Automatic Differentiation
 
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multiply_log.hpp
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1#ifndef STAN_MATH_PRIM_FUN_MULTIPLY_LOG_HPP
2#define STAN_MATH_PRIM_FUN_MULTIPLY_LOG_HPP
3
7#include <cmath>
8
9namespace stan {
10namespace math {
11
47template <typename T_a, typename T_b,
48 require_all_arithmetic_t<T_a, T_b>* = nullptr>
49inline return_type_t<T_a, T_b> multiply_log(const T_a a, const T_b b) {
50 using std::log;
51 if (b == 0.0 && a == 0.0) {
52 return 0.0;
53 }
54
55 return a * log(b);
56}
57
68template <typename T1, typename T2, require_any_container_t<T1, T2>* = nullptr,
69 require_all_not_var_matrix_t<T1, T2>* = nullptr>
70inline auto multiply_log(const T1& a, const T2& b) {
72 a, b, [&](const auto& c, const auto& d) { return multiply_log(c, d); });
73}
74
75} // namespace math
76} // namespace stan
77
78#endif
typename return_type< Ts... >::type return_type_t
Convenience type for the return type of the specified template parameters.
fvar< T > multiply_log(const fvar< T > &x1, const fvar< T > &x2)
fvar< T > log(const fvar< T > &x)
Definition log.hpp:15
auto apply_scalar_binary(const T1 &x, const T2 &y, const F &f)
Base template function for vectorization of binary scalar functions defined by applying a functor to ...
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...