1 #ifndef STAN_MATH_FWD_MAT_FUN_SOFTMAX_HPP 2 #define STAN_MATH_FWD_MAT_FUN_SOFTMAX_HPP 12 inline Eigen::Matrix<fvar<T>, Eigen::Dynamic, 1>
softmax(
13 const Eigen::Matrix<
fvar<T>, Eigen::Dynamic, 1>& alpha) {
17 Matrix<T, Dynamic, 1> alpha_t(alpha.size());
18 for (
int k = 0; k < alpha.size(); ++k)
19 alpha_t(k) = alpha(k).val_;
21 Matrix<T, Dynamic, 1> softmax_alpha_t =
softmax(alpha_t);
23 Matrix<fvar<T>, Dynamic, 1> softmax_alpha(alpha.size());
24 for (
int k = 0; k < alpha.size(); ++k) {
25 softmax_alpha(k).val_ = softmax_alpha_t(k);
26 softmax_alpha(k).d_ = 0;
29 for (
int m = 0; m < alpha.size(); ++m) {
30 T negative_alpha_m_d_times_softmax_alpha_t_m
31 = -alpha(m).d_ * softmax_alpha_t(m);
32 for (
int k = 0; k < alpha.size(); ++k) {
36 * (alpha(m).d_ + negative_alpha_m_d_times_softmax_alpha_t_m);
39 += negative_alpha_m_d_times_softmax_alpha_t_m * softmax_alpha_t(k);
Eigen::Matrix< fvar< T >, Eigen::Dynamic, 1 > softmax(const Eigen::Matrix< fvar< T >, Eigen::Dynamic, 1 > &alpha)
This template class represents scalars used in forward-mode automatic differentiation, which consist of values and directional derivatives of the specified template type.