Stan Math Library  2.20.0
reverse mode automatic differentiation
floor.hpp
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1 #ifndef STAN_MATH_REV_SCAL_FUN_FLOOR_HPP
2 #define STAN_MATH_REV_SCAL_FUN_FLOOR_HPP
3 
4 #include <stan/math/rev/meta.hpp>
5 #include <stan/math/rev/core.hpp>
7 #include <cmath>
8 #include <limits>
9 
10 namespace stan {
11 namespace math {
12 
13 namespace internal {
14 class floor_vari : public op_v_vari {
15  public:
16  explicit floor_vari(vari* avi) : op_v_vari(std::floor(avi->val_), avi) {}
17  void chain() {
18  if (unlikely(is_nan(avi_->val_)))
19  avi_->adj_ = std::numeric_limits<double>::quiet_NaN();
20  }
21 };
22 } // namespace internal
23 
58 inline var floor(const var& a) { return var(new internal::floor_vari(a.vi_)); }
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: floor.hpp:17
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
#define unlikely(x)
Definition: likely.hpp:9
vari * vi_
Pointer to the implementation of this variable.
Definition: var.hpp:45
fvar< T > floor(const fvar< T > &x)
Definition: floor.hpp:12
double adj_
The adjoint of this variable, which is the partial derivative of this variable with respect to the ro...
Definition: vari.hpp:44
int is_nan(const fvar< T > &x)
Returns 1 if the input&#39;s value is NaN and 0 otherwise.
Definition: is_nan.hpp:20

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