Fix bug - stepsize from windowed adaptation is reset to 1 when term_buffer == 0

src/stan/mcmc/covar_adaptation.hpp

@@ -14,13 +14,11 @@ class covar_adaptation : public windowed_adaptation {
   explicit covar_adaptation(int n)
       : windowed_adaptation("covariance"), estimator_(n) {}
 
-  bool learn_covariance(Eigen::MatrixXd& covar, const Eigen::VectorXd& q) {
-    if (adaptation_window())
+  void learn_covariance(Eigen::MatrixXd& covar, const Eigen::VectorXd& q) {
+    if (in_phase2_window())
       estimator_.add_sample(q);
 
-    if (end_adaptation_window()) {
-      compute_next_window();
-
+    if (end_phase2_window()) {
       estimator_.sample_covariance(covar);
 
       double n = static_cast<double>(estimator_.num_samples());
@@ -35,15 +33,7 @@ class covar_adaptation : public windowed_adaptation {
             "unconstrained space; this may happen when the posterior density "
             "function is too wide or improper. "
             "There may be problems with your model specification.");
-
-      estimator_.restart();
-
-      ++adapt_window_counter_;
-      return true;
     }
-
-    ++adapt_window_counter_;
-    return false;
   }
 
  protected:

src/stan/mcmc/hmc/nuts/adapt_dense_e_nuts.hpp

@@ -29,15 +29,18 @@ class adapt_dense_e_nuts : public dense_e_nuts<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);
+      if (this->covar_adaptation_.in_phase2_window()) {
+        this->covar_adaptation_.learn_covariance(this->z_.inv_e_metric_,
+                                                 this->z_.q);
+      }
 
-      if (update) {
+      if (this->covar_adaptation_.end_phase2_window()) {
         this->init_stepsize(logger);
-
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
+        this->covar_adaptation_.compute_next_window();
       }
+      this->covar_adaptation_.cur_iter_++;
     }
     return s;
   }

src/stan/mcmc/hmc/nuts/adapt_diag_e_nuts.hpp

@@ -29,15 +29,18 @@ class adapt_diag_e_nuts : public diag_e_nuts<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);
+      if (this->var_adaptation_.in_phase2_window()) {
+        this->var_adaptation_.learn_variance(this->z_.inv_e_metric_,
+                                             this->z_.q);
+      }
 
-      if (update) {
+      if (this->var_adaptation_.end_phase2_window()) {
         this->init_stepsize(logger);
-
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
+        this->var_adaptation_.compute_next_window();
       }
+      this->var_adaptation_.cur_iter_++;
     }
     return s;
   }

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

@@ -29,15 +29,18 @@ 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);
+      if (this->covar_adaptation_.in_phase2_window()) {
+        this->covar_adaptation_.learn_covariance(this->z_.inv_e_metric_,
+                                                 this->z_.q);
+      }
 
-      if (update) {
+      if (this->covar_adaptation_.end_phase2_window()) {
         this->init_stepsize(logger);
-
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
+        this->covar_adaptation_.compute_next_window();
       }
+      this->covar_adaptation_.cur_iter_++;
     }
     return s;
   }

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

@@ -30,15 +30,18 @@ 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_,
-                                                         this->z_.q);
+      if (this->var_adaptation_.in_phase2_window()) {
+        this->var_adaptation_.learn_variance(this->z_.inv_e_metric_,
+                                             this->z_.q);
+      }
 
-      if (update) {
+      if (this->var_adaptation_.end_phase2_window()) {
         this->init_stepsize(logger);
-
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
+        this->var_adaptation_.compute_next_window();
       }
+      this->var_adaptation_.cur_iter_++;
     }
     return s;
   }

src/stan/mcmc/hmc/static/adapt_dense_e_static_hmc.hpp

@@ -32,16 +32,17 @@ class adapt_dense_e_static_hmc : public dense_e_static_hmc<Model, BaseRNG>,
                                                 s.accept_stat());
       this->update_L_();
 
-      bool update = this->covar_adaptation_.learn_covariance(
-          this->z_.inv_e_metric_, this->z_.q);
+      if (this->covar_adaptation_.in_phase2_window())
+        this->covar_adaptation_.learn_covariance(this->z_.inv_e_metric_,
+                                                 this->z_.q);
 
-      if (update) {
+      if (this->covar_adaptation_.end_phase2_window()) {
         this->init_stepsize(logger);
         this->update_L_();
-
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
-        this->stepsize_adaptation_.restart();
+        this->covar_adaptation_.compute_next_window();
       }
+      this->covar_adaptation_.cur_iter_++;
     }
     return s;
   }

src/stan/mcmc/hmc/static/adapt_diag_e_static_hmc.hpp

@@ -32,16 +32,18 @@ class adapt_diag_e_static_hmc : public diag_e_static_hmc<Model, BaseRNG>,
                                                 s.accept_stat());
       this->update_L_();
 
-      bool update = this->var_adaptation_.learn_variance(this->z_.inv_e_metric_,
-                                                         this->z_.q);
+      if (this->var_adaptation_.in_phase2_window())
+        this->var_adaptation_.learn_variance(this->z_.inv_e_metric_,
+                                             this->z_.q);
 
-      if (update) {
+      if (this->var_adaptation_.end_phase2_window()) {
         this->init_stepsize(logger);
         this->update_L_();
 
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
-        this->stepsize_adaptation_.restart();
+        this->var_adaptation_.compute_next_window();
       }
+      this->var_adaptation_.cur_iter_++;
     }
     return s;
   }

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

@@ -31,14 +31,18 @@ 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);
+      if (this->covar_adaptation_.in_phase2_window()) {
+        this->covar_adaptation_.learn_covariance(this->z_.inv_e_metric_,
+                                                 this->z_.q);
+      }
 
-      if (update) {
+      if (this->covar_adaptation_.end_phase2_window()) {
         this->init_stepsize(logger);
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
+        this->covar_adaptation_.compute_next_window();
       }
+      this->covar_adaptation_.cur_iter_++;
     }
     return s;
   }

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

@@ -31,13 +31,18 @@ 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);
-      if (update) {
+      if (this->var_adaptation_.in_phase2_window()) {
+        this->var_adaptation_.learn_variance(this->z_.inv_e_metric_,
+                                             this->z_.q);
+      }
+
+      if (this->var_adaptation_.end_phase2_window()) {
         this->init_stepsize(logger);
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
+        this->var_adaptation_.compute_next_window();
       }
+      this->var_adaptation_.cur_iter_++;
     }
     return s;
   }

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

@@ -29,15 +29,18 @@ 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);
+      if (this->covar_adaptation_.in_phase2_window()) {
+        this->covar_adaptation_.learn_covariance(this->z_.inv_e_metric_,
+                                                 this->z_.q);
+      }
 
-      if (update) {
+      if (this->covar_adaptation_.end_phase2_window()) {
         this->init_stepsize(logger);
-
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
+        this->covar_adaptation_.compute_next_window();
       }
+      this->covar_adaptation_.cur_iter_++;
     }
     return s;
   }

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

@@ -29,15 +29,18 @@ 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);
+      if (this->var_adaptation_.in_phase2_window()) {
+        this->var_adaptation_.learn_variance(this->z_.inv_e_metric_,
+                                             this->z_.q);
+      }
 
-      if (update) {
+      if (this->var_adaptation_.end_phase2_window()) {
         this->init_stepsize(logger);
-
         this->stepsize_adaptation_.set_mu(log(10 * this->nom_epsilon_));
         this->stepsize_adaptation_.restart();
+        this->var_adaptation_.compute_next_window();
       }
+      this->var_adaptation_.cur_iter_++;
     }
     return s;
   }

src/stan/mcmc/stepsize_adaptation.hpp

@@ -70,7 +70,10 @@ class stepsize_adaptation : public base_adaptation {
     epsilon = std::exp(x);
   }
 
-  void complete_adaptation(double& epsilon) { epsilon = std::exp(x_bar_); }
+  void complete_adaptation(double& epsilon) {
+    if (x_bar_ > 0)
+      epsilon = std::exp(x_bar_);
+  }
 
  protected:
   double counter_;  // Adaptation iteration

src/stan/mcmc/var_adaptation.hpp

@@ -14,13 +14,11 @@ class var_adaptation : public windowed_adaptation {
   explicit var_adaptation(int n)
       : windowed_adaptation("variance"), estimator_(n) {}
 
-  bool learn_variance(Eigen::VectorXd& var, const Eigen::VectorXd& q) {
-    if (adaptation_window())
+  void learn_variance(Eigen::VectorXd& var, const Eigen::VectorXd& q) {
+    if (in_phase2_window())
       estimator_.add_sample(q);
 
-    if (end_adaptation_window()) {
-      compute_next_window();
-
+    if (end_phase2_window()) {
       estimator_.sample_variance(var);
 
       double n = static_cast<double>(estimator_.num_samples());
@@ -34,15 +32,7 @@ class var_adaptation : public windowed_adaptation {
             "unconstrained space; this may happen when the posterior density "
             "function is too wide or improper. "
             "There may be problems with your model specification.");
-
-      estimator_.restart();
-
-      ++adapt_window_counter_;
-      return true;
     }
-
-    ++adapt_window_counter_;
-    return false;
   }
 
  protected: