1 #ifndef STAN_MATH_FWD_MAT_FUN_LOG_SOFTMAX_HPP 2 #define STAN_MATH_FWD_MAT_FUN_LOG_SOFTMAX_HPP 15 const Eigen::Matrix<
fvar<T>, Eigen::Dynamic, 1>& alpha) {
19 Matrix<T, Dynamic, 1> alpha_t(alpha.size());
20 for (
int k = 0; k < alpha.size(); ++k)
21 alpha_t(k) = alpha(k).val_;
23 Matrix<T, Dynamic, 1> softmax_alpha_t =
softmax(alpha_t);
24 Matrix<T, Dynamic, 1> log_softmax_alpha_t =
log_softmax(alpha_t);
26 Matrix<fvar<T>, Dynamic, 1> log_softmax_alpha(alpha.size());
27 for (
int k = 0; k < alpha.size(); ++k) {
28 log_softmax_alpha(k).val_ = log_softmax_alpha_t(k);
29 log_softmax_alpha(k).d_ = 0;
32 for (
int m = 0; m < alpha.size(); ++m) {
33 T negative_alpha_m_d_times_softmax_alpha_t_m
34 = -alpha(m).d_ * softmax_alpha_t(m);
35 for (
int k = 0; k < alpha.size(); ++k) {
37 log_softmax_alpha(k).d_
38 += alpha(m).d_ + negative_alpha_m_d_times_softmax_alpha_t_m;
40 log_softmax_alpha(k).d_ += negative_alpha_m_d_times_softmax_alpha_t_m;
44 return log_softmax_alpha;
Eigen::Matrix< fvar< T >, Eigen::Dynamic, 1 > softmax(const Eigen::Matrix< fvar< T >, Eigen::Dynamic, 1 > &alpha)
Eigen::Matrix< fvar< T >, Eigen::Dynamic, 1 > log_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.