Fixes #278

src/limbo/model/gp.hpp

@@ -408,14 +408,31 @@ namespace limbo {
             void set_log_loo_cv(double log_loo_cv) { _log_loo_cv = log_loo_cv; }
 
             /// LLT matrix (from Cholesky decomposition)
-            //const Eigen::LLT<Eigen::MatrixXd>& llt() const { return _llt; }
             const Eigen::MatrixXd& matrixL() const { return _matrixL; }
 
             const Eigen::MatrixXd& alpha() const { return _alpha; }
 
-            /// return the list of samples that have been tested so far
+            /// return the list of samples
             const std::vector<Eigen::VectorXd>& samples() const { return _samples; }
 
+            /// return the list of observations
+            std::vector<Eigen::VectorXd> observations() const
+            {
+                std::vector<Eigen::VectorXd> observations;
+                for (int i = 0; i < _observations.rows(); i++) {
+                    observations.push_back(_observations.row(i));
+                }
+
+                return observations;
+            }
+
+            /// return the observations (in matrix form)
+            /// (NxD), where N is the number of points and D is the dimension output
+            const Eigen::MatrixXd& observations_matrix() const
+            {
+                return _observations;
+            }
+
             bool inv_kernel_computed() { return _inv_kernel_updated; }
 
             /// save the parameters and the data for the GP to the archive (text or binary)

src/limbo/model/multi_gp.hpp

@@ -265,13 +265,32 @@ namespace limbo {
                     });
             }
 
-            /// return the list of samples that have been tested so far
+            /// return the list of samples
             const std::vector<Eigen::VectorXd>& samples() const
             {
                 assert(_gp_models.size());
                 return _gp_models[0].samples();
             }
 
+            /// return the list of observations
+            const std::vector<Eigen::VectorXd>& observations() const
+            {
+                return _observations;
+            }
+
+            /// return the observations (in matrix form)
+            /// (NxD), where N is the number of points and D is the dimension output
+            Eigen::MatrixXd observations_matrix() const
+            {
+                assert(_dim_out > 0);
+                Eigen::MatrixXd observations(_observations.size(), _dim_out);
+                for (int i = 0; i < _observations.size(); i++) {
+                    observations.row(i) = _observations[i];
+                }
+
+                return observations;
+            }
+
             /// return the mean observation
             Eigen::VectorXd mean_observation() const
             {