logistic_cdf.hpp

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#ifndef STAN_MATH_PRIM_PROB_LOGISTIC_CDF_HPP
#define STAN_MATH_PRIM_PROB_LOGISTIC_CDF_HPP
 
#include <stan/math/prim/meta.hpp>
#include <stan/math/prim/err.hpp>
#include <stan/math/prim/fun/constants.hpp>
#include <stan/math/prim/fun/exp.hpp>
#include <stan/math/prim/fun/max_size.hpp>
#include <stan/math/prim/fun/scalar_seq_view.hpp>
#include <stan/math/prim/fun/size.hpp>
#include <stan/math/prim/fun/size_zero.hpp>
#include <stan/math/prim/fun/value_of.hpp>
#include <stan/math/prim/fun/inv_logit.hpp>
#include <stan/math/prim/prob/logistic_log.hpp>
#include <stan/math/prim/functor/partials_propagator.hpp>
#include <cmath>
 
namespace stan {
namespace math {
 
// Logistic(y|mu, sigma) [sigma > 0]
template <typename T_y, typename T_loc, typename T_scale,
          require_all_not_nonscalar_prim_or_rev_kernel_expression_t<
              T_y, T_loc, T_scale>* = nullptr>
return_type_t<T_y, T_loc, T_scale> logistic_cdf(const T_y& y, const T_loc& mu,
                                                const T_scale& sigma) {
  using T_partials_return = partials_return_t<T_y, T_loc, T_scale>;
  using std::exp;
  using T_y_ref = ref_type_t<T_y>;
  using T_mu_ref = ref_type_t<T_loc>;
  using T_sigma_ref = ref_type_t<T_scale>;
  static constexpr const char* function = "logistic_cdf";
  check_consistent_sizes(function, "Random variable", y, "Location parameter",
                         mu, "Scale parameter", sigma);
  T_y_ref y_ref = y;
  T_mu_ref mu_ref = mu;
  T_sigma_ref sigma_ref = sigma;
  check_not_nan(function, "Random variable", y_ref);
  check_finite(function, "Location parameter", mu_ref);
  check_positive_finite(function, "Scale parameter", sigma_ref);
 
  if (size_zero(y, mu, sigma)) {
    return 1.0;
  }
 
  T_partials_return P(1.0);
  auto ops_partials = make_partials_propagator(y_ref, mu_ref, sigma_ref);
 
  scalar_seq_view<T_y_ref> y_vec(y_ref);
  scalar_seq_view<T_mu_ref> mu_vec(mu_ref);
  scalar_seq_view<T_sigma_ref> sigma_vec(sigma_ref);
  size_t N = max_size(y, mu, sigma);
 
  // Explicit return for extreme values
  // The gradients are technically ill-defined, but treated as zero
  for (size_t i = 0; i < stan::math::size(y); i++) {
    if (y_vec.val(i) == NEGATIVE_INFTY) {
      return ops_partials.build(0.0);
    }
  }
 
  for (size_t n = 0; n < N; n++) {
    // Explicit results for extreme values
    // The gradients are technically ill-defined, but treated as zero
    if (y_vec.val(n) == INFTY) {
      continue;
    }
 
    const T_partials_return y_dbl = y_vec.val(n);
    const T_partials_return mu_dbl = mu_vec.val(n);
    const T_partials_return sigma_dbl = sigma_vec.val(n);
    const T_partials_return sigma_inv_vec = 1.0 / sigma_vec.val(n);
 
    // TODO(Andrew) Further simplify derivatives and log scale below
    const T_partials_return Pn = inv_logit((y_dbl - mu_dbl) * sigma_inv_vec);
 
    P *= Pn;
 
    if (!is_constant_all<T_y>::value) {
      partials<0>(ops_partials)[n]
          += exp(logistic_log(y_dbl, mu_dbl, sigma_dbl)) / Pn;
    }
    if (!is_constant_all<T_loc>::value) {
      partials<1>(ops_partials)[n]
          += -exp(logistic_log(y_dbl, mu_dbl, sigma_dbl)) / Pn;
    }
    if (!is_constant_all<T_scale>::value) {
      partials<2>(ops_partials)[n]
          += -(y_dbl - mu_dbl) * sigma_inv_vec
             * exp(logistic_log(y_dbl, mu_dbl, sigma_dbl)) / Pn;
    }
  }
 
  if (!is_constant_all<T_y>::value) {
    for (size_t n = 0; n < stan::math::size(y); ++n) {
      partials<0>(ops_partials)[n] *= P;
    }
  }
  if (!is_constant_all<T_loc>::value) {
    for (size_t n = 0; n < stan::math::size(mu); ++n) {
      partials<1>(ops_partials)[n] *= P;
    }
  }
  if (!is_constant_all<T_scale>::value) {
    for (size_t n = 0; n < stan::math::size(sigma); ++n) {
      partials<2>(ops_partials)[n] *= P;
    }
  }
  return ops_partials.build(P);
}
 
}  // namespace math
}  // namespace stan
#endif