1 #ifndef STAN_MATH_REV_MAT_FUNCTOR_JACOBIAN_HPP
2 #define STAN_MATH_REV_MAT_FUNCTOR_JACOBIAN_HPP
15 const Eigen::Matrix<double, Eigen::Dynamic, 1>& x,
16 Eigen::Matrix<double, Eigen::Dynamic, 1>& fx,
17 Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic>& J) {
23 Matrix<var, Dynamic, 1> x_var(x.size());
24 for (
int k = 0; k < x.size(); ++k)
26 Matrix<var, Dynamic, 1> fx_var = f(x_var);
27 fx.resize(fx_var.size());
28 for (
int i = 0; i < fx_var.size(); ++i)
29 fx(i) = fx_var(i).val();
30 J.resize(x.size(), fx_var.size());
31 for (
int i = 0; i < fx_var.size(); ++i) {
35 for (
int k = 0; k < x.size(); ++k)
36 J(k, i) = x_var(k).adj();
38 }
catch (
const std::exception&
e) {
static void set_zero_all_adjoints()
Reset all adjoint values in the stack to zero.
Independent (input) and dependent (output) variables for gradients.
static void grad(vari *vi)
Compute the gradient for all variables starting from the specified root variable implementation.
void jacobian(const F &f, const Eigen::Matrix< T, Eigen::Dynamic, 1 > &x, Eigen::Matrix< T, Eigen::Dynamic, 1 > &fx, Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > &J)
double e()
Return the base of the natural logarithm.
static void recover_memory_nested()
Recover only the memory used for the top nested call.
static void start_nested()
Record the current position so that recover_memory_nested() can find it.