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Cleanup unused local typedefs #2505

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merged 2 commits into from
Jun 12, 2021
Merged

Cleanup unused local typedefs #2505

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467719c
Cleanup unused typedefs
Jun 10, 2021
ea2134d
Add warning flag for headers tests
Jun 11, 2021
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2 changes: 1 addition & 1 deletion make/tests
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Expand Up @@ -100,7 +100,7 @@ else
endif

%.hpp-test : %.hpp test/dummy.cpp
$(COMPILE.cpp) $(CXXFLAGS) -O0 -include $^ -o $(DEV_NULL)
$(COMPILE.cpp) $(CXXFLAGS) -O0 -include $^ -o $(DEV_NULL) -Wunused-local-typedefs

test/dummy.cpp:
@mkdir -p test
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1 change: 0 additions & 1 deletion stan/math/fwd/fun/mdivide_left.hpp
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Expand Up @@ -60,7 +60,6 @@ template <typename T1, typename T2,
inline Eigen::Matrix<value_type_t<T2>, T1::RowsAtCompileTime,
T2::ColsAtCompileTime>
mdivide_left(const T1& A, const T2& b) {
using T = typename value_type_t<T2>::Scalar;
constexpr int S1 = T1::RowsAtCompileTime;
constexpr int C2 = T2::ColsAtCompileTime;

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1 change: 0 additions & 1 deletion stan/math/fwd/fun/mdivide_left_tri_low.hpp
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Expand Up @@ -55,7 +55,6 @@ template <typename T1, typename T2, require_eigen_t<T1>* = nullptr,
inline Eigen::Matrix<value_type_t<T2>, T1::RowsAtCompileTime,
T2::ColsAtCompileTime>
mdivide_left_tri_low(const T1& A, const T2& b) {
using T = typename value_type_t<T2>::Scalar;
constexpr int S1 = T1::RowsAtCompileTime;
constexpr int C2 = T2::ColsAtCompileTime;

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2 changes: 0 additions & 2 deletions stan/math/prim/fun/generalized_inverse.hpp
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Expand Up @@ -31,8 +31,6 @@ template <typename EigMat, require_eigen_t<EigMat>* = nullptr,
inline Eigen::Matrix<value_type_t<EigMat>, EigMat::ColsAtCompileTime,
EigMat::RowsAtCompileTime>
generalized_inverse(const EigMat& G) {
using value_t = value_type_t<EigMat>;

if (G.size() == 0)
return {};

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1 change: 0 additions & 1 deletion stan/math/prim/fun/tail.hpp
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Expand Up @@ -39,7 +39,6 @@ inline auto tail(const T& v, size_t n) {
*/
template <typename T>
std::vector<T> tail(const std::vector<T>& sv, size_t n) {
using idx_t = index_type_t<std::vector<T>>;
if (n != 0) {
check_std_vector_index("tail", "n", sv, n);
}
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4 changes: 0 additions & 4 deletions stan/math/prim/prob/bernoulli_logit_glm_lpmf.hpp
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Expand Up @@ -56,10 +56,6 @@ return_type_t<T_x, T_alpha, T_beta> bernoulli_logit_glm_lpmf(
using std::exp;
constexpr int T_x_rows = T_x::RowsAtCompileTime;
using T_partials_return = partials_return_t<T_y, T_x, T_alpha, T_beta>;
using T_y_val =
typename std::conditional_t<is_vector<T_y>::value,
Eigen::Matrix<partials_return_t<T_y>, -1, 1>,
partials_return_t<T_y>>;
using T_ytheta_tmp =
typename std::conditional_t<T_x_rows == 1, T_partials_return,
Array<T_partials_return, Dynamic, 1>>;
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1 change: 0 additions & 1 deletion stan/math/prim/prob/beta_lpdf.hpp
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Expand Up @@ -47,7 +47,6 @@ template <bool propto, typename T_y, typename T_scale_succ,
return_type_t<T_y, T_scale_succ, T_scale_fail> beta_lpdf(
const T_y& y, const T_scale_succ& alpha, const T_scale_fail& beta) {
using T_partials_return = partials_return_t<T_y, T_scale_succ, T_scale_fail>;
using T_partials_matrix = Eigen::Matrix<T_partials_return, Eigen::Dynamic, 1>;
using T_y_ref = ref_type_if_t<!is_constant<T_y>::value, T_y>;
using T_alpha_ref
= ref_type_if_t<!is_constant<T_scale_succ>::value, T_scale_succ>;
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2 changes: 0 additions & 2 deletions stan/math/prim/prob/beta_proportion_lpdf.hpp
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Expand Up @@ -52,8 +52,6 @@ return_type_t<T_y, T_loc, T_prec> beta_proportion_lpdf(const T_y& y,
const T_loc& mu,
const T_prec& kappa) {
using T_partials_return = partials_return_t<T_y, T_loc, T_prec>;
using T_partials_return_kappa = return_type_t<T_prec>;
using T_partials_array = Eigen::Array<T_partials_return, Eigen::Dynamic, 1>;
using std::log;
using T_y_ref = ref_type_if_t<!is_constant<T_y>::value, T_y>;
using T_mu_ref = ref_type_if_t<!is_constant<T_loc>::value, T_loc>;
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1 change: 0 additions & 1 deletion stan/math/prim/prob/cauchy_lpdf.hpp
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Expand Up @@ -43,7 +43,6 @@ template <bool propto, typename T_y, typename T_loc, typename T_scale,
return_type_t<T_y, T_loc, T_scale> cauchy_lpdf(const T_y& y, const T_loc& mu,
const T_scale& sigma) {
using T_partials_return = partials_return_t<T_y, T_loc, T_scale>;
using T_partials_array = Eigen::Array<T_partials_return, Eigen::Dynamic, 1>;
using std::log;
using T_y_ref = ref_type_if_t<!is_constant<T_y>::value, T_y>;
using T_mu_ref = ref_type_if_t<!is_constant<T_loc>::value, T_loc>;
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1 change: 0 additions & 1 deletion stan/math/prim/prob/dirichlet_lpdf.hpp
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Expand Up @@ -59,7 +59,6 @@ template <bool propto, typename T_prob, typename T_prior_size,
return_type_t<T_prob, T_prior_size> dirichlet_lpdf(const T_prob& theta,
const T_prior_size& alpha) {
using T_partials_return = partials_return_t<T_prob, T_prior_size>;
using T_partials_vec = typename Eigen::Matrix<T_partials_return, -1, 1>;
using T_partials_array = typename Eigen::Array<T_partials_return, -1, -1>;
using T_theta_ref = ref_type_t<T_prob>;
using T_alpha_ref = ref_type_t<T_prior_size>;
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1 change: 0 additions & 1 deletion stan/math/prim/prob/double_exponential_lpdf.hpp
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Expand Up @@ -42,7 +42,6 @@ template <bool propto, typename T_y, typename T_loc, typename T_scale,
return_type_t<T_y, T_loc, T_scale> double_exponential_lpdf(
const T_y& y, const T_loc& mu, const T_scale& sigma) {
using T_partials_return = partials_return_t<T_y, T_loc, T_scale>;
using T_partials_array = Eigen::Array<T_partials_return, Eigen::Dynamic, 1>;
using T_y_ref = ref_type_if_t<!is_constant<T_y>::value, T_y>;
using T_mu_ref = ref_type_if_t<!is_constant<T_loc>::value, T_loc>;
using T_sigma_ref = ref_type_if_t<!is_constant<T_scale>::value, T_scale>;
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1 change: 0 additions & 1 deletion stan/math/prim/prob/frechet_cdf.hpp
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Expand Up @@ -29,7 +29,6 @@ return_type_t<T_y, T_shape, T_scale> frechet_cdf(const T_y& y,
const T_shape& alpha,
const T_scale& sigma) {
using T_partials_return = partials_return_t<T_y, T_shape, T_scale>;
using T_partials_array = Eigen::Array<T_partials_return, Eigen::Dynamic, 1>;
using T_y_ref = ref_type_t<T_y>;
using T_alpha_ref = ref_type_t<T_shape>;
using T_sigma_ref = ref_type_t<T_scale>;
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3 changes: 0 additions & 3 deletions stan/math/prim/prob/multi_normal_cholesky_lpdf.hpp
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Expand Up @@ -53,9 +53,6 @@ return_type_t<T_y, T_loc, T_covar> multi_normal_cholesky_lpdf(
using T_partials_return = partials_return_t<T_y, T_loc, T_covar>;
using matrix_partials_t
= Eigen::Matrix<T_partials_return, Eigen::Dynamic, Eigen::Dynamic>;
using vector_partials_t = Eigen::Matrix<T_partials_return, Eigen::Dynamic, 1>;
using row_vector_partials_t
= Eigen::Matrix<T_partials_return, 1, Eigen::Dynamic>;
using T_y_ref = ref_type_t<T_y>;
using T_mu_ref = ref_type_t<T_loc>;
using T_L_ref = ref_type_t<T_covar>;
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1 change: 0 additions & 1 deletion stan/math/prim/prob/ordered_logistic_lpmf.hpp
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Expand Up @@ -76,7 +76,6 @@ return_type_t<T_loc, T_cut> ordered_logistic_lpmf(const T_y& y,
const T_loc& lambda,
const T_cut& c) {
using T_partials_return = partials_return_t<T_loc, T_cut>;
using T_partials_array = Eigen::Array<T_partials_return, -1, 1>;
using T_cuts_val = partials_return_t<T_cut>;
using T_y_ref = ref_type_t<T_y>;
using T_lambda_ref = ref_type_if_t<!is_constant<T_loc>::value, T_loc>;
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4 changes: 0 additions & 4 deletions stan/math/prim/prob/poisson_log_glm_lpmf.hpp
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Expand Up @@ -59,10 +59,6 @@ return_type_t<T_x, T_alpha, T_beta> poisson_log_glm_lpmf(const T_y& y,
using std::exp;
constexpr int T_x_rows = T_x::RowsAtCompileTime;
using T_partials_return = partials_return_t<T_y, T_x, T_alpha, T_beta>;
using T_alpha_val = typename std::conditional_t<
is_vector<T_alpha>::value,
Eigen::Array<partials_return_t<T_alpha>, -1, 1>,
partials_return_t<T_alpha>>;
using T_theta_tmp =
typename std::conditional_t<T_x_rows == 1, T_partials_return,
Array<T_partials_return, Dynamic, 1>>;
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1 change: 0 additions & 1 deletion stan/math/prim/prob/skew_double_exponential_lpdf.hpp
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Expand Up @@ -44,7 +44,6 @@ return_type_t<T_y, T_loc, T_scale, T_skewness> skew_double_exponential_lpdf(
const T_y& y, const T_loc& mu, const T_scale& sigma,
const T_skewness& tau) {
using T_partials_return = partials_return_t<T_y, T_loc, T_scale, T_skewness>;
using T_partials_array = Eigen::Array<T_partials_return, Eigen::Dynamic, 1>;
using T_y_ref = ref_type_if_t<!is_constant<T_y>::value, T_y>;
using T_mu_ref = ref_type_if_t<!is_constant<T_loc>::value, T_loc>;
using T_sigma_ref = ref_type_if_t<!is_constant<T_scale>::value, T_scale>;
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1 change: 0 additions & 1 deletion stan/math/rev/fun/cholesky_decompose.hpp
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Expand Up @@ -80,7 +80,6 @@ inline auto unblocked_cholesky_lambda(T1& L_A, T2& L, T3& A) {
template <typename T1, typename T2, typename T3>
inline auto cholesky_lambda(T1& L_A, T2& L, T3& A) {
return [L_A, L, A]() mutable {
using Block_ = Eigen::Block<Eigen::MatrixXd>;
using Eigen::Lower;
using Eigen::StrictlyUpper;
using Eigen::Upper;
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1 change: 0 additions & 1 deletion stan/math/rev/fun/cholesky_factor_constrain.hpp
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Expand Up @@ -29,7 +29,6 @@ namespace math {
template <typename T, require_var_vector_t<T>* = nullptr>
var_value<Eigen::MatrixXd> cholesky_factor_constrain(const T& x, int M, int N) {
using std::exp;
using T_scalar = value_type_t<T>;
check_greater_or_equal("cholesky_factor_constrain",
"num rows (must be greater or equal to num cols)", M,
N);
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1 change: 0 additions & 1 deletion stan/math/rev/fun/generalized_inverse.hpp
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Expand Up @@ -61,7 +61,6 @@ inline auto generalized_inverse_lambda(T1& G_arena, T2& inv_G) {
*/
template <typename VarMat, require_rev_matrix_t<VarMat>* = nullptr>
inline auto generalized_inverse(const VarMat& G) {
using value_t = value_type_t<VarMat>;
using ret_type = promote_var_matrix_t<VarMat, VarMat>;

if (G.size() == 0)
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2 changes: 0 additions & 2 deletions stan/math/rev/fun/lub_constrain.hpp
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Expand Up @@ -482,7 +482,6 @@ inline auto lub_constrain(const T& x, const L& lb, const U& ub) {
auto lb_val = value_of(arena_lb).array();
auto ub_val = value_of(arena_ub).array();
check_less("lub_constrain", "lb", lb_val, ub_val);
using plain_x_array = plain_type_t<decltype(arena_x_val.array())>;
auto inv_logit_x = to_arena(inv_logit(arena_x_val.array()));
auto is_lb_inf = to_arena((lb_val == NEGATIVE_INFTY));
auto is_ub_inf = to_arena((ub_val == INFTY));
Expand Down Expand Up @@ -562,7 +561,6 @@ inline auto lub_constrain(const T& x, const L& lb, const U& ub,
auto lb_val = value_of(arena_lb).array();
auto ub_val = value_of(arena_ub).array();
check_less("lub_constrain", "lb", lb_val, ub_val);
using plain_x_array = plain_type_t<decltype(arena_x_val.array())>;
auto inv_logit_x = to_arena(inv_logit(arena_x_val.array()));
auto is_lb_inf = to_arena((lb_val == NEGATIVE_INFTY));
auto is_ub_inf = to_arena((ub_val == INFTY));
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