Stan Math Library  2.20.0
reverse mode automatic differentiation
binary_log_loss.hpp
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1 #ifndef STAN_MATH_REV_SCAL_FUN_BINARY_LOG_LOSS_HPP
2 #define STAN_MATH_REV_SCAL_FUN_BINARY_LOG_LOSS_HPP
3 
4 #include <stan/math/rev/meta.hpp>
5 #include <stan/math/rev/core.hpp>
7 
8 namespace stan {
9 namespace math {
10 
11 namespace internal {
13  public:
15  : op_v_vari(-std::log(avi->val_), avi) {}
16  void chain() { avi_->adj_ -= adj_ / avi_->val_; }
17 };
18 
20  public:
22  : op_v_vari(-log1p(-avi->val_), avi) {}
23  void chain() { avi_->adj_ += adj_ / (1.0 - avi_->val_); }
24 };
25 } // namespace internal
26 
61 inline var binary_log_loss(int y, const var& y_hat) {
62  if (y == 0)
63  return var(new internal::binary_log_loss_0_vari(y_hat.vi_));
64  else
65  return var(new internal::binary_log_loss_1_vari(y_hat.vi_));
66 }
67 
68 } // namespace math
69 } // namespace stan
70 #endif
void chain()
Apply the chain rule to this variable based on the variables on which it depends. ...
fvar< T > log(const fvar< T > &x)
Definition: log.hpp:12
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. ...
fvar< T > binary_log_loss(int y, const fvar< T > &y_hat)
vari * vi_
Pointer to the implementation of this variable.
Definition: var.hpp:45
fvar< T > log1p(const fvar< T > &x)
Definition: log1p.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

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