Fixed lint issues (Issue #2881)

stan-dev · Oct 29, 2020 · 6f165f9 · 6f165f9
1 parent a182f78
commit 6f165f9
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Showing 12 changed files with 40 additions and 28 deletions.
diff --git a/src/stan/mcmc/hmc/hamiltonians/dense_e_metric.hpp b/src/stan/mcmc/hmc/hamiltonians/dense_e_metric.hpp
@@ -50,7 +50,8 @@ class dense_e_metric : public base_hamiltonian<Model, dense_e_point, BaseRNG> {
     for (idx_t i = 0; i < u.size(); ++i)
       u(i) = rand_dense_gaus();
 
-    z.p = z.get_transpose_llt_inv_metric().triangularView<Eigen::Upper>().solve(u);
+    z.p = z.get_transpose_llt_inv_metric().triangularView<Eigen::Upper>()
+      .solve(u);
   }
 };
 

diff --git a/src/stan/mcmc/hmc/hamiltonians/diag_e_point.hpp b/src/stan/mcmc/hmc/hamiltonians/diag_e_point.hpp
@@ -35,8 +35,9 @@ class diag_e_point : public ps_point {
    * @param inv_e_metric initial mass matrix
    */
   void set_inv_metric(const Eigen::VectorXd& inv_e_metric) {
-    math::check_size_match("set_inv_metric", "Number of rows in old inverse metric", inv_e_metric_.size(),
-			   "number of rows in new inverse metric", inv_e_metric.size());
+    math::check_size_match("set_inv_metric",
+    "Number of rows in old inverse metric", inv_e_metric_.size(),
+    "number of rows in new inverse metric", inv_e_metric.size());
     inv_e_metric_ = inv_e_metric;
   }
 

diff --git a/src/stan/mcmc/hmc/nuts/adapt_dense_e_nuts.hpp b/src/stan/mcmc/hmc/nuts/adapt_dense_e_nuts.hpp
@@ -31,7 +31,8 @@ class adapt_dense_e_nuts : public dense_e_nuts<Model, BaseRNG>,
 
       Eigen::MatrixXd inv_metric;
 
-      bool update = this->covar_adaptation_.learn_covariance(inv_metric, this->z_.q);
+      bool update = this->covar_adaptation_.learn_covariance(inv_metric,
+        this->z_.q);
 
       if (update) {
         this->init_stepsize(logger);

diff --git a/src/stan/mcmc/hmc/nuts/adapt_diag_e_nuts.hpp b/src/stan/mcmc/hmc/nuts/adapt_diag_e_nuts.hpp
@@ -31,7 +31,8 @@ class adapt_diag_e_nuts : public diag_e_nuts<Model, BaseRNG>,
 
       Eigen::VectorXd inv_metric;
 
-      bool update = this->var_adaptation_.learn_variance(inv_metric, this->z_.q);
+      bool update = this->var_adaptation_.learn_variance(inv_metric,
+        this->z_.q);
 
       if (update) {
         this->init_stepsize(logger);

diff --git a/src/stan/mcmc/hmc/nuts_classic/adapt_dense_e_nuts_classic.hpp b/src/stan/mcmc/hmc/nuts_classic/adapt_dense_e_nuts_classic.hpp
@@ -31,13 +31,14 @@ class adapt_dense_e_nuts_classic : public dense_e_nuts_classic<Model, BaseRNG>,
 
       Eigen::MatrixXd inv_metric;
 
-      bool update = this->covar_adaptation_.learn_covariance(inv_metric, this->z_.q);
+      bool update = this->covar_adaptation_.learn_covariance(inv_metric,
+        this->z_.q);
 
       if (update) {
         this->init_stepsize(logger);
 
-	this->z_.set_inv_metric(inv_metric);
-	
+        this->z_.set_inv_metric(inv_metric);
+
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
       }

diff --git a/src/stan/mcmc/hmc/nuts_classic/adapt_diag_e_nuts_classic.hpp b/src/stan/mcmc/hmc/nuts_classic/adapt_diag_e_nuts_classic.hpp
@@ -31,14 +31,14 @@ class adapt_diag_e_nuts_classic : public diag_e_nuts_classic<Model, BaseRNG>,
                                                 s.accept_stat());
 
       Eigen::VectorXd inv_metric;
-      
+
       bool update = this->var_adaptation_.learn_variance(inv_metric,
                                                          this->z_.q);
 
       if (update) {
         this->init_stepsize(logger);
 
-	this->z_.set_inv_metric(inv_metric);
+        this->z_.set_inv_metric(inv_metric);
 
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();

diff --git a/src/stan/mcmc/hmc/nuts_classic/diag_e_nuts_classic.hpp b/src/stan/mcmc/hmc/nuts_classic/diag_e_nuts_classic.hpp
@@ -22,8 +22,10 @@ class diag_e_nuts_classic
   // since start.get_inv_metric() = finish.get_inv_metric()
   bool compute_criterion(ps_point& start, diag_e_point& finish,
                          Eigen::VectorXd& rho) {
-    return finish.get_inv_metric().cwiseProduct(finish.p).dot(rho - finish.p) > 0
-           && finish.get_inv_metric().cwiseProduct(start.p).dot(rho - start.p) > 0;
+    return finish.get_inv_metric().cwiseProduct(finish.p).dot(rho - finish.p)
+        > 0
+      && finish.get_inv_metric().cwiseProduct(start.p).dot(rho - start.p)
+        > 0;
   }
 };
 

diff --git a/src/stan/mcmc/hmc/static/adapt_dense_e_static_hmc.hpp b/src/stan/mcmc/hmc/static/adapt_dense_e_static_hmc.hpp
@@ -34,7 +34,8 @@ class adapt_dense_e_static_hmc : public dense_e_static_hmc<Model, BaseRNG>,
 
       Eigen::MatrixXd inv_metric;
 
-      bool update = this->covar_adaptation_.learn_covariance(inv_metric, this->z_.q);
+      bool update = this->covar_adaptation_.learn_covariance(inv_metric,
+        this->z_.q);
 
       if (update) {
         this->init_stepsize(logger);

diff --git a/src/stan/mcmc/hmc/static_uniform/adapt_dense_e_static_uniform.hpp b/src/stan/mcmc/hmc/static_uniform/adapt_dense_e_static_uniform.hpp
@@ -32,14 +32,15 @@ class adapt_dense_e_static_uniform
                                                 s.accept_stat());
 
       Eigen::MatrixXd inv_metric;
-
-      bool update = this->covar_adaptation_.learn_covariance(inv_metric, this->z_.q);
+
+      bool update = this->covar_adaptation_.learn_covariance(inv_metric,
+        this->z_.q);
 
       if (update) {
         this->init_stepsize(logger);
 
-	this->z_.set_inv_metric(inv_metric);
-	
+        this->z_.set_inv_metric(inv_metric);
+
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
       }

diff --git a/src/stan/mcmc/hmc/static_uniform/adapt_diag_e_static_uniform.hpp b/src/stan/mcmc/hmc/static_uniform/adapt_diag_e_static_uniform.hpp
@@ -32,13 +32,14 @@ class adapt_diag_e_static_uniform
                                                 s.accept_stat());
 
       Eigen::VectorXd inv_metric;
-
-      bool update = this->var_adaptation_.learn_variance(inv_metric, this->z_.q);
+
+      bool update = this->var_adaptation_.learn_variance(inv_metric,
+        this->z_.q);
       if (update) {
         this->init_stepsize(logger);
 
-	this->z_.set_inv_metric(inv_metric);
-	
+        this->z_.set_inv_metric(inv_metric);
+
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
       }

diff --git a/src/stan/mcmc/hmc/xhmc/adapt_dense_e_xhmc.hpp b/src/stan/mcmc/hmc/xhmc/adapt_dense_e_xhmc.hpp
@@ -30,14 +30,15 @@ class adapt_dense_e_xhmc : public dense_e_xhmc<Model, BaseRNG>,
                                                 s.accept_stat());
 
       Eigen::MatrixXd inv_metric;
-
-      bool update = this->covar_adaptation_.learn_covariance(inv_metric, this->z_.q);
+
+      bool update = this->covar_adaptation_.learn_covariance(inv_metric,
+        this->z_.q);
 
       if (update) {
         this->init_stepsize(logger);
 
-	this->z_.set_inv_metric(inv_metric);
-	
+        this->z_.set_inv_metric(inv_metric);
+
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
       }

diff --git a/src/stan/mcmc/hmc/xhmc/adapt_diag_e_xhmc.hpp b/src/stan/mcmc/hmc/xhmc/adapt_diag_e_xhmc.hpp
@@ -30,13 +30,14 @@ class adapt_diag_e_xhmc : public diag_e_xhmc<Model, BaseRNG>,
                                                 s.accept_stat());
 
       Eigen::VectorXd inv_metric;
-
-      bool update = this->var_adaptation_.learn_variance(inv_metric, this->z_.q);
+
+      bool update = this->var_adaptation_.learn_variance(inv_metric,
+        this->z_.q);
 
       if (update) {
         this->init_stepsize(logger);
 
-	this->z_.set_inv_metric(inv_metric);
+        this->z_.set_inv_metric(inv_metric);
 
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();