1

Here the definition of that function from the header:

    // Enables supplying an arma probability
    template <class T> 
    T sample(const T &x, const int size, const bool replace, arma::vec &prob_){
      return sample_main(x, size, replace, prob_);
    }

Note that it expects a arma::vec == arma::colvec , while you are providing a arma::rowvec . So it should work if you change p0 and alph to arma::vec . Untested because of missing sample data ...

BTW, there is meanwhile also a Rcpp:::sample() function in case you are not really needing Armadillo for other tasks.

Concerning the performance questions raised by @JosephWood in the comments: I have the impression that both Rcpp::sample() and Rcpp::RcppArmadillo::sample() are based on do_sample() . So they should be quite similar in most cases, but I have not benchmarked them. The higher performance of R for unweighted sampling without replacement for larger numbers comes from the hash algorithm , which is selected at R level in such cases. It is also interesting to note that R 3.6 will have a new method for sampling in order to remove a bias present in the current method.