cpplint

src/stan/services/pathfinder/multi.hpp

@@ -79,10 +79,11 @@ namespace pathfinder {
  * `error_codes::SOFTWARE` for failures
  * @param[in] return_lp Whether single pathfinder should return lp calculations.
  *  If `true`, calculates the joint log probability for each sample.
- *  If `false`, (`num_draws` - `num_elbo_draws`) of the joint log probability calculations will be `NA` and psis resampling will not be performed.
- * @param[in] psis_resampling If `true`, psis resampling is performed over the 
- *  samples returned by all of the individual pathfinders and `num_multi_draws` 
- *  samples are returned. If `false`, no psis resampling is performed 
+ *  If `false`, (`num_draws` - `num_elbo_draws`) of the joint log probability
+ *  calculations will be `NA` and psis resampling will not be performed.
+ * @param[in] psis_resampling If `true`, psis resampling is performed over the
+ *  samples returned by all of the individual pathfinders and `num_multi_draws`
+ *  samples are returned. If `false`, no psis resampling is performed
  *  and (`num_paths` * `num_draws`) samples are returned.
  * @return error_codes::OK if successful
  */
@@ -166,7 +167,7 @@ inline int pathfinder_lbfgs_multi(
   for (auto&& ilpr : individual_lp_ratios) {
     num_returned_samples += ilpr.size();
   }
-  // Rows are individual parameters and columns are samples per iteration  
+  // Rows are individual parameters and columns are samples per iteration
   Eigen::Array<double, Eigen::Dynamic, Eigen::Dynamic> samples(
       individual_samples[0].rows(), num_returned_samples);
   Eigen::Index filling_start_row = 0;
@@ -188,19 +189,19 @@ inline int pathfinder_lbfgs_multi(
     }
 
     const auto tail_len = std::min(0.2 * num_returned_samples,
-                                  3 * std::sqrt(num_returned_samples));
+                                   3 * std::sqrt(num_returned_samples));
     Eigen::Array<double, Eigen::Dynamic, 1> weight_vals
         = stan::services::psis::psis_weights(lp_ratios, tail_len, logger);
     boost::ecuyer1988 rng
         = util::create_rng<boost::ecuyer1988>(random_seed, stride_id);
     boost::variate_generator<
         boost::ecuyer1988&,
         boost::random::discrete_distribution<Eigen::Index, double>>
-        rand_psis_idx(rng,
-                      boost::random::discrete_distribution<Eigen::Index, double>(
-                          boost::iterator_range<double*>(
-                              weight_vals.data(),
-                              weight_vals.data() + weight_vals.size())));
+        rand_psis_idx(
+            rng, boost::random::discrete_distribution<Eigen::Index, double>(
+                     boost::iterator_range<double*>(
+                         weight_vals.data(),
+                         weight_vals.data() + weight_vals.size())));
     for (size_t i = 0; i <= num_multi_draws - 1; ++i) {
       parameter_writer(samples.col(rand_psis_idx()));
     }

src/stan/services/pathfinder/single.hpp

@@ -217,8 +217,8 @@ inline elbo_est_t est_approx_draws(LPF&& lp_fun, ConstrainF&& constrain_fun,
                                    RNG&& rng,
                                    const taylor_approx_t& taylor_approx,
                                    size_t num_samples, const EigVec& alpha,
-                                   const std::string& iter_msg,
-                                   Logger&& logger, bool return_lp = true) {
+                                   const std::string& iter_msg, Logger&& logger,
+                                   bool return_lp = true) {
   boost::variate_generator<boost::ecuyer1988&, boost::normal_distribution<>>
       rand_unit_gaus(rng, boost::normal_distribution<>());
   const auto num_params = taylor_approx.x_center.size();
@@ -253,10 +253,12 @@ inline elbo_est_t est_approx_draws(LPF&& lp_fun, ConstrainF&& constrain_fun,
       }
       log_stream(logger, pathfinder_ss, iter_msg);
     }
-    lp_ratio = lp_mat.col(1) - lp_mat.col(0);    
+    lp_ratio = lp_mat.col(1) - lp_mat.col(0);
   } else {
-    lp_ratio = Eigen::Array<double, Eigen::Dynamic, 1>::Constant(lp_mat.rows(), std::numeric_limits<double>::quiet_NaN());
-    lp_mat.col(1) = Eigen::Matrix<double, Eigen::Dynamic, 1>::Constant(lp_mat.rows(), std::numeric_limits<double>::quiet_NaN());
+    lp_ratio = Eigen::Array<double, Eigen::Dynamic, 1>::Constant(
+        lp_mat.rows(), std::numeric_limits<double>::quiet_NaN());
+    lp_mat.col(1) = Eigen::Matrix<double, Eigen::Dynamic, 1>::Constant(
+        lp_mat.rows(), std::numeric_limits<double>::quiet_NaN());
   }
   if (ReturnElbo) {
     double elbo = lp_ratio.mean();
@@ -582,8 +584,9 @@ auto pathfinder_impl(RNG&& rng, LPFun&& lp_fun, ConstrainFun&& constrain_fun,
  * @param[in,out] diagnostic_writer output for diagnostics values
  * @param[in] return_lp Whether single pathfinder should return lp calculations.
  *  If `true`, calculates the joint log probability for each sample.
- *  If `false`, (`num_draws` - `num_elbo_draws`) of the joint log probability calculations will be `NA` and psis resampling will not be performed.
- *  Setting this parameter to `false` will also set all of the lp ratios to `NaN`.
+ *  If `false`, (`num_draws` - `num_elbo_draws`) of the joint log probability
+ * calculations will be `NA` and psis resampling will not be performed. Setting
+ * this parameter to `false` will also set all of the lp ratios to `NaN`.
  * @return If `ReturnLpSamples` is `true`, returns a tuple of the error code,
  * approximate draws, and a vector of the lp ratio. If `false`, only returns an
  * error code `error_codes::OK` if successful, `error_codes::SOFTWARE`
@@ -596,7 +599,7 @@ inline auto pathfinder_lbfgs_single(
     unsigned int stride_id, double init_radius, int max_history_size,
     double init_alpha, double tol_obj, double tol_rel_obj, double tol_grad,
     double tol_rel_grad, double tol_param, int num_iterations,
-    int num_elbo_draws, int num_draws, bool save_iterations, int refresh, 
+    int num_elbo_draws, int num_draws, bool save_iterations, int refresh,
     callbacks::interrupt& interrupt, callbacks::logger& logger,
     callbacks::writer& init_writer, ParamWriter& parameter_writer,
     DiagnosticWriter& diagnostic_writer, bool return_lp = true) {
@@ -868,8 +871,8 @@ inline auto pathfinder_lbfgs_single(
       auto&& new_lp_ratio = est_draws.lp_ratio;
       auto&& lp_draws = est_draws.lp_mat;
       auto&& new_draws = est_draws.repeat_draws;
-      lp_ratio = Eigen::Array<double, Eigen::Dynamic, 1>(
-          elbo_lp_ratio.size() + new_lp_ratio.size());
+      lp_ratio = Eigen::Array<double, Eigen::Dynamic, 1>(elbo_lp_ratio.size()
+                                                         + new_lp_ratio.size());
       lp_ratio.head(elbo_lp_ratio.size()) = elbo_lp_ratio.array();
       lp_ratio.tail(new_lp_ratio.size()) = new_lp_ratio.array();
       const auto total_size = elbo_draws.cols() + new_draws.cols();