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student_t_qf only returns column eigen vectors now #3317

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1 change: 0 additions & 1 deletion stan/math/prim/meta.hpp
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Expand Up @@ -71,7 +71,6 @@
#include <stan/math/prim/meta/ad_promotable.hpp>
#include <stan/math/prim/meta/append_return_type.hpp>
#include <stan/math/prim/meta/base_type.hpp>
#include <stan/math/prim/meta/common_container_type.hpp>
#include <stan/math/prim/meta/contains_std_vector.hpp>
#include <stan/math/prim/meta/contains_tuple.hpp>
#include <stan/math/prim/meta/error_index.hpp>
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77 changes: 0 additions & 77 deletions stan/math/prim/meta/common_container_type.hpp
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5 changes: 4 additions & 1 deletion stan/math/prim/meta/is_vector.hpp
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Expand Up @@ -184,7 +184,10 @@ using require_eigen_row_vector_t
* member with a value of false.
*/
template <typename T>
struct is_row_vector : internal::is_row_vector_impl<T> {};
struct is_row_vector : internal::is_row_vector_impl<std::decay_t<T>> {};

template <typename T>
inline constexpr bool is_row_vector_v = is_row_vector<T>::value;

/*! \ingroup require_eigens_types */
/*! \defgroup row_vector_types row_vector */
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21 changes: 11 additions & 10 deletions stan/math/prim/prob/student_t_qf.hpp
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Expand Up @@ -6,6 +6,7 @@
#include <stan/math/prim/fun/sqrt.hpp>
#include <stan/math/prim/fun/inv_inc_beta.hpp>
#include <stan/math/prim/fun/max_size.hpp>
#include <stan/math/prim/fun/to_ref.hpp>

namespace stan {
namespace math {
Expand Down Expand Up @@ -70,20 +71,20 @@ template <typename T_p, typename T_nu, typename T_mu, typename T_sigma,
require_any_vector_t<T_p, T_nu, T_mu, T_sigma>* = nullptr>
inline auto student_t_qf(const T_p& p, const T_nu& nu, const T_mu& mu,
const T_sigma& sigma) {
using T_container = common_container_t<T_p, T_nu, T_mu, T_sigma>;
using ret_scalar = return_type_t<T_p, T_nu, T_mu, T_sigma>;
static constexpr const char* function = "student_t_qf";
const size_t max_size_all = max_size(p, nu, mu, sigma);
T_container result(max_size_all);
Eigen::Matrix<ret_scalar, -1, 1> result(max_size_all);

ref_type_t<T_p> p_ref = p;
ref_type_t<T_nu> nu_ref = nu;
ref_type_t<T_mu> mu_ref = mu;
ref_type_t<T_sigma> sigma_ref = sigma;
decltype(auto) p_ref = to_ref(p);
decltype(auto) nu_ref = to_ref(nu);
decltype(auto) mu_ref = to_ref(mu);
decltype(auto) sigma_ref = to_ref(sigma);

scalar_seq_view<ref_type_t<T_p>> p_vec(p_ref);
scalar_seq_view<ref_type_t<T_nu>> nu_vec(nu_ref);
scalar_seq_view<ref_type_t<T_mu>> mu_vec(mu_ref);
scalar_seq_view<ref_type_t<T_sigma>> sigma_vec(sigma_ref);
scalar_seq_view<decltype(p_ref)> p_vec(p_ref);
scalar_seq_view<decltype(nu_ref)> nu_vec(nu_ref);
scalar_seq_view<decltype(mu_ref)> mu_vec(mu_ref);
scalar_seq_view<decltype(sigma_ref)> sigma_vec(sigma_ref);

for (size_t i = 0; i < max_size_all; ++i) {
result[i] = student_t_qf(p_vec[i], nu_vec[i], mu_vec[i], sigma_vec[i]);
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9 changes: 3 additions & 6 deletions test/unit/math/mix/prob/student_t_qf_test.cpp
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Expand Up @@ -16,14 +16,11 @@ TEST_F(AgradRev, mathMixProb_student_t_qf) {
stan::test::expect_ad(f, 0.8, 0.5, 0.1);
stan::test::expect_ad(f, 0.1, 3, 3);

Eigen::VectorXd p(3);
p << 0.3, 0.8, 0.1;
Eigen::VectorXd p{{0.3, 0.8, 0.1}};

Eigen::VectorXd nu(3);
nu << 0.5, 0.5, 3;
Eigen::VectorXd nu{{0.5, 0.5, 3}};

Eigen::VectorXd sigma(3);
sigma << 3, 0.1, 3;
Eigen::VectorXd sigma{{3, 0.1, 3}};

stan::test::expect_ad(f, p, nu, sigma);
}
11 changes: 4 additions & 7 deletions test/unit/math/prim/prob/student_t_qf_test.cpp
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Expand Up @@ -6,8 +6,7 @@
TEST(MathFunctions, student_t_qf_vals) {
using stan::math::student_t_qf;

Eigen::VectorXd p(5);
p << 0.1, 0.2, 0.5, 0.8, 0.9;
Eigen::VectorXd p{{0.1, 0.2, 0.5, 0.8, 0.9}};

Eigen::VectorXd res = student_t_qf(p, 4, 2, 3);

Expand All @@ -21,11 +20,9 @@ TEST(MathFunctions, student_t_qf_vals) {
TEST(MathFunctions, student_t_qf_vec) {
using stan::math::student_t_qf;

Eigen::VectorXd p(5);
p << 0.1, 0.2, 0.5, 0.8, 0.9;
Eigen::VectorXd p{{0.1, 0.2, 0.5, 0.8, 0.9}};

Eigen::VectorXd nu(5);
nu << 4, 4, 3, 3, 3;
Eigen::VectorXd nu{{4, 4, 3, 3, 3}};

Eigen::VectorXd res(5);
for (int i = 0; i < 5; ++i) {
Expand All @@ -37,7 +34,7 @@ TEST(MathFunctions, student_t_qf_vec) {
Eigen::RowVectorXd nu_rowvec = nu.transpose();
Eigen::RowVectorXd res_rowvec = res.transpose();

EXPECT_MATRIX_FLOAT_EQ(res_rowvec, student_t_qf(p_rowvec, nu_rowvec, 2, 3));
EXPECT_MATRIX_FLOAT_EQ(res, student_t_qf(p_rowvec, nu_rowvec, 2, 3));
}

TEST(MathFunctions, student_t_qf_inf) {
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