1 #ifndef STAN_MATH_PRIM_FUN_ORDERED_CONSTRAIN_HPP
2 #define STAN_MATH_PRIM_FUN_ORDERED_CONSTRAIN_HPP
3
4 #include <stan/math/prim/meta.hpp>
5 #include <stan/math/prim/fun/Eigen.hpp>
6 #include <stan/math/prim/fun/exp.hpp>
7 #include <stan/math/prim/fun/sum.hpp>
8 #include <stan/math/prim/fun/to_ref.hpp>
9 #include <cmath>
10
11 namespace stan {
12 namespace math {
13
14 /**
15 * Return an increasing ordered vector derived from the specified
16 * free vector. The returned constrained vector will have the
17 * same dimensionality as the specified free vector.
18 *
19 * @tparam T type of the vector
20 * @param x Free vector of scalars.
21 * @return Positive, increasing ordered vector.
22 * @tparam T Type of scalar.
23 */
24 template <typename EigVec, require_eigen_col_vector_t<EigVec>* = nullptr,
25 require_not_st_var<EigVec>* = nullptr>
ordered_constrain(const EigVec & x)26 inline plain_type_t<EigVec> ordered_constrain(const EigVec& x) {
27 using std::exp;
28 Eigen::Index k = x.size();
29 plain_type_t<EigVec> y(k);
30 const auto& x_ref = to_ref(x);
31 if (unlikely(k == 0)) {
32 return y;
33 }
34 y[0] = x_ref[0];
35 for (Eigen::Index i = 1; i < k; ++i) {
36 y.coeffRef(i) = y.coeff(i - 1) + exp(x_ref.coeff(i));
37 }
38 return y;
39 }
40
41 /**
42 * Return a positive valued, increasing ordered vector derived
43 * from the specified free vector and increment the specified log
44 * probability reference with the log absolute Jacobian determinant
45 * of the transform. The returned constrained vector
46 * will have the same dimensionality as the specified free vector.
47 *
48 * @tparam T type of the vector
49 * @param x Free vector of scalars.
50 * @param lp Log probability reference.
51 * @return Positive, increasing ordered vector.
52 */
53 template <typename EigVec, require_eigen_col_vector_t<EigVec>* = nullptr>
ordered_constrain(const EigVec & x,value_type_t<EigVec> & lp)54 inline auto ordered_constrain(const EigVec& x, value_type_t<EigVec>& lp) {
55 const auto& x_ref = to_ref(x);
56 if (likely(x.size() > 1)) {
57 lp += sum(x_ref.tail(x.size() - 1));
58 }
59 return ordered_constrain(x_ref);
60 }
61
62 /**
63 * Return a positive valued, increasing ordered vector derived from the
64 * specified free vector. The returned constrained vector will have the same
65 * dimensionality as the specified free vector. If the `Jacobian` parameter is
66 * `true`, the log density accumulator is incremented with the log absolute
67 * Jacobian determinant of the transform. All of the transforms are specified
68 * with their Jacobians in the *Stan Reference Manual* chapter Constraint
69 * Transforms.
70 *
71 * @tparam Jacobian if `true`, increment log density accumulator with log
72 * absolute Jacobian determinant of constraining transform
73 * @tparam T A type inheriting from `Eigen::DenseBase` or a `var_value` with
74 * inner type inheriting from `Eigen::DenseBase` with compile time dynamic rows
75 * and 1 column
76 * @param x Free vector of scalars
77 * @param[in, out] lp log density accumulator
78 * @return Positive, increasing ordered vector.
79 */
80 template <bool Jacobian, typename T, require_not_std_vector_t<T>* = nullptr>
ordered_constrain(const T & x,return_type_t<T> & lp)81 inline auto ordered_constrain(const T& x, return_type_t<T>& lp) {
82 if (Jacobian) {
83 return ordered_constrain(x, lp);
84 } else {
85 return ordered_constrain(x);
86 }
87 }
88
89 /**
90 * Return a positive valued, increasing ordered vector derived from the
91 * specified free vector. The returned constrained vector will have the same
92 * dimensionality as the specified free vector. If the `Jacobian` parameter is
93 * `true`, the log density accumulator is incremented with the log absolute
94 * Jacobian determinant of the transform. All of the transforms are specified
95 * with their Jacobians in the *Stan Reference Manual* chapter Constraint
96 * Transforms.
97 *
98 * @tparam Jacobian if `true`, increment log density accumulator with log
99 * absolute Jacobian determinant of constraining transform
100 * @tparam T A standard vector with inner type inheriting from
101 * `Eigen::DenseBase` or a `var_value` with inner type inheriting from
102 * `Eigen::DenseBase` with compile time dynamic rows and 1 column
103 * @param x Free vector of scalars
104 * @param[in, out] lp log density accumulator
105 * @return Positive, increasing ordered vector.
106 */
107 template <bool Jacobian, typename T, require_std_vector_t<T>* = nullptr>
ordered_constrain(const T & x,return_type_t<T> & lp)108 inline auto ordered_constrain(const T& x, return_type_t<T>& lp) {
109 return apply_vector_unary<T>::apply(
110 x, [&lp](auto&& v) { return ordered_constrain<Jacobian>(v, lp); });
111 }
112
113 } // namespace math
114 } // namespace stan
115
116 #endif
117