[examples][pollution] add the bootstrapping arguments.

examples/pollution/test_pollution.py

@@ -118,6 +118,11 @@ if __name__ == "__main__":
     # 7)EBL: Emperical Bayesian Learning
     MetaModelOpts.pce_reg_method = 'FastARD'
 
+    # Bootstraping
+    # 1) normal 2) fast
+    MetaModelOpts.bootstrap_method = 'fast'
+    MetaModelOpts.n_bootstrap_itrs = 100
+
     # Specify the max degree to be compared by the adaptive algorithm:
     # The degree with the lowest Leave-One-Out cross-validation (LOO)
     # error (or the highest score=1-LOO)estimator is chosen as the final

examples/pollution/test_valid_pollution.py

@@ -97,9 +97,9 @@ if __name__ == "__main__":
     MetaModelOpts = MetaModel(Inputs)
 
     # Select if you want to preserve the spatial/temporal depencencies
-    # MetaModelOpts.dim_red_method = 'PCA'
+    MetaModelOpts.dim_red_method = 'PCA'
     # MetaModelOpts.var_pca_threshold = 99.999
-    # MetaModelOpts.n_pca_components = 12
+    # MetaModelOpts.n_pca_components = 5
 
     # Select your metamodel method
     # 1) PCE (Polynomial Chaos Expansion) 2) aPCE (arbitrary PCE)
@@ -118,6 +118,11 @@ if __name__ == "__main__":
     # 7)EBL: Emperical Bayesian Learning
     MetaModelOpts.pce_reg_method = 'FastARD'
 
+    # Bootstraping
+    # 1) normal 2) fast
+    MetaModelOpts.bootstrap_method = 'fast'
+    MetaModelOpts.n_bootstrap_itrs = 100
+
     # Specify the max degree to be compared by the adaptive algorithm:
     # The degree with the lowest Leave-One-Out cross-validation (LOO)
     # error (or the highest score=1-LOO)estimator is chosen as the final
@@ -170,7 +175,7 @@ if __name__ == "__main__":
 
     # Plot the sobol indices
     if MetaModelOpts.meta_model_type != 'GPE':
-        sobol_cell, total_sobol = PostPCE.sobol_indices()
+        total_sobol = PostPCE.sobol_indices()
 
     # Compute and print RMSE error
     valid_samples = np.load("data/validSet.npy")
@@ -205,7 +210,7 @@ if __name__ == "__main__":
     DiscOutputOpts.add_marginals()
     DiscOutputOpts.Marginals[0].name = '$\\sigma^2_{\\epsilon}$'
     DiscOutputOpts.Marginals[0].dist_type = 'uniform'
-    DiscOutputOpts.Marginals[0].parameters = [0.0, 5.0]
+    DiscOutputOpts.Marginals[0].parameters = [0.0, 0.1]
     BayesOpts.Discrepancy = Discrepancy(DiscOutputOpts)
 
     # Start the inference