categorical_logit_rng.hpp

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#ifndef STAN_MATH_PRIM_PROB_CATEGORICAL_LOGIT_RNG_HPP
#define STAN_MATH_PRIM_PROB_CATEGORICAL_LOGIT_RNG_HPP
 
#include <stan/math/prim/meta.hpp>
#include <stan/math/prim/err.hpp>
#include <stan/math/prim/fun/cumulative_sum.hpp>
#include <stan/math/prim/fun/softmax.hpp>
#include <stan/math/prim/fun/Eigen.hpp>
#include <stan/math/prim/fun/constants.hpp>
#include <boost/random/uniform_01.hpp>
#include <boost/random/variate_generator.hpp>
 
namespace stan {
namespace math {
 
template <class RNG>
inline int categorical_logit_rng(const Eigen::VectorXd& beta, RNG& rng) {
  using boost::uniform_01;
  using boost::variate_generator;
  static constexpr const char* function = "categorical_logit_rng";
  // Throws in nan and all -inf case
  check_not_nan(function, "Log odds parameter", beta);
  check_nonzero_size(function, "Log odds parameter", beta);
  check_greater(function, "Log odds parameter", beta.maxCoeff(),
                NEGATIVE_INFTY);
 
  auto is_pos_inf = (beta.array() == INFTY);
  int num_infty = is_pos_inf.count();
 
  Eigen::VectorXd theta;
 
  // softmax is NaN with +inf, so split probability evenly over the
  // +inf entries and give every other entry zero probability
  if (num_infty > 0) {
    theta = is_pos_inf.template cast<double>() / num_infty;
  } else {
    theta = softmax(beta);
  }
 
  variate_generator<RNG&, uniform_01<> > uniform01_rng(rng, uniform_01<>());
  Eigen::VectorXd index = cumulative_sum(theta);
 
  // index may sum to just below 1, so stop at the last nonzero entry
  int last = theta.size() - 1;
  while (theta(last) == 0) {
    last--;
  }
  double c = uniform01_rng();
  int b = 0;
  while (b < last && c >= index(b)) {
    b++;
  }
  return b + 1;
}
}  // namespace math
}  // namespace stan
#endif