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stan-dev · Jul 30, 2024 · 0e3eb28 · 0e3eb28
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Showing 25 changed files with 1,094 additions and 2,011 deletions.
diff --git a/stan/math/mix/functor/hessian_times_vector.hpp b/stan/math/mix/functor/hessian_times_vector.hpp
@@ -15,10 +15,12 @@ namespace math {
  * and pstream.
  */
 template <typename F, typename Eta, require_eigen_t<Eta>* = nullptr,
-typename... Args>
-inline Eigen::VectorXd hessian_times_vector(
-    const F& f, const Eigen::VectorXd& x, const Eta& eta,
-    const Eigen::VectorXd& v, Args&&... args) {
+          typename... Args>
+inline Eigen::VectorXd hessian_times_vector(const F& f,
+                                            const Eigen::VectorXd& x,
+                                            const Eta& eta,
+                                            const Eigen::VectorXd& v,
+                                            Args&&... args) {
   nested_rev_autodiff nested;
   const Eigen::Index x_size = x.size();
   Eigen::Matrix<var, Eigen::Dynamic, 1> x_var = x;

diff --git a/stan/math/mix/functor/laplace_base_rng.hpp b/stan/math/mix/functor/laplace_base_rng.hpp
@@ -30,17 +30,16 @@ inline Eigen::VectorXd laplace_base_rng(
     D&& ll_fun, LLArgs&& ll_args, CovarFun&& covariance_function,
     const ThetaMatrix& eta, const EtaMatrix& theta_0,
     const laplace_options& options, TrainTuple&& train_tuple,
-    PredTuple&& pred_tuple, RNG& rng, std::ostream* msgs,
-    Args&&... args) {
+    PredTuple&& pred_tuple, RNG& rng, std::ostream* msgs, Args&&... args) {
   using Eigen::MatrixXd;
   using Eigen::VectorXd;
   auto args_dbl = std::make_tuple(to_ref(value_of(args))...);
   auto eta_dbl = value_of(eta);
   auto md_est = apply(
       [&](auto&&... args_val) {
-        return laplace_marginal_density_est(ll_fun, ll_args, covariance_function,
-                                            eta_dbl, value_of(theta_0), msgs,
-                                            options, args_val...);
+        return laplace_marginal_density_est(
+            ll_fun, ll_args, covariance_function, eta_dbl, value_of(theta_0),
+            msgs, options, args_val...);
       },
       std::tuple_cat(std::forward<TrainTuple>(train_tuple), args_dbl));
   // Modified R&W method

diff --git a/stan/math/mix/functor/laplace_likelihood.hpp b/stan/math/mix/functor/laplace_likelihood.hpp
@@ -17,7 +17,8 @@ namespace internal {
 template <typename F, typename Theta, typename Eta, typename... Args,
           require_eigen_vector_t<Theta>* = nullptr,
           require_eigen_t<Eta>* = nullptr>
-inline auto log_likelihood(F&& f, const Theta& theta, const Eta& eta, Args&&... args) {
+inline auto log_likelihood(F&& f, const Theta& theta, const Eta& eta,
+                           Args&&... args) {
   return f(theta, eta, args...);
 }
 
@@ -62,7 +63,6 @@ inline Eigen::SparseMatrix<double> diff(F&& f, const Theta& theta,
   }
 }
 
-
 template <typename F, typename Theta, typename Eta, typename... Args,
           require_eigen_vector_t<Theta>* = nullptr,
           require_eigen_t<Eta>* = nullptr>
@@ -179,16 +179,19 @@ inline plain_type_t<Eta> diff_eta_implicit(F&& f, const Theta& v,
   return eta_var.adj();
 }
 
-}
+}  // namespace internal
 
 template <typename F, typename Theta, typename Eta, typename TupleArgs,
           require_eigen_vector_t<Theta>* = nullptr,
-          require_eigen_t<Eta>* = nullptr, require_tuple_t<TupleArgs>* = nullptr>
+          require_eigen_t<Eta>* = nullptr,
+          require_tuple_t<TupleArgs>* = nullptr>
 inline auto log_likelihood(F&& f, const Theta& theta, const Eta& eta,
                            TupleArgs&& ll_tup, std::ostream* msgs) {
-  return apply([](auto&& f, auto&& theta, auto&& eta, auto&& msgs,
-                  auto&&... args) { return internal::log_likelihood(f, theta, eta, args..., msgs); },
-               ll_tup, f, theta, eta, msgs);
+  return apply(
+      [](auto&& f, auto&& theta, auto&& eta, auto&& msgs, auto&&... args) {
+        return internal::log_likelihood(f, theta, eta, args..., msgs);
+      },
+      ll_tup, f, theta, eta, msgs);
 }
 
 template <typename F, typename Theta, typename Eta, typename TupleArgs,
@@ -204,7 +207,8 @@ inline Eigen::SparseMatrix<double> diff(F&& f, const Theta& theta,
   return apply(
       [](auto&& f, auto&& theta, auto&& eta, auto&& gradient,
          auto hessian_block_size, auto* msgs, auto&&... args) {
-        return internal::diff(f, theta, eta, gradient, hessian_block_size, args..., msgs);
+        return internal::diff(f, theta, eta, gradient, hessian_block_size,
+                              args..., msgs);
       },
       ll_tuple, f, theta, eta, gradient, hessian_block_size, msgs);
 }
@@ -233,7 +237,8 @@ inline Eigen::VectorXd compute_s2(F&& f, const Theta& theta, const Eta& eta,
   return apply(
       [](auto&& f, auto&& theta, auto&& eta, auto&& A, auto hessian_block_size,
          auto* msgs, auto&&... args) {
-        return internal::compute_s2(f, theta, eta, A, hessian_block_size, args..., msgs);
+        return internal::compute_s2(f, theta, eta, A, hessian_block_size,
+                                    args..., msgs);
       },
       ll_args, f, theta, eta, A, hessian_block_size, msgs);
 }

diff --git a/stan/math/mix/functor/laplace_marginal_density.hpp b/stan/math/mix/functor/laplace_marginal_density.hpp
@@ -647,8 +647,7 @@ inline auto laplace_marginal_density(const D& ll_fun, LLArgs&& ll_args,
 
   // TODO: Why does remove this phi_size != 0 check work but for eta it fails?
   // Because we have an eta_dummy sometimes...
-  if (is_any_var<Args...>::value && !is_constant<Eta>::value
-      && eta_size != 0) {
+  if (is_any_var<Args...>::value && !is_constant<Eta>::value && eta_size != 0) {
     {
       const nested_rev_autodiff nested;
       Eigen::Matrix<var, Eigen::Dynamic, Eigen::Dynamic> K_var

diff --git a/stan/math/mix/prob/laplace_bernoulli_logit_rng.hpp b/stan/math/mix/prob/laplace_bernoulli_logit_rng.hpp
@@ -24,20 +24,18 @@ template <typename CovarFun, typename ThetaMatrix, class RNG,
 inline Eigen::VectorXd  // CHECK -- right return type
 laplace_marginal_tol_bernoulli_logit_rng(
     const std::vector<int>& y, const std::vector<int>& n_samples,
-    const ThetaMatrix& theta_0,
-    CovarFun&& covariance_function,
-    TrainTuple&& train_tuple, PredTuple&& pred_tuple,
-    const double tolerance, const long int max_num_steps,
-    const int hessian_block_size, const int solver,
-    const int max_steps_line_search, RNG& rng, std::ostream* msgs,
-    Args&&... args) {
+    const ThetaMatrix& theta_0, CovarFun&& covariance_function,
+    TrainTuple&& train_tuple, PredTuple&& pred_tuple, const double tolerance,
+    const long int max_num_steps, const int hessian_block_size,
+    const int solver, const int max_steps_line_search, RNG& rng,
+    std::ostream* msgs, Args&&... args) {
   laplace_options ops{hessian_block_size, solver, max_steps_line_search,
                       tolerance, max_num_steps};
   Eigen::Matrix<double, 0, 0> eta_dummy;
-  return laplace_base_rng(bernoulli_logit_likelihood{},
-      std::forward_as_tuple(to_vector(y), n_samples),
-      covariance_function, eta_dummy, theta_0, rng, msgs, ops,
-      std::forward<TrainTuple>(train_tuple),
+  return laplace_base_rng(
+      bernoulli_logit_likelihood{},
+      std::forward_as_tuple(to_vector(y), n_samples), covariance_function,
+      eta_dummy, theta_0, rng, msgs, ops, std::forward<TrainTuple>(train_tuple),
       std::forward<PredTuple>(pred_tuple), std::forward<Args>(args)...);
 }
 
@@ -50,17 +48,15 @@ laplace_marginal_bernoulli_logit_rng(const std::vector<int>& y,
                                      const ThetaMatrix& theta_0,
                                      CovarFun&& covariance_function,
                                      TrainTuple&& train_tuple,
-                                     PredTuple&& pred_tuple,
-                                     RNG& rng,
-                                     std::ostream* msgs,
-                                     Args&&... args) {
+                                     PredTuple&& pred_tuple, RNG& rng,
+                                     std::ostream* msgs, Args&&... args) {
   constexpr laplace_options ops{1, 1, 0, 1e-6, 100};
   Eigen::Matrix<double, 0, 0> eta_dummy;
   return laplace_base_rng(bernoulli_logit_likelihood{},
-      std::forward_as_tuple(to_vector(y), n_samples),
-      covariance_function, eta_dummy, theta_0, ops,
-      std::make_tuple(), std::make_tuple(), rng, msgs,
-      std::forward<Args>(args)...);
+                          std::forward_as_tuple(to_vector(y), n_samples),
+                          covariance_function, eta_dummy, theta_0, ops,
+                          std::make_tuple(), std::make_tuple(), rng, msgs,
+                          std::forward<Args>(args)...);
 }
 
 }  // namespace math

diff --git a/stan/math/mix/prob/laplace_marginal_bernoulli_logit_lpmf.hpp b/stan/math/mix/prob/laplace_marginal_bernoulli_logit_lpmf.hpp
@@ -42,18 +42,17 @@ template <typename CovarF, typename ThetaMatrix, typename... Args,
           require_eigen_t<ThetaMatrix>* = nullptr>
 inline auto laplace_marginal_tol_bernoulli_logit_lpmf(
     const std::vector<int>& y, const std::vector<int>& n_samples,
-    const ThetaMatrix& theta_0, CovarF&& covariance_function,
-    double tolerance, long int max_num_steps, const int hessian_block_size,
-    const int solver, const int max_steps_line_search,
-    std::ostream* msgs, Args&&... args) {
+    const ThetaMatrix& theta_0, CovarF&& covariance_function, double tolerance,
+    long int max_num_steps, const int hessian_block_size, const int solver,
+    const int max_steps_line_search, std::ostream* msgs, Args&&... args) {
   // TODO: change this to a VectorXd once we have operands & partials.
   Eigen::Matrix<double, 0, 0> eta_dummy;
   laplace_options ops{hessian_block_size, solver, max_steps_line_search,
                       tolerance, max_num_steps};
-  return laplace_marginal_density(bernoulli_logit_likelihood{},
-      std::forward_as_tuple(to_vector(y), n_samples),
-      covariance_function, eta_dummy, theta_0, msgs, ops,
-      std::forward<Args>(args)...);
+  return laplace_marginal_density(
+      bernoulli_logit_likelihood{},
+      std::forward_as_tuple(to_vector(y), n_samples), covariance_function,
+      eta_dummy, theta_0, msgs, ops, std::forward<Args>(args)...);
 }
 
 template <typename CovarF, typename ThetaMatrix, typename... Args,
@@ -65,10 +64,10 @@ inline auto laplace_marginal_bernoulli_logit_lpmf(
   // TODO: change this to a VectorXd once we have operands & partials.
   Eigen::Matrix<double, 0, 0> eta_dummy;
   constexpr laplace_options ops{1, 1, 0, 1e-6, 100};
-  return laplace_marginal_density(bernoulli_logit_likelihood{},
-      std::forward_as_tuple(to_vector(y), n_samples),
-      covariance_function, eta_dummy, theta_0, msgs, ops,
-      std::forward<Args>(args)...);
+  return laplace_marginal_density(
+      bernoulli_logit_likelihood{},
+      std::forward_as_tuple(to_vector(y), n_samples), covariance_function,
+      eta_dummy, theta_0, msgs, ops, std::forward<Args>(args)...);
 }
 
 }  // namespace math