Stan Math Library  2.20.0
reverse mode automatic differentiation
binary_log_loss.hpp
Go to the documentation of this file.
1 #ifndef STAN_MATH_FWD_SCAL_FUN_BINARY_LOG_LOSS_HPP
2 #define STAN_MATH_FWD_SCAL_FUN_BINARY_LOG_LOSS_HPP
3 
4 #include <stan/math/fwd/meta.hpp>
5 #include <stan/math/fwd/core.hpp>
7 
8 namespace stan {
9 namespace math {
10 
11 template <typename T>
12 inline fvar<T> binary_log_loss(int y, const fvar<T>& y_hat) {
13  if (y)
14  return fvar<T>(binary_log_loss(y, y_hat.val_), -y_hat.d_ / y_hat.val_);
15  else
16  return fvar<T>(binary_log_loss(y, y_hat.val_),
17  y_hat.d_ / (1.0 - y_hat.val_));
18 }
19 } // namespace math
20 } // namespace stan
21 #endif
T d_
The tangent (derivative) of this variable.
Definition: fvar.hpp:50
T val_
The value of this variable.
Definition: fvar.hpp:45
fvar< T > binary_log_loss(int y, const fvar< T > &y_hat)
This template class represents scalars used in forward-mode automatic differentiation, which consist of values and directional derivatives of the specified template type.
Definition: fvar.hpp:41

     [ Stan Home Page ] © 2011–2018, Stan Development Team.