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+</ul>
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+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.create_psi.html">bayesvalidrox.surrogate_models.surrogate_models.create_psi</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</a></li>
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+ <section id="bayesvalidrox-bayes-inference-bayes-inference-bayesinference">
+<h1>bayesvalidrox.bayes_inference.bayes_inference.BayesInference<a class="headerlink" href="#bayesvalidrox-bayes-inference-bayes-inference-bayesinference" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_inference.BayesInference">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.bayes_inference.bayes_inference.</span></span><span class="sig-name descname"><span class="pre">BayesInference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">discrepancy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">emulator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Calib'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">req_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prior_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">measured_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inference_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rejection'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mcmc_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bayes_loocv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap_itrs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perturbed_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">just_analysis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">valid_metrics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['BME']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_post_pred</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_map_pred</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_a_posteriori</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">corner_title_fmt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'.2e'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bmc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>A class to perform Bayesian Analysis.</p>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl>
+<dt>MetaModel<span class="classifier">obj</span></dt><dd><p>Meta model object.</p>
+</dd>
+<dt>discrepancy<span class="classifier">obj</span></dt><dd><p>The discrepancy object for the sigma2s, i.e. the diagonal entries
+of the variance matrix for a multivariate normal likelihood.</p>
+</dd>
+<dt>name<span class="classifier">str, optional</span></dt><dd><p>The type of analysis, either calibration (<cite>Calib</cite>) or validation
+(<cite>Valid</cite>). The default is <cite>‘Calib’</cite>. # TODO: what is going on here for validation?</p>
+</dd>
+<dt>emulator<span class="classifier">bool, optional</span></dt><dd><p>Analysis with emulator (MetaModel). The default is <cite>True</cite>.</p>
+</dd>
+<dt>bootstrap<span class="classifier">bool, optional</span></dt><dd><p>Bootstrap the analysis. The default is <cite>False</cite>.</p>
+</dd>
+<dt>req_outputs<span class="classifier">list, optional</span></dt><dd><p>The list of requested output to be used for the analysis.
+The default is <cite>None</cite>. If None, all the defined outputs for the model
+object is used.</p>
+</dd>
+<dt>selected_indices<span class="classifier">dict, optional</span></dt><dd><p>A dictionary with the selected indices of each model output. The
+default is <cite>None</cite>. If <cite>None</cite>, all measurement points are used in the
+analysis.</p>
+</dd>
+<dt>prior_samples<span class="classifier">array of shape (n_samples, n_params), optional</span></dt><dd><p>The samples to be used in the analysis. The default is <cite>None</cite>. If
+None the samples are drawn from the probablistic input parameter
+object of the MetaModel object.</p>
+</dd>
+<dt>n_prior_samples<span class="classifier">int, optional</span></dt><dd><p>Number of samples to be used in the analysis. The default is <cite>500000</cite>.
+If samples is not <cite>None</cite>, this argument will be assigned based on the
+number of samples given.</p>
+</dd>
+<dt>measured_data<span class="classifier">dict, optional</span></dt><dd><p>A dictionary containing the observation data. The default is <cite>None</cite>.
+if <cite>None</cite>, the observation defined in the Model object of the
+MetaModel is used.</p>
+</dd>
+<dt>inference_method<span class="classifier">str, optional</span></dt><dd><p>A method for approximating the posterior distribution in the Bayesian
+inference step. The default is <cite>‘rejection’</cite>, which stands for
+rejection sampling. A Markov Chain Monte Carlo sampler can be simply
+selected by passing <cite>‘MCMC’</cite>.</p>
+</dd>
+<dt>mcmc_params<span class="classifier">dict, optional</span></dt><dd><p>A dictionary with args required for the Bayesian inference with
+<cite>MCMC</cite>. The default is <cite>None</cite>.</p>
+<p>Pass the mcmc_params like the following:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mcmc_params</span><span class="p">:{</span>
+<span class="go"> 'init_samples': None, # initial samples</span>
+<span class="go"> 'n_walkers': 100, # number of walkers (chain)</span>
+<span class="go"> 'n_steps': 100000, # number of maximum steps</span>
+<span class="go"> 'n_burn': 200, # number of burn-in steps</span>
+<span class="go"> 'moves': None, # Moves for the emcee sampler</span>
+<span class="go"> 'multiprocessing': False, # multiprocessing</span>
+<span class="go"> 'verbose': False # verbosity</span>
+<span class="go"> }</span>
+</pre></div>
+</div>
+<p>The items shown above are the default values. If any parmeter is
+not defined, the default value will be assigned to it.</p>
+</dd>
+<dt>bayes_loocv<span class="classifier">bool, optional</span></dt><dd><p>Bayesian Leave-one-out Cross Validation. The default is <cite>False</cite>. If
+<cite>True</cite>, the LOOCV procedure is used to estimate the bayesian Model
+Evidence (BME).</p>
+</dd>
+<dt>n_bootstrap_itrs<span class="classifier">int, optional</span></dt><dd><p>Number of bootstrap iteration. The default is <cite>1</cite>. If bayes_loocv is
+<cite>True</cite>, this is qualt to the total length of the observation data
+set.</p>
+</dd>
+<dt>perturbed_data<span class="classifier">array of shape (n_bootstrap_itrs, n_obs), optional</span></dt><dd><p>User defined perturbed data. The default is <cite>[]</cite>.</p>
+</dd>
+<dt>bootstrap_noise<span class="classifier">float, optional</span></dt><dd><p>A noise level to perturb the data set. The default is <cite>0.05</cite>.</p>
+</dd>
+<dt>just_analysis<span class="classifier">bool, optional</span></dt><dd><p>Justifiability analysis. The default is False.</p>
+</dd>
+<dt>valid_metrics<span class="classifier">list, optional</span></dt><dd><p>List of the validation metrics. The following metrics are supported:</p>
+<ol class="arabic simple">
+<li><p>log_BME : logarithm of the Bayesian model evidence</p></li>
+<li><p>KLD : Kullback-Leibler Divergence</p></li>
+</ol>
+<p>3. inf_entropy: Information entropy
+The default is <cite>[‘log_BME’]</cite>.</p>
+</dd>
+<dt>plot_post_pred<span class="classifier">bool, optional</span></dt><dd><p>Plot posterior predictive plots. The default is <cite>True</cite>.</p>
+</dd>
+<dt>plot_map_pred<span class="classifier">bool, optional</span></dt><dd><p>Plot the model outputs vs the metamodel predictions for the maximum
+a posteriori (defined as <cite>max_a_posteriori</cite>) parameter set. The
+default is <cite>False</cite>.</p>
+</dd>
+<dt>max_a_posteriori<span class="classifier">str, optional</span></dt><dd><p>Maximum a posteriori. <cite>‘mean’</cite> and <cite>‘mode’</cite> are available. The default
+is <cite>‘mean’</cite>.</p>
+</dd>
+<dt>corner_title_fmt<span class="classifier">str, optional</span></dt><dd><p>Title format for the posterior distribution plot with python
+package <cite>corner</cite>. The default is <cite>‘.2e’</cite>.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">discrepancy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">emulator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Calib'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">req_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prior_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prior_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">measured_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inference_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rejection'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mcmc_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bayes_loocv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap_itrs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perturbed_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_noise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">just_analysis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">valid_metrics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['BME']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_post_pred</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_map_pred</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_a_posteriori</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">corner_title_fmt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'.2e'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bmc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.__init__" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(engine[, discrepancy, emulator, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_error_model" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_error_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_error_model</span></code></a>([type_, opt_sigma, sampler])</p></td>
+<td><p>Creates an error model in the engine.MetaModel based on input dist samples of the chosen type</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_inference" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_inference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_inference</span></code></a>()</p></td>
+<td><p>Starts the inference.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.normpdf" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.normpdf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normpdf</span></code></a>(outputs, obs_data, total_sigma2s[, ...])</p></td>
+<td><p>Calculates the likelihood of simulation outputs compared with observation data.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.perform_bootstrap" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.perform_bootstrap"><code class="xref py py-obj docutils literal notranslate"><span class="pre">perform_bootstrap</span></code></a>(total_sigma2)</p></td>
+<td><p>Perform bootstrap to get TOM (??)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_log_BME" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_log_BME"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_log_BME</span></code></a>()</p></td>
+<td><p>Plots the log_BME if bootstrap is active.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_post_params" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_post_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_post_params</span></code></a>(opt_sigma)</p></td>
+<td><p>Plots the multivar.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.setup_inference" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.setup_inference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setup_inference</span></code></a>()</p></td>
+<td><p>This function sets up the inference by checking the inputs and getting needed data.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_error_model">
+<span class="sig-name descname"><span class="pre">create_error_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'posterior'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opt_sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'B'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_error_model" title="Link to this definition">¶</a></dt>
+<dd><p>Creates an error model in the engine.MetaModel based on input dist
+samples of the chosen type</p>
+<section id="parameters">
+<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>opt_sigma<span class="classifier">string, optional</span></dt><dd><p>Type of uncertainty description, only used if type_==’posterior’.
+The default is ‘B’</p>
+</dd>
+<dt><a href="#id9"><span class="problematic" id="id10">type_</span></a><span class="classifier">string</span></dt><dd><p>Type of parameter samples to use, either ‘prior’ or ‘posterior’.
+The default is ‘posterior’.</p>
+</dd>
+<dt>sampler<span class="classifier">MCMC, optional</span></dt><dd><p>Should be an MCMC object if type==’posterior’ and MCMC is used in
+the inference.In al other cases this parameter is not needed.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_inference">
+<span class="sig-name descname"><span class="pre">create_inference</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_inference" title="Link to this definition">¶</a></dt>
+<dd><p>Starts the inference.</p>
+<section id="id1">
+<h3>Returns<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>BayesInference<span class="classifier">obj</span></dt><dd><p>The Bayes inference object.</p>
+</dd>
+</dl>
+<p># TODO: should this function really return the class?</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.normpdf">
+<span class="sig-name descname"><span class="pre">normpdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_sigma2s</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.normpdf" title="Link to this definition">¶</a></dt>
+<dd><p>Calculates the likelihood of simulation outputs compared with
+observation data.</p>
+<section id="id2">
+<h3>Parameters<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>outputs<span class="classifier">dict</span></dt><dd><p>A dictionary containing the simulation outputs as array of shape
+(n_samples, n_measurement) for each model output.</p>
+</dd>
+<dt>obs_data<span class="classifier">dict</span></dt><dd><p>A dictionary/dataframe containing the observation data.</p>
+</dd>
+<dt>total_sigma2s<span class="classifier">dict</span></dt><dd><p>A dictionary with known values of the covariance diagonal entries,
+a.k.a. sigma^2.</p>
+</dd>
+<dt>sigma2<span class="classifier">array, optional</span></dt><dd><p>An array of the sigma^2 samples, when the covariance diagonal
+entries are unknown and are being jointly inferred. The default is
+None.</p>
+</dd>
+<dt>std<span class="classifier">dict, optional</span></dt><dd><p>A dictionary containing the root mean squared error as array of
+shape (n_samples, n_measurement) for each model output. The default
+is None.</p>
+</dd>
+</dl>
+</section>
+<section id="id3">
+<h3>Returns<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>logLik<span class="classifier">array of shape (n_samples)</span></dt><dd><p>Likelihoods.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.perform_bootstrap">
+<span class="sig-name descname"><span class="pre">perform_bootstrap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_sigma2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.perform_bootstrap" title="Link to this definition">¶</a></dt>
+<dd><p>Perform bootstrap to get TOM (??)</p>
+<section id="id4">
+<h3>Parameters<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>total_sigma2<span class="classifier">dict</span></dt><dd><p>Dictionary containing the sigma2 for the training(?) data</p>
+</dd>
+</dl>
+</section>
+<section id="id5">
+<h3>Returns<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_log_BME">
+<span class="sig-name descname"><span class="pre">plot_log_BME</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_log_BME" title="Link to this definition">¶</a></dt>
+<dd><p>Plots the log_BME if bootstrap is active.</p>
+<section id="id6">
+<h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_post_params">
+<span class="sig-name descname"><span class="pre">plot_post_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">opt_sigma</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_post_params" title="Link to this definition">¶</a></dt>
+<dd><p>Plots the multivar. posterior parameter distribution.</p>
+<section id="id7">
+<h3>Parameters<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>opt_sigma<span class="classifier">string</span></dt><dd><p>Type of uncertainty description available.</p>
+</dd>
+</dl>
+</section>
+<section id="id8">
+<h3>Returns<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.setup_inference">
+<span class="sig-name descname"><span class="pre">setup_inference</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.setup_inference" title="Link to this definition">¶</a></dt>
+<dd><p>This function sets up the inference by checking the inputs and getting
+needed data.</p>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
+ </div>
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+<li><a class="reference internal" href="#">bayesvalidrox.bayes_inference.bayes_inference.BayesInference</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference"><code class="docutils literal notranslate"><span class="pre">BayesInference</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.__init__"><code class="docutils literal notranslate"><span class="pre">BayesInference.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_error_model"><code class="docutils literal notranslate"><span class="pre">BayesInference.create_error_model()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_inference"><code class="docutils literal notranslate"><span class="pre">BayesInference.create_inference()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.normpdf"><code class="docutils literal notranslate"><span class="pre">BayesInference.normpdf()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.perform_bootstrap"><code class="docutils literal notranslate"><span class="pre">BayesInference.perform_bootstrap()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_log_BME"><code class="docutils literal notranslate"><span class="pre">BayesInference.plot_log_BME()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_post_params"><code class="docutils literal notranslate"><span class="pre">BayesInference.plot_post_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.setup_inference"><code class="docutils literal notranslate"><span class="pre">BayesInference.setup_inference()</span></code></a></li>
+</ul>
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+<span id="bayesvalidrox-bayes-inference-bayes-inference"></span><h1>bayesvalidrox.bayes_inference.bayes_inference<a class="headerlink" href="#module-bayesvalidrox.bayes_inference.bayes_inference" title="Link to this heading">¶</a></h1>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BayesInference</span></code></a>(engine[, discrepancy, ...])</p></td>
+<td><p>A class to perform Bayesian Analysis.</p></td>
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+ <section id="bayesvalidrox-bayes-inference-bayes-model-comparison-bayesmodelcomparison">
+<h1>bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison<a class="headerlink" href="#bayesvalidrox-bayes-inference-bayes-model-comparison-bayesmodelcomparison" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.bayes_inference.bayes_model_comparison.</span></span><span class="sig-name descname"><span class="pre">BayesModelComparison</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">justifiability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perturbed_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_Bayes_settings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">emulator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Outputs_Comparison/'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>A class to perform Bayesian Analysis.</p>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>justifiability<span class="classifier">bool, optional</span></dt><dd><p>Whether to perform the justifiability analysis. The default is
+<cite>True</cite>.</p>
+</dd>
+<dt>perturbed_data<span class="classifier">array of shape (n_bootstrap_itrs, n_obs), optional</span></dt><dd><p>User defined perturbed data. The default is <cite>None</cite>.</p>
+</dd>
+<dt>n_bootstrap<span class="classifier">int</span></dt><dd><p>Number of bootstrap iteration. The default is <cite>1000</cite>.</p>
+</dd>
+<dt>data_noise_level<span class="classifier">float</span></dt><dd><p>A noise level to perturb the data set. The default is <cite>0.01</cite>.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">justifiability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perturbed_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_Bayes_settings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">emulator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Outputs_Comparison/'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.__init__" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([justifiability, perturbed_data, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.cal_model_weight" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.cal_model_weight"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cal_model_weight</span></code></a>(BME_dict[, justifiability, ...])</p></td>
+<td><p>Normalize the BME (Asumption: Model Prior weights are equal for models)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_bayes_factors" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_bayes_factors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calc_bayes_factors</span></code></a>(model_dict, opts_dict)</p></td>
+<td><p>Calculate the BayesFactors for each pair of models in the model_dict with respect to given data.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_justifiability_analysis" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_justifiability_analysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calc_justifiability_analysis</span></code></a>(model_dict, ...)</p></td>
+<td><p>Perform justifiability analysis by calculating the confusion matrix</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_model_weights" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_model_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calc_model_weights</span></code></a>(model_dict, opts_dict)</p></td>
+<td><p>Calculate the model weights from BME evaluations for Bayes factors.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.generate_dataset" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.generate_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_dataset</span></code></a>(model_dict[, ...])</p></td>
+<td><p>Generates the perturbed data set for the Bayes factor calculations and the data set for the justifiability analysis.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.model_comparison_all" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.model_comparison_all"><code class="xref py py-obj docutils literal notranslate"><span class="pre">model_comparison_all</span></code></a>(model_dict, opts_dict)</p></td>
+<td><p>Perform all three types of model comparison:</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_bayes_factor" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_bayes_factor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_bayes_factor</span></code></a>(BME_dict[, plot_name])</p></td>
+<td><p>Plots the Bayes factor distibutions in a <span class="math notranslate nohighlight">\(N_m \times N_m\)</span> matrix, where <span class="math notranslate nohighlight">\(N_m\)</span> is the number of the models.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_just_analysis" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_just_analysis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_just_analysis</span></code></a>(model_weights_dict)</p></td>
+<td><p>Visualizes the confusion matrix and the model wights for the justifiability analysis.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_model_weights" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_model_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_model_weights</span></code></a>(model_weights, plot_name)</p></td>
+<td><p>Visualizes the model weights resulting from BMS via the observation data.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.setup" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.setup"><code class="xref py py-obj docutils literal notranslate"><span class="pre">setup</span></code></a>(model_dict)</p></td>
+<td><p>Initialize parameters that are needed for all types of model comparison</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.cal_model_weight">
+<span class="sig-name descname"><span class="pre">cal_model_weight</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">BME_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">justifiability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.cal_model_weight" title="Link to this definition">¶</a></dt>
+<dd><p>Normalize the BME (Asumption: Model Prior weights are equal for models)</p>
+<section id="parameters">
+<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>BME_dict<span class="classifier">dict</span></dt><dd><p>A dictionary containing the BME values.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>model_weights<span class="classifier">array</span></dt><dd><p>Model weights.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_bayes_factors">
+<span class="sig-name descname"><span class="pre">calc_bayes_factors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opts_dict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_bayes_factors" title="Link to this definition">¶</a></dt>
+<dd><p>Calculate the BayesFactors for each pair of models in the model_dict
+with respect to given data.</p>
+<section id="id1">
+<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>model_dict<span class="classifier">dict</span></dt><dd><p>A dictionary including the metamodels.</p>
+</dd>
+<dt>opts_dict<span class="classifier">dict</span></dt><dd><p>A dictionary given the <cite>BayesInference</cite> options.</p>
+</dd>
+</dl>
+</section>
+<section id="id2">
+<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_justifiability_analysis">
+<span class="sig-name descname"><span class="pre">calc_justifiability_analysis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opts_dict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_justifiability_analysis" title="Link to this definition">¶</a></dt>
+<dd><p>Perform justifiability analysis by calculating the confusion matrix</p>
+<section id="id3">
+<h3>Parameters<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>model_dict<span class="classifier">dict</span></dt><dd><p>A dictionary including the metamodels.</p>
+</dd>
+<dt>opts_dict<span class="classifier">dict</span></dt><dd><p>A dictionary given the <cite>BayesInference</cite> options.</p>
+</dd>
+</dl>
+</section>
+<section id="id4">
+<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>confusion_matrix: dict</dt><dd><p>The averaged confusion matrix</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_model_weights">
+<span class="sig-name descname"><span class="pre">calc_model_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opts_dict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_model_weights" title="Link to this definition">¶</a></dt>
+<dd><p>Calculate the model weights from BME evaluations for Bayes factors.</p>
+<section id="id5">
+<h3>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>model_dict<span class="classifier">TYPE</span></dt><dd><p>DESCRIPTION.</p>
+</dd>
+<dt>opts_dict<span class="classifier">TYPE</span></dt><dd><p>DESCRIPTION.</p>
+</dd>
+</dl>
+</section>
+<section id="id6">
+<h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.generate_dataset">
+<span class="sig-name descname"><span class="pre">generate_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">justifiability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.generate_dataset" title="Link to this definition">¶</a></dt>
+<dd><p>Generates the perturbed data set for the Bayes factor calculations and
+the data set for the justifiability analysis.</p>
+<section id="id7">
+<h3>Parameters<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>model_dict<span class="classifier">dict</span></dt><dd><p>A dictionary including the metamodels.</p>
+</dd>
+<dt>bool, optional</dt><dd><p>Whether to perform the justifiability analysis. The default is
+<cite>False</cite>.</p>
+</dd>
+<dt>n_bootstrap<span class="classifier">int, optional</span></dt><dd><p>Number of bootstrap iterations. The default is <cite>1</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id8">
+<h3>Returns<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>all_just_data: array</dt><dd><p>Created data set.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.model_comparison_all">
+<span class="sig-name descname"><span class="pre">model_comparison_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opts_dict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.model_comparison_all" title="Link to this definition">¶</a></dt>
+<dd><dl class="simple">
+<dt>Perform all three types of model comparison: </dt><dd><ul class="simple">
+<li><p>Bayes Factors</p></li>
+<li><p>Model weights</p></li>
+<li><p>Justifiability analysis</p></li>
+</ul>
+</dd>
+</dl>
+<section id="id9">
+<h3>Parameters<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>model_dict<span class="classifier">dict</span></dt><dd><p>A dictionary including the metamodels.</p>
+</dd>
+<dt>opts_dict<span class="classifier">dict</span></dt><dd><p>A dictionary given the <cite>BayesInference</cite> options.</p>
+</dd>
+</dl>
+</section>
+<section id="id10">
+<h3>Returns<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>results<span class="classifier">dict</span></dt><dd><p>A dictionary that contains the calculated BME values, model weights
+and confusion matrix</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_bayes_factor">
+<span class="sig-name descname"><span class="pre">plot_bayes_factor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">BME_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_bayes_factor" title="Link to this definition">¶</a></dt>
+<dd><p>Plots the Bayes factor distibutions in a <span class="math notranslate nohighlight">\(N_m \times N_m\)</span>
+matrix, where <span class="math notranslate nohighlight">\(N_m\)</span> is the number of the models.</p>
+<section id="id11">
+<h3>Parameters<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>BME_dict<span class="classifier">dict</span></dt><dd><p>A dictionary containing the BME values of the models.</p>
+</dd>
+<dt>plot_name<span class="classifier">str, optional</span></dt><dd><p>Plot name. The default is ‘’.</p>
+</dd>
+</dl>
+</section>
+<section id="id12">
+<h3>Returns<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_just_analysis">
+<span class="sig-name descname"><span class="pre">plot_just_analysis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_weights_dict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_just_analysis" title="Link to this definition">¶</a></dt>
+<dd><p>Visualizes the confusion matrix and the model wights for the
+justifiability analysis.</p>
+<section id="id13">
+<h3>Parameters<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>model_weights_dict<span class="classifier">dict</span></dt><dd><p>Model weights.</p>
+</dd>
+</dl>
+</section>
+<section id="id14">
+<h3>Returns<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_model_weights">
+<span class="sig-name descname"><span class="pre">plot_model_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_weights</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_name</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_model_weights" title="Link to this definition">¶</a></dt>
+<dd><p>Visualizes the model weights resulting from BMS via the observation
+data.</p>
+<section id="id15">
+<h3>Parameters<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>model_weights<span class="classifier">array</span></dt><dd><p>Model weights.</p>
+</dd>
+<dt>plot_name<span class="classifier">str</span></dt><dd><p>Plot name.</p>
+</dd>
+</dl>
+</section>
+<section id="id16">
+<h3>Returns<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.setup">
+<span class="sig-name descname"><span class="pre">setup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_dict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.setup" title="Link to this definition">¶</a></dt>
+<dd><p>Initialize parameters that are needed for all types of model comparison</p>
+<section id="id17">
+<h3>Returns<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
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+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.__init__"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.cal_model_weight"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.cal_model_weight()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_bayes_factors"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.calc_bayes_factors()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_justifiability_analysis"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.calc_justifiability_analysis()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_model_weights"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.calc_model_weights()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.generate_dataset"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.generate_dataset()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.model_comparison_all"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.model_comparison_all()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_bayes_factor"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.plot_bayes_factor()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_just_analysis"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.plot_just_analysis()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_model_weights"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.plot_model_weights()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.setup"><code class="docutils literal notranslate"><span class="pre">BayesModelComparison.setup()</span></code></a></li>
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+ <section id="bayesvalidrox-bayes-inference-discrepancy-discrepancy">
+<h1>bayesvalidrox.bayes_inference.discrepancy.Discrepancy<a class="headerlink" href="#bayesvalidrox-bayes-inference-discrepancy-discrepancy" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.discrepancy.Discrepancy">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.bayes_inference.discrepancy.</span></span><span class="sig-name descname"><span class="pre">Discrepancy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">InputDisc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disc_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.discrepancy.Discrepancy" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Discrepancy class for Bayesian inference method.
+We define the reference or reality to be equal to what we can model and a
+descripancy term ( epsilon ). We consider the followin format:</p>
+<p>$$textbf{y}_{text{reality}} = mathcal{M}(theta) + epsilon,$$</p>
+<p>where ( epsilon in R^{N_{out}} ) represents the the effects of
+measurement error and model inaccuracy. For simplicity, it can be defined
+as an additive Gaussian disrepancy with zeromean and given covariance
+matrix ( Sigma ):</p>
+<p>$$epsilon sim mathcal{N}(epsilon|0, Sigma). $$</p>
+<p>In the context of model inversion or calibration, an observation point
+( textbf{y}_i in mathcal{y} ) is a realization of a Gaussian
+distribution with mean value of (mathcal{M}(theta) ) and covariance
+matrix of ( Sigma ).</p>
+<dl class="simple">
+<dt>$$ p(textbf{y}|theta) = mathcal{N}(textbf{y}|mathcal{M}</dt><dd><p>(theta))$$</p>
+</dd>
+</dl>
+<p>The following options are available:</p>
+<ul class="simple">
+<li><p>Option A: With known redidual covariance matrix (Sigma) for</p></li>
+</ul>
+<p>independent measurements.</p>
+<ul class="simple">
+<li><p>Option B: With unknown redidual covariance matrix (Sigma),</p></li>
+</ul>
+<p>paramethrized as (Sigma(theta_{epsilon})=sigma^2 textbf{I}_
+{N_{out}}) with unknown residual variances (sigma^2).
+This term will be jointly infered with the uncertain input parameters. For
+the inversion, you need to define a prior marginal via <cite>Input</cite> class. Note
+that (sigma^2) is only a single scalar multiplier for the diagonal
+entries of the covariance matrix (Sigma).</p>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>InputDisc<span class="classifier">obj</span></dt><dd><p>Input object. When the (sigma^2) is expected to be inferred
+jointly with the parameters (<cite>Option B</cite>).If multiple output groups are
+defined by <cite>Model.Output.names</cite>, each model output needs to have.
+a prior marginal using the <cite>Input</cite> class. The default is <cite>‘’</cite>.</p>
+</dd>
+<dt>disc_type<span class="classifier">str</span></dt><dd><p>Type of the noise definition. <cite>‘Gaussian’</cite> is only supported so far.</p>
+</dd>
+<dt>parameters<span class="classifier">dict or pandas.DataFrame</span></dt><dd><p>Known residual variance (sigma^2), i.e. diagonal entry of the
+covariance matrix of the multivariate normal likelihood in case of
+<cite>Option A</cite>.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.discrepancy.Discrepancy.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">InputDisc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disc_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.discrepancy.Discrepancy.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.discrepancy.Discrepancy.__init__" title="bayesvalidrox.bayes_inference.discrepancy.Discrepancy.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([InputDisc, disc_type, parameters])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.discrepancy.Discrepancy.get_sample" title="bayesvalidrox.bayes_inference.discrepancy.Discrepancy.get_sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_sample</span></code></a>(n_samples)</p></td>
+<td><p>Generate samples for the (sigma^2), i.e. the diagonal entries of the variance-covariance matrix in the multivariate normal distribution.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.discrepancy.Discrepancy.get_sample">
+<span class="sig-name descname"><span class="pre">get_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.discrepancy.Discrepancy.get_sample" title="Link to this definition">¶</a></dt>
+<dd><p>Generate samples for the (sigma^2), i.e. the diagonal entries of
+the variance-covariance matrix in the multivariate normal distribution.</p>
+<section id="parameters">
+<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>n_samples<span class="classifier">int</span></dt><dd><p>Number of samples (parameter sets).</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>sigma2_prior: array of shape (n_samples, n_params)</dt><dd><p>(sigma^2) samples.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.discrepancy.Discrepancy"><code class="docutils literal notranslate"><span class="pre">Discrepancy</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.discrepancy.Discrepancy.__init__"><code class="docutils literal notranslate"><span class="pre">Discrepancy.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.discrepancy.Discrepancy.get_sample"><code class="docutils literal notranslate"><span class="pre">Discrepancy.get_sample()</span></code></a></li>
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+<span id="bayesvalidrox-bayes-inference-discrepancy"></span><h1>bayesvalidrox.bayes_inference.discrepancy<a class="headerlink" href="#module-bayesvalidrox.bayes_inference.discrepancy" title="Link to this heading">¶</a></h1>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html#bayesvalidrox.bayes_inference.discrepancy.Discrepancy" title="bayesvalidrox.bayes_inference.discrepancy.Discrepancy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Discrepancy</span></code></a>([InputDisc, disc_type, parameters])</p></td>
+<td><p>Discrepancy class for Bayesian inference method.</p></td>
+</tr>
+</tbody>
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+ <section id="bayesvalidrox-bayes-inference-mcmc-mcmc">
+<h1>bayesvalidrox.bayes_inference.mcmc.MCMC<a class="headerlink" href="#bayesvalidrox-bayes-inference-mcmc-mcmc" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.mcmc.MCMC">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.bayes_inference.mcmc.</span></span><span class="sig-name descname"><span class="pre">MCMC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mcmc_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Discrepancy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">req_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">emulator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BiasInputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.mcmc.MCMC" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>A class for bayesian inference via a Markov-Chain Monte-Carlo (MCMC)
+Sampler to approximate the posterior distribution of the Bayes theorem:
+$$p(theta|mathcal{y}) = frac{p(mathcal{y}|theta) p(theta)}</p>
+<blockquote>
+<div><p>{p(mathcal{y})}.$$</p>
+</div></blockquote>
+<p>This class make inference with emcee package [1] using an Affine Invariant
+Ensemble sampler (AIES) [2].</p>
+<dl class="simple">
+<dt>[1] Foreman-Mackey, D., Hogg, D.W., Lang, D. and Goodman, J., 2013.emcee:</dt><dd><p>the MCMC hammer. Publications of the Astronomical Society of the
+Pacific, 125(925), p.306. <a class="reference external" href="https://emcee.readthedocs.io/en/stable/">https://emcee.readthedocs.io/en/stable/</a></p>
+</dd>
+<dt>[2] Goodman, J. and Weare, J., 2010. Ensemble samplers with affine</dt><dd><p>invariance. Communications in applied mathematics and computational
+science, 5(1), pp.65-80.</p>
+</dd>
+</dl>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>BayesOpts<span class="classifier">obj</span></dt><dd><p>Bayes object.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.mcmc.MCMC.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mcmc_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Discrepancy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">req_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selected_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">emulator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BiasInputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.__init__" title="bayesvalidrox.bayes_inference.mcmc.MCMC.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(engine, mcmc_params, Discrepancy, ...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.eval_model" title="bayesvalidrox.bayes_inference.mcmc.MCMC.eval_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_model</span></code></a>(theta)</p></td>
+<td><p>Evaluates the (meta-) model at the given theta.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.log_likelihood" title="bayesvalidrox.bayes_inference.mcmc.MCMC.log_likelihood"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log_likelihood</span></code></a>(theta)</p></td>
+<td><p>Computes likelihood ( p(mathcal{Y}|theta)) of the performance of the (meta-)model in reproducing the observation data.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.log_posterior" title="bayesvalidrox.bayes_inference.mcmc.MCMC.log_posterior"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log_posterior</span></code></a>(theta)</p></td>
+<td><p>Computes the posterior likelihood (p(theta| mathcal{Y})) for the given parameterset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.log_prior" title="bayesvalidrox.bayes_inference.mcmc.MCMC.log_prior"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log_prior</span></code></a>(theta)</p></td>
+<td><p>Calculates the log prior likelihood ( p(theta)) for the given parameter set(s) ( theta ).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.normpdf" title="bayesvalidrox.bayes_inference.mcmc.MCMC.normpdf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normpdf</span></code></a>(outputs, obs_data, total_sigma2s[, ...])</p></td>
+<td><p>Calculates the likelihood of simulation outputs compared with observation data.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.run_sampler" title="bayesvalidrox.bayes_inference.mcmc.MCMC.run_sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_sampler</span></code></a>(observation, total_sigma2)</p></td>
+<td><p>Run the MCMC sampler for the given observations and stdevs.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.train_error_model" title="bayesvalidrox.bayes_inference.mcmc.MCMC.train_error_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train_error_model</span></code></a>(sampler)</p></td>
+<td><p>Trains an error model using a Gaussian Process Regression.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.mcmc.MCMC.eval_model">
+<span class="sig-name descname"><span class="pre">eval_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.eval_model" title="Link to this definition">¶</a></dt>
+<dd><p>Evaluates the (meta-) model at the given theta.</p>
+<section id="parameters">
+<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>theta<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Parameter set, i.e. proposals of the MCMC chains.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>mean_pred<span class="classifier">dict</span></dt><dd><p>Mean model prediction.</p>
+</dd>
+<dt>std_pred<span class="classifier">dict</span></dt><dd><p>Std of model prediction.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.mcmc.MCMC.log_likelihood">
+<span class="sig-name descname"><span class="pre">log_likelihood</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.log_likelihood" title="Link to this definition">¶</a></dt>
+<dd><p>Computes likelihood ( p(mathcal{Y}|theta)) of the performance
+of the (meta-)model in reproducing the observation data.</p>
+<section id="id1">
+<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>theta<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Parameter set, i.e. proposals of the MCMC chains.</p>
+</dd>
+</dl>
+</section>
+<section id="id2">
+<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>log_like<span class="classifier">array of shape (n_samples)</span></dt><dd><p>Log likelihood.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.mcmc.MCMC.log_posterior">
+<span class="sig-name descname"><span class="pre">log_posterior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.log_posterior" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the posterior likelihood (p(theta| mathcal{Y})) for
+the given parameterset.</p>
+<section id="id3">
+<h3>Parameters<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>theta<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Parameter set, i.e. proposals of the MCMC chains.</p>
+</dd>
+</dl>
+</section>
+<section id="id4">
+<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>log_like<span class="classifier">array of shape (n_samples)</span></dt><dd><p>Log posterior likelihood.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.mcmc.MCMC.log_prior">
+<span class="sig-name descname"><span class="pre">log_prior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.log_prior" title="Link to this definition">¶</a></dt>
+<dd><p>Calculates the log prior likelihood ( p(theta)) for the given
+parameter set(s) ( theta ).</p>
+<section id="id5">
+<h3>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>theta<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Parameter sets, i.e. proposals of MCMC chains.</p>
+</dd>
+</dl>
+</section>
+<section id="id6">
+<h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>logprior: float or array of shape n_samples</dt><dd><p>Log prior likelihood. If theta has only one row, a single value is
+returned otherwise an array.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.mcmc.MCMC.normpdf">
+<span class="sig-name descname"><span class="pre">normpdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_sigma2s</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.normpdf" title="Link to this definition">¶</a></dt>
+<dd><p>Calculates the likelihood of simulation outputs compared with
+observation data.</p>
+<section id="id7">
+<h3>Parameters<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>outputs<span class="classifier">dict</span></dt><dd><p>A dictionary containing the simulation outputs as array of shape
+(n_samples, n_measurement) for each model output.</p>
+</dd>
+<dt>obs_data<span class="classifier">dict</span></dt><dd><p>A dictionary/dataframe containing the observation data.</p>
+</dd>
+<dt>total_sigma2s<span class="classifier">dict</span></dt><dd><p>A dictionary with known values of the covariance diagonal entries,
+a.k.a. sigma^2.</p>
+</dd>
+<dt>sigma2<span class="classifier">array, optional</span></dt><dd><p>An array of the sigma^2 samples, when the covariance diagonal
+entries are unknown and are being jointly inferred. The default is
+None.</p>
+</dd>
+<dt>std<span class="classifier">dict, optional</span></dt><dd><p>A dictionary containing the root mean squared error as array of
+shape (n_samples, n_measurement) for each model output. The default
+is None.</p>
+</dd>
+</dl>
+</section>
+<section id="id8">
+<h3>Returns<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>logLik<span class="classifier">array of shape (n_samples)</span></dt><dd><p>Likelihoods.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.mcmc.MCMC.run_sampler">
+<span class="sig-name descname"><span class="pre">run_sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_sigma2</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.run_sampler" title="Link to this definition">¶</a></dt>
+<dd><p>Run the MCMC sampler for the given observations and stdevs.</p>
+<section id="id9">
+<h3>Parameters<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>observation<span class="classifier">TYPE</span></dt><dd><p>DESCRIPTION.</p>
+</dd>
+<dt>total_sigma2<span class="classifier">TYPE</span></dt><dd><p>DESCRIPTION.</p>
+</dd>
+</dl>
+</section>
+<section id="id10">
+<h3>Returns<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>Posterior_df<span class="classifier">TYPE</span></dt><dd><p>DESCRIPTION.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.mcmc.MCMC.train_error_model">
+<span class="sig-name descname"><span class="pre">train_error_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sampler</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.train_error_model" title="Link to this definition">¶</a></dt>
+<dd><p>Trains an error model using a Gaussian Process Regression.</p>
+<section id="id11">
+<h3>Parameters<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>sampler<span class="classifier">obj</span></dt><dd><p>emcee sampler.</p>
+</dd>
+</dl>
+</section>
+<section id="id12">
+<h3>Returns<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>error_MetaModel<span class="classifier">obj</span></dt><dd><p>A error model.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
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+<li><a class="reference internal" href="#">bayesvalidrox.bayes_inference.mcmc.MCMC</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC"><code class="docutils literal notranslate"><span class="pre">MCMC</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.__init__"><code class="docutils literal notranslate"><span class="pre">MCMC.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.eval_model"><code class="docutils literal notranslate"><span class="pre">MCMC.eval_model()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.log_likelihood"><code class="docutils literal notranslate"><span class="pre">MCMC.log_likelihood()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.log_posterior"><code class="docutils literal notranslate"><span class="pre">MCMC.log_posterior()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.log_prior"><code class="docutils literal notranslate"><span class="pre">MCMC.log_prior()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.normpdf"><code class="docutils literal notranslate"><span class="pre">MCMC.normpdf()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.run_sampler"><code class="docutils literal notranslate"><span class="pre">MCMC.run_sampler()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.MCMC.train_error_model"><code class="docutils literal notranslate"><span class="pre">MCMC.train_error_model()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
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+<h1>bayesvalidrox.bayes_inference.mcmc.gelman_rubin<a class="headerlink" href="#bayesvalidrox-bayes-inference-mcmc-gelman-rubin" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.bayes_inference.mcmc.gelman_rubin">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.bayes_inference.mcmc.</span></span><span class="sig-name descname"><span class="pre">gelman_rubin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">chain</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_var</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.bayes_inference.mcmc.gelman_rubin" title="Link to this definition">¶</a></dt>
+<dd><p>The potential scale reduction factor (PSRF) defined by the variance
+within one chain, W, with the variance between chains B.
+Both variances are combined in a weighted sum to obtain an estimate of
+the variance of a parameter ( theta ).The square root of the
+ratio of this estimates variance to the within chain variance is called
+the potential scale reduction.
+For a well converged chain it should approach 1. Values greater than
+1.1 typically indicate that the chains have not yet fully converged.</p>
+<p>Source: <a class="reference external" href="http://joergdietrich.github.io/emcee-convergence.html">http://joergdietrich.github.io/emcee-convergence.html</a></p>
+<p><a class="reference external" href="https://github.com/jwalton3141/jwalton3141.github.io/blob/master/assets/posts/ESS/rwmh.py">https://github.com/jwalton3141/jwalton3141.github.io/blob/master/assets/posts/ESS/rwmh.py</a></p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>chain<span class="classifier">array (n_walkers, n_steps, n_params)</span></dt><dd><p>The emcee ensamples.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>R_hat<span class="classifier">float</span></dt><dd><p>The Gelman-Robin values.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#bayesvalidrox.bayes_inference.mcmc.gelman_rubin"><code class="docutils literal notranslate"><span class="pre">gelman_rubin()</span></code></a></li>
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+ <section id="module-bayesvalidrox.bayes_inference.mcmc">
+<span id="bayesvalidrox-bayes-inference-mcmc"></span><h1>bayesvalidrox.bayes_inference.mcmc<a class="headerlink" href="#module-bayesvalidrox.bayes_inference.mcmc" title="Link to this heading">¶</a></h1>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.bayes_inference.mcmc.gelman_rubin.html#bayesvalidrox.bayes_inference.mcmc.gelman_rubin" title="bayesvalidrox.bayes_inference.mcmc.gelman_rubin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gelman_rubin</span></code></a>(chain[, return_var])</p></td>
+<td><p>The potential scale reduction factor (PSRF) defined by the variance within one chain, W, with the variance between chains B.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.bayes_inference.mcmc.MCMC.html#bayesvalidrox.bayes_inference.mcmc.MCMC" title="bayesvalidrox.bayes_inference.mcmc.MCMC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MCMC</span></code></a>(engine, mcmc_params, Discrepancy, ...)</p></td>
+<td><p>A class for bayesian inference via a Markov-Chain Monte-Carlo (MCMC) Sampler to approximate the posterior distribution of the Bayes theorem: $$p(theta|mathcal{y}) = frac{p(mathcal{y}|theta) p(theta)} {p(mathcal{y})}.$$</p></td>
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+ <article role="main">
+ <section id="bayesvalidrox-post-processing-post-processing-postprocessing">
+<h1>bayesvalidrox.post_processing.post_processing.PostProcessing<a class="headerlink" href="#bayesvalidrox-post-processing-post-processing-postprocessing" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.post_processing.post_processing.PostProcessing">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.post_processing.post_processing.</span></span><span class="sig-name descname"><span class="pre">PostProcessing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'calib'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.post_processing.post_processing.PostProcessing" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>This class provides many helper functions to post-process the trained
+meta-model.</p>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>MetaModel<span class="classifier">obj</span></dt><dd><p>MetaModel object to do postprocessing on.</p>
+</dd>
+<dt>name<span class="classifier">str</span></dt><dd><p>Type of the anaylsis. The default is <cite>‘calib’</cite>. If a validation is
+expected to be performed change this to <cite>‘valid’</cite>.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.post_processing.post_processing.PostProcessing.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'calib'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.__init__" title="bayesvalidrox.post_processing.post_processing.PostProcessing.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(engine[, name])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.check_accuracy" title="bayesvalidrox.post_processing.post_processing.PostProcessing.check_accuracy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_accuracy</span></code></a>([n_samples, samples, outputs])</p></td>
+<td><p>Checks accuracy of the metamodel by computing the root mean square error and validation error for all outputs.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.check_reg_quality" title="bayesvalidrox.post_processing.post_processing.PostProcessing.check_reg_quality"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_reg_quality</span></code></a>([n_samples, samples])</p></td>
+<td><p>Checks the quality of the metamodel for single output models based on: <a class="reference external" href="https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685">https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685</a></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.compute_pce_moments" title="bayesvalidrox.post_processing.post_processing.PostProcessing.compute_pce_moments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_pce_moments</span></code></a>()</p></td>
+<td><p>Computes the first two moments using the PCE-based meta-model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_pce_model_3d</span></code>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.plot_moments" title="bayesvalidrox.post_processing.post_processing.PostProcessing.plot_moments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_moments</span></code></a>([xlabel, plot_type])</p></td>
+<td><p>Plots the moments in a pdf format in the directory <cite>Outputs_PostProcessing</cite>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.plot_seq_design_diagnostics" title="bayesvalidrox.post_processing.post_processing.PostProcessing.plot_seq_design_diagnostics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_seq_design_diagnostics</span></code></a>([ref_BME_KLD])</p></td>
+<td><p>Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence (KLD) for the sequential design.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.sobol_indices" title="bayesvalidrox.post_processing.post_processing.PostProcessing.sobol_indices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sobol_indices</span></code></a>([xlabel, plot_type])</p></td>
+<td><p>Provides Sobol indices as a sensitivity measure to infer the importance of the input parameters.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.valid_metamodel" title="bayesvalidrox.post_processing.post_processing.PostProcessing.valid_metamodel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">valid_metamodel</span></code></a>([n_samples, samples, ...])</p></td>
+<td><p>Evaluates and plots the meta model and the PCEModel outputs for the given number of samples or the given samples.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.post_processing.post_processing.PostProcessing.check_accuracy">
+<span class="sig-name descname"><span class="pre">check_accuracy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.check_accuracy" title="Link to this definition">¶</a></dt>
+<dd><p>Checks accuracy of the metamodel by computing the root mean square
+error and validation error for all outputs.</p>
+<section id="parameters">
+<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>n_samples<span class="classifier">int, optional</span></dt><dd><p>Number of samples. The default is None.</p>
+</dd>
+<dt>samples<span class="classifier">array of shape (n_samples, n_params), optional</span></dt><dd><p>Parameter sets to be checked. The default is None.</p>
+</dd>
+<dt>outputs<span class="classifier">dict, optional</span></dt><dd><p>Output dictionary with model outputs for all given output types in
+<cite>Model.Output.names</cite>. The default is None.</p>
+</dd>
+</dl>
+</section>
+<section id="raises">
+<h3>Raises<a class="headerlink" href="#raises" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>Exception</dt><dd><p>When neither n_samples nor samples are provided.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>rmse: dict</dt><dd><p>Root mean squared error for each output.</p>
+</dd>
+<dt>valid_error<span class="classifier">dict</span></dt><dd><p>Validation error for each output.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.post_processing.post_processing.PostProcessing.check_reg_quality">
+<span class="sig-name descname"><span class="pre">check_reg_quality</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.check_reg_quality" title="Link to this definition">¶</a></dt>
+<dd><p>Checks the quality of the metamodel for single output models based on:
+<a class="reference external" href="https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685">https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685</a></p>
+<section id="id1">
+<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>n_samples<span class="classifier">int, optional</span></dt><dd><p>Number of parameter sets to use for the check. The default is 1000.</p>
+</dd>
+<dt>samples<span class="classifier">array of shape (n_samples, n_params), optional</span></dt><dd><p>Parameter sets to use for the check. The default is None.</p>
+</dd>
+</dl>
+</section>
+<section id="id2">
+<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.post_processing.post_processing.PostProcessing.compute_pce_moments">
+<span class="sig-name descname"><span class="pre">compute_pce_moments</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.compute_pce_moments" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the first two moments using the PCE-based meta-model.</p>
+<section id="id3">
+<h3>Returns<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>pce_means: dict</dt><dd><p>The first moments (mean) of outpust.</p>
+</dd>
+<dt>pce_means: dict</dt><dd><p>The first moments (mean) of outpust.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.post_processing.post_processing.PostProcessing.plot_moments">
+<span class="sig-name descname"><span class="pre">plot_moments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Time</span> <span class="pre">[s]'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.plot_moments" title="Link to this definition">¶</a></dt>
+<dd><p>Plots the moments in a pdf format in the directory
+<cite>Outputs_PostProcessing</cite>.</p>
+<section id="id4">
+<h3>Parameters<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>xlabel<span class="classifier">str, optional</span></dt><dd><p>String to be displayed as x-label. The default is <cite>‘Time [s]’</cite>.</p>
+</dd>
+<dt>plot_type<span class="classifier">str, optional</span></dt><dd><p>Options: bar or line. The default is <cite>None</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id5">
+<h3>Returns<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>pce_means: dict</dt><dd><p>Mean of the model outputs.</p>
+</dd>
+<dt>pce_means: dict</dt><dd><p>Standard deviation of the model outputs.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.post_processing.post_processing.PostProcessing.plot_seq_design_diagnostics">
+<span class="sig-name descname"><span class="pre">plot_seq_design_diagnostics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_BME_KLD</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.plot_seq_design_diagnostics" title="Link to this definition">¶</a></dt>
+<dd><p>Plots the Bayesian Model Evidence (BME) and Kullback-Leibler divergence
+(KLD) for the sequential design.</p>
+<section id="id6">
+<h3>Parameters<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>ref_BME_KLD<span class="classifier">array, optional</span></dt><dd><p>Reference BME and KLD . The default is <cite>None</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id7">
+<h3>Returns<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.post_processing.post_processing.PostProcessing.sobol_indices">
+<span class="sig-name descname"><span class="pre">sobol_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Time</span> <span class="pre">[s]'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.sobol_indices" title="Link to this definition">¶</a></dt>
+<dd><p>Provides Sobol indices as a sensitivity measure to infer the importance
+of the input parameters. See Eq. 27 in [1] for more details. For the
+case with Principal component analysis refer to [2].</p>
+<p>[1] Global sensitivity analysis: A flexible and efficient framework
+with an example from stochastic hydrogeology S. Oladyshkin, F.P.
+de Barros, W. Nowak <a class="reference external" href="https://doi.org/10.1016/j.advwatres.2011.11.001">https://doi.org/10.1016/j.advwatres.2011.11.001</a></p>
+<p>[2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
+component analysis and sparse polynomial chaos expansions for global
+sensitivity analysis and model calibration: Application to urban
+drainage simulation. Reliability Engineering & System Safety, 195,
+p.106737.</p>
+<section id="id8">
+<h3>Parameters<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>xlabel<span class="classifier">str, optional</span></dt><dd><p>Label of the x-axis. The default is <cite>‘Time [s]’</cite>.</p>
+</dd>
+<dt>plot_type<span class="classifier">str, optional</span></dt><dd><p>Plot type. The default is <cite>None</cite>. This corresponds to line plot.
+Bar chart can be selected by <cite>bar</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id9">
+<h3>Returns<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>sobol_cell: dict</dt><dd><p>Sobol indices.</p>
+</dd>
+<dt>total_sobol: dict</dt><dd><p>Total Sobol indices.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.post_processing.post_processing.PostProcessing.valid_metamodel">
+<span class="sig-name descname"><span class="pre">valid_metamodel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_out_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Time</span> <span class="pre">[s]'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.valid_metamodel" title="Link to this definition">¶</a></dt>
+<dd><p>Evaluates and plots the meta model and the PCEModel outputs for the
+given number of samples or the given samples.</p>
+<section id="id10">
+<h3>Parameters<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>n_samples<span class="classifier">int, optional</span></dt><dd><p>Number of samples to be evaluated. The default is 1.</p>
+</dd>
+<dt>samples<span class="classifier">array of shape (n_samples, n_params), optional</span></dt><dd><p>Samples to be evaluated. The default is None.</p>
+</dd>
+<dt>model_out_dict: dict</dt><dd><p>The model runs using the samples provided.</p>
+</dd>
+<dt>x_axis<span class="classifier">str, optional</span></dt><dd><p>Label of x axis. The default is <cite>‘Time [s]’</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id11">
+<h3>Returns<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
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+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.post_processing.post_processing.PostProcessing</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing"><code class="docutils literal notranslate"><span class="pre">PostProcessing</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.__init__"><code class="docutils literal notranslate"><span class="pre">PostProcessing.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.check_accuracy"><code class="docutils literal notranslate"><span class="pre">PostProcessing.check_accuracy()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.check_reg_quality"><code class="docutils literal notranslate"><span class="pre">PostProcessing.check_reg_quality()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.compute_pce_moments"><code class="docutils literal notranslate"><span class="pre">PostProcessing.compute_pce_moments()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.plot_moments"><code class="docutils literal notranslate"><span class="pre">PostProcessing.plot_moments()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.plot_seq_design_diagnostics"><code class="docutils literal notranslate"><span class="pre">PostProcessing.plot_seq_design_diagnostics()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.sobol_indices"><code class="docutils literal notranslate"><span class="pre">PostProcessing.sobol_indices()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.post_processing.post_processing.PostProcessing.valid_metamodel"><code class="docutils literal notranslate"><span class="pre">PostProcessing.valid_metamodel()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
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+<span id="bayesvalidrox-post-processing-post-processing"></span><h1>bayesvalidrox.post_processing.post_processing<a class="headerlink" href="#module-bayesvalidrox.post_processing.post_processing" title="Link to this heading">¶</a></h1>
+<p class="rubric">Classes</p>
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+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing" title="bayesvalidrox.post_processing.post_processing.PostProcessing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PostProcessing</span></code></a>(engine[, name])</p></td>
+<td><p>This class provides many helper functions to post-process the trained meta-model.</p></td>
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+<h1>bayesvalidrox.pylink.pylink.PyLinkForwardModel<a class="headerlink" href="#bayesvalidrox-pylink-pylink-pylinkforwardmodel" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.pylink.pylink.</span></span><span class="sig-name descname"><span class="pre">PyLinkForwardModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">link_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pylink'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">py_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shell_command</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_template</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exe_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_file_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_parser</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cpus</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meas_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meas_file_valid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mc_ref_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_dict_valid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mc_ref_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>A forward model binder</p>
+<p>This calss serves as a code wrapper. This wrapper allows the execution of
+a third-party software/solver within the scope of BayesValidRox.</p>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>link_type<span class="classifier">str</span></dt><dd><p>The type of the wrapper. The default is <cite>‘pylink’</cite>. This runs the
+third-party software or an executable using a shell command with given
+input files.
+Second option is <cite>‘function’</cite> which assumed that model can be run using
+a function written separately in a Python script.</p>
+</dd>
+<dt>name<span class="classifier">str</span></dt><dd><p>Name of the model.</p>
+</dd>
+<dt>py_file<span class="classifier">str</span></dt><dd><p>Python file name without <cite>.py</cite> extension to be run for the <cite>‘function’</cite>
+wrapper. Note that the name of the python file and that of the function
+must be simillar. This function must recieve the parameters in an array
+of shape <cite>(n_samples, n_params)</cite> and returns a dictionary with the
+x_values and output arrays for given output names.</p>
+</dd>
+<dt>func_args<span class="classifier">dict</span></dt><dd><p>Additional arguments for the python file. The default is <cite>{}</cite>.</p>
+</dd>
+<dt>shell_command<span class="classifier">str</span></dt><dd><p>Shell command to be executed for the <cite>‘pylink’</cite> wrapper.</p>
+</dd>
+<dt>input_file<span class="classifier">str or list</span></dt><dd><p>The input file to be passed to the <cite>‘pylink’</cite> wrapper.</p>
+</dd>
+<dt>input_template<span class="classifier">str or list</span></dt><dd><p>A template input file to be passed to the <cite>‘pylink’</cite> wrapper. This file
+must be a copy of <cite>input_file</cite> with <cite><Xi></cite> place holder for the input
+parameters defined using <cite>inputs</cite> class, with i being the number of
+parameter. The file name ending should include <cite>.tpl</cite> before the actual
+extension of the input file, for example, <cite>params.tpl.input</cite>.</p>
+</dd>
+<dt>aux_file<span class="classifier">str or list</span></dt><dd><p>The list of auxiliary files needed for the <cite>‘pylink’</cite> wrapper.</p>
+</dd>
+<dt>exe_path<span class="classifier">str</span></dt><dd><p>Execution path if you wish to run the model for the <cite>‘pylink’</cite> wrapper
+in another directory. The default is <cite>None</cite>, which corresponds to the
+currecnt working directory.</p>
+</dd>
+<dt>output_file_names<span class="classifier">list of str</span></dt><dd><p>List of the name of the model output text files for the <cite>‘pylink’</cite>
+wrapper.</p>
+</dd>
+<dt>output_names<span class="classifier">list of str</span></dt><dd><p>List of the model outputs to be used for the analysis.</p>
+</dd>
+<dt>output_parser<span class="classifier">str</span></dt><dd><p>Name of the model parser file (without <cite>.py</cite> extension) that recieves
+the <cite>output_file_names</cite> and returns a 2d-array with the first row being
+the x_values, e.g. x coordinates or time and the rest of raws pass the
+simulation output for each model output defined in <cite>output_names</cite>. Note
+that again here the name of the file and that of the function must be
+the same.</p>
+</dd>
+<dt>multi_process: bool</dt><dd><p>Whether the model runs to be executed in parallel for the <cite>‘pylink’</cite>
+wrapper. The default is <cite>True</cite>.</p>
+</dd>
+<dt>n_cpus: int</dt><dd><p>The number of cpus to be used for the parallel model execution for the
+<cite>‘pylink’</cite> wrapper. The default is <cite>None</cite>, which corresponds to all
+available cpus.</p>
+</dd>
+<dt>meas_file<span class="classifier">str</span></dt><dd><p>The name of the measurement text-based file. This file must contain
+x_values as the first column and one column for each model output. The
+default is <cite>None</cite>. Only needed for the Bayesian Inference.</p>
+</dd>
+<dt>meas_file_valid<span class="classifier">str</span></dt><dd><p>The name of the measurement text-based file for the validation. The
+default is <cite>None</cite>. Only needed for the validation with Bayesian
+Inference.</p>
+</dd>
+<dt>mc_ref_file<span class="classifier">str</span></dt><dd><p>The name of the text file for the Monte-Carlo reference (mean and
+standard deviation) values. It must contain <cite>x_values</cite> as the first
+column, <cite>mean</cite> as the second column and <cite>std</cite> as the third. It can be
+used to compare the estimated moments using meta-model in the post-
+processing step. This is only available for one output.</p>
+</dd>
+<dt>obs_dict<span class="classifier">dict</span></dt><dd><p>A dictionary containing the measurement text-based file. It must
+contain <cite>x_values</cite> as the first item and one item for each model output
+. The default is <cite>{}</cite>. Only needed for the Bayesian Inference.</p>
+</dd>
+<dt>obs_dict_valid<span class="classifier">dict</span></dt><dd><p>A dictionary containing the validation measurement text-based file. It
+must contain <cite>x_values</cite> as the first item and one item for each model
+output. The default is <cite>{}</cite>.</p>
+</dd>
+<dt>mc_ref_dict<span class="classifier">dict</span></dt><dd><p>A dictionary containing the Monte-Carlo reference (mean and standard
+deviation) values. It must contain <cite>x_values</cite> as the first item and
+<cite>mean</cite> as the second item and <cite>std</cite> as the third. The default is <cite>{}</cite>.
+This is only available for one output.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">link_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pylink'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">py_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shell_command</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_template</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aux_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exe_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_file_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_parser</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cpus</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meas_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meas_file_valid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mc_ref_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_dict_valid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mc_ref_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.__init__" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([link_type, name, py_file, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_observation" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_observation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read_observation</span></code></a>([case])</p></td>
+<td><p>Reads/prepare the observation/measurement data for calibration.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_output" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_output"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read_output</span></code></a>()</p></td>
+<td><p>Reads the the parser output file and returns it as an</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_command" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_command"><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_command</span></code></a>(command, output_file_names)</p></td>
+<td><p>Runs the execution command given by the user to run the given model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_forwardmodel" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_forwardmodel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_forwardmodel</span></code></a>(xx)</p></td>
+<td><p>This function creates subdirectory for the current run and copies the necessary files to this directory and renames them.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_model_parallel" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_model_parallel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_model_parallel</span></code></a>(c_points[, prevRun_No, ...])</p></td>
+<td><p>Runs model simulations. If mp is true (default), then the simulations</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.uMBridge_model" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel.uMBridge_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uMBridge_model</span></code></a>(params)</p></td>
+<td><p>Function that calls a UMBridge model and transforms its output into the shape expected for the surrogate.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.update_input_params" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel.update_input_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_input_params</span></code></a>(new_input_file, param_set)</p></td>
+<td><p>Finds this pattern with <X1> in the new_input_file and replace it with</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.zip_subdirs" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel.zip_subdirs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zip_subdirs</span></code></a>(dir_name, key)</p></td>
+<td><p>Zips all the files containing the key(word).</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel.OutputData">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">OutputData</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parser</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.OutputData" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_observation">
+<span class="sig-name descname"><span class="pre">read_observation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">case</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'calib'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_observation" title="Link to this definition">¶</a></dt>
+<dd><p>Reads/prepare the observation/measurement data for
+calibration.</p>
+<section id="parameters">
+<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>case<span class="classifier">str</span></dt><dd><p>The type of observation to read in. Can be either ‘calib’,
+‘valid’ or ‘mc_ref’</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>DataFrame</dt><dd><p>A dataframe with the calibration data.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_output">
+<span class="sig-name descname"><span class="pre">read_output</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_output" title="Link to this definition">¶</a></dt>
+<dd><dl class="simple">
+<dt>Reads the the parser output file and returns it as an</dt><dd><p>executable function. It is required when the models returns the
+simulation outputs in csv files.</p>
+</dd>
+</dl>
+<section id="id1">
+<h3>Returns<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>Output<span class="classifier">func</span></dt><dd><p>Output parser function.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_command">
+<span class="sig-name descname"><span class="pre">run_command</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">command</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_file_names</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_command" title="Link to this definition">¶</a></dt>
+<dd><p>Runs the execution command given by the user to run the given model.
+It checks if the output files have been generated. If yes, the jobe is
+done and it extracts and returns the requested output(s). Otherwise,
+it executes the command again.</p>
+<section id="id2">
+<h3>Parameters<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>command<span class="classifier">str</span></dt><dd><p>The shell command to be executed.</p>
+</dd>
+<dt>output_file_names<span class="classifier">list</span></dt><dd><p>Name of the output file names.</p>
+</dd>
+</dl>
+</section>
+<section id="id3">
+<h3>Returns<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>simulation_outputs<span class="classifier">array of shape (n_obs, n_outputs)</span></dt><dd><p>Simulation outputs.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_forwardmodel">
+<span class="sig-name descname"><span class="pre">run_forwardmodel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">xx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_forwardmodel" title="Link to this definition">¶</a></dt>
+<dd><p>This function creates subdirectory for the current run and copies the
+necessary files to this directory and renames them. Next, it executes
+the given command.</p>
+<section id="id4">
+<h3>Parameters<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>xx<span class="classifier">tuple</span></dt><dd><p>A tuple including parameter set, simulation number and key string.</p>
+</dd>
+</dl>
+</section>
+<section id="id5">
+<h3>Returns<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>output<span class="classifier">array of shape (n_outputs+1, n_obs)</span></dt><dd><p>An array passed by the output paraser containing the x_values as
+the first row and the simulations results stored in the the rest of
+the array.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_model_parallel">
+<span class="sig-name descname"><span class="pre">run_model_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">c_points</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevRun_No</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_str</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_model_parallel" title="Link to this definition">¶</a></dt>
+<dd><dl class="simple">
+<dt>Runs model simulations. If mp is true (default), then the simulations</dt><dd><p>are started in parallel.</p>
+</dd>
+</dl>
+<section id="id6">
+<h3>Parameters<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>c_points<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Collocation points (training set).</p>
+</dd>
+<dt>prevRun_No<span class="classifier">int, optional</span></dt><dd><p>Previous run number, in case the sequential design is selected.
+The default is <cite>0</cite>.</p>
+</dd>
+<dt>key_str<span class="classifier">str, optional</span></dt><dd><p>A descriptive string for validation runs. The default is <cite>‘’</cite>.</p>
+</dd>
+<dt>mp<span class="classifier">bool, optional</span></dt><dd><p>Multiprocessing. The default is <cite>True</cite>.</p>
+</dd>
+<dt>verbose: bool, optional</dt><dd><p>Verbosity. The default is <cite>True</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id7">
+<h3>Returns<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>all_outputs<span class="classifier">dict</span></dt><dd><p>A dictionary with x values (time step or point id) and all outputs.
+Each key contains an array of the shape <cite>(n_samples, n_obs)</cite>.</p>
+</dd>
+<dt>new_c_points<span class="classifier">array</span></dt><dd><p>Updated collocation points (training set). If a simulation does not
+executed successfully, the parameter set is removed.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel.uMBridge_model">
+<span class="sig-name descname"><span class="pre">uMBridge_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.uMBridge_model" title="Link to this definition">¶</a></dt>
+<dd><p>Function that calls a UMBridge model and transforms its output into the
+shape expected for the surrogate.</p>
+<section id="id8">
+<h3>Parameters<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>params<span class="classifier">2d np.array, shape (#samples, #params)</span></dt><dd><p>The parameter values for which the model is run.</p>
+</dd>
+</dl>
+</section>
+<section id="id9">
+<h3>Returns<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>dict</dt><dd><p>The transformed model outputs.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel.update_input_params">
+<span class="sig-name descname"><span class="pre">update_input_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_input_file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_set</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.update_input_params" title="Link to this definition">¶</a></dt>
+<dd><dl class="simple">
+<dt>Finds this pattern with <X1> in the new_input_file and replace it with</dt><dd><p>the new value from the array param_sets.</p>
+</dd>
+</dl>
+<section id="id10">
+<h3>Parameters<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>new_input_file<span class="classifier">list</span></dt><dd><p>List of the input files with the adapted names.</p>
+</dd>
+<dt>param_set<span class="classifier">array of shape (n_params)</span></dt><dd><p>Parameter set.</p>
+</dd>
+</dl>
+</section>
+<section id="id11">
+<h3>Returns<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.PyLinkForwardModel.zip_subdirs">
+<span class="sig-name descname"><span class="pre">zip_subdirs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dir_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.zip_subdirs" title="Link to this definition">¶</a></dt>
+<dd><p>Zips all the files containing the key(word).</p>
+<section id="id12">
+<h3>Parameters<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>dir_name<span class="classifier">str</span></dt><dd><p>Directory name.</p>
+</dd>
+<dt>key<span class="classifier">str</span></dt><dd><p>Keyword to search for.</p>
+</dd>
+</dl>
+</section>
+<section id="id13">
+<h3>Returns<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
+ </div>
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+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.pylink.pylink.PyLinkForwardModel</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.__init__"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.OutputData"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel.OutputData</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_observation"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel.read_observation()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_output"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel.read_output()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_command"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel.run_command()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_forwardmodel"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel.run_forwardmodel()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_model_parallel"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel.run_model_parallel()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.uMBridge_model"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel.uMBridge_model()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.update_input_params"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel.update_input_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.PyLinkForwardModel.zip_subdirs"><code class="docutils literal notranslate"><span class="pre">PyLinkForwardModel.zip_subdirs()</span></code></a></li>
+</ul>
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+ <section id="module-bayesvalidrox.pylink.pylink">
+<span id="bayesvalidrox-pylink-pylink"></span><h1>bayesvalidrox.pylink.pylink<a class="headerlink" href="#module-bayesvalidrox.pylink.pylink" title="Link to this heading">¶</a></h1>
+<p>Calls to the model and evaluations</p>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.pylink.pylink.within_range.html#bayesvalidrox.pylink.pylink.within_range" title="bayesvalidrox.pylink.pylink.within_range"><code class="xref py py-obj docutils literal notranslate"><span class="pre">within_range</span></code></a>(out, minout, maxout)</p></td>
+<td><p>Checks if all the values in out lie between minout and maxout</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PyLinkForwardModel</span></code></a>([link_type, name, ...])</p></td>
+<td><p>A forward model binder</p></td>
+</tr>
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+</table>
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+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.pylink.pylink.within_range">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.pylink.pylink.</span></span><span class="sig-name descname"><span class="pre">within_range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">minout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maxout</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.pylink.pylink.within_range" title="Link to this definition">¶</a></dt>
+<dd><p>Checks if all the values in out lie between minout and maxout</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>out<span class="classifier">array or list</span></dt><dd><p>Data to check against range</p>
+</dd>
+<dt>minout<span class="classifier">int</span></dt><dd><p>Lower bound of the range</p>
+</dd>
+<dt>maxout<span class="classifier">int</span></dt><dd><p>Upper bound of the range</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>inside<span class="classifier">bool</span></dt><dd><p>True if all values in out are in the specified range</p>
+</dd>
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+<li><a class="reference internal" href="#">bayesvalidrox.pylink.pylink.within_range</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.pylink.pylink.within_range"><code class="docutils literal notranslate"><span class="pre">within_range()</span></code></a></li>
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+<h1>bayesvalidrox.surrogate_models.adaptPlot.adaptPlot<a class="headerlink" href="#bayesvalidrox-surrogate-models-adaptplot-adaptplot" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.adaptPlot.adaptPlot">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.adaptPlot.</span></span><span class="sig-name descname"><span class="pre">adaptPlot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">PCEModel</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y_Val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y_PC_Val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Y_PC_Val_std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plotED</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">SaveFig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.adaptPlot.adaptPlot" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
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+<span id="bayesvalidrox-surrogate-models-adaptplot"></span><h1>bayesvalidrox.surrogate_models.adaptPlot<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.adaptPlot" title="Link to this heading">¶</a></h1>
+<p>Created on Thu Aug 13 13:46:24 2020</p>
+<p>@author: farid</p>
+<p class="rubric">Functions</p>
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+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.html#bayesvalidrox.surrogate_models.adaptPlot.adaptPlot" title="bayesvalidrox.surrogate_models.adaptPlot.adaptPlot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adaptPlot</span></code></a>(PCEModel, Y_Val, Y_PC_Val, ...[, ...])</p></td>
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+ <section id="bayesvalidrox-surrogate-models-apoly-construction-apoly-construction">
+<h1>bayesvalidrox.surrogate_models.apoly_construction.apoly_construction<a class="headerlink" href="#bayesvalidrox-surrogate-models-apoly-construction-apoly-construction" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.apoly_construction.apoly_construction">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.apoly_construction.</span></span><span class="sig-name descname"><span class="pre">apoly_construction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">degree</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.apoly_construction.apoly_construction" title="Link to this definition">¶</a></dt>
+<dd><p>Construction of Data-driven Orthonormal Polynomial Basis
+Author: Dr.-Ing. habil. Sergey Oladyshkin
+Department of Stochastic Simulation and Safety Research for Hydrosystems
+Institute for Modelling Hydraulic and Environmental Systems
+Universitaet Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart
+E-mail: <a class="reference external" href="mailto:Sergey.Oladyshkin%40iws.uni-stuttgart.de">Sergey<span>.</span>Oladyshkin<span>@</span>iws<span>.</span>uni-stuttgart<span>.</span>de</a>
+<a class="reference external" href="http://www.iws-ls3.uni-stuttgart.de">http://www.iws-ls3.uni-stuttgart.de</a>
+The current script is based on definition of arbitrary polynomial chaos
+expansion (aPC), which is presented in the following manuscript:
+Oladyshkin, S. and W. Nowak. Data-driven uncertainty quantification using
+the arbitrary polynomial chaos expansion. Reliability Engineering & System
+Safety, Elsevier, V. 106, P. 179-190, 2012.
+DOI: 10.1016/j.ress.2012.05.002.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>Data<span class="classifier">array</span></dt><dd><p>Raw data.</p>
+</dd>
+<dt>degree<span class="classifier">int</span></dt><dd><p>Maximum polynomial degree.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>Polynomial<span class="classifier">array</span></dt><dd><p>The coefficients of the univariate orthonormal polynomials.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
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+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.apoly_construction.apoly_construction"><code class="docutils literal notranslate"><span class="pre">apoly_construction()</span></code></a></li>
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+<span id="bayesvalidrox-surrogate-models-apoly-construction"></span><h1>bayesvalidrox.surrogate_models.apoly_construction<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.apoly_construction" title="Link to this heading">¶</a></h1>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
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+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.html#bayesvalidrox.surrogate_models.apoly_construction.apoly_construction" title="bayesvalidrox.surrogate_models.apoly_construction.apoly_construction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apoly_construction</span></code></a>(Data, degree)</p></td>
+<td><p>Construction of Data-driven Orthonormal Polynomial Basis Author: Dr.-Ing.</p></td>
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+ <section id="bayesvalidrox-surrogate-models-bayes-linear-bayesianlinearregression">
+<h1>bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression<a class="headerlink" href="#bayesvalidrox-surrogate-models-bayes-linear-bayesianlinearregression" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.bayes_linear.</span></span><span class="sig-name descname"><span class="pre">BayesianLinearRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_iter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">RegressorMixin</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">LinearModel</span></code></p>
+<p>Superclass for Empirical Bayes and Variational Bayes implementations of
+Bayesian Linear Regression Model</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_iter</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.__init__" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(n_iter, tol, fit_intercept, copy_X, ...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.fit" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y)</p></td>
+<td><p>Fit model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_metadata_routing" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
+<td><p>Get metadata routing of this object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_params" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
+<td><p>Get parameters for this estimator.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X)</p></td>
+<td><p>Predict using the linear model.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict_dist" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_dist</span></code></a>(X)</p></td>
+<td><p>Calculates mean and variance of predictive distribution for each data point of test set.(Note predictive distribution for each data point is Gaussian, therefore it is uniquely determined by mean and variance)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.score" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y[, sample_weight])</p></td>
+<td><p>Return the coefficient of determination of the prediction.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_params" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
+<td><p>Set the parameters of this estimator.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_score_request" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_score_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_score_request</span></code></a>(*[, sample_weight])</p></td>
+<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.fit">
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.fit" title="Link to this definition">¶</a></dt>
+<dd><p>Fit model.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_metadata_routing">
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_metadata_routing" title="Link to this definition">¶</a></dt>
+<dd><p>Get metadata routing of this object.</p>
+<p>Please check <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>routing<span class="classifier">MetadataRequest</span></dt><dd><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code> encapsulating
+routing information.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_params">
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_params" title="Link to this definition">¶</a></dt>
+<dd><p>Get parameters for this estimator.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>deep<span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
+contained subobjects that are estimators.</p>
+</dd>
+</dl>
+</section>
+<section id="id1">
+<h2>Returns<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>params<span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict" title="Link to this definition">¶</a></dt>
+<dd><p>Predict using the linear model.</p>
+<section id="id2">
+<h2>Parameters<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>X<span class="classifier">array-like or sparse matrix, shape (n_samples, n_features)</span></dt><dd><p>Samples.</p>
+</dd>
+</dl>
+</section>
+<section id="id3">
+<h2>Returns<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>C<span class="classifier">array, shape (n_samples,)</span></dt><dd><p>Returns predicted values.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict_dist">
+<span class="sig-name descname"><span class="pre">predict_dist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict_dist" title="Link to this definition">¶</a></dt>
+<dd><p>Calculates mean and variance of predictive distribution for each data
+point of test set.(Note predictive distribution for each data point is
+Gaussian, therefore it is uniquely determined by mean and variance)</p>
+<section id="id4">
+<h2>Parameters<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>x: array-like of size (n_test_samples, n_features)</dt><dd><p>Set of features for which corresponding responses should be predicted</p>
+</dd>
+</dl>
+</section>
+<section id="id5">
+<h2>Returns<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h2>
+<p>:list of two numpy arrays [mu_pred, var_pred]</p>
+<blockquote>
+<div><dl class="simple">
+<dt>mu_pred<span class="classifier">numpy array of size (n_test_samples,)</span></dt><dd><p>Mean of predictive distribution</p>
+</dd>
+<dt>var_pred: numpy array of size (n_test_samples,)</dt><dd><p>Variance of predictive distribution</p>
+</dd>
+</dl>
+</div></blockquote>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.score">
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.score" title="Link to this definition">¶</a></dt>
+<dd><p>Return the coefficient of determination of the prediction.</p>
+<p>The coefficient of determination <span class="math notranslate nohighlight">\(R^2\)</span> is defined as
+<span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
+sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_pred)**</span> <span class="pre">2).sum()</span></code> and <span class="math notranslate nohighlight">\(v\)</span>
+is the total sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_true.mean())</span> <span class="pre">**</span> <span class="pre">2).sum()</span></code>.
+The best possible score is 1.0 and it can be negative (because the
+model can be arbitrarily worse). A constant model that always predicts
+the expected value of <cite>y</cite>, disregarding the input features, would get
+a <span class="math notranslate nohighlight">\(R^2\)</span> score of 0.0.</p>
+<section id="id6">
+<h2>Parameters<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>X<span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples. For some estimators this may be a precomputed
+kernel matrix or a list of generic objects instead with shape
+<code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_samples_fitted)</span></code>, where <code class="docutils literal notranslate"><span class="pre">n_samples_fitted</span></code>
+is the number of samples used in the fitting for the estimator.</p>
+</dd>
+<dt>y<span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for <cite>X</cite>.</p>
+</dd>
+<dt>sample_weight<span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
+</dd>
+</dl>
+</section>
+<section id="id7">
+<h2>Returns<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>score<span class="classifier">float</span></dt><dd><p><span class="math notranslate nohighlight">\(R^2\)</span> of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> w.r.t. <cite>y</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="notes">
+<h2>Notes<a class="headerlink" href="#notes" title="Link to this heading">¶</a></h2>
+<p>The <span class="math notranslate nohighlight">\(R^2\)</span> score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor uses
+<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
+with default value of <code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score()</span></code>.
+This influences the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput
+regressors (except for
+<code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code>).</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_params">
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_params" title="Link to this definition">¶</a></dt>
+<dd><p>Set the parameters of this estimator.</p>
+<p>The method works on simple estimators as well as on nested objects
+(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
+parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
+possible to update each component of a nested object.</p>
+<section id="id8">
+<h2>Parameters<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt><a href="#id9"><span class="problematic" id="id10">**</span></a>params<span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
+</dd>
+</dl>
+</section>
+<section id="id11">
+<h2>Returns<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>self<span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression"><span class="pre">BayesianLinearRegression</span></a></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_score_request" title="Link to this definition">¶</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<section id="id12">
+<h2>Parameters<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>sample_weight<span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+</dl>
+</section>
+<section id="id13">
+<h2>Returns<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>self<span class="classifier">object</span></dt><dd><p>The updated object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+ </article>
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+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression"><code class="docutils literal notranslate"><span class="pre">BayesianLinearRegression</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.__init__"><code class="docutils literal notranslate"><span class="pre">BayesianLinearRegression.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">BayesianLinearRegression.fit()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">BayesianLinearRegression.get_metadata_routing()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_params"><code class="docutils literal notranslate"><span class="pre">BayesianLinearRegression.get_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">BayesianLinearRegression.predict()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict_dist"><code class="docutils literal notranslate"><span class="pre">BayesianLinearRegression.predict_dist()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.score"><code class="docutils literal notranslate"><span class="pre">BayesianLinearRegression.score()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_params"><code class="docutils literal notranslate"><span class="pre">BayesianLinearRegression.set_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_score_request"><code class="docutils literal notranslate"><span class="pre">BayesianLinearRegression.set_score_request()</span></code></a></li>
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new file mode 100644
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+ <section id="bayesvalidrox-surrogate-models-bayes-linear-eblinearregression">
+<h1>bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression<a class="headerlink" href="#bayesvalidrox-surrogate-models-bayes-linear-eblinearregression" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.bayes_linear.</span></span><span class="sig-name descname"><span class="pre">EBLinearRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fp'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perfect_fit_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">BayesianLinearRegression</span></code></a></p>
+<p>Bayesian Regression with type II maximum likelihood (Empirical Bayes)</p>
+<section id="parameters">
+<h2>Parameters:<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>n_iter: int, optional (DEFAULT = 300)</dt><dd><p>Maximum number of iterations</p>
+</dd>
+<dt>tol: float, optional (DEFAULT = 1e-3)</dt><dd><p>Threshold for convergence</p>
+</dd>
+<dt>optimizer: str, optional (DEFAULT = ‘fp’)</dt><dd><p>Method for optimization , either Expectation Maximization or
+Fixed Point Gull-MacKay {‘em’,’fp’}. Fixed point iterations are
+faster, but can be numerically unstable (especially in case of near perfect fit).</p>
+</dd>
+<dt>fit_intercept: bool, optional (DEFAULT = True)</dt><dd><p>If True includes bias term in model</p>
+</dd>
+<dt>perfect_fit_tol: float (DEAFAULT = 1e-5)</dt><dd><p>Prevents overflow of precision parameters (this is smallest value RSS can have).
+( !!! Note if using EM instead of fixed-point, try smaller values
+of perfect_fit_tol, for better estimates of variance of predictive distribution )</p>
+</dd>
+<dt>alpha: float (DEFAULT = 1)</dt><dd><p>Initial value of precision paramter for coefficients ( by default we define
+very broad distribution )</p>
+</dd>
+<dt>copy_X<span class="classifier">boolean, optional (DEFAULT = True)</span></dt><dd><p>If True, X will be copied, otherwise will be</p>
+</dd>
+<dt>verbose: bool, optional (Default = False)</dt><dd><p>If True at each iteration progress report is printed out</p>
+</dd>
+</dl>
+</section>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt><a href="#id19"><span class="problematic" id="id20">coef_</span></a><span class="classifier">array, shape = (n_features)</span></dt><dd><p>Coefficients of the regression model (mean of posterior distribution)</p>
+</dd>
+<dt><a href="#id21"><span class="problematic" id="id22">intercept_</span></a>: float</dt><dd><p>Value of bias term (if fit_intercept is False, then <a href="#id23"><span class="problematic" id="id24">intercept_</span></a> = 0)</p>
+</dd>
+<dt><a href="#id25"><span class="problematic" id="id26">alpha_</span></a><span class="classifier">float</span></dt><dd><p>Estimated precision of coefficients</p>
+</dd>
+<dt><a href="#id27"><span class="problematic" id="id28">beta_</span></a><span class="classifier">float </span></dt><dd><p>Estimated precision of noise</p>
+</dd>
+<dt><a href="#id29"><span class="problematic" id="id30">eigvals_</span></a><span class="classifier">array, shape = (n_features, )</span></dt><dd><p>Eigenvalues of covariance matrix (from posterior distribution of weights)</p>
+</dd>
+<dt><a href="#id31"><span class="problematic" id="id32">eigvecs_</span></a><span class="classifier">array, shape = (n_features, n_featues)</span></dt><dd><p>Eigenvectors of covariance matrix (from posterior distribution of weights)</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fp'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perfect_fit_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.__init__" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([n_iter, tol, optimizer, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.fit" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y)</p></td>
+<td><p>Fits Bayesian Linear Regression using Empirical Bayes</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_metadata_routing" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
+<td><p>Get metadata routing of this object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_params" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
+<td><p>Get parameters for this estimator.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X[, return_std])</p></td>
+<td><p>Computes predictive distribution for test set.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict_dist" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_dist</span></code></a>(X)</p></td>
+<td><p>Calculates mean and variance of predictive distribution for each data point of test set.(Note predictive distribution for each data point is Gaussian, therefore it is uniquely determined by mean and variance)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.score" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y[, sample_weight])</p></td>
+<td><p>Return the coefficient of determination of the prediction.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_params" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
+<td><p>Set the parameters of this estimator.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_predict_request" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_predict_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_predict_request</span></code></a>(*[, return_std])</p></td>
+<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_score_request" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_score_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_score_request</span></code></a>(*[, sample_weight])</p></td>
+<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.fit" title="Link to this definition">¶</a></dt>
+<dd><p>Fits Bayesian Linear Regression using Empirical Bayes</p>
+<section id="id1">
+<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X: array-like of size [n_samples,n_features]</dt><dd><p>Matrix of explanatory variables (should not include bias term)</p>
+</dd>
+<dt>y: array-like of size [n_features]</dt><dd><p>Vector of dependent variables.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>object: self</dt><dd><p>self</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_metadata_routing">
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_metadata_routing" title="Link to this definition">¶</a></dt>
+<dd><p>Get metadata routing of this object.</p>
+<p>Please check <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<section id="id2">
+<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>routing<span class="classifier">MetadataRequest</span></dt><dd><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code> encapsulating
+routing information.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_params">
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_params" title="Link to this definition">¶</a></dt>
+<dd><p>Get parameters for this estimator.</p>
+<section id="id3">
+<h3>Parameters<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>deep<span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
+contained subobjects that are estimators.</p>
+</dd>
+</dl>
+</section>
+<section id="id4">
+<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>params<span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict" title="Link to this definition">¶</a></dt>
+<dd><p>Computes predictive distribution for test set.
+Predictive distribution for each data point is one dimensional
+Gaussian and therefore is characterised by mean and variance.</p>
+<section id="id5">
+<h3>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X: {array-like, sparse} (n_samples_test, n_features)</dt><dd><p>Test data, matrix of explanatory variables</p>
+</dd>
+</dl>
+</section>
+<section id="id6">
+<h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<p>: list of length two [y_hat, var_hat]</p>
+<blockquote>
+<div><dl class="simple">
+<dt>y_hat: numpy array of size (n_samples_test,)</dt><dd><p>Estimated values of targets on test set (i.e. mean of predictive
+distribution)</p>
+</dd>
+<dt>var_hat: numpy array of size (n_samples_test,)</dt><dd><p>Variance of predictive distribution</p>
+</dd>
+</dl>
+</div></blockquote>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict_dist">
+<span class="sig-name descname"><span class="pre">predict_dist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict_dist" title="Link to this definition">¶</a></dt>
+<dd><p>Calculates mean and variance of predictive distribution for each data
+point of test set.(Note predictive distribution for each data point is
+Gaussian, therefore it is uniquely determined by mean and variance)</p>
+<section id="id7">
+<h3>Parameters<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>x: array-like of size (n_test_samples, n_features)</dt><dd><p>Set of features for which corresponding responses should be predicted</p>
+</dd>
+</dl>
+</section>
+<section id="id8">
+<h3>Returns<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<p>:list of two numpy arrays [mu_pred, var_pred]</p>
+<blockquote>
+<div><dl class="simple">
+<dt>mu_pred<span class="classifier">numpy array of size (n_test_samples,)</span></dt><dd><p>Mean of predictive distribution</p>
+</dd>
+<dt>var_pred: numpy array of size (n_test_samples,)</dt><dd><p>Variance of predictive distribution</p>
+</dd>
+</dl>
+</div></blockquote>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.score">
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.score" title="Link to this definition">¶</a></dt>
+<dd><p>Return the coefficient of determination of the prediction.</p>
+<p>The coefficient of determination <span class="math notranslate nohighlight">\(R^2\)</span> is defined as
+<span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
+sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_pred)**</span> <span class="pre">2).sum()</span></code> and <span class="math notranslate nohighlight">\(v\)</span>
+is the total sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_true.mean())</span> <span class="pre">**</span> <span class="pre">2).sum()</span></code>.
+The best possible score is 1.0 and it can be negative (because the
+model can be arbitrarily worse). A constant model that always predicts
+the expected value of <cite>y</cite>, disregarding the input features, would get
+a <span class="math notranslate nohighlight">\(R^2\)</span> score of 0.0.</p>
+<section id="id9">
+<h3>Parameters<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples. For some estimators this may be a precomputed
+kernel matrix or a list of generic objects instead with shape
+<code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_samples_fitted)</span></code>, where <code class="docutils literal notranslate"><span class="pre">n_samples_fitted</span></code>
+is the number of samples used in the fitting for the estimator.</p>
+</dd>
+<dt>y<span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for <cite>X</cite>.</p>
+</dd>
+<dt>sample_weight<span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
+</dd>
+</dl>
+</section>
+<section id="id10">
+<h3>Returns<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>score<span class="classifier">float</span></dt><dd><p><span class="math notranslate nohighlight">\(R^2\)</span> of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> w.r.t. <cite>y</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="notes">
+<h3>Notes<a class="headerlink" href="#notes" title="Link to this heading">¶</a></h3>
+<p>The <span class="math notranslate nohighlight">\(R^2\)</span> score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor uses
+<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
+with default value of <code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score()</span></code>.
+This influences the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput
+regressors (except for
+<code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code>).</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_params">
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_params" title="Link to this definition">¶</a></dt>
+<dd><p>Set the parameters of this estimator.</p>
+<p>The method works on simple estimators as well as on nested objects
+(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
+parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
+possible to update each component of a nested object.</p>
+<section id="id11">
+<h3>Parameters<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt><a href="#id12"><span class="problematic" id="id13">**</span></a>params<span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
+</dd>
+</dl>
+</section>
+<section id="id14">
+<h3>Returns<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_predict_request">
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression"><span class="pre">EBLinearRegression</span></a></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_predict_request" title="Link to this definition">¶</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">predict</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<section id="id15">
+<h3>Parameters<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>return_std<span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">return_std</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p>
+</dd>
+</dl>
+</section>
+<section id="id16">
+<h3>Returns<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">object</span></dt><dd><p>The updated object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression"><span class="pre">EBLinearRegression</span></a></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_score_request" title="Link to this definition">¶</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<section id="id17">
+<h3>Parameters<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>sample_weight<span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+</dl>
+</section>
+<section id="id18">
+<h3>Returns<a class="headerlink" href="#id18" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">object</span></dt><dd><p>The updated object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
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+ <div class="title">bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression</div>
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+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.__init__"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression.fit()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression.get_metadata_routing()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_params"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression.get_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression.predict()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict_dist"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression.predict_dist()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.score"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression.score()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_params"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression.set_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_predict_request"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression.set_predict_request()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_score_request"><code class="docutils literal notranslate"><span class="pre">EBLinearRegression.set_score_request()</span></code></a></li>
+</ul>
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+ <section id="bayesvalidrox-surrogate-models-bayes-linear-vblinearregression">
+<h1>bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression<a class="headerlink" href="#bayesvalidrox-surrogate-models-bayes-linear-vblinearregression" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.bayes_linear.</span></span><span class="sig-name descname"><span class="pre">VBLinearRegression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression"><code class="xref py py-class docutils literal notranslate"><span class="pre">BayesianLinearRegression</span></code></a></p>
+<p>Implements Bayesian Linear Regression using mean-field approximation.
+Assumes gamma prior on precision parameters of coefficients and noise.</p>
+<section id="parameters">
+<h2>Parameters:<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>n_iter: int, optional (DEFAULT = 100)</dt><dd><p>Maximum number of iterations for KL minimization</p>
+</dd>
+<dt>tol: float, optional (DEFAULT = 1e-3)</dt><dd><p>Convergence threshold</p>
+</dd>
+<dt>fit_intercept: bool, optional (DEFAULT = True)</dt><dd><p>If True will use bias term in model fitting</p>
+</dd>
+<dt>a: float, optional (Default = 1e-4)</dt><dd><p>Shape parameter of Gamma prior for precision of coefficients</p>
+</dd>
+<dt>b: float, optional (Default = 1e-4)</dt><dd><p>Rate parameter of Gamma prior for precision coefficients</p>
+</dd>
+<dt>c: float, optional (Default = 1e-4)</dt><dd><p>Shape parameter of Gamma prior for precision of noise</p>
+</dd>
+<dt>d: float, optional (Default = 1e-4)</dt><dd><p>Rate parameter of Gamma prior for precision of noise</p>
+</dd>
+<dt>verbose: bool, optional (Default = False)</dt><dd><p>If True at each iteration progress report is printed out</p>
+</dd>
+</dl>
+</section>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt><a href="#id19"><span class="problematic" id="id20">coef_</span></a><span class="classifier">array, shape = (n_features)</span></dt><dd><p>Coefficients of the regression model (mean of posterior distribution)</p>
+</dd>
+<dt><a href="#id21"><span class="problematic" id="id22">intercept_</span></a>: float</dt><dd><p>Value of bias term (if fit_intercept is False, then <a href="#id23"><span class="problematic" id="id24">intercept_</span></a> = 0)</p>
+</dd>
+<dt><a href="#id25"><span class="problematic" id="id26">alpha_</span></a><span class="classifier">float</span></dt><dd><p>Mean of precision of coefficients</p>
+</dd>
+<dt><a href="#id27"><span class="problematic" id="id28">beta_</span></a><span class="classifier">float </span></dt><dd><p>Mean of precision of noise</p>
+</dd>
+<dt><a href="#id29"><span class="problematic" id="id30">eigvals_</span></a><span class="classifier">array, shape = (n_features, )</span></dt><dd><p>Eigenvalues of covariance matrix (from posterior distribution of weights)</p>
+</dd>
+<dt><a href="#id31"><span class="problematic" id="id32">eigvecs_</span></a><span class="classifier">array, shape = (n_features, n_featues)</span></dt><dd><p>Eigenvectors of covariance matrix (from posterior distribution of weights)</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.__init__" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([n_iter, tol, fit_intercept, a, b, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.fit" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y)</p></td>
+<td><p>Fits Variational Bayesian Linear Regression Model</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_metadata_routing" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
+<td><p>Get metadata routing of this object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_params" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
+<td><p>Get parameters for this estimator.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X[, return_std])</p></td>
+<td><p>Computes predictive distribution for test set.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict_dist" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict_dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_dist</span></code></a>(X)</p></td>
+<td><p>Calculates mean and variance of predictive distribution for each data point of test set.(Note predictive distribution for each data point is Gaussian, therefore it is uniquely determined by mean and variance)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.score" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y[, sample_weight])</p></td>
+<td><p>Return the coefficient of determination of the prediction.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_params" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
+<td><p>Set the parameters of this estimator.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_predict_request" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_predict_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_predict_request</span></code></a>(*[, return_std])</p></td>
+<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_score_request" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_score_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_score_request</span></code></a>(*[, sample_weight])</p></td>
+<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.fit" title="Link to this definition">¶</a></dt>
+<dd><p>Fits Variational Bayesian Linear Regression Model</p>
+<section id="id1">
+<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X: array-like of size [n_samples,n_features]</dt><dd><p>Matrix of explanatory variables (should not include bias term)</p>
+</dd>
+<dt>Y: array-like of size [n_features]</dt><dd><p>Vector of dependent variables.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>object: self</dt><dd><p>self</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_metadata_routing">
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_metadata_routing" title="Link to this definition">¶</a></dt>
+<dd><p>Get metadata routing of this object.</p>
+<p>Please check <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<section id="id2">
+<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>routing<span class="classifier">MetadataRequest</span></dt><dd><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code> encapsulating
+routing information.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_params">
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_params" title="Link to this definition">¶</a></dt>
+<dd><p>Get parameters for this estimator.</p>
+<section id="id3">
+<h3>Parameters<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>deep<span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
+contained subobjects that are estimators.</p>
+</dd>
+</dl>
+</section>
+<section id="id4">
+<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>params<span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict" title="Link to this definition">¶</a></dt>
+<dd><p>Computes predictive distribution for test set.
+Predictive distribution for each data point is one dimensional
+Gaussian and therefore is characterised by mean and variance.</p>
+<section id="id5">
+<h3>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X: {array-like, sparse} (n_samples_test, n_features)</dt><dd><p>Test data, matrix of explanatory variables</p>
+</dd>
+</dl>
+</section>
+<section id="id6">
+<h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<p>: list of length two [y_hat, var_hat]</p>
+<blockquote>
+<div><dl class="simple">
+<dt>y_hat: numpy array of size (n_samples_test,)</dt><dd><p>Estimated values of targets on test set (i.e. mean of predictive
+distribution)</p>
+</dd>
+<dt>var_hat: numpy array of size (n_samples_test,)</dt><dd><p>Variance of predictive distribution</p>
+</dd>
+</dl>
+</div></blockquote>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict_dist">
+<span class="sig-name descname"><span class="pre">predict_dist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict_dist" title="Link to this definition">¶</a></dt>
+<dd><p>Calculates mean and variance of predictive distribution for each data
+point of test set.(Note predictive distribution for each data point is
+Gaussian, therefore it is uniquely determined by mean and variance)</p>
+<section id="id7">
+<h3>Parameters<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>x: array-like of size (n_test_samples, n_features)</dt><dd><p>Set of features for which corresponding responses should be predicted</p>
+</dd>
+</dl>
+</section>
+<section id="id8">
+<h3>Returns<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<p>:list of two numpy arrays [mu_pred, var_pred]</p>
+<blockquote>
+<div><dl class="simple">
+<dt>mu_pred<span class="classifier">numpy array of size (n_test_samples,)</span></dt><dd><p>Mean of predictive distribution</p>
+</dd>
+<dt>var_pred: numpy array of size (n_test_samples,)</dt><dd><p>Variance of predictive distribution</p>
+</dd>
+</dl>
+</div></blockquote>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.score">
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.score" title="Link to this definition">¶</a></dt>
+<dd><p>Return the coefficient of determination of the prediction.</p>
+<p>The coefficient of determination <span class="math notranslate nohighlight">\(R^2\)</span> is defined as
+<span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
+sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_pred)**</span> <span class="pre">2).sum()</span></code> and <span class="math notranslate nohighlight">\(v\)</span>
+is the total sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_true.mean())</span> <span class="pre">**</span> <span class="pre">2).sum()</span></code>.
+The best possible score is 1.0 and it can be negative (because the
+model can be arbitrarily worse). A constant model that always predicts
+the expected value of <cite>y</cite>, disregarding the input features, would get
+a <span class="math notranslate nohighlight">\(R^2\)</span> score of 0.0.</p>
+<section id="id9">
+<h3>Parameters<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples. For some estimators this may be a precomputed
+kernel matrix or a list of generic objects instead with shape
+<code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_samples_fitted)</span></code>, where <code class="docutils literal notranslate"><span class="pre">n_samples_fitted</span></code>
+is the number of samples used in the fitting for the estimator.</p>
+</dd>
+<dt>y<span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for <cite>X</cite>.</p>
+</dd>
+<dt>sample_weight<span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
+</dd>
+</dl>
+</section>
+<section id="id10">
+<h3>Returns<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>score<span class="classifier">float</span></dt><dd><p><span class="math notranslate nohighlight">\(R^2\)</span> of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> w.r.t. <cite>y</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="notes">
+<h3>Notes<a class="headerlink" href="#notes" title="Link to this heading">¶</a></h3>
+<p>The <span class="math notranslate nohighlight">\(R^2\)</span> score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor uses
+<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
+with default value of <code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score()</span></code>.
+This influences the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput
+regressors (except for
+<code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code>).</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_params">
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_params" title="Link to this definition">¶</a></dt>
+<dd><p>Set the parameters of this estimator.</p>
+<p>The method works on simple estimators as well as on nested objects
+(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
+parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
+possible to update each component of a nested object.</p>
+<section id="id11">
+<h3>Parameters<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt><a href="#id12"><span class="problematic" id="id13">**</span></a>params<span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
+</dd>
+</dl>
+</section>
+<section id="id14">
+<h3>Returns<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_predict_request">
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression"><span class="pre">VBLinearRegression</span></a></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_predict_request" title="Link to this definition">¶</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">predict</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<section id="id15">
+<h3>Parameters<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>return_std<span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">return_std</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p>
+</dd>
+</dl>
+</section>
+<section id="id16">
+<h3>Returns<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">object</span></dt><dd><p>The updated object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression"><span class="pre">VBLinearRegression</span></a></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_score_request" title="Link to this definition">¶</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<section id="id17">
+<h3>Parameters<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>sample_weight<span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+</dl>
+</section>
+<section id="id18">
+<h3>Returns<a class="headerlink" href="#id18" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">object</span></dt><dd><p>The updated object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
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+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.__init__"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.fit"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression.fit()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression.get_metadata_routing()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_params"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression.get_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression.predict()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict_dist"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression.predict_dist()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.score"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression.score()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_params"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression.set_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_predict_request"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression.set_predict_request()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_score_request"><code class="docutils literal notranslate"><span class="pre">VBLinearRegression.set_score_request()</span></code></a></li>
+</ul>
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+<h1>bayesvalidrox.surrogate_models.bayes_linear.gamma_mean<a class="headerlink" href="#bayesvalidrox-surrogate-models-bayes-linear-gamma-mean" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.bayes_linear.gamma_mean">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.bayes_linear.</span></span><span class="sig-name descname"><span class="pre">gamma_mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.bayes_linear.gamma_mean" title="Link to this definition">¶</a></dt>
+<dd><p>Computes mean of gamma distribution</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>a: float</dt><dd><p>Shape parameter of Gamma distribution</p>
+</dd>
+<dt>b: float</dt><dd><p>Rate parameter of Gamma distribution</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>: float</dt><dd><p>Mean of Gamma distribution</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
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+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.bayes_linear.gamma_mean"><code class="docutils literal notranslate"><span class="pre">gamma_mean()</span></code></a></li>
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+ <section id="module-bayesvalidrox.surrogate_models.bayes_linear">
+<span id="bayesvalidrox-surrogate-models-bayes-linear"></span><h1>bayesvalidrox.surrogate_models.bayes_linear<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.bayes_linear" title="Link to this heading">¶</a></h1>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.html#bayesvalidrox.surrogate_models.bayes_linear.gamma_mean" title="bayesvalidrox.surrogate_models.bayes_linear.gamma_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gamma_mean</span></code></a>(a, b)</p></td>
+<td><p>Computes mean of gamma distribution</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression" title="bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BayesianLinearRegression</span></code></a>(n_iter, tol, ...)</p></td>
+<td><p>Superclass for Empirical Bayes and Variational Bayes implementations of Bayesian Linear Regression Model</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression" title="bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EBLinearRegression</span></code></a>([n_iter, tol, optimizer, ...])</p></td>
+<td><p>Bayesian Regression with type II maximum likelihood (Empirical Bayes)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression" title="bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VBLinearRegression</span></code></a>([n_iter, tol, ...])</p></td>
+<td><p>Implements Bayesian Linear Regression using mean-field approximation.</p></td>
+</tr>
+</tbody>
+</table>
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+ <section id="bayesvalidrox-surrogate-models-engine-engine">
+<h1>bayesvalidrox.surrogate_models.engine.Engine<a class="headerlink" href="#bayesvalidrox-surrogate-models-engine-engine" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.engine.</span></span><span class="sig-name descname"><span class="pre">Engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MetaMod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ExpDes</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">MetaMod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ExpDes</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.__init__" title="bayesvalidrox.surrogate_models.engine.Engine.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(MetaMod, Model, ExpDes)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.choose_next_sample" title="bayesvalidrox.surrogate_models.engine.Engine.choose_next_sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">choose_next_sample</span></code></a>([sigma2, n_candidates, var])</p></td>
+<td><p>Runs optimal sequential design.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.dual_annealing" title="bayesvalidrox.surrogate_models.engine.Engine.dual_annealing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dual_annealing</span></code></a>(method, Bounds, sigma2Dict, ...)</p></td>
+<td><p>Exploration algorithm to find the optimum parameter space.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.eval_metamodel" title="bayesvalidrox.surrogate_models.engine.Engine.eval_metamodel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_metamodel</span></code></a>([samples, nsamples, ...])</p></td>
+<td><p>Evaluates metamodel at the requested samples.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.run_util_func" title="bayesvalidrox.surrogate_models.engine.Engine.run_util_func"><code class="xref py py-obj docutils literal notranslate"><span class="pre">run_util_func</span></code></a>(method, candidates, index[, ...])</p></td>
+<td><p>Runs the utility function based on the given method.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.start_engine" title="bayesvalidrox.surrogate_models.engine.Engine.start_engine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">start_engine</span></code></a>()</p></td>
+<td><p>Do all the preparations that need to be run before the actual training</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.tradeoff_weights" title="bayesvalidrox.surrogate_models.engine.Engine.tradeoff_weights"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tradeoff_weights</span></code></a>(tradeoff_scheme, old_EDX, ...)</p></td>
+<td><p>Calculates weights for exploration scores based on the requested scheme: <cite>None</cite>, <cite>equal</cite>, <cite>epsilon-decreasing</cite> and <cite>adaptive</cite>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.train_normal" title="bayesvalidrox.surrogate_models.engine.Engine.train_normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train_normal</span></code></a>([parallel, verbose, save])</p></td>
+<td><p>Trains surrogate on static samples only.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.train_seq_design" title="bayesvalidrox.surrogate_models.engine.Engine.train_seq_design"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train_seq_design</span></code></a>([parallel, verbose])</p></td>
+<td><p>Starts the adaptive sequential design for refining the surrogate model by selecting training points in a sequential manner.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.train_sequential" title="bayesvalidrox.surrogate_models.engine.Engine.train_sequential"><code class="xref py py-obj docutils literal notranslate"><span class="pre">train_sequential</span></code></a>([parallel, verbose])</p></td>
+<td><p>Train the surrogate in a sequential manner.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.util_AlphOptDesign" title="bayesvalidrox.surrogate_models.engine.Engine.util_AlphOptDesign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">util_AlphOptDesign</span></code></a>(candidates[, var])</p></td>
+<td><p>Enriches the Experimental design with the requested alphabetic criterion based on exploring the space with number of sampling points.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.util_BayesianActiveDesign" title="bayesvalidrox.surrogate_models.engine.Engine.util_BayesianActiveDesign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">util_BayesianActiveDesign</span></code></a>(y_hat, std, sigma2Dict)</p></td>
+<td><p>Computes scores based on Bayesian active design criterion (var).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.util_BayesianDesign" title="bayesvalidrox.surrogate_models.engine.Engine.util_BayesianDesign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">util_BayesianDesign</span></code></a>(X_can, X_MC, sigma2Dict)</p></td>
+<td><p>Computes scores based on Bayesian sequential design criterion (var).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.util_VarBasedDesign" title="bayesvalidrox.surrogate_models.engine.Engine.util_VarBasedDesign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">util_VarBasedDesign</span></code></a>(X_can, index[, util_func])</p></td>
+<td><p>Computes the exploitation scores based on: active learning MacKay(ALM) and active learning Cohn (ALC) Paper: Sequential Design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami Model by Beck and Guillas (2016)</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.choose_next_sample">
+<span class="sig-name descname"><span class="pre">choose_next_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_candidates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'DKL'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.choose_next_sample" title="Link to this definition">¶</a></dt>
+<dd><p>Runs optimal sequential design.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>sigma2<span class="classifier">dict, optional</span></dt><dd><p>A dictionary containing the measurement errors (sigma^2). The
+default is None.</p>
+</dd>
+<dt>n_candidates<span class="classifier">int, optional</span></dt><dd><p>Number of candidate samples. The default is 5.</p>
+</dd>
+<dt>var<span class="classifier">string, optional</span></dt><dd><p>Utility function. The default is None. # TODO: default is set to DKL, not none</p>
+</dd>
+</dl>
+</section>
+<section id="raises">
+<h2>Raises<a class="headerlink" href="#raises" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>NameError</dt><dd><p>Wrong utility function.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>Xnew<span class="classifier">array (n_samples, n_params)</span></dt><dd><p>Selected new training point(s).</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.dual_annealing">
+<span class="sig-name descname"><span class="pre">dual_annealing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Bounds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma2Dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Run_No</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.dual_annealing" title="Link to this definition">¶</a></dt>
+<dd><p>Exploration algorithm to find the optimum parameter space.</p>
+<section id="id1">
+<h2>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>method<span class="classifier">string</span></dt><dd><p>Exploitation method: <cite>VarOptDesign</cite>, <cite>BayesActDesign</cite> and
+<cite>BayesOptDesign</cite>.</p>
+</dd>
+<dt>Bounds<span class="classifier">list of tuples</span></dt><dd><p>List of lower and upper boundaries of parameters.</p>
+</dd>
+<dt>sigma2Dict<span class="classifier">dict</span></dt><dd><p>A dictionary containing the measurement errors (sigma^2).</p>
+</dd>
+</dl>
+<p>var : unknown
+Run_No : int</p>
+<blockquote>
+<div><p>Run number.</p>
+</div></blockquote>
+<dl class="simple">
+<dt>verbose<span class="classifier">bool, optional</span></dt><dd><p>Print out a summary. The default is False.</p>
+</dd>
+</dl>
+</section>
+<section id="id2">
+<h2>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>Run_No<span class="classifier">int</span></dt><dd><p>Run number.</p>
+</dd>
+<dt>array</dt><dd><p>Optimial candidate.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.eval_metamodel">
+<span class="sig-name descname"><span class="pre">eval_metamodel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nsamples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.eval_metamodel" title="Link to this definition">¶</a></dt>
+<dd><p>Evaluates metamodel at the requested samples. One can also generate
+nsamples.</p>
+<section id="id3">
+<h2>Parameters<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>samples<span class="classifier">array of shape (n_samples, n_params), optional</span></dt><dd><p>Samples to evaluate metamodel at. The default is None.</p>
+</dd>
+<dt>nsamples<span class="classifier">int, optional</span></dt><dd><p>Number of samples to generate, if no <cite>samples</cite> is provided. The
+default is None.</p>
+</dd>
+<dt>sampling_method<span class="classifier">str, optional</span></dt><dd><p>Type of sampling, if no <cite>samples</cite> is provided. The default is
+‘random’.</p>
+</dd>
+<dt>return_samples<span class="classifier">bool, optional</span></dt><dd><p>Retun samples, if no <cite>samples</cite> is provided. The default is False.</p>
+</dd>
+</dl>
+</section>
+<section id="id4">
+<h2>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>mean_pred<span class="classifier">dict</span></dt><dd><p>Mean of the predictions.</p>
+</dd>
+<dt>std_pred<span class="classifier">dict</span></dt><dd><p>Standard deviatioon of the predictions.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.run_util_func">
+<span class="sig-name descname"><span class="pre">run_util_func</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">candidates</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma2Dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_MC</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.run_util_func" title="Link to this definition">¶</a></dt>
+<dd><p>Runs the utility function based on the given method.</p>
+<section id="id5">
+<h2>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>method<span class="classifier">string</span></dt><dd><p>Exploitation method: <cite>VarOptDesign</cite>, <cite>BayesActDesign</cite> and
+<cite>BayesOptDesign</cite>.</p>
+</dd>
+<dt>candidates<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>All candidate parameter sets.</p>
+</dd>
+<dt>index<span class="classifier">int</span></dt><dd><p>ExpDesign index.</p>
+</dd>
+<dt>sigma2Dict<span class="classifier">dict, optional</span></dt><dd><p>A dictionary containing the measurement errors (sigma^2). The
+default is None.</p>
+</dd>
+<dt>var<span class="classifier">string, optional</span></dt><dd><p>Utility function. The default is None.</p>
+</dd>
+<dt>X_MC<span class="classifier">TYPE, optional</span></dt><dd><p>DESCRIPTION. The default is None.</p>
+</dd>
+</dl>
+</section>
+<section id="id6">
+<h2>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>index<span class="classifier">TYPE</span></dt><dd><p>DESCRIPTION.</p>
+</dd>
+<dt>List</dt><dd><p>Scores.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.start_engine">
+<span class="sig-name descname"><span class="pre">start_engine</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.start_engine" title="Link to this definition">¶</a></dt>
+<dd><p>Do all the preparations that need to be run before the actual training</p>
+<section id="id7">
+<h2>Returns<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h2>
+<p>None</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.tradeoff_weights">
+<span class="sig-name descname"><span class="pre">tradeoff_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tradeoff_scheme</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old_EDX</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old_EDY</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.tradeoff_weights" title="Link to this definition">¶</a></dt>
+<dd><p>Calculates weights for exploration scores based on the requested
+scheme: <cite>None</cite>, <cite>equal</cite>, <cite>epsilon-decreasing</cite> and <cite>adaptive</cite>.</p>
+<p><cite>None</cite>: No exploration.
+<cite>equal</cite>: Same weights for exploration and exploitation scores.
+<cite>epsilon-decreasing</cite>: Start with more exploration and increase the</p>
+<blockquote>
+<div><p>influence of exploitation along the way with an exponential decay
+function</p>
+</div></blockquote>
+<dl class="simple">
+<dt><cite>adaptive</cite>: An adaptive method based on:</dt><dd><p>Liu, Haitao, Jianfei Cai, and Yew-Soon Ong. “An adaptive sampling
+approach for Kriging metamodeling by maximizing expected prediction
+error.†Computers & Chemical Engineering 106 (2017): 171-182.</p>
+</dd>
+</dl>
+<section id="id8">
+<h2>Parameters<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>tradeoff_scheme<span class="classifier">string</span></dt><dd><p>Trade-off scheme for exloration and exploitation scores.</p>
+</dd>
+<dt>old_EDX<span class="classifier">array (n_samples, n_params)</span></dt><dd><p>Old experimental design (training points).</p>
+</dd>
+<dt>old_EDY<span class="classifier">dict</span></dt><dd><p>Old model responses (targets).</p>
+</dd>
+</dl>
+</section>
+<section id="id9">
+<h2>Returns<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>exploration_weight<span class="classifier">float</span></dt><dd><p>Exploration weight.</p>
+</dd>
+<dt>exploitation_weight: float</dt><dd><p>Exploitation weight.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.train_normal">
+<span class="sig-name descname"><span class="pre">train_normal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parallel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.train_normal" title="Link to this definition">¶</a></dt>
+<dd><p>Trains surrogate on static samples only.
+Samples are taken from the experimental design and the specified
+model is run on them.
+Alternatively the samples can be read in from a provided hdf5 file.</p>
+<section id="id10">
+<h2>Returns<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h2>
+<p>None</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.train_seq_design">
+<span class="sig-name descname"><span class="pre">train_seq_design</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parallel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.train_seq_design" title="Link to this definition">¶</a></dt>
+<dd><p>Starts the adaptive sequential design for refining the surrogate model
+by selecting training points in a sequential manner.</p>
+<section id="id11">
+<h2>Returns<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>MetaModel<span class="classifier">object</span></dt><dd><p>Meta model object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.train_sequential">
+<span class="sig-name descname"><span class="pre">train_sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parallel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.train_sequential" title="Link to this definition">¶</a></dt>
+<dd><p>Train the surrogate in a sequential manner.
+First build and train evereything on the static samples, then iterate
+choosing more samples and refitting the surrogate on them.</p>
+<section id="id12">
+<h2>Returns<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h2>
+<p>None</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.util_AlphOptDesign">
+<span class="sig-name descname"><span class="pre">util_AlphOptDesign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">candidates</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'D-Opt'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.util_AlphOptDesign" title="Link to this definition">¶</a></dt>
+<dd><p>Enriches the Experimental design with the requested alphabetic
+criterion based on exploring the space with number of sampling points.</p>
+<p>Ref: Hadigol, M., & Doostan, A. (2018). Least squares polynomial chaos
+expansion: A review of sampling strategies., Computer Methods in
+Applied Mechanics and Engineering, 332, 382-407.</p>
+<section id="arguments">
+<h2>Arguments<a class="headerlink" href="#arguments" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>candidates<span class="classifier">int?</span></dt><dd><p>Number of candidate points to be searched</p>
+</dd>
+<dt>var<span class="classifier">string</span></dt><dd><p>Alphabetic optimality criterion</p>
+</dd>
+</dl>
+</section>
+<section id="id13">
+<h2>Returns<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>X_new<span class="classifier">array of shape (1, n_params)</span></dt><dd><p>The new sampling location in the input space.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.util_BayesianActiveDesign">
+<span class="sig-name descname"><span class="pre">util_BayesianActiveDesign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma2Dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'DKL'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.util_BayesianActiveDesign" title="Link to this definition">¶</a></dt>
+<dd><p>Computes scores based on Bayesian active design criterion (var).</p>
+<p>It is based on the following paper:
+Oladyshkin, Sergey, Farid Mohammadi, Ilja Kroeker, and Wolfgang Nowak.
+“Bayesian3 active learning for the gaussian process emulator using
+information theory.†Entropy 22, no. 8 (2020): 890.</p>
+<section id="id14">
+<h2>Parameters<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h2>
+<p>y_hat : unknown
+std : unknown
+sigma2Dict : dict</p>
+<blockquote>
+<div><p>A dictionary containing the measurement errors (sigma^2).</p>
+</div></blockquote>
+<dl class="simple">
+<dt>var<span class="classifier">string, optional</span></dt><dd><p>BAL design criterion. The default is ‘DKL’.</p>
+</dd>
+</dl>
+</section>
+<section id="id15">
+<h2>Returns<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>float</dt><dd><p>Score.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.util_BayesianDesign">
+<span class="sig-name descname"><span class="pre">util_BayesianDesign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_can</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X_MC</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma2Dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'DKL'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.util_BayesianDesign" title="Link to this definition">¶</a></dt>
+<dd><p>Computes scores based on Bayesian sequential design criterion (var).</p>
+<section id="id16">
+<h2>Parameters<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>X_can<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Candidate samples.</p>
+</dd>
+</dl>
+<p>X_MC : unknown
+sigma2Dict : dict</p>
+<blockquote>
+<div><p>A dictionary containing the measurement errors (sigma^2).</p>
+</div></blockquote>
+<dl class="simple">
+<dt>var<span class="classifier">string, optional</span></dt><dd><p>Bayesian design criterion. The default is ‘DKL’.</p>
+</dd>
+</dl>
+</section>
+<section id="id17">
+<h2>Returns<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>float</dt><dd><p>Score.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.Engine.util_VarBasedDesign">
+<span class="sig-name descname"><span class="pre">util_VarBasedDesign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X_can</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">util_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Entropy'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.Engine.util_VarBasedDesign" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the exploitation scores based on:
+active learning MacKay(ALM) and active learning Cohn (ALC)
+Paper: Sequential Design with Mutual Information for Computer
+Experiments (MICE): Emulation of a Tsunami Model by Beck and Guillas
+(2016)</p>
+<section id="id18">
+<h2>Parameters<a class="headerlink" href="#id18" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>X_can<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Candidate samples.</p>
+</dd>
+<dt>index<span class="classifier">int</span></dt><dd><p>Model output index.</p>
+</dd>
+<dt>util_func<span class="classifier">string, optional</span></dt><dd><p>Exploitation utility function. The default is ‘Entropy’.</p>
+</dd>
+</dl>
+</section>
+<section id="id19">
+<h2>Returns<a class="headerlink" href="#id19" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>float</dt><dd><p>Score.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+ </article>
+ </div>
+ <footer>
+
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+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.engine.Engine</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine"><code class="docutils literal notranslate"><span class="pre">Engine</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.__init__"><code class="docutils literal notranslate"><span class="pre">Engine.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.choose_next_sample"><code class="docutils literal notranslate"><span class="pre">Engine.choose_next_sample()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.dual_annealing"><code class="docutils literal notranslate"><span class="pre">Engine.dual_annealing()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.eval_metamodel"><code class="docutils literal notranslate"><span class="pre">Engine.eval_metamodel()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.run_util_func"><code class="docutils literal notranslate"><span class="pre">Engine.run_util_func()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.start_engine"><code class="docutils literal notranslate"><span class="pre">Engine.start_engine()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.tradeoff_weights"><code class="docutils literal notranslate"><span class="pre">Engine.tradeoff_weights()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.train_normal"><code class="docutils literal notranslate"><span class="pre">Engine.train_normal()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.train_seq_design"><code class="docutils literal notranslate"><span class="pre">Engine.train_seq_design()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.train_sequential"><code class="docutils literal notranslate"><span class="pre">Engine.train_sequential()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.util_AlphOptDesign"><code class="docutils literal notranslate"><span class="pre">Engine.util_AlphOptDesign()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.util_BayesianActiveDesign"><code class="docutils literal notranslate"><span class="pre">Engine.util_BayesianActiveDesign()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.util_BayesianDesign"><code class="docutils literal notranslate"><span class="pre">Engine.util_BayesianDesign()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.Engine.util_VarBasedDesign"><code class="docutils literal notranslate"><span class="pre">Engine.util_VarBasedDesign()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
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+ <section id="bayesvalidrox-surrogate-models-engine-hellinger-distance">
+<h1>bayesvalidrox.surrogate_models.engine.hellinger_distance<a class="headerlink" href="#bayesvalidrox-surrogate-models-engine-hellinger-distance" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.hellinger_distance">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.engine.</span></span><span class="sig-name descname"><span class="pre">hellinger_distance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.hellinger_distance" title="Link to this definition">¶</a></dt>
+<dd><p>Hellinger distance between two continuous distributions.</p>
+<p>The maximum distance 1 is achieved when P assigns probability zero to
+every set to which Q assigns a positive probability, and vice versa.
+0 (identical) and 1 (maximally different)</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>P<span class="classifier">array</span></dt><dd><p>Reference likelihood.</p>
+</dd>
+<dt>Q<span class="classifier">array</span></dt><dd><p>Estimated likelihood.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>float</dt><dd><p>Hellinger distance of two distributions.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+
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+ <section id="module-bayesvalidrox.surrogate_models.engine">
+<span id="bayesvalidrox-surrogate-models-engine"></span><h1>bayesvalidrox.surrogate_models.engine<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.engine" title="Link to this heading">¶</a></h1>
+<p>Engine to train the surrogate</p>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.hellinger_distance.html#bayesvalidrox.surrogate_models.engine.hellinger_distance" title="bayesvalidrox.surrogate_models.engine.hellinger_distance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hellinger_distance</span></code></a>(P, Q)</p></td>
+<td><p>Hellinger distance between two continuous distributions.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.logpdf.html#bayesvalidrox.surrogate_models.engine.logpdf" title="bayesvalidrox.surrogate_models.engine.logpdf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logpdf</span></code></a>(x, mean, cov)</p></td>
+<td><p>Computes the likelihood based on a multivariate normal distribution.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.subdomain.html#bayesvalidrox.surrogate_models.engine.subdomain" title="bayesvalidrox.surrogate_models.engine.subdomain"><code class="xref py py-obj docutils literal notranslate"><span class="pre">subdomain</span></code></a>(Bounds, n_new_samples)</p></td>
+<td><p>Divides a domain defined by Bounds into subdomains.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Engine</span></code></a>(MetaMod, Model, ExpDes)</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
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+ <article role="main">
+ <section id="bayesvalidrox-surrogate-models-engine-logpdf">
+<h1>bayesvalidrox.surrogate_models.engine.logpdf<a class="headerlink" href="#bayesvalidrox-surrogate-models-engine-logpdf" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.logpdf">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.engine.</span></span><span class="sig-name descname"><span class="pre">logpdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.logpdf" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the likelihood based on a multivariate normal distribution.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>x<span class="classifier">TYPE</span></dt><dd><p>DESCRIPTION.</p>
+</dd>
+<dt>mean<span class="classifier">array_like</span></dt><dd><p>Observation data.</p>
+</dd>
+<dt>cov<span class="classifier">2d array</span></dt><dd><p>Covariance matrix of the distribution.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>log_lik<span class="classifier">float</span></dt><dd><p>Log likelihood.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+
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+<h1>bayesvalidrox.surrogate_models.engine.subdomain<a class="headerlink" href="#bayesvalidrox-surrogate-models-engine-subdomain" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.engine.subdomain">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.engine.</span></span><span class="sig-name descname"><span class="pre">subdomain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Bounds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_new_samples</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.engine.subdomain" title="Link to this definition">¶</a></dt>
+<dd><p>Divides a domain defined by Bounds into subdomains.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>Bounds<span class="classifier">list of tuples</span></dt><dd><p>List of lower and upper bounds.</p>
+</dd>
+<dt>n_new_samples<span class="classifier">int</span></dt><dd><p>Number of samples to divide the domain for.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>Subdomains<span class="classifier">List of tuples of tuples</span></dt><dd><p>Each tuple of tuples divides one set of bounds into n_new_samples parts.</p>
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+ <section id="bayesvalidrox-surrogate-models-eval-rec-rule-eval-rec-rule">
+<h1>bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule<a class="headerlink" href="#bayesvalidrox-surrogate-models-eval-rec-rule-eval-rec-rule" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.eval_rec_rule.</span></span><span class="sig-name descname"><span class="pre">eval_rec_rule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_deg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">poly_type</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule" title="Link to this definition">¶</a></dt>
+<dd><p>Evaluates the polynomial that corresponds to the Jacobi matrix defined
+from the AB.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>x<span class="classifier">array (n_samples)</span></dt><dd><p>Points where the polynomials are evaluated.</p>
+</dd>
+<dt>max_deg<span class="classifier">int</span></dt><dd><p>Maximum degree.</p>
+</dd>
+<dt>poly_type<span class="classifier">string</span></dt><dd><p>Polynomial type.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>values<span class="classifier">array of shape (n_samples, max_deg+1)</span></dt><dd><p>Polynomials corresponding to the Jacobi matrix.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule"><code class="docutils literal notranslate"><span class="pre">eval_rec_rule()</span></code></a></li>
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+<h1>bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary<a class="headerlink" href="#bayesvalidrox-surrogate-models-eval-rec-rule-eval-rec-rule-arbitrary" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.eval_rec_rule.</span></span><span class="sig-name descname"><span class="pre">eval_rec_rule_arbitrary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_deg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">poly_coeffs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary" title="Link to this definition">¶</a></dt>
+<dd><p>Evaluates the polynomial at sample array x.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>x<span class="classifier">array (n_samples)</span></dt><dd><p>Points where the polynomials are evaluated.</p>
+</dd>
+<dt>max_deg<span class="classifier">int</span></dt><dd><p>Maximum degree.</p>
+</dd>
+<dt>poly_coeffs<span class="classifier">dict</span></dt><dd><p>Polynomial coefficients computed based on moments.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>values<span class="classifier">array of shape (n_samples, max_deg+1)</span></dt><dd><p>Univariate Polynomials evaluated at samples.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary"><code class="docutils literal notranslate"><span class="pre">eval_rec_rule_arbitrary()</span></code></a></li>
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+ <section id="bayesvalidrox-surrogate-models-eval-rec-rule-eval-univ-basis">
+<h1>bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis<a class="headerlink" href="#bayesvalidrox-surrogate-models-eval-rec-rule-eval-univ-basis" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.eval_rec_rule.</span></span><span class="sig-name descname"><span class="pre">eval_univ_basis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_deg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">poly_types</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apoly_coeffs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis" title="Link to this definition">¶</a></dt>
+<dd><p>Evaluates univariate regressors along input directions.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>x<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Training samples.</p>
+</dd>
+<dt>max_deg<span class="classifier">int</span></dt><dd><p>Maximum polynomial degree.</p>
+</dd>
+<dt>poly_types<span class="classifier">list of strings</span></dt><dd><p>List of polynomial types for all parameters.</p>
+</dd>
+<dt>apoly_coeffs<span class="classifier">dict , optional</span></dt><dd><p>Polynomial coefficients computed based on moments. The default is None.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>univ_vals<span class="classifier">array of shape (n_samples, n_params, max_deg+1)</span></dt><dd><p>Univariate polynomials for all degrees and parameters evaluated at x.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
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+
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+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis"><code class="docutils literal notranslate"><span class="pre">eval_univ_basis()</span></code></a></li>
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+ <section id="module-bayesvalidrox.surrogate_models.eval_rec_rule">
+<span id="bayesvalidrox-surrogate-models-eval-rec-rule"></span><h1>bayesvalidrox.surrogate_models.eval_rec_rule<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.eval_rec_rule" title="Link to this heading">¶</a></h1>
+<p>Based on the implementation in UQLab [1].</p>
+<p>References:
+1. S. Marelli, and B. Sudret, UQLab: A framework for uncertainty quantification
+in Matlab, Proc. 2nd Int. Conf. on Vulnerability, Risk Analysis and Management
+(ICVRAM2014), Liverpool, United Kingdom, 2014, 2554-2563.</p>
+<p>2. S. Marelli, N. Lüthen, B. Sudret, UQLab user manual – Polynomial chaos
+expansions, Report # UQLab-V1.4-104, Chair of Risk, Safety and Uncertainty
+Quantification, ETH Zurich, Switzerland, 2021.</p>
+<p>Author: Farid Mohammadi, M.Sc.
+E-Mail: <a class="reference external" href="mailto:farid.mohammadi%40iws.uni-stuttgart.de">farid<span>.</span>mohammadi<span>@</span>iws<span>.</span>uni-stuttgart<span>.</span>de</a>
+Department of Hydromechanics and Modelling of Hydrosystems (LH2)
+Institute for Modelling Hydraulic and Environmental Systems (IWS), University
+of Stuttgart, www.iws.uni-stuttgart.de/lh2/
+Pfaffenwaldring 61
+70569 Stuttgart</p>
+<p>Created on Fri Jan 14 2022</p>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.html#bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule" title="bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_rec_rule</span></code></a>(x, max_deg, poly_type)</p></td>
+<td><p>Evaluates the polynomial that corresponds to the Jacobi matrix defined from the AB.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.html#bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary" title="bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_rec_rule_arbitrary</span></code></a>(x, max_deg, poly_coeffs)</p></td>
+<td><p>Evaluates the polynomial at sample array x.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.html#bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis" title="bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_univ_basis</span></code></a>(x, max_deg, poly_types[, ...])</p></td>
+<td><p>Evaluates univariate regressors along input directions.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.html#bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs" title="bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">poly_rec_coeffs</span></code></a>(n_max, poly_type[, params])</p></td>
+<td><p>Computes the recurrence coefficients for classical Wiener-Askey orthogonal polynomials.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.html
new file mode 100644
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+<h1>bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs<a class="headerlink" href="#bayesvalidrox-surrogate-models-eval-rec-rule-poly-rec-coeffs" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.eval_rec_rule.</span></span><span class="sig-name descname"><span class="pre">poly_rec_coeffs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">poly_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the recurrence coefficients for classical Wiener-Askey orthogonal
+polynomials.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>n_max<span class="classifier">int</span></dt><dd><p>Maximum polynomial degree.</p>
+</dd>
+<dt>poly_type<span class="classifier">string</span></dt><dd><p>Polynomial type.</p>
+</dd>
+<dt>params<span class="classifier">list, optional</span></dt><dd><p>Parameters required for <cite>laguerre</cite> poly type. The default is None.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>AB<span class="classifier">dict</span></dt><dd><p>The 3 term recursive coefficients and the applicable ranges.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
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+
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+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs"><code class="docutils literal notranslate"><span class="pre">poly_rec_coeffs()</span></code></a></li>
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+ <section id="bayesvalidrox-surrogate-models-exp-designs-expdesigns">
+<h1>bayesvalidrox.surrogate_models.exp_designs.ExpDesigns<a class="headerlink" href="#bayesvalidrox-surrogate-models-exp-designs-expdesigns" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.exp_designs.</span></span><span class="sig-name descname"><span class="pre">ExpDesigns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_Model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pce'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hdf5_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_new_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mod_LOO_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tradeoff_scheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_canddidate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explore_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploit_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">util_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cand_groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_replication</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_a_post</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adapt_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_func_itr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace" title="bayesvalidrox.surrogate_models.input_space.InputSpace"><code class="xref py py-class docutils literal notranslate"><span class="pre">InputSpace</span></code></a></p>
+<p>This class generates samples from the prescribed marginals for the model
+parameters using the <cite>Input</cite> object.</p>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>input_object<span class="classifier">obj</span></dt><dd><p>Input object containing the parameter marginals, i.e. name,
+distribution type and distribution parameters or available raw data.</p>
+</dd>
+<dt>meta_Model_type<span class="classifier">str</span></dt><dd><p>Type of the meta_Model_type.</p>
+</dd>
+<dt>sampling_method<span class="classifier">str</span></dt><dd><p>Name of the sampling method for the experimental design. The following
+sampling method are supported:</p>
+<ul class="simple">
+<li><p>random</p></li>
+<li><p>latin_hypercube</p></li>
+<li><p>sobol</p></li>
+<li><p>halton</p></li>
+<li><p>hammersley</p></li>
+<li><p>chebyshev(FT)</p></li>
+<li><p>grid(FT)</p></li>
+<li><p>user</p></li>
+</ul>
+</dd>
+<dt>hdf5_file<span class="classifier">str</span></dt><dd><p>Name of the hdf5 file that contains the experimental design.</p>
+</dd>
+<dt>n_new_samples<span class="classifier">int</span></dt><dd><p>Number of (initial) training points.</p>
+</dd>
+<dt>n_max_samples<span class="classifier">int</span></dt><dd><p>Number of maximum training points.</p>
+</dd>
+<dt>mod_LOO_threshold<span class="classifier">float</span></dt><dd><p>The modified leave-one-out cross validation threshold where the
+sequential design stops.</p>
+</dd>
+<dt>tradeoff_scheme<span class="classifier">str</span></dt><dd><p>Trade-off scheme to assign weights to the exploration and exploitation
+scores in the sequential design.</p>
+</dd>
+<dt>n_canddidate<span class="classifier">int</span></dt><dd><p>Number of candidate training sets to calculate the scores for.</p>
+</dd>
+<dt>explore_method<span class="classifier">str</span></dt><dd><p>Type of the exploration method for the sequential design. The following
+methods are supported:</p>
+<ul class="simple">
+<li><p>Voronoi</p></li>
+<li><p>random</p></li>
+<li><p>latin_hypercube</p></li>
+<li><p>LOOCV</p></li>
+<li><p>dual annealing</p></li>
+</ul>
+</dd>
+<dt>exploit_method<span class="classifier">str</span></dt><dd><p>Type of the exploitation method for the sequential design. The
+following methods are supported:</p>
+<ul class="simple">
+<li><p>BayesOptDesign</p></li>
+<li><p>BayesActDesign</p></li>
+<li><p>VarOptDesign</p></li>
+<li><p>alphabetic</p></li>
+<li><p>Space-filling</p></li>
+</ul>
+</dd>
+<dt>util_func<span class="classifier">str or list</span></dt><dd><p>The utility function to be specified for the <cite>exploit_method</cite>. For the
+available utility functions see Note section.</p>
+</dd>
+<dt>n_cand_groups<span class="classifier">int</span></dt><dd><p>Number of candidate groups. Each group of candidate training sets will
+be evaulated separately in parallel.</p>
+</dd>
+<dt>n_replication<span class="classifier">int</span></dt><dd><p>Number of replications. Only for comparison. The default is 1.</p>
+</dd>
+<dt>post_snapshot<span class="classifier">int</span></dt><dd><p>Whether to plot the posterior in the sequential design. The default is
+<cite>True</cite>.</p>
+</dd>
+<dt>step_snapshot<span class="classifier">int</span></dt><dd><p>The number of steps to plot the posterior in the sequential design. The
+default is 1.</p>
+</dd>
+<dt>max_a_post<span class="classifier">list or array</span></dt><dd><p>Maximum a posteriori of the posterior distribution, if known. The
+default is <cite>[]</cite>.</p>
+</dd>
+<dt>adapt_verbose<span class="classifier">bool</span></dt><dd><p>Whether to plot the model response vs that of metamodel for the new
+trining point in the sequential design.</p>
+</dd>
+</dl>
+</section>
+<section id="note">
+<h2>Note<a class="headerlink" href="#note" title="Link to this heading">¶</a></h2>
+<p>The following utiliy functions for the <strong>exploitation</strong> methods are
+supported:</p>
+<p>#### BayesOptDesign (when data is available)
+- DKL (Kullback-Leibler Divergence)
+- DPP (D-Posterior-percision)
+- APP (A-Posterior-percision)</p>
+<p>#### VarBasedOptDesign -> when data is not available
+- Entropy (Entropy/MMSE/active learning)
+- EIGF (Expected Improvement for Global fit)
+- LOOCV (Leave-one-out Cross Validation)</p>
+<p>#### alphabetic
+- D-Opt (D-Optimality)
+- A-Opt (A-Optimality)
+- K-Opt (K-Optimality)</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_Model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pce'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hdf5_file</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_new_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_max_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mod_LOO_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tradeoff_scheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_canddidate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explore_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exploit_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">util_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Space-filling'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_cand_groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_replication</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_snapshot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_a_post</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">adapt_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_func_itr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.__init__" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(input_object[, meta_Model_type, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.build_polytypes" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.build_polytypes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_polytypes</span></code></a>(rosenblatt)</p></td>
+<td><p>Creates the polynomial types to be passed to univ_basis_vals method of the MetaModel object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.check_valid_inputs" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.check_valid_inputs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_valid_inputs</span></code></a>()</p></td>
+<td><p>Check if the given InputObj is valid to use for further calculations: 1) Has some Marginals 2) The Marginals have valid priors 3) All Marginals given as the same type (samples vs dist)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_ED" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_ED"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_ED</span></code></a>(n_samples[, max_pce_deg])</p></td>
+<td><p>Generates experimental designs (training set) with the given method.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_samples" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_samples</span></code></a>(n_samples[, sampling_method])</p></td>
+<td><p>Generates samples with given sampling method</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.init_param_space" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.init_param_space"><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_param_space</span></code></a>([max_deg])</p></td>
+<td><p>Initializes parameter space.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.pcm_sampler" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.pcm_sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pcm_sampler</span></code></a>(n_samples, max_deg)</p></td>
+<td><p>Generates collocation points based on the root of the polynomial degrees.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_sampler</span></code></a>(n_samples[, max_deg])</p></td>
+<td><p>Samples the given raw data randomly.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read_from_file</span></code></a>(out_names)</p></td>
+<td><p>Reads in the ExpDesign from a provided h5py file and saves the results.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(X[, params, method])</p></td>
+<td><p>Transforms the samples via either a Rosenblatt or an isoprobabilistic transformation.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.build_polytypes">
+<span class="sig-name descname"><span class="pre">build_polytypes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rosenblatt</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.build_polytypes" title="Link to this definition">¶</a></dt>
+<dd><p>Creates the polynomial types to be passed to univ_basis_vals method of
+the MetaModel object.</p>
+<section id="parameters">
+<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>rosenblatt<span class="classifier">bool</span></dt><dd><p>Rosenblatt transformation flag.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>orig_space_dist<span class="classifier">object</span></dt><dd><p>A chaospy JDist object or a gaussian_kde object.</p>
+</dd>
+<dt>poly_types<span class="classifier">list</span></dt><dd><p>A list of polynomial types for the parameters.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.check_valid_inputs">
+<span class="sig-name descname"><span class="pre">check_valid_inputs</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.check_valid_inputs" title="Link to this definition">¶</a></dt>
+<dd><p>Check if the given InputObj is valid to use for further calculations:
+1) Has some Marginals
+2) The Marginals have valid priors
+3) All Marginals given as the same type (samples vs dist)</p>
+<section id="id1">
+<h3>Returns<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<p>None</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_ED">
+<span class="sig-name descname"><span class="pre">generate_ED</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pce_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_ED" title="Link to this definition">¶</a></dt>
+<dd><p>Generates experimental designs (training set) with the given method.</p>
+<section id="id2">
+<h3>Parameters<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>n_samples<span class="classifier">int</span></dt><dd><p>Number of requested training points.</p>
+</dd>
+<dt>max_pce_deg<span class="classifier">int, optional</span></dt><dd><p>Maximum PCE polynomial degree. The default is <cite>None</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id3">
+<h3>Returns<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<p>None</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_samples">
+<span class="sig-name descname"><span class="pre">generate_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampling_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_samples" title="Link to this definition">¶</a></dt>
+<dd><p>Generates samples with given sampling method</p>
+<section id="id4">
+<h3>Parameters<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>n_samples<span class="classifier">int</span></dt><dd><p>Number of requested samples.</p>
+</dd>
+<dt>sampling_method<span class="classifier">str, optional</span></dt><dd><p>Sampling method. The default is <cite>‘random’</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id5">
+<h3>Returns<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>samples: array of shape (n_samples, n_params)</dt><dd><p>Generated samples from defined model input object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.init_param_space">
+<span class="sig-name descname"><span class="pre">init_param_space</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.init_param_space" title="Link to this definition">¶</a></dt>
+<dd><p>Initializes parameter space.</p>
+<section id="id6">
+<h3>Parameters<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>max_deg<span class="classifier">int, optional</span></dt><dd><p>Maximum degree. The default is <cite>None</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id7">
+<h3>Returns<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>raw_data<span class="classifier">array of shape (n_params, n_samples)</span></dt><dd><p>Raw data.</p>
+</dd>
+<dt>bound_tuples<span class="classifier">list of tuples</span></dt><dd><p>A list containing lower and upper bounds of parameters.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.pcm_sampler">
+<span class="sig-name descname"><span class="pre">pcm_sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_deg</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.pcm_sampler" title="Link to this definition">¶</a></dt>
+<dd><p>Generates collocation points based on the root of the polynomial
+degrees.</p>
+<section id="id8">
+<h3>Parameters<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>n_samples<span class="classifier">int</span></dt><dd><p>Number of requested samples.</p>
+</dd>
+<dt>max_deg<span class="classifier">int</span></dt><dd><p>Maximum degree defined by user. Will also be used to run
+init_param_space if that has not been done beforehand.</p>
+</dd>
+</dl>
+</section>
+<section id="id9">
+<h3>Returns<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>opt_col_points: array of shape (n_samples, n_params)</dt><dd><p>Collocation points.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler">
+<span class="sig-name descname"><span class="pre">random_sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler" title="Link to this definition">¶</a></dt>
+<dd><p>Samples the given raw data randomly.</p>
+<section id="id10">
+<h3>Parameters<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>n_samples<span class="classifier">int</span></dt><dd><p>Number of requested samples.</p>
+</dd>
+<dt>max_deg<span class="classifier">int, optional</span></dt><dd><p>Maximum degree. The default is <cite>None</cite>.
+This will be used to run init_param_space, if it has not been done
+until now.</p>
+</dd>
+</dl>
+</section>
+<section id="id11">
+<h3>Returns<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>samples: array of shape (n_samples, n_params)</dt><dd><p>The sampling locations in the input space.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file">
+<span class="sig-name descname"><span class="pre">read_from_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_names</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file" title="Link to this definition">¶</a></dt>
+<dd><p>Reads in the ExpDesign from a provided h5py file and saves the results.</p>
+<section id="id12">
+<h3>Parameters<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>out_names<span class="classifier">list of strings</span></dt><dd><p>The keys that are in the outputs (y) saved in the provided file.</p>
+</dd>
+</dl>
+</section>
+<section id="id13">
+<h3>Returns<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform" title="Link to this definition">¶</a></dt>
+<dd><p>Transforms the samples via either a Rosenblatt or an isoprobabilistic
+transformation.</p>
+<section id="id14">
+<h3>Parameters<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">array of shape (n_samples,n_params)</span></dt><dd><p>Samples to be transformed.</p>
+</dd>
+<dt>params<span class="classifier">list</span></dt><dd><p>Parameters for laguerre/gamma-type distribution.</p>
+</dd>
+<dt>method<span class="classifier">string</span></dt><dd><p>If transformation method is ‘user’ transform X, else just pass X.</p>
+</dd>
+</dl>
+</section>
+<section id="id15">
+<h3>Returns<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>tr_X: array of shape (n_samples,n_params)</dt><dd><p>Transformed samples.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
+ </div>
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+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.exp_designs.ExpDesigns</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns"><code class="docutils literal notranslate"><span class="pre">ExpDesigns</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.__init__"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.build_polytypes"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.build_polytypes()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.check_valid_inputs"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.check_valid_inputs()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_ED"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.generate_ED()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_samples"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.generate_samples()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.init_param_space"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.init_param_space()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.pcm_sampler"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.pcm_sampler()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.random_sampler()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.read_from_file()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.transform()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+</ul>
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+ <section id="bayesvalidrox-surrogate-models-exp-designs-check-ranges">
+<h1>bayesvalidrox.surrogate_models.exp_designs.check_ranges<a class="headerlink" href="#bayesvalidrox-surrogate-models-exp-designs-check-ranges" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exp_designs.check_ranges">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.exp_designs.</span></span><span class="sig-name descname"><span class="pre">check_ranges</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ranges</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exp_designs.check_ranges" title="Link to this definition">¶</a></dt>
+<dd><p>This function checks if theta lies in the given ranges.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>theta<span class="classifier">array</span></dt><dd><p>Proposed parameter set.</p>
+</dd>
+<dt>ranges<span class="classifier">nested list</span></dt><dd><p>The parameter ranges.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>c<span class="classifier">bool</span></dt><dd><p>If it lies in the given range, it returns True else False.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.exp_designs.check_ranges</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exp_designs.check_ranges"><code class="docutils literal notranslate"><span class="pre">check_ranges()</span></code></a></li>
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new file mode 100644
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+<span id="bayesvalidrox-surrogate-models-exp-designs"></span><h1>bayesvalidrox.surrogate_models.exp_designs<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.exp_designs" title="Link to this heading">¶</a></h1>
+<p>Experimental design with associated sampling methods</p>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.exp_designs.check_ranges.html#bayesvalidrox.surrogate_models.exp_designs.check_ranges" title="bayesvalidrox.surrogate_models.exp_designs.check_ranges"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_ranges</span></code></a>(theta, ranges)</p></td>
+<td><p>This function checks if theta lies in the given ranges.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ExpDesigns</span></code></a>(input_object[, meta_Model_type, ...])</p></td>
+<td><p>This class generates samples from the prescribed marginals for the model parameters using the <cite>Input</cite> object.</p></td>
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+ <section id="bayesvalidrox-surrogate-models-exploration-exploration">
+<h1>bayesvalidrox.surrogate_models.exploration.Exploration<a class="headerlink" href="#bayesvalidrox-surrogate-models-exploration-exploration" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exploration.Exploration">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.exploration.</span></span><span class="sig-name descname"><span class="pre">Exploration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ExpDesign</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_candidate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mc_criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mc-intersite-proj-th'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exploration.Exploration" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Created based on the Surrogate Modeling Toolbox (SUMO) [1].</p>
+<dl class="simple">
+<dt>[1] Gorissen, D., Couckuyt, I., Demeester, P., Dhaene, T. and Crombecq, K.,</dt><dd><p>2010. A surrogate modeling and adaptive sampling toolbox for computer
+based design. Journal of machine learning research.-Cambridge, Mass.,
+11, pp.2051-2055. <a class="reference external" href="mailto:sumo%40sumo.intec.ugent.be">sumo<span>@</span>sumo<span>.</span>intec<span>.</span>ugent<span>.</span>be</a> - <a class="reference external" href="http://sumo.intec.ugent.be">http://sumo.intec.ugent.be</a></p>
+</dd>
+</dl>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>ExpDesign<span class="classifier">obj</span></dt><dd><p>ExpDesign object.</p>
+</dd>
+<dt>n_candidate<span class="classifier">int</span></dt><dd><p>Number of candidate samples.</p>
+</dd>
+<dt>mc_criterion<span class="classifier">str</span></dt><dd><p>Selection crieterion. The default is <cite>‘mc-intersite-proj-th’</cite>. Another
+option is <cite>‘mc-intersite-proj’</cite>.</p>
+</dd>
+<dt>w<span class="classifier">int</span></dt><dd><p>Number of random points in the domain for each sample of the
+training set.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exploration.Exploration.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ExpDesign</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_candidate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mc_criterion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mc-intersite-proj-th'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exploration.Exploration.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration.__init__" title="bayesvalidrox.surrogate_models.exploration.Exploration.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(ExpDesign, n_candidate[, mc_criterion])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration.approximate_voronoi" title="bayesvalidrox.surrogate_models.exploration.Exploration.approximate_voronoi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">approximate_voronoi</span></code></a>(w, samples)</p></td>
+<td><p>An approximate (monte carlo) version of Matlab's voronoi command.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration.get_exploration_samples" title="bayesvalidrox.surrogate_models.exploration.Exploration.get_exploration_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_exploration_samples</span></code></a>()</p></td>
+<td><p>This function generates candidates to be selected as new design and their associated exploration scores.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration.get_mc_samples" title="bayesvalidrox.surrogate_models.exploration.Exploration.get_mc_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_mc_samples</span></code></a>([all_candidates])</p></td>
+<td><p>This function generates random samples based on Global Monte Carlo methods and their corresponding scores, based on [1].</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration.get_vornoi_samples" title="bayesvalidrox.surrogate_models.exploration.Exploration.get_vornoi_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_vornoi_samples</span></code></a>()</p></td>
+<td><p>This function generates samples based on voronoi cells and their corresponding scores</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exploration.Exploration.approximate_voronoi">
+<span class="sig-name descname"><span class="pre">approximate_voronoi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exploration.Exploration.approximate_voronoi" title="Link to this definition">¶</a></dt>
+<dd><p>An approximate (monte carlo) version of Matlab’s voronoi command.</p>
+<section id="arguments">
+<h3>Arguments<a class="headerlink" href="#arguments" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>samples<span class="classifier">array</span></dt><dd><p>Old experimental design to be used as center points for voronoi
+cells.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>areas<span class="classifier">array</span></dt><dd><p>An approximation of the voronoi cells’ areas.</p>
+</dd>
+<dt>all_candidates: list of arrays</dt><dd><p>A list of samples in each voronoi cell.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exploration.Exploration.get_exploration_samples">
+<span class="sig-name descname"><span class="pre">get_exploration_samples</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exploration.Exploration.get_exploration_samples" title="Link to this definition">¶</a></dt>
+<dd><p>This function generates candidates to be selected as new design and
+their associated exploration scores.</p>
+<section id="id1">
+<h3>Returns<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>all_candidates<span class="classifier">array of shape (n_candidate, n_params)</span></dt><dd><p>A list of samples.</p>
+</dd>
+<dt>exploration_scores: arrays of shape (n_candidate)</dt><dd><p>Exploration scores.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exploration.Exploration.get_mc_samples">
+<span class="sig-name descname"><span class="pre">get_mc_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">all_candidates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exploration.Exploration.get_mc_samples" title="Link to this definition">¶</a></dt>
+<dd><p>This function generates random samples based on Global Monte Carlo
+methods and their corresponding scores, based on [1].</p>
+<dl class="simple">
+<dt>[1] Crombecq, K., Laermans, E. and Dhaene, T., 2011. Efficient</dt><dd><p>space-filling and non-collapsing sequential design strategies for
+simulation-based modeling. European Journal of Operational Research
+, 214(3), pp.683-696.
+DOI: <a class="reference external" href="https://doi.org/10.1016/j.ejor.2011.05.032">https://doi.org/10.1016/j.ejor.2011.05.032</a></p>
+</dd>
+<dt>Implemented methods to compute scores:</dt><dd><ol class="arabic simple">
+<li><p>mc-intersite-proj</p></li>
+<li><p>mc-intersite-proj-th</p></li>
+</ol>
+</dd>
+</dl>
+<section id="id2">
+<h3>Arguments<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>all_candidates<span class="classifier">array, optional</span></dt><dd><p>Samples to compute the scores for. The default is <cite>None</cite>. In this
+case, samples will be generated by defined model input marginals.</p>
+</dd>
+</dl>
+</section>
+<section id="id3">
+<h3>Returns<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>new_samples<span class="classifier">array of shape (n_candidate, n_params)</span></dt><dd><p>A list of samples.</p>
+</dd>
+<dt>exploration_scores: arrays of shape (n_candidate)</dt><dd><p>Exploration scores.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.exploration.Exploration.get_vornoi_samples">
+<span class="sig-name descname"><span class="pre">get_vornoi_samples</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.exploration.Exploration.get_vornoi_samples" title="Link to this definition">¶</a></dt>
+<dd><p>This function generates samples based on voronoi cells and their
+corresponding scores</p>
+<section id="id4">
+<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>new_samples<span class="classifier">array of shape (n_candidate, n_params)</span></dt><dd><p>A list of samples.</p>
+</dd>
+<dt>exploration_scores: arrays of shape (n_candidate)</dt><dd><p>Exploration scores.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
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+
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+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.exploration.Exploration</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration"><code class="docutils literal notranslate"><span class="pre">Exploration</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration.__init__"><code class="docutils literal notranslate"><span class="pre">Exploration.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration.approximate_voronoi"><code class="docutils literal notranslate"><span class="pre">Exploration.approximate_voronoi()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration.get_exploration_samples"><code class="docutils literal notranslate"><span class="pre">Exploration.get_exploration_samples()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration.get_mc_samples"><code class="docutils literal notranslate"><span class="pre">Exploration.get_mc_samples()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.exploration.Exploration.get_vornoi_samples"><code class="docutils literal notranslate"><span class="pre">Exploration.get_vornoi_samples()</span></code></a></li>
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+<span id="bayesvalidrox-surrogate-models-exploration"></span><h1>bayesvalidrox.surrogate_models.exploration<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.exploration" title="Link to this heading">¶</a></h1>
+<p>Exploration for sequential training of metamodels</p>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.exploration.Exploration.html#bayesvalidrox.surrogate_models.exploration.Exploration" title="bayesvalidrox.surrogate_models.exploration.Exploration"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Exploration</span></code></a>(ExpDesign, n_candidate[, ...])</p></td>
+<td><p>Created based on the Surrogate Modeling Toolbox (SUMO) [1].</p></td>
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+ <section id="bayesvalidrox-surrogate-models-glexindex-cross-truncate">
+<h1>bayesvalidrox.surrogate_models.glexindex.cross_truncate<a class="headerlink" href="#bayesvalidrox-surrogate-models-glexindex-cross-truncate" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.glexindex.cross_truncate">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.glexindex.</span></span><span class="sig-name descname"><span class="pre">cross_truncate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bound</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.glexindex.cross_truncate" title="Link to this definition">¶</a></dt>
+<dd><p>Truncate of indices using L_p norm.
+.. math:</p>
+<blockquote>
+<div><p>L_p(x) = sum_i <a href="#id1"><span class="problematic" id="id2">|</span></a>x_i/b_i|^p ^{1/p} leq 1</p>
+</div></blockquote>
+<p>where <span class="math notranslate nohighlight">\(b_i\)</span> are bounds that each <span class="math notranslate nohighlight">\(x_i\)</span> should follow.
+Args:</p>
+<blockquote>
+<div><dl class="simple">
+<dt>indices (Sequence[int]):</dt><dd><p>Indices to be truncated.</p>
+</dd>
+<dt>bound (int, Sequence[int]):</dt><dd><p>The bound function for witch the indices can not be larger than.</p>
+</dd>
+<dt>norm (float, Sequence[float]):</dt><dd><p>The <cite>p</cite> in the <cite>L_p</cite>-norm. Support includes both <cite>L_0</cite> and <cite>L_inf</cite>.</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl>
+<dt>Returns:</dt><dd><p>Boolean indices to <code class="docutils literal notranslate"><span class="pre">indices</span></code> with True for each index where the
+truncation criteria holds.</p>
+</dd>
+<dt>Examples:</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">indices</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">numpy</span><span class="o">.</span><span class="n">mgrid</span><span class="p">[:</span><span class="mi">10</span><span class="p">,</span> <span class="p">:</span><span class="mi">10</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+<span class="gp">>>> </span><span class="n">indices</span><span class="p">[</span><span class="n">cross_truncate</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="mi">0</span><span class="p">)]</span><span class="o">.</span><span class="n">T</span>
+<span class="go">array([[0, 0, 0, 1, 2],</span>
+<span class="go"> [0, 1, 2, 0, 0]])</span>
+<span class="gp">>>> </span><span class="n">indices</span><span class="p">[</span><span class="n">cross_truncate</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span><span class="o">.</span><span class="n">T</span>
+<span class="go">array([[0, 0, 0, 1, 1, 2],</span>
+<span class="go"> [0, 1, 2, 0, 1, 0]])</span>
+<span class="gp">>>> </span><span class="n">indices</span><span class="p">[</span><span class="n">cross_truncate</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">norm</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span><span class="o">.</span><span class="n">T</span>
+<span class="go">array([[0, 0],</span>
+<span class="go"> [0, 1]])</span>
+</pre></div>
+</div>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
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+ <section id="bayesvalidrox-surrogate-models-glexindex-glexindex">
+<h1>bayesvalidrox.surrogate_models.glexindex.glexindex<a class="headerlink" href="#bayesvalidrox-surrogate-models-glexindex-glexindex" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.glexindex.glexindex">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.glexindex.</span></span><span class="sig-name descname"><span class="pre">glexindex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dimensions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cross_truncation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graded</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.glexindex.glexindex" title="Link to this definition">¶</a></dt>
+<dd><p>Generate graded lexicographical multi-indices for the monomial exponents.
+Args:</p>
+<blockquote>
+<div><dl class="simple">
+<dt>start (Union[int, numpy.ndarray]):</dt><dd><p>The lower order of the indices. If array of int, counts as lower
+bound for each axis.</p>
+</dd>
+<dt>stop (Union[int, numpy.ndarray, None]):</dt><dd><p>The maximum shape included. If omitted: stop <- start; start <- 0
+If int is provided, set as largest total order. If array of int,
+set as upper bound for each axis.</p>
+</dd>
+<dt>dimensions (int):</dt><dd><p>The number of dimensions in the expansion.</p>
+</dd>
+<dt>cross_truncation (float, Tuple[float, float]):</dt><dd><p>Use hyperbolic cross truncation scheme to reduce the number of
+terms in expansion. If two values are provided, first is low bound
+truncation, while the latter upper bound. If only one value, upper
+bound is assumed.</p>
+</dd>
+<dt>graded (bool):</dt><dd><p>Graded sorting, meaning the indices are always sorted by the index
+sum. E.g. <code class="docutils literal notranslate"><span class="pre">(2,</span> <span class="pre">2,</span> <span class="pre">2)</span></code> has a sum of 6, and will therefore be
+consider larger than both <code class="docutils literal notranslate"><span class="pre">(3,</span> <span class="pre">1,</span> <span class="pre">1)</span></code> and <code class="docutils literal notranslate"><span class="pre">(1,</span> <span class="pre">1,</span> <span class="pre">3)</span></code>.</p>
+</dd>
+<dt>reverse (bool):</dt><dd><p>Reversed lexicographical sorting meaning that <code class="docutils literal notranslate"><span class="pre">(1,</span> <span class="pre">3)</span></code> is
+considered smaller than <code class="docutils literal notranslate"><span class="pre">(3,</span> <span class="pre">1)</span></code>, instead of the opposite.</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl>
+<dt>Returns:</dt><dd><dl class="simple">
+<dt>list:</dt><dd><p>Order list of indices.</p>
+</dd>
+</dl>
+</dd>
+<dt>Examples:</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">numpoly</span><span class="o">.</span><span class="n">glexindex</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="go">[[0], [1], [2], [3]]</span>
+<span class="gp">>>> </span><span class="n">numpoly</span><span class="o">.</span><span class="n">glexindex</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">dimensions</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="go">[[0, 0], [1, 0], [0, 1]]</span>
+<span class="gp">>>> </span><span class="n">numpoly</span><span class="o">.</span><span class="n">glexindex</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stop</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">dimensions</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="go">[[2, 0], [1, 1], [0, 2]]</span>
+<span class="gp">>>> </span><span class="n">numpoly</span><span class="o">.</span><span class="n">glexindex</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="go">[[0, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 2]]</span>
+<span class="gp">>>> </span><span class="n">numpoly</span><span class="o">.</span><span class="n">glexindex</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">cross_truncation</span><span class="o">=</span><span class="n">numpy</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+<span class="go">[[0, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 1], [0, 0, 2], [0, 1, 2]]</span>
+</pre></div>
+</div>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.glexindex.glexindex"><code class="docutils literal notranslate"><span class="pre">glexindex()</span></code></a></li>
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+ <section id="module-bayesvalidrox.surrogate_models.glexindex">
+<span id="bayesvalidrox-surrogate-models-glexindex"></span><h1>bayesvalidrox.surrogate_models.glexindex<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.glexindex" title="Link to this heading">¶</a></h1>
+<p>Multi indices for monomial exponents.
+Credit: Jonathan Feinberg
+<a class="reference external" href="https://github.com/jonathf/numpoly/blob/master/numpoly/utils/glexindex.py">https://github.com/jonathf/numpoly/blob/master/numpoly/utils/glexindex.py</a></p>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.glexindex.cross_truncate.html#bayesvalidrox.surrogate_models.glexindex.cross_truncate" title="bayesvalidrox.surrogate_models.glexindex.cross_truncate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_truncate</span></code></a>(indices, bound, norm)</p></td>
+<td><p>Truncate of indices using L_p norm. .. math: L_p(x) = sum_i <a href="#id1"><span class="problematic" id="id2">|</span></a>x_i/b_i|^p ^{1/p} leq 1 where <span class="math notranslate nohighlight">\(b_i\)</span> are bounds that each <span class="math notranslate nohighlight">\(x_i\)</span> should follow. Args: indices (Sequence[int]): Indices to be truncated. bound (int, Sequence[int]): The bound function for witch the indices can not be larger than. norm (float, Sequence[float]): The <cite>p</cite> in the <cite>L_p</cite>-norm. Support includes both <cite>L_0</cite> and <cite>L_inf</cite>. Returns: Boolean indices to <code class="docutils literal notranslate"><span class="pre">indices</span></code> with True for each index where the truncation criteria holds. Examples: >>> indices = numpy.array(numpy.mgrid[:10, :10]).reshape(2, -1).T >>> indices[cross_truncate(indices, 2, norm=0)].T array([[0, 0, 0, 1, 2], [0, 1, 2, 0, 0]]) >>> indices[cross_truncate(indices, 2, norm=1)].T array([[0, 0, 0, 1, 1, 2], [0, 1, 2, 0, 1, 0]]) >>> indices[cross_truncate(indices, [0, 1], norm=1)].T array([[0, 0], [0, 1]]).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.glexindex.glexindex.html#bayesvalidrox.surrogate_models.glexindex.glexindex" title="bayesvalidrox.surrogate_models.glexindex.glexindex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">glexindex</span></code></a>(start[, stop, dimensions, ...])</p></td>
+<td><p>Generate graded lexicographical multi-indices for the monomial exponents. Args: start (Union[int, numpy.ndarray]): The lower order of the indices. If array of int, counts as lower bound for each axis. stop (Union[int, numpy.ndarray, None]): The maximum shape included. If omitted: stop <- start; start <- 0 If int is provided, set as largest total order. If array of int, set as upper bound for each axis. dimensions (int): The number of dimensions in the expansion. cross_truncation (float, Tuple[float, float]): Use hyperbolic cross truncation scheme to reduce the number of terms in expansion. If two values are provided, first is low bound truncation, while the latter upper bound. If only one value, upper bound is assumed. graded (bool): Graded sorting, meaning the indices are always sorted by the index sum. E.g. <code class="docutils literal notranslate"><span class="pre">(2,</span> <span class="pre">2,</span> <span class="pre">2)</span></code> has a sum of 6, and will therefore be consider larger than both <code class="docutils literal notranslate"><span class="pre">(3,</span> <span class="pre">1,</span> <span class="pre">1)</span></code> and <code class="docutils literal notranslate"><span class="pre">(1,</span> <span class="pre">1,</span> <span class="pre">3)</span></code>. reverse (bool): Reversed lexicographical sorting meaning that <code class="docutils literal notranslate"><span class="pre">(1,</span> <span class="pre">3)</span></code> is considered smaller than <code class="docutils literal notranslate"><span class="pre">(3,</span> <span class="pre">1)</span></code>, instead of the opposite. Returns: list: Order list of indices. Examples: >>> numpoly.glexindex(4).tolist() [[0], [1], [2], [3]] >>> numpoly.glexindex(2, dimensions=2).tolist() [[0, 0], [1, 0], [0, 1]] >>> numpoly.glexindex(start=2, stop=3, dimensions=2).tolist() [[2, 0], [1, 1], [0, 2]] >>> numpoly.glexindex([1, 2, 3]).tolist() [[0, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 2]] >>> numpoly.glexindex([1, 2, 3], cross_truncation=numpy.inf).tolist() [[0, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 1], [0, 0, 2], [0, 1, 2]].</p></td>
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+ <section id="module-bayesvalidrox.surrogate_models">
+<span id="bayesvalidrox-surrogate-models"></span><h1>bayesvalidrox.surrogate_models<a class="headerlink" href="#module-bayesvalidrox.surrogate_models" title="Link to this heading">¶</a></h1>
+<p class="rubric">Modules</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.adaptPlot.html#module-bayesvalidrox.surrogate_models.adaptPlot" title="bayesvalidrox.surrogate_models.adaptPlot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.adaptPlot</span></code></a></p></td>
+<td><p>Created on Thu Aug 13 13:46:24 2020</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction" title="bayesvalidrox.surrogate_models.apoly_construction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.apoly_construction</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear" title="bayesvalidrox.surrogate_models.bayes_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.bayes_linear</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine" title="bayesvalidrox.surrogate_models.engine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine</span></code></a></p></td>
+<td><p>Engine to train the surrogate</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.eval_rec_rule.html#module-bayesvalidrox.surrogate_models.eval_rec_rule" title="bayesvalidrox.surrogate_models.eval_rec_rule"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.eval_rec_rule</span></code></a></p></td>
+<td><p>Based on the implementation in UQLab [1].</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs" title="bayesvalidrox.surrogate_models.exp_designs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.exp_designs</span></code></a></p></td>
+<td><p>Experimental design with associated sampling methods</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration" title="bayesvalidrox.surrogate_models.exploration"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.exploration</span></code></a></p></td>
+<td><p>Exploration for sequential training of metamodels</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex" title="bayesvalidrox.surrogate_models.glexindex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.glexindex</span></code></a></p></td>
+<td><p>Multi indices for monomial exponents.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.input_space.html#module-bayesvalidrox.surrogate_models.input_space" title="bayesvalidrox.surrogate_models.input_space"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.input_space</span></code></a></p></td>
+<td><p>Input space built from set prior distributions</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html#module-bayesvalidrox.surrogate_models.inputs" title="bayesvalidrox.surrogate_models.inputs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs</span></code></a></p></td>
+<td><p>Inputs and related marginal distributions</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</span></code></a></p></td>
+<td><p>Created on Fri Jul 15 14:08:59 2022</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html#module-bayesvalidrox.surrogate_models.reg_fast_ard" title="bayesvalidrox.surrogate_models.reg_fast_ard"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.reg_fast_ard</span></code></a></p></td>
+<td><p>Created on Tue Mar 24 19:41:45 2020</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html#module-bayesvalidrox.surrogate_models.reg_fast_laplace" title="bayesvalidrox.surrogate_models.reg_fast_laplace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.reg_fast_laplace</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models" title="bayesvalidrox.surrogate_models.surrogate_models"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models</span></code></a></p></td>
+<td><p>Implementation of metamodel as either PC, aPC or GPE</p></td>
+</tr>
+</tbody>
+</table>
+</div>
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+ <section id="bayesvalidrox-surrogate-models-input-space-inputspace">
+<h1>bayesvalidrox.surrogate_models.input_space.InputSpace<a class="headerlink" href="#bayesvalidrox-surrogate-models-input-space-inputspace" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.input_space.InputSpace">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.input_space.</span></span><span class="sig-name descname"><span class="pre">InputSpace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_Model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pce'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.input_space.InputSpace" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>This class generates the input space for the metamodel from the
+distributions provided using the <cite>Input</cite> object.</p>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>Input<span class="classifier">obj</span></dt><dd><p>Input object containing the parameter marginals, i.e. name,
+distribution type and distribution parameters or available raw data.</p>
+</dd>
+<dt>meta_Model_type<span class="classifier">str</span></dt><dd><p>Type of the meta_Model_type.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.input_space.InputSpace.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_Model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pce'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.__init__" title="bayesvalidrox.surrogate_models.input_space.InputSpace.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(input_object[, meta_Model_type])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.build_polytypes" title="bayesvalidrox.surrogate_models.input_space.InputSpace.build_polytypes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_polytypes</span></code></a>(rosenblatt)</p></td>
+<td><p>Creates the polynomial types to be passed to univ_basis_vals method of the MetaModel object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.check_valid_inputs" title="bayesvalidrox.surrogate_models.input_space.InputSpace.check_valid_inputs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_valid_inputs</span></code></a>()</p></td>
+<td><p>Check if the given InputObj is valid to use for further calculations: 1) Has some Marginals 2) The Marginals have valid priors 3) All Marginals given as the same type (samples vs dist)</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.init_param_space" title="bayesvalidrox.surrogate_models.input_space.InputSpace.init_param_space"><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_param_space</span></code></a>([max_deg])</p></td>
+<td><p>Initializes parameter space.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.transform" title="bayesvalidrox.surrogate_models.input_space.InputSpace.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(X[, params, method])</p></td>
+<td><p>Transforms the samples via either a Rosenblatt or an isoprobabilistic transformation.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.input_space.InputSpace.build_polytypes">
+<span class="sig-name descname"><span class="pre">build_polytypes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rosenblatt</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.build_polytypes" title="Link to this definition">¶</a></dt>
+<dd><p>Creates the polynomial types to be passed to univ_basis_vals method of
+the MetaModel object.</p>
+<section id="parameters">
+<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>rosenblatt<span class="classifier">bool</span></dt><dd><p>Rosenblatt transformation flag.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>orig_space_dist<span class="classifier">object</span></dt><dd><p>A chaospy JDist object or a gaussian_kde object.</p>
+</dd>
+<dt>poly_types<span class="classifier">list</span></dt><dd><p>A list of polynomial types for the parameters.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.input_space.InputSpace.check_valid_inputs">
+<span class="sig-name descname"><span class="pre">check_valid_inputs</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.check_valid_inputs" title="Link to this definition">¶</a></dt>
+<dd><p>Check if the given InputObj is valid to use for further calculations:
+1) Has some Marginals
+2) The Marginals have valid priors
+3) All Marginals given as the same type (samples vs dist)</p>
+<section id="id1">
+<h3>Returns<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<p>None</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.input_space.InputSpace.init_param_space">
+<span class="sig-name descname"><span class="pre">init_param_space</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.init_param_space" title="Link to this definition">¶</a></dt>
+<dd><p>Initializes parameter space.</p>
+<section id="id2">
+<h3>Parameters<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>max_deg<span class="classifier">int, optional</span></dt><dd><p>Maximum degree. The default is <cite>None</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id3">
+<h3>Returns<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>raw_data<span class="classifier">array of shape (n_params, n_samples)</span></dt><dd><p>Raw data.</p>
+</dd>
+<dt>bound_tuples<span class="classifier">list of tuples</span></dt><dd><p>A list containing lower and upper bounds of parameters.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.input_space.InputSpace.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.transform" title="Link to this definition">¶</a></dt>
+<dd><p>Transforms the samples via either a Rosenblatt or an isoprobabilistic
+transformation.</p>
+<section id="id4">
+<h3>Parameters<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">array of shape (n_samples,n_params)</span></dt><dd><p>Samples to be transformed.</p>
+</dd>
+<dt>params<span class="classifier">list</span></dt><dd><p>Parameters for laguerre/gamma-type distribution.</p>
+</dd>
+<dt>method<span class="classifier">string</span></dt><dd><p>If transformation method is ‘user’ transform X, else just pass X.</p>
+</dd>
+</dl>
+</section>
+<section id="id5">
+<h3>Returns<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>tr_X: array of shape (n_samples,n_params)</dt><dd><p>Transformed samples.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
+ </div>
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+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.input_space.InputSpace</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace"><code class="docutils literal notranslate"><span class="pre">InputSpace</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.__init__"><code class="docutils literal notranslate"><span class="pre">InputSpace.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.build_polytypes"><code class="docutils literal notranslate"><span class="pre">InputSpace.build_polytypes()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.check_valid_inputs"><code class="docutils literal notranslate"><span class="pre">InputSpace.check_valid_inputs()</span></code></a></li>
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+<span id="bayesvalidrox-surrogate-models-input-space"></span><h1>bayesvalidrox.surrogate_models.input_space<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.input_space" title="Link to this heading">¶</a></h1>
+<p>Input space built from set prior distributions</p>
+<p class="rubric">Classes</p>
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+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace" title="bayesvalidrox.surrogate_models.input_space.InputSpace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InputSpace</span></code></a>(input_object[, meta_Model_type])</p></td>
+<td><p>This class generates the input space for the metamodel from the distributions provided using the <cite>Input</cite> object.</p></td>
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+ <section id="bayesvalidrox-surrogate-models-inputs-input">
+<h1>bayesvalidrox.surrogate_models.inputs.Input<a class="headerlink" href="#bayesvalidrox-surrogate-models-inputs-input" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.inputs.Input">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.inputs.</span></span><span class="sig-name descname"><span class="pre">Input</span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.inputs.Input" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>A class to define the uncertain input parameters.</p>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>Marginals<span class="classifier">obj</span></dt><dd><p>Marginal objects. See <cite>inputs.Marginal</cite>.</p>
+</dd>
+<dt>Rosenblatt<span class="classifier">bool</span></dt><dd><p>If Rossenblatt transformation is required for the dependent input
+parameters.</p>
+</dd>
+</dl>
+</section>
+<section id="examples">
+<h2>Examples<a class="headerlink" href="#examples" title="Link to this heading">¶</a></h2>
+<p>Marginals can be defined as following:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">inputs</span> <span class="o">=</span> <span class="n">Inputs</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="n">inputs</span><span class="o">.</span><span class="n">add_marginals</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="n">inputs</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'X_1'</span>
+<span class="gp">>>> </span><span class="n">inputs</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">dist_type</span> <span class="o">=</span> <span class="s1">'uniform'</span>
+<span class="gp">>>> </span><span class="n">inputs</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>If there is no common data is avaliable, the input data can be given
+as following:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">inputs</span><span class="o">.</span><span class="n">add_marginals</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="n">inputs</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'X_1'</span>
+<span class="gp">>>> </span><span class="n">inputs</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">input_data</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.inputs.Input.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.inputs.Input.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.inputs.Input.__init__" title="bayesvalidrox.surrogate_models.inputs.Input.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.inputs.Input.add_marginals" title="bayesvalidrox.surrogate_models.inputs.Input.add_marginals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_marginals</span></code></a>()</p></td>
+<td><p>Adds a new Marginal object to the input object.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Attributes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">poly_coeffs_flag</span></code></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.inputs.Input.add_marginals">
+<span class="sig-name descname"><span class="pre">add_marginals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.inputs.Input.add_marginals" title="Link to this definition">¶</a></dt>
+<dd><p>Adds a new Marginal object to the input object.</p>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
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+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.inputs.Input"><code class="docutils literal notranslate"><span class="pre">Input</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.inputs.Input.__init__"><code class="docutils literal notranslate"><span class="pre">Input.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.inputs.Input.add_marginals"><code class="docutils literal notranslate"><span class="pre">Input.add_marginals()</span></code></a></li>
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+ <section id="bayesvalidrox-surrogate-models-inputs-marginal">
+<h1>bayesvalidrox.surrogate_models.inputs.Marginal<a class="headerlink" href="#bayesvalidrox-surrogate-models-inputs-marginal" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.inputs.Marginal">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.inputs.</span></span><span class="sig-name descname"><span class="pre">Marginal</span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.inputs.Marginal" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>An object containing the specifications of the marginals for each uncertain
+parameter.</p>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>name<span class="classifier">string</span></dt><dd><p>Name of the parameter. The default is <cite>‘$x_1$’</cite>.</p>
+</dd>
+<dt>dist_type<span class="classifier">string</span></dt><dd><p>Name of the distribution. The default is <cite>None</cite>.</p>
+</dd>
+<dt>parameters<span class="classifier">list</span></dt><dd><p>Parameters corresponding to the distribution type. The
+default is <cite>None</cite>.</p>
+</dd>
+<dt>input_data<span class="classifier">array</span></dt><dd><p>Available input data. The default is <cite>[]</cite>.</p>
+</dd>
+<dt>moments<span class="classifier">list</span></dt><dd><p>Moments of the distribution. The default is <cite>None</cite>.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.inputs.Marginal.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.inputs.Marginal.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.inputs.Marginal.__init__" title="bayesvalidrox.surrogate_models.inputs.Marginal.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.inputs.Marginal</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.inputs.Marginal"><code class="docutils literal notranslate"><span class="pre">Marginal</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.inputs.Marginal.__init__"><code class="docutils literal notranslate"><span class="pre">Marginal.__init__()</span></code></a></li>
+</ul>
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+<span id="bayesvalidrox-surrogate-models-inputs"></span><h1>bayesvalidrox.surrogate_models.inputs<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.inputs" title="Link to this heading">¶</a></h1>
+<p>Inputs and related marginal distributions</p>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input" title="bayesvalidrox.surrogate_models.inputs.Input"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Input</span></code></a>()</p></td>
+<td><p>A class to define the uncertain input parameters.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.Marginal.html#bayesvalidrox.surrogate_models.inputs.Marginal" title="bayesvalidrox.surrogate_models.inputs.Marginal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Marginal</span></code></a>()</p></td>
+<td><p>An object containing the specifications of the marginals for each uncertain parameter.</p></td>
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+ <section id="bayesvalidrox-surrogate-models-orthogonal-matching-pursuit-orthogonalmatchingpursuit">
+<h1>bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit<a class="headerlink" href="#bayesvalidrox-surrogate-models-orthogonal-matching-pursuit-orthogonalmatchingpursuit" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.</span></span><span class="sig-name descname"><span class="pre">OrthogonalMatchingPursuit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">LinearModel</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">RegressorMixin</span></code></p>
+<p>Regression with Orthogonal Matching Pursuit [1].</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>fit_intercept<span class="classifier">boolean, optional (DEFAULT = True)</span></dt><dd><p>whether to calculate the intercept for this model. If set
+to false, no intercept will be used in calculations
+(e.g. data is expected to be already centered).</p>
+</dd>
+<dt>copy_X<span class="classifier">boolean, optional (DEFAULT = True)</span></dt><dd><p>If True, X will be copied; else, it may be overwritten.</p>
+</dd>
+<dt>verbose<span class="classifier">boolean, optional (DEFAULT = FALSE)</span></dt><dd><p>Verbose mode when fitting the model</p>
+</dd>
+</dl>
+</section>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt><a href="#id19"><span class="problematic" id="id20">coef_</span></a><span class="classifier">array, shape = (n_features)</span></dt><dd><p>Coefficients of the regression model (mean of posterior distribution)</p>
+</dd>
+<dt><a href="#id21"><span class="problematic" id="id22">active_</span></a><span class="classifier">array, dtype = np.bool, shape = (n_features)</span></dt><dd><p>True for non-zero coefficients, False otherwise</p>
+</dd>
+</dl>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>[1] Pati, Y., Rezaiifar, R., Krishnaprasad, P. (1993). Orthogonal matching</dt><dd><p>pursuit: recursive function approximation with application to wavelet
+decomposition. Proceedings of 27th Asilomar Conference on Signals,
+Systems and Computers, 40-44.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.__init__" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([fit_intercept, normalize, copy_X, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.blockwise_inverse" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.blockwise_inverse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">blockwise_inverse</span></code></a>(Ainv, B, C, D)</p></td>
+<td><p>non-singular square matrix M defined as M = [[A B]; [C D]] .</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.fit" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y)</p></td>
+<td><p>Fits Regression with Orthogonal Matching Pursuit Algorithm.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_metadata_routing" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
+<td><p>Get metadata routing of this object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_params" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
+<td><p>Get parameters for this estimator.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.loo_error" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.loo_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">loo_error</span></code></a>(psi, inv_inf_matrix, y, coeffs)</p></td>
+<td><p>Calculates the corrected LOO error for regression on regressor matrix <cite>psi</cite> that generated the coefficients based on [1] and [2].</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.predict" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X)</p></td>
+<td><p>Computes predictive distribution for test set.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.score" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y[, sample_weight])</p></td>
+<td><p>Return the coefficient of determination of the prediction.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_params" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
+<td><p>Set the parameters of this estimator.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_score_request" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_score_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_score_request</span></code></a>(*[, sample_weight])</p></td>
+<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.blockwise_inverse">
+<span class="sig-name descname"><span class="pre">blockwise_inverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Ainv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">D</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.blockwise_inverse" title="Link to this definition">¶</a></dt>
+<dd><p>non-singular square matrix M defined as M = [[A B]; [C D]] .
+B, C and D can have any dimension, provided their combination defines
+a square matrix M.</p>
+<section id="id1">
+<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>Ainv<span class="classifier">float or array</span></dt><dd><p>inverse of the square-submatrix A.</p>
+</dd>
+<dt>B<span class="classifier">float or array</span></dt><dd><p>Information matrix with all new regressor.</p>
+</dd>
+<dt>C<span class="classifier">float or array</span></dt><dd><p>Transpose of B.</p>
+</dd>
+<dt>D<span class="classifier">float or array</span></dt><dd><p>Information matrix with all selected regressors.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>M<span class="classifier">array</span></dt><dd><p>Inverse of the information matrix.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.fit" title="Link to this definition">¶</a></dt>
+<dd><p>Fits Regression with Orthogonal Matching Pursuit Algorithm.</p>
+<section id="id2">
+<h3>Parameters<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X: {array-like, sparse matrix} of size (n_samples, n_features)</dt><dd><p>Training data, matrix of explanatory variables</p>
+</dd>
+<dt>y: array-like of size [n_samples, n_features]</dt><dd><p>Target values</p>
+</dd>
+</dl>
+</section>
+<section id="id3">
+<h3>Returns<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">object</span></dt><dd><p>Returns self.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_metadata_routing">
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_metadata_routing" title="Link to this definition">¶</a></dt>
+<dd><p>Get metadata routing of this object.</p>
+<p>Please check <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<section id="id4">
+<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>routing<span class="classifier">MetadataRequest</span></dt><dd><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code> encapsulating
+routing information.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_params">
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_params" title="Link to this definition">¶</a></dt>
+<dd><p>Get parameters for this estimator.</p>
+<section id="id5">
+<h3>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>deep<span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
+contained subobjects that are estimators.</p>
+</dd>
+</dl>
+</section>
+<section id="id6">
+<h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>params<span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.loo_error">
+<span class="sig-name descname"><span class="pre">loo_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inv_inf_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coeffs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.loo_error" title="Link to this definition">¶</a></dt>
+<dd><p>Calculates the corrected LOO error for regression on regressor
+matrix <cite>psi</cite> that generated the coefficients based on [1] and [2].</p>
+<dl class="simple">
+<dt>[1] Blatman, G., 2009. Adaptive sparse polynomial chaos expansions for</dt><dd><p>uncertainty propagation and sensitivity analysis (Doctoral
+dissertation, Clermont-Ferrand 2).</p>
+</dd>
+<dt>[2] Blatman, G. and Sudret, B., 2011. Adaptive sparse polynomial chaos</dt><dd><p>expansion based on least angle regression. Journal of computational
+Physics, 230(6), pp.2345-2367.</p>
+</dd>
+</dl>
+<section id="id7">
+<h3>Parameters<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>psi<span class="classifier">array of shape (n_samples, n_feature)</span></dt><dd><p>Orthogonal bases evaluated at the samples.</p>
+</dd>
+<dt>inv_inf_matrix<span class="classifier">array</span></dt><dd><p>Inverse of the information matrix.</p>
+</dd>
+<dt>y<span class="classifier">array of shape (n_samples, )</span></dt><dd><p>Targets.</p>
+</dd>
+<dt>coeffs<span class="classifier">array</span></dt><dd><p>Computed regresssor cofficients.</p>
+</dd>
+</dl>
+</section>
+<section id="id8">
+<h3>Returns<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>loo_error<span class="classifier">float</span></dt><dd><p>Modified LOOCV error.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.predict" title="Link to this definition">¶</a></dt>
+<dd><p>Computes predictive distribution for test set.</p>
+<section id="id9">
+<h3>Parameters<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X: {array-like, sparse} (n_samples_test, n_features)</dt><dd><p>Test data, matrix of explanatory variables</p>
+</dd>
+</dl>
+</section>
+<section id="id10">
+<h3>Returns<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>y_hat: numpy array of size (n_samples_test,)</dt><dd><p>Estimated values of targets on test set (i.e. mean of
+predictive distribution)</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.score">
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.score" title="Link to this definition">¶</a></dt>
+<dd><p>Return the coefficient of determination of the prediction.</p>
+<p>The coefficient of determination <span class="math notranslate nohighlight">\(R^2\)</span> is defined as
+<span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
+sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_pred)**</span> <span class="pre">2).sum()</span></code> and <span class="math notranslate nohighlight">\(v\)</span>
+is the total sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_true.mean())</span> <span class="pre">**</span> <span class="pre">2).sum()</span></code>.
+The best possible score is 1.0 and it can be negative (because the
+model can be arbitrarily worse). A constant model that always predicts
+the expected value of <cite>y</cite>, disregarding the input features, would get
+a <span class="math notranslate nohighlight">\(R^2\)</span> score of 0.0.</p>
+<section id="id11">
+<h3>Parameters<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples. For some estimators this may be a precomputed
+kernel matrix or a list of generic objects instead with shape
+<code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_samples_fitted)</span></code>, where <code class="docutils literal notranslate"><span class="pre">n_samples_fitted</span></code>
+is the number of samples used in the fitting for the estimator.</p>
+</dd>
+<dt>y<span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for <cite>X</cite>.</p>
+</dd>
+<dt>sample_weight<span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
+</dd>
+</dl>
+</section>
+<section id="id12">
+<h3>Returns<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>score<span class="classifier">float</span></dt><dd><p><span class="math notranslate nohighlight">\(R^2\)</span> of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> w.r.t. <cite>y</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="notes">
+<h3>Notes<a class="headerlink" href="#notes" title="Link to this heading">¶</a></h3>
+<p>The <span class="math notranslate nohighlight">\(R^2\)</span> score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor uses
+<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
+with default value of <code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score()</span></code>.
+This influences the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput
+regressors (except for
+<code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code>).</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_params">
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_params" title="Link to this definition">¶</a></dt>
+<dd><p>Set the parameters of this estimator.</p>
+<p>The method works on simple estimators as well as on nested objects
+(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
+parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
+possible to update each component of a nested object.</p>
+<section id="id13">
+<h3>Parameters<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt><a href="#id14"><span class="problematic" id="id15">**</span></a>params<span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
+</dd>
+</dl>
+</section>
+<section id="id16">
+<h3>Returns<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit"><span class="pre">OrthogonalMatchingPursuit</span></a></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_score_request" title="Link to this definition">¶</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<section id="id17">
+<h3>Parameters<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>sample_weight<span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+</dl>
+</section>
+<section id="id18">
+<h3>Returns<a class="headerlink" href="#id18" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">object</span></dt><dd><p>The updated object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
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+ <div class="toc-tree">
+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.__init__"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.blockwise_inverse"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit.blockwise_inverse()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.fit"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit.fit()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit.get_metadata_routing()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_params"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit.get_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.loo_error"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit.loo_error()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.predict"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit.predict()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.score"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit.score()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_params"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit.set_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_score_request"><code class="docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit.set_score_request()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
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+<h1>bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr<a class="headerlink" href="#bayesvalidrox-surrogate-models-orthogonal-matching-pursuit-corr" title="Link to this heading">¶</a></h1>
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+<span id="bayesvalidrox-surrogate-models-orthogonal-matching-pursuit"></span><h1>bayesvalidrox.surrogate_models.orthogonal_matching_pursuit<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit" title="Link to this heading">¶</a></h1>
+<p>Created on Fri Jul 15 14:08:59 2022</p>
+<p>@author: farid</p>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">corr</span></code></a>(x, y)</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit</span></code></a>([fit_intercept, ...])</p></td>
+<td><p>Regression with Orthogonal Matching Pursuit [1].</p></td>
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+ <section id="bayesvalidrox-surrogate-models-reg-fast-ard-regressionfastard">
+<h1>bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD<a class="headerlink" href="#bayesvalidrox-surrogate-models-reg-fast-ard-regressionfastard" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.reg_fast_ard.</span></span><span class="sig-name descname"><span class="pre">RegressionFastARD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">LinearModel</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">RegressorMixin</span></code></p>
+<p>Regression with Automatic Relevance Determination (Fast Version uses
+Sparse Bayesian Learning)
+<a class="reference external" href="https://github.com/AmazaspShumik/sklearn-bayes/blob/master/skbayes/rvm_ard_models/fast_rvm.py">https://github.com/AmazaspShumik/sklearn-bayes/blob/master/skbayes/rvm_ard_models/fast_rvm.py</a></p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>n_iter: int, optional (DEFAULT = 100)</dt><dd><p>Maximum number of iterations</p>
+</dd>
+<dt>start: list, optional (DEFAULT = None)</dt><dd><p>Initial selected features.</p>
+</dd>
+<dt>tol: float, optional (DEFAULT = 1e-3)</dt><dd><p>If absolute change in precision parameter for weights is below threshold
+algorithm terminates.</p>
+</dd>
+<dt>fit_intercept<span class="classifier">boolean, optional (DEFAULT = True)</span></dt><dd><p>whether to calculate the intercept for this model. If set
+to false, no intercept will be used in calculations
+(e.g. data is expected to be already centered).</p>
+</dd>
+<dt>copy_X<span class="classifier">boolean, optional (DEFAULT = True)</span></dt><dd><p>If True, X will be copied; else, it may be overwritten.</p>
+</dd>
+<dt>compute_score<span class="classifier">bool, default=False</span></dt><dd><p>If True, compute the log marginal likelihood at each iteration of the
+optimization.</p>
+</dd>
+<dt>verbose<span class="classifier">boolean, optional (DEFAULT = FALSE)</span></dt><dd><p>Verbose mode when fitting the model</p>
+</dd>
+</dl>
+</section>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt><a href="#id18"><span class="problematic" id="id19">coef_</span></a><span class="classifier">array, shape = (n_features)</span></dt><dd><p>Coefficients of the regression model (mean of posterior distribution)</p>
+</dd>
+<dt><a href="#id20"><span class="problematic" id="id21">alpha_</span></a><span class="classifier">float</span></dt><dd><p>estimated precision of the noise</p>
+</dd>
+<dt><a href="#id22"><span class="problematic" id="id23">active_</span></a><span class="classifier">array, dtype = np.bool, shape = (n_features)</span></dt><dd><p>True for non-zero coefficients, False otherwise</p>
+</dd>
+<dt><a href="#id24"><span class="problematic" id="id25">lambda_</span></a><span class="classifier">array, shape = (n_features)</span></dt><dd><p>estimated precisions of the coefficients</p>
+</dd>
+<dt><a href="#id26"><span class="problematic" id="id27">sigma_</span></a><span class="classifier">array, shape = (n_features, n_features)</span></dt><dd><p>estimated covariance matrix of the weights, computed only
+for non-zero coefficients</p>
+</dd>
+<dt><a href="#id28"><span class="problematic" id="id29">scores_</span></a><span class="classifier">array-like of shape (n_iter_+1,)</span></dt><dd><p>If computed_score is True, value of the log marginal likelihood (to be
+maximized) at each iteration of the optimization.</p>
+</dd>
+</dl>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">¶</a></h2>
+<p>[1] Fast marginal likelihood maximisation for sparse Bayesian models
+(Tipping & Faul 2003) (<a class="reference external" href="http://www.miketipping.com/papers/met-fastsbl.pdf">http://www.miketipping.com/papers/met-fastsbl.pdf</a>)
+[2] Analysis of sparse Bayesian learning (Tipping & Faul 2001)</p>
+<blockquote>
+<div><p>(<a class="reference external" href="http://www.miketipping.com/abstracts.htm#Faul:NIPS01">http://www.miketipping.com/abstracts.htm#Faul:NIPS01</a>)</p>
+</div></blockquote>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_score</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.__init__" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([n_iter, start, tol, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.fit" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y)</p></td>
+<td><p>Fits ARD Regression with Sequential Sparse Bayes Algorithm.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_metadata_routing" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_metadata_routing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_metadata_routing</span></code></a>()</p></td>
+<td><p>Get metadata routing of this object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_params" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
+<td><p>Get parameters for this estimator.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.log_marginal_like" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.log_marginal_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log_marginal_like</span></code></a>(XXa, XYa, Aa, beta)</p></td>
+<td><p>Computes the log of the marginal likelihood.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.predict" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X[, return_std])</p></td>
+<td><p>Computes predictive distribution for test set.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.score" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X, y[, sample_weight])</p></td>
+<td><p>Return the coefficient of determination of the prediction.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_params" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
+<td><p>Set the parameters of this estimator.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_predict_request" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_predict_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_predict_request</span></code></a>(*[, return_std])</p></td>
+<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_score_request" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_score_request"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_score_request</span></code></a>(*[, sample_weight])</p></td>
+<td><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.fit" title="Link to this definition">¶</a></dt>
+<dd><p>Fits ARD Regression with Sequential Sparse Bayes Algorithm.</p>
+<section id="id1">
+<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X: {array-like, sparse matrix} of size (n_samples, n_features)</dt><dd><p>Training data, matrix of explanatory variables</p>
+</dd>
+<dt>y: array-like of size [n_samples, n_features]</dt><dd><p>Target values</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">object</span></dt><dd><p>Returns self.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_metadata_routing">
+<span class="sig-name descname"><span class="pre">get_metadata_routing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_metadata_routing" title="Link to this definition">¶</a></dt>
+<dd><p>Get metadata routing of this object.</p>
+<p>Please check <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<section id="id2">
+<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>routing<span class="classifier">MetadataRequest</span></dt><dd><p>A <code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code> encapsulating
+routing information.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_params">
+<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_params" title="Link to this definition">¶</a></dt>
+<dd><p>Get parameters for this estimator.</p>
+<section id="id3">
+<h3>Parameters<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>deep<span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
+contained subobjects that are estimators.</p>
+</dd>
+</dl>
+</section>
+<section id="id4">
+<h3>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>params<span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.log_marginal_like">
+<span class="sig-name descname"><span class="pre">log_marginal_like</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">XXa</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">XYa</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Aa</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.log_marginal_like" title="Link to this definition">¶</a></dt>
+<dd><p>Computes the log of the marginal likelihood.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.predict" title="Link to this definition">¶</a></dt>
+<dd><p>Computes predictive distribution for test set.
+Predictive distribution for each data point is one dimensional
+Gaussian and therefore is characterised by mean and variance based on
+Ref.[1] Section 3.3.2.</p>
+<section id="id5">
+<h3>Parameters<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X: {array-like, sparse} (n_samples_test, n_features)</dt><dd><p>Test data, matrix of explanatory variables</p>
+</dd>
+</dl>
+</section>
+<section id="id6">
+<h3>Returns<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<p>: list of length two [y_hat, var_hat]</p>
+<blockquote>
+<div><dl>
+<dt>y_hat: numpy array of size (n_samples_test,)</dt><dd><blockquote>
+<div><p>Estimated values of targets on test set (i.e. mean of
+predictive distribution)</p>
+</div></blockquote>
+<dl class="simple">
+<dt>var_hat: numpy array of size (n_samples_test,)</dt><dd><p>Variance of predictive distribution</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</div></blockquote>
+</section>
+<section id="id7">
+<h3>References<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<p>[1] Bishop, C. M. (2006). Pattern recognition and machine learning.
+springer.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.score">
+<span class="sig-name descname"><span class="pre">score</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.score" title="Link to this definition">¶</a></dt>
+<dd><p>Return the coefficient of determination of the prediction.</p>
+<p>The coefficient of determination <span class="math notranslate nohighlight">\(R^2\)</span> is defined as
+<span class="math notranslate nohighlight">\((1 - \frac{u}{v})\)</span>, where <span class="math notranslate nohighlight">\(u\)</span> is the residual
+sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_pred)**</span> <span class="pre">2).sum()</span></code> and <span class="math notranslate nohighlight">\(v\)</span>
+is the total sum of squares <code class="docutils literal notranslate"><span class="pre">((y_true</span> <span class="pre">-</span> <span class="pre">y_true.mean())</span> <span class="pre">**</span> <span class="pre">2).sum()</span></code>.
+The best possible score is 1.0 and it can be negative (because the
+model can be arbitrarily worse). A constant model that always predicts
+the expected value of <cite>y</cite>, disregarding the input features, would get
+a <span class="math notranslate nohighlight">\(R^2\)</span> score of 0.0.</p>
+<section id="id8">
+<h3>Parameters<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Test samples. For some estimators this may be a precomputed
+kernel matrix or a list of generic objects instead with shape
+<code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_samples_fitted)</span></code>, where <code class="docutils literal notranslate"><span class="pre">n_samples_fitted</span></code>
+is the number of samples used in the fitting for the estimator.</p>
+</dd>
+<dt>y<span class="classifier">array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>True values for <cite>X</cite>.</p>
+</dd>
+<dt>sample_weight<span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Sample weights.</p>
+</dd>
+</dl>
+</section>
+<section id="id9">
+<h3>Returns<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>score<span class="classifier">float</span></dt><dd><p><span class="math notranslate nohighlight">\(R^2\)</span> of <code class="docutils literal notranslate"><span class="pre">self.predict(X)</span></code> w.r.t. <cite>y</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="notes">
+<h3>Notes<a class="headerlink" href="#notes" title="Link to this heading">¶</a></h3>
+<p>The <span class="math notranslate nohighlight">\(R^2\)</span> score used when calling <code class="docutils literal notranslate"><span class="pre">score</span></code> on a regressor uses
+<code class="docutils literal notranslate"><span class="pre">multioutput='uniform_average'</span></code> from version 0.23 to keep consistent
+with default value of <code class="xref py py-func docutils literal notranslate"><span class="pre">r2_score()</span></code>.
+This influences the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of all the multioutput
+regressors (except for
+<code class="xref py py-class docutils literal notranslate"><span class="pre">MultiOutputRegressor</span></code>).</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_params">
+<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_params" title="Link to this definition">¶</a></dt>
+<dd><p>Set the parameters of this estimator.</p>
+<p>The method works on simple estimators as well as on nested objects
+(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
+parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
+possible to update each component of a nested object.</p>
+<section id="id10">
+<h3>Parameters<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt><a href="#id11"><span class="problematic" id="id12">**</span></a>params<span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
+</dd>
+</dl>
+</section>
+<section id="id13">
+<h3>Returns<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_predict_request">
+<span class="sig-name descname"><span class="pre">set_predict_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD"><span class="pre">RegressionFastARD</span></a></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_predict_request" title="Link to this definition">¶</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">predict</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">predict</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<section id="id14">
+<h3>Parameters<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>return_std<span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">return_std</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">predict</span></code>.</p>
+</dd>
+</dl>
+</section>
+<section id="id15">
+<h3>Returns<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">object</span></dt><dd><p>The updated object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_score_request">
+<span class="sig-name descname"><span class="pre">set_score_request</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">None</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'$UNCHANGED$'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD"><span class="pre">RegressionFastARD</span></a></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_score_request" title="Link to this definition">¶</a></dt>
+<dd><p>Request metadata passed to the <code class="docutils literal notranslate"><span class="pre">score</span></code> method.</p>
+<p>Note that this method is only relevant if
+<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing=True</span></code> (see <code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.set_config()</span></code>).
+Please see <span class="xref std std-ref">User Guide</span> on how the routing
+mechanism works.</p>
+<p>The options for each parameter are:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: metadata is requested, and passed to <code class="docutils literal notranslate"><span class="pre">score</span></code> if provided. The request is ignored if metadata is not provided.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: metadata is not requested and the meta-estimator will not pass it to <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">str</span></code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p></li>
+</ul>
+<p>The default (<code class="docutils literal notranslate"><span class="pre">sklearn.utils.metadata_routing.UNCHANGED</span></code>) retains the
+existing request. This allows you to change the request for some
+parameters and not others.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">Added in version 1.3.</span></p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method is only relevant if this estimator is used as a
+sub-estimator of a meta-estimator, e.g. used inside a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">pipeline.Pipeline</span></code>. Otherwise it has no effect.</p>
+</div>
+<section id="id16">
+<h3>Parameters<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>sample_weight<span class="classifier">str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED</span></dt><dd><p>Metadata routing for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> parameter in <code class="docutils literal notranslate"><span class="pre">score</span></code>.</p>
+</dd>
+</dl>
+</section>
+<section id="id17">
+<h3>Returns<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self<span class="classifier">object</span></dt><dd><p>The updated object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
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+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.__init__"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.fit"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD.fit()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_metadata_routing"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD.get_metadata_routing()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_params"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD.get_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.log_marginal_like"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD.log_marginal_like()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.predict"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD.predict()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.score"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD.score()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_params"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD.set_params()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_predict_request"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD.set_predict_request()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_score_request"><code class="docutils literal notranslate"><span class="pre">RegressionFastARD.set_score_request()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+ <section id="module-bayesvalidrox.surrogate_models.reg_fast_ard">
+<span id="bayesvalidrox-surrogate-models-reg-fast-ard"></span><h1>bayesvalidrox.surrogate_models.reg_fast_ard<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.reg_fast_ard" title="Link to this heading">¶</a></h1>
+<p>Created on Tue Mar 24 19:41:45 2020</p>
+<p>@author: farid</p>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.html#bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions" title="bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_precisions</span></code></a>(Q, S, q, s, A, active, ...)</p></td>
+<td><p>Selects one feature to be added/recomputed/deleted to model based on effect it will have on value of log marginal likelihood.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
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+<h1>bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions<a class="headerlink" href="#bayesvalidrox-surrogate-models-reg-fast-ard-update-precisions" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.reg_fast_ard.</span></span><span class="sig-name descname"><span class="pre">update_precisions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">S</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">s</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clf_bias</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions" title="Link to this definition">¶</a></dt>
+<dd><p>Selects one feature to be added/recomputed/deleted to model based on
+effect it will have on value of log marginal likelihood.</p>
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+ <section id="bayesvalidrox-surrogate-models-reg-fast-laplace-regressionfastlaplace">
+<h1>bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace<a class="headerlink" href="#bayesvalidrox-surrogate-models-reg-fast-laplace-regressionfastlaplace" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.reg_fast_laplace.</span></span><span class="sig-name descname"><span class="pre">RegressionFastLaplace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_Kfold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_term</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Sparse regression with Bayesian Compressive Sensing as described in Alg. 1
+(Fast Laplace) of Ref.[1], which updated formulas from [2].</p>
+<p>sigma2: noise precision (sigma^2)
+nu fixed to 0</p>
+<p>uqlab/lib/uq_regression/BCS/uq_bsc.m</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>n_iter: int, optional (DEFAULT = 1000)</dt><dd><p>Maximum number of iterations</p>
+</dd>
+<dt>tol: float, optional (DEFAULT = 1e-7)</dt><dd><p>If absolute change in precision parameter for weights is below
+threshold algorithm terminates.</p>
+</dd>
+<dt>fit_intercept<span class="classifier">boolean, optional (DEFAULT = True)</span></dt><dd><p>whether to calculate the intercept for this model. If set
+to false, no intercept will be used in calculations
+(e.g. data is expected to be already centered).</p>
+</dd>
+<dt>copy_X<span class="classifier">boolean, optional (DEFAULT = True)</span></dt><dd><p>If True, X will be copied; else, it may be overwritten.</p>
+</dd>
+<dt>verbose<span class="classifier">boolean, optional (DEFAULT = FALSE)</span></dt><dd><p>Verbose mode when fitting the model</p>
+</dd>
+</dl>
+</section>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt><a href="#id3"><span class="problematic" id="id4">coef_</span></a><span class="classifier">array, shape = (n_features)</span></dt><dd><p>Coefficients of the regression model (mean of posterior distribution)</p>
+</dd>
+<dt><a href="#id5"><span class="problematic" id="id6">alpha_</span></a><span class="classifier">float</span></dt><dd><p>estimated precision of the noise</p>
+</dd>
+<dt><a href="#id7"><span class="problematic" id="id8">active_</span></a><span class="classifier">array, dtype = np.bool, shape = (n_features)</span></dt><dd><p>True for non-zero coefficients, False otherwise</p>
+</dd>
+<dt><a href="#id9"><span class="problematic" id="id10">lambda_</span></a><span class="classifier">array, shape = (n_features)</span></dt><dd><p>estimated precisions of the coefficients</p>
+</dd>
+<dt><a href="#id11"><span class="problematic" id="id12">sigma_</span></a><span class="classifier">array, shape = (n_features, n_features)</span></dt><dd><p>estimated covariance matrix of the weights, computed only
+for non-zero coefficients</p>
+</dd>
+</dl>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>[1] Babacan, S. D., Molina, R., & Katsaggelos, A. K. (2009). Bayesian</dt><dd><p>compressive sensing using Laplace priors. IEEE Transactions on image
+processing, 19(1), 53-63.</p>
+</dd>
+<dt>[2] Fast marginal likelihood maximisation for sparse Bayesian models</dt><dd><p>(Tipping & Faul 2003).
+(<a class="reference external" href="http://www.miketipping.com/papers/met-fastsbl.pdf">http://www.miketipping.com/papers/met-fastsbl.pdf</a>)</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_Kfold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_intercept</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_term</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy_X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.__init__" title="bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([n_iter, n_Kfold, tol, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code>(X, y)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_</span></code>(X, y, sigma2)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.predict" title="bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X[, return_std])</p></td>
+<td><p>Computes predictive distribution for test set.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.predict" title="Link to this definition">¶</a></dt>
+<dd><p>Computes predictive distribution for test set.
+Predictive distribution for each data point is one dimensional
+Gaussian and therefore is characterised by mean and variance based on
+Ref.[1] Section 3.3.2.</p>
+<section id="id1">
+<h3>Parameters<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X: {array-like, sparse} (n_samples_test, n_features)</dt><dd><p>Test data, matrix of explanatory variables</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<p>: list of length two [y_hat, var_hat]</p>
+<blockquote>
+<div><dl>
+<dt>y_hat: numpy array of size (n_samples_test,)</dt><dd><blockquote>
+<div><p>Estimated values of targets on test set (i.e. mean of
+predictive distribution)</p>
+</div></blockquote>
+<dl class="simple">
+<dt>var_hat: numpy array of size (n_samples_test,)</dt><dd><p>Variance of predictive distribution</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</div></blockquote>
+</section>
+<section id="id2">
+<h3>References<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<p>[1] Bishop, C. M. (2006). Pattern recognition and machine learning.
+springer.</p>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace"><code class="docutils literal notranslate"><span class="pre">RegressionFastLaplace</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.__init__"><code class="docutils literal notranslate"><span class="pre">RegressionFastLaplace.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.predict"><code class="docutils literal notranslate"><span class="pre">RegressionFastLaplace.predict()</span></code></a></li>
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+<p class="rubric">Classes</p>
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+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace" title="bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RegressionFastLaplace</span></code></a>([n_iter, n_Kfold, ...])</p></td>
+<td><p>Sparse regression with Bayesian Compressive Sensing as described in Alg.</p></td>
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new file mode 100644
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+ <section id="bayesvalidrox-surrogate-models-surrogate-models-metamodel">
+<h1>bayesvalidrox.surrogate_models.surrogate_models.MetaModel<a class="headerlink" href="#bayesvalidrox-surrogate-models-surrogate-models-metamodel" title="Link to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models.</span></span><span class="sig-name descname"><span class="pre">MetaModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'PCE'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pce_reg_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'OLS'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fast'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap_itrs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pce_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pce_q_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_red_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_constraints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Meta (surrogate) model</p>
+<p>This class trains a surrogate model. It accepts an input object (input_obj)
+containing the specification of the distributions for uncertain parameters
+and a model object with instructions on how to run the computational model.</p>
+<section id="attributes">
+<h2>Attributes<a class="headerlink" href="#attributes" title="Link to this heading">¶</a></h2>
+<dl>
+<dt>input_obj<span class="classifier">obj</span></dt><dd><p>Input object with the information on the model input parameters.</p>
+</dd>
+<dt>meta_model_type<span class="classifier">str</span></dt><dd><p>Surrogate model types. Three surrogate model types are supported:
+polynomial chaos expansion (<cite>PCE</cite>), arbitrary PCE (<cite>aPCE</cite>) and
+Gaussian process regression (<cite>GPE</cite>). Default is PCE.</p>
+</dd>
+<dt>pce_reg_method<span class="classifier">str</span></dt><dd><p>PCE regression method to compute the coefficients. The following
+regression methods are available:</p>
+<ol class="arabic simple">
+<li><p>OLS: Ordinary Least Square method</p></li>
+<li><p>BRR: Bayesian Ridge Regression</p></li>
+<li><p>LARS: Least angle regression</p></li>
+<li><p>ARD: Bayesian ARD Regression</p></li>
+<li><p>FastARD: Fast Bayesian ARD Regression</p></li>
+<li><p>VBL: Variational Bayesian Learning</p></li>
+</ol>
+<p>7. EBL: Emperical Bayesian Learning
+Default is <cite>OLS</cite>.</p>
+</dd>
+<dt>bootstrap_method<span class="classifier">str</span></dt><dd><p>Bootstraping method. Options are <cite>‘normal’</cite> and <cite>‘fast’</cite>. The default
+is <cite>‘fast’</cite>. It means that in each iteration except the first one, only
+the coefficent are recalculated with the ordinary least square method.</p>
+</dd>
+<dt>n_bootstrap_itrs<span class="classifier">int</span></dt><dd><p>Number of iterations for the bootstrap sampling. The default is <cite>1</cite>.</p>
+</dd>
+<dt>pce_deg<span class="classifier">int or list of int</span></dt><dd><p>Polynomial degree(s). If a list is given, an adaptive algorithm is used
+to find the best degree with the lowest Leave-One-Out cross-validation
+(LOO) error (or the highest score=1-LOO). Default is <cite>1</cite>.</p>
+</dd>
+<dt>pce_q_norm<span class="classifier">float</span></dt><dd><p>Hyperbolic (or q-norm) truncation for multi-indices of multivariate
+polynomials. Default is <cite>1.0</cite>.</p>
+</dd>
+<dt>dim_red_method<span class="classifier">str</span></dt><dd><p>Dimensionality reduction method for the output space. The available
+method is based on principal component analysis (PCA). The Default is
+<cite>‘no’</cite>. There are two ways to select number of components: use
+percentage of the explainable variance threshold (between 0 and 100)
+(Option A) or direct prescription of components’ number (Option B):</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaModelOpts</span> <span class="o">=</span> <span class="n">MetaModel</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">dim_red_method</span> <span class="o">=</span> <span class="s1">'PCA'</span>
+<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">var_pca_threshold</span> <span class="o">=</span> <span class="mf">99.999</span> <span class="c1"># Option A</span>
+<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">n_pca_components</span> <span class="o">=</span> <span class="mi">12</span> <span class="c1"># Option B</span>
+</pre></div>
+</div>
+</dd>
+<dt>apply_constraints<span class="classifier">bool</span></dt><dd><p>If set to true constraints will be applied during training.
+In this case the training uses OLS. In this version the constraints
+need to be set explicitly in this class.</p>
+</dd>
+<dt>verbose<span class="classifier">bool</span></dt><dd><p>Prints summary of the regression results. Default is <cite>False</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="note">
+<h2>Note<a class="headerlink" href="#note" title="Link to this heading">¶</a></h2>
+<p>To define the sampling methods and the training set, an experimental design
+instance shall be defined. This can be done by:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">add_InputSpace</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Two experimental design schemes are supported: one-shot (<cite>normal</cite>) and
+adaptive sequential (<cite>sequential</cite>) designs.
+For experimental design refer to <cite>InputSpace</cite>.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">meta_model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'PCE'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pce_reg_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'OLS'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fast'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap_itrs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pce_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pce_q_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_red_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_constraints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.__init__" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.__init__" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(input_obj[, meta_model_type, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.adaptive_regression" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.adaptive_regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adaptive_regression</span></code></a>(ED_Y, varIdx[, verbose])</p></td>
+<td><p>Adaptively fits the PCE model by comparing the scores of different degrees and q-norm.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.add_InputSpace" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.add_InputSpace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_InputSpace</span></code></a>()</p></td>
+<td><p>Instanciates experimental design object.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.build_metamodel" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.build_metamodel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">build_metamodel</span></code></a>([n_init_samples])</p></td>
+<td><p>Builds the parts for the metamodel (polynomes,...) that are neede before fitting.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.copy_meta_model_opts" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.copy_meta_model_opts"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy_meta_model_opts</span></code></a>()</p></td>
+<td><p>This method is a convinient function to copy the metamodel options.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.create_model_error" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.create_model_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_model_error</span></code></a>(X, y, MeasuredData)</p></td>
+<td><p>Fits a GPE-based model error.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_metamodel" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_metamodel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_metamodel</span></code></a>(samples)</p></td>
+<td><p>Evaluates metamodel at the requested samples.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_model_error" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_model_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_model_error</span></code></a>(X, y_pred)</p></td>
+<td><p>Evaluates the error model.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.fit" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X, y[, parallel, verbose])</p></td>
+<td><p>Fits the surrogate to the given data (samples X, outputs y).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.generate_polynomials" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.generate_polynomials"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_polynomials</span></code></a>([max_deg])</p></td>
+<td><p>Generates (univariate) polynomials.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.pca_transformation" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.pca_transformation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pca_transformation</span></code></a>(target[, verbose])</p></td>
+<td><p>Transforms the targets (outputs) via Principal Component Analysis</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.regression" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.regression"><code class="xref py py-obj docutils literal notranslate"><span class="pre">regression</span></code></a>(X, y, basis_indices[, ...])</p></td>
+<td><p>Fit regression using the regression method provided.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.univ_basis_vals" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.univ_basis_vals"><code class="xref py py-obj docutils literal notranslate"><span class="pre">univ_basis_vals</span></code></a>(samples[, n_max])</p></td>
+<td><p>Evaluates univariate regressors along input directions.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.update_pce_coeffs" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.update_pce_coeffs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_pce_coeffs</span></code></a>(X, y[, out_dict])</p></td>
+<td><p>Updates the PCE coefficents using only the ordinary least square method for the fast version of the bootstrapping.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.adaptive_regression">
+<span class="sig-name descname"><span class="pre">adaptive_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ED_Y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">varIdx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.adaptive_regression" title="Link to this definition">¶</a></dt>
+<dd><p>Adaptively fits the PCE model by comparing the scores of different
+degrees and q-norm.</p>
+<section id="parameters">
+<h3>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>ED_Y<span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Target values, i.e. simulation results for the Experimental design.</p>
+</dd>
+<dt>varIdx<span class="classifier">int</span></dt><dd><p>Index of the output.</p>
+</dd>
+<dt>verbose<span class="classifier">bool, optional</span></dt><dd><p>Print out summary. The default is False.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h3>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>returnVars<span class="classifier">Dict</span></dt><dd><p>Fitted estimator, best degree, best q-norm, LOOCVScore and
+coefficients.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.add_InputSpace">
+<span class="sig-name descname"><span class="pre">add_InputSpace</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.add_InputSpace" title="Link to this definition">¶</a></dt>
+<dd><p>Instanciates experimental design object.</p>
+<section id="id1">
+<h3>Returns<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">auto_vivification</span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification" title="Link to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></p>
+<p>Implementation of perl’s AutoVivification feature.</p>
+<p>Source: <a class="reference external" href="https://stackoverflow.com/a/651879/18082457">https://stackoverflow.com/a/651879/18082457</a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.clear">
+<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None.</span>  <span class="pre">Remove</span> <span class="pre">all</span> <span class="pre">items</span> <span class="pre">from</span> <span class="pre">D.</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.clear" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.copy">
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">a</span> <span class="pre">shallow</span> <span class="pre">copy</span> <span class="pre">of</span> <span class="pre">D</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.copy" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.fromkeys">
+<span class="sig-name descname"><span class="pre">fromkeys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.fromkeys" title="Link to this definition">¶</a></dt>
+<dd><p>Create a new dictionary with keys from iterable and values set to value.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.get">
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.get" title="Link to this definition">¶</a></dt>
+<dd><p>Return the value for key if key is in the dictionary, else default.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.items">
+<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">a</span> <span class="pre">set-like</span> <span class="pre">object</span> <span class="pre">providing</span> <span class="pre">a</span> <span class="pre">view</span> <span class="pre">on</span> <span class="pre">D's</span> <span class="pre">items</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.items" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.keys">
+<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">a</span> <span class="pre">set-like</span> <span class="pre">object</span> <span class="pre">providing</span> <span class="pre">a</span> <span class="pre">view</span> <span class="pre">on</span> <span class="pre">D's</span> <span class="pre">keys</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.keys" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.pop">
+<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span></em><span class="optional">[</span>, <em class="sig-param"><span class="n"><span class="pre">d</span></span></em><span class="optional">]</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">v,</span> <span class="pre">remove</span> <span class="pre">specified</span> <span class="pre">key</span> <span class="pre">and</span> <span class="pre">return</span> <span class="pre">the</span> <span class="pre">corresponding</span> <span class="pre">value.</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.pop" title="Link to this definition">¶</a></dt>
+<dd><p>If the key is not found, return the default if given; otherwise,
+raise a KeyError.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.popitem">
+<span class="sig-name descname"><span class="pre">popitem</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.popitem" title="Link to this definition">¶</a></dt>
+<dd><p>Remove and return a (key, value) pair as a 2-tuple.</p>
+<p>Pairs are returned in LIFO (last-in, first-out) order.
+Raises KeyError if the dict is empty.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.setdefault">
+<span class="sig-name descname"><span class="pre">setdefault</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.setdefault" title="Link to this definition">¶</a></dt>
+<dd><p>Insert key with a value of default if key is not in the dictionary.</p>
+<p>Return the value for key if key is in the dictionary, else default.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.update">
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><span class="optional">[</span><em class="sig-param"><span class="n"><span class="pre">E</span></span></em>, <span class="optional">]</span><em class="sig-param"><span class="n"><span class="pre">**F</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None.</span>  <span class="pre">Update</span> <span class="pre">D</span> <span class="pre">from</span> <span class="pre">dict/iterable</span> <span class="pre">E</span> <span class="pre">and</span> <span class="pre">F.</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.update" title="Link to this definition">¶</a></dt>
+<dd><p>If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
+If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
+In either case, this is followed by: for k in F: D[k] = F[k]</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.values">
+<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">an</span> <span class="pre">object</span> <span class="pre">providing</span> <span class="pre">a</span> <span class="pre">view</span> <span class="pre">on</span> <span class="pre">D's</span> <span class="pre">values</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.values" title="Link to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.build_metamodel">
+<span class="sig-name descname"><span class="pre">build_metamodel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_init_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.build_metamodel" title="Link to this definition">¶</a></dt>
+<dd><p>Builds the parts for the metamodel (polynomes,…) that are neede before fitting.</p>
+<section id="id2">
+<h3>Returns<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>None</dt><dd><p>DESCRIPTION.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.copy_meta_model_opts">
+<span class="sig-name descname"><span class="pre">copy_meta_model_opts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.copy_meta_model_opts" title="Link to this definition">¶</a></dt>
+<dd><p>This method is a convinient function to copy the metamodel options.</p>
+<section id="id3">
+<h3>Returns<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>new_MetaModelOpts<span class="classifier">object</span></dt><dd><p>The copied object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.create_model_error">
+<span class="sig-name descname"><span class="pre">create_model_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">MeasuredData</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.create_model_error" title="Link to this definition">¶</a></dt>
+<dd><p>Fits a GPE-based model error.</p>
+<section id="id4">
+<h3>Parameters<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">array of shape (n_outputs, n_inputs)</span></dt><dd><dl class="simple">
+<dt>Input array. It can contain any forcing inputs or coordinates of</dt><dd><p>extracted data.</p>
+</dd>
+</dl>
+</dd>
+<dt>y<span class="classifier">array of shape (n_outputs,)</span></dt><dd><p>The model response for the MAP parameter set.</p>
+</dd>
+</dl>
+<p>MeasuredData :</p>
+</section>
+<section id="id5">
+<h3>Returns<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>self: object</dt><dd><p>Self object.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_metamodel">
+<span class="sig-name descname"><span class="pre">eval_metamodel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">samples</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_metamodel" title="Link to this definition">¶</a></dt>
+<dd><p>Evaluates metamodel at the requested samples. One can also generate
+nsamples.</p>
+<section id="id6">
+<h3>Parameters<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>samples<span class="classifier">array of shape (n_samples, n_params), optional</span></dt><dd><p>Samples to evaluate metamodel at. The default is None.</p>
+</dd>
+</dl>
+</section>
+<section id="id7">
+<h3>Returns<a class="headerlink" href="#id7" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>mean_pred<span class="classifier">dict</span></dt><dd><p>Mean of the predictions.</p>
+</dd>
+<dt>std_pred<span class="classifier">dict</span></dt><dd><p>Standard deviatioon of the predictions.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_model_error">
+<span class="sig-name descname"><span class="pre">eval_model_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_model_error" title="Link to this definition">¶</a></dt>
+<dd><p>Evaluates the error model.</p>
+<section id="id8">
+<h3>Parameters<a class="headerlink" href="#id8" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">array</span></dt><dd><p>Inputs.</p>
+</dd>
+<dt>y_pred<span class="classifier">dict</span></dt><dd><p>Predictions.</p>
+</dd>
+</dl>
+</section>
+<section id="id9">
+<h3>Returns<a class="headerlink" href="#id9" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>mean_pred<span class="classifier">dict</span></dt><dd><p>Mean predition of the GPE-based error model.</p>
+</dd>
+<dt>std_pred<span class="classifier">dict</span></dt><dd><p>standard deviation of the GPE-based error model.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parallel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.fit" title="Link to this definition">¶</a></dt>
+<dd><p>Fits the surrogate to the given data (samples X, outputs y).
+Note here that the samples X should be the transformed samples provided
+by the experimental design if the transformation is used there.</p>
+<section id="id10">
+<h3>Parameters<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">2D list or np.array of shape (#samples, #dim)</span></dt><dd><p>The parameter value combinations that the model was evaluated at.</p>
+</dd>
+<dt>y<span class="classifier">dict of 2D lists or arrays of shape (#samples, #timesteps)</span></dt><dd><p>The respective model evaluations.</p>
+</dd>
+<dt>parallel<span class="classifier">bool</span></dt><dd><p>Set to True to run the training in parallel for various keys.
+The default is False.</p>
+</dd>
+<dt>verbose<span class="classifier">bool</span></dt><dd><p>Set to True to obtain more information during runtime.
+The default is False.</p>
+</dd>
+</dl>
+</section>
+<section id="id11">
+<h3>Returns<a class="headerlink" href="#id11" title="Link to this heading">¶</a></h3>
+<p>None.</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.generate_polynomials">
+<span class="sig-name descname"><span class="pre">generate_polynomials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_deg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.generate_polynomials" title="Link to this definition">¶</a></dt>
+<dd><p>Generates (univariate) polynomials.</p>
+<section id="id12">
+<h3>Parameters<a class="headerlink" href="#id12" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>max_deg<span class="classifier">int</span></dt><dd><p>Maximum polynomial degree.</p>
+</dd>
+</dl>
+</section>
+<section id="id13">
+<h3>Returns<a class="headerlink" href="#id13" title="Link to this heading">¶</a></h3>
+<p>None</p>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.pca_transformation">
+<span class="sig-name descname"><span class="pre">pca_transformation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.pca_transformation" title="Link to this definition">¶</a></dt>
+<dd><p>Transforms the targets (outputs) via Principal Component Analysis</p>
+<section id="id14">
+<h3>Parameters<a class="headerlink" href="#id14" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>target<span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Target values.</p>
+</dd>
+<dt>verbose<span class="classifier">bool</span></dt><dd><p>Set to True to get more information during functtion call.
+The default is False.</p>
+</dd>
+</dl>
+</section>
+<section id="id15">
+<h3>Returns<a class="headerlink" href="#id15" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>pca<span class="classifier">obj</span></dt><dd><p>Fitted sklearnPCA object.</p>
+</dd>
+<dt>OutputMatrix<span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Transformed target values.</p>
+</dd>
+<dt>n_pca_components<span class="classifier">int</span></dt><dd><p>Number of selected principal components.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.regression">
+<span class="sig-name descname"><span class="pre">regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">basis_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reg_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparsity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.regression" title="Link to this definition">¶</a></dt>
+<dd><p>Fit regression using the regression method provided.</p>
+<section id="id16">
+<h3>Parameters<a class="headerlink" href="#id16" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">array of shape (n_samples, n_features)</span></dt><dd><p>Training vector, where n_samples is the number of samples and
+n_features is the number of features.</p>
+</dd>
+<dt>y<span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Target values.</p>
+</dd>
+<dt>basis_indices<span class="classifier">array of shape (n_terms, n_params)</span></dt><dd><p>Multi-indices of multivariate polynomials.</p>
+</dd>
+<dt>reg_method<span class="classifier">str, optional</span></dt><dd><p>DESCRIPTION. The default is None.</p>
+</dd>
+<dt>sparsity<span class="classifier">bool</span></dt><dd><p>Use with sparsity-inducing training methods. The default is True</p>
+</dd>
+</dl>
+</section>
+<section id="id17">
+<h3>Returns<a class="headerlink" href="#id17" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>return_out_dict<span class="classifier">Dict</span></dt><dd><p>Fitted estimator, spareMulti-Index, sparseX and coefficients.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.univ_basis_vals">
+<span class="sig-name descname"><span class="pre">univ_basis_vals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.univ_basis_vals" title="Link to this definition">¶</a></dt>
+<dd><p>Evaluates univariate regressors along input directions.</p>
+<section id="id18">
+<h3>Parameters<a class="headerlink" href="#id18" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>samples<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Samples.</p>
+</dd>
+<dt>n_max<span class="classifier">int, optional</span></dt><dd><p>Maximum polynomial degree. The default is <cite>None</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="id19">
+<h3>Returns<a class="headerlink" href="#id19" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>univ_basis: array of shape (n_samples, n_params, n_max+1)</dt><dd><p>All univariate regressors up to n_max.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.update_pce_coeffs">
+<span class="sig-name descname"><span class="pre">update_pce_coeffs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.update_pce_coeffs" title="Link to this definition">¶</a></dt>
+<dd><p>Updates the PCE coefficents using only the ordinary least square method
+for the fast version of the bootstrapping.</p>
+<section id="id20">
+<h3>Parameters<a class="headerlink" href="#id20" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>X<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Training set.</p>
+</dd>
+<dt>y<span class="classifier">array of shape (n_samples, n_outs)</span></dt><dd><p>The (transformed) model responses.</p>
+</dd>
+<dt>out_dict<span class="classifier">dict</span></dt><dd><p>The training output dictionary of the first iteration, i.e.
+the surrogate model for the original experimental design.</p>
+</dd>
+</dl>
+</section>
+<section id="id21">
+<h3>Returns<a class="headerlink" href="#id21" title="Link to this heading">¶</a></h3>
+<dl class="simple">
+<dt>final_out_dict<span class="classifier">dict</span></dt><dd><p>The updated training output dictionary.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+</dd></dl>
+
+</section>
+
+ </article>
+ </div>
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+
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+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel"><code class="docutils literal notranslate"><span class="pre">MetaModel</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.__init__"><code class="docutils literal notranslate"><span class="pre">MetaModel.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.adaptive_regression"><code class="docutils literal notranslate"><span class="pre">MetaModel.adaptive_regression()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.add_InputSpace"><code class="docutils literal notranslate"><span class="pre">MetaModel.add_InputSpace()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification</span></code></a><ul>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.clear"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.clear()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.copy"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.copy()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.fromkeys"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.fromkeys()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.get"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.get()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.items"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.items()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.keys"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.keys()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.pop"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.pop()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.popitem"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.popitem()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.setdefault"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.setdefault()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.update"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.update()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.values"><code class="docutils literal notranslate"><span class="pre">MetaModel.auto_vivification.values()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.build_metamodel"><code class="docutils literal notranslate"><span class="pre">MetaModel.build_metamodel()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.copy_meta_model_opts"><code class="docutils literal notranslate"><span class="pre">MetaModel.copy_meta_model_opts()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.create_model_error"><code class="docutils literal notranslate"><span class="pre">MetaModel.create_model_error()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_metamodel"><code class="docutils literal notranslate"><span class="pre">MetaModel.eval_metamodel()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_model_error"><code class="docutils literal notranslate"><span class="pre">MetaModel.eval_model_error()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.fit"><code class="docutils literal notranslate"><span class="pre">MetaModel.fit()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.generate_polynomials"><code class="docutils literal notranslate"><span class="pre">MetaModel.generate_polynomials()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.pca_transformation"><code class="docutils literal notranslate"><span class="pre">MetaModel.pca_transformation()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.regression"><code class="docutils literal notranslate"><span class="pre">MetaModel.regression()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.univ_basis_vals"><code class="docutils literal notranslate"><span class="pre">MetaModel.univ_basis_vals()</span></code></a></li>
+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.update_pce_coeffs"><code class="docutils literal notranslate"><span class="pre">MetaModel.update_pce_coeffs()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+ </div>
+ </div>
+ </div>
+
+
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diff --git a/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html b/docs/build/html/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html
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+<h1>bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error<a class="headerlink" href="#bayesvalidrox-surrogate-models-surrogate-models-corr-loocv-error" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models.</span></span><span class="sig-name descname"><span class="pre">corr_loocv_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">clf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">psi</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coeffs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error" title="Link to this definition">¶</a></dt>
+<dd><p>Calculates the corrected LOO error for regression on regressor
+matrix <cite>psi</cite> that generated the coefficients based on [1] and [2].</p>
+<dl class="simple">
+<dt>[1] Blatman, G., 2009. Adaptive sparse polynomial chaos expansions for</dt><dd><p>uncertainty propagation and sensitivity analysis (Doctoral
+dissertation, Clermont-Ferrand 2).</p>
+</dd>
+<dt>[2] Blatman, G. and Sudret, B., 2011. Adaptive sparse polynomial chaos</dt><dd><p>expansion based on least angle regression. Journal of computational
+Physics, 230(6), pp.2345-2367.</p>
+</dd>
+</dl>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>clf<span class="classifier">object</span></dt><dd><p>Fitted estimator.</p>
+</dd>
+<dt>psi<span class="classifier">array of shape (n_samples, n_features)</span></dt><dd><p>The multivariate orthogonal polynomials (regressor).</p>
+</dd>
+<dt>coeffs<span class="classifier">array-like of shape (n_features,)</span></dt><dd><p>Estimated cofficients.</p>
+</dd>
+<dt>y<span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Target values.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>R_2<span class="classifier">float</span></dt><dd><p>LOOCV Validation score (1-LOOCV erro).</p>
+</dd>
+<dt>residual<span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Residual values (y - predicted targets).</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error"><code class="docutils literal notranslate"><span class="pre">corr_loocv_error()</span></code></a></li>
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+ <section id="bayesvalidrox-surrogate-models-surrogate-models-create-psi">
+<h1>bayesvalidrox.surrogate_models.surrogate_models.create_psi<a class="headerlink" href="#bayesvalidrox-surrogate-models-surrogate-models-create-psi" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.create_psi">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models.</span></span><span class="sig-name descname"><span class="pre">create_psi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">basis_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">univ_p_val</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.create_psi" title="Link to this definition">¶</a></dt>
+<dd><p>This function assemble the design matrix Psi from the given basis index
+set INDICES and the univariate polynomial evaluations univ_p_val.</p>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>basis_indices<span class="classifier">array of shape (n_terms, n_params)</span></dt><dd><p>Multi-indices of multivariate polynomials.</p>
+</dd>
+<dt>univ_p_val<span class="classifier">array of (n_samples, n_params, n_max+1)</span></dt><dd><p>All univariate regressors up to <cite>n_max</cite>.</p>
+</dd>
+</dl>
+</section>
+<section id="raises">
+<h2>Raises<a class="headerlink" href="#raises" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>ValueError</dt><dd><p>n_terms in arguments do not match.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>psi<span class="classifier">array of shape (n_samples, n_terms)</span></dt><dd><p>Multivariate regressors.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
+</section>
+
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+<h1>bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator<a class="headerlink" href="#bayesvalidrox-surrogate-models-surrogate-models-gaussian-process-emulator" title="Link to this heading">¶</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator">
+<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models.</span></span><span class="sig-name descname"><span class="pre">gaussian_process_emulator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nug_term</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autoSelect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">varIdx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator" title="Link to this definition">¶</a></dt>
+<dd><dl class="simple">
+<dt>Fits a Gaussian Process Emulator to the target given the training</dt><dd><p>points.</p>
+</dd>
+</dl>
+<section id="parameters">
+<h2>Parameters<a class="headerlink" href="#parameters" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>X<span class="classifier">array of shape (n_samples, n_params)</span></dt><dd><p>Training points.</p>
+</dd>
+<dt>y<span class="classifier">array of shape (n_samples,)</span></dt><dd><p>Target values.</p>
+</dd>
+<dt>nug_term<span class="classifier">float, optional</span></dt><dd><p>Nugget term. The default is None, i.e. variance of y.</p>
+</dd>
+<dt>autoSelect<span class="classifier">bool, optional</span></dt><dd><p>Loop over some kernels and select the best. The default is False.</p>
+</dd>
+<dt>varIdx<span class="classifier">int, optional</span></dt><dd><p>The index number. The default is None.</p>
+</dd>
+</dl>
+</section>
+<section id="returns">
+<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
+<dl class="simple">
+<dt>gp<span class="classifier">object</span></dt><dd><p>Fitted estimator.</p>
+</dd>
+</dl>
+</section>
+</dd></dl>
+
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+
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+<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator"><code class="docutils literal notranslate"><span class="pre">gaussian_process_emulator()</span></code></a></li>
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+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html">bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error</a></li>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.create_psi.html">bayesvalidrox.surrogate_models.surrogate_models.create_psi</a></li>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html">bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator</a></li>
+<li class="toctree-l5"><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
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+</div>
+
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+
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+ </aside>
+ <div class="main">
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+ <a href="#" class="back-to-top muted-link">
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+ <label class="toc-overlay-icon toc-content-icon" for="__toc">
+ <div class="visually-hidden">Toggle table of contents sidebar</div>
+ <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+ </label>
+ </div>
+ <article role="main">
+ <section id="module-bayesvalidrox.surrogate_models.surrogate_models">
+<span id="bayesvalidrox-surrogate-models-surrogate-models"></span><h1>bayesvalidrox.surrogate_models.surrogate_models<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.surrogate_models" title="Link to this heading">¶</a></h1>
+<p>Implementation of metamodel as either PC, aPC or GPE</p>
+<p class="rubric">Functions</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html#bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error" title="bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">corr_loocv_error</span></code></a>(clf, psi, coeffs, y)</p></td>
+<td><p>Calculates the corrected LOO error for regression on regressor matrix <cite>psi</cite> that generated the coefficients based on [1] and [2].</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.create_psi.html#bayesvalidrox.surrogate_models.surrogate_models.create_psi" title="bayesvalidrox.surrogate_models.surrogate_models.create_psi"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_psi</span></code></a>(basis_indices, univ_p_val)</p></td>
+<td><p>This function assemble the design matrix Psi from the given basis index set INDICES and the univariate polynomial evaluations univ_p_val.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html#bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator" title="bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process_emulator</span></code></a>(X, y[, nug_term, ...])</p></td>
+<td><p>Fits a Gaussian Process Emulator to the target given the training</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p class="rubric">Classes</p>
+<div class="table-wrapper autosummary longtable docutils container">
+<table class="autosummary longtable docutils align-default">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MetaModel</span></code></a>(input_obj[, meta_model_type, ...])</p></td>
+<td><p>Meta (surrogate) model</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+</section>
+
+ </article>
+ </div>
+ <footer>
+
+ <div class="related-pages">
+ <a class="next-page" href="bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html">
+ <div class="page-info">
+ <div class="context">
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+ <div class="title">bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error</div>
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+ </a>
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+ <div class="context">
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+ <div class="title">bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace</div>
+
+ </div>
+ </a>
+ </div>
+ <div class="bottom-of-page">
+ <div class="left-details">
+ <div class="copyright">
+ Copyright © 2023, Farid Mohammadi, Rebecca Kohlhaas
+ </div>
+ Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+
+ <a href="https://github.com/pradyunsg/furo">Furo</a>
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+ <div class="right-details">
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+ <div class="toc-sticky toc-scroll">
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+ On this page
+ </span>
+ </div>
+ <div class="toc-tree-container">
+ <div class="toc-tree">
+ <ul>
+<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.surrogate_models</a><ul>
+</ul>
+</li>
+</ul>
+
+ </div>
+ </div>
+ </div>
+
+
+ </aside>
+ </div>
+</div><script src="../_static/documentation_options.js?v=282f96c0"></script>
+ <script src="../_static/doctools.js?v=9a2dae69"></script>
+ <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
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+</html>
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diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f129d634120070b7264822b901279a02983e8498
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.rst.txt
@@ -0,0 +1,32 @@
+bayesvalidrox.bayes\_inference.bayes\_inference.BayesInference
+==============================================================
+
+.. currentmodule:: bayesvalidrox.bayes_inference.bayes_inference
+
+.. autoclass:: BayesInference
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~BayesInference.__init__
+ ~BayesInference.create_error_model
+ ~BayesInference.create_inference
+ ~BayesInference.normpdf
+ ~BayesInference.perform_bootstrap
+ ~BayesInference.plot_log_BME
+ ~BayesInference.plot_post_params
+ ~BayesInference.setup_inference
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ebc205c86c7eeb677c26a76f2c26255f327fb909
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.rst.txt
@@ -0,0 +1,31 @@
+bayesvalidrox.bayes\_inference.bayes\_inference
+===============================================
+
+.. automodule:: bayesvalidrox.bayes_inference.bayes_inference
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ BayesInference
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..69d7f471f16058189e0ceb1c1b5196437f946baf
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.rst.txt
@@ -0,0 +1,35 @@
+bayesvalidrox.bayes\_inference.bayes\_model\_comparison.BayesModelComparison
+============================================================================
+
+.. currentmodule:: bayesvalidrox.bayes_inference.bayes_model_comparison
+
+.. autoclass:: BayesModelComparison
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~BayesModelComparison.__init__
+ ~BayesModelComparison.cal_model_weight
+ ~BayesModelComparison.calc_bayes_factors
+ ~BayesModelComparison.calc_justifiability_analysis
+ ~BayesModelComparison.calc_model_weights
+ ~BayesModelComparison.generate_dataset
+ ~BayesModelComparison.model_comparison_all
+ ~BayesModelComparison.plot_bayes_factor
+ ~BayesModelComparison.plot_just_analysis
+ ~BayesModelComparison.plot_model_weights
+ ~BayesModelComparison.setup
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a0599ad4816f88709c1fb3eec268f4e63a920c7b
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.rst.txt
@@ -0,0 +1,31 @@
+bayesvalidrox.bayes\_inference.bayes\_model\_comparison
+=======================================================
+
+.. automodule:: bayesvalidrox.bayes_inference.bayes_model_comparison
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ BayesModelComparison
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..39c9e8e960e81d3d423feec02d61e3809647ba39
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.rst.txt
@@ -0,0 +1,26 @@
+bayesvalidrox.bayes\_inference.discrepancy.Discrepancy
+======================================================
+
+.. currentmodule:: bayesvalidrox.bayes_inference.discrepancy
+
+.. autoclass:: Discrepancy
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Discrepancy.__init__
+ ~Discrepancy.get_sample
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b97bd05f0c5af897fe049ff86dbf844cbafdc236
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.rst.txt
@@ -0,0 +1,31 @@
+bayesvalidrox.bayes\_inference.discrepancy
+==========================================
+
+.. automodule:: bayesvalidrox.bayes_inference.discrepancy
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ Discrepancy
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3037f65657ea50ae5bc288d590e5523e432607ee
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.rst.txt
@@ -0,0 +1,32 @@
+bayesvalidrox.bayes\_inference.mcmc.MCMC
+========================================
+
+.. currentmodule:: bayesvalidrox.bayes_inference.mcmc
+
+.. autoclass:: MCMC
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~MCMC.__init__
+ ~MCMC.eval_model
+ ~MCMC.log_likelihood
+ ~MCMC.log_posterior
+ ~MCMC.log_prior
+ ~MCMC.normpdf
+ ~MCMC.run_sampler
+ ~MCMC.train_error_model
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..00d208a8f0c8e92fea9c697f828c6b0eec893e64
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.bayes\_inference.mcmc.gelman\_rubin
+=================================================
+
+.. currentmodule:: bayesvalidrox.bayes_inference.mcmc
+
+.. autofunction:: gelman_rubin
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..685757475adef946caf395de71c6c9d0146d171a
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.rst.txt
@@ -0,0 +1,38 @@
+bayesvalidrox.bayes\_inference.mcmc
+===================================
+
+.. automodule:: bayesvalidrox.bayes_inference.mcmc
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ gelman_rubin
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ MCMC
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2d1f540c9e4975adb3578e477d29eac97f2330ad
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.bayes_inference.rst.txt
@@ -0,0 +1,35 @@
+bayesvalidrox.bayes\_inference
+==============================
+
+.. automodule:: bayesvalidrox.bayes_inference
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+
+ bayesvalidrox.bayes_inference.bayes_inference
+ bayesvalidrox.bayes_inference.bayes_model_comparison
+ bayesvalidrox.bayes_inference.discrepancy
+ bayesvalidrox.bayes_inference.mcmc
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9efcfaaa0b53ab697fc1c5f9c080472b2d4a5f67
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.rst.txt
@@ -0,0 +1,33 @@
+bayesvalidrox.post\_processing.post\_processing.PostProcessing
+==============================================================
+
+.. currentmodule:: bayesvalidrox.post_processing.post_processing
+
+.. autoclass:: PostProcessing
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~PostProcessing.__init__
+ ~PostProcessing.check_accuracy
+ ~PostProcessing.check_reg_quality
+ ~PostProcessing.compute_pce_moments
+ ~PostProcessing.eval_pce_model_3d
+ ~PostProcessing.plot_moments
+ ~PostProcessing.plot_seq_design_diagnostics
+ ~PostProcessing.sobol_indices
+ ~PostProcessing.valid_metamodel
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1aae5db20ef1bf96c930a464e6ed713f1f166d73
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.rst.txt
@@ -0,0 +1,31 @@
+bayesvalidrox.post\_processing.post\_processing
+===============================================
+
+.. automodule:: bayesvalidrox.post_processing.post_processing
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ PostProcessing
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.post_processing.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.post_processing.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c37004bd561d420fdb3ff51ff488415983214aa3
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.post_processing.rst.txt
@@ -0,0 +1,32 @@
+bayesvalidrox.post\_processing
+==============================
+
+.. automodule:: bayesvalidrox.post_processing
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+
+ bayesvalidrox.post_processing.post_processing
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..52664818dbe0e571253d3341790bd66040372021
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.rst.txt
@@ -0,0 +1,33 @@
+bayesvalidrox.pylink.pylink.PyLinkForwardModel
+==============================================
+
+.. currentmodule:: bayesvalidrox.pylink.pylink
+
+.. autoclass:: PyLinkForwardModel
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~PyLinkForwardModel.__init__
+ ~PyLinkForwardModel.read_observation
+ ~PyLinkForwardModel.read_output
+ ~PyLinkForwardModel.run_command
+ ~PyLinkForwardModel.run_forwardmodel
+ ~PyLinkForwardModel.run_model_parallel
+ ~PyLinkForwardModel.uMBridge_model
+ ~PyLinkForwardModel.update_input_params
+ ~PyLinkForwardModel.zip_subdirs
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.pylink.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.pylink.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..267ef0c041ce8afdf5de17feb9aeb4e6946869d8
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.pylink.rst.txt
@@ -0,0 +1,38 @@
+bayesvalidrox.pylink.pylink
+===========================
+
+.. automodule:: bayesvalidrox.pylink.pylink
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ within_range
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ PyLinkForwardModel
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.pylink.within_range.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.pylink.within_range.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..70192a94373df9e6122d9a78e9d726a483a441ab
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.pylink.within_range.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.pylink.pylink.within\_range
+=========================================
+
+.. currentmodule:: bayesvalidrox.pylink.pylink
+
+.. autofunction:: within_range
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1b9ae788bafcbf0e83252161478c7817f9af499f
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.pylink.rst.txt
@@ -0,0 +1,32 @@
+bayesvalidrox.pylink
+====================
+
+.. automodule:: bayesvalidrox.pylink
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+
+ bayesvalidrox.pylink.pylink
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1e904c9f78c3f74d596b2a0803dd6fb3f63f741b
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.rst.txt
@@ -0,0 +1,35 @@
+bayesvalidrox
+=============
+
+.. automodule:: bayesvalidrox
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+
+ bayesvalidrox.bayes_inference
+ bayesvalidrox.post_processing
+ bayesvalidrox.pylink
+ bayesvalidrox.surrogate_models
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c4a5ddc2f41dab3812601a432a824e8e6ce91853
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.adaptPlot.adaptPlot
+===================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.adaptPlot
+
+.. autofunction:: adaptPlot
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..860e8ec2c3aee6f8a624a518c9fe3115eb74c57b
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.rst.txt
@@ -0,0 +1,30 @@
+bayesvalidrox.surrogate\_models.adaptPlot
+=========================================
+
+.. automodule:: bayesvalidrox.surrogate_models.adaptPlot
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ adaptPlot
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..049182d7af9f8bdf0c7420a9b1b107de0ca1c562
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.apoly\_construction.apoly\_construction
+=======================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.apoly_construction
+
+.. autofunction:: apoly_construction
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..73c72f8f33fa684448060c14ac2f17435fc4ea82
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.rst.txt
@@ -0,0 +1,30 @@
+bayesvalidrox.surrogate\_models.apoly\_construction
+===================================================
+
+.. automodule:: bayesvalidrox.surrogate_models.apoly_construction
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ apoly_construction
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..60ca9858f0b4a1e304c67c0cbd22d42e9262b73b
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.rst.txt
@@ -0,0 +1,33 @@
+bayesvalidrox.surrogate\_models.bayes\_linear.BayesianLinearRegression
+======================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.bayes_linear
+
+.. autoclass:: BayesianLinearRegression
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~BayesianLinearRegression.__init__
+ ~BayesianLinearRegression.fit
+ ~BayesianLinearRegression.get_metadata_routing
+ ~BayesianLinearRegression.get_params
+ ~BayesianLinearRegression.predict
+ ~BayesianLinearRegression.predict_dist
+ ~BayesianLinearRegression.score
+ ~BayesianLinearRegression.set_params
+ ~BayesianLinearRegression.set_score_request
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..42f6224c3e023d5e673166950929ababecdd11d6
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.rst.txt
@@ -0,0 +1,34 @@
+bayesvalidrox.surrogate\_models.bayes\_linear.EBLinearRegression
+================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.bayes_linear
+
+.. autoclass:: EBLinearRegression
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~EBLinearRegression.__init__
+ ~EBLinearRegression.fit
+ ~EBLinearRegression.get_metadata_routing
+ ~EBLinearRegression.get_params
+ ~EBLinearRegression.predict
+ ~EBLinearRegression.predict_dist
+ ~EBLinearRegression.score
+ ~EBLinearRegression.set_params
+ ~EBLinearRegression.set_predict_request
+ ~EBLinearRegression.set_score_request
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e47223ddbd54560f1b505c36d8094f4e68880d45
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.rst.txt
@@ -0,0 +1,34 @@
+bayesvalidrox.surrogate\_models.bayes\_linear.VBLinearRegression
+================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.bayes_linear
+
+.. autoclass:: VBLinearRegression
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~VBLinearRegression.__init__
+ ~VBLinearRegression.fit
+ ~VBLinearRegression.get_metadata_routing
+ ~VBLinearRegression.get_params
+ ~VBLinearRegression.predict
+ ~VBLinearRegression.predict_dist
+ ~VBLinearRegression.score
+ ~VBLinearRegression.set_params
+ ~VBLinearRegression.set_predict_request
+ ~VBLinearRegression.set_score_request
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c8f24a14d3ae45038b8226a74238d28600e47237
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.bayes\_linear.gamma\_mean
+=========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.bayes_linear
+
+.. autofunction:: gamma_mean
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..40200d4b0376ed1fcc5f7885feece9304995a1b6
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.rst.txt
@@ -0,0 +1,40 @@
+bayesvalidrox.surrogate\_models.bayes\_linear
+=============================================
+
+.. automodule:: bayesvalidrox.surrogate_models.bayes_linear
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ gamma_mean
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ BayesianLinearRegression
+ EBLinearRegression
+ VBLinearRegression
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.Engine.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.Engine.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..21a9e34806d5b3671103d4a334fcdfd92880c1bc
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.Engine.rst.txt
@@ -0,0 +1,38 @@
+bayesvalidrox.surrogate\_models.engine.Engine
+=============================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.engine
+
+.. autoclass:: Engine
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. 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
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8e71cfe6e44cc70921aa0ccfa6fbaf42bec53b0e
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.engine.hellinger\_distance
+==========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.engine
+
+.. autofunction:: hellinger_distance
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1c0ed0353adc92a0517b8fb929fbd966fd900a63
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.engine.logpdf
+=============================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.engine
+
+.. autofunction:: logpdf
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..43998aeade22fc4feb5c04282973e72a1ae1d72e
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.rst.txt
@@ -0,0 +1,40 @@
+bayesvalidrox.surrogate\_models.engine
+======================================
+
+.. automodule:: bayesvalidrox.surrogate_models.engine
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ hellinger_distance
+ logpdf
+ subdomain
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ Engine
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b660535bb3a75530ff0f7c8ed247e84105d81fdd
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.engine.subdomain
+================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.engine
+
+.. autofunction:: subdomain
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b443c4a1248c566c717c3ee11f965e24db9ac762
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.eval\_rec\_rule.eval\_rec\_rule
+===============================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.eval_rec_rule
+
+.. autofunction:: eval_rec_rule
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..58c6ae25d017f74106f17fda960141e1495129c3
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.eval\_rec\_rule.eval\_rec\_rule\_arbitrary
+==========================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.eval_rec_rule
+
+.. autofunction:: eval_rec_rule_arbitrary
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3f9ccc16cc17970150861a5e98ed881a40f67f3c
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.eval\_rec\_rule.eval\_univ\_basis
+=================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.eval_rec_rule
+
+.. autofunction:: eval_univ_basis
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f0f06e3163c9f099b6cefc33b0360daa4aedd24e
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.eval\_rec\_rule.poly\_rec\_coeffs
+=================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.eval_rec_rule
+
+.. autofunction:: poly_rec_coeffs
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b5055daeb3eed74f3ee4245cbb6b6d4885c53369
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.rst.txt
@@ -0,0 +1,33 @@
+bayesvalidrox.surrogate\_models.eval\_rec\_rule
+===============================================
+
+.. automodule:: bayesvalidrox.surrogate_models.eval_rec_rule
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ eval_rec_rule
+ eval_rec_rule_arbitrary
+ eval_univ_basis
+ poly_rec_coeffs
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..87376d6c45519e53f1323a61d30def9c57adb745
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst.txt
@@ -0,0 +1,34 @@
+bayesvalidrox.surrogate\_models.exp\_designs.ExpDesigns
+=======================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.exp_designs
+
+.. autoclass:: ExpDesigns
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~ExpDesigns.__init__
+ ~ExpDesigns.build_polytypes
+ ~ExpDesigns.check_valid_inputs
+ ~ExpDesigns.generate_ED
+ ~ExpDesigns.generate_samples
+ ~ExpDesigns.init_param_space
+ ~ExpDesigns.pcm_sampler
+ ~ExpDesigns.random_sampler
+ ~ExpDesigns.read_from_file
+ ~ExpDesigns.transform
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e49711dd03aac4f9be9aa1a5a878bc76c07d69ce
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.exp\_designs.check\_ranges
+==========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.exp_designs
+
+.. autofunction:: check_ranges
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e5d704bd5bdc24df135e0520c42fa8956adf6f80
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.rst.txt
@@ -0,0 +1,38 @@
+bayesvalidrox.surrogate\_models.exp\_designs
+============================================
+
+.. automodule:: bayesvalidrox.surrogate_models.exp_designs
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ check_ranges
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ ExpDesigns
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6ccff094faf883249b40597f8a51e6c7de2fe954
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.rst.txt
@@ -0,0 +1,29 @@
+bayesvalidrox.surrogate\_models.exploration.Exploration
+=======================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.exploration
+
+.. autoclass:: Exploration
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Exploration.__init__
+ ~Exploration.approximate_voronoi
+ ~Exploration.get_exploration_samples
+ ~Exploration.get_mc_samples
+ ~Exploration.get_vornoi_samples
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aa60c432480e2e890958d0f086e5412c56305f13
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.rst.txt
@@ -0,0 +1,31 @@
+bayesvalidrox.surrogate\_models.exploration
+===========================================
+
+.. automodule:: bayesvalidrox.surrogate_models.exploration
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ Exploration
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c0d050f1d1a4cfad20314b1baec4ea0eff36d413
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.glexindex.cross\_truncate
+=========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.glexindex
+
+.. autofunction:: cross_truncate
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c76918bd1fd5f06600a8c267351c67eca1fe0757
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.glexindex.glexindex
+===================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.glexindex
+
+.. autofunction:: glexindex
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bcc161fcf44863062d7faa51f42b34d0844daf30
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.rst.txt
@@ -0,0 +1,31 @@
+bayesvalidrox.surrogate\_models.glexindex
+=========================================
+
+.. automodule:: bayesvalidrox.surrogate_models.glexindex
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ cross_truncate
+ glexindex
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e106f4d59aefb3f1e8957d8d6608ae045f8b8b90
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.rst.txt
@@ -0,0 +1,29 @@
+bayesvalidrox.surrogate\_models.input\_space.InputSpace
+=======================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.input_space
+
+.. autoclass:: InputSpace
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~InputSpace.__init__
+ ~InputSpace.build_polytypes
+ ~InputSpace.check_valid_inputs
+ ~InputSpace.init_param_space
+ ~InputSpace.transform
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1dd0d58b0cbcf8c5db054eb283d9585911277d8d
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.rst.txt
@@ -0,0 +1,31 @@
+bayesvalidrox.surrogate\_models.input\_space
+============================================
+
+.. automodule:: bayesvalidrox.surrogate_models.input_space
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ InputSpace
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c4b68c252ea8fa4c6acc0e0c7311855548a8d321
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.rst.txt
@@ -0,0 +1,32 @@
+bayesvalidrox.surrogate\_models.inputs.Input
+============================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.inputs
+
+.. autoclass:: Input
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Input.__init__
+ ~Input.add_marginals
+
+
+
+
+
+ .. rubric:: Attributes
+
+ .. autosummary::
+
+ ~Input.poly_coeffs_flag
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2fa2e281575c3cf340f22561aa1732c48ebef78a
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.rst.txt
@@ -0,0 +1,25 @@
+bayesvalidrox.surrogate\_models.inputs.Marginal
+===============================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.inputs
+
+.. autoclass:: Marginal
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Marginal.__init__
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..687dc2f794cf9ff634b100818664c0fafb037c10
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.rst.txt
@@ -0,0 +1,32 @@
+bayesvalidrox.surrogate\_models.inputs
+======================================
+
+.. automodule:: bayesvalidrox.surrogate_models.inputs
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ Input
+ Marginal
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e835d0c2b01cea1fa91215d72cdd97b95c98ebde
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.rst.txt
@@ -0,0 +1,34 @@
+bayesvalidrox.surrogate\_models.orthogonal\_matching\_pursuit.OrthogonalMatchingPursuit
+=======================================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
+
+.. autoclass:: OrthogonalMatchingPursuit
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~OrthogonalMatchingPursuit.__init__
+ ~OrthogonalMatchingPursuit.blockwise_inverse
+ ~OrthogonalMatchingPursuit.fit
+ ~OrthogonalMatchingPursuit.get_metadata_routing
+ ~OrthogonalMatchingPursuit.get_params
+ ~OrthogonalMatchingPursuit.loo_error
+ ~OrthogonalMatchingPursuit.predict
+ ~OrthogonalMatchingPursuit.score
+ ~OrthogonalMatchingPursuit.set_params
+ ~OrthogonalMatchingPursuit.set_score_request
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9a6786588d090b06429a68485bbf3bf1a4fb8f19
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.orthogonal\_matching\_pursuit.corr
+==================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
+
+.. autofunction:: corr
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a64e2c4550d56783e48482cbae6c0d31536fdf92
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst.txt
@@ -0,0 +1,38 @@
+bayesvalidrox.surrogate\_models.orthogonal\_matching\_pursuit
+=============================================================
+
+.. automodule:: bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ corr
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ OrthogonalMatchingPursuit
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..137bdc1565ed4d52cd60319bff4ab8d6cf20df7a
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.rst.txt
@@ -0,0 +1,34 @@
+bayesvalidrox.surrogate\_models.reg\_fast\_ard.RegressionFastARD
+================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.reg_fast_ard
+
+.. autoclass:: RegressionFastARD
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~RegressionFastARD.__init__
+ ~RegressionFastARD.fit
+ ~RegressionFastARD.get_metadata_routing
+ ~RegressionFastARD.get_params
+ ~RegressionFastARD.log_marginal_like
+ ~RegressionFastARD.predict
+ ~RegressionFastARD.score
+ ~RegressionFastARD.set_params
+ ~RegressionFastARD.set_predict_request
+ ~RegressionFastARD.set_score_request
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f739234816e896208e107eca177c35967eb36964
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.rst.txt
@@ -0,0 +1,38 @@
+bayesvalidrox.surrogate\_models.reg\_fast\_ard
+==============================================
+
+.. automodule:: bayesvalidrox.surrogate_models.reg_fast_ard
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ update_precisions
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ RegressionFastARD
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0ff8616c845ad03b7898a0ae6da4d5c7fad1bead
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.reg\_fast\_ard.update\_precisions
+=================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.reg_fast_ard
+
+.. autofunction:: update_precisions
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d5b18ed0a0f744edd2af59f58f1e6c09972ce154
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.rst.txt
@@ -0,0 +1,28 @@
+bayesvalidrox.surrogate\_models.reg\_fast\_laplace.RegressionFastLaplace
+========================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.reg_fast_laplace
+
+.. autoclass:: RegressionFastLaplace
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~RegressionFastLaplace.__init__
+ ~RegressionFastLaplace.fit
+ ~RegressionFastLaplace.fit_
+ ~RegressionFastLaplace.predict
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2cc1f44c5533ad742b113bb38a481191a0d9b307
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.rst.txt
@@ -0,0 +1,31 @@
+bayesvalidrox.surrogate\_models.reg\_fast\_laplace
+==================================================
+
+.. automodule:: bayesvalidrox.surrogate_models.reg_fast_laplace
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ RegressionFastLaplace
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fbd49ba297a2f28c516c5818e5f844d545986446
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.rst.txt
@@ -0,0 +1,45 @@
+bayesvalidrox.surrogate\_models
+===============================
+
+.. automodule:: bayesvalidrox.surrogate_models
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+
+ bayesvalidrox.surrogate_models.adaptPlot
+ bayesvalidrox.surrogate_models.apoly_construction
+ bayesvalidrox.surrogate_models.bayes_linear
+ bayesvalidrox.surrogate_models.engine
+ bayesvalidrox.surrogate_models.eval_rec_rule
+ bayesvalidrox.surrogate_models.exp_designs
+ bayesvalidrox.surrogate_models.exploration
+ bayesvalidrox.surrogate_models.glexindex
+ bayesvalidrox.surrogate_models.input_space
+ bayesvalidrox.surrogate_models.inputs
+ bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
+ bayesvalidrox.surrogate_models.reg_fast_ard
+ bayesvalidrox.surrogate_models.reg_fast_laplace
+ bayesvalidrox.surrogate_models.surrogate_models
+
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f4cd883ed344f535e9aaf285734e6df56266e72d
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.rst.txt
@@ -0,0 +1,38 @@
+bayesvalidrox.surrogate\_models.surrogate\_models.MetaModel
+===========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.surrogate_models
+
+.. autoclass:: MetaModel
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~MetaModel.__init__
+ ~MetaModel.adaptive_regression
+ ~MetaModel.add_InputSpace
+ ~MetaModel.build_metamodel
+ ~MetaModel.copy_meta_model_opts
+ ~MetaModel.create_model_error
+ ~MetaModel.eval_metamodel
+ ~MetaModel.eval_model_error
+ ~MetaModel.fit
+ ~MetaModel.generate_polynomials
+ ~MetaModel.pca_transformation
+ ~MetaModel.regression
+ ~MetaModel.univ_basis_vals
+ ~MetaModel.update_pce_coeffs
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7972de4abea5cb1a96c319ecaae7771d776deccb
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.surrogate\_models.corr\_loocv\_error
+====================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.surrogate_models
+
+.. autofunction:: corr_loocv_error
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d6f9c97a90b030aebe4996b1d75086cedc1d5f6f
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.surrogate\_models.create\_psi
+=============================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.surrogate_models
+
+.. autofunction:: create_psi
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c5eb1db492e525fba32b60ba56f84643ba7e501b
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.rst.txt
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.surrogate\_models.gaussian\_process\_emulator
+=============================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.surrogate_models
+
+.. autofunction:: gaussian_process_emulator
\ No newline at end of file
diff --git a/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.rst.txt b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e8b46319c04c860e4832eb513b76c74c06fb0b74
--- /dev/null
+++ b/docs/build/html/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.rst.txt
@@ -0,0 +1,40 @@
+bayesvalidrox.surrogate\_models.surrogate\_models
+=================================================
+
+.. automodule:: bayesvalidrox.surrogate_models.surrogate_models
+
+
+
+
+
+
+
+ .. rubric:: Functions
+
+ .. autosummary::
+ :toctree:
+
+ corr_loocv_error
+ create_psi
+ gaussian_process_emulator
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ MetaModel
+
+
+
+
+
+
+
+
+
diff --git a/docs/build/html/_sources/api.rst.txt b/docs/build/html/_sources/api.rst.txt
index 67bae2b9df328547809007067292450a1036483f..b9e02468e27edaea904a2e4184b7752b6d50d184 100644
--- a/docs/build/html/_sources/api.rst.txt
+++ b/docs/build/html/_sources/api.rst.txt
@@ -1,52 +1,10 @@
API
***
-
-SURROGATE MODELS
-----------------
-
-.. autosummary::
- :toctree: generated
-
- bayesvalidrox.surrogate_models.adaptPlot
- bayesvalidrox.surrogate_models.apoly_construction
- bayesvalidrox.surrogate_models.bayes_linear
- bayesvalidrox.surrogate_models.eval_rec_rule
- bayesvalidrox.surrogate_models.exp_designs
- bayesvalidrox.surrogate_models.exploration
- bayesvalidrox.surrogate_models.glexindex
- bayesvalidrox.surrogate_models.inputs
- bayesvalidrox.surrogate_models.engine
- bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
- bayesvalidrox.surrogate_models.reg_fast_ard
- bayesvalidrox.surrogate_models.reg_fast_laplace
- bayesvalidrox.surrogate_models.surrogate_models
-
-
-PYLINK
-------
-
-.. autosummary::
- :toctree: generated
-
- bayesvalidrox.pylink.pylink
-
-
-POST PROCESSING
----------------
-
-.. autosummary::
- :toctree: generated
-
- bayesvalidrox.post_processing.post_processing
-
-
-BAYES INFERENCE
----------------
+Here you can find the api for the package **bayesvalidrox**.
.. autosummary::
- :toctree: generated
+ :toctree: _autosummary
+ :template: custom-module-template.rst
+ :recursive:
- bayesvalidrox.bayes_inference.bayes_inference
- bayesvalidrox.bayes_inference.bayes_model_comparison
- bayesvalidrox.bayes_inference.discrepancy
- bayesvalidrox.bayes_inference.mcmc
\ No newline at end of file
+ bayesvalidrox
\ No newline at end of file
diff --git a/docs/build/html/_sources/tutorial.rst.txt b/docs/build/html/_sources/tutorial.rst.txt
index 6ca026f77cfedb64b2bd3c6209e265efa630a776..03d5a7462d5611d13371bca7f841c91e6eafe53c 100644
--- a/docs/build/html/_sources/tutorial.rst.txt
+++ b/docs/build/html/_sources/tutorial.rst.txt
@@ -33,7 +33,7 @@ These names will also be adopted by the surrogate and used in all plots.
>>> Model.Output.names = ['Z']
-Bonus: For this example, we have a Monte-Carlo reference solution for the first moments (mean and standard deviation) of the analytical function.
+For this example, we have a Monte-Carlo reference solution for the first moments (mean and standard deviation) of the analytical function.
The numpy (``*.npy``) files for this can be found in the ``data\`` directory of the example.
We will later discuss the estimation of the first two moments from the surrogate model, so let us import the mc-reference here.
These values can be passed in a form of a dictionary to the object variable ``mc_reference``.
@@ -136,38 +136,52 @@ A value of 1 results in standard truncation of the expansion, while smaller valu
Set the experimental design
---------------------------
-The experimental design provides instructions on how to sample the input parameter space for static training of the surrogate.
-Various sampling methods are available, but the samples can also be given by the user.
+The experimental design provides instructions on how to sample the input parameter space for training and evaluating the surrogate.
+Various sampling methods are available, and the samples can also be given by the user.
->>> MetaModelOpts.add_ExpDesign()
->>> MetaModelOpts.ExpDesign.Method = 'normal'
->>> MetaModelOpts.ExpDesign.n_init_samples = 100
->>> MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
+>>> ExpDesign = ExpDesign(Inputs)
+>>> ExpDesign.Method = 'normal'
+>>> ExpDesign.n_init_samples = 100
+>>> ExpDesign.sampling_method = 'latin_hypercube'
-Train the surrogate
--------------------
-Now, we can start training the surrogate (meta-) model by using the method ``create_metamodel`` and passing the model object as the only argument.
-This method returns the trained metamodel which we save as a ``pkl`` object.
+Train the surrogate with an engine
+----------------------------------
+Training is done by giving the model, experimental design and the surrogate model to an engine, which performs the training for us.
+The engine is of class :py:class:`bayesvalidrox.surrogate_models.engine.Engine`.
+
+>>> engine = Engine(MetaModelOpts, Model, ExpDesign)
+
+Now, we can start training the surrogate (meta-) model by starting the engine and using the method ``train_normal``.
+The engine obtains the training samples from the experimental design using the sampling strategy we set, runs the model on these samples and trains the surrogate on the results.
+
+>>> engine.start_engine()
+>>> engine.train_normal()
+
+Once this has run through we can obtain the trained metamodel from the engine.
+
+>>> MetaModel_trained = engine.MetaModel
+
+As **bayesvalidrox** uses the engine class for postprocessing and inference, we will save it at this point as a ``pkl`` object.
+This can be easily read in to avoid retraining the surrogate.
->>> PCEModel = MetaModelOpts.create_metamodel(Model)
->>> with open(f'PCEModel_{Model.name}.pkl', 'wb') as output:
->>> joblib.dump(PCEModel, output, 2)
+>>> with open(f'PCEengine_{Model.name}.pkl', 'wb') as output:
+>>> joblib.dump(engine, output, 2)
Sequential training
-------------------
The basic surrogate training that we just performed is done only on one static set of data.
**bayesvalidrox** also provide the option of sequential training, also known as active learning, where additional samples to be trained on are chosen by the surrogate.
-This can be activated by setting the ``method`` of the experimental design to ``'sequential'``.
+This can be activated by setting the ``method`` of the experimental design as ``'sequential'``.
->>> MetaModelOpts.ExpDesign.method = 'sequential'
+>>> ExpDesign.method = 'sequential'
This will split the training into two parts.
In the first part the training is performed as before, though the
size of this initial training set can a bit smaller.
->>> MetaModelOpts.ExpDesign.n_init_samples = 3*ndim
->>> MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
+>>> ExpDesign.n_init_samples = 3*ndim
+>>> ExpDesign.sampling_method = 'latin_hypercube'
The options for sequential training are listed at ...........
New samples are set by exploration and exploitation.
@@ -175,16 +189,16 @@ Exploration refers to samples that are randomly drawn from the prior input space
while exploitation can use different metrics.
The tradeoff between the two helps to avoid overfitting, while keeping the faster convergence given by the exploitation methods.
->>> MetaModelOpts.ExpDesign.n_new_samples = 1
->>> MetaModelOpts.ExpDesign.n_max_samples = 150
->>> MetaModelOpts.ExpDesign.mod_LOO_threshold = 1e-16
+>>> ExpDesign.n_new_samples = 1
+>>> ExpDesign.n_max_samples = 150
+>>> ExpDesign.mod_LOO_threshold = 1e-16
>>>
->>> MetaModelOpts.ExpDesign.tradeoff_scheme = None
->>> MetaModelOpts.ExpDesign.explore_method = 'random'
+>>> ExpDesign.tradeoff_scheme = None
+>>> ExpDesign.explore_method = 'random'
>>>
>>> # Use when 'Voronoi' or 'random' or 'latin_hypercube' chosen
->>> MetaModelOpts.ExpDesign.n_canddidate = 1000
->>> MetaModelOpts.ExpDesign.n_cand_groups = 4
+>>> ExpDesign.n_canddidate = 1000
+>>> ExpDesign.n_cand_groups = 4
Here we set the exploitaiton method to be Bayesian Active Learning .....cite......
@@ -192,8 +206,8 @@ Here we set the exploitaiton method to be Bayesian Active Learning .....cite....
In addition we need to set the information metric to use, here ``'DKL'`` is chosen.
->>> MetaModelOpts.ExpDesign.exploit_method = 'BayesActDesign'
->>> MetaModelOpts.ExpDesign.util_func = 'DKL'
+>>> ExpDesign.exploit_method = 'BayesActDesign'
+>>> ExpDesign.util_func = 'DKL'
This active learning strategy also relies on the data uncertainty, so we set this to follow a Gaussian distribution around all values
with standard deviations that are as large as the values themselves.
@@ -206,23 +220,22 @@ with standard deviations that are as large as the values themselves.
The measures calculated in each training iteration can also be plotted.
->>> MetaModelOpts.ExpDesign.post_snapshot = False
->>> MetaModelOpts.ExpDesign.step_snapshot = 1
->>> MetaModelOpts.ExpDesign.max_a_post = [0] * ndim
+>>> ExpDesign.post_snapshot = False
+>>> ExpDesign.step_snapshot = 1
+>>> ExpDesign.max_a_post = [0] * ndim
For calculating and plotting the validation error of the surrogate in each iteration, additional references can be given.
>>> prior = np.load(f"data/Prior_{ndim}.npy")
>>> prior_outputs = np.load(f"data/origModelOutput_{ndim}.npy")
>>> likelihood = np.load(f"data/validLikelihoods_{ndim}.npy")
->>> MetaModelOpts.valid_samples = prior[:500]
->>> MetaModelOpts.valid_model_runs = {'Z': prior_outputs[:500]}
+>>> ExpDesign.valid_samples = prior[:500]
+>>> ExpDesign.valid_model_runs = {'Z': prior_outputs[:500]}
-The sequential training is performed by the class :py:class:`bayesvalidrox.surrogate_models.meta_model_engine.MetaModelEngine`.
+The sequential training is again performed by the class :py:class:`bayesvalidrox.surrogate_models.engine.Engine`, but using the function `train_sequential`.
->>> meta_model_engine = MetaModelEngine(MetaModelOpts)
->>> meta_model_engine.run()
->>> PCEModel = meta_model_engine.MetaModel
+>>> engine.ExpDesign = ExpDesign
+>>> engine.train_sequential()
@@ -232,9 +245,9 @@ The available post-processing methods for **bayesvalidrox** are given in the cla
All results created by this class are automatically stored in an output-folder ``Outputs_PostProcessing_*modelname``.
>>> from bayesvalidrox import PostProcessing
->>> PostPCE = PostProcessing(PCEModel)
+>>> PostPCE = PostProcessing(engine)
-Since the reference moments obtained from a Monte-Carlo simulation is available, we only need to call the method ``plotMoments`` method from the PostProcessing object.
+Since the reference moments obtained from a Monte-Carlo simulation is available, we only need to call the method ``plot_moments`` method from the PostProcessing object.
This method generates a plot and stores it in the output folder.
>>> PostPCE.plot_moments()
@@ -260,7 +273,7 @@ Bayesian Inference
Inverse parameter estimation can be done in **bayesvalidrox** with the class :py:class:`bayesvalidrox.bayes_inf.bayes_inf.BayesInference`.
>>> from bayesvalidrox import BayesInference
->>> BayesOpts = BayesInference(PCEModel)
+>>> BayesOpts = BayesInference(engine)
If we set ``emulator`` to be true the Bayesian Inference will be performed based on the emulator.
Some posterior predictions will be plotted by setting ``plot_post_pred``.
diff --git a/docs/build/html/api.html b/docs/build/html/api.html
index 2d9e3bd533c184b190e17b109b90d481c5e5ce4e..76ac032311224ccee59f436ae4a1f4eab0e6bba9 100644
--- a/docs/build/html/api.html
+++ b/docs/build/html/api.html
@@ -3,7 +3,7 @@
<head><meta charset="utf-8"/>
<meta name="viewport" content="width=device-width,initial-scale=1"/>
<meta name="color-scheme" content="light dark"><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
-<link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="bayesvalidrox.surrogate_models.adaptPlot" href="generated/bayesvalidrox.surrogate_models.adaptPlot.html" /><link rel="prev" title="PACKAGE DESCRIPTION" href="packagedescription.html" />
+<link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="bayesvalidrox" href="_autosummary/bayesvalidrox.html" /><link rel="prev" title="PACKAGE DESCRIPTION" href="packagedescription.html" />
<!-- Generated with Sphinx 7.3.7 and Furo 2023.09.10 -->
<title>API - bayesvalidrox 0.0.5 documentation</title>
@@ -134,7 +134,7 @@
<svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
</button>
</div>
- <label class="toc-overlay-icon toc-header-icon" for="__toc">
+ <label class="toc-overlay-icon toc-header-icon no-toc" for="__toc">
<div class="visually-hidden">Toggle table of contents sidebar</div>
<i class="icon"><svg><use href="#svg-toc"></use></svg></i>
</label>
@@ -168,25 +168,120 @@
</li>
<li class="toctree-l1"><a class="reference internal" href="packagedescription.html">PACKAGE DESCRIPTION</a></li>
<li class="toctree-l1 current has-children current-page"><a class="current reference internal" href="#">API</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.adaptPlot.html">bayesvalidrox.surrogate_models.adaptPlot</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.apoly_construction.html">bayesvalidrox.surrogate_models.apoly_construction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.bayes_linear.html">bayesvalidrox.surrogate_models.bayes_linear</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.eval_rec_rule.html">bayesvalidrox.surrogate_models.eval_rec_rule</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.exp_designs.html">bayesvalidrox.surrogate_models.exp_designs</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.glexindex.html">bayesvalidrox.surrogate_models.glexindex</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.surrogate_models.html">bayesvalidrox.surrogate_models.surrogate_models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.pylink.pylink.html">bayesvalidrox.pylink.pylink</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.post_processing.post_processing.html">bayesvalidrox.post_processing.post_processing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.bayes_inference.html">bayesvalidrox.bayes_inference.bayes_inference</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html">bayesvalidrox.bayes_inference.bayes_model_comparison</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.discrepancy.html">bayesvalidrox.bayes_inference.discrepancy</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.mcmc.html">bayesvalidrox.bayes_inference.mcmc</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.html">bayesvalidrox</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.html">bayesvalidrox.bayes_inference.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html">bayesvalidrox.bayes_inference.bayes_inference.BayesInference</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.html">bayesvalidrox.bayes_inference.bayes_model_comparison</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference.bayes_model_comparison</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html">bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.html">bayesvalidrox.bayes_inference.discrepancy</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference.discrepancy</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html">bayesvalidrox.bayes_inference.discrepancy.Discrepancy</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.mcmc.html">bayesvalidrox.bayes_inference.mcmc</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference.mcmc</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.html">bayesvalidrox.bayes_inference.mcmc.gelman_rubin</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html">bayesvalidrox.bayes_inference.mcmc.MCMC</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.post_processing.html">bayesvalidrox.post_processing</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of bayesvalidrox.post_processing</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.post_processing.post_processing.html">bayesvalidrox.post_processing.post_processing</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of bayesvalidrox.post_processing.post_processing</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html">bayesvalidrox.post_processing.post_processing.PostProcessing</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.pylink.html">bayesvalidrox.pylink</a><input class="toctree-checkbox" id="toctree-checkbox-11" name="toctree-checkbox-11" role="switch" type="checkbox"/><label for="toctree-checkbox-11"><div class="visually-hidden">Toggle navigation of bayesvalidrox.pylink</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html">bayesvalidrox.pylink.pylink.PyLinkForwardModel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.html">bayesvalidrox.surrogate_models</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.adaptPlot.html">bayesvalidrox.surrogate_models.adaptPlot</a><input class="toctree-checkbox" id="toctree-checkbox-14" name="toctree-checkbox-14" role="switch" type="checkbox"/><label for="toctree-checkbox-14"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.adaptPlot</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.html">bayesvalidrox.surrogate_models.adaptPlot.adaptPlot</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html">bayesvalidrox.surrogate_models.apoly_construction</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.apoly_construction</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.html">bayesvalidrox.surrogate_models.apoly_construction.apoly_construction</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.html">bayesvalidrox.surrogate_models.bayes_linear</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.bayes_linear</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.html">bayesvalidrox.surrogate_models.bayes_linear.gamma_mean</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html">bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html">bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html">bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.engine</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.html">bayesvalidrox.surrogate_models.engine.hellinger_distance</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.html">bayesvalidrox.surrogate_models.engine.logpdf</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.html">bayesvalidrox.surrogate_models.engine.subdomain</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html">bayesvalidrox.surrogate_models.engine.Engine</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.html">bayesvalidrox.surrogate_models.eval_rec_rule</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.eval_rec_rule</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.html">bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.html">bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.html">bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.html">bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.html">bayesvalidrox.surrogate_models.exp_designs</a><input class="toctree-checkbox" id="toctree-checkbox-19" name="toctree-checkbox-19" role="switch" type="checkbox"/><label for="toctree-checkbox-19"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.exp_designs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.html">bayesvalidrox.surrogate_models.exp_designs.check_ranges</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html">bayesvalidrox.surrogate_models.exp_designs.ExpDesigns</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a><input class="toctree-checkbox" id="toctree-checkbox-20" name="toctree-checkbox-20" role="switch" type="checkbox"/><label for="toctree-checkbox-20"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.exploration</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html">bayesvalidrox.surrogate_models.exploration.Exploration</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.glexindex.html">bayesvalidrox.surrogate_models.glexindex</a><input class="toctree-checkbox" id="toctree-checkbox-21" name="toctree-checkbox-21" role="switch" type="checkbox"/><label for="toctree-checkbox-21"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.glexindex</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.html">bayesvalidrox.surrogate_models.glexindex.cross_truncate</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.html">bayesvalidrox.surrogate_models.glexindex.glexindex</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.input_space.html">bayesvalidrox.surrogate_models.input_space</a><input class="toctree-checkbox" id="toctree-checkbox-22" name="toctree-checkbox-22" role="switch" type="checkbox"/><label for="toctree-checkbox-22"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.input_space</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html">bayesvalidrox.surrogate_models.input_space.InputSpace</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a><input class="toctree-checkbox" id="toctree-checkbox-23" name="toctree-checkbox-23" role="switch" type="checkbox"/><label for="toctree-checkbox-23"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.inputs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html">bayesvalidrox.surrogate_models.inputs.Input</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html">bayesvalidrox.surrogate_models.inputs.Marginal</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a><input class="toctree-checkbox" id="toctree-checkbox-24" name="toctree-checkbox-24" role="switch" type="checkbox"/><label for="toctree-checkbox-24"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a><input class="toctree-checkbox" id="toctree-checkbox-25" name="toctree-checkbox-25" role="switch" type="checkbox"/><label for="toctree-checkbox-25"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.reg_fast_ard</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.html">bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html">bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a><input class="toctree-checkbox" id="toctree-checkbox-26" name="toctree-checkbox-26" role="switch" type="checkbox"/><label for="toctree-checkbox-26"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.reg_fast_laplace</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html">bayesvalidrox.surrogate_models.surrogate_models</a><input class="toctree-checkbox" id="toctree-checkbox-27" name="toctree-checkbox-27" role="switch" type="checkbox"/><label for="toctree-checkbox-27"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.surrogate_models</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html">bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.html">bayesvalidrox.surrogate_models.surrogate_models.create_psi</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html">bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
</ul>
</li>
</ul>
@@ -217,7 +312,7 @@
<svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
</button>
</div>
- <label class="toc-overlay-icon toc-content-icon" for="__toc">
+ <label class="toc-overlay-icon toc-content-icon no-toc" for="__toc">
<div class="visually-hidden">Toggle table of contents sidebar</div>
<i class="icon"><svg><use href="#svg-toc"></use></svg></i>
</label>
@@ -225,99 +320,16 @@
<article role="main">
<section id="api">
<h1>API<a class="headerlink" href="#api" title="Link to this heading">¶</a></h1>
-<section id="surrogate-models">
-<h2>SURROGATE MODELS<a class="headerlink" href="#surrogate-models" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.adaptPlot.html#module-bayesvalidrox.surrogate_models.adaptPlot" title="bayesvalidrox.surrogate_models.adaptPlot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.adaptPlot</span></code></a></p></td>
-<td><p>Created on Thu Aug 13 13:46:24 2020</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction" title="bayesvalidrox.surrogate_models.apoly_construction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.apoly_construction</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear" title="bayesvalidrox.surrogate_models.bayes_linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.bayes_linear</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.eval_rec_rule.html#module-bayesvalidrox.surrogate_models.eval_rec_rule" title="bayesvalidrox.surrogate_models.eval_rec_rule"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.eval_rec_rule</span></code></a></p></td>
-<td><p>Based on the implementation in UQLab [1].</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs" title="bayesvalidrox.surrogate_models.exp_designs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.exp_designs</span></code></a></p></td>
-<td><p>Experimental design with associated sampling methods</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration" title="bayesvalidrox.surrogate_models.exploration"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.exploration</span></code></a></p></td>
-<td><p>Exploration for sequential training of metamodels</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex" title="bayesvalidrox.surrogate_models.glexindex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.glexindex</span></code></a></p></td>
-<td><p>Multi indices for monomial exponents.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.inputs.html#module-bayesvalidrox.surrogate_models.inputs" title="bayesvalidrox.surrogate_models.inputs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs</span></code></a></p></td>
-<td><p>Inputs and related marginal distributions</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine" title="bayesvalidrox.surrogate_models.engine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine</span></code></a></p></td>
-<td><p>Engine to train the surrogate</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit" title="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</span></code></a></p></td>
-<td><p>Created on Fri Jul 15 14:08:59 2022</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.reg_fast_ard.html#module-bayesvalidrox.surrogate_models.reg_fast_ard" title="bayesvalidrox.surrogate_models.reg_fast_ard"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.reg_fast_ard</span></code></a></p></td>
-<td><p>Created on Tue Mar 24 19:41:45 2020</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html#module-bayesvalidrox.surrogate_models.reg_fast_laplace" title="bayesvalidrox.surrogate_models.reg_fast_laplace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.reg_fast_laplace</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models" title="bayesvalidrox.surrogate_models.surrogate_models"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models</span></code></a></p></td>
-<td><p>Implementation of metamodel as either PC, aPC or GPE</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="pylink">
-<h2>PYLINK<a class="headerlink" href="#pylink" title="Link to this heading">¶</a></h2>
+<p>Here you can find the api for the package <strong>bayesvalidrox</strong>.</p>
<div class="table-wrapper autosummary longtable docutils container">
<table class="autosummary longtable docutils align-default">
<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.pylink.pylink.html#module-bayesvalidrox.pylink.pylink" title="bayesvalidrox.pylink.pylink"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.pylink.pylink</span></code></a></p></td>
-<td><p>Calls to the model and evaluations</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="post-processing">
-<h2>POST PROCESSING<a class="headerlink" href="#post-processing" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.post_processing.post_processing.html#module-bayesvalidrox.post_processing.post_processing" title="bayesvalidrox.post_processing.post_processing"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.post_processing.post_processing</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="bayes-inference">
-<h2>BAYES INFERENCE<a class="headerlink" href="#bayes-inference" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper autosummary longtable docutils container">
-<table class="autosummary longtable docutils align-default">
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.bayes_inference.html#module-bayesvalidrox.bayes_inference.bayes_inference" title="bayesvalidrox.bayes_inference.bayes_inference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.bayes_inference</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html#module-bayesvalidrox.bayes_inference.bayes_model_comparison" title="bayesvalidrox.bayes_inference.bayes_model_comparison"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.bayes_model_comparison</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.discrepancy.html#module-bayesvalidrox.bayes_inference.discrepancy" title="bayesvalidrox.bayes_inference.discrepancy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.discrepancy</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.mcmc.html#module-bayesvalidrox.bayes_inference.mcmc" title="bayesvalidrox.bayes_inference.mcmc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.mcmc</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/bayesvalidrox.html#module-bayesvalidrox" title="bayesvalidrox"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox</span></code></a></p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
</div>
-</section>
</section>
</article>
@@ -325,12 +337,12 @@
<footer>
<div class="related-pages">
- <a class="next-page" href="generated/bayesvalidrox.surrogate_models.adaptPlot.html">
+ <a class="next-page" href="_autosummary/bayesvalidrox.html">
<div class="page-info">
<div class="context">
<span>Next</span>
</div>
- <div class="title">bayesvalidrox.surrogate_models.adaptPlot</div>
+ <div class="title">bayesvalidrox</div>
</div>
<svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
</a>
@@ -363,31 +375,9 @@
</footer>
</div>
- <aside class="toc-drawer">
+ <aside class="toc-drawer no-toc">
- <div class="toc-sticky toc-scroll">
- <div class="toc-title-container">
- <span class="toc-title">
- On this page
- </span>
- </div>
- <div class="toc-tree-container">
- <div class="toc-tree">
- <ul>
-<li><a class="reference internal" href="#">API</a><ul>
-<li><a class="reference internal" href="#surrogate-models">SURROGATE MODELS</a></li>
-<li><a class="reference internal" href="#pylink">PYLINK</a></li>
-<li><a class="reference internal" href="#post-processing">POST PROCESSING</a></li>
-<li><a class="reference internal" href="#bayes-inference">BAYES INFERENCE</a></li>
-</ul>
-</li>
-</ul>
-
- </div>
- </div>
- </div>
-
</aside>
</div>
diff --git a/docs/build/html/generated/bayesvalidrox.bayes_inference.bayes_inference.html b/docs/build/html/generated/bayesvalidrox.bayes_inference.bayes_inference.html
index d1420a7d57e5f3a49a11a7c04f494eb349956d99..6c509b0515470f50675d7b4ed80f87ac2cb5be18 100644
--- a/docs/build/html/generated/bayesvalidrox.bayes_inference.bayes_inference.html
+++ b/docs/build/html/generated/bayesvalidrox.bayes_inference.bayes_inference.html
@@ -176,6 +176,7 @@
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
diff --git a/docs/build/html/generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html b/docs/build/html/generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html
index 82c90d9fc8db3086f7b9090a6acba35a590b25ff..dac8cb532fcefeef4143798be7725e47451d4714 100644
--- a/docs/build/html/generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html
+++ b/docs/build/html/generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html
@@ -176,6 +176,7 @@
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
diff --git a/docs/build/html/generated/bayesvalidrox.bayes_inference.discrepancy.html b/docs/build/html/generated/bayesvalidrox.bayes_inference.discrepancy.html
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--- a/docs/build/html/generated/bayesvalidrox.bayes_inference.discrepancy.html
+++ b/docs/build/html/generated/bayesvalidrox.bayes_inference.discrepancy.html
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
diff --git a/docs/build/html/generated/bayesvalidrox.bayes_inference.mcmc.html b/docs/build/html/generated/bayesvalidrox.bayes_inference.mcmc.html
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--- a/docs/build/html/generated/bayesvalidrox.bayes_inference.mcmc.html
+++ b/docs/build/html/generated/bayesvalidrox.bayes_inference.mcmc.html
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
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+++ b/docs/build/html/generated/bayesvalidrox.post_processing.post_processing.html
@@ -176,6 +176,7 @@
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
diff --git a/docs/build/html/generated/bayesvalidrox.pylink.pylink.html b/docs/build/html/generated/bayesvalidrox.pylink.pylink.html
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--- a/docs/build/html/generated/bayesvalidrox.pylink.pylink.html
+++ b/docs/build/html/generated/bayesvalidrox.pylink.pylink.html
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.glexindex.html">bayesvalidrox.surrogate_models.glexindex</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
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+++ b/docs/build/html/generated/bayesvalidrox.surrogate_models.adaptPlot.html
@@ -176,6 +176,7 @@
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a></li>
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
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--- a/docs/build/html/generated/bayesvalidrox.surrogate_models.bayes_linear.html
+++ b/docs/build/html/generated/bayesvalidrox.surrogate_models.bayes_linear.html
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
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--- a/docs/build/html/generated/bayesvalidrox.surrogate_models.eval_rec_rule.html
+++ b/docs/build/html/generated/bayesvalidrox.surrogate_models.eval_rec_rule.html
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a></li>
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
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+++ b/docs/build/html/generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html
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+<link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="bayesvalidrox.surrogate_models.reg_fast_ard" href="bayesvalidrox.surrogate_models.reg_fast_ard.html" /><link rel="prev" title="bayesvalidrox.surrogate_models.engine" href="bayesvalidrox.surrogate_models.engine.html" />
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<title>bayesvalidrox.surrogate_models.orthogonal_matching_pursuit - bayesvalidrox 0.0.5 documentation</title>
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a></li>
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2 current current-page"><a class="current reference internal" href="#">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
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</div>
<svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
</a>
- <a class="prev-page" href="bayesvalidrox.surrogate_models.inputs.html">
+ <a class="prev-page" href="bayesvalidrox.surrogate_models.engine.html">
<svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
<div class="page-info">
<div class="context">
<span>Previous</span>
</div>
- <div class="title">bayesvalidrox.surrogate_models.inputs</div>
+ <div class="title">bayesvalidrox.surrogate_models.engine</div>
</div>
</a>
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+++ b/docs/build/html/generated/bayesvalidrox.surrogate_models.reg_fast_ard.html
@@ -176,6 +176,7 @@
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2 current current-page"><a class="current reference internal" href="#">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
diff --git a/docs/build/html/generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html b/docs/build/html/generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html
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--- a/docs/build/html/generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html
+++ b/docs/build/html/generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html
@@ -176,6 +176,7 @@
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.glexindex.html">bayesvalidrox.surrogate_models.glexindex</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
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<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
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<li class="toctree-l2 current current-page"><a class="current reference internal" href="#">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
diff --git a/docs/build/html/generated/bayesvalidrox.surrogate_models.surrogate_models.html b/docs/build/html/generated/bayesvalidrox.surrogate_models.surrogate_models.html
index 940f60dec9d2dc5a8c99d524ac24eeaa239683f9..fc487219f2f87f81ea81bff702ef0ce7d9f40b44 100644
--- a/docs/build/html/generated/bayesvalidrox.surrogate_models.surrogate_models.html
+++ b/docs/build/html/generated/bayesvalidrox.surrogate_models.surrogate_models.html
@@ -176,6 +176,7 @@
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.glexindex.html">bayesvalidrox.surrogate_models.glexindex</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index 65a5be7f301a5e05e5d7387a4a2c46b5dd1173b4..53ae4bc7a8a5aa0ecfc93c0e4e7b0e2b4913c092 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -166,25 +166,120 @@
</li>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a><input class="toctree-checkbox" id="toctree-checkbox-23" name="toctree-checkbox-23" role="switch" type="checkbox"/><label for="toctree-checkbox-23"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.inputs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html">bayesvalidrox.surrogate_models.inputs.Input</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html">bayesvalidrox.surrogate_models.inputs.Marginal</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a><input class="toctree-checkbox" id="toctree-checkbox-24" name="toctree-checkbox-24" role="switch" type="checkbox"/><label for="toctree-checkbox-24"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a><input class="toctree-checkbox" id="toctree-checkbox-25" name="toctree-checkbox-25" role="switch" type="checkbox"/><label for="toctree-checkbox-25"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.reg_fast_ard</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.html">bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html">bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a><input class="toctree-checkbox" id="toctree-checkbox-26" name="toctree-checkbox-26" role="switch" type="checkbox"/><label for="toctree-checkbox-26"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.reg_fast_laplace</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html">bayesvalidrox.surrogate_models.surrogate_models</a><input class="toctree-checkbox" id="toctree-checkbox-27" name="toctree-checkbox-27" role="switch" type="checkbox"/><label for="toctree-checkbox-27"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.surrogate_models</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html">bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.html">bayesvalidrox.surrogate_models.surrogate_models.create_psi</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html">bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
</ul>
</li>
</ul>
@@ -223,148 +318,528 @@
<section class="genindex-section">
<h1 id="index">Index</h1>
- <div class="genindex-jumpbox"><a href="#B"><strong>B</strong></a> | <a href="#M"><strong>M</strong></a></div>
+ <div class="genindex-jumpbox"><a href="#_"><strong>_</strong></a> | <a href="#A"><strong>A</strong></a> | <a href="#B"><strong>B</strong></a> | <a href="#C"><strong>C</strong></a> | <a href="#D"><strong>D</strong></a> | <a href="#E"><strong>E</strong></a> | <a href="#F"><strong>F</strong></a> | <a href="#G"><strong>G</strong></a> | <a href="#H"><strong>H</strong></a> | <a href="#I"><strong>I</strong></a> | <a href="#K"><strong>K</strong></a> | <a href="#L"><strong>L</strong></a> | <a href="#M"><strong>M</strong></a> | <a href="#N"><strong>N</strong></a> | <a href="#O"><strong>O</strong></a> | <a href="#P"><strong>P</strong></a> | <a href="#R"><strong>R</strong></a> | <a href="#S"><strong>S</strong></a> | <a href="#T"><strong>T</strong></a> | <a href="#U"><strong>U</strong></a> | <a href="#V"><strong>V</strong></a> | <a href="#W"><strong>W</strong></a> | <a href="#Z"><strong>Z</strong></a></div>
</section>
+<section id="_" class="genindex-section">
+ <h2>_</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.__init__">__init__() (bayesvalidrox.bayes_inference.bayes_inference.BayesInference method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.__init__">(bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html#bayesvalidrox.bayes_inference.discrepancy.Discrepancy.__init__">(bayesvalidrox.bayes_inference.discrepancy.Discrepancy method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html#bayesvalidrox.bayes_inference.mcmc.MCMC.__init__">(bayesvalidrox.bayes_inference.mcmc.MCMC method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing.__init__">(bayesvalidrox.post_processing.post_processing.PostProcessing method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.__init__">(bayesvalidrox.pylink.pylink.PyLinkForwardModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.__init__">(bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.__init__">(bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.__init__">(bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.__init__">(bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.__init__">(bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html#bayesvalidrox.surrogate_models.exploration.Exploration.__init__">(bayesvalidrox.surrogate_models.exploration.Exploration method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace.__init__">(bayesvalidrox.surrogate_models.input_space.InputSpace method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input.__init__">(bayesvalidrox.surrogate_models.inputs.Input method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html#bayesvalidrox.surrogate_models.inputs.Marginal.__init__">(bayesvalidrox.surrogate_models.inputs.Marginal method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.__init__">(bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.__init__">(bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.__init__">(bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.__init__">(bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="A" class="genindex-section">
+ <h2>A</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.adaptive_regression">adaptive_regression() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.html#bayesvalidrox.surrogate_models.adaptPlot.adaptPlot">adaptPlot() (in module bayesvalidrox.surrogate_models.adaptPlot)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.add_InputSpace">add_InputSpace() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input.add_marginals">add_marginals() (bayesvalidrox.surrogate_models.inputs.Input method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.html#bayesvalidrox.surrogate_models.apoly_construction.apoly_construction">apoly_construction() (in module bayesvalidrox.surrogate_models.apoly_construction)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html#bayesvalidrox.surrogate_models.exploration.Exploration.approximate_voronoi">approximate_voronoi() (bayesvalidrox.surrogate_models.exploration.Exploration method)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
<section id="B" class="genindex-section">
<h2>B</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression">BayesianLinearRegression (class in bayesvalidrox.surrogate_models.bayes_linear)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference">BayesInference (class in bayesvalidrox.bayes_inference.bayes_inference)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison">BayesModelComparison (class in bayesvalidrox.bayes_inference.bayes_model_comparison)</a>
+</li>
+ <li>
+ bayesvalidrox
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.html#module-bayesvalidrox">module</a>
+</li>
+ </ul></li>
+ <li>
+ bayesvalidrox.bayes_inference
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.html#module-bayesvalidrox.bayes_inference">module</a>
+</li>
+ </ul></li>
<li>
bayesvalidrox.bayes_inference.bayes_inference
<ul>
- <li><a href="generated/bayesvalidrox.bayes_inference.bayes_inference.html#module-bayesvalidrox.bayes_inference.bayes_inference">module</a>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.html#module-bayesvalidrox.bayes_inference.bayes_inference">module</a>, <a href="generated/bayesvalidrox.bayes_inference.bayes_inference.html#module-bayesvalidrox.bayes_inference.bayes_inference">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.bayes_inference.bayes_model_comparison
<ul>
- <li><a href="generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html#module-bayesvalidrox.bayes_inference.bayes_model_comparison">module</a>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.html#module-bayesvalidrox.bayes_inference.bayes_model_comparison">module</a>, <a href="generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html#module-bayesvalidrox.bayes_inference.bayes_model_comparison">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.bayes_inference.discrepancy
<ul>
- <li><a href="generated/bayesvalidrox.bayes_inference.discrepancy.html#module-bayesvalidrox.bayes_inference.discrepancy">module</a>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.html#module-bayesvalidrox.bayes_inference.discrepancy">module</a>, <a href="generated/bayesvalidrox.bayes_inference.discrepancy.html#module-bayesvalidrox.bayes_inference.discrepancy">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.bayes_inference.mcmc
<ul>
- <li><a href="generated/bayesvalidrox.bayes_inference.mcmc.html#module-bayesvalidrox.bayes_inference.mcmc">module</a>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.html#module-bayesvalidrox.bayes_inference.mcmc">module</a>, <a href="generated/bayesvalidrox.bayes_inference.mcmc.html#module-bayesvalidrox.bayes_inference.mcmc">[1]</a>
+</li>
+ </ul></li>
+ <li>
+ bayesvalidrox.post_processing
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.html#module-bayesvalidrox.post_processing">module</a>
</li>
</ul></li>
<li>
bayesvalidrox.post_processing.post_processing
<ul>
- <li><a href="generated/bayesvalidrox.post_processing.post_processing.html#module-bayesvalidrox.post_processing.post_processing">module</a>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.html#module-bayesvalidrox.post_processing.post_processing">module</a>, <a href="generated/bayesvalidrox.post_processing.post_processing.html#module-bayesvalidrox.post_processing.post_processing">[1]</a>
+</li>
+ </ul></li>
+ <li>
+ bayesvalidrox.pylink
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.pylink.html#module-bayesvalidrox.pylink">module</a>
</li>
</ul></li>
<li>
bayesvalidrox.pylink.pylink
<ul>
- <li><a href="generated/bayesvalidrox.pylink.pylink.html#module-bayesvalidrox.pylink.pylink">module</a>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.html#module-bayesvalidrox.pylink.pylink">module</a>, <a href="generated/bayesvalidrox.pylink.pylink.html#module-bayesvalidrox.pylink.pylink">[1]</a>
+</li>
+ </ul></li>
+ <li>
+ bayesvalidrox.surrogate_models
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.html#module-bayesvalidrox.surrogate_models">module</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.adaptPlot
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.adaptPlot.html#module-bayesvalidrox.surrogate_models.adaptPlot">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.adaptPlot.html#module-bayesvalidrox.surrogate_models.adaptPlot">module</a>, <a href="generated/bayesvalidrox.surrogate_models.adaptPlot.html#module-bayesvalidrox.surrogate_models.adaptPlot">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.apoly_construction
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction">module</a>, <a href="generated/bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction">[1]</a>
</li>
</ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
<li>
bayesvalidrox.surrogate_models.bayes_linear
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear">module</a>, <a href="generated/bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.engine
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine">module</a>, <a href="generated/bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine">[1]</a>
</li>
</ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
<li>
bayesvalidrox.surrogate_models.eval_rec_rule
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.eval_rec_rule.html#module-bayesvalidrox.surrogate_models.eval_rec_rule">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.html#module-bayesvalidrox.surrogate_models.eval_rec_rule">module</a>, <a href="generated/bayesvalidrox.surrogate_models.eval_rec_rule.html#module-bayesvalidrox.surrogate_models.eval_rec_rule">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.exp_designs
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs">module</a>, <a href="generated/bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.exploration
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration">module</a>, <a href="generated/bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.glexindex
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex">module</a>, <a href="generated/bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex">[1]</a>
+</li>
+ </ul></li>
+ <li>
+ bayesvalidrox.surrogate_models.input_space
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.input_space.html#module-bayesvalidrox.surrogate_models.input_space">module</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.inputs
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.inputs.html#module-bayesvalidrox.surrogate_models.inputs">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.inputs.html#module-bayesvalidrox.surrogate_models.inputs">module</a>, <a href="generated/bayesvalidrox.surrogate_models.inputs.html#module-bayesvalidrox.surrogate_models.inputs">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit">module</a>, <a href="generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.reg_fast_ard
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.reg_fast_ard.html#module-bayesvalidrox.surrogate_models.reg_fast_ard">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html#module-bayesvalidrox.surrogate_models.reg_fast_ard">module</a>, <a href="generated/bayesvalidrox.surrogate_models.reg_fast_ard.html#module-bayesvalidrox.surrogate_models.reg_fast_ard">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.reg_fast_laplace
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html#module-bayesvalidrox.surrogate_models.reg_fast_laplace">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html#module-bayesvalidrox.surrogate_models.reg_fast_laplace">module</a>, <a href="generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html#module-bayesvalidrox.surrogate_models.reg_fast_laplace">[1]</a>
</li>
</ul></li>
<li>
bayesvalidrox.surrogate_models.surrogate_models
<ul>
- <li><a href="generated/bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models">module</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models">module</a>, <a href="generated/bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models">[1]</a>
+</li>
+ </ul></li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.blockwise_inverse">blockwise_inverse() (bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.build_metamodel">build_metamodel() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.build_polytypes">build_polytypes() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace.build_polytypes">(bayesvalidrox.surrogate_models.input_space.InputSpace method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="C" class="genindex-section">
+ <h2>C</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.cal_model_weight">cal_model_weight() (bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_bayes_factors">calc_bayes_factors() (bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_justifiability_analysis">calc_justifiability_analysis() (bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.calc_model_weights">calc_model_weights() (bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing.check_accuracy">check_accuracy() (bayesvalidrox.post_processing.post_processing.PostProcessing method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.html#bayesvalidrox.surrogate_models.exp_designs.check_ranges">check_ranges() (in module bayesvalidrox.surrogate_models.exp_designs)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing.check_reg_quality">check_reg_quality() (bayesvalidrox.post_processing.post_processing.PostProcessing method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.check_valid_inputs">check_valid_inputs() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace.check_valid_inputs">(bayesvalidrox.surrogate_models.input_space.InputSpace method)</a>
+</li>
+ </ul></li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.choose_next_sample">choose_next_sample() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.clear">clear() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing.compute_pce_moments">compute_pce_moments() (bayesvalidrox.post_processing.post_processing.PostProcessing method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.copy">copy() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.copy_meta_model_opts">copy_meta_model_opts() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr">corr() (in module bayesvalidrox.surrogate_models.orthogonal_matching_pursuit)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html#bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error">corr_loocv_error() (in module bayesvalidrox.surrogate_models.surrogate_models)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_error_model">create_error_model() (bayesvalidrox.bayes_inference.bayes_inference.BayesInference method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.create_inference">create_inference() (bayesvalidrox.bayes_inference.bayes_inference.BayesInference method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.create_model_error">create_model_error() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.html#bayesvalidrox.surrogate_models.surrogate_models.create_psi">create_psi() (in module bayesvalidrox.surrogate_models.surrogate_models)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.html#bayesvalidrox.surrogate_models.glexindex.cross_truncate">cross_truncate() (in module bayesvalidrox.surrogate_models.glexindex)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="D" class="genindex-section">
+ <h2>D</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html#bayesvalidrox.bayes_inference.discrepancy.Discrepancy">Discrepancy (class in bayesvalidrox.bayes_inference.discrepancy)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.dual_annealing">dual_annealing() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="E" class="genindex-section">
+ <h2>E</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression">EBLinearRegression (class in bayesvalidrox.surrogate_models.bayes_linear)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine">Engine (class in bayesvalidrox.surrogate_models.engine)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.eval_metamodel">eval_metamodel() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_metamodel">(bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ </ul></li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html#bayesvalidrox.bayes_inference.mcmc.MCMC.eval_model">eval_model() (bayesvalidrox.bayes_inference.mcmc.MCMC method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.eval_model_error">eval_model_error() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.html#bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule">eval_rec_rule() (in module bayesvalidrox.surrogate_models.eval_rec_rule)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.html#bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary">eval_rec_rule_arbitrary() (in module bayesvalidrox.surrogate_models.eval_rec_rule)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.html#bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis">eval_univ_basis() (in module bayesvalidrox.surrogate_models.eval_rec_rule)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns">ExpDesigns (class in bayesvalidrox.surrogate_models.exp_designs)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html#bayesvalidrox.surrogate_models.exploration.Exploration">Exploration (class in bayesvalidrox.surrogate_models.exploration)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="F" class="genindex-section">
+ <h2>F</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.fit">fit() (bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.fit">(bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.fit">(bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.fit">(bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.fit">(bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.fit">(bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.fromkeys">fromkeys() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="G" class="genindex-section">
+ <h2>G</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.html#bayesvalidrox.surrogate_models.bayes_linear.gamma_mean">gamma_mean() (in module bayesvalidrox.surrogate_models.bayes_linear)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html#bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator">gaussian_process_emulator() (in module bayesvalidrox.surrogate_models.surrogate_models)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.html#bayesvalidrox.bayes_inference.mcmc.gelman_rubin">gelman_rubin() (in module bayesvalidrox.bayes_inference.mcmc)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.generate_dataset">generate_dataset() (bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_ED">generate_ED() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.generate_polynomials">generate_polynomials() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.generate_samples">generate_samples() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.get">get() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html#bayesvalidrox.surrogate_models.exploration.Exploration.get_exploration_samples">get_exploration_samples() (bayesvalidrox.surrogate_models.exploration.Exploration method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html#bayesvalidrox.surrogate_models.exploration.Exploration.get_mc_samples">get_mc_samples() (bayesvalidrox.surrogate_models.exploration.Exploration method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_metadata_routing">get_metadata_routing() (bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_metadata_routing">(bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_metadata_routing">(bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_metadata_routing">(bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_metadata_routing">(bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.get_params">get_params() (bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.get_params">(bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.get_params">(bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.get_params">(bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.get_params">(bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD method)</a>
+</li>
+ </ul></li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html#bayesvalidrox.bayes_inference.discrepancy.Discrepancy.get_sample">get_sample() (bayesvalidrox.bayes_inference.discrepancy.Discrepancy method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html#bayesvalidrox.surrogate_models.exploration.Exploration.get_vornoi_samples">get_vornoi_samples() (bayesvalidrox.surrogate_models.exploration.Exploration method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.html#bayesvalidrox.surrogate_models.glexindex.glexindex">glexindex() (in module bayesvalidrox.surrogate_models.glexindex)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="H" class="genindex-section">
+ <h2>H</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.html#bayesvalidrox.surrogate_models.engine.hellinger_distance">hellinger_distance() (in module bayesvalidrox.surrogate_models.engine)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="I" class="genindex-section">
+ <h2>I</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.init_param_space">init_param_space() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace.init_param_space">(bayesvalidrox.surrogate_models.input_space.InputSpace method)</a>
</li>
</ul></li>
</ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input">Input (class in bayesvalidrox.surrogate_models.inputs)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace">InputSpace (class in bayesvalidrox.surrogate_models.input_space)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.items">items() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="K" class="genindex-section">
+ <h2>K</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.keys">keys() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="L" class="genindex-section">
+ <h2>L</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html#bayesvalidrox.bayes_inference.mcmc.MCMC.log_likelihood">log_likelihood() (bayesvalidrox.bayes_inference.mcmc.MCMC method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.log_marginal_like">log_marginal_like() (bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html#bayesvalidrox.bayes_inference.mcmc.MCMC.log_posterior">log_posterior() (bayesvalidrox.bayes_inference.mcmc.MCMC method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html#bayesvalidrox.bayes_inference.mcmc.MCMC.log_prior">log_prior() (bayesvalidrox.bayes_inference.mcmc.MCMC method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.html#bayesvalidrox.surrogate_models.engine.logpdf">logpdf() (in module bayesvalidrox.surrogate_models.engine)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.loo_error">loo_error() (bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit method)</a>
+</li>
+ </ul></td>
</tr></table>
</section>
@@ -372,49 +847,351 @@
<h2>M</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html#bayesvalidrox.surrogate_models.inputs.Marginal">Marginal (class in bayesvalidrox.surrogate_models.inputs)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html#bayesvalidrox.bayes_inference.mcmc.MCMC">MCMC (class in bayesvalidrox.bayes_inference.mcmc)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel">MetaModel (class in bayesvalidrox.surrogate_models.surrogate_models)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification">MetaModel.auto_vivification (class in bayesvalidrox.surrogate_models.surrogate_models)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.model_comparison_all">model_comparison_all() (bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
<li>
module
<ul>
- <li><a href="generated/bayesvalidrox.bayes_inference.bayes_inference.html#module-bayesvalidrox.bayes_inference.bayes_inference">bayesvalidrox.bayes_inference.bayes_inference</a>
+ <li><a href="_autosummary/bayesvalidrox.html#module-bayesvalidrox">bayesvalidrox</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.html#module-bayesvalidrox.bayes_inference">bayesvalidrox.bayes_inference</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.html#module-bayesvalidrox.bayes_inference.bayes_inference">bayesvalidrox.bayes_inference.bayes_inference</a>, <a href="generated/bayesvalidrox.bayes_inference.bayes_inference.html#module-bayesvalidrox.bayes_inference.bayes_inference">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.html#module-bayesvalidrox.bayes_inference.bayes_model_comparison">bayesvalidrox.bayes_inference.bayes_model_comparison</a>, <a href="generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html#module-bayesvalidrox.bayes_inference.bayes_model_comparison">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.html#module-bayesvalidrox.bayes_inference.discrepancy">bayesvalidrox.bayes_inference.discrepancy</a>, <a href="generated/bayesvalidrox.bayes_inference.discrepancy.html#module-bayesvalidrox.bayes_inference.discrepancy">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.html#module-bayesvalidrox.bayes_inference.mcmc">bayesvalidrox.bayes_inference.mcmc</a>, <a href="generated/bayesvalidrox.bayes_inference.mcmc.html#module-bayesvalidrox.bayes_inference.mcmc">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.html#module-bayesvalidrox.post_processing">bayesvalidrox.post_processing</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.html#module-bayesvalidrox.post_processing.post_processing">bayesvalidrox.post_processing.post_processing</a>, <a href="generated/bayesvalidrox.post_processing.post_processing.html#module-bayesvalidrox.post_processing.post_processing">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.pylink.html#module-bayesvalidrox.pylink">bayesvalidrox.pylink</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.html#module-bayesvalidrox.pylink.pylink">bayesvalidrox.pylink.pylink</a>, <a href="generated/bayesvalidrox.pylink.pylink.html#module-bayesvalidrox.pylink.pylink">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.html#module-bayesvalidrox.surrogate_models">bayesvalidrox.surrogate_models</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.adaptPlot.html#module-bayesvalidrox.surrogate_models.adaptPlot">bayesvalidrox.surrogate_models.adaptPlot</a>, <a href="generated/bayesvalidrox.surrogate_models.adaptPlot.html#module-bayesvalidrox.surrogate_models.adaptPlot">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction">bayesvalidrox.surrogate_models.apoly_construction</a>, <a href="generated/bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear">bayesvalidrox.surrogate_models.bayes_linear</a>, <a href="generated/bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine">bayesvalidrox.surrogate_models.engine</a>, <a href="generated/bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.html#module-bayesvalidrox.surrogate_models.eval_rec_rule">bayesvalidrox.surrogate_models.eval_rec_rule</a>, <a href="generated/bayesvalidrox.surrogate_models.eval_rec_rule.html#module-bayesvalidrox.surrogate_models.eval_rec_rule">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs">bayesvalidrox.surrogate_models.exp_designs</a>, <a href="generated/bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration">bayesvalidrox.surrogate_models.exploration</a>, <a href="generated/bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex">bayesvalidrox.surrogate_models.glexindex</a>, <a href="generated/bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.input_space.html#module-bayesvalidrox.surrogate_models.input_space">bayesvalidrox.surrogate_models.input_space</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.inputs.html#module-bayesvalidrox.surrogate_models.inputs">bayesvalidrox.surrogate_models.inputs</a>, <a href="generated/bayesvalidrox.surrogate_models.inputs.html#module-bayesvalidrox.surrogate_models.inputs">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a>, <a href="generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html#module-bayesvalidrox.surrogate_models.reg_fast_ard">bayesvalidrox.surrogate_models.reg_fast_ard</a>, <a href="generated/bayesvalidrox.surrogate_models.reg_fast_ard.html#module-bayesvalidrox.surrogate_models.reg_fast_ard">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html#module-bayesvalidrox.surrogate_models.reg_fast_laplace">bayesvalidrox.surrogate_models.reg_fast_laplace</a>, <a href="generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html#module-bayesvalidrox.surrogate_models.reg_fast_laplace">[1]</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models">bayesvalidrox.surrogate_models.surrogate_models</a>, <a href="generated/bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models">[1]</a>
+</li>
+ </ul></li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="N" class="genindex-section">
+ <h2>N</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.normpdf">normpdf() (bayesvalidrox.bayes_inference.bayes_inference.BayesInference method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html#bayesvalidrox.bayes_inference.mcmc.MCMC.normpdf">(bayesvalidrox.bayes_inference.mcmc.MCMC method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="O" class="genindex-section">
+ <h2>O</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit">OrthogonalMatchingPursuit (class in bayesvalidrox.surrogate_models.orthogonal_matching_pursuit)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="P" class="genindex-section">
+ <h2>P</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.pca_transformation">pca_transformation() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.pcm_sampler">pcm_sampler() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.perform_bootstrap">perform_bootstrap() (bayesvalidrox.bayes_inference.bayes_inference.BayesInference method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_bayes_factor">plot_bayes_factor() (bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_just_analysis">plot_just_analysis() (bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_log_BME">plot_log_BME() (bayesvalidrox.bayes_inference.bayes_inference.BayesInference method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.plot_model_weights">plot_model_weights() (bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing.plot_moments">plot_moments() (bayesvalidrox.post_processing.post_processing.PostProcessing method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.plot_post_params">plot_post_params() (bayesvalidrox.bayes_inference.bayes_inference.BayesInference method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing.plot_seq_design_diagnostics">plot_seq_design_diagnostics() (bayesvalidrox.post_processing.post_processing.PostProcessing method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.html#bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs">poly_rec_coeffs() (in module bayesvalidrox.surrogate_models.eval_rec_rule)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.pop">pop() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.popitem">popitem() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing">PostProcessing (class in bayesvalidrox.post_processing.post_processing)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict">predict() (bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict">(bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict">(bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.predict">(bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.predict">(bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.predict">(bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace method)</a>
+</li>
+ </ul></li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.predict_dist">predict_dist() (bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.predict_dist">(bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.predict_dist">(bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression method)</a>
+</li>
+ </ul></li>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel">PyLinkForwardModel (class in bayesvalidrox.pylink.pylink)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.OutputData">PyLinkForwardModel.OutputData (class in bayesvalidrox.pylink.pylink)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="R" class="genindex-section">
+ <h2>R</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.random_sampler">random_sampler() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.read_from_file">read_from_file() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_observation">read_observation() (bayesvalidrox.pylink.pylink.PyLinkForwardModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.read_output">read_output() (bayesvalidrox.pylink.pylink.PyLinkForwardModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.regression">regression() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD">RegressionFastARD (class in bayesvalidrox.surrogate_models.reg_fast_ard)</a>
</li>
- <li><a href="generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html#module-bayesvalidrox.bayes_inference.bayes_model_comparison">bayesvalidrox.bayes_inference.bayes_model_comparison</a>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html#bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace">RegressionFastLaplace (class in bayesvalidrox.surrogate_models.reg_fast_laplace)</a>
</li>
- <li><a href="generated/bayesvalidrox.bayes_inference.discrepancy.html#module-bayesvalidrox.bayes_inference.discrepancy">bayesvalidrox.bayes_inference.discrepancy</a>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_command">run_command() (bayesvalidrox.pylink.pylink.PyLinkForwardModel method)</a>
</li>
- <li><a href="generated/bayesvalidrox.bayes_inference.mcmc.html#module-bayesvalidrox.bayes_inference.mcmc">bayesvalidrox.bayes_inference.mcmc</a>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_forwardmodel">run_forwardmodel() (bayesvalidrox.pylink.pylink.PyLinkForwardModel method)</a>
</li>
- <li><a href="generated/bayesvalidrox.post_processing.post_processing.html#module-bayesvalidrox.post_processing.post_processing">bayesvalidrox.post_processing.post_processing</a>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_model_parallel">run_model_parallel() (bayesvalidrox.pylink.pylink.PyLinkForwardModel method)</a>
</li>
- <li><a href="generated/bayesvalidrox.pylink.pylink.html#module-bayesvalidrox.pylink.pylink">bayesvalidrox.pylink.pylink</a>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html#bayesvalidrox.bayes_inference.mcmc.MCMC.run_sampler">run_sampler() (bayesvalidrox.bayes_inference.mcmc.MCMC method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.adaptPlot.html#module-bayesvalidrox.surrogate_models.adaptPlot">bayesvalidrox.surrogate_models.adaptPlot</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.run_util_func">run_util_func() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction">bayesvalidrox.surrogate_models.apoly_construction</a>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="S" class="genindex-section">
+ <h2>S</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.score">score() (bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.score">(bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.score">(bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.score">(bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear">bayesvalidrox.surrogate_models.bayes_linear</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.score">(bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine">bayesvalidrox.surrogate_models.engine</a>
+ </ul></li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_params">set_params() (bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_params">(bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.eval_rec_rule.html#module-bayesvalidrox.surrogate_models.eval_rec_rule">bayesvalidrox.surrogate_models.eval_rec_rule</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_params">(bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs">bayesvalidrox.surrogate_models.exp_designs</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_params">(bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration">bayesvalidrox.surrogate_models.exploration</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_params">(bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex">bayesvalidrox.surrogate_models.glexindex</a>
+ </ul></li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_predict_request">set_predict_request() (bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_predict_request">(bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.inputs.html#module-bayesvalidrox.surrogate_models.inputs">bayesvalidrox.surrogate_models.inputs</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_predict_request">(bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a>
+ </ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.set_score_request">set_score_request() (bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.set_score_request">(bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.reg_fast_ard.html#module-bayesvalidrox.surrogate_models.reg_fast_ard">bayesvalidrox.surrogate_models.reg_fast_ard</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.set_score_request">(bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html#module-bayesvalidrox.surrogate_models.reg_fast_laplace">bayesvalidrox.surrogate_models.reg_fast_laplace</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html#bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.set_score_request">(bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit method)</a>
</li>
- <li><a href="generated/bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models">bayesvalidrox.surrogate_models.surrogate_models</a>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.set_score_request">(bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD method)</a>
</li>
</ul></li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.setdefault">setdefault() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.setup">setup() (bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference.setup_inference">setup_inference() (bayesvalidrox.bayes_inference.bayes_inference.BayesInference method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing.sobol_indices">sobol_indices() (bayesvalidrox.post_processing.post_processing.PostProcessing method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.start_engine">start_engine() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.html#bayesvalidrox.surrogate_models.engine.subdomain">subdomain() (in module bayesvalidrox.surrogate_models.engine)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="T" class="genindex-section">
+ <h2>T</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.tradeoff_weights">tradeoff_weights() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html#bayesvalidrox.bayes_inference.mcmc.MCMC.train_error_model">train_error_model() (bayesvalidrox.bayes_inference.mcmc.MCMC method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.train_normal">train_normal() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.train_seq_design">train_seq_design() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.train_sequential">train_sequential() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.transform">transform() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns method)</a>
+
+ <ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace.transform">(bayesvalidrox.surrogate_models.input_space.InputSpace method)</a>
+</li>
+ </ul></li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="U" class="genindex-section">
+ <h2>U</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.uMBridge_model">uMBridge_model() (bayesvalidrox.pylink.pylink.PyLinkForwardModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.univ_basis_vals">univ_basis_vals() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.update">update() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.update_input_params">update_input_params() (bayesvalidrox.pylink.pylink.PyLinkForwardModel method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.update_pce_coeffs">update_pce_coeffs() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.html#bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions">update_precisions() (in module bayesvalidrox.surrogate_models.reg_fast_ard)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.util_AlphOptDesign">util_AlphOptDesign() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.util_BayesianActiveDesign">util_BayesianActiveDesign() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.util_BayesianDesign">util_BayesianDesign() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine.util_VarBasedDesign">util_VarBasedDesign() (bayesvalidrox.surrogate_models.engine.Engine method)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="V" class="genindex-section">
+ <h2>V</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing.valid_metamodel">valid_metamodel() (bayesvalidrox.post_processing.post_processing.PostProcessing method)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification.values">values() (bayesvalidrox.surrogate_models.surrogate_models.MetaModel.auto_vivification method)</a>
+</li>
+ <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html#bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression">VBLinearRegression (class in bayesvalidrox.surrogate_models.bayes_linear)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="W" class="genindex-section">
+ <h2>W</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.within_range.html#bayesvalidrox.pylink.pylink.within_range">within_range() (in module bayesvalidrox.pylink.pylink)</a>
+</li>
+ </ul></td>
+ </tr></table>
+</section>
+
+<section id="Z" class="genindex-section">
+ <h2>Z</h2>
+ <table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.zip_subdirs">zip_subdirs() (bayesvalidrox.pylink.pylink.PyLinkForwardModel method)</a>
+</li>
</ul></td>
</tr></table>
</section>
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index 40b22e21de35a7d4314a9b29d7d443c90da2eb3a..af514a6bf41e67b48235c60b4c53b8a247696da1 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -168,25 +168,120 @@
</li>
<li class="toctree-l1"><a class="reference internal" href="packagedescription.html">PACKAGE DESCRIPTION</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="api.html">API</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.adaptPlot.html">bayesvalidrox.surrogate_models.adaptPlot</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.apoly_construction.html">bayesvalidrox.surrogate_models.apoly_construction</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.bayes_linear.html">bayesvalidrox.surrogate_models.bayes_linear</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.eval_rec_rule.html">bayesvalidrox.surrogate_models.eval_rec_rule</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.exp_designs.html">bayesvalidrox.surrogate_models.exp_designs</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.glexindex.html">bayesvalidrox.surrogate_models.glexindex</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.surrogate_models.html">bayesvalidrox.surrogate_models.surrogate_models</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.pylink.pylink.html">bayesvalidrox.pylink.pylink</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.post_processing.post_processing.html">bayesvalidrox.post_processing.post_processing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.bayes_inference.html">bayesvalidrox.bayes_inference.bayes_inference</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.bayes_model_comparison.html">bayesvalidrox.bayes_inference.bayes_model_comparison</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.discrepancy.html">bayesvalidrox.bayes_inference.discrepancy</a></li>
-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.mcmc.html">bayesvalidrox.bayes_inference.mcmc</a></li>
+<li class="toctree-l2 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.html">bayesvalidrox</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" role="switch" type="checkbox"/><label for="toctree-checkbox-3"><div class="visually-hidden">Toggle navigation of bayesvalidrox</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.html">bayesvalidrox.surrogate_models.eval_rec_rule</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.eval_rec_rule</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.html">bayesvalidrox.surrogate_models.exp_designs</a><input class="toctree-checkbox" id="toctree-checkbox-19" name="toctree-checkbox-19" role="switch" type="checkbox"/><label for="toctree-checkbox-19"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.exp_designs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.glexindex.html">bayesvalidrox.surrogate_models.glexindex</a><input class="toctree-checkbox" id="toctree-checkbox-21" name="toctree-checkbox-21" role="switch" type="checkbox"/><label for="toctree-checkbox-21"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.glexindex</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a><input class="toctree-checkbox" id="toctree-checkbox-26" name="toctree-checkbox-26" role="switch" type="checkbox"/><label for="toctree-checkbox-26"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.reg_fast_laplace</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html">bayesvalidrox.surrogate_models.surrogate_models</a><input class="toctree-checkbox" id="toctree-checkbox-27" name="toctree-checkbox-27" role="switch" type="checkbox"/><label for="toctree-checkbox-27"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.surrogate_models</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.html">bayesvalidrox.surrogate_models.surrogate_models.create_psi</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html">bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
</ul>
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</ul>
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
index d102abf2f8087a4a7b60cf67cbf22a3b9a9a2b9f..8ffa0a71fe6112833acd54c5057ea1ae8bcae447 100644
Binary files a/docs/build/html/objects.inv and b/docs/build/html/objects.inv differ
diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html
index 174aa6374d9a814a54fff68c0ff37202f85ce3cd..ce1832ceac070a254f4aaa6ebc3d44538dfd7757 100644
--- a/docs/build/html/py-modindex.html
+++ b/docs/build/html/py-modindex.html
@@ -166,25 +166,120 @@
</li>
<li class="toctree-l1"><a class="reference internal" href="packagedescription.html">PACKAGE DESCRIPTION</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="api.html">API</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of API</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+</ul>
+</li>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.html">bayesvalidrox.surrogate_models.surrogate_models.create_psi</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html">bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
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</ul>
@@ -236,121 +331,151 @@
<td><img src="_static/minus.png" class="toggler"
id="toggle-1" style="display: none" alt="-" /></td>
<td>
- <code class="xref">bayesvalidrox</code></td><td>
+ <a href="_autosummary/bayesvalidrox.html#module-bayesvalidrox"><code class="xref">bayesvalidrox</code></a></td><td>
+ <em></em></td>
+ </tr>
+ <tr class="cg-1">
+ <td></td>
+ <td>   
+ <a href="_autosummary/bayesvalidrox.bayes_inference.html#module-bayesvalidrox.bayes_inference"><code class="xref">bayesvalidrox.bayes_inference</code></a></td><td>
+ <em></em></td>
+ </tr>
+ <tr class="cg-1">
+ <td></td>
+ <td>   
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+ <em></em></td>
+ </tr>
+ <tr class="cg-1">
+ <td></td>
+ <td>   
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+ <em></em></td>
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+ <tr class="cg-1">
+ <td></td>
+ <td>   
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+ <em></em></td>
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+ <tr class="cg-1">
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<td></td>
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<td></td>
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<td></td>
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- <a href="generated/bayesvalidrox.pylink.pylink.html#module-bayesvalidrox.pylink.pylink"><code class="xref">bayesvalidrox.pylink.pylink</code></a></td><td>
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<td>   
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<td>   
- <a href="generated/bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine"><code class="xref">bayesvalidrox.surrogate_models.engine</code></a></td><td>
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+ <a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit"><code class="xref">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</code></a></td><td>
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+ <a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html#module-bayesvalidrox.surrogate_models.reg_fast_laplace"><code class="xref">bayesvalidrox.surrogate_models.reg_fast_laplace</code></a></td><td>
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<td>   
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+ <a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models"><code class="xref">bayesvalidrox.surrogate_models.surrogate_models</code></a></td><td>
<em></em></td>
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diff --git a/docs/build/html/search.html b/docs/build/html/search.html
index 1343efb8f2c99fb33d8db64a098d2602f444a3a0..7746d0c0d3e5e2be5a78e1ad160eb08bda00c54d 100644
--- a/docs/build/html/search.html
+++ b/docs/build/html/search.html
@@ -165,25 +165,120 @@
</li>
<li class="toctree-l1"><a class="reference internal" href="packagedescription.html">PACKAGE DESCRIPTION</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.bayes_inference.mcmc.html">bayesvalidrox.bayes_inference.mcmc</a></li>
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+<li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.html">bayesvalidrox.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" role="switch" type="checkbox"/><label for="toctree-checkbox-4"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.html">bayesvalidrox.bayes_inference.bayes_inference</a><input class="toctree-checkbox" id="toctree-checkbox-5" name="toctree-checkbox-5" role="switch" type="checkbox"/><label for="toctree-checkbox-5"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference.bayes_inference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html">bayesvalidrox.bayes_inference.bayes_inference.BayesInference</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.html">bayesvalidrox.bayes_inference.bayes_model_comparison</a><input class="toctree-checkbox" id="toctree-checkbox-6" name="toctree-checkbox-6" role="switch" type="checkbox"/><label for="toctree-checkbox-6"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference.bayes_model_comparison</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.html">bayesvalidrox.bayes_inference.discrepancy</a><input class="toctree-checkbox" id="toctree-checkbox-7" name="toctree-checkbox-7" role="switch" type="checkbox"/><label for="toctree-checkbox-7"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference.discrepancy</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.mcmc.html">bayesvalidrox.bayes_inference.mcmc</a><input class="toctree-checkbox" id="toctree-checkbox-8" name="toctree-checkbox-8" role="switch" type="checkbox"/><label for="toctree-checkbox-8"><div class="visually-hidden">Toggle navigation of bayesvalidrox.bayes_inference.mcmc</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.html">bayesvalidrox.bayes_inference.mcmc.MCMC</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.post_processing.html">bayesvalidrox.post_processing</a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" role="switch" type="checkbox"/><label for="toctree-checkbox-9"><div class="visually-hidden">Toggle navigation of bayesvalidrox.post_processing</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.post_processing.post_processing.html">bayesvalidrox.post_processing.post_processing</a><input class="toctree-checkbox" id="toctree-checkbox-10" name="toctree-checkbox-10" role="switch" type="checkbox"/><label for="toctree-checkbox-10"><div class="visually-hidden">Toggle navigation of bayesvalidrox.post_processing.post_processing</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html">bayesvalidrox.post_processing.post_processing.PostProcessing</a></li>
+</ul>
+</li>
+</ul>
+</li>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html">bayesvalidrox.pylink.pylink.PyLinkForwardModel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.html">bayesvalidrox.surrogate_models</a><input class="toctree-checkbox" id="toctree-checkbox-13" name="toctree-checkbox-13" role="switch" type="checkbox"/><label for="toctree-checkbox-13"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html">bayesvalidrox.surrogate_models.apoly_construction</a><input class="toctree-checkbox" id="toctree-checkbox-15" name="toctree-checkbox-15" role="switch" type="checkbox"/><label for="toctree-checkbox-15"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.apoly_construction</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+</ul>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.html">bayesvalidrox.surrogate_models.bayes_linear</a><input class="toctree-checkbox" id="toctree-checkbox-16" name="toctree-checkbox-16" role="switch" type="checkbox"/><label for="toctree-checkbox-16"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.bayes_linear</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.html">bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.html">bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression</a></li>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a><input class="toctree-checkbox" id="toctree-checkbox-17" name="toctree-checkbox-17" role="switch" type="checkbox"/><label for="toctree-checkbox-17"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.engine</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.html">bayesvalidrox.surrogate_models.engine.hellinger_distance</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.html">bayesvalidrox.surrogate_models.engine.logpdf</a></li>
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+</ul>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.html">bayesvalidrox.surrogate_models.eval_rec_rule</a><input class="toctree-checkbox" id="toctree-checkbox-18" name="toctree-checkbox-18" role="switch" type="checkbox"/><label for="toctree-checkbox-18"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.eval_rec_rule</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.html">bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.html">bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.html">bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.html">bayesvalidrox.surrogate_models.exp_designs</a><input class="toctree-checkbox" id="toctree-checkbox-19" name="toctree-checkbox-19" role="switch" type="checkbox"/><label for="toctree-checkbox-19"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.exp_designs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html">bayesvalidrox.surrogate_models.exp_designs.ExpDesigns</a></li>
+</ul>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a><input class="toctree-checkbox" id="toctree-checkbox-20" name="toctree-checkbox-20" role="switch" type="checkbox"/><label for="toctree-checkbox-20"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.exploration</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.html">bayesvalidrox.surrogate_models.exploration.Exploration</a></li>
+</ul>
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+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.glexindex.html">bayesvalidrox.surrogate_models.glexindex</a><input class="toctree-checkbox" id="toctree-checkbox-21" name="toctree-checkbox-21" role="switch" type="checkbox"/><label for="toctree-checkbox-21"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.glexindex</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.html">bayesvalidrox.surrogate_models.glexindex.cross_truncate</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.html">bayesvalidrox.surrogate_models.glexindex.glexindex</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.input_space.html">bayesvalidrox.surrogate_models.input_space</a><input class="toctree-checkbox" id="toctree-checkbox-22" name="toctree-checkbox-22" role="switch" type="checkbox"/><label for="toctree-checkbox-22"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.input_space</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html">bayesvalidrox.surrogate_models.input_space.InputSpace</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a><input class="toctree-checkbox" id="toctree-checkbox-23" name="toctree-checkbox-23" role="switch" type="checkbox"/><label for="toctree-checkbox-23"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.inputs</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html">bayesvalidrox.surrogate_models.inputs.Marginal</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a><input class="toctree-checkbox" id="toctree-checkbox-24" name="toctree-checkbox-24" role="switch" type="checkbox"/><label for="toctree-checkbox-24"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a><input class="toctree-checkbox" id="toctree-checkbox-25" name="toctree-checkbox-25" role="switch" type="checkbox"/><label for="toctree-checkbox-25"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.reg_fast_ard</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.html">bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.html">bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a><input class="toctree-checkbox" id="toctree-checkbox-26" name="toctree-checkbox-26" role="switch" type="checkbox"/><label for="toctree-checkbox-26"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.reg_fast_laplace</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace</a></li>
+</ul>
+</li>
+<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html">bayesvalidrox.surrogate_models.surrogate_models</a><input class="toctree-checkbox" id="toctree-checkbox-27" name="toctree-checkbox-27" role="switch" type="checkbox"/><label for="toctree-checkbox-27"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.surrogate_models</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
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+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.html">bayesvalidrox.surrogate_models.surrogate_models.create_psi</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.html">bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator</a></li>
+<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
</ul>
</li>
</ul>
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 236b5726456ff12765daa1175ebb581eaa44d0d6..19a9e4c13100b11db07ffb9991bfec0f1c4ef5db 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
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validation of computational models", "Example: ishigami", "Example: model comparison", "Example: OHagan-function", "PACKAGE DESCRIPTION", "Example: pollution", "TUTORIAL"], "titleterms": {"1": 90, "3": 90, "adaptplot": [19, 20, 75], "an": 94, "analyt": 64, "api": 65, "apoly_construct": [21, 22, 76], "argument": [29, 42], "assist": 88, "attribut": [3, 5, 7, 9, 13, 16, 25, 26, 39, 42, 47, 49, 50, 52, 55, 58, 60], "bay": [], "bayes_infer": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 69, 70, 71, 72], "bayes_linear": [23, 24, 25, 26, 27, 77], "bayes_model_comparison": [4, 5, 70], "bayesian": [88, 94], "bayesianlinearregress": 24, "bayesinfer": 3, "bayesmodelcomparison": 5, "bayesvalidrox": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87], "beam": 66, "borehol": 67, "check_rang": 40, "choic": [64, 66, 67, 89, 90, 91, 93], "comparison": 90, "comput": 88, "content": 88, "corr": 53, "corr_loocv_error": 61, "create_psi": 62, "cross_trunc": 44, "data": [64, 66, 67, 89, 91, 93, 94], "defin": 94, "descript": 92, "design": [92, 94], "discrep": [6, 7, 64, 66, 71], "eblinearregress": 25, "engin": [28, 29, 30, 31, 32, 78, 94], "eval_rec_rul": [33, 34, 35, 36, 37, 79], "eval_rec_rule_arbitrari": 35, "eval_univ_basi": 36, "exampl": [49, 64, 66, 67, 68, 89, 90, 91, 93], "exp_design": [38, 39, 40, 80], "expdesign": 39, "experiment": [92, 94], "explor": [41, 42, 81], "function": [64, 91], "gamma_mean": 27, "gaussian_process_emul": 63, "gelman_rubin": 10, "glexindex": [43, 44, 45, 82], "hellinger_dist": 30, "import": 94, "indic": 88, "infer": 94, "input": [48, 49, 50, 83, 94], "input_spac": [46, 47], "inputspac": 47, "ishigami": 89, "l2_model": 90, "librari": 94, "logpdf": 31, "margin": 50, "mcmc": [8, 9, 10, 72], "meta": 94, "meta_model_engin": [], "metamodel": [60, 64, 66, 67, 89, 90, 91, 93], "model": [64, 66, 67, 88, 89, 90, 91, 93, 94], "model1": 90, "necessari": 94, "nl2_model": 90, "nl4_model": 90, "note": [24, 25, 26, 39, 52, 55, 60], "ohagan": 91, "orthogonal_matching_pursuit": [51, 52, 53, 84], "orthogonalmatchingpursuit": 52, "packag": 92, "paramet": [3, 5, 7, 9, 10, 13, 16, 17, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 47, 52, 55, 58, 60, 61, 62, 63], "pollut": 93, "poly_rec_coeff": 37, "post": 94, "post_process": [11, 12, 13, 73], "postprocess": 13, "prior": [64, 66, 67, 89, 91, 92, 93], "priors1": 90, "probabilist": 94, "process": 94, "pylink": [14, 15, 16, 17, 64, 66, 67, 74, 89, 90, 91, 93], "pylinkforwardmodel": [16, 94], "rais": [13, 29, 62], "refer": [52, 55, 58], "reg_fast_ard": [54, 55, 56, 85], "reg_fast_laplac": [57, 58, 86], "regressionfastard": 55, "regressionfastlaplac": 58, "return": [3, 5, 7, 9, 10, 13, 16, 17, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 42, 47, 49, 52, 55, 58, 60, 61, 62, 63], "sequenti": 94, "set": [64, 66, 67, 89, 90, 91, 93, 94], "subdomain": 32, "surrog": [64, 66, 67, 88, 89, 90, 91, 92, 93, 94], "surrogate_model": [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87], "tabl": 88, "train": [64, 66, 67, 89, 90, 91, 93, 94], "tutori": 94, "uncertainti": 94, "update_precis": 56, "valid": 88, "vblinearregress": 26, "within_rang": 17}})
\ No newline at end of file
diff --git a/docs/build/html/tutorial.html b/docs/build/html/tutorial.html
index 9244f1a572f7ad51da3c3e1c07d9d3e58cf3a92e..7e66c9a9ffc622e3e3820cba6b0ddf6c2167ae9f 100644
--- a/docs/build/html/tutorial.html
+++ b/docs/build/html/tutorial.html
@@ -176,6 +176,7 @@
<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.exploration.html">bayesvalidrox.surrogate_models.exploration</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.glexindex.html">bayesvalidrox.surrogate_models.glexindex</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.inputs.html">bayesvalidrox.surrogate_models.inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.engine.html">bayesvalidrox.surrogate_models.engine</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html">bayesvalidrox.surrogate_models.orthogonal_matching_pursuit</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.reg_fast_ard.html">bayesvalidrox.surrogate_models.reg_fast_ard</a></li>
<li class="toctree-l2"><a class="reference internal" href="generated/bayesvalidrox.surrogate_models.reg_fast_laplace.html">bayesvalidrox.surrogate_models.reg_fast_laplace</a></li>
@@ -257,7 +258,7 @@ These names will also be adopted by the surrogate and used in all plots.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">Output</span><span class="o">.</span><span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Z'</span><span class="p">]</span>
</pre></div>
</div>
-<p>Bonus: For this example, we have a Monte-Carlo reference solution for the first moments (mean and standard deviation) of the analytical function.
+<p>For this example, we have a Monte-Carlo reference solution for the first moments (mean and standard deviation) of the analytical function.
The numpy (<code class="docutils literal notranslate"><span class="pre">*.npy</span></code>) files for this can be found in the <code class="docutils literal notranslate"><span class="pre">data\</span></code> directory of the example.
We will later discuss the estimation of the first two moments from the surrogate model, so let us import the mc-reference here.
These values can be passed in a form of a dictionary to the object variable <code class="docutils literal notranslate"><span class="pre">mc_reference</span></code>.</p>
@@ -351,22 +352,36 @@ A value of 1 results in standard truncation of the expansion, while smaller valu
</section>
<section id="set-the-experimental-design">
<h2>Set the experimental design<a class="headerlink" href="#set-the-experimental-design" title="Link to this heading">¶</a></h2>
-<p>The experimental design provides instructions on how to sample the input parameter space for static training of the surrogate.
-Various sampling methods are available, but the samples can also be given by the user.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">add_ExpDesign</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">Method</span> <span class="o">=</span> <span class="s1">'normal'</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_init_samples</span> <span class="o">=</span> <span class="mi">100</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">sampling_method</span> <span class="o">=</span> <span class="s1">'latin_hypercube'</span>
+<p>The experimental design provides instructions on how to sample the input parameter space for training and evaluating the surrogate.
+Various sampling methods are available, and the samples can also be given by the user.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ExpDesign</span> <span class="o">=</span> <span class="n">ExpDesign</span><span class="p">(</span><span class="n">Inputs</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">Method</span> <span class="o">=</span> <span class="s1">'normal'</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_init_samples</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">sampling_method</span> <span class="o">=</span> <span class="s1">'latin_hypercube'</span>
</pre></div>
</div>
</section>
-<section id="train-the-surrogate">
-<h2>Train the surrogate<a class="headerlink" href="#train-the-surrogate" title="Link to this heading">¶</a></h2>
-<p>Now, we can start training the surrogate (meta-) model by using the method <code class="docutils literal notranslate"><span class="pre">create_metamodel</span></code> and passing the model object as the only argument.
-This method returns the trained metamodel which we save as a <code class="docutils literal notranslate"><span class="pre">pkl</span></code> object.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">PCEModel</span> <span class="o">=</span> <span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">create_metamodel</span><span class="p">(</span><span class="n">Model</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s1">'PCEModel_</span><span class="si">{</span><span class="n">Model</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s1">.pkl'</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">output</span><span class="p">:</span>
-<span class="gp">>>> </span> <span class="n">joblib</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">PCEModel</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<section id="train-the-surrogate-with-an-engine">
+<h2>Train the surrogate with an engine<a class="headerlink" href="#train-the-surrogate-with-an-engine" title="Link to this heading">¶</a></h2>
+<p>Training is done by giving the model, experimental design and the surrogate model to an engine, which performs the training for us.
+The engine is of class <code class="xref py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">engine</span> <span class="o">=</span> <span class="n">Engine</span><span class="p">(</span><span class="n">MetaModelOpts</span><span class="p">,</span> <span class="n">Model</span><span class="p">,</span> <span class="n">ExpDesign</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Now, we can start training the surrogate (meta-) model by starting the engine and using the method <code class="docutils literal notranslate"><span class="pre">train_normal</span></code>.
+The engine obtains the training samples from the experimental design using the sampling strategy we set, runs the model on these samples and trains the surrogate on the results.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">engine</span><span class="o">.</span><span class="n">start_engine</span><span class="p">()</span>
+<span class="gp">>>> </span><span class="n">engine</span><span class="o">.</span><span class="n">train_normal</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Once this has run through we can obtain the trained metamodel from the engine.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaModel_trained</span> <span class="o">=</span> <span class="n">engine</span><span class="o">.</span><span class="n">MetaModel</span>
+</pre></div>
+</div>
+<p>As <strong>bayesvalidrox</strong> uses the engine class for postprocessing and inference, we will save it at this point as a <code class="docutils literal notranslate"><span class="pre">pkl</span></code> object.
+This can be easily read in to avoid retraining the surrogate.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s1">'PCEengine_</span><span class="si">{</span><span class="n">Model</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s1">.pkl'</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">output</span><span class="p">:</span>
+<span class="gp">>>> </span> <span class="n">joblib</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">engine</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
</pre></div>
</div>
</section>
@@ -374,15 +389,15 @@ This method returns the trained metamodel which we save as a <code class="docuti
<h2>Sequential training<a class="headerlink" href="#sequential-training" title="Link to this heading">¶</a></h2>
<p>The basic surrogate training that we just performed is done only on one static set of data.
<strong>bayesvalidrox</strong> also provide the option of sequential training, also known as active learning, where additional samples to be trained on are chosen by the surrogate.
-This can be activated by setting the <code class="docutils literal notranslate"><span class="pre">method</span></code> of the experimental design to <code class="docutils literal notranslate"><span class="pre">'sequential'</span></code>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="s1">'sequential'</span>
+This can be activated by setting the <code class="docutils literal notranslate"><span class="pre">method</span></code> of the experimental design as <code class="docutils literal notranslate"><span class="pre">'sequential'</span></code>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="s1">'sequential'</span>
</pre></div>
</div>
<p>This will split the training into two parts.
In the first part the training is performed as before, though the
size of this initial training set can a bit smaller.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_init_samples</span> <span class="o">=</span> <span class="mi">3</span><span class="o">*</span><span class="n">ndim</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">sampling_method</span> <span class="o">=</span> <span class="s1">'latin_hypercube'</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_init_samples</span> <span class="o">=</span> <span class="mi">3</span><span class="o">*</span><span class="n">ndim</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">sampling_method</span> <span class="o">=</span> <span class="s1">'latin_hypercube'</span>
</pre></div>
</div>
<p>The options for sequential training are listed at ………..
@@ -390,23 +405,23 @@ New samples are set by exploration and exploitation.
Exploration refers to samples that are randomly drawn from the prior input space,
while exploitation can use different metrics.
The tradeoff between the two helps to avoid overfitting, while keeping the faster convergence given by the exploitation methods.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_new_samples</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_max_samples</span> <span class="o">=</span> <span class="mi">150</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">mod_LOO_threshold</span> <span class="o">=</span> <span class="mf">1e-16</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_new_samples</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_max_samples</span> <span class="o">=</span> <span class="mi">150</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">mod_LOO_threshold</span> <span class="o">=</span> <span class="mf">1e-16</span>
<span class="gp">>>></span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">tradeoff_scheme</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">explore_method</span> <span class="o">=</span> <span class="s1">'random'</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">tradeoff_scheme</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">explore_method</span> <span class="o">=</span> <span class="s1">'random'</span>
<span class="gp">>>></span>
<span class="gp">>>> </span><span class="c1"># Use when 'Voronoi' or 'random' or 'latin_hypercube' chosen</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_canddidate</span> <span class="o">=</span> <span class="mi">1000</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_cand_groups</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_canddidate</span> <span class="o">=</span> <span class="mi">1000</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">n_cand_groups</span> <span class="o">=</span> <span class="mi">4</span>
</pre></div>
</div>
<p>Here we set the exploitaiton method to be Bayesian Active Learning …..cite……
, which chooses the new samples based on the information gain with respect to some given data, here the model results described earlier.</p>
<p>In addition we need to set the information metric to use, here <code class="docutils literal notranslate"><span class="pre">'DKL'</span></code> is chosen.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">exploit_method</span> <span class="o">=</span> <span class="s1">'BayesActDesign'</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">util_func</span> <span class="o">=</span> <span class="s1">'DKL'</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">exploit_method</span> <span class="o">=</span> <span class="s1">'BayesActDesign'</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">util_func</span> <span class="o">=</span> <span class="s1">'DKL'</span>
</pre></div>
</div>
<p>This active learning strategy also relies on the data uncertainty, so we set this to follow a Gaussian distribution around all values
@@ -419,23 +434,22 @@ with standard deviations that are as large as the values themselves.</p>
</pre></div>
</div>
<p>The measures calculated in each training iteration can also be plotted.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">post_snapshot</span> <span class="o">=</span> <span class="kc">False</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">step_snapshot</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">max_a_post</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">ndim</span>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">post_snapshot</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">step_snapshot</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">max_a_post</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">ndim</span>
</pre></div>
</div>
<p>For calculating and plotting the validation error of the surrogate in each iteration, additional references can be given.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">prior</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="sa">f</span><span class="s2">"data/Prior_</span><span class="si">{</span><span class="n">ndim</span><span class="si">}</span><span class="s2">.npy"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">prior_outputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="sa">f</span><span class="s2">"data/origModelOutput_</span><span class="si">{</span><span class="n">ndim</span><span class="si">}</span><span class="s2">.npy"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">likelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="sa">f</span><span class="s2">"data/validLikelihoods_</span><span class="si">{</span><span class="n">ndim</span><span class="si">}</span><span class="s2">.npy"</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">valid_samples</span> <span class="o">=</span> <span class="n">prior</span><span class="p">[:</span><span class="mi">500</span><span class="p">]</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">valid_model_runs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Z'</span><span class="p">:</span> <span class="n">prior_outputs</span><span class="p">[:</span><span class="mi">500</span><span class="p">]}</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">valid_samples</span> <span class="o">=</span> <span class="n">prior</span><span class="p">[:</span><span class="mi">500</span><span class="p">]</span>
+<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">valid_model_runs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Z'</span><span class="p">:</span> <span class="n">prior_outputs</span><span class="p">[:</span><span class="mi">500</span><span class="p">]}</span>
</pre></div>
</div>
-<p>The sequential training is performed by the class <code class="xref py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.meta_model_engine.MetaModelEngine</span></code>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">meta_model_engine</span> <span class="o">=</span> <span class="n">MetaModelEngine</span><span class="p">(</span><span class="n">MetaModelOpts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">meta_model_engine</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="n">PCEModel</span> <span class="o">=</span> <span class="n">meta_model_engine</span><span class="o">.</span><span class="n">MetaModel</span>
+<p>The sequential training is again performed by the class <code class="xref py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code>, but using the function <cite>train_sequential</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">engine</span><span class="o">.</span><span class="n">ExpDesign</span> <span class="o">=</span> <span class="n">ExpDesign</span>
+<span class="gp">>>> </span><span class="n">engine</span><span class="o">.</span><span class="n">train_sequential</span><span class="p">()</span>
</pre></div>
</div>
</section>
@@ -444,10 +458,10 @@ with standard deviations that are as large as the values themselves.</p>
<p>The available post-processing methods for <strong>bayesvalidrox</strong> are given in the class <code class="xref py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.post_processing.post_processing.PostProcessing</span></code>.
All results created by this class are automatically stored in an output-folder <code class="docutils literal notranslate"><span class="pre">Outputs_PostProcessing_*modelname</span></code>.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">bayesvalidrox</span> <span class="kn">import</span> <span class="n">PostProcessing</span>
-<span class="gp">>>> </span><span class="n">PostPCE</span> <span class="o">=</span> <span class="n">PostProcessing</span><span class="p">(</span><span class="n">PCEModel</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">PostPCE</span> <span class="o">=</span> <span class="n">PostProcessing</span><span class="p">(</span><span class="n">engine</span><span class="p">)</span>
</pre></div>
</div>
-<p>Since the reference moments obtained from a Monte-Carlo simulation is available, we only need to call the method <code class="docutils literal notranslate"><span class="pre">plotMoments</span></code> method from the PostProcessing object.
+<p>Since the reference moments obtained from a Monte-Carlo simulation is available, we only need to call the method <code class="docutils literal notranslate"><span class="pre">plot_moments</span></code> method from the PostProcessing object.
This method generates a plot and stores it in the output folder.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">PostPCE</span><span class="o">.</span><span class="n">plot_moments</span><span class="p">()</span>
</pre></div>
@@ -472,7 +486,7 @@ The method <code class="docutils literal notranslate"><span class="pre">sobolInd
<h2>Bayesian Inference<a class="headerlink" href="#bayesian-inference" title="Link to this heading">¶</a></h2>
<p>Inverse parameter estimation can be done in <strong>bayesvalidrox</strong> with the class <code class="xref py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inf.bayes_inf.BayesInference</span></code>.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">bayesvalidrox</span> <span class="kn">import</span> <span class="n">BayesInference</span>
-<span class="gp">>>> </span><span class="n">BayesOpts</span> <span class="o">=</span> <span class="n">BayesInference</span><span class="p">(</span><span class="n">PCEModel</span><span class="p">)</span>
+<span class="gp">>>> </span><span class="n">BayesOpts</span> <span class="o">=</span> <span class="n">BayesInference</span><span class="p">(</span><span class="n">engine</span><span class="p">)</span>
</pre></div>
</div>
<p>If we set <code class="docutils literal notranslate"><span class="pre">emulator</span></code> to be true the Bayesian Inference will be performed based on the emulator.
@@ -622,7 +636,7 @@ plots of posterior predictions if wanted.</p>
<li><a class="reference internal" href="#define-probabilistic-input-model">Define probabilistic input model</a></li>
<li><a class="reference internal" href="#define-surrogate-meta-model">Define surrogate (meta) model</a></li>
<li><a class="reference internal" href="#set-the-experimental-design">Set the experimental design</a></li>
-<li><a class="reference internal" href="#train-the-surrogate">Train the surrogate</a></li>
+<li><a class="reference internal" href="#train-the-surrogate-with-an-engine">Train the surrogate with an engine</a></li>
<li><a class="reference internal" href="#sequential-training">Sequential training</a></li>
<li><a class="reference internal" href="#post-processing">Post-processing</a></li>
<li><a class="reference internal" href="#bayesian-inference">Bayesian Inference</a></li>
diff --git a/docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.rst b/docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f129d634120070b7264822b901279a02983e8498
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.rst
@@ -0,0 +1,32 @@
+bayesvalidrox.bayes\_inference.bayes\_inference.BayesInference
+==============================================================
+
+.. currentmodule:: bayesvalidrox.bayes_inference.bayes_inference
+
+.. autoclass:: BayesInference
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~BayesInference.__init__
+ ~BayesInference.create_error_model
+ ~BayesInference.create_inference
+ ~BayesInference.normpdf
+ ~BayesInference.perform_bootstrap
+ ~BayesInference.plot_log_BME
+ ~BayesInference.plot_post_params
+ ~BayesInference.setup_inference
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.bayes_inference.bayes_inference.rst b/docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.rst
similarity index 67%
rename from docs/source/generated/bayesvalidrox.bayes_inference.bayes_inference.rst
rename to docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.rst
index acff1be63e333b89f5048a7bd555091bd9ac48ea..ebc205c86c7eeb677c26a76f2c26255f327fb909 100644
--- a/docs/source/generated/bayesvalidrox.bayes_inference.bayes_inference.rst
+++ b/docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.bayes\_inference.bayes\_inference
+bayesvalidrox.bayes\_inference.bayes\_inference
===============================================
.. automodule:: bayesvalidrox.bayes_inference.bayes_inference
@@ -16,6 +16,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
BayesInference
diff --git a/docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.rst b/docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.rst
new file mode 100644
index 0000000000000000000000000000000000000000..69d7f471f16058189e0ceb1c1b5196437f946baf
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.rst
@@ -0,0 +1,35 @@
+bayesvalidrox.bayes\_inference.bayes\_model\_comparison.BayesModelComparison
+============================================================================
+
+.. currentmodule:: bayesvalidrox.bayes_inference.bayes_model_comparison
+
+.. autoclass:: BayesModelComparison
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~BayesModelComparison.__init__
+ ~BayesModelComparison.cal_model_weight
+ ~BayesModelComparison.calc_bayes_factors
+ ~BayesModelComparison.calc_justifiability_analysis
+ ~BayesModelComparison.calc_model_weights
+ ~BayesModelComparison.generate_dataset
+ ~BayesModelComparison.model_comparison_all
+ ~BayesModelComparison.plot_bayes_factor
+ ~BayesModelComparison.plot_just_analysis
+ ~BayesModelComparison.plot_model_weights
+ ~BayesModelComparison.setup
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.bayes_inference.bayes_model_comparison.rst b/docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.rst
similarity index 68%
rename from docs/source/generated/bayesvalidrox.bayes_inference.bayes_model_comparison.rst
rename to docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.rst
index 0841445694edce22b731744391150b3abe0caf40..a0599ad4816f88709c1fb3eec268f4e63a920c7b 100644
--- a/docs/source/generated/bayesvalidrox.bayes_inference.bayes_model_comparison.rst
+++ b/docs/source/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.bayes\_inference.bayes\_model\_comparison
+bayesvalidrox.bayes\_inference.bayes\_model\_comparison
=======================================================
.. automodule:: bayesvalidrox.bayes_inference.bayes_model_comparison
@@ -16,6 +16,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
BayesModelComparison
diff --git a/docs/source/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.rst b/docs/source/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.rst
new file mode 100644
index 0000000000000000000000000000000000000000..39c9e8e960e81d3d423feec02d61e3809647ba39
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.rst
@@ -0,0 +1,26 @@
+bayesvalidrox.bayes\_inference.discrepancy.Discrepancy
+======================================================
+
+.. currentmodule:: bayesvalidrox.bayes_inference.discrepancy
+
+.. autoclass:: Discrepancy
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Discrepancy.__init__
+ ~Discrepancy.get_sample
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.bayes_inference.discrepancy.rst b/docs/source/_autosummary/bayesvalidrox.bayes_inference.discrepancy.rst
similarity index 67%
rename from docs/source/generated/bayesvalidrox.bayes_inference.discrepancy.rst
rename to docs/source/_autosummary/bayesvalidrox.bayes_inference.discrepancy.rst
index b7f09cb9c0611a28398caae1e92ebc798e9dbc0a..b97bd05f0c5af897fe049ff86dbf844cbafdc236 100644
--- a/docs/source/generated/bayesvalidrox.bayes_inference.discrepancy.rst
+++ b/docs/source/_autosummary/bayesvalidrox.bayes_inference.discrepancy.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.bayes\_inference.discrepancy
+bayesvalidrox.bayes\_inference.discrepancy
==========================================
.. automodule:: bayesvalidrox.bayes_inference.discrepancy
@@ -16,6 +16,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
Discrepancy
diff --git a/docs/source/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.rst b/docs/source/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3037f65657ea50ae5bc288d590e5523e432607ee
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.rst
@@ -0,0 +1,32 @@
+bayesvalidrox.bayes\_inference.mcmc.MCMC
+========================================
+
+.. currentmodule:: bayesvalidrox.bayes_inference.mcmc
+
+.. autoclass:: MCMC
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~MCMC.__init__
+ ~MCMC.eval_model
+ ~MCMC.log_likelihood
+ ~MCMC.log_posterior
+ ~MCMC.log_prior
+ ~MCMC.normpdf
+ ~MCMC.run_sampler
+ ~MCMC.train_error_model
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.rst b/docs/source/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.rst
new file mode 100644
index 0000000000000000000000000000000000000000..00d208a8f0c8e92fea9c697f828c6b0eec893e64
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.bayes\_inference.mcmc.gelman\_rubin
+=================================================
+
+.. currentmodule:: bayesvalidrox.bayes_inference.mcmc
+
+.. autofunction:: gelman_rubin
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.bayes_inference.mcmc.rst b/docs/source/_autosummary/bayesvalidrox.bayes_inference.mcmc.rst
similarity index 69%
rename from docs/source/generated/bayesvalidrox.bayes_inference.mcmc.rst
rename to docs/source/_autosummary/bayesvalidrox.bayes_inference.mcmc.rst
index ebdba31cd98a821bfaf3271beb6fcb074576ff56..685757475adef946caf395de71c6c9d0146d171a 100644
--- a/docs/source/generated/bayesvalidrox.bayes_inference.mcmc.rst
+++ b/docs/source/_autosummary/bayesvalidrox.bayes_inference.mcmc.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.bayes\_inference.mcmc
+bayesvalidrox.bayes\_inference.mcmc
===================================
.. automodule:: bayesvalidrox.bayes_inference.mcmc
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
gelman_rubin
@@ -22,6 +23,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
MCMC
diff --git a/docs/source/_autosummary/bayesvalidrox.bayes_inference.rst b/docs/source/_autosummary/bayesvalidrox.bayes_inference.rst
new file mode 100644
index 0000000000000000000000000000000000000000..2d1f540c9e4975adb3578e477d29eac97f2330ad
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.bayes_inference.rst
@@ -0,0 +1,35 @@
+bayesvalidrox.bayes\_inference
+==============================
+
+.. automodule:: bayesvalidrox.bayes_inference
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+
+ bayesvalidrox.bayes_inference.bayes_inference
+ bayesvalidrox.bayes_inference.bayes_model_comparison
+ bayesvalidrox.bayes_inference.discrepancy
+ bayesvalidrox.bayes_inference.mcmc
+
diff --git a/docs/source/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.rst b/docs/source/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.rst
new file mode 100644
index 0000000000000000000000000000000000000000..9efcfaaa0b53ab697fc1c5f9c080472b2d4a5f67
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.rst
@@ -0,0 +1,33 @@
+bayesvalidrox.post\_processing.post\_processing.PostProcessing
+==============================================================
+
+.. currentmodule:: bayesvalidrox.post_processing.post_processing
+
+.. autoclass:: PostProcessing
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~PostProcessing.__init__
+ ~PostProcessing.check_accuracy
+ ~PostProcessing.check_reg_quality
+ ~PostProcessing.compute_pce_moments
+ ~PostProcessing.eval_pce_model_3d
+ ~PostProcessing.plot_moments
+ ~PostProcessing.plot_seq_design_diagnostics
+ ~PostProcessing.sobol_indices
+ ~PostProcessing.valid_metamodel
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.post_processing.post_processing.rst b/docs/source/_autosummary/bayesvalidrox.post_processing.post_processing.rst
similarity index 67%
rename from docs/source/generated/bayesvalidrox.post_processing.post_processing.rst
rename to docs/source/_autosummary/bayesvalidrox.post_processing.post_processing.rst
index cc095065c785cfb465f5aee1b91ce040f1030f6f..1aae5db20ef1bf96c930a464e6ed713f1f166d73 100644
--- a/docs/source/generated/bayesvalidrox.post_processing.post_processing.rst
+++ b/docs/source/_autosummary/bayesvalidrox.post_processing.post_processing.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.post\_processing.post\_processing
+bayesvalidrox.post\_processing.post\_processing
===============================================
.. automodule:: bayesvalidrox.post_processing.post_processing
@@ -16,6 +16,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
PostProcessing
diff --git a/docs/source/_autosummary/bayesvalidrox.post_processing.rst b/docs/source/_autosummary/bayesvalidrox.post_processing.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c37004bd561d420fdb3ff51ff488415983214aa3
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.post_processing.rst
@@ -0,0 +1,32 @@
+bayesvalidrox.post\_processing
+==============================
+
+.. automodule:: bayesvalidrox.post_processing
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+
+ bayesvalidrox.post_processing.post_processing
+
diff --git a/docs/source/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.rst b/docs/source/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.rst
new file mode 100644
index 0000000000000000000000000000000000000000..52664818dbe0e571253d3341790bd66040372021
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.rst
@@ -0,0 +1,33 @@
+bayesvalidrox.pylink.pylink.PyLinkForwardModel
+==============================================
+
+.. currentmodule:: bayesvalidrox.pylink.pylink
+
+.. autoclass:: PyLinkForwardModel
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~PyLinkForwardModel.__init__
+ ~PyLinkForwardModel.read_observation
+ ~PyLinkForwardModel.read_output
+ ~PyLinkForwardModel.run_command
+ ~PyLinkForwardModel.run_forwardmodel
+ ~PyLinkForwardModel.run_model_parallel
+ ~PyLinkForwardModel.uMBridge_model
+ ~PyLinkForwardModel.update_input_params
+ ~PyLinkForwardModel.zip_subdirs
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.pylink.pylink.rst b/docs/source/_autosummary/bayesvalidrox.pylink.pylink.rst
similarity index 70%
rename from docs/source/generated/bayesvalidrox.pylink.pylink.rst
rename to docs/source/_autosummary/bayesvalidrox.pylink.pylink.rst
index 499b04ff9a95672ae9015c5bcc2826ae2003c816..267ef0c041ce8afdf5de17feb9aeb4e6946869d8 100644
--- a/docs/source/generated/bayesvalidrox.pylink.pylink.rst
+++ b/docs/source/_autosummary/bayesvalidrox.pylink.pylink.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.pylink.pylink
+bayesvalidrox.pylink.pylink
===========================
.. automodule:: bayesvalidrox.pylink.pylink
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
within_range
@@ -22,6 +23,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
PyLinkForwardModel
diff --git a/docs/source/_autosummary/bayesvalidrox.pylink.pylink.within_range.rst b/docs/source/_autosummary/bayesvalidrox.pylink.pylink.within_range.rst
new file mode 100644
index 0000000000000000000000000000000000000000..70192a94373df9e6122d9a78e9d726a483a441ab
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.pylink.pylink.within_range.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.pylink.pylink.within\_range
+=========================================
+
+.. currentmodule:: bayesvalidrox.pylink.pylink
+
+.. autofunction:: within_range
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.pylink.rst b/docs/source/_autosummary/bayesvalidrox.pylink.rst
new file mode 100644
index 0000000000000000000000000000000000000000..1b9ae788bafcbf0e83252161478c7817f9af499f
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.pylink.rst
@@ -0,0 +1,32 @@
+bayesvalidrox.pylink
+====================
+
+.. automodule:: bayesvalidrox.pylink
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+
+ bayesvalidrox.pylink.pylink
+
diff --git a/docs/source/_autosummary/bayesvalidrox.rst b/docs/source/_autosummary/bayesvalidrox.rst
new file mode 100644
index 0000000000000000000000000000000000000000..1e904c9f78c3f74d596b2a0803dd6fb3f63f741b
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.rst
@@ -0,0 +1,35 @@
+bayesvalidrox
+=============
+
+.. automodule:: bayesvalidrox
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+
+ bayesvalidrox.bayes_inference
+ bayesvalidrox.post_processing
+ bayesvalidrox.pylink
+ bayesvalidrox.surrogate_models
+
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c4a5ddc2f41dab3812601a432a824e8e6ce91853
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.adaptPlot.adaptPlot
+===================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.adaptPlot
+
+.. autofunction:: adaptPlot
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.adaptPlot.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.rst
similarity index 78%
rename from docs/source/generated/bayesvalidrox.surrogate_models.adaptPlot.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.rst
index dcdbd3d79137b15e44975aa97fd549e33073c05d..860e8ec2c3aee6f8a624a518c9fe3115eb74c57b 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.adaptPlot.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.adaptPlot
+bayesvalidrox.surrogate\_models.adaptPlot
=========================================
.. automodule:: bayesvalidrox.surrogate_models.adaptPlot
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
adaptPlot
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.rst
new file mode 100644
index 0000000000000000000000000000000000000000..049182d7af9f8bdf0c7420a9b1b107de0ca1c562
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.apoly\_construction.apoly\_construction
+=======================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.apoly_construction
+
+.. autofunction:: apoly_construction
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.apoly_construction.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.rst
similarity index 77%
rename from docs/source/generated/bayesvalidrox.surrogate_models.apoly_construction.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.rst
index a5a45131f65c8841cb770e1a5a245b1549c3f993..73c72f8f33fa684448060c14ac2f17435fc4ea82 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.apoly_construction.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.apoly\_construction
+bayesvalidrox.surrogate\_models.apoly\_construction
===================================================
.. automodule:: bayesvalidrox.surrogate_models.apoly_construction
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
apoly_construction
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.rst
new file mode 100644
index 0000000000000000000000000000000000000000..60ca9858f0b4a1e304c67c0cbd22d42e9262b73b
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.rst
@@ -0,0 +1,33 @@
+bayesvalidrox.surrogate\_models.bayes\_linear.BayesianLinearRegression
+======================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.bayes_linear
+
+.. autoclass:: BayesianLinearRegression
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~BayesianLinearRegression.__init__
+ ~BayesianLinearRegression.fit
+ ~BayesianLinearRegression.get_metadata_routing
+ ~BayesianLinearRegression.get_params
+ ~BayesianLinearRegression.predict
+ ~BayesianLinearRegression.predict_dist
+ ~BayesianLinearRegression.score
+ ~BayesianLinearRegression.set_params
+ ~BayesianLinearRegression.set_score_request
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.rst
new file mode 100644
index 0000000000000000000000000000000000000000..42f6224c3e023d5e673166950929ababecdd11d6
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.rst
@@ -0,0 +1,34 @@
+bayesvalidrox.surrogate\_models.bayes\_linear.EBLinearRegression
+================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.bayes_linear
+
+.. autoclass:: EBLinearRegression
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~EBLinearRegression.__init__
+ ~EBLinearRegression.fit
+ ~EBLinearRegression.get_metadata_routing
+ ~EBLinearRegression.get_params
+ ~EBLinearRegression.predict
+ ~EBLinearRegression.predict_dist
+ ~EBLinearRegression.score
+ ~EBLinearRegression.set_params
+ ~EBLinearRegression.set_predict_request
+ ~EBLinearRegression.set_score_request
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e47223ddbd54560f1b505c36d8094f4e68880d45
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.rst
@@ -0,0 +1,34 @@
+bayesvalidrox.surrogate\_models.bayes\_linear.VBLinearRegression
+================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.bayes_linear
+
+.. autoclass:: VBLinearRegression
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~VBLinearRegression.__init__
+ ~VBLinearRegression.fit
+ ~VBLinearRegression.get_metadata_routing
+ ~VBLinearRegression.get_params
+ ~VBLinearRegression.predict
+ ~VBLinearRegression.predict_dist
+ ~VBLinearRegression.score
+ ~VBLinearRegression.set_params
+ ~VBLinearRegression.set_predict_request
+ ~VBLinearRegression.set_score_request
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c8f24a14d3ae45038b8226a74238d28600e47237
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.bayes\_linear.gamma\_mean
+=========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.bayes_linear
+
+.. autofunction:: gamma_mean
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.bayes_linear.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.rst
similarity index 73%
rename from docs/source/generated/bayesvalidrox.surrogate_models.bayes_linear.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.rst
index 2d866f9107c48b8c17e972d15e22f03d48b6065e..40200d4b0376ed1fcc5f7885feece9304995a1b6 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.bayes_linear.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.bayes\_linear
+bayesvalidrox.surrogate\_models.bayes\_linear
=============================================
.. automodule:: bayesvalidrox.surrogate_models.bayes_linear
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
gamma_mean
@@ -22,6 +23,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
BayesianLinearRegression
EBLinearRegression
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.Engine.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.Engine.rst
new file mode 100644
index 0000000000000000000000000000000000000000..21a9e34806d5b3671103d4a334fcdfd92880c1bc
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.Engine.rst
@@ -0,0 +1,38 @@
+bayesvalidrox.surrogate\_models.engine.Engine
+=============================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.engine
+
+.. autoclass:: Engine
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. 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
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.rst
new file mode 100644
index 0000000000000000000000000000000000000000..8e71cfe6e44cc70921aa0ccfa6fbaf42bec53b0e
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.engine.hellinger\_distance
+==========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.engine
+
+.. autofunction:: hellinger_distance
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.rst
new file mode 100644
index 0000000000000000000000000000000000000000..1c0ed0353adc92a0517b8fb929fbd966fd900a63
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.engine.logpdf
+=============================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.engine
+
+.. autofunction:: logpdf
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.engine.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.rst
similarity index 71%
rename from docs/source/generated/bayesvalidrox.surrogate_models.engine.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.rst
index 17def78a4473e17e19bffb364cae7ce928b8bd91..43998aeade22fc4feb5c04282973e72a1ae1d72e 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.engine.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.engine
+bayesvalidrox.surrogate\_models.engine
======================================
.. automodule:: bayesvalidrox.surrogate_models.engine
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
hellinger_distance
logpdf
@@ -24,6 +25,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
Engine
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.rst
new file mode 100644
index 0000000000000000000000000000000000000000..b660535bb3a75530ff0f7c8ed247e84105d81fdd
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.engine.subdomain
+================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.engine
+
+.. autofunction:: subdomain
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.rst
new file mode 100644
index 0000000000000000000000000000000000000000..b443c4a1248c566c717c3ee11f965e24db9ac762
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.eval\_rec\_rule.eval\_rec\_rule
+===============================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.eval_rec_rule
+
+.. autofunction:: eval_rec_rule
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.rst
new file mode 100644
index 0000000000000000000000000000000000000000..58c6ae25d017f74106f17fda960141e1495129c3
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.eval\_rec\_rule.eval\_rec\_rule\_arbitrary
+==========================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.eval_rec_rule
+
+.. autofunction:: eval_rec_rule_arbitrary
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3f9ccc16cc17970150861a5e98ed881a40f67f3c
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.eval\_rec\_rule.eval\_univ\_basis
+=================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.eval_rec_rule
+
+.. autofunction:: eval_univ_basis
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f0f06e3163c9f099b6cefc33b0360daa4aedd24e
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.eval\_rec\_rule.poly\_rec\_coeffs
+=================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.eval_rec_rule
+
+.. autofunction:: poly_rec_coeffs
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.eval_rec_rule.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.rst
similarity index 81%
rename from docs/source/generated/bayesvalidrox.surrogate_models.eval_rec_rule.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.rst
index 00c5a6de1ad158877a1c238260e1d86a05c39cf0..b5055daeb3eed74f3ee4245cbb6b6d4885c53369 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.eval_rec_rule.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.eval\_rec\_rule
+bayesvalidrox.surrogate\_models.eval\_rec\_rule
===============================================
.. automodule:: bayesvalidrox.surrogate_models.eval_rec_rule
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
eval_rec_rule
eval_rec_rule_arbitrary
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst
new file mode 100644
index 0000000000000000000000000000000000000000..87376d6c45519e53f1323a61d30def9c57adb745
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst
@@ -0,0 +1,34 @@
+bayesvalidrox.surrogate\_models.exp\_designs.ExpDesigns
+=======================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.exp_designs
+
+.. autoclass:: ExpDesigns
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~ExpDesigns.__init__
+ ~ExpDesigns.build_polytypes
+ ~ExpDesigns.check_valid_inputs
+ ~ExpDesigns.generate_ED
+ ~ExpDesigns.generate_samples
+ ~ExpDesigns.init_param_space
+ ~ExpDesigns.pcm_sampler
+ ~ExpDesigns.random_sampler
+ ~ExpDesigns.read_from_file
+ ~ExpDesigns.transform
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e49711dd03aac4f9be9aa1a5a878bc76c07d69ce
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.exp\_designs.check\_ranges
+==========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.exp_designs
+
+.. autofunction:: check_ranges
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.exp_designs.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.rst
similarity index 69%
rename from docs/source/generated/bayesvalidrox.surrogate_models.exp_designs.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.rst
index 2b744abd69d57056e87bec986a98635c98ac63b7..e5d704bd5bdc24df135e0520c42fa8956adf6f80 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.exp_designs.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exp_designs.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.exp\_designs
+bayesvalidrox.surrogate\_models.exp\_designs
============================================
.. automodule:: bayesvalidrox.surrogate_models.exp_designs
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
check_ranges
@@ -22,6 +23,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
ExpDesigns
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.rst
new file mode 100644
index 0000000000000000000000000000000000000000..6ccff094faf883249b40597f8a51e6c7de2fe954
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.rst
@@ -0,0 +1,29 @@
+bayesvalidrox.surrogate\_models.exploration.Exploration
+=======================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.exploration
+
+.. autoclass:: Exploration
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Exploration.__init__
+ ~Exploration.approximate_voronoi
+ ~Exploration.get_exploration_samples
+ ~Exploration.get_mc_samples
+ ~Exploration.get_vornoi_samples
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.exploration.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exploration.rst
similarity index 67%
rename from docs/source/generated/bayesvalidrox.surrogate_models.exploration.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.exploration.rst
index a7f91722cb3496a032cfe16f7d51cabde319a6d3..aa60c432480e2e890958d0f086e5412c56305f13 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.exploration.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.exploration.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.exploration
+bayesvalidrox.surrogate\_models.exploration
===========================================
.. automodule:: bayesvalidrox.surrogate_models.exploration
@@ -16,6 +16,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
Exploration
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c0d050f1d1a4cfad20314b1baec4ea0eff36d413
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.glexindex.cross\_truncate
+=========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.glexindex
+
+.. autofunction:: cross_truncate
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c76918bd1fd5f06600a8c267351c67eca1fe0757
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.glexindex.glexindex
+===================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.glexindex
+
+.. autofunction:: glexindex
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.glexindex.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.glexindex.rst
similarity index 79%
rename from docs/source/generated/bayesvalidrox.surrogate_models.glexindex.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.glexindex.rst
index 66547f9ddcf046fdc4eac94b1dd974765cce126c..bcc161fcf44863062d7faa51f42b34d0844daf30 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.glexindex.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.glexindex.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.glexindex
+bayesvalidrox.surrogate\_models.glexindex
=========================================
.. automodule:: bayesvalidrox.surrogate_models.glexindex
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
cross_truncate
glexindex
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e106f4d59aefb3f1e8957d8d6608ae045f8b8b90
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.rst
@@ -0,0 +1,29 @@
+bayesvalidrox.surrogate\_models.input\_space.InputSpace
+=======================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.input_space
+
+.. autoclass:: InputSpace
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~InputSpace.__init__
+ ~InputSpace.build_polytypes
+ ~InputSpace.check_valid_inputs
+ ~InputSpace.init_param_space
+ ~InputSpace.transform
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.input_space.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.input_space.rst
new file mode 100644
index 0000000000000000000000000000000000000000..1dd0d58b0cbcf8c5db054eb283d9585911277d8d
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.input_space.rst
@@ -0,0 +1,31 @@
+bayesvalidrox.surrogate\_models.input\_space
+============================================
+
+.. automodule:: bayesvalidrox.surrogate_models.input_space
+
+
+
+
+
+
+
+
+
+
+
+ .. rubric:: Classes
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+
+ InputSpace
+
+
+
+
+
+
+
+
+
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c4b68c252ea8fa4c6acc0e0c7311855548a8d321
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.rst
@@ -0,0 +1,32 @@
+bayesvalidrox.surrogate\_models.inputs.Input
+============================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.inputs
+
+.. autoclass:: Input
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Input.__init__
+ ~Input.add_marginals
+
+
+
+
+
+ .. rubric:: Attributes
+
+ .. autosummary::
+
+ ~Input.poly_coeffs_flag
+
+
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.rst
new file mode 100644
index 0000000000000000000000000000000000000000..2fa2e281575c3cf340f22561aa1732c48ebef78a
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.rst
@@ -0,0 +1,25 @@
+bayesvalidrox.surrogate\_models.inputs.Marginal
+===============================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.inputs
+
+.. autoclass:: Marginal
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~Marginal.__init__
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.inputs.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.inputs.rst
similarity index 68%
rename from docs/source/generated/bayesvalidrox.surrogate_models.inputs.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.inputs.rst
index 4b2f678327e6fd7f054a09ede531473e9c461ebf..687dc2f794cf9ff634b100818664c0fafb037c10 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.inputs.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.inputs.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.inputs
+bayesvalidrox.surrogate\_models.inputs
======================================
.. automodule:: bayesvalidrox.surrogate_models.inputs
@@ -16,6 +16,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
Input
Marginal
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e835d0c2b01cea1fa91215d72cdd97b95c98ebde
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.rst
@@ -0,0 +1,34 @@
+bayesvalidrox.surrogate\_models.orthogonal\_matching\_pursuit.OrthogonalMatchingPursuit
+=======================================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
+
+.. autoclass:: OrthogonalMatchingPursuit
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~OrthogonalMatchingPursuit.__init__
+ ~OrthogonalMatchingPursuit.blockwise_inverse
+ ~OrthogonalMatchingPursuit.fit
+ ~OrthogonalMatchingPursuit.get_metadata_routing
+ ~OrthogonalMatchingPursuit.get_params
+ ~OrthogonalMatchingPursuit.loo_error
+ ~OrthogonalMatchingPursuit.predict
+ ~OrthogonalMatchingPursuit.score
+ ~OrthogonalMatchingPursuit.set_params
+ ~OrthogonalMatchingPursuit.set_score_request
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.rst
new file mode 100644
index 0000000000000000000000000000000000000000..9a6786588d090b06429a68485bbf3bf1a4fb8f19
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.orthogonal\_matching\_pursuit.corr
+==================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
+
+.. autofunction:: corr
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst
similarity index 69%
rename from docs/source/generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst
index bf6808c42d05f13d6c7072127ad8d22f80649ecb..a64e2c4550d56783e48482cbae6c0d31536fdf92 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.orthogonal\_matching\_pursuit
+bayesvalidrox.surrogate\_models.orthogonal\_matching\_pursuit
=============================================================
.. automodule:: bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
corr
@@ -22,6 +23,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
OrthogonalMatchingPursuit
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.rst
new file mode 100644
index 0000000000000000000000000000000000000000..137bdc1565ed4d52cd60319bff4ab8d6cf20df7a
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.rst
@@ -0,0 +1,34 @@
+bayesvalidrox.surrogate\_models.reg\_fast\_ard.RegressionFastARD
+================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.reg_fast_ard
+
+.. autoclass:: RegressionFastARD
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~RegressionFastARD.__init__
+ ~RegressionFastARD.fit
+ ~RegressionFastARD.get_metadata_routing
+ ~RegressionFastARD.get_params
+ ~RegressionFastARD.log_marginal_like
+ ~RegressionFastARD.predict
+ ~RegressionFastARD.score
+ ~RegressionFastARD.set_params
+ ~RegressionFastARD.set_predict_request
+ ~RegressionFastARD.set_score_request
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.reg_fast_ard.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.rst
similarity index 70%
rename from docs/source/generated/bayesvalidrox.surrogate_models.reg_fast_ard.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.rst
index 1768efccaf46f85dc86a8fbcf5f738dcd5a0a09a..f739234816e896208e107eca177c35967eb36964 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.reg_fast_ard.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.reg\_fast\_ard
+bayesvalidrox.surrogate\_models.reg\_fast\_ard
==============================================
.. automodule:: bayesvalidrox.surrogate_models.reg_fast_ard
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
update_precisions
@@ -22,6 +23,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
RegressionFastARD
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.rst
new file mode 100644
index 0000000000000000000000000000000000000000..0ff8616c845ad03b7898a0ae6da4d5c7fad1bead
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.reg\_fast\_ard.update\_precisions
+=================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.reg_fast_ard
+
+.. autofunction:: update_precisions
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d5b18ed0a0f744edd2af59f58f1e6c09972ce154
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.rst
@@ -0,0 +1,28 @@
+bayesvalidrox.surrogate\_models.reg\_fast\_laplace.RegressionFastLaplace
+========================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.reg_fast_laplace
+
+.. autoclass:: RegressionFastLaplace
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~RegressionFastLaplace.__init__
+ ~RegressionFastLaplace.fit
+ ~RegressionFastLaplace.fit_
+ ~RegressionFastLaplace.predict
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.reg_fast_laplace.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.rst
similarity index 68%
rename from docs/source/generated/bayesvalidrox.surrogate_models.reg_fast_laplace.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.rst
index 5d1d0b387addb4404f2ce15291e482bf1584b9dd..2cc1f44c5533ad742b113bb38a481191a0d9b307 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.reg_fast_laplace.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.reg\_fast\_laplace
+bayesvalidrox.surrogate\_models.reg\_fast\_laplace
==================================================
.. automodule:: bayesvalidrox.surrogate_models.reg_fast_laplace
@@ -16,6 +16,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
RegressionFastLaplace
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.rst
new file mode 100644
index 0000000000000000000000000000000000000000..fbd49ba297a2f28c516c5818e5f844d545986446
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.rst
@@ -0,0 +1,45 @@
+bayesvalidrox.surrogate\_models
+===============================
+
+.. automodule:: bayesvalidrox.surrogate_models
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+
+ bayesvalidrox.surrogate_models.adaptPlot
+ bayesvalidrox.surrogate_models.apoly_construction
+ bayesvalidrox.surrogate_models.bayes_linear
+ bayesvalidrox.surrogate_models.engine
+ bayesvalidrox.surrogate_models.eval_rec_rule
+ bayesvalidrox.surrogate_models.exp_designs
+ bayesvalidrox.surrogate_models.exploration
+ bayesvalidrox.surrogate_models.glexindex
+ bayesvalidrox.surrogate_models.input_space
+ bayesvalidrox.surrogate_models.inputs
+ bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
+ bayesvalidrox.surrogate_models.reg_fast_ard
+ bayesvalidrox.surrogate_models.reg_fast_laplace
+ bayesvalidrox.surrogate_models.surrogate_models
+
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f4cd883ed344f535e9aaf285734e6df56266e72d
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.rst
@@ -0,0 +1,38 @@
+bayesvalidrox.surrogate\_models.surrogate\_models.MetaModel
+===========================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.surrogate_models
+
+.. autoclass:: MetaModel
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~MetaModel.__init__
+ ~MetaModel.adaptive_regression
+ ~MetaModel.add_InputSpace
+ ~MetaModel.build_metamodel
+ ~MetaModel.copy_meta_model_opts
+ ~MetaModel.create_model_error
+ ~MetaModel.eval_metamodel
+ ~MetaModel.eval_model_error
+ ~MetaModel.fit
+ ~MetaModel.generate_polynomials
+ ~MetaModel.pca_transformation
+ ~MetaModel.regression
+ ~MetaModel.univ_basis_vals
+ ~MetaModel.update_pce_coeffs
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.rst
new file mode 100644
index 0000000000000000000000000000000000000000..7972de4abea5cb1a96c319ecaae7771d776deccb
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.surrogate\_models.corr\_loocv\_error
+====================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.surrogate_models
+
+.. autofunction:: corr_loocv_error
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d6f9c97a90b030aebe4996b1d75086cedc1d5f6f
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.surrogate\_models.create\_psi
+=============================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.surrogate_models
+
+.. autofunction:: create_psi
\ No newline at end of file
diff --git a/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c5eb1db492e525fba32b60ba56f84643ba7e501b
--- /dev/null
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.rst
@@ -0,0 +1,6 @@
+bayesvalidrox.surrogate\_models.surrogate\_models.gaussian\_process\_emulator
+=============================================================================
+
+.. currentmodule:: bayesvalidrox.surrogate_models.surrogate_models
+
+.. autofunction:: gaussian_process_emulator
\ No newline at end of file
diff --git a/docs/source/generated/bayesvalidrox.surrogate_models.surrogate_models.rst b/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.rst
similarity index 72%
rename from docs/source/generated/bayesvalidrox.surrogate_models.surrogate_models.rst
rename to docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.rst
index 33e765714797f57ccef1377dff3c3e38bc64a5d0..e8b46319c04c860e4832eb513b76c74c06fb0b74 100644
--- a/docs/source/generated/bayesvalidrox.surrogate_models.surrogate_models.rst
+++ b/docs/source/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.rst
@@ -1,4 +1,4 @@
-bayesvalidrox.surrogate\_models.surrogate\_models
+bayesvalidrox.surrogate\_models.surrogate\_models
=================================================
.. automodule:: bayesvalidrox.surrogate_models.surrogate_models
@@ -12,6 +12,7 @@
.. rubric:: Functions
.. autosummary::
+ :toctree:
corr_loocv_error
create_psi
@@ -24,6 +25,8 @@
.. rubric:: Classes
.. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
MetaModel
diff --git a/docs/source/_templates/custom-class-template.rst b/docs/source/_templates/custom-class-template.rst
new file mode 100644
index 0000000000000000000000000000000000000000..7bdbf10028cca46af4fa3cd525713c3931a5cb06
--- /dev/null
+++ b/docs/source/_templates/custom-class-template.rst
@@ -0,0 +1,32 @@
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+ :members:
+ :show-inheritance:
+ :inherited-members:
+
+ {% block methods %}
+ .. automethod:: __init__
+
+ {% if methods %}
+ .. rubric:: {{ _('Methods') }}
+
+ .. autosummary::
+ {% for item in methods %}
+ ~{{ name }}.{{ item }}
+ {%- endfor %}
+ {% endif %}
+ {% endblock %}
+
+ {% block attributes %}
+ {% if attributes %}
+ .. rubric:: {{ _('Attributes') }}
+
+ .. autosummary::
+ {% for item in attributes %}
+ ~{{ name }}.{{ item }}
+ {%- endfor %}
+ {% endif %}
+ {% endblock %}
diff --git a/docs/source/_templates/custom-module-template.rst b/docs/source/_templates/custom-module-template.rst
new file mode 100644
index 0000000000000000000000000000000000000000..aaae1599c4b74fdc537e7cecc130fab88a39a6e1
--- /dev/null
+++ b/docs/source/_templates/custom-module-template.rst
@@ -0,0 +1,66 @@
+{{ fullname | escape | underline}}
+
+.. automodule:: {{ fullname }}
+
+ {% block attributes %}
+ {% if attributes %}
+ .. rubric:: {{ _('Module Attributes') }}
+
+ .. autosummary::
+ :toctree:
+ {% for item in attributes %}
+ {{ item }}
+ {%- endfor %}
+ {% endif %}
+ {% endblock %}
+
+ {% block functions %}
+ {% if functions %}
+ .. rubric:: {{ _('Functions') }}
+
+ .. autosummary::
+ :toctree:
+ {% for item in functions %}
+ {{ item }}
+ {%- endfor %}
+ {% endif %}
+ {% endblock %}
+
+ {% block classes %}
+ {% if classes %}
+ .. rubric:: {{ _('Classes') }}
+
+ .. autosummary::
+ :toctree:
+ :template: custom-class-template.rst
+ {% for item in classes %}
+ {{ item }}
+ {%- endfor %}
+ {% endif %}
+ {% endblock %}
+
+ {% block exceptions %}
+ {% if exceptions %}
+ .. rubric:: {{ _('Exceptions') }}
+
+ .. autosummary::
+ :toctree:
+ {% for item in exceptions %}
+ {{ item }}
+ {%- endfor %}
+ {% endif %}
+ {% endblock %}
+
+{% block modules %}
+{% if modules %}
+.. rubric:: Modules
+
+.. autosummary::
+ :toctree:
+ :template: custom-module-template.rst
+ :recursive:
+{% for item in modules %}
+ {{ item }}
+{%- endfor %}
+{% endif %}
+{% endblock %}
diff --git a/docs/source/api.rst b/docs/source/api.rst
index 67bae2b9df328547809007067292450a1036483f..b9e02468e27edaea904a2e4184b7752b6d50d184 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -1,52 +1,10 @@
API
***
-
-SURROGATE MODELS
-----------------
-
-.. autosummary::
- :toctree: generated
-
- bayesvalidrox.surrogate_models.adaptPlot
- bayesvalidrox.surrogate_models.apoly_construction
- bayesvalidrox.surrogate_models.bayes_linear
- bayesvalidrox.surrogate_models.eval_rec_rule
- bayesvalidrox.surrogate_models.exp_designs
- bayesvalidrox.surrogate_models.exploration
- bayesvalidrox.surrogate_models.glexindex
- bayesvalidrox.surrogate_models.inputs
- bayesvalidrox.surrogate_models.engine
- bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
- bayesvalidrox.surrogate_models.reg_fast_ard
- bayesvalidrox.surrogate_models.reg_fast_laplace
- bayesvalidrox.surrogate_models.surrogate_models
-
-
-PYLINK
-------
-
-.. autosummary::
- :toctree: generated
-
- bayesvalidrox.pylink.pylink
-
-
-POST PROCESSING
----------------
-
-.. autosummary::
- :toctree: generated
-
- bayesvalidrox.post_processing.post_processing
-
-
-BAYES INFERENCE
----------------
+Here you can find the api for the package **bayesvalidrox**.
.. autosummary::
- :toctree: generated
+ :toctree: _autosummary
+ :template: custom-module-template.rst
+ :recursive:
- bayesvalidrox.bayes_inference.bayes_inference
- bayesvalidrox.bayes_inference.bayes_model_comparison
- bayesvalidrox.bayes_inference.discrepancy
- bayesvalidrox.bayes_inference.mcmc
\ No newline at end of file
+ bayesvalidrox
\ No newline at end of file
diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst
index 6ca026f77cfedb64b2bd3c6209e265efa630a776..03d5a7462d5611d13371bca7f841c91e6eafe53c 100644
--- a/docs/source/tutorial.rst
+++ b/docs/source/tutorial.rst
@@ -33,7 +33,7 @@ These names will also be adopted by the surrogate and used in all plots.
>>> Model.Output.names = ['Z']
-Bonus: For this example, we have a Monte-Carlo reference solution for the first moments (mean and standard deviation) of the analytical function.
+For this example, we have a Monte-Carlo reference solution for the first moments (mean and standard deviation) of the analytical function.
The numpy (``*.npy``) files for this can be found in the ``data\`` directory of the example.
We will later discuss the estimation of the first two moments from the surrogate model, so let us import the mc-reference here.
These values can be passed in a form of a dictionary to the object variable ``mc_reference``.
@@ -136,38 +136,52 @@ A value of 1 results in standard truncation of the expansion, while smaller valu
Set the experimental design
---------------------------
-The experimental design provides instructions on how to sample the input parameter space for static training of the surrogate.
-Various sampling methods are available, but the samples can also be given by the user.
+The experimental design provides instructions on how to sample the input parameter space for training and evaluating the surrogate.
+Various sampling methods are available, and the samples can also be given by the user.
->>> MetaModelOpts.add_ExpDesign()
->>> MetaModelOpts.ExpDesign.Method = 'normal'
->>> MetaModelOpts.ExpDesign.n_init_samples = 100
->>> MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
+>>> ExpDesign = ExpDesign(Inputs)
+>>> ExpDesign.Method = 'normal'
+>>> ExpDesign.n_init_samples = 100
+>>> ExpDesign.sampling_method = 'latin_hypercube'
-Train the surrogate
--------------------
-Now, we can start training the surrogate (meta-) model by using the method ``create_metamodel`` and passing the model object as the only argument.
-This method returns the trained metamodel which we save as a ``pkl`` object.
+Train the surrogate with an engine
+----------------------------------
+Training is done by giving the model, experimental design and the surrogate model to an engine, which performs the training for us.
+The engine is of class :py:class:`bayesvalidrox.surrogate_models.engine.Engine`.
+
+>>> engine = Engine(MetaModelOpts, Model, ExpDesign)
+
+Now, we can start training the surrogate (meta-) model by starting the engine and using the method ``train_normal``.
+The engine obtains the training samples from the experimental design using the sampling strategy we set, runs the model on these samples and trains the surrogate on the results.
+
+>>> engine.start_engine()
+>>> engine.train_normal()
+
+Once this has run through we can obtain the trained metamodel from the engine.
+
+>>> MetaModel_trained = engine.MetaModel
+
+As **bayesvalidrox** uses the engine class for postprocessing and inference, we will save it at this point as a ``pkl`` object.
+This can be easily read in to avoid retraining the surrogate.
->>> PCEModel = MetaModelOpts.create_metamodel(Model)
->>> with open(f'PCEModel_{Model.name}.pkl', 'wb') as output:
->>> joblib.dump(PCEModel, output, 2)
+>>> with open(f'PCEengine_{Model.name}.pkl', 'wb') as output:
+>>> joblib.dump(engine, output, 2)
Sequential training
-------------------
The basic surrogate training that we just performed is done only on one static set of data.
**bayesvalidrox** also provide the option of sequential training, also known as active learning, where additional samples to be trained on are chosen by the surrogate.
-This can be activated by setting the ``method`` of the experimental design to ``'sequential'``.
+This can be activated by setting the ``method`` of the experimental design as ``'sequential'``.
->>> MetaModelOpts.ExpDesign.method = 'sequential'
+>>> ExpDesign.method = 'sequential'
This will split the training into two parts.
In the first part the training is performed as before, though the
size of this initial training set can a bit smaller.
->>> MetaModelOpts.ExpDesign.n_init_samples = 3*ndim
->>> MetaModelOpts.ExpDesign.sampling_method = 'latin_hypercube'
+>>> ExpDesign.n_init_samples = 3*ndim
+>>> ExpDesign.sampling_method = 'latin_hypercube'
The options for sequential training are listed at ...........
New samples are set by exploration and exploitation.
@@ -175,16 +189,16 @@ Exploration refers to samples that are randomly drawn from the prior input space
while exploitation can use different metrics.
The tradeoff between the two helps to avoid overfitting, while keeping the faster convergence given by the exploitation methods.
->>> MetaModelOpts.ExpDesign.n_new_samples = 1
->>> MetaModelOpts.ExpDesign.n_max_samples = 150
->>> MetaModelOpts.ExpDesign.mod_LOO_threshold = 1e-16
+>>> ExpDesign.n_new_samples = 1
+>>> ExpDesign.n_max_samples = 150
+>>> ExpDesign.mod_LOO_threshold = 1e-16
>>>
->>> MetaModelOpts.ExpDesign.tradeoff_scheme = None
->>> MetaModelOpts.ExpDesign.explore_method = 'random'
+>>> ExpDesign.tradeoff_scheme = None
+>>> ExpDesign.explore_method = 'random'
>>>
>>> # Use when 'Voronoi' or 'random' or 'latin_hypercube' chosen
->>> MetaModelOpts.ExpDesign.n_canddidate = 1000
->>> MetaModelOpts.ExpDesign.n_cand_groups = 4
+>>> ExpDesign.n_canddidate = 1000
+>>> ExpDesign.n_cand_groups = 4
Here we set the exploitaiton method to be Bayesian Active Learning .....cite......
@@ -192,8 +206,8 @@ Here we set the exploitaiton method to be Bayesian Active Learning .....cite....
In addition we need to set the information metric to use, here ``'DKL'`` is chosen.
->>> MetaModelOpts.ExpDesign.exploit_method = 'BayesActDesign'
->>> MetaModelOpts.ExpDesign.util_func = 'DKL'
+>>> ExpDesign.exploit_method = 'BayesActDesign'
+>>> ExpDesign.util_func = 'DKL'
This active learning strategy also relies on the data uncertainty, so we set this to follow a Gaussian distribution around all values
with standard deviations that are as large as the values themselves.
@@ -206,23 +220,22 @@ with standard deviations that are as large as the values themselves.
The measures calculated in each training iteration can also be plotted.
->>> MetaModelOpts.ExpDesign.post_snapshot = False
->>> MetaModelOpts.ExpDesign.step_snapshot = 1
->>> MetaModelOpts.ExpDesign.max_a_post = [0] * ndim
+>>> ExpDesign.post_snapshot = False
+>>> ExpDesign.step_snapshot = 1
+>>> ExpDesign.max_a_post = [0] * ndim
For calculating and plotting the validation error of the surrogate in each iteration, additional references can be given.
>>> prior = np.load(f"data/Prior_{ndim}.npy")
>>> prior_outputs = np.load(f"data/origModelOutput_{ndim}.npy")
>>> likelihood = np.load(f"data/validLikelihoods_{ndim}.npy")
->>> MetaModelOpts.valid_samples = prior[:500]
->>> MetaModelOpts.valid_model_runs = {'Z': prior_outputs[:500]}
+>>> ExpDesign.valid_samples = prior[:500]
+>>> ExpDesign.valid_model_runs = {'Z': prior_outputs[:500]}
-The sequential training is performed by the class :py:class:`bayesvalidrox.surrogate_models.meta_model_engine.MetaModelEngine`.
+The sequential training is again performed by the class :py:class:`bayesvalidrox.surrogate_models.engine.Engine`, but using the function `train_sequential`.
->>> meta_model_engine = MetaModelEngine(MetaModelOpts)
->>> meta_model_engine.run()
->>> PCEModel = meta_model_engine.MetaModel
+>>> engine.ExpDesign = ExpDesign
+>>> engine.train_sequential()
@@ -232,9 +245,9 @@ The available post-processing methods for **bayesvalidrox** are given in the cla
All results created by this class are automatically stored in an output-folder ``Outputs_PostProcessing_*modelname``.
>>> from bayesvalidrox import PostProcessing
->>> PostPCE = PostProcessing(PCEModel)
+>>> PostPCE = PostProcessing(engine)
-Since the reference moments obtained from a Monte-Carlo simulation is available, we only need to call the method ``plotMoments`` method from the PostProcessing object.
+Since the reference moments obtained from a Monte-Carlo simulation is available, we only need to call the method ``plot_moments`` method from the PostProcessing object.
This method generates a plot and stores it in the output folder.
>>> PostPCE.plot_moments()
@@ -260,7 +273,7 @@ Bayesian Inference
Inverse parameter estimation can be done in **bayesvalidrox** with the class :py:class:`bayesvalidrox.bayes_inf.bayes_inf.BayesInference`.
>>> from bayesvalidrox import BayesInference
->>> BayesOpts = BayesInference(PCEModel)
+>>> BayesOpts = BayesInference(engine)
If we set ``emulator`` to be true the Bayesian Inference will be performed based on the emulator.
Some posterior predictions will be plotted by setting ``plot_post_pred``.