Converted some other samplers over to use accessor functions on dense…

… and diagonal metrics (Issue #2881)

src/stan/mcmc/hmc/nuts_classic/adapt_dense_e_nuts_classic.hpp

@@ -29,12 +29,15 @@ class adapt_dense_e_nuts_classic : public dense_e_nuts_classic<Model, BaseRNG>,
       this->stepsize_adaptation_.learn_stepsize(this->nom_epsilon_,
                                                 s.accept_stat());
 
-      bool update = this->covar_adaptation_.learn_covariance(
-          this->z_.inv_e_metric_, this->z_.q);
+      Eigen::MatrixXd inv_metric;
+
+      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->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
       }

src/stan/mcmc/hmc/nuts_classic/adapt_diag_e_nuts_classic.hpp

@@ -30,12 +30,16 @@ class adapt_diag_e_nuts_classic : public diag_e_nuts_classic<Model, BaseRNG>,
       this->stepsize_adaptation_.learn_stepsize(this->nom_epsilon_,
                                                 s.accept_stat());
 
-      bool update = this->var_adaptation_.learn_variance(this->z_.inv_e_metric_,
+      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->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
       }

src/stan/mcmc/hmc/nuts_classic/dense_e_nuts_classic.hpp

@@ -22,8 +22,8 @@ class dense_e_nuts_classic
   // here since start.inv_e_metric_ = finish.inv_e_metric_
   bool compute_criterion(ps_point& start, dense_e_point& finish,
                          Eigen::VectorXd& rho) {
-    return finish.p.transpose() * finish.inv_e_metric_ * (rho - finish.p) > 0
-           && start.p.transpose() * finish.inv_e_metric_ * (rho - start.p) > 0;
+    return finish.p.transpose() * finish.get_inv_metric() * (rho - finish.p) > 0
+      && start.p.transpose() * finish.get_inv_metric() * (rho - start.p) > 0;
   }
 };
 

src/stan/mcmc/hmc/nuts_classic/diag_e_nuts_classic.hpp

@@ -22,8 +22,8 @@ class diag_e_nuts_classic
   // since start.inv_e_metric_ = finish.inv_e_metric_
   bool compute_criterion(ps_point& start, diag_e_point& finish,
                          Eigen::VectorXd& rho) {
-    return finish.inv_e_metric_.cwiseProduct(finish.p).dot(rho - finish.p) > 0
-           && finish.inv_e_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;
   }
 };
 

src/stan/mcmc/hmc/static_uniform/adapt_dense_e_static_uniform.hpp

@@ -31,11 +31,15 @@ class adapt_dense_e_static_uniform
       this->stepsize_adaptation_.learn_stepsize(this->nom_epsilon_,
                                                 s.accept_stat());
 
-      bool update = this->covar_adaptation_.learn_covariance(
-          this->z_.inv_e_metric_, this->z_.q);
+      Eigen::MatrixXd inv_metric;
+
+      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->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
       }

src/stan/mcmc/hmc/static_uniform/adapt_diag_e_static_uniform.hpp

@@ -31,10 +31,14 @@ class adapt_diag_e_static_uniform
       this->stepsize_adaptation_.learn_stepsize(this->nom_epsilon_,
                                                 s.accept_stat());
 
-      bool update = this->var_adaptation_.learn_variance(this->z_.inv_e_metric_,
-                                                         this->z_.q);
+      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->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
       }

src/stan/mcmc/hmc/xhmc/adapt_dense_e_xhmc.hpp

@@ -29,12 +29,15 @@ class adapt_dense_e_xhmc : public dense_e_xhmc<Model, BaseRNG>,
       this->stepsize_adaptation_.learn_stepsize(this->nom_epsilon_,
                                                 s.accept_stat());
 
-      bool update = this->covar_adaptation_.learn_covariance(
-          this->z_.inv_e_metric_, this->z_.q);
+      Eigen::MatrixXd inv_metric;
+
+      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->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
       }

src/stan/mcmc/hmc/xhmc/adapt_diag_e_xhmc.hpp

@@ -29,12 +29,15 @@ class adapt_diag_e_xhmc : public diag_e_xhmc<Model, BaseRNG>,
       this->stepsize_adaptation_.learn_stepsize(this->nom_epsilon_,
                                                 s.accept_stat());
 
-      bool update = this->var_adaptation_.learn_variance(this->z_.inv_e_metric_,
-                                                         this->z_.q);
+      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->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
       }