Merge branch 'revert-02d880e5' into 'master'

Revert "Merge branch 'fix/BMC' into 'master'"

See merge request !19

.idea/.gitignore

-# Default ignored files
-/shelf/
-/workspace.xml
-# Editor-based HTTP Client requests
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml

.idea/bayesvalidrox.iml

-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$">
-      <sourceFolder url="file://$MODULE_DIR$/src" isTestSource="false" />
-      <sourceFolder url="file://$MODULE_DIR$/tests" isTestSource="true" />
-    </content>
-    <orderEntry type="inheritedJdk" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-  <component name="PyDocumentationSettings">
-    <option name="format" value="NUMPY" />
-    <option name="myDocStringFormat" value="NumPy" />
-  </component>
-  <component name="TestRunnerService">
-    <option name="PROJECT_TEST_RUNNER" value="py.test" />
-  </component>
-</module>
\ No newline at end of file

.idea/inspectionProfiles/profiles_settings.xml

-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>
\ No newline at end of file

.idea/misc.xml

-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="Black">
-    <option name="sdkName" value="Python 3.11" />
-  </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11" project-jdk-type="Python SDK" />
-</project>
\ No newline at end of file

.idea/modules.xml

-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/bayesvalidrox.iml" filepath="$PROJECT_DIR$/.idea/bayesvalidrox.iml" />
-    </modules>
-  </component>
-</project>
\ No newline at end of file

.idea/other.xml

-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="PySciProjectComponent">
-    <option name="PY_INTERACTIVE_PLOTS_SUGGESTED" value="true" />
-  </component>
-</project>
\ No newline at end of file

.idea/vcs.xml

-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="" vcs="Git" />
-  </component>
-</project>
\ No newline at end of file

examples/analytical-function/example_analytical_function.py

@@ -141,7 +141,7 @@ if __name__ == "__main__":
 
     # One-shot (normal) or Sequential Adaptive (sequential) Design
     ExpDesign.method = 'sequential'
-    ExpDesign.n_init_samples = 140#00#3*ndim
+    ExpDesign.n_init_samples = 100#3*ndim
 
     # Sampling methods
     # 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley
@@ -227,8 +227,7 @@ if __name__ == "__main__":
     MetaModelOpts.ExpDesign = ExpDesign
     engine = Engine(MetaModelOpts, Model, ExpDesign)
     engine.start_engine()
-    #engine.train_sequential()
-    engine.train_normal()
+    engine.train_sequential()
 
     # Load the objects
     # with open(f"PCEModel_{Model.name}.pkl", "rb") as input:
@@ -267,12 +266,12 @@ if __name__ == "__main__":
 
     # BayesOpts.selected_indices = [0, 3, 5,  7, 9]
     # BME Bootstrap
-    BayesOpts.bootstrap = True
-    BayesOpts.n_bootstrap_itrs = 500
-    BayesOpts.bootstrap_noise = 100
+    # BayesOpts.bootstrap = True
+    # BayesOpts.n_bootstrap_itrs = 500
+    # BayesOpts.bootstrap_noise = 100
 
     # Bayesian cross validation
-    BayesOpts.bayes_loocv = True  # TODO: test what this does
+    # BayesOpts.bayes_loocv = True
 
     # Select the inference method
     import emcee
@@ -297,34 +296,31 @@ if __name__ == "__main__":
     BayesOpts.Discrepancy = DiscrepancyOpts
 
     # -- (Option C) --
-    if 0:
-        DiscOutputOpts = Input()
-        # # # OutputName = 'Z'
-        DiscOutputOpts.add_marginals()
-        DiscOutputOpts.Marginals[0].Nnme = '$\sigma^2_{\epsilon}$'
-        DiscOutputOpts.Marginals[0].dist_type = 'uniform'
-        DiscOutputOpts.Marginals[0].parameters =  [0, 10]
-        #BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
-        #                          'infer': Discrepancy(DiscOutputOpts)}
-    
-        BayesOpts.bias_inputs = {'Z':np.arange(0, 10, 1.).reshape(-1,1) / 9}
-        
-        DiscOutputOpts = Input()
-        # OutputName = 'lambda'
-        DiscOutputOpts.add_marginals()
-        DiscOutputOpts.Marginals[0].name = '$\lambda$'
-        DiscOutputOpts.Marginals[0].dist_type = 'uniform'
-        DiscOutputOpts.Marginals[0].parameters = [0, 1]
-    
-        # # OutputName = 'sigma_f'
-        DiscOutputOpts.add_marginals()
-        DiscOutputOpts.Marginals[1].Name = '$\sigma_f$'
-        DiscOutputOpts.Marginals[1].dist_type = 'uniform'
-        DiscOutputOpts.Marginals[1].parameters = [0, 1e-4]
-        #BayesOpts.Discrepancy = Discrepancy(DiscOutputOpts)
-        BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
-                                  'infer': Discrepancy(DiscOutputOpts)}
-    
+    # DiscOutputOpts = Input()
+    # # # OutputName = 'Z'
+    # DiscOutputOpts.add_marginals()
+    # DiscOutputOpts.Marginals[0].Nnme = '$\sigma^2_{\epsilon}$'
+    # DiscOutputOpts.Marginals[0].dist_type = 'uniform'
+    # DiscOutputOpts.Marginals[0].parameters =  [0, 10]
+    # BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
+    #                           'infer': Discrepancy(DiscOutputOpts)}
+
+    # BayesOpts.bias_inputs = {'Z':np.arange(0, 10, 1.).reshape(-1,1) / 9}
+    # DiscOutputOpts = Input()
+    # # OutputName = 'lambda'
+    # DiscOutputOpts.add_marginals()
+    # DiscOutputOpts.Marginals[0].name = '$\lambda$'
+    # DiscOutputOpts.Marginals[0].dist_type = 'uniform'
+    # DiscOutputOpts.Marginals[0].parameters = [0, 1]
+
+    # # OutputName = 'sigma_f'
+    # DiscOutputOpts.add_marginals()
+    # DiscOutputOpts.Marginals[1].Name = '$\sigma_f$'
+    # DiscOutputOpts.Marginals[1].dist_type = 'uniform'
+    # DiscOutputOpts.Marginals[1].parameters = [0, 1e-4]
+    # BayesOpts.Discrepancy = Discrepancy(DiscOutputOpts)
+    # BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
+    #                           'infer': Discrepancy(DiscOutputOpts)}
     # Start the calibration/inference
     Bayes_PCE = BayesOpts.create_inference()