Automatic Differentiation
 
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log1p_exp.hpp
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1#ifndef STAN_MATH_PRIM_FUN_LOG1P_EXP_HPP
2#define STAN_MATH_PRIM_FUN_LOG1P_EXP_HPP
3
9#include <cmath>
10
11namespace stan {
12namespace math {
13
45inline double log1p_exp(double a) {
46 using std::exp;
47 // like log_sum_exp below with b=0.0; prevents underflow
48 if (a > 0.0) {
49 return a + log1p(exp(-a));
50 }
51 return log1p(exp(a));
52}
53
62 template <typename T>
63 static inline auto fun(T&& x) {
64 return log1p_exp(std::forward<T>(x));
65 }
66};
67
75template <typename T,
77 require_container_t<T>* = nullptr,
79inline auto log1p_exp(T&& x) {
80 return apply_scalar_unary<log1p_exp_fun, T>::apply(std::forward<T>(x));
81}
82
83} // namespace math
84} // namespace stan
85
86#endif
require_t< is_container< std::decay_t< T > > > require_container_t
Require type satisfies is_container.
require_not_t< is_nonscalar_prim_or_rev_kernel_expression< std::decay_t< T > > > require_not_nonscalar_prim_or_rev_kernel_expression_t
Require type does not satisfy is_nonscalar_prim_or_rev_kernel_expression.
require_not_t< is_var_matrix< std::decay_t< T > > > require_not_var_matrix_t
Require type does not satisfy is_var_matrix.
fvar< T > log1p_exp(const fvar< T > &x)
Definition log1p_exp.hpp:14
fvar< T > log1p(const fvar< T > &x)
Definition log1p.hpp:12
fvar< T > exp(const fvar< T > &x)
Definition exp.hpp:15
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Base template class for vectorization of unary scalar functions defined by a template class F to a sc...
static auto fun(T &&x)
Definition log1p_exp.hpp:63
Structure to wrap log1p_exp() so that it can be vectorized.
Definition log1p_exp.hpp:61