Stan Math Library  2.20.0
reverse mode automatic differentiation
log1m_exp.hpp
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1 #ifndef STAN_MATH_REV_SCAL_FUN_LOG1M_EXP_HPP
2 #define STAN_MATH_REV_SCAL_FUN_LOG1M_EXP_HPP
3 
4 #include <stan/math/rev/meta.hpp>
5 #include <stan/math/rev/core.hpp>
8 
9 namespace stan {
10 namespace math {
11 
12 namespace internal {
13 class log1m_exp_v_vari : public op_v_vari {
14  public:
15  explicit log1m_exp_v_vari(vari* avi) : op_v_vari(log1m_exp(avi->val_), avi) {}
16 
17  void chain() { avi_->adj_ -= adj_ / expm1(-(avi_->val_)); }
18 };
19 } // namespace internal
20 
32 inline var log1m_exp(const var& x) {
33  return var(new internal::log1m_exp_v_vari(x.vi_));
34 }
35 
36 } // namespace math
37 } // namespace stan
38 #endif
The variable implementation base class.
Definition: vari.hpp:30
Independent (input) and dependent (output) variables for gradients.
Definition: var.hpp:33
friend class var
Definition: vari.hpp:32
const double val_
The value of this variable.
Definition: vari.hpp:38
void chain()
Apply the chain rule to this variable based on the variables on which it depends. ...
Definition: log1m_exp.hpp:17
fvar< T > expm1(const fvar< T > &x)
Definition: expm1.hpp:13
fvar< T > log1m_exp(const fvar< T > &x)
Return the natural logarithm of one minus the exponentiation of the specified argument.
Definition: log1m_exp.hpp:23
vari * vi_
Pointer to the implementation of this variable.
Definition: var.hpp:45
double adj_
The adjoint of this variable, which is the partial derivative of this variable with respect to the ro...
Definition: vari.hpp:44

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