Stan Math Library  2.20.0
reverse mode automatic differentiation
tgamma.hpp
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1 #ifndef STAN_MATH_REV_SCAL_FUN_TGAMMA_HPP
2 #define STAN_MATH_REV_SCAL_FUN_TGAMMA_HPP
3 
4 #include <stan/math/rev/meta.hpp>
7 #include <stan/math/rev/core.hpp>
8 
9 namespace stan {
10 namespace math {
11 
12 namespace internal {
13 class tgamma_vari : public op_v_vari {
14  public:
15  explicit tgamma_vari(vari* avi) : op_v_vari(tgamma(avi->val_), avi) {}
16  void chain() { avi_->adj_ += adj_ * val_ * digamma(avi_->val_); }
17 };
18 } // namespace internal
19 
56 inline var tgamma(const var& a) {
57  return var(new internal::tgamma_vari(a.vi_));
58 }
59 
60 } // namespace math
61 } // namespace stan
62 #endif
void chain()
Apply the chain rule to this variable based on the variables on which it depends. ...
Definition: tgamma.hpp:16
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
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
fvar< T > tgamma(const fvar< T > &x)
Return the result of applying the gamma function to the specified argument.
Definition: tgamma.hpp:21
fvar< T > digamma(const fvar< T > &x)
Return the derivative of the log gamma function at the specified argument.
Definition: digamma.hpp:23

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