Stan Math Library
2.20.0
reverse mode automatic differentiation
stan
math
fwd
scal
fun
binary_log_loss.hpp
Go to the documentation of this file.
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#ifndef STAN_MATH_FWD_SCAL_FUN_BINARY_LOG_LOSS_HPP
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#define STAN_MATH_FWD_SCAL_FUN_BINARY_LOG_LOSS_HPP
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#include <
stan/math/fwd/meta.hpp
>
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#include <
stan/math/fwd/core.hpp
>
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#include <
stan/math/prim/scal/fun/binary_log_loss.hpp
>
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namespace
stan
{
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namespace
math {
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template
<
typename
T>
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inline
fvar<T>
binary_log_loss
(
int
y,
const
fvar<T>
& y_hat) {
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if
(y)
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return
fvar<T>
(
binary_log_loss
(y, y_hat.
val_
), -y_hat.
d_
/ y_hat.
val_
);
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else
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return
fvar<T>
(
binary_log_loss
(y, y_hat.
val_
),
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y_hat.
d_
/ (1.0 - y_hat.
val_
));
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}
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}
// namespace math
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}
// namespace stan
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#endif
core.hpp
stan::math::fvar::d_
T d_
The tangent (derivative) of this variable.
Definition:
fvar.hpp:50
stan
Definition:
log_sum_exp.hpp:8
binary_log_loss.hpp
stan::math::fvar::val_
T val_
The value of this variable.
Definition:
fvar.hpp:45
meta.hpp
stan::math::binary_log_loss
fvar< T > binary_log_loss(int y, const fvar< T > &y_hat)
Definition:
binary_log_loss.hpp:12
stan::math::fvar
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
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