1 #ifndef STAN_MATH_PRIM_MAT_FUN_CHOLESKY_CORR_CONSTRAIN_HPP 2 #define STAN_MATH_PRIM_MAT_FUN_CHOLESKY_CORR_CONSTRAIN_HPP 15 Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>
21 int k_choose_2 = (K * (K - 1)) / 2;
24 "k_choose_2", k_choose_2);
25 Matrix<T, Dynamic, 1> z(k_choose_2);
26 for (
int i = 0; i < k_choose_2; ++i)
28 Matrix<T, Dynamic, Dynamic> x(K, K);
31 for (
int j = 1; j < K; ++j)
32 for (
int i = 0; i < j; ++i)
36 for (
int i = 1; i < K; ++i) {
38 T sum_sqs(
square(x(i, 0)));
39 for (
int j = 1; j < i; ++j) {
40 x(i, j) = z(k++) *
sqrt(1.0 - sum_sqs);
41 sum_sqs +=
square(x(i, j));
43 x(i, i) =
sqrt(1.0 - sum_sqs);
50 Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>
57 int k_choose_2 = (K * (K - 1)) / 2;
60 "k_choose_2", k_choose_2);
61 Matrix<T, Dynamic, 1> z(k_choose_2);
62 for (
int i = 0; i < k_choose_2; ++i)
64 Matrix<T, Dynamic, Dynamic> x(K, K);
67 for (
int j = 1; j < K; ++j)
68 for (
int i = 0; i < j; ++i)
72 for (
int i = 1; i < K; ++i) {
74 T sum_sqs =
square(x(i, 0));
75 for (
int j = 1; j < i; ++j) {
76 lp += 0.5 *
log1m(sum_sqs);
77 x(i, j) = z(k++) *
sqrt(1.0 - sum_sqs);
78 sum_sqs +=
square(x(i, j));
80 x(i, i) =
sqrt(1.0 - sum_sqs);
fvar< T > sqrt(const fvar< T > &x)
void check_size_match(const char *function, const char *name_i, T_size1 i, const char *name_j, T_size2 j)
Check if the provided sizes match.
fvar< T > square(const fvar< T > &x)
Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > cholesky_corr_constrain(const Eigen::Matrix< T, Eigen::Dynamic, 1 > &y, int K)
fvar< T > log1m(const fvar< T > &x)
T corr_constrain(const T x)
Return the result of transforming the specified scalar to have a valid correlation value between -1 a...