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reverse mode automatic differentiation
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mat
fun
grad.hpp
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#ifndef STAN_MATH_REV_MAT_FUN_GRAD_HPP
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#define STAN_MATH_REV_MAT_FUN_GRAD_HPP
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#include <
stan/math/prim/mat/fun/Eigen.hpp
>
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#include <
stan/math/rev/mat/fun/Eigen_NumTraits.hpp
>
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#include <
stan/math/rev/core.hpp
>
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namespace
stan
{
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namespace
math {
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void
grad
(
var
& v,
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Eigen::Matrix<var, Eigen::Dynamic, 1>& x,
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Eigen::VectorXd& g) {
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stan::math::grad
(v.
vi_
);
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g.resize(x.size());
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for
(
int
i = 0; i < x.size(); ++i)
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g(i) = x(i).vi_->adj_;
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}
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}
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}
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#endif
core.hpp
Eigen_NumTraits.hpp
stan
Definition:
log_sum_exp.hpp:8
stan::math::var
Independent (input) and dependent (output) variables for gradients.
Definition:
var.hpp:31
stan::math::grad
static void grad(vari *vi)
Compute the gradient for all variables starting from the specified root variable implementation.
Definition:
grad.hpp:30
stan::math::var::vi_
vari * vi_
Pointer to the implementation of this variable.
Definition:
var.hpp:43
Eigen.hpp
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