[docs] Basic BMC user guide

docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.Engine.rst

@@ -17,19 +17,11 @@ bayesvalidrox.surrogate\_models.engine.Engine
    .. autosummary::
    
       ~Engine.__init__
-      ~Engine.choose_next_sample
-      ~Engine.dual_annealing
       ~Engine.eval_metamodel
-      ~Engine.run_util_func
       ~Engine.start_engine
-      ~Engine.tradeoff_weights
       ~Engine.train_normal
       ~Engine.train_seq_design
       ~Engine.train_sequential
-      ~Engine.util_AlphOptDesign
-      ~Engine.util_BayesianActiveDesign
-      ~Engine.util_BayesianDesign
-      ~Engine.util_VarBasedDesign
    
    
 

docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.rst

@@ -9,15 +9,6 @@ bayesvalidrox.surrogate\_models.engine
 
    
    
-   .. rubric:: Functions
-
-   .. autosummary::
-      :toctree:     
-   
-      hellinger_distance
-      logpdf
-      subdomain
-   
    
 
    

docs/source/_autosummary/bayesvalidrox.surrogate_models.rst

-bayesvalidrox.surrogate\_models
+bayesvalidrox.surrogate\_models
 ===============================
 
 .. automodule:: bayesvalidrox.surrogate_models
@@ -41,5 +41,6 @@ bayesvalidrox.surrogate\_models
    bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
    bayesvalidrox.surrogate_models.reg_fast_ard
    bayesvalidrox.surrogate_models.reg_fast_laplace
+   bayesvalidrox.surrogate_models.sequential_design
    bayesvalidrox.surrogate_models.surrogate_models
 

docs/source/_autosummary/bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.rst

+bayesvalidrox.surrogate\_models.sequential\_design.SequentialDesign
+===================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.sequential_design
+
+.. autoclass:: SequentialDesign
+   :members:                                   
+   :show-inheritance:                           
+   :inherited-members:                          
+
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~SequentialDesign.__init__
+      ~SequentialDesign.choose_next_sample
+      ~SequentialDesign.dual_annealing
+      ~SequentialDesign.run_util_func
+      ~SequentialDesign.start_seqdesign
+      ~SequentialDesign.tradeoff_weights
+      ~SequentialDesign.util_AlphOptDesign
+      ~SequentialDesign.util_BayesianActiveDesign
+      ~SequentialDesign.util_BayesianDesign
+      ~SequentialDesign.util_VarBasedDesign
+   
+   
+
+   
+   
+   
\ No newline at end of file

docs/source/_autosummary/bayesvalidrox.surrogate_models.sequential_design.hellinger_distance.rst

+bayesvalidrox.surrogate\_models.sequential\_design.hellinger\_distance
+======================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.sequential_design
+
+.. autofunction:: hellinger_distance
\ No newline at end of file

docs/source/_autosummary/bayesvalidrox.surrogate_models.sequential_design.logpdf.rst

+bayesvalidrox.surrogate\_models.sequential\_design.logpdf
+=========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.sequential_design
+
+.. autofunction:: logpdf
\ No newline at end of file

docs/source/_autosummary/bayesvalidrox.surrogate_models.sequential_design.rst

+bayesvalidrox.surrogate\_models.sequential\_design
+==================================================
+
+.. automodule:: bayesvalidrox.surrogate_models.sequential_design
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:     
+   
+      hellinger_distance
+      logpdf
+      subdomain
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:     
+      :template: custom-class-template.rst  
+   
+      SequentialDesign
+   
+   
+
+   
+   
+   
+
+
+

docs/source/_autosummary/bayesvalidrox.surrogate_models.sequential_design.subdomain.rst

+bayesvalidrox.surrogate\_models.sequential\_design.subdomain
+============================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.sequential_design
+
+.. autofunction:: subdomain
\ No newline at end of file

docs/source/bmc_description.rst

@@ -18,5 +18,66 @@ Bayesian multi-model comparison
 Example
 =======
 To perform model comparison, we first need to define the set of competing models.
-For this, we create two additional models based on the example model from :any:`model_description`.
-The first of these models 
+For this, we create an additional model in the file ``model2.py`` based on the example model from :any:`model_description`.
+
\ No newline at end of file
+>>> def model2(samples, x_values):
+>>>     poly = samples[0]*np.power(x_values, 3)
+>>>     outputs = {'A': poly, 'x_values': x_values}
+>>>     return outputs
+
+Then we can build another surrogate for this model, following the same code as for the surrogate in :any:`surrogate_description`.
+
+>>> Model2 = PyLinkForwardModel()
+>>> Model2.link_type = 'Function'
+>>> Model2.py_file = 'model2'
+>>> Model2.name = 'model2'
+>>> Model2.Output.names = ['A']
+>>> Model2.func_args = {'x_values': x_values}
+>>> Model2.store = False
+    
+>>> MetaMod2 = MetaModel(Inputs)
+>>> MetaMod2.meta_model_type = 'aPCE'
+>>> MetaMod2.pce_reg_method = 'FastARD'
+>>> MetaMod2.pce_deg = 3
+>>> MetaMod2.pce_q_norm = 1
+    
+>>> ExpDesign2 = ExpDesigns(Inputs)
+>>> ExpDesign2.n_init_samples = 30
+>>> ExpDesign2.sampling_method = 'random'
+    
+>>> Engine_2 = Engine(MetaMod2, Model2, ExpDesign2)
+>>> Engine_2.train_normal()
+
+To perform model comparison we use the class :any:`bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison`.
+
+>>> from bayesvalidrox import BayesModelComparison`
+
+We collect the engines that should be compared in a dictionary, and assign them names.
+
+>>> meta_models = {
+>>>     "linear": Engine_,
+>>>     "degthree": Engine_2
+>>>     }
+	
+Then we create an object of class ``BayesModelComparison``.
+
+>>> BayesOpts = BayesModelComparison()	
+
+As the comparison uses the class :any:`bayesvalidrox.bayes_inference.bayes_inference.BayesInference`, we can also set the properties for this class as well.
+These are collected in a dictionary and given to the function calls that perform the model comparison.
+In this example we use the following settings.
+
+>>> opts_bootstrap = {
+>>>     "bootstrap": True,
+>>>     "n_samples": 100,
+>>>     "Discrepancy": DiscrepancyOpts,
+>>>     "emulator": True,
+>>>     "plot_post_pred": False
+>>>     }
+
+Now we can run the full model comparison.
+
+>>> output_dict = BayesOpts.model_comparison_all(meta_models, opts_bootstrap)
+
+The created plots are saved in the folder ``Outputs_Comparison``.
+