exponential_lcdf.hpp

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#ifndef STAN_MATH_OPENCL_PRIM_EXPONENTIAL_LCDF_HPP
#define STAN_MATH_OPENCL_PRIM_EXPONENTIAL_LCDF_HPP
#ifdef STAN_OPENCL
 
#include <stan/math/prim/meta.hpp>
#include <stan/math/prim/err.hpp>
#include <stan/math/prim/fun/constants.hpp>
#include <stan/math/prim/fun/elt_divide.hpp>
#include <stan/math/prim/fun/elt_multiply.hpp>
#include <stan/math/opencl/kernel_generator.hpp>
#include <stan/math/prim/functor/partials_propagator.hpp>
 
namespace stan {
namespace math {
 
template <typename T_y_cl, typename T_inv_scale_cl,
          require_all_prim_or_rev_kernel_expression_t<
              T_y_cl, T_inv_scale_cl>* = nullptr,
          require_any_not_stan_scalar_t<T_y_cl, T_inv_scale_cl>* = nullptr>
inline return_type_t<T_y_cl, T_inv_scale_cl> exponential_lcdf(
    const T_y_cl& y, const T_inv_scale_cl& beta) {
  static constexpr const char* function = "exponential_lcdf(OpenCL)";
  using T_partials_return = partials_return_t<T_y_cl, T_inv_scale_cl>;
  using std::isfinite;
  using std::isnan;
 
  check_consistent_sizes(function, "Random variable", y,
                         "Inverse scale parameter", beta);
  const size_t N = max_size(y, beta);
  if (N == 0) {
    return 0.0;
  }
 
  const auto& y_col = as_column_vector_or_scalar(y);
  const auto& beta_col = as_column_vector_or_scalar(beta);
 
  const auto& y_val = value_of(y_col);
  const auto& beta_val = value_of(beta_col);
 
  auto check_y_nonnegative
      = check_cl(function, "Random variable", y_val, "nonnegative");
  auto y_nonnegative_expr = y_val >= 0.0;
  auto check_beta_positive_finite = check_cl(
      function, "Inverse scale parameter", beta_val, "positive finite");
  auto beta_positive_finite_expr = 0.0 < beta_val && isfinite(beta_val);
 
  auto exp_val = exp(elt_multiply(-beta_val, y_val));
  auto lcdf_expr = colwise_sum(log1m(exp_val));
 
  auto rep_deriv = elt_divide(exp_val, 1.0 - exp_val);
  auto y_deriv = elt_multiply(beta_val, rep_deriv);
  auto beta_deriv = elt_multiply(y_val, rep_deriv);
 
  matrix_cl<double> lcdf_cl;
  matrix_cl<double> y_deriv_cl;
  matrix_cl<double> beta_deriv_cl;
 
  results(check_y_nonnegative, check_beta_positive_finite, lcdf_cl, y_deriv_cl,
          beta_deriv_cl)
      = expressions(y_nonnegative_expr, beta_positive_finite_expr, lcdf_expr,
                    calc_if<is_autodiff_v<T_y_cl>>(y_deriv),
                    calc_if<is_autodiff_v<T_inv_scale_cl>>(beta_deriv));
 
  T_partials_return lcdf = (from_matrix_cl(lcdf_cl)).sum();
 
  auto ops_partials = make_partials_propagator(y_col, beta_col);
 
  if constexpr (is_autodiff_v<T_y_cl>) {
    partials<0>(ops_partials) = std::move(y_deriv);
  }
  if constexpr (is_autodiff_v<T_inv_scale_cl>) {
    partials<1>(ops_partials) = std::move(beta_deriv);
  }
  return ops_partials.build(lcdf);
}
 
}  // namespace math
}  // namespace stan
#endif
#endif