From c52017f6ebedf756d7669027b6ef3b893acfa0a4 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Fri, 19 Jul 2024 08:48:58 +0200
Subject: [PATCH] Delete public folder
---
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diff --git a/public/.doctrees/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.doctree b/public/.doctrees/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.doctree
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diff --git a/public/.doctrees/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.doctree b/public/.doctrees/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.doctree
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diff --git a/public/.doctrees/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.doctree b/public/.doctrees/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.doctree
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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>
-
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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>
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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>
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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>()</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><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>
-
-</section>
-</dd></dl>
-
-</section>
-
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-<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>
-</ul>
-</li>
-</ul>
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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>
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-<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>
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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>
-<dt>engine<span class="classifier">bayesvalidrox.Engine</span></dt><dd><p>Engine object that contains the surrogate, model and expdesign</p>
-</dd>
-<dt>mcmc_params<span class="classifier">dict</span></dt><dd><p>Dictionary of parameters for the mcmc. Required are
-- init_samples
-- n_steps
-- n_walkers
-- n_burn
-- moves
-- multiplrocessing
-- verbose</p>
-</dd>
-<dt>Discrepancy<span class="classifier">bayesvalidrox.Discrepancy</span></dt><dd><p>Discrepancy object that described the uncertainty of the data.</p>
-</dd>
-</dl>
-<p>bias_inputs :</p>
-<p>error_model :</p>
-<p>req_outputs :</p>
-<p>selected_indices :</p>
-<p>emulator :</p>
-<dl class="simple">
-<dt>out_dir<span class="classifier">string</span></dt><dd><p>Directory to write the outputs to.</p>
-</dd>
-<dt>name<span class="classifier">string</span></dt><dd><p>Name of this MCMC selection (?)</p>
-</dd>
-<dt>BiasInputs<span class="classifier"></span></dt><dd><p>The default is None.</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>
- </div>
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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>
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-<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>
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-<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>
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-<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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-<span id="bayesvalidrox"></span><h1>bayesvalidrox<a class="headerlink" href="#module-bayesvalidrox" title="Link to this heading">¶</a></h1>
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- <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>
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-<section id="id11">
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-<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>
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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>, <em class="sig-param"><span class="n"><span class="pre">store</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">''</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>, <em class="sig-param"><span class="n"><span class="pre">store</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">''</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>
-
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-<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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-<p>Calls to the model and evaluations</p>
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-<dd><p>Checks if all the values in out lie between minout and maxout</p>
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-<dt>out<span class="classifier">array or list</span></dt><dd><p>Data to check against range</p>
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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>
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- <section id="module-bayesvalidrox.surrogate_models.adaptPlot">
-<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>
-<div class="table-wrapper autosummary longtable docutils container">
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-<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>
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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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- <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</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>
- </div>
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- <ul>
-<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>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
-
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- </div>
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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</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</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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-<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>
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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</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</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>
- </div>
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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>
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-<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>
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-</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>
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-<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>
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-<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>
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-<p class="rubric">Classes</p>
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-<td><p>Superclass for Empirical Bayes and Variational Bayes implementations of Bayesian Linear Regression Model</p></td>
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-<td><p>Bayesian Regression with type II maximum likelihood (Empirical Bayes)</p></td>
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-<td><p>Implements Bayesian Linear Regression using mean-field approximation.</p></td>
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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.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.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-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>
-</tbody>
-</table>
-</div>
-<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>, <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><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="parameters">
-<h2>Parameters<a class="headerlink" href="#parameters" 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>
-<dt>parallel<span class="classifier">bool, optional</span></dt><dd><p>Set to true if the evaluations should be done in parallel.
-The default is False.</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>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.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="id1">
-<h2>Returns<a class="headerlink" href="#id1" 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_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="id2">
-<h2>Returns<a class="headerlink" href="#id2" 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="id3">
-<h2>Returns<a class="headerlink" href="#id3" 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="id4">
-<h2>Returns<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h2>
-<p>None</p>
-</section>
-</dd></dl>
-
-</dd></dl>
-
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-<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.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.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.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>
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-<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>
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-</dd></dl>
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-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.engine.hellinger_distance"><code class="docutils literal notranslate"><span class="pre">hellinger_distance()</span></code></a></li>
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-<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>
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-<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>
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-<dt>cov<span class="classifier">2d array</span></dt><dd><p>Covariance matrix of the distribution.</p>
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-<h2>Returns<a class="headerlink" href="#returns" title="Link to this heading">¶</a></h2>
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-<dt>log_lik<span class="classifier">float</span></dt><dd><p>Log likelihood.</p>
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-<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>
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-<dt>poly_type<span class="classifier">string</span></dt><dd><p>Polynomial type.</p>
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-</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>
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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>
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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>
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-<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>
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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>
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-<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>, <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><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>, <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><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.plot_samples" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.plot_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_samples</span></code></a>(samples)</p></td>
-<td><p>Visualizes generated samples over their given distributions.</p></td>
-</tr>
-<tr class="row-odd"><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-even"><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-odd"><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">1</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 1.</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">1</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>1</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.plot_samples">
-<span class="sig-name descname"><span class="pre">plot_samples</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.exp_designs.ExpDesigns.plot_samples" title="Link to this definition">¶</a></dt>
-<dd><p>Visualizes generated samples over their given distributions.</p>
-<section id="id10">
-<h3>Parameters<a class="headerlink" href="#id10" title="Link to this heading">¶</a></h3>
-<dl class="simple">
-<dt>samples<span class="classifier">array</span></dt><dd><p>The samples to visualize.</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.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">1</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="id12">
-<h3>Parameters<a class="headerlink" href="#id12" 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 1.
-This will be used to run init_param_space, if it has not been done
-until now.</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>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="id14">
-<h3>Parameters<a class="headerlink" href="#id14" 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="id15">
-<h3>Returns<a class="headerlink" href="#id15" 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="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_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="id17">
-<h3>Returns<a class="headerlink" href="#id17" 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>
-
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-<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.plot_samples"><code class="docutils literal notranslate"><span class="pre">ExpDesigns.plot_samples()</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>
-</li>
-</ul>
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-<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>
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-<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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-<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>
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-<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>
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-<p class="rubric">Classes</p>
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-<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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-<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>
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-<td><p>Created based on the Surrogate Modeling Toolbox (SUMO) [1].</p></td>
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-<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>
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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>
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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>
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-<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>
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-<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.sequential_design.html#module-bayesvalidrox.surrogate_models.sequential_design" title="bayesvalidrox.surrogate_models.sequential_design"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.sequential_design</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.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>
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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">1</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>1</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>
-
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-<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>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.init_param_space"><code class="docutils literal notranslate"><span class="pre">InputSpace.init_param_space()</span></code></a></li>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.input_space.InputSpace.transform"><code class="docutils literal notranslate"><span class="pre">InputSpace.transform()</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>
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-<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>
-
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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>
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-<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>
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-<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>
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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>
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-<td><p>A class to define the uncertain input parameters.</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</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>
-
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-
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-<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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-<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>
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-<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>
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-<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</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</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>
- </div>
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-<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>
-</li>
-</ul>
-
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-<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">
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-<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>
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-<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.reg_fast_ard.RegressionFastARD.html#bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD" title="bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RegressionFastARD</span></code></a>([n_iter, start, tol, ...])</p></td>
-<td><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></td>
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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>
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-<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>
-
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-</dd></dl>
-
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- <section id="bayesvalidrox-surrogate-models-sequential-design-sequentialdesign">
-<h1>bayesvalidrox.surrogate_models.sequential_design.SequentialDesign<a class="headerlink" href="#bayesvalidrox-surrogate-models-sequential-design-sequentialdesign" title="Link to this heading">¶</a></h1>
-<dl class="py class">
-<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.sequential_design.</span></span><span class="sig-name descname"><span class="pre">SequentialDesign</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>, <em class="sig-param"><span class="n"><span class="pre">engine</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><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign" 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>Contains options for choosing the next training sample iteratively.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.__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>, <em class="sig-param"><span class="n"><span class="pre">engine</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><span class="sig-paren">)</span><a class="headerlink" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.__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.sequential_design.SequentialDesign.__init__" title="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(MetaMod, Model, ExpDes, engine[, ...])</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.choose_next_sample" title="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.dual_annealing" title="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.run_util_func" title="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.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-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.start_seqdesign" title="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.start_seqdesign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">start_seqdesign</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-even"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.tradeoff_weights" title="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.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-odd"><td><p><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.util_AlphOptDesign" title="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.util_BayesianActiveDesign" title="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.util_BayesianDesign" title="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.util_VarBasedDesign" title="bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.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>
-</dl>
-<p># TODO: BayesActDesign has no corresponding function call in this function!
-Bounds : list of tuples</p>
-<blockquote>
-<div><p>List of lower and upper boundaries of parameters.</p>
-</div></blockquote>
-<dl class="simple">
-<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.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.run_util_func" title="Link to this definition">¶</a></dt>
-<dd><p>Runs the utility function based on the given method.</p>
-<section id="id3">
-<h2>Parameters<a class="headerlink" href="#id3" 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="id4">
-<h2>Returns<a class="headerlink" href="#id4" 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.sequential_design.SequentialDesign.start_seqdesign">
-<span class="sig-name descname"><span class="pre">start_seqdesign</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.sequential_design.SequentialDesign.start_seqdesign" 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="id5">
-<h2>Returns<a class="headerlink" href="#id5" 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.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.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="id6">
-<h2>Parameters<a class="headerlink" href="#id6" 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="id7">
-<h2>Returns<a class="headerlink" href="#id7" 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.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.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="id8">
-<h2>Returns<a class="headerlink" href="#id8" 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.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.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="id9">
-<h2>Parameters<a class="headerlink" href="#id9" 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="id10">
-<h2>Returns<a class="headerlink" href="#id10" 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.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.util_BayesianDesign" title="Link to this definition">¶</a></dt>
-<dd><p>Computes scores based on Bayesian sequential design criterion (var).</p>
-<section id="id11">
-<h2>Parameters<a class="headerlink" href="#id11" 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="id12">
-<h2>Returns<a class="headerlink" href="#id12" 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.sequential_design.SequentialDesign.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.sequential_design.SequentialDesign.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="id13">
-<h2>Parameters<a class="headerlink" href="#id13" 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="id14">
-<h2>Returns<a class="headerlink" href="#id14" 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>
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-<li><a class="reference internal" href="#">bayesvalidrox.surrogate_models.sequential_design.SequentialDesign</a><ul>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign"><code class="docutils literal notranslate"><span class="pre">SequentialDesign</span></code></a><ul>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.__init__"><code class="docutils literal notranslate"><span class="pre">SequentialDesign.__init__()</span></code></a></li>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.choose_next_sample"><code class="docutils literal notranslate"><span class="pre">SequentialDesign.choose_next_sample()</span></code></a></li>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.dual_annealing"><code class="docutils literal notranslate"><span class="pre">SequentialDesign.dual_annealing()</span></code></a></li>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.run_util_func"><code class="docutils literal notranslate"><span class="pre">SequentialDesign.run_util_func()</span></code></a></li>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.start_seqdesign"><code class="docutils literal notranslate"><span class="pre">SequentialDesign.start_seqdesign()</span></code></a></li>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.tradeoff_weights"><code class="docutils literal notranslate"><span class="pre">SequentialDesign.tradeoff_weights()</span></code></a></li>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.util_AlphOptDesign"><code class="docutils literal notranslate"><span class="pre">SequentialDesign.util_AlphOptDesign()</span></code></a></li>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.util_BayesianActiveDesign"><code class="docutils literal notranslate"><span class="pre">SequentialDesign.util_BayesianActiveDesign()</span></code></a></li>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.util_BayesianDesign"><code class="docutils literal notranslate"><span class="pre">SequentialDesign.util_BayesianDesign()</span></code></a></li>
-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.util_VarBasedDesign"><code class="docutils literal notranslate"><span class="pre">SequentialDesign.util_VarBasedDesign()</span></code></a></li>
-</ul>
-</li>
-</ul>
-</li>
-</ul>
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-<h1>bayesvalidrox.surrogate_models.sequential_design.hellinger_distance<a class="headerlink" href="#bayesvalidrox-surrogate-models-sequential-design-hellinger-distance" title="Link to this heading">¶</a></h1>
-<dl class="py function">
-<dt class="sig sig-object py" id="bayesvalidrox.surrogate_models.sequential_design.hellinger_distance">
-<span class="sig-prename descclassname"><span class="pre">bayesvalidrox.surrogate_models.sequential_design.</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.sequential_design.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>
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-<li><a class="reference internal" href="#bayesvalidrox.surrogate_models.sequential_design.hellinger_distance"><code class="docutils literal notranslate"><span class="pre">hellinger_distance()</span></code></a></li>
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- <section id="module-bayesvalidrox.surrogate_models.sequential_design">
-<span id="bayesvalidrox-surrogate-models-sequential-design"></span><h1>bayesvalidrox.surrogate_models.sequential_design<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.sequential_design" 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.sequential_design.hellinger_distance.html#bayesvalidrox.surrogate_models.sequential_design.hellinger_distance" title="bayesvalidrox.surrogate_models.sequential_design.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.sequential_design.logpdf.html#bayesvalidrox.surrogate_models.sequential_design.logpdf" title="bayesvalidrox.surrogate_models.sequential_design.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>
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-<td><p>Divides a domain defined by Bounds into subdomains.</p></td>
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-<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)</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><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.
-The number of features is set by <cite>self.n_pca_components</cite>.
-If this is not given, <cite>self.var_pca_threshold</cite> is used as a threshold.</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>
-</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. These samples should be already transformed.</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>
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-<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>
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diff --git a/public/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.html b/public/_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>
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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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-<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>
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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">
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-<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>
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- <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">
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-<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>
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-<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>
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-<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>
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-<p class="rubric">Classes</p>
-<div class="table-wrapper autosummary longtable docutils container">
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-<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>
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- <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=4ebf8126"></script>
- <script src="../_static/doctools.js?v=9a2dae69"></script>
- <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
- <script src="../_static/scripts/furo.js?v=32e29ea5"></script>
- </body>
-</html>
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diff --git a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.rst.txt b/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.rst.txt
deleted file mode 100644
index f129d6341..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.rst.txt b/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.rst.txt
deleted file mode 100644
index ebc205c86..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_inference.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-bayesvalidrox.bayes\_inference.bayes\_inference
-===============================================
-
-.. automodule:: bayesvalidrox.bayes_inference.bayes_inference
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
- :toctree:
- :template: custom-class-template.rst
-
- BayesInference
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.rst.txt b/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.rst.txt
deleted file mode 100644
index 69d7f471f..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.rst.txt b/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.rst.txt
deleted file mode 100644
index a0599ad48..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.rst.txt b/public/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.rst.txt
deleted file mode 100644
index 39c9e8e96..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.rst.txt
+++ /dev/null
@@ -1,26 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.rst.txt b/public/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.rst.txt
deleted file mode 100644
index b97bd05f0..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.discrepancy.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-bayesvalidrox.bayes\_inference.discrepancy
-==========================================
-
-.. automodule:: bayesvalidrox.bayes_inference.discrepancy
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
- :toctree:
- :template: custom-class-template.rst
-
- Discrepancy
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.rst.txt b/public/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.rst.txt
deleted file mode 100644
index 3037f6565..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.MCMC.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.rst.txt b/public/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.rst.txt
deleted file mode 100644
index 00d208a8f..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.gelman_rubin.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.bayes\_inference.mcmc.gelman\_rubin
-=================================================
-
-.. currentmodule:: bayesvalidrox.bayes_inference.mcmc
-
-.. autofunction:: gelman_rubin
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.rst.txt b/public/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.rst.txt
deleted file mode 100644
index 685757475..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.mcmc.rst.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.bayes_inference.rst.txt b/public/_sources/_autosummary/bayesvalidrox.bayes_inference.rst.txt
deleted file mode 100644
index 2d1f540c9..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.bayes_inference.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.rst.txt b/public/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.rst.txt
deleted file mode 100644
index 9efcfaaa0..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.rst.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.rst.txt b/public/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.rst.txt
deleted file mode 100644
index 1aae5db20..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.post_processing.post_processing.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-bayesvalidrox.post\_processing.post\_processing
-===============================================
-
-.. automodule:: bayesvalidrox.post_processing.post_processing
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
- :toctree:
- :template: custom-class-template.rst
-
- PostProcessing
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.post_processing.rst.txt b/public/_sources/_autosummary/bayesvalidrox.post_processing.rst.txt
deleted file mode 100644
index c37004bd5..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.post_processing.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.rst.txt b/public/_sources/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.rst.txt
deleted file mode 100644
index 52664818d..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.rst.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.pylink.pylink.rst.txt b/public/_sources/_autosummary/bayesvalidrox.pylink.pylink.rst.txt
deleted file mode 100644
index 267ef0c04..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.pylink.pylink.rst.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.pylink.pylink.within_range.rst.txt b/public/_sources/_autosummary/bayesvalidrox.pylink.pylink.within_range.rst.txt
deleted file mode 100644
index 70192a943..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.pylink.pylink.within_range.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.pylink.pylink.within\_range
-=========================================
-
-.. currentmodule:: bayesvalidrox.pylink.pylink
-
-.. autofunction:: within_range
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.pylink.rst.txt b/public/_sources/_autosummary/bayesvalidrox.pylink.rst.txt
deleted file mode 100644
index 1b9ae788b..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.pylink.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-bayesvalidrox.pylink
-====================
-
-.. automodule:: bayesvalidrox.pylink
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. rubric:: Modules
-
-.. autosummary::
- :toctree:
- :template: custom-module-template.rst
- :recursive:
-
- bayesvalidrox.pylink.pylink
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.rst.txt b/public/_sources/_autosummary/bayesvalidrox.rst.txt
deleted file mode 100644
index 1e904c9f7..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.rst.txt
deleted file mode 100644
index c4a5ddc2f..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.adaptPlot.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.surrogate\_models.adaptPlot.adaptPlot
-===================================================
-
-.. currentmodule:: bayesvalidrox.surrogate_models.adaptPlot
-
-.. autofunction:: adaptPlot
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.rst.txt
deleted file mode 100644
index 860e8ec2c..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.adaptPlot.rst.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-bayesvalidrox.surrogate\_models.adaptPlot
-=========================================
-
-.. automodule:: bayesvalidrox.surrogate_models.adaptPlot
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
- :toctree:
-
- adaptPlot
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.rst.txt
deleted file mode 100644
index 049182d7a..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.apoly_construction.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.rst.txt
deleted file mode 100644
index 73c72f8f3..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.apoly_construction.rst.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-bayesvalidrox.surrogate\_models.apoly\_construction
-===================================================
-
-.. automodule:: bayesvalidrox.surrogate_models.apoly_construction
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
- :toctree:
-
- apoly_construction
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.rst.txt
deleted file mode 100644
index 60ca9858f..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.BayesianLinearRegression.rst.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.rst.txt
deleted file mode 100644
index 42f6224c3..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.EBLinearRegression.rst.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.rst.txt
deleted file mode 100644
index e47223ddb..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.VBLinearRegression.rst.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.rst.txt
deleted file mode 100644
index c8f24a14d..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.gamma_mean.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.rst.txt
deleted file mode 100644
index 40200d4b0..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.bayes_linear.rst.txt
+++ /dev/null
@@ -1,40 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.Engine.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.Engine.rst.txt
deleted file mode 100644
index 00a9dcd93..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.Engine.rst.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-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.eval_metamodel
- ~Engine.start_engine
- ~Engine.train_normal
- ~Engine.train_seq_design
- ~Engine.train_sequential
-
-
-
-
-
-
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.rst.txt
deleted file mode 100644
index 8e71cfe6e..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.hellinger_distance.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.surrogate\_models.engine.hellinger\_distance
-==========================================================
-
-.. currentmodule:: bayesvalidrox.surrogate_models.engine
-
-.. autofunction:: hellinger_distance
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.rst.txt
deleted file mode 100644
index 1c0ed0353..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.logpdf.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.surrogate\_models.engine.logpdf
-=============================================
-
-.. currentmodule:: bayesvalidrox.surrogate_models.engine
-
-.. autofunction:: logpdf
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.rst.txt
deleted file mode 100644
index a73ff3387..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-bayesvalidrox.surrogate\_models.engine
-======================================
-
-.. automodule:: bayesvalidrox.surrogate_models.engine
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
- :toctree:
- :template: custom-class-template.rst
-
- Engine
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.rst.txt
deleted file mode 100644
index b660535bb..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.engine.subdomain.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.surrogate\_models.engine.subdomain
-================================================
-
-.. currentmodule:: bayesvalidrox.surrogate_models.engine
-
-.. autofunction:: subdomain
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.rst.txt
deleted file mode 100644
index b443c4a12..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.rst.txt
deleted file mode 100644
index 58c6ae25d..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_rec_rule_arbitrary.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.rst.txt
deleted file mode 100644
index 3f9ccc16c..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.eval_univ_basis.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.rst.txt
deleted file mode 100644
index f0f06e316..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.poly_rec_coeffs.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.rst.txt
deleted file mode 100644
index b5055daeb..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.rst.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst.txt
deleted file mode 100644
index 07efe980f..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-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.plot_samples
- ~ExpDesigns.random_sampler
- ~ExpDesigns.read_from_file
- ~ExpDesigns.transform
-
-
-
-
-
-
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.rst.txt
deleted file mode 100644
index e49711dd0..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.check_ranges.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.rst.txt
deleted file mode 100644
index e5d704bd5..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exp_designs.rst.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.rst.txt
deleted file mode 100644
index 6ccff094f..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.Exploration.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.rst.txt
deleted file mode 100644
index aa60c4324..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.exploration.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-bayesvalidrox.surrogate\_models.exploration
-===========================================
-
-.. automodule:: bayesvalidrox.surrogate_models.exploration
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
- :toctree:
- :template: custom-class-template.rst
-
- Exploration
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.rst.txt
deleted file mode 100644
index c0d050f1d..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.cross_truncate.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.surrogate\_models.glexindex.cross\_truncate
-=========================================================
-
-.. currentmodule:: bayesvalidrox.surrogate_models.glexindex
-
-.. autofunction:: cross_truncate
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.rst.txt
deleted file mode 100644
index c76918bd1..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.glexindex.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.surrogate\_models.glexindex.glexindex
-===================================================
-
-.. currentmodule:: bayesvalidrox.surrogate_models.glexindex
-
-.. autofunction:: glexindex
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.rst.txt
deleted file mode 100644
index bcc161fcf..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.glexindex.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-bayesvalidrox.surrogate\_models.glexindex
-=========================================
-
-.. automodule:: bayesvalidrox.surrogate_models.glexindex
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
- :toctree:
-
- cross_truncate
- glexindex
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.rst.txt
deleted file mode 100644
index e106f4d59..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.rst.txt
deleted file mode 100644
index 1dd0d58b0..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.input_space.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-bayesvalidrox.surrogate\_models.input\_space
-============================================
-
-.. automodule:: bayesvalidrox.surrogate_models.input_space
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
- :toctree:
- :template: custom-class-template.rst
-
- InputSpace
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.rst.txt
deleted file mode 100644
index c4b68c252..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Input.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.rst.txt
deleted file mode 100644
index 2fa2e2815..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.rst.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.rst.txt
deleted file mode 100644
index 687dc2f79..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.inputs.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-bayesvalidrox.surrogate\_models.inputs
-======================================
-
-.. automodule:: bayesvalidrox.surrogate_models.inputs
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
- :toctree:
- :template: custom-class-template.rst
-
- Input
- Marginal
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.rst.txt
deleted file mode 100644
index e835d0c2b..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.OrthogonalMatchingPursuit.rst.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.rst.txt
deleted file mode 100644
index 9a6786588..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.corr.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst.txt
deleted file mode 100644
index a64e2c455..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.rst.txt
deleted file mode 100644
index 137bdc156..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD.rst.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.rst.txt
deleted file mode 100644
index f73923481..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.rst.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.rst.txt
deleted file mode 100644
index 0ff8616c8..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.update_precisions.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.rst.txt
deleted file mode 100644
index d5b18ed0a..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.RegressionFastLaplace.rst.txt
+++ /dev/null
@@ -1,28 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.rst.txt
deleted file mode 100644
index 2cc1f44c5..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.rst.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.rst.txt
deleted file mode 100644
index 19ea84682..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.rst.txt
+++ /dev/null
@@ -1,46 +0,0 @@
-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.sequential_design
- bayesvalidrox.surrogate_models.surrogate_models
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.rst.txt
deleted file mode 100644
index 85bf390ce..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.rst.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-bayesvalidrox.surrogate\_models.sequential\_design.SequentialDesign
-===================================================================
-
-.. currentmodule:: bayesvalidrox.surrogate_models.sequential_design
-
-.. autoclass:: SequentialDesign
- :members:
- :show-inheritance:
- :inherited-members:
-
-
- .. automethod:: __init__
-
-
- .. rubric:: Methods
-
- .. autosummary::
-
- ~SequentialDesign.__init__
- ~SequentialDesign.choose_next_sample
- ~SequentialDesign.dual_annealing
- ~SequentialDesign.run_util_func
- ~SequentialDesign.start_seqdesign
- ~SequentialDesign.tradeoff_weights
- ~SequentialDesign.util_AlphOptDesign
- ~SequentialDesign.util_BayesianActiveDesign
- ~SequentialDesign.util_BayesianDesign
- ~SequentialDesign.util_VarBasedDesign
-
-
-
-
-
-
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.hellinger_distance.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.hellinger_distance.rst.txt
deleted file mode 100644
index e242998b2..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.hellinger_distance.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.surrogate\_models.sequential\_design.hellinger\_distance
-======================================================================
-
-.. currentmodule:: bayesvalidrox.surrogate_models.sequential_design
-
-.. autofunction:: hellinger_distance
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.logpdf.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.logpdf.rst.txt
deleted file mode 100644
index 124f9a6d5..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.logpdf.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.surrogate\_models.sequential\_design.logpdf
-=========================================================
-
-.. currentmodule:: bayesvalidrox.surrogate_models.sequential_design
-
-.. autofunction:: logpdf
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.rst.txt
deleted file mode 100644
index 2e13f6766..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.rst.txt
+++ /dev/null
@@ -1,40 +0,0 @@
-bayesvalidrox.surrogate\_models.sequential\_design
-==================================================
-
-.. automodule:: bayesvalidrox.surrogate_models.sequential_design
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
- :toctree:
-
- hellinger_distance
- logpdf
- subdomain
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
- :toctree:
- :template: custom-class-template.rst
-
- SequentialDesign
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.subdomain.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.subdomain.rst.txt
deleted file mode 100644
index abef419d3..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.sequential_design.subdomain.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-bayesvalidrox.surrogate\_models.sequential\_design.subdomain
-============================================================
-
-.. currentmodule:: bayesvalidrox.surrogate_models.sequential_design
-
-.. autofunction:: subdomain
\ No newline at end of file
diff --git a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.rst.txt
deleted file mode 100644
index f4cd883ed..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.rst.txt
+++ /dev/null
@@ -1,38 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.rst.txt
deleted file mode 100644
index 7972de4ab..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.corr_loocv_error.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.rst.txt
deleted file mode 100644
index d6f9c97a9..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.create_psi.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.rst.txt
deleted file mode 100644
index c5eb1db49..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.gaussian_process_emulator.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-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/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.rst.txt b/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.rst.txt
deleted file mode 100644
index e8b46319c..000000000
--- a/public/_sources/_autosummary/bayesvalidrox.surrogate_models.surrogate_models.rst.txt
+++ /dev/null
@@ -1,40 +0,0 @@
-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/public/_sources/al_description.rst.txt b/public/_sources/al_description.rst.txt
deleted file mode 100644
index ac52ef0a6..000000000
--- a/public/_sources/al_description.rst.txt
+++ /dev/null
@@ -1,59 +0,0 @@
-Active learning: iteratively expanding the training set
-*******************************************************
-Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model.
-The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.
-
-.. warning::
- The active learning methods are currently being reworked.
- This should not change the function call ``Engine.train_sequential()``, but will change the associated class structures.
-
-.. image:: ../diagrams/active_learning_reduced.png
- :width: 550
- :alt: UML diagram for the classes and functions used in active learning in BayesValidRox.
-
-In BayesValidRox AL is realized by additional properties of the :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns` and :any:`bayesvalidrox.surrogate_models.engine.Engine` classes without any changes to the surrogate model.
-
-Exploration, exploitation and tradeoff
-======================================
-**Exploration** methods choose the new samples in a space-filling manner, while **exploitation methods** make use of available data or properties of the surrogate models, such as the estimated surrogate standard deviation.
-Exploration methods in BayesValidRox include random or latin-hypercube sampling, voronoi sampling, choice based on leave-one-out cross validation or dual-annealing.
-Exploitation can be set to Bayesian designs, such as Bayesian3 Active Learning, or variance-based designs.
-
-The tradeoff between exploration and exploitation is defined by **tradeoff-schemes**, such as an equal split, epsilon-decreaseing or adaptive schemes.
-
-
-Example
-=======
-We take the engine from :any:`surrogate_description` and change the settings to perform sequential training.
-
-This mainly changes the experimental design.
-For this example we start with the 10 initial samples from :any:`surrogate_description` and increase them iteratively to the number of samples given in ``n_max_samples``.
-The parameter ``n_new_samples`` sets the number of new samples that are chosen in each iteration, while ``mod_LOO_threshold`` sets an additional stopping condition.
-
->>> ExpDesign.n_max_samples = 14
->>> ExpDesign.n_new_samples = 1
->>> ExpDesign.mod_LOO_threshold = 1e-16
-
-Here we do not set a ``tradeoff_scheme``.
-This will result in all samples being chosen based on the exploration weights.
-
->>> ExpDesign.tradeoff_scheme = None
-
-As the proposed samples come from the exploration method, we still need to define this.
-
->>> ExpDesign.explore_method = 'random'
->>> ExpDesign.n_canddidate = 1000
->>> ExpDesign.n_cand_groups = 4
-
-For the exploitation method we use a variance-based method, as no data is given.
-
->>> ExpDesign.exploit_method = 'VarOptDesign'
->>> ExpDesign.util_func = 'EIGF'
-
-Once all properties are set, we can assemble the engine and start it.
-This time we use ``train_sequential``.
-
->>> Engine_ = Engine(MetaMod, Model, ExpDesign)
->>> Engine_.start_engine()
->>> Engine_.train_sequential()
-
\ No newline at end of file
diff --git a/public/_sources/al_training.rst.txt b/public/_sources/al_training.rst.txt
deleted file mode 100644
index e5a68dc7f..000000000
--- a/public/_sources/al_training.rst.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-Active learning: iteratively expanding the training set
-*******************************************************
\ No newline at end of file
diff --git a/public/_sources/analyticalfunction.rst.txt b/public/_sources/analyticalfunction.rst.txt
deleted file mode 100644
index d0e56b071..000000000
--- a/public/_sources/analyticalfunction.rst.txt
+++ /dev/null
@@ -1,84 +0,0 @@
-Example: Analytical function
-****************************
-This example shows a surrogate-assisted Bayesian calibration of a time dependent analytical function.
-A detailed explanation of this example is provided in the :any:`tutorial`.
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function
- * - Number of input parameters
- - Set to either 2 or 10
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - Time-dependent output, 10 steps in time
- * - MC reference
- - Yes
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - 0-10
- - Uniform in (-5,5)
-
-.. list-table:: Discrepancy
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Distribution type
- - Gaussian
- * - Characteristic value
- - variance: data^2
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - 'analytical function'
- * - degree choices
- - max degree 12, q-norm truncation 0.85 or 0.5 depending on number of inputs
- * - regression
- - FastARD with bootstrapping
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 3*#inputparams
- * - Number of total samples
- - 150
- * - Number of samples per AL iteration
- - 1
- * - AL tradeoff scheme
- - None
- * - AL exploration method
- - random, n_candidates=1000, n_cand_groups=4
- * - AL exploitation method
- - Bayesian Active Design (BAL) with DKL
\ No newline at end of file
diff --git a/public/_sources/api.rst.txt b/public/_sources/api.rst.txt
deleted file mode 100644
index b9e02468e..000000000
--- a/public/_sources/api.rst.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-API
-***
-Here you can find the api for the package **bayesvalidrox**.
-
-.. autosummary::
- :toctree: _autosummary
- :template: custom-module-template.rst
- :recursive:
-
- bayesvalidrox
\ No newline at end of file
diff --git a/public/_sources/bayes_description.rst.txt b/public/_sources/bayes_description.rst.txt
deleted file mode 100644
index 5017bceea..000000000
--- a/public/_sources/bayes_description.rst.txt
+++ /dev/null
@@ -1,125 +0,0 @@
-Bayesian inference
-******************
-.. container:: twocol
-
- .. container:: leftside
-
- With Bayesian inference we ask the question 'how does our understanding of the inputs change given some observation of the outputs of the model?', i.e. we perform an updating step of the prior distributions to posterior, based on some observations.
- Bayesvalidrox provides a dedicated class to perform this task, :any:`bayesvalidrox.bayes_inference.bayes_inference.BayesInference`, which infers the posterior via ``rejection-sampling`` or ``MCMC``.
- The likelihood in rejection sampling is estimated with the help of ``bootstrapping``.
- MCMC-specific parameters are to be given as a dictionary called ``mcmc_params`` and can include
-
- * ``init_samples``: initial samples
- * ``n_steps``: number of steps
- * ``n_walkers``: number of walkers
- * ``n_burn``: length of the burn-in
- * ``moves``: function to use for the moves, e.g. taken from ``emcee``
- * ``multiprocessing``: setting for multiprocessing
- * ``verbose``: verbosity
-
- .. container:: rightside
-
- .. image:: ../diagrams/bayesian_validation.png
- :width: 300
- :alt: UML diagram for classes related to Bayesian inference.
-
-The observation should be set as ``Model.observations`` in the ``Engine``, and an estimation of its uncertainty can be provided as a :any:`bayesvalidrox.bayes_inference.discrepancy.Discrepancy` object.
-
-Example
-=======
-For this example we need to add the following imports.
-
->>> from bayesvalidrox import Discrepancy, BayesInference
-
-In order to run Bayesian inference we first need to provide an observation.
-For this example we take an evaluation of the model on some chosen sample and add the resulting values as ``Model.observations``.
-As this expects a 1D-array for each output key, we need to change the format slightly.
-
->>> true_sample = [[2]]
->>> observation = Model.run_model_parallel(true_sample)
->>> Model.observations = {}
->>> for key in observation:
->>> if key == 'x_values':
->>> Model.observations[key]=observation[key]
->>> else:
->>> Model.observations[key]=observation[key][0]
-
-Next we define the uncertainty on the observation with the class :any:`bayesvalidrox.bayes_inference.discrepancy.Discrepancy`.
-For this example we set the uncertainty to be zero-mean gaussian and dependent on the values in the observation, i.e. larger values have a larger uncertainty associated with them.
-The ``parameters`` contain the variance for each point in the observation.
-
-.. warning::
- For models with only a single uncertain input parameter, numerical issues can appear when the discrepancy is set only depending on the observed data.
- To resolve this, a small value can be added to the variance of the discrepancy.
-
->>> obsData = pd.DataFrame(Model.observations, columns=Model.Output.names)
->>> DiscrepancyOpts = Discrepancy('')
->>> DiscrepancyOpts.type = 'Gaussian'
->>> DiscrepancyOpts.parameters = obsData**2+0.01
-
-Now we can initialize an object of class :any:`bayesvalidrox.bayes_inference.bayes_inference.BayesInference` with all the wanted properties.
-This object has to be given our ``Engine``.
-If it should use the surrogate during inference, set ``emulator`` to ``True``, otherwise the model will be evaluated directly.
-We also set the defined ``Discrepancy``. and set ``post_plot_pred`` if posterior predictions should be visualized.
-
->>> BayesObj = BayesInference(Engine_)
->>> BayesObj.emulator = True
->>> BayesObj.Discrepancy = DiscrepancyOpts
->>> BayesObj.plot_post_pred = True
-
-In order to run with rejection sampling, we set the ``inference_method`` accordingly and add properties for ``bootstrap``.
-
->>> BayesObj.inference_method = 'rejection'
->>> BayesObj.bootstrap = True
->>> BayesObj.n_bootstrap_itrs = 500
->>> BayesObj.bootstrap_noise = 2
-
-If the sampling should be done with MCMC, then this is set as the ``inference_method`` and additional properties are given in ``mcmc_params``.
-For this example we use the python package ``emcee`` to define the MCMC moves.
-
->>> BayesObj.inference_method = 'MCMC'
->>> import emcee
->>> BayesObj.mcmc_params = {
->>> 'n_steps': 1e4,
->>> 'n_walkers': 30,
->>> 'moves': emcee.moves.KDEMove(),
->>> 'multiprocessing': False,
->>> 'verbose': False
->>> }
-
-Then we run the inference.
-
->>> BayesObj.create_inference()
-
-If the output directory ``BayesObj.out_dir`` is not set otherwise, the outputs are written into the folder ``Outputs_Bayes_model_Calib``.
-This folder includes the posterior distribution of the input parameters, as well as the predictions resulting from the mean of the posterior.
-For inference with MCMC, chain diagnostics are also written out in the console.
-
-.. container:: twocol
-
- .. container:: leftside
-
- .. code-block:: py
-
- ---------------Posterior diagnostics---------------
- Mean auto-correlation time: 2.057
- Thin: 1
- Burn-in: 4
- Flat chain shape: (13380, 1)
- Mean acceptance fraction*: 0.752
- Gelman-Rubin Test**: [1.001]
-
- * This value must lay between 0.234 and 0.5.
- ** These values must be smaller than 1.1.
- --------------------------------------------------
-
- .. container:: rightside
-
- .. image:: ../../examples/user_guide/Outputs_Bayes_model_Calib/Posterior_Dist_model_emulator.pdf
- :width: 400
- :alt: Posterior distribution of the input parameter
-
- .. image:: ../../examples/user_guide/Outputs_Bayes_model_Calib/Post_Prior_Perd_model_emulator_A.pdf
- :width: 400
- :alt: Comparison of posterior prediction to the observation
-
\ No newline at end of file
diff --git a/public/_sources/beam.rst.txt b/public/_sources/beam.rst.txt
deleted file mode 100644
index 61db6729d..000000000
--- a/public/_sources/beam.rst.txt
+++ /dev/null
@@ -1,85 +0,0 @@
-Example: beam
-*************
-This example shows how a surrogate for a beam deflection model can be created and
-illustrates how a model with an executable and an
-input file can be linked with the bayesvalidrox package.
-
-The surrogate is trained without active learning and no inference is performed, though reference data is available.
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Runs model via given shell command and parser
- * - Number of input parameters
- - 4
- * - Number of output parameters
- - 1: deflection [m]
- * - Time- or space- dependency
- - ??
- * - MC reference
- - Yes
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - Beam width
- - lognormal
- * - Beam height
- - lognormal
- * - Youngs modulus
- - lognormal
- * - Uniform load
- - lognormal
-
-.. list-table:: Discrepancy
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Distribution type
- - Gaussian
- * - Characteristic value
- - variance: data^2
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - PCE
- * - associated model
- - 'Beam9points'
- * - degree choices
- - max degree 6, q-norm truncation 0.75
- * - regression
- - FastARD
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 100
- * - Number of total samples
- - 100
\ No newline at end of file
diff --git a/public/_sources/bmc_description.rst.txt b/public/_sources/bmc_description.rst.txt
deleted file mode 100644
index 3ebd9d525..000000000
--- a/public/_sources/bmc_description.rst.txt
+++ /dev/null
@@ -1,83 +0,0 @@
-Bayesian multi-model comparison
-*******************************
-.. container:: twocol
-
- .. container:: leftside
-
- Bayesvalidrox provides three distinct methods to compare sets of models against each other given some observation of the outputs, Bayes' Factors, model weights and confusion matrices.
- These are contained within the class :any:`bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison` and can be called one-at-a-time with their respective functions, or consecutively with the function ``model_comparison_all()``.
-
-
- .. container:: rightside
-
- .. image:: ../diagrams/bayesian_model_comparison.png
- :width: 400
- :alt: UML diagram for classes related to Bayesian multi-model comparison.
-
-
-Example
-=======
-To perform model comparison, we first need to define the set of competing models.
-For this, we create an additional model in the file ``model2.py`` based on the example model from :any:`model_description`.
-
->>> def model2(samples, x_values):
->>> poly = samples[0]*np.power(x_values, 3)
->>> outputs = {'A': poly, 'x_values': x_values}
->>> return outputs
-
-Then we can build another surrogate for this model, following the same code as for the surrogate in :any:`surrogate_description`.
-
->>> Model2 = PyLinkForwardModel()
->>> Model2.link_type = 'Function'
->>> Model2.py_file = 'model2'
->>> Model2.name = 'model2'
->>> Model2.Output.names = ['A']
->>> Model2.func_args = {'x_values': x_values}
->>> Model2.store = False
-
->>> MetaMod2 = MetaModel(Inputs)
->>> MetaMod2.meta_model_type = 'aPCE'
->>> MetaMod2.pce_reg_method = 'FastARD'
->>> MetaMod2.pce_deg = 3
->>> MetaMod2.pce_q_norm = 1
-
->>> ExpDesign2 = ExpDesigns(Inputs)
->>> ExpDesign2.n_init_samples = 30
->>> ExpDesign2.sampling_method = 'random'
-
->>> Engine_2 = Engine(MetaMod2, Model2, ExpDesign2)
->>> Engine_2.train_normal()
-
-To perform model comparison we use the class :any:`bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison`.
-
->>> from bayesvalidrox import BayesModelComparison`
-
-We collect the engines that should be compared in a dictionary, and assign them names.
-
->>> meta_models = {
->>> "linear": Engine_,
->>> "degthree": Engine_2
->>> }
-
-Then we create an object of class ``BayesModelComparison``.
-
->>> BayesOpts = BayesModelComparison()
-
-As the comparison uses the class :any:`bayesvalidrox.bayes_inference.bayes_inference.BayesInference`, we can also set the properties for this class as well.
-These are collected in a dictionary and given to the function calls that perform the model comparison.
-In this example we use the following settings.
-
->>> opts_bootstrap = {
->>> "bootstrap": True,
->>> "n_samples": 100,
->>> "Discrepancy": DiscrepancyOpts,
->>> "emulator": True,
->>> "plot_post_pred": False
->>> }
-
-Now we can run the full model comparison.
-
->>> output_dict = BayesOpts.model_comparison_all(meta_models, opts_bootstrap)
-
-The created plots are saved in the folder ``Outputs_Comparison``.
-
diff --git a/public/_sources/borehole.rst.txt b/public/_sources/borehole.rst.txt
deleted file mode 100644
index 36cdfd325..000000000
--- a/public/_sources/borehole.rst.txt
+++ /dev/null
@@ -1,115 +0,0 @@
-Example: borehole
-*****************
-This test deals with the surrogate modeling of a Borehole function.
-You will see how to check the quality of your regression model and perform sensitivity analysis via Sobol Indices
-
-
- BOREHOLE FUNCTION
-
- Authors: Sonja Surjanovic, Simon Fraser University
- Derek Bingham, Simon Fraser University
-
- Questions/Comments: Please email Derek Bingham at dbingham@stat.sfu.ca.
-
- Copyright 2013. Derek Bingham, Simon Fraser University.
-
- THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY
- FOR THE USE OF THIS SOFTWARE. If software is modified to produce
- derivative works, such modified software should be clearly marked.
- Additionally, this program is free software; you can redistribute it
- and/or modify it under the terms of the GNU General Public License as
- published by the Free Software Foundation; version 2.0 of the License.
- Accordingly, this program is distributed in the hope that it will be
- useful, but WITHOUT ANY WARRANTY; without even the implied warranty
- of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- General Public License for more details.
-
- For function details and reference information, see:
- https://www.sfu.ca/~ssurjano/ishigami.html
-
-no reference data given
-Surrogate with AL - OMP for regression and Space-filling sequential exploitaiton scheme (no data)
-
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function
- * - Number of input parameters
- - 8
- * - Number of output parameters
- - 1: flow rate [m$^3$/yr]
- * - Time- or space- dependency
- - ??
- * - MC reference
- - No
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - r_w
- - uniform
- * - L
- - uniform
- * - K_w
- - uniform
- * - T_u
- - uniform
- * - T_l
- - uniform
- * - H_u
- - uniform
- * - H_l
- - uniform
- * - r
- - lognormal
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - 'borehole'
- * - degree choices
- - max degree 5, q-norm truncation 1.0
- * - regression
- - OMP (Orthogonal matching pursuit)
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 50
- * - Number of total samples
- - 300
- * - Number of samples per AL iteration
- - 1
- * - AL tradeoff scheme
- - None
- * - AL exploration method
- - latin-hypercube, n_candidates=5000, n_cand_groups=4
- * - AL exploitation method
- - space-filling
\ No newline at end of file
diff --git a/public/_sources/examples.rst.txt b/public/_sources/examples.rst.txt
deleted file mode 100644
index 6941b83f3..000000000
--- a/public/_sources/examples.rst.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-EXAMPLES
-********
-Within the git repository of **bayesvalidrox** a set of examples can be found.
-Here we provide short introductions into the used models and what each example can teach about the functionality of the package.
-
-The following examples are provided:
-
-.. toctree::
- :maxdepth: 1
-
- Analytical function <analyticalfunction>
- Beam <beam>
- Borehole <borehole>
- Ishigami <ishigami>
- Model comparison <modelcomparison>
- OHagan-function <ohaganfunction>
- Pollution <pollution>
\ No newline at end of file
diff --git a/public/_sources/generated/bayesvalidrox.bayes_inference.bayes_inference.rst.txt b/public/_sources/generated/bayesvalidrox.bayes_inference.bayes_inference.rst.txt
deleted file mode 100644
index acff1be63..000000000
--- a/public/_sources/generated/bayesvalidrox.bayes_inference.bayes_inference.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-bayesvalidrox.bayes\_inference.bayes\_inference
-===============================================
-
-.. automodule:: bayesvalidrox.bayes_inference.bayes_inference
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- BayesInference
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.bayes_inference.bayes_model_comparison.rst.txt b/public/_sources/generated/bayesvalidrox.bayes_inference.bayes_model_comparison.rst.txt
deleted file mode 100644
index 084144569..000000000
--- a/public/_sources/generated/bayesvalidrox.bayes_inference.bayes_model_comparison.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-bayesvalidrox.bayes\_inference.bayes\_model\_comparison
-=======================================================
-
-.. automodule:: bayesvalidrox.bayes_inference.bayes_model_comparison
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- BayesModelComparison
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.bayes_inference.discrepancy.rst.txt b/public/_sources/generated/bayesvalidrox.bayes_inference.discrepancy.rst.txt
deleted file mode 100644
index b7f09cb9c..000000000
--- a/public/_sources/generated/bayesvalidrox.bayes_inference.discrepancy.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-bayesvalidrox.bayes\_inference.discrepancy
-==========================================
-
-.. automodule:: bayesvalidrox.bayes_inference.discrepancy
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- Discrepancy
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.bayes_inference.mcmc.rst.txt b/public/_sources/generated/bayesvalidrox.bayes_inference.mcmc.rst.txt
deleted file mode 100644
index ebdba31cd..000000000
--- a/public/_sources/generated/bayesvalidrox.bayes_inference.mcmc.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-bayesvalidrox.bayes\_inference.mcmc
-===================================
-
-.. automodule:: bayesvalidrox.bayes_inference.mcmc
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- gelman_rubin
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- MCMC
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.post_processing.post_processing.rst.txt b/public/_sources/generated/bayesvalidrox.post_processing.post_processing.rst.txt
deleted file mode 100644
index cc095065c..000000000
--- a/public/_sources/generated/bayesvalidrox.post_processing.post_processing.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-bayesvalidrox.post\_processing.post\_processing
-===============================================
-
-.. automodule:: bayesvalidrox.post_processing.post_processing
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- PostProcessing
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.pylink.pylink.rst.txt b/public/_sources/generated/bayesvalidrox.pylink.pylink.rst.txt
deleted file mode 100644
index 499b04ff9..000000000
--- a/public/_sources/generated/bayesvalidrox.pylink.pylink.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-bayesvalidrox.pylink.pylink
-===========================
-
-.. automodule:: bayesvalidrox.pylink.pylink
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- within_range
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- PyLinkForwardModel
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.adaptPlot.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.adaptPlot.rst.txt
deleted file mode 100644
index dcdbd3d79..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.adaptPlot.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-bayesvalidrox.surrogate\_models.adaptPlot
-=========================================
-
-.. automodule:: bayesvalidrox.surrogate_models.adaptPlot
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- adaptPlot
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.apoly_construction.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.apoly_construction.rst.txt
deleted file mode 100644
index a5a45131f..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.apoly_construction.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-bayesvalidrox.surrogate\_models.apoly\_construction
-===================================================
-
-.. automodule:: bayesvalidrox.surrogate_models.apoly_construction
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- apoly_construction
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.bayes_linear.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.bayes_linear.rst.txt
deleted file mode 100644
index 2d866f910..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.bayes_linear.rst.txt
+++ /dev/null
@@ -1,37 +0,0 @@
-bayesvalidrox.surrogate\_models.bayes\_linear
-=============================================
-
-.. automodule:: bayesvalidrox.surrogate_models.bayes_linear
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- gamma_mean
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- BayesianLinearRegression
- EBLinearRegression
- VBLinearRegression
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.engine.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.engine.rst.txt
deleted file mode 100644
index 17def78a4..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.engine.rst.txt
+++ /dev/null
@@ -1,37 +0,0 @@
-bayesvalidrox.surrogate\_models.engine
-======================================
-
-.. automodule:: bayesvalidrox.surrogate_models.engine
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- hellinger_distance
- logpdf
- subdomain
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- Engine
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.eval_rec_rule.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.eval_rec_rule.rst.txt
deleted file mode 100644
index 00c5a6de1..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.eval_rec_rule.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-bayesvalidrox.surrogate\_models.eval\_rec\_rule
-===============================================
-
-.. automodule:: bayesvalidrox.surrogate_models.eval_rec_rule
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- eval_rec_rule
- eval_rec_rule_arbitrary
- eval_univ_basis
- poly_rec_coeffs
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.exp_designs.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.exp_designs.rst.txt
deleted file mode 100644
index 2b744abd6..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.exp_designs.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-bayesvalidrox.surrogate\_models.exp\_designs
-============================================
-
-.. automodule:: bayesvalidrox.surrogate_models.exp_designs
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- check_ranges
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- ExpDesigns
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.exploration.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.exploration.rst.txt
deleted file mode 100644
index a7f91722c..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.exploration.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-bayesvalidrox.surrogate\_models.exploration
-===========================================
-
-.. automodule:: bayesvalidrox.surrogate_models.exploration
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- Exploration
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.glexindex.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.glexindex.rst.txt
deleted file mode 100644
index 66547f9dd..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.glexindex.rst.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-bayesvalidrox.surrogate\_models.glexindex
-=========================================
-
-.. automodule:: bayesvalidrox.surrogate_models.glexindex
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- cross_truncate
- glexindex
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.inputs.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.inputs.rst.txt
deleted file mode 100644
index 4b2f67832..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.inputs.rst.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-bayesvalidrox.surrogate\_models.inputs
-======================================
-
-.. automodule:: bayesvalidrox.surrogate_models.inputs
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- Input
- Marginal
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.meta_model_engine.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.meta_model_engine.rst.txt
deleted file mode 100644
index 0b9add9a6..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.meta_model_engine.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-bayesvalidrox.surrogate\_models.meta\_model\_engine
-===================================================
-
-.. automodule:: bayesvalidrox.surrogate_models.meta_model_engine
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- MetaModelEngine
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst.txt
deleted file mode 100644
index bf6808c42..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-bayesvalidrox.surrogate\_models.orthogonal\_matching\_pursuit
-=============================================================
-
-.. automodule:: bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- corr
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- OrthogonalMatchingPursuit
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.reg_fast_ard.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.reg_fast_ard.rst.txt
deleted file mode 100644
index 1768efcca..000000000
--- a/public/_sources/generated/bayesvalidrox.surrogate_models.reg_fast_ard.rst.txt
+++ /dev/null
@@ -1,35 +0,0 @@
-bayesvalidrox.surrogate\_models.reg\_fast\_ard
-==============================================
-
-.. automodule:: bayesvalidrox.surrogate_models.reg_fast_ard
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- update_precisions
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- RegressionFastARD
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.reg_fast_laplace.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.reg_fast_laplace.rst.txt
deleted file mode 100644
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--- a/public/_sources/generated/bayesvalidrox.surrogate_models.reg_fast_laplace.rst.txt
+++ /dev/null
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-bayesvalidrox.surrogate\_models.reg\_fast\_laplace
-==================================================
-
-.. automodule:: bayesvalidrox.surrogate_models.reg_fast_laplace
-
-
-
-
-
-
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- RegressionFastLaplace
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/generated/bayesvalidrox.surrogate_models.surrogate_models.rst.txt b/public/_sources/generated/bayesvalidrox.surrogate_models.surrogate_models.rst.txt
deleted file mode 100644
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--- a/public/_sources/generated/bayesvalidrox.surrogate_models.surrogate_models.rst.txt
+++ /dev/null
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-bayesvalidrox.surrogate\_models.surrogate\_models
-=================================================
-
-.. automodule:: bayesvalidrox.surrogate_models.surrogate_models
-
-
-
-
-
-
-
- .. rubric:: Functions
-
- .. autosummary::
-
- corr_loocv_error
- create_psi
- gaussian_process_emulator
-
-
-
-
-
- .. rubric:: Classes
-
- .. autosummary::
-
- MetaModel
-
-
-
-
-
-
-
-
-
diff --git a/public/_sources/index.rst.txt b/public/_sources/index.rst.txt
deleted file mode 100644
index 8671dc83e..000000000
--- a/public/_sources/index.rst.txt
+++ /dev/null
@@ -1,129 +0,0 @@
-.. bayesvalidrox documentation master file, created by
- sphinx-quickstart on Wed Dec 13 11:14:59 2023.
-
-Surrogate-assisted Bayesian validation of computational models
-==============================================================
-**BayesValidRox** is an open-source python package that provides methods for surrogate modeling, Bayesian inference and model comparison.
-
-.. image:: ../diagrams/balance.png
- :width: 600
- :alt: Weighting model results against data with associated uncertainty, costs and sparsity
-
-An introductory tutorial to the overall workflow with **bayesvalidrox** is provided in :any:`tutorial` and descriptions of the available examples can be found in :any:`examples`.
-The functionality and options for the different classes is described more in-depth in :any:`packagedescription` and a list of all the classes and functions is provided in :any:`api`.
-
-Links
------
-* GitHub: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/tree/master?ref_type=heads
-* PyPI: https://pypi.org/project/bayesvalidrox/
-* Documentation:
-
-
-Installation
-------------
-This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0.0 on, use pip to install:
-
-.. code-block:: bash
-
- pip install bayesvalidrox
-
-
-Quickstart
-----------
-Here we show a minimal example to get started on working with BayesValidRox.
-The :any:`packagedescription` goes into more detail on the available options and proposed workflow.
-
-The central functionalities of BayesValidRox all depend on building an object of class ``Engine`` that includes an interface to a model and a definition of an input space and sampling option in the form of an ``ExpDesigns`` object.
-It can contain and build a surrogate model of class ``MetaModel``, but also functions without one.
-
-We import the needed classes in our main file ``main.py``.
-
->>> from bayesvalidrox import PyLinkForwardModel, InputSpace, ExpDesigns, Engine, MetaModel
-
-Here we use a simple linear model.
-This is defined in another python file in the same folder, here we call it ``model.py``.
-This file contains a python function that expects samples of two parameter and returns a linear combination of them.
-For a detailed description of the expected output format see :any:`model_description`.
-
->>> def model(samples):
->>> return {'Z':samples[:,0]+2*samples[:,1], 'x_values':[0]}
-
-With this we can create the interface to the model in ``main.py``.
-
->>> model = PyLinkForwardModel()
->>> model.link_type = 'Function'
->>> model.py_file = 'model'
->>> model.name = 'linear model'
->>> model.Output.names = ['Z']
-
-We specify marginal distributions on the inputs in an object of class ``InputSpace`` and use this to build the experimental design.
-
->>> inputs = InputSpace()
->>> inputs.add_Marginals()
->>> inputs.Marginals[0].name = 'input0'
->>> inputs.Marginals[0].dist_type = 'unif'
->>> inputs.Marginals[0].parameters = [0,1]
->>> inputs.add_Marginals()
->>> inputs.Marginals[1].name = 'input1'
->>> inputs.Marginals[1].dist_type = 'unif'
->>> inputs.Marginals[1].parameters = [0,1]
-
->>> expdes = ExpDesigns(inputs)
->>> expdes.sampling_method = 'random'
-
-If we do not want to build a surrogate model, we can define the engine from these objects.
-
->>> engine = Engine(None, model, expdes)
-
-If we want to build a surrogate model, we create and object of class ``MetaModel`` and set its properties.
-Here we build an arbitrary Polynomial Chaos Expansion and train it on samples given by the experimental design and the model.
-
->>> metamodel = MetaModel(Inputs)
->>> metamodel.meta_model_type = 'aPCE'
->>> metamodel.pce_reg_method = 'FastARD'
->>> metamodel.pce_deg = 3
->>> MetaMod.pce_q_norm = 0.85
-
->>> expdes.n_init_samples = 10
-
->>> engine = Engine(metamodel, model, expdes)
->>> engine.start_engine()
->>> engine.train_normal()
-
-The engine with the trained metamodel can now be used for postprocessing, Bayesian inference, of Bayesian model comparison.
-
-License
--------
-BayesValidRox is licensed under the MIT license_.
-
-.. _license: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/master/LICENCE.md
-
-Contribution
-------------
-We would be happy for you to contribute to **BayesValidRox**.
-This can include e.g. reporting issues, proposing new features, working on features, or support with the documentation.
-If you want to contibute, check out our contribution_ guidelines.
-You can contact us on the gitlab_ page.
-
-.. _gitlab: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox
-
-.. _contribution: https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/docs/sphinx_new/CONTRIBUTING.md?ref_type=heads
-
-Further contents
-----------------
-
-.. toctree::
- :maxdepth: 1
-
- packagedescription
- tutorial
- examples
- api
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/public/_sources/input_description.rst.txt b/public/_sources/input_description.rst.txt
deleted file mode 100644
index ee506fc23..000000000
--- a/public/_sources/input_description.rst.txt
+++ /dev/null
@@ -1,81 +0,0 @@
-Priors, input space and experimental design
-*******************************************
-The surrogate models, as used in BayesValidRox, consider model formulations where at least one of the input parameters is associated with uncertainty.
-This uncertainty can be described as probability distributions over possible values for the parameter.
-
-.. container:: twocol
-
- .. container:: leftside
-
- Four classes contained in bayesvalidrox are associated with describing uncertain inputs: :any:`bayesvalidrox.surrogate_models.inputs.Marginal`, :any:`bayesvalidrox.surrogate_models.inputs.Input`, :any:`bayesvalidrox.surrogate_models.input_space.InputSpace` and :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns`.
-
- Uncertain parameters are specified via their marginal distributions in :any:`bayesvalidrox.surrogate_models.inputs.Marginal` objects as either distribution types with associated parameters, or via a set of realizations.
- Supported distribution types include ``unif``, ``norm``, ``gamma``, ``beta``, ``lognorm``, ``expon`` and ``weibull``.
-
- All marginals contained in an :any:`bayesvalidrox.surrogate_models.inputs.Input` object should be defined in the same manner, mixing definitions via distribution type and sampels is not supported.
-
- If ``rosenblatt`` is set as ``True``, then a Rosenblatt transform will be applied for training the surrogate.
-
- .. container:: rightside
-
- .. image:: ../diagrams/input_classes.png
- :width: 300
- :alt: UML diagram for input-related classes in bayesvalidrox
-
-.. note::
- When using a polynomial-type surrogate setting ``rosenblatt`` to ``True`` results in all hermite polynomials.
- If all uncertain parameters are specified as samples, the corresponding polynomials are automatically calculated following an aPCE.
-
- #TODO Recheck this second point!
-
-The :any:`bayesvalidrox.surrogate_models.input_space.InputSpace` checks the validity of the given marginals and builds the input space.
-This includes sampling from the distributions and applying the Rosenblatt transform or an isoprobabilistic transformation.
-
-The :any:`bayesvalidrox.surrogate_models.input_space.InputSpace` is used mainly in the surrogate model.
-The class :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns` additionally contains methods and attributes related to sampling from the input space for static and iterative training of the surrogate model.
-Supported sampling methods include ``random``, ``latin-hypercube``, ``sobol``, ``halton``, ``hammersley``, ``chebyshev(FT)``, ``grid(FT)`` and ``user`` for user-defined sampling.
-
-The options for iterative metamodel training are detailed in :any:`surrogate_description`.
-
-Example
-=======
-In practice, only the classes :any:`bayesvalidrox.surrogate_models.inputs.Input` and :any:`bayesvalidrox.surrogate_models.exp_designs.ExpDesigns` are directly used.
-
->>> from bayesvalidrox import Input, ExpDesign
-
-Marginals of uncertain parameters can be directly added and defined on an ``Input`` object.
-
->>> Inputs = Input()
-
-If they are defined via distribution types, the ``name``, ``dist_type`` and ``parameters`` for the distribution should be set.
-
->>> Inputs.add_marginals()
->>> Inputs.Marginals[0].name = '$X$'
->>> Inputs.Marginals[0].dist_type = 'unif'
->>> Inputs.Marginals[0].parameters = [-5, 5]
-
-If they are given via data, only ``name`` and ``input_data`` are relevant.
-
->>> inputParams = np.random.uniform(-5,-5,100)
->>> Inputs.add_marginals()
->>> Inputs.Marginals[0].name = '$X$'
->>> Inputs.Marginals[0].input_data = inputParams
-
-An experimental design can be constructed based on these inputs.
-
->>> ExpDesign = ExpDesigns(Inputs)
-
-Samples of the marginals can be created by specifying a sampling method and generating the wanted number of samples.
-
->>> ExpDesign.sampling_method = 'latin_hypercube'
->>> samples = ExpDesign.generate_samples(100)
-
-The generated samples can be visualized against their marginal distributions.
-
->>> ExpDesign.plot_samples(samples)
-
-The results will be saved in the folder ``Outputs_Priors``.
-
-.. image:: ../../examples/user_guide/Outputs_Priors/prior_$X$.png
- :width: 400
- :alt: Generated samples against their marginal distribution
diff --git a/public/_sources/ishigami.rst.txt b/public/_sources/ishigami.rst.txt
deleted file mode 100644
index 8dfe217f3..000000000
--- a/public/_sources/ishigami.rst.txt
+++ /dev/null
@@ -1,95 +0,0 @@
-Example: ishigami
-*****************
-This example deals with the surrogate modeling of a Ishigami function.
-You will see how to check the quality of your regression model and perform
-sensitivity analysis via Sobol Indices.
-
- ISHIGAMI FUNCTION
-
- Authors: Sonja Surjanovic, Simon Fraser University
- Derek Bingham, Simon Fraser University
-
- Questions/Comments: Please email Derek Bingham at dbingham@stat.sfu.ca.
-
- Copyright 2013. Derek Bingham, Simon Fraser University.
-
- THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY
- FOR THE USE OF THIS SOFTWARE. If software is modified to produce
- derivative works, such modified software should be clearly marked.
- Additionally, this program is free software; you can redistribute it
- and/or modify it under the terms of the GNU General Public License as
- published by the Free Software Foundation; version 2.0 of the License.
- Accordingly, this program is distributed in the hope that it will be
- useful, but WITHOUT ANY WARRANTY; without even the implied warranty
- of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- General Public License for more details.
-
- For function details and reference information, see:
- https://www.sfu.ca/~ssurjano/ishigami.html
-
-No reference data is given for this example, the surrogate is trained with BCS as the regression method and no active learning.
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function
- * - Number of input parameters
- - 3
- * - Number of output parameters
- - 1: flow rate [m$^3$/yr]
- * - Time- or space- dependency
- - ??
- * - MC reference
- - No
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - X_1
- - uniform
- * - X_2
- - uniform
- * - X_3
- - uniform
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - 'Ishigami'
- * - degree choices
- - max degree 14, q-norm truncation 1.0
- * - regression
- - BCS (Bayesian Compressive Sensing)
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 200
- * - Number of total samples
- - 200
\ No newline at end of file
diff --git a/public/_sources/model_description.rst.txt b/public/_sources/model_description.rst.txt
deleted file mode 100644
index 42103caa6..000000000
--- a/public/_sources/model_description.rst.txt
+++ /dev/null
@@ -1,61 +0,0 @@
-Models
-******
-
-.. container:: twocol
-
- .. container:: leftside
-
- BayesValidRox gives options to create interfaces for a variety of models with the class :any:`bayesvalidrox.pylink.pylink.PyLinkForwardModel`.
- Its main function is to run the model on given samples and to read in and contain MC references and observations.
-
- Models can be defined via python functions, shell commands or as general executables.
- This allows for the use of BayesValidRox with a broad range of models and easy extension to models that are defined with e.g. UM-Bridge.
-
- .. container:: rightside
-
- .. image:: ../diagrams/model.png
- :width: 150
- :alt: UML diagram for the bayesvalidrox class :any:`bayesvalidrox.pylink.pylink.PyLinkForwardModel`.
-
-Example
-=======
-For this example we use a very simple model that maps the uncertain parameter :math:`X` from the example in :any:`input_description` to two outputs, the original value and its square.
-We define this model as a function ``model`` in a new file ``model.py``.
-This function takes a single realization of the uncertain parameter as a 2-dimensional ``np.array`` and returns a dictionary of model results.
-Here we use the key ``A`` for the sample values and ``B`` for their squares.
-Under the key ``x_values`` a list should be given that is of the same length as each output of the model for a single input.
-The values in this list can denote e.g. timesteps and are used in postprocessing as labels of the x-axis.
-If we want to set the ``x_values`` outside of the model, it can also be given as an additional parameter
-
->>> def model(samples, x_values):
->>> sample = samples[0]*x_values
->>> square = np.power(samples[0]*x_values, 2)
->>> outputs = {'A': sample, 'B': square, 'x_values': [0]}
->>> return outputs
-
-In the main python file we can create an object of :any:`bayesvalidrox.pylink.pylink.PyLinkForwardModel` that links to this model.
-
->>> from bayesvalidrox import PyLinkForwardModel
-
-We create an object of this class and set the ``link_type`` as 'Function' to indicate that this is a function in a python file.
-``py_file`` should be set to the filename of the model and ``name`` to the name of the function.
-Lastly we list the keys of the outputs that we are interested in.
-
->>> Model = PyLinkForwardModel()
->>> Model.link_type = 'Function'
->>> Model.py_file = 'model'
->>> Model.name = 'model'
->>> Model.Output.names = ['A']
-
-Any parameters to the model function, that are not the samples, can be set via the ``func_args`` argument.
-In this case we define ``x_values`` as a ``np.array`` and include it.
-
->>> x_values = np.arange(0,1,0.1)
->>> Model.func_args = {'x_values':x_values}
-
-With this we have completed an interface to our model.
-We can now evaluate this model on the samples created in the input example.
-
->>> output, samples = Model.run_model_parallel(samples)
-
-Other types of model links can be found in :any:`examples`, e.g. :any:`beam` uses a shell command and parser.
diff --git a/public/_sources/modelcomparison.rst.txt b/public/_sources/modelcomparison.rst.txt
deleted file mode 100644
index c89ea1f9c..000000000
--- a/public/_sources/modelcomparison.rst.txt
+++ /dev/null
@@ -1,131 +0,0 @@
-Example: model comparison
-*************************
-This example shows the multi-model comparison.
-You will see how to perform a multi-model comparison
-
-Provided are three models, a linear models with 2 input parameters, a nonlinear model with 2 input parameters
-and a nonlinear model with 4 input parameters.
-The data to base the comparison on is given in an extra file.
-
-.. note::
- A detailed explanation of this example will be provided in future as part of the tutorial.
-
-Model 1: L2_model
-=================
-
-.. list-table:: Pylink model1
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function (linear)
- * - Number of input parameters
- - 2
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - space-dependency
- * - MC reference
- - No
-
-.. list-table:: Priors1
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - 0-2
- - given as correlated samples
-
-Model 1: NL2_model
-==================
-
-.. list-table:: Pylink model1
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function (exponential)
- * - Number of input parameters
- - 2
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - space-dependency
- * - MC reference
- - No
-
-.. list-table:: Priors1
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - 0-2
- - given as correlated samples
-
-Model 1: NL4_model
-==================
-
-.. list-table:: Pylink model1
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function (cosine)
- * - Number of input parameters
- - 4
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - space-dependency
- * - MC reference
- - No
-
-.. list-table:: Priors1
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - 0-4
- - given as correlated samples
-
-Surrogates 1-3
-==============
-All surrogates share the same setup and only differ in the given model.
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - see lists above
- * - degree choices
- - 1-12, q-norm truncation 1.0
- * - regression
- - OMP (Orthogonal matching pursuit)
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 100
- * - Number of total samples
- - 100
\ No newline at end of file
diff --git a/public/_sources/ohaganfunction.rst.txt b/public/_sources/ohaganfunction.rst.txt
deleted file mode 100644
index 2b3949128..000000000
--- a/public/_sources/ohaganfunction.rst.txt
+++ /dev/null
@@ -1,88 +0,0 @@
-Example: OHagan-function
-************************
-This example deals with the surrogate modeling of O'Hagan function with 15
-parameters.
-You will see how to check the quality of your regression model and perform sensitivity analysis via Sobol Indices
-
-
- Oakley & O'Hagan (2004) Function
-
- This function's a-coefficients are chosen so that 5 of the input variables
- contribute significantly to the output variance, 5 have a much smaller
- effect, and the remaining 5 have almost no effect on the output variance.
-
- O'Hagan, 2004, Probabilistic sensitivity analysis of complex models: a
- Bayesian approach J. R. Statist. Soc. B (2004) 66, Part 3, pp. 751-769.
-
-This example trains a surrogate with AL.
-FastARD is set as the regression type and the space-filling sequential exploitaiton scheme is chosen as no data is given.
-
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function
- * - Number of input parameters
- - 15
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - ??
- * - MC reference
- - No
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - 0-14
- - gaussian
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - 'OHagan'
- * - degree choices
- - max degree 7, q-norm truncation 0.65
- * - regression
- - FastARD
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 100
- * - Number of total samples
- - 500
- * - Number of samples per AL iteration
- - 1
- * - AL tradeoff scheme
- - None
- * - AL exploration method
- - latin-hypercube, n_candidates=10000, n_cand_groups=4
- * - AL exploitation method
- - space-filling
\ No newline at end of file
diff --git a/public/_sources/packagedescription.rst.txt b/public/_sources/packagedescription.rst.txt
deleted file mode 100644
index 1d1fd6517..000000000
--- a/public/_sources/packagedescription.rst.txt
+++ /dev/null
@@ -1,72 +0,0 @@
-USER GUIDE
-**********
-
-Installation
-------------
-BayesValidRox provides functionalities for describing uncertain parameters, building surrogate models based on model outputs and evaluating them with Bayesian validation methods.
-
-This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0.0 on.
-It can be installed with pip, best practice is to do so inside a virtual environment.
-
-.. code-block:: bash
-
- python3 -m venv bayes_env
- cd bayes_env
- source bin/activate
-
-Here replace ``bayes_env`` with your preferred name.
-Then install the latest release of BayesValidRox inside the venv.
-
-.. code-block:: bash
-
- pip install bayesvalidrox
-
-The current master can be installed by cloning the repository.
-
-.. code-block:: bash
-
- git clone https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox.git
- cd bayesvalidrox
- pip install .
-
-Overview
-========
-This package is split into multiple aspects corresponding to its folder structure.
-
-
-.. image:: ../diagrams/folder_structure.png
- :width: 600
- :alt: Folder structure of **bayesvalidrox**
-
-The folder ``surrogate_models`` contains all the functions and classes that are necessary in order to create and train the surrogate model.
-This includes
-
-* defining the input marginals
-* setting properties of the sampling in an experimental design
-* choosing the surrogate model and its properties
-* training the surrogate model on model evaluations in a straightforward manner or iteratively with active learning
-
-The computational model is linked via a ``pylink`` interface.
-
-Multiple post-processing options are available, including the calculation of Sobol' indices, checking the accuracy of the surrogate model and visualizations of the moments of the surrogate.
-
-Bayesian inference can be performed with rejection sampling or MCMC, while taking into account the estimated uncertainty of the data that the (surrogate) model is compared to.
-If multiple (surrogate) models are given, they can be compared against each other with pairwise Bayes Factors, model weights or a justifiability analysis.
-
-..
- We split this into the aspects :any:`input_description` and :any:`surrogate_description` to provide insight into the options available in bayesvalidrox.
-..
- :any:`post_description` can be applied to trained surrogate models, or using the underlying models themselves.
-
-The next pages lead through the topics given in BayesValidRox and describe the available classes and give brief examples for their use.
-
-.. toctree::
- :maxdepth: 1
-
- input_description
- model_description
- surrogate_description
- al_description
- post_description
- bayes_description
- bmc_description
diff --git a/public/_sources/pollution.rst.txt b/public/_sources/pollution.rst.txt
deleted file mode 100644
index 77cb7e217..000000000
--- a/public/_sources/pollution.rst.txt
+++ /dev/null
@@ -1,100 +0,0 @@
-Example: pollution
-******************
-This test shows a surrogate-assisted Bayesian calibration of a time dependent
-pollution function. Here, the noise will be jointly inferred with the input parameters.
-
- ENVIRONMENTAL MODEL FUNCTION
-
- Authors: Sonja Surjanovic, Simon Fraser University
- Derek Bingham, Simon Fraser University
-
- Questions/Comments: Please email Derek Bingham at dbingham@stat.sfu.ca.
-
- Copyright 2013. Derek Bingham, Simon Fraser University.
-
- THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY
- FOR THE USE OF THIS SOFTWARE. If software is modified to produce
- derivative works, such modified software should be clearly marked.
- Additionally, this program is free software; you can redistribute it
- and/or modify it under the terms of the GNU General Public License as
- published by the Free Software Foundation; version 2.0 of the License.
- Accordingly, this program is distributed in the hope that it will be
- useful, but WITHOUT ANY WARRANTY; without even the implied warranty
- of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- General Public License for more details.
-
- For function details and reference information, see:
- http://www.sfu.ca/~ssurjano/
-
-This example trains a surrogate and performs Bayesian Inference based on the given data.
-Active Learning can be activated and will then be performed with Variance Optimal Design based on Entropy and an epsilon-decreasing tradeoff scheme.
-
-.. note::
- This example contains two ``test_*`` files that can be run.
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function
- * - Number of input parameters
- - 4
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - Yes, ??
- * - MC reference
- - No
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - M
- - uniform
- * - D
- - uniform
- * - L
- - uniform
- * - tau
- - uniform
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - 'pollution'
- * - degree choices
- - max degree 8, q-norm truncation 1.0
- * - regression
- - BCS (Bayesian Compressive Sensing) with ``'fast'`` bootstrap
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 150
- * - Number of total samples
- - 150
\ No newline at end of file
diff --git a/public/_sources/post_description.rst.txt b/public/_sources/post_description.rst.txt
deleted file mode 100644
index 987f18e7d..000000000
--- a/public/_sources/post_description.rst.txt
+++ /dev/null
@@ -1,66 +0,0 @@
-Postprocessing
-**************
-.. container:: twocol
-
- .. container:: leftside
-
- Postprocessing refers to evaluations and checks performed on a model to get an understanding of its properties and estimate its quality.
- The BayesValidRox class :any:`bayesvalidrox.post_processing.post_processing.PostProcessing` includes functions that are applicable to all types of surrogate model.
-
- * ``valid_metamodel``: visualizing some metamodel runs against the corresponding model runs
- * ``check_accuracy``: computing the RMSE error of the surrogate model
-
- .. container:: rightside
-
- .. image:: ../diagrams/postprocessing.png
- :width: 300
- :alt: UML diagram for the classes and functions used in active learning in BayesValidRox.
-
-Postprocessing methods that are specific to a type of surrogate or training include
-
-* ``plot_moments``: visualizing the moments of a polynomial chaos expansion (PCE, aPCE)
-* ``sobol_indices``: calculating the Sobol' and Total Sobol' indices (PCE, aPCE)
-* ``plot_seq_design_diagnostics``: visualizing diagnostics that were evaluated during sequential training (surrogate trained with ``train_sequential``)
-
-
-Example
-=======
-We want to compare out trained surrogate from :any:`al_description` against its original model.
-
->>> from bayesvalidrox import PostProcessing
-
-The postprocessing object expects the full engine. This allows it to perform both sampling, running the model and running the surrogate model.
-
->>> PostProc = PostProcessing(Engine_)
-
-To gain an understanding of the approximation quality we use both ``valid_metamodel`` and ``check_accuracy``.
-
->>> PostProc.valid_metamodel(n_samples=1)
->>> PostProc.check_accuracy(n_samples=10)
-
-The visual comparison of model and metamodel results is saved in the folder ``Outputs_PostProcessing_calib``, while the accuracy check is given as console output.
-
-.. container:: twocol
-
- .. container:: leftside
-
- .. code-block:: py
-
- >>>>> Errors of A <<<<<
-
- Index | RMSE | Validation Error
- -----------------------------------
- 1 | 1.513e-07 | 2.224e-15
-
- >>>>> Errors of B <<<<<
-
- Index | RMSE | Validation Error
- -----------------------------------
- 1 | 5.045e-05 | 2.406e-11
-
- .. container:: rightside
-
- .. image:: ../../examples/user_guide/Outputs_PostProcessing_calib/Model_vs_PCEModel_y_1.pdf
- :width: 400
- :alt: Visual comparison of model and metamodel outputs
-
\ No newline at end of file
diff --git a/public/_sources/surrogate_description.rst.txt b/public/_sources/surrogate_description.rst.txt
deleted file mode 100644
index 0d17b06ce..000000000
--- a/public/_sources/surrogate_description.rst.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-Training surrogate models
-*************************
-Surrogate models, also called metamodels, are models that are built on evaluations of full models with the goal to capture the full behaviour, but reduce the cost of evaluations.
-
-The surrogate models are trained on datasets :math:`\mathcal{D}=(x_i, y_i)_{i=1,\dots,M)` that consist of :math:`M` samples of the uncertain parameters and the corresponding model outputs.
-We call this dataset the training data, with training samples :math:`(x_i)_{i=1,\dots,M)`.
-
-BayesValidRox creates surrogate models as objects of the class :any:`bayesvalidrox.surrogate_models.surrogate_models.MetaModel`.
-Training is performed by the class :any:`bayesvalidrox.surrogate_models.engine.Engine`.
-
-.. image:: ../diagrams/metamod_training_reduced.png
- :width: 800
- :alt: UML diagram for metamodel-related classes in bayesvalidrox
-
-MetaModel options
-=================
-In BayesValidRox two types of surrogate model are available, Polynomial Chaos Expansion (PCE) and Gaussian Processes (GP).
-The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
-Gaussian processes (GP) give kernel-based representations of the model results.
-
-We provide a broad range of regression methods for useage with PCE-surrogates that can be set by the parameter ``MetaModel.pce_reg_method``.
-These include Ordinary Least Squares (``ols``), Bayesian Ridge Regression (``brr``), Least angle regression (``lars``), Bayesian ARD Regression (``ard``), Fast Bayesian ARD Regression (``fastard``), Variational Bayesian Learning (``vbl``) and Emperical Bayesian Learning (``ebl``).
-Depending on the chosen regression method, the surrogate outputs a mean approximation and an associated standard deviation.
-
-Dimensionality reduction can be performed on outputs with Principal Component Analysis (PCA).
-PCA is applied on the set of surrogates built for the ``x_values`` defined in the model.
-
-If bootstrapping is used, multiple surrogates will be created based on bootstrapped training data, and jointly evaluated.
-The final outputs will then be the mean and standard deviation of their approximations.
-
-
-Training with the engine
-========================
-For training a surrogate model we use an object of class :any:`bayesvalidrox.surrogate_models.engine.Engine`.
-This needs to be given three things: the metamodel itself, the model that the metamodel should replace and the experimental design that matches the uncertain inputs for the model and metamodel.
-
-The standard method of training the surrogate is performed by the function ``train_normal()``.
-Other available training methods in BayesValidRox are presented in :any:`al_description`.
-
-.. container:: twocol
-
- .. container:: leftside
-
- For training the engine performs three main steps.
-
- 1) Generating training samples from the experimental design.
- 2) Evaluating the model on the training samples.
- 3) Fitting the surrogate to the training dataset.
-
- .. container:: rightside
-
- .. image:: ../diagrams/engine_train_normal.png
- :width: 800
- :alt: Diagram of main steps in ``Engine.train_normal()``
-
-Example
-=======
-We now build a surrogate model for the simple model from :any:`model_description` using the experimental design from :any:`input_description`.
-For this we need the classes :any:`bayesvalidrox.surrogate_models.surrogate_models.MetaModel` and :any:`bayesvalidrox.surrogate_models.engine.Engine`.
-
->> from bayesvalidrox import MetaModel, Engine
-
-First we set up the surrogate model and tell it to consider the uncertain parameters defined in ``Inputs`` as its input parameters.
-
->>> MetaMod = MetaModel(Inputs)
-
-Then we specify what type of surrogate we want and its properties.
-Here we use an aPCE with maximal polynomial degree 3 and want to use FastARD as the regression method.
-We set the value of the q-norm truncation scheme to 0.9.
-This combination will give us a sparse aPCE.
-
->>> MetaMod.meta_model_type = 'aPCE'
->>> MetaMod.pce_reg_method = 'FastARD'
->>> MetaMod.pce_deg = 3
->>> MetaMod.pce_q_norm = 0.85
-
-Before we start the actual training we set ``n_init_samples`` to our wanted number of training samples.
-
->>> ExpDesign.n_init_samples = 10
-
-Like this the experimental design will generate 10 samples according to our previously set sampling method.
-Alternatively we can set the samples that we generated in :any:`input_description` as the training samples.
-For this the sampling method should be set to 'user' and our samples given as ``X``.
-
->>> ExpDesign.sampling_method = 'user'
->>> ExpDesign.root_samples = samples
-
-Now we create an engine object with the model, experimental design and surrogate model.
-With the function ``start_engine`` the engine performs its preparations for training.
-
->>> Engine_ = Engine(MetaMod, Model, ExpDesign)
->>> Engine_.start_engine()
-
-Then we train the surrogate model.
-
->>> Engine.train_normal()
-
-We can evaluate the trained surrogate model in two ways, via the engine, or directly.
-The evaluations return the mean approximation of the surrogate and its associated standard deviation.
-Evaluation via the surrogate model can make use of the sampling in the experimental design,
-
->>> mean, stdev = Engine_.eval_metamodel(nsamples = 10)
-
-while for direct evaluation the exact set of samples has to be given.
-
->>> mean, stdev = Engine_.MetaModel.eval_metamodel(samples)
diff --git a/public/_sources/tutorial.rst.txt b/public/_sources/tutorial.rst.txt
deleted file mode 100644
index 5ad40b9dd..000000000
--- a/public/_sources/tutorial.rst.txt
+++ /dev/null
@@ -1,347 +0,0 @@
-TUTORIAL
-********
-Here we provide an introductory tutorial for the general workflow with **bayesvalidrox**.
-This tutorial follows along the example :any:`analyticalfunction` to explain the basic functionality of working with **bayesvalidrox** surrogates, postprocessing and the Bayesian classes.
-The full code can be found in :py:mod:`examples.analytical-function.test_analytical_function`.
-
-Import necessary libraries
---------------------------
->>> import numpy as np
->>> import pandas as pd
->>> import sys
->>> import joblib
->>> from IPython.display import IFrame
-
-Define the model with PyLinkForwardModel
-----------------------------------------
-We use the :any:`bayesvalidrox.pylink.pylink.PyLinkForwardModel` object to define the model.
-
->>> from bayesvalidrox import PyLinkForwardModel
->>> Model = PyLinkForwardModel()
-
-Since the analytical function is implmented as a python function in a separate file, we can set the type of the link to ``Function`` and only need to pass it's name (without the ``.py`` extension) to the object variable ``py_file``.
-Note that the function name in the python script should match that of the script.
-
-The name variable takes any user defined string.
-
->>> Model.link_type = 'Function'
->>> Model.py_file = 'analytical_function'
->>> Model.name = 'AnalyticFunc'
-
-The model output names are defined as a list of strings.
-These names will also be adopted by the surrogate and used in all plots.
-
->>> Model.Output.names = ['Z']
-
-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``.
-
->>> Model.mc_reference = {}
->>> Model.mc_reference['Time [s]'] = np.arange(0, 10, 1.) / 9
->>> Model.mc_reference['mean'] = np.load(f"data/mean_2.npy")
->>> Model.mc_reference['std'] = np.load(f"data/std_2.npy")
-
-Some of the training methods and evaluations make use of given data.
-For this we choose the model results for the parameter values X=[0,0].
-
->>> Model.observations = {}
->>> Model.observations['Time [s]'] = np.arange(0, 10, 1.) / 9
->>> Model.observations['Z'] = np.repeat([2.], 10)
-
-
-Define probabilistic input model
---------------------------------
-Import and instantiate the input object
-
->>> from bayesvalidrox import Input
->>> Inputs = Input()
-
-Now, we define the distribution of the model inputs.
-**bayesvalidrox** accepts the definition in two ways: by defining the distribution directly or by passing available data.
-The latter is handy when little information is available on the parameters or they do not follow any typical distributions.
-Here we show both options, the associated example :any:`analyticalfunction` read the input parameters from a numpy file in the ``data/`` directory.
-
-**Option I:** Define distribution directy with their name, type and parameters
-
->>> # First parameter
->>> Inputs.add_marginals()
->>> Inputs.Marginals[0].name = '$X_1$'
->>> Inputs.Marginals[0].dist_type = 'unif'
->>> Inputs.Marginals[0].parameters = [-5, 5]
->>>
->>> # Second parameter
->>> Inputs.add_marginals()
->>> Inputs.Marginals[1].name = '$X_2$'
->>> Inputs.Marginals[1].dist_type = 'unif'
->>> Inputs.Marginals[1].parameters = [-5, 5]
-
-**Option II:** Pass available data for input parameters
-
->>> inputParams = np.load('data/InputParameters_2.npy')
->>>
->>> # First parameter
->>> Inputs.add_marginals()
->>> Inputs.Marginals[0].name = '$X_1$'
->>> Inputs.Marginals[0].input_data = inputParams[:, 0]
->>>
->>> # Second parameter
->>> Inputs.add_marginals()
->>> Inputs.Marginals[1].name = '$X_2$'
->>> Inputs.Marginals[1].input_data = inputParams[:, 1]
-
-
-Define surrogate (meta) model
------------------------------
-In **bayesvalidrox** a metamodel is an object of class :any:`bayesvalidrox.surrogate_models.surrogate_models.MetaModel`.
-This object also accepts the inputs we defined in the previous section as an argument.
-
-
->>> from bayesvalidrox import MetaModel
->>> MetaModelOpts = MetaModel(Inputs)
-
-In this example, we use the arbitrary Polynomial Chaos Expansion (``'aPCE'``) as our ``meta_model_type``.
-
->>> MetaModelOpts.meta_model_type = 'aPCE'
-
-.. _2002: https://epubs.siam.org/doi/10.1137/S1064827501387826/
-
-.. note::
- In **bayesvalidrox** two implementations of the Polynomial Chaos Expansion are available, ``'PCE'`` and ``'aPCE'``.
- The tag ``'PCE'`` uses the generalized Polynomial Chaos Expansion by Xiu & Karniadakis (2002_).
- while using ``'aPCE'`` results in its arbitrary extension by Oladyshkin & Nowak (2012_).
-
-.. _2012: https://www.sciencedirect.com/science/article/pii/S0951832012000853?via%3Dihub/
-
-
-In **bayesvalidrox** the calculation of the polynomial coefficient is by standard done as described in Blatman & Sudret (2011_).
-
-.. _2011: https://www.sciencedirect.com/science/article/pii/S0021999110006856/
-
-In addition the regression method to be used at the core of this calculation has to be set.
-Here we choose ``'FastARD'``, as it also induces sparsity in the expansion.
-
->>> MetaModelOpts.pce_reg_method = 'FastARD'
-
-The polynomial degrees to consider can be either set as a range, or a single value to indicate the highest allowed degree.
-
->>> MetaModelOpts.pce_deg = np.arange(9)
-
-The truncation shceme of the expansion is given as the q-norm, with a value between 0 and 1.
-A value of 1 results in standard truncation of the expansion, while smaller values make the expansion prefer less combined terms.
-
->>> MetaModelOpts.pce_q_norm = 0.75
-
-
-Set the experimental design
----------------------------
-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.
-
->>> ExpDesign = ExpDesign(Inputs)
->>> ExpDesign.n_init_samples = 100
->>> ExpDesign.sampling_method = 'latin_hypercube'
-
-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 :any:`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.
-
->>> 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 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.
-
->>> ExpDesign.n_init_samples = 3*ndim
->>> ExpDesign.sampling_method = 'latin_hypercube'
-
-The options for sequential training are listed in the dedicated page of the :any:`packagedescription`.
-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.
-
->>> ExpDesign.n_new_samples = 1
->>> ExpDesign.n_max_samples = 150
->>> ExpDesign.mod_LOO_threshold = 1e-16
->>>
->>> ExpDesign.tradeoff_scheme = None
->>> ExpDesign.explore_method = 'random'
->>>
->>> # Use when 'Voronoi' or 'random' or 'latin_hypercube' chosen
->>> ExpDesign.n_canddidate = 1000
->>> ExpDesign.n_cand_groups = 4
-
-
-Here we set the exploitaiton method to be Bayesian Active Learning, which chooses the new samples based on the information gain with respect to some given data, here the model results described earlier.
-
-In addition we need to set the information metric to use, here ``'DKL'`` is chosen.
-
->>> 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.
-
->>> obsData = pd.DataFrame(Model.observations, columns=Model.Output.names)
->>> DiscrepancyOpts = Discrepancy('')
->>> DiscrepancyOpts.type = 'Gaussian'
->>> DiscrepancyOpts.parameters = obsData**2
->>> MetaModelOpts.Discrepancy = DiscrepancyOpts
-
-The measures calculated in each training iteration can also be plotted.
-
->>> 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")
->>> ExpDesign.valid_samples = prior[:500]
->>> ExpDesign.valid_model_runs = {'Z': prior_outputs[:500]}
-
-The sequential training is again performed by the class :any:`bayesvalidrox.surrogate_models.engine.Engine`, but using the function ``train_sequential``.
-
->>> engine.ExpDesign = ExpDesign
->>> engine.train_sequential()
-
-
-
-Post-processing
----------------
-The available post-processing methods for **bayesvalidrox** are given in the class :any:`bayesvalidrox.post_processing.post_processing.PostProcessing`.
-All results created by this class are automatically stored in an output folder ``Outputs_PostProcessing_*modelname``.
-
->>> from bayesvalidrox import PostProcessing
->>> PostPCE = PostProcessing(engine)
-
-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()
-
-The method ``valid_metamodel`` allows for visual comparison between the model and surrogate for samples from the prior parameter distribution.
-The samples can be drawn randomly, or set by the user.
-
->>> PostPCE.valid_metamodel(n_samples=3)
-
-Another way to check the accuracy of the meta model is to use the method ``accuracyCheckMetaModel`` to show the Root Mean Square Error (RMSE) and the validation error.
-
->>> PostPCE.accuracyCheckMetaModel(nSamples=200)
-
-Global sensitivity analysis can be performed on the surrogate via the Total Sobol Indices.
-These are especially cheap to compute for PCE as they can exploit the properties of the calculated coefficients.
-The method ``sobolIndicesPCE`` returns a dictionary that contains the total sobol indices and stores their plots.
-
->>> total_sobol = PostPCE.sobolIndicesPCE()
-
-
-Bayesian Inference
-------------------
-Inverse parameter estimation can be done in **bayesvalidrox** with the class :any:`bayesvalidrox.bayes_inference.bayes_inference.BayesInference`.
-
->>> from bayesvalidrox import BayesInference
->>> 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``.
-More options for Bayesian inference are listed at :any:`bayes_description`.
-
-.. note::
- Setting ``emulator = False`` means that the inference is based on actual model runs and not the surrogate.
- This can also be achieved by initializing the Engine without a surrogate object.
-
->>> BayesOpts.emulator = True
->>> BayesOpts.plot_post_pred = True
-
-We use MCMC to approximate the posterior distribution, rejection sampling is also available.
-
->>> import emcee
->>> BayesOpts.inference_method = "MCMC"
->>> BayesOpts.mcmc_params = {
->>> 'n_steps': 1e5,
->>> 'n_walkers': 30,
->>> 'moves': emcee.moves.KDEMove(),
->>> 'multiprocessing': False,
->>> 'verbose': False
->>> }
-
-Define the data uncertainty
----------------------------
-The estimated uncertainty of the data is used for the Bayesian Inference.
-**bayesvalidrox** provides three option to add this.
-For this tutorial we assume the uncertainty of the data to be distributed according to a Gaussian distribution around the known data with a standard deviation that is as large as the values in the data.
-
-**Option I:** Set error directly for all data
-
->>> BayesOpts.measurement_error = obsData
-
-.. warning::
- This option will become deprecated.
-
-**Option II:** Set discrepancy distributions all at once
-
->>> DiscrepancyOpts = Discrepancy('')
->>> DiscrepancyOpts.type = 'Gaussian'
->>> DiscrepancyOpts.parameters = obsData**2
->>> BayesOpts.Discrepancy = DiscrepancyOpts
-
-**Option III:** Set discrepancy distributions for each parameter at a time
-
->>> BayesOpts.bias_inputs = {'Z':np.arange(0, 10, 1.).reshape(-1,1) / 9}
-
->>> DiscOutputOpts = Input()
->>> DiscOutputOpts.add_marginals()
->>> DiscOutputOpts.Marginals[0].Nnme = '$\sigma^2_{\epsilon}$'
->>> DiscOutputOpts.Marginals[0].dist_type = 'uniform'
->>> DiscOutputOpts.Marginals[0].parameters = [0, 10]
->>> BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
->>> 'infer': Discrepancy(DiscOutputOpts)}
-
->>> DiscOutputOpts = Input()
->>> DiscOutputOpts.add_marginals()
->>> DiscOutputOpts.Marginals[0].name = '$\lambda$'
->>> DiscOutputOpts.Marginals[0].dist_type = 'uniform'
->>> DiscOutputOpts.Marginals[0].parameters = [0, 1]
-
->>> DiscOutputOpts.add_marginals()
->>> DiscOutputOpts.Marginals[1].Name = '$\sigma_f$'
->>> DiscOutputOpts.Marginals[1].dist_type = 'uniform'
->>> DiscOutputOpts.Marginals[1].parameters = [0, 1e-4]
->>> BayesOpts.Discrepancy = Discrepancy(DiscOutputOpts)
->>> BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
->>> 'infer': Discrepancy(DiscOutputOpts)}
-
-With the method ``create_inference`` we start the calibration/inference and save the results in a a``.pkl`` file.
-This also creates and saves multiple plots in the folder ``Output_Bayes_*modelname``.
-The saved plots include a histogram of the BME of the surrogate and TOM, the posterior distribution with its most likely values and
-plots of posterior predictions if wanted.
-
->>> Bayes_PCE = BayesOpts.create_inference()
->>> with open(f'Bayes_{Model.name}.pkl', 'wb') as output:
->>> joblib.dump(Bayes_PCE, output, 2)
diff --git a/public/_static/_sphinx_javascript_frameworks_compat.js b/public/_static/_sphinx_javascript_frameworks_compat.js
deleted file mode 100644
index 8549469dc..000000000
--- a/public/_static/_sphinx_javascript_frameworks_compat.js
+++ /dev/null
@@ -1,134 +0,0 @@
-/*
- * _sphinx_javascript_frameworks_compat.js
- * ~~~~~~~~~~
- *
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- *
- * WILL BE REMOVED IN Sphinx 6.0
- * xref RemovedInSphinx60Warning
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- "target": rect});
- }
- }
- }
- else if (!jQuery(node).is("button, select, textarea")) {
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- highlight(this, addItems);
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- jQuery(addItems[i].parent).before(addItems[i].target);
- }
- return result;
-};
-
-/*
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- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
- jQuery.uaMatch = function(ua) {
- ua = ua.toLowerCase();
-
- var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
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- };
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diff --git a/public/_static/alabaster.css b/public/_static/alabaster.css
deleted file mode 100644
index 0eddaeb07..000000000
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+++ /dev/null
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diff --git a/public/_static/custom.css b/public/_static/custom.css
deleted file mode 100644
index 9a890582e..000000000
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\ No newline at end of file
diff --git a/public/_static/debug.css b/public/_static/debug.css
deleted file mode 100644
index 74d4aec33..000000000
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-}
diff --git a/public/_static/doctools.js b/public/_static/doctools.js
deleted file mode 100644
index 4d67807d1..000000000
--- a/public/_static/doctools.js
+++ /dev/null
@@ -1,156 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Base JavaScript utilities for all Sphinx HTML documentation.
- *
- * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
- "TEXTAREA",
- "INPUT",
- "SELECT",
- "BUTTON",
-]);
-
-const _ready = (callback) => {
- if (document.readyState !== "loading") {
- callback();
- } else {
- document.addEventListener("DOMContentLoaded", callback);
- }
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const Documentation = {
- init: () => {
- Documentation.initDomainIndexTable();
- Documentation.initOnKeyListeners();
- },
-
- /**
- * i18n support
- */
- TRANSLATIONS: {},
- PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
- LOCALE: "unknown",
-
- // gettext and ngettext don't access this so that the functions
- // can safely bound to a different name (_ = Documentation.gettext)
- gettext: (string) => {
- const translated = Documentation.TRANSLATIONS[string];
- switch (typeof translated) {
- case "undefined":
- return string; // no translation
- case "string":
- return translated; // translation exists
- default:
- return translated[0]; // (singular, plural) translation tuple exists
- }
- },
-
- ngettext: (singular, plural, n) => {
- const translated = Documentation.TRANSLATIONS[singular];
- if (typeof translated !== "undefined")
- return translated[Documentation.PLURAL_EXPR(n)];
- return n === 1 ? singular : plural;
- },
-
- addTranslations: (catalog) => {
- Object.assign(Documentation.TRANSLATIONS, catalog.messages);
- Documentation.PLURAL_EXPR = new Function(
- "n",
- `return (${catalog.plural_expr})`
- );
- Documentation.LOCALE = catalog.locale;
- },
-
- /**
- * helper function to focus on search bar
- */
- focusSearchBar: () => {
- document.querySelectorAll("input[name=q]")[0]?.focus();
- },
-
- /**
- * Initialise the domain index toggle buttons
- */
- initDomainIndexTable: () => {
- const toggler = (el) => {
- const idNumber = el.id.substr(7);
- const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
- if (el.src.substr(-9) === "minus.png") {
- el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
- toggledRows.forEach((el) => (el.style.display = "none"));
- } else {
- el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
- toggledRows.forEach((el) => (el.style.display = ""));
- }
- };
-
- const togglerElements = document.querySelectorAll("img.toggler");
- togglerElements.forEach((el) =>
- el.addEventListener("click", (event) => toggler(event.currentTarget))
- );
- togglerElements.forEach((el) => (el.style.display = ""));
- if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
- },
-
- initOnKeyListeners: () => {
- // only install a listener if it is really needed
- if (
- !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
- !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
- )
- return;
-
- document.addEventListener("keydown", (event) => {
- // bail for input elements
- if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
- // bail with special keys
- if (event.altKey || event.ctrlKey || event.metaKey) return;
-
- if (!event.shiftKey) {
- switch (event.key) {
- case "ArrowLeft":
- if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
- const prevLink = document.querySelector('link[rel="prev"]');
- if (prevLink && prevLink.href) {
- window.location.href = prevLink.href;
- event.preventDefault();
- }
- break;
- case "ArrowRight":
- if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
-
- const nextLink = document.querySelector('link[rel="next"]');
- if (nextLink && nextLink.href) {
- window.location.href = nextLink.href;
- event.preventDefault();
- }
- break;
- }
- }
-
- // some keyboard layouts may need Shift to get /
- switch (event.key) {
- case "/":
- if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
- Documentation.focusSearchBar();
- event.preventDefault();
- }
- });
- },
-};
-
-// quick alias for translations
-const _ = Documentation.gettext;
-
-_ready(Documentation.init);
diff --git a/public/_static/documentation_options.js b/public/_static/documentation_options.js
deleted file mode 100644
index 1675f3b58..000000000
--- a/public/_static/documentation_options.js
+++ /dev/null
@@ -1,13 +0,0 @@
-const DOCUMENTATION_OPTIONS = {
- VERSION: '1.0.0',
- LANGUAGE: 'en',
- COLLAPSE_INDEX: false,
- BUILDER: 'html',
- FILE_SUFFIX: '.html',
- LINK_SUFFIX: '.html',
- HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt',
- NAVIGATION_WITH_KEYS: true,
- SHOW_SEARCH_SUMMARY: true,
- ENABLE_SEARCH_SHORTCUTS: true,
-};
\ No newline at end of file
diff --git a/public/_static/jquery-3.6.0.js b/public/_static/jquery-3.6.0.js
deleted file mode 100644
index fc6c299b7..000000000
--- a/public/_static/jquery-3.6.0.js
+++ /dev/null
@@ -1,10881 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.6.0
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright OpenJS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2021-03-02T17:08Z
- */
-( function( global, factory ) {
-
- "use strict";
-
- if ( typeof module === "object" && typeof module.exports === "object" ) {
-
- // For CommonJS and CommonJS-like environments where a proper `window`
- // is present, execute the factory and get jQuery.
- // For environments that do not have a `window` with a `document`
- // (such as Node.js), expose a factory as module.exports.
- // This accentuates the need for the creation of a real `window`.
- // e.g. var jQuery = require("jquery")(window);
- // See ticket #14549 for more info.
- module.exports = global.document ?
- factory( global, true ) :
- function( w ) {
- if ( !w.document ) {
- throw new Error( "jQuery requires a window with a document" );
- }
- return factory( w );
- };
- } else {
- factory( global );
- }
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
- return arr.flat.call( array );
-} : function( array ) {
- return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
- // Support: Chrome <=57, Firefox <=52
- // In some browsers, typeof returns "function" for HTML <object> elements
- // (i.e., `typeof document.createElement( "object" ) === "function"`).
- // We don't want to classify *any* DOM node as a function.
- // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
- // Plus for old WebKit, typeof returns "function" for HTML collections
- // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
- return typeof obj === "function" && typeof obj.nodeType !== "number" &&
- typeof obj.item !== "function";
- };
-
-
-var isWindow = function isWindow( obj ) {
- return obj != null && obj === obj.window;
- };
-
-
-var document = window.document;
-
-
-
- var preservedScriptAttributes = {
- type: true,
- src: true,
- nonce: true,
- noModule: true
- };
-
- function DOMEval( code, node, doc ) {
- doc = doc || document;
-
- var i, val,
- script = doc.createElement( "script" );
-
- script.text = code;
- if ( node ) {
- for ( i in preservedScriptAttributes ) {
-
- // Support: Firefox 64+, Edge 18+
- // Some browsers don't support the "nonce" property on scripts.
- // On the other hand, just using `getAttribute` is not enough as
- // the `nonce` attribute is reset to an empty string whenever it
- // becomes browsing-context connected.
- // See https://github.com/whatwg/html/issues/2369
- // See https://html.spec.whatwg.org/#nonce-attributes
- // The `node.getAttribute` check was added for the sake of
- // `jQuery.globalEval` so that it can fake a nonce-containing node
- // via an object.
- val = node[ i ] || node.getAttribute && node.getAttribute( i );
- if ( val ) {
- script.setAttribute( i, val );
- }
- }
- }
- doc.head.appendChild( script ).parentNode.removeChild( script );
- }
-
-
-function toType( obj ) {
- if ( obj == null ) {
- return obj + "";
- }
-
- // Support: Android <=2.3 only (functionish RegExp)
- return typeof obj === "object" || typeof obj === "function" ?
- class2type[ toString.call( obj ) ] || "object" :
- typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
- version = "3.6.0",
-
- // Define a local copy of jQuery
- jQuery = function( selector, context ) {
-
- // The jQuery object is actually just the init constructor 'enhanced'
- // Need init if jQuery is called (just allow error to be thrown if not included)
- return new jQuery.fn.init( selector, context );
- };
-
-jQuery.fn = jQuery.prototype = {
-
- // The current version of jQuery being used
- jquery: version,
-
- constructor: jQuery,
-
- // The default length of a jQuery object is 0
- length: 0,
-
- toArray: function() {
- return slice.call( this );
- },
-
- // Get the Nth element in the matched element set OR
- // Get the whole matched element set as a clean array
- get: function( num ) {
-
- // Return all the elements in a clean array
- if ( num == null ) {
- return slice.call( this );
- }
-
- // Return just the one element from the set
- return num < 0 ? this[ num + this.length ] : this[ num ];
- },
-
- // Take an array of elements and push it onto the stack
- // (returning the new matched element set)
- pushStack: function( elems ) {
-
- // Build a new jQuery matched element set
- var ret = jQuery.merge( this.constructor(), elems );
-
- // Add the old object onto the stack (as a reference)
- ret.prevObject = this;
-
- // Return the newly-formed element set
- return ret;
- },
-
- // Execute a callback for every element in the matched set.
- each: function( callback ) {
- return jQuery.each( this, callback );
- },
-
- map: function( callback ) {
- return this.pushStack( jQuery.map( this, function( elem, i ) {
- return callback.call( elem, i, elem );
- } ) );
- },
-
- slice: function() {
- return this.pushStack( slice.apply( this, arguments ) );
- },
-
- first: function() {
- return this.eq( 0 );
- },
-
- last: function() {
- return this.eq( -1 );
- },
-
- even: function() {
- return this.pushStack( jQuery.grep( this, function( _elem, i ) {
- return ( i + 1 ) % 2;
- } ) );
- },
-
- odd: function() {
- return this.pushStack( jQuery.grep( this, function( _elem, i ) {
- return i % 2;
- } ) );
- },
-
- eq: function( i ) {
- var len = this.length,
- j = +i + ( i < 0 ? len : 0 );
- return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
- },
-
- end: function() {
- return this.prevObject || this.constructor();
- },
-
- // For internal use only.
- // Behaves like an Array's method, not like a jQuery method.
- push: push,
- sort: arr.sort,
- splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
- var options, name, src, copy, copyIsArray, clone,
- target = arguments[ 0 ] || {},
- i = 1,
- length = arguments.length,
- deep = false;
-
- // Handle a deep copy situation
- if ( typeof target === "boolean" ) {
- deep = target;
-
- // Skip the boolean and the target
- target = arguments[ i ] || {};
- i++;
- }
-
- // Handle case when target is a string or something (possible in deep copy)
- if ( typeof target !== "object" && !isFunction( target ) ) {
- target = {};
- }
-
- // Extend jQuery itself if only one argument is passed
- if ( i === length ) {
- target = this;
- i--;
- }
-
- for ( ; i < length; i++ ) {
-
- // Only deal with non-null/undefined values
- if ( ( options = arguments[ i ] ) != null ) {
-
- // Extend the base object
- for ( name in options ) {
- copy = options[ name ];
-
- // Prevent Object.prototype pollution
- // Prevent never-ending loop
- if ( name === "__proto__" || target === copy ) {
- continue;
- }
-
- // Recurse if we're merging plain objects or arrays
- if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
- ( copyIsArray = Array.isArray( copy ) ) ) ) {
- src = target[ name ];
-
- // Ensure proper type for the source value
- if ( copyIsArray && !Array.isArray( src ) ) {
- clone = [];
- } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
- clone = {};
- } else {
- clone = src;
- }
- copyIsArray = false;
-
- // Never move original objects, clone them
- target[ name ] = jQuery.extend( deep, clone, copy );
-
- // Don't bring in undefined values
- } else if ( copy !== undefined ) {
- target[ name ] = copy;
- }
- }
- }
- }
-
- // Return the modified object
- return target;
-};
-
-jQuery.extend( {
-
- // Unique for each copy of jQuery on the page
- expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
- // Assume jQuery is ready without the ready module
- isReady: true,
-
- error: function( msg ) {
- throw new Error( msg );
- },
-
- noop: function() {},
-
- isPlainObject: function( obj ) {
- var proto, Ctor;
-
- // Detect obvious negatives
- // Use toString instead of jQuery.type to catch host objects
- if ( !obj || toString.call( obj ) !== "[object Object]" ) {
- return false;
- }
-
- proto = getProto( obj );
-
- // Objects with no prototype (e.g., `Object.create( null )`) are plain
- if ( !proto ) {
- return true;
- }
-
- // Objects with prototype are plain iff they were constructed by a global Object function
- Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
- return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
- },
-
- isEmptyObject: function( obj ) {
- var name;
-
- for ( name in obj ) {
- return false;
- }
- return true;
- },
-
- // Evaluates a script in a provided context; falls back to the global one
- // if not specified.
- globalEval: function( code, options, doc ) {
- DOMEval( code, { nonce: options && options.nonce }, doc );
- },
-
- each: function( obj, callback ) {
- var length, i = 0;
-
- if ( isArrayLike( obj ) ) {
- length = obj.length;
- for ( ; i < length; i++ ) {
- if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
- break;
- }
- }
- } else {
- for ( i in obj ) {
- if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
- break;
- }
- }
- }
-
- return obj;
- },
-
- // results is for internal usage only
- makeArray: function( arr, results ) {
- var ret = results || [];
-
- if ( arr != null ) {
- if ( isArrayLike( Object( arr ) ) ) {
- jQuery.merge( ret,
- typeof arr === "string" ?
- [ arr ] : arr
- );
- } else {
- push.call( ret, arr );
- }
- }
-
- return ret;
- },
-
- inArray: function( elem, arr, i ) {
- return arr == null ? -1 : indexOf.call( arr, elem, i );
- },
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- merge: function( first, second ) {
- var len = +second.length,
- j = 0,
- i = first.length;
-
- for ( ; j < len; j++ ) {
- first[ i++ ] = second[ j ];
- }
-
- first.length = i;
-
- return first;
- },
-
- grep: function( elems, callback, invert ) {
- var callbackInverse,
- matches = [],
- i = 0,
- length = elems.length,
- callbackExpect = !invert;
-
- // Go through the array, only saving the items
- // that pass the validator function
- for ( ; i < length; i++ ) {
- callbackInverse = !callback( elems[ i ], i );
- if ( callbackInverse !== callbackExpect ) {
- matches.push( elems[ i ] );
- }
- }
-
- return matches;
- },
-
- // arg is for internal usage only
- map: function( elems, callback, arg ) {
- var length, value,
- i = 0,
- ret = [];
-
- // Go through the array, translating each of the items to their new values
- if ( isArrayLike( elems ) ) {
- length = elems.length;
- for ( ; i < length; i++ ) {
- value = callback( elems[ i ], i, arg );
-
- if ( value != null ) {
- ret.push( value );
- }
- }
-
- // Go through every key on the object,
- } else {
- for ( i in elems ) {
- value = callback( elems[ i ], i, arg );
-
- if ( value != null ) {
- ret.push( value );
- }
- }
- }
-
- // Flatten any nested arrays
- return flat( ret );
- },
-
- // A global GUID counter for objects
- guid: 1,
-
- // jQuery.support is not used in Core but other projects attach their
- // properties to it so it needs to exist.
- support: support
-} );
-
-if ( typeof Symbol === "function" ) {
- jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
- function( _i, name ) {
- class2type[ "[object " + name + "]" ] = name.toLowerCase();
- } );
-
-function isArrayLike( obj ) {
-
- // Support: real iOS 8.2 only (not reproducible in simulator)
- // `in` check used to prevent JIT error (gh-2145)
- // hasOwn isn't used here due to false negatives
- // regarding Nodelist length in IE
- var length = !!obj && "length" in obj && obj.length,
- type = toType( obj );
-
- if ( isFunction( obj ) || isWindow( obj ) ) {
- return false;
- }
-
- return type === "array" || length === 0 ||
- typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.6
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2021-02-16
- */
-( function( window ) {
-var i,
- support,
- Expr,
- getText,
- isXML,
- tokenize,
- compile,
- select,
- outermostContext,
- sortInput,
- hasDuplicate,
-
- // Local document vars
- setDocument,
- document,
- docElem,
- documentIsHTML,
- rbuggyQSA,
- rbuggyMatches,
- matches,
- contains,
-
- // Instance-specific data
- expando = "sizzle" + 1 * new Date(),
- preferredDoc = window.document,
- dirruns = 0,
- done = 0,
- classCache = createCache(),
- tokenCache = createCache(),
- compilerCache = createCache(),
- nonnativeSelectorCache = createCache(),
- sortOrder = function( a, b ) {
- if ( a === b ) {
- hasDuplicate = true;
- }
- return 0;
- },
-
- // Instance methods
- hasOwn = ( {} ).hasOwnProperty,
- arr = [],
- pop = arr.pop,
- pushNative = arr.push,
- push = arr.push,
- slice = arr.slice,
-
- // Use a stripped-down indexOf as it's faster than native
- // https://jsperf.com/thor-indexof-vs-for/5
- indexOf = function( list, elem ) {
- var i = 0,
- len = list.length;
- for ( ; i < len; i++ ) {
- if ( list[ i ] === elem ) {
- return i;
- }
- }
- return -1;
- },
-
- booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
- "ismap|loop|multiple|open|readonly|required|scoped",
-
- // Regular expressions
-
- // http://www.w3.org/TR/css3-selectors/#whitespace
- whitespace = "[\\x20\\t\\r\\n\\f]",
-
- // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
- identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
- "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
- // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
- attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
- // Operator (capture 2)
- "*([*^$|!~]?=)" + whitespace +
-
- // "Attribute values must be CSS identifiers [capture 5]
- // or strings [capture 3 or capture 4]"
- "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
- whitespace + "*\\]",
-
- pseudos = ":(" + identifier + ")(?:\\((" +
-
- // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
- // 1. quoted (capture 3; capture 4 or capture 5)
- "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
- // 2. simple (capture 6)
- "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
- // 3. anything else (capture 2)
- ".*" +
- ")\\)|)",
-
- // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
- rwhitespace = new RegExp( whitespace + "+", "g" ),
- rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
- whitespace + "+$", "g" ),
-
- rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
- rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
- "*" ),
- rdescend = new RegExp( whitespace + "|>" ),
-
- rpseudo = new RegExp( pseudos ),
- ridentifier = new RegExp( "^" + identifier + "$" ),
-
- matchExpr = {
- "ID": new RegExp( "^#(" + identifier + ")" ),
- "CLASS": new RegExp( "^\\.(" + identifier + ")" ),
- "TAG": new RegExp( "^(" + identifier + "|[*])" ),
- "ATTR": new RegExp( "^" + attributes ),
- "PSEUDO": new RegExp( "^" + pseudos ),
- "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
- whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
- whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
- "bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
- // For use in libraries implementing .is()
- // We use this for POS matching in `select`
- "needsContext": new RegExp( "^" + whitespace +
- "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
- "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
- },
-
- rhtml = /HTML$/i,
- rinputs = /^(?:input|select|textarea|button)$/i,
- rheader = /^h\d$/i,
-
- rnative = /^[^{]+\{\s*\[native \w/,
-
- // Easily-parseable/retrievable ID or TAG or CLASS selectors
- rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
- rsibling = /[+~]/,
-
- // CSS escapes
- // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
- runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
- funescape = function( escape, nonHex ) {
- var high = "0x" + escape.slice( 1 ) - 0x10000;
-
- return nonHex ?
-
- // Strip the backslash prefix from a non-hex escape sequence
- nonHex :
-
- // Replace a hexadecimal escape sequence with the encoded Unicode code point
- // Support: IE <=11+
- // For values outside the Basic Multilingual Plane (BMP), manually construct a
- // surrogate pair
- high < 0 ?
- String.fromCharCode( high + 0x10000 ) :
- String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
- },
-
- // CSS string/identifier serialization
- // https://drafts.csswg.org/cssom/#common-serializing-idioms
- rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
- fcssescape = function( ch, asCodePoint ) {
- if ( asCodePoint ) {
-
- // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
- if ( ch === "\0" ) {
- return "\uFFFD";
- }
-
- // Control characters and (dependent upon position) numbers get escaped as code points
- return ch.slice( 0, -1 ) + "\\" +
- ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
- }
-
- // Other potentially-special ASCII characters get backslash-escaped
- return "\\" + ch;
- },
-
- // Used for iframes
- // See setDocument()
- // Removing the function wrapper causes a "Permission Denied"
- // error in IE
- unloadHandler = function() {
- setDocument();
- },
-
- inDisabledFieldset = addCombinator(
- function( elem ) {
- return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
- },
- { dir: "parentNode", next: "legend" }
- );
-
-// Optimize for push.apply( _, NodeList )
-try {
- push.apply(
- ( arr = slice.call( preferredDoc.childNodes ) ),
- preferredDoc.childNodes
- );
-
- // Support: Android<4.0
- // Detect silently failing push.apply
- // eslint-disable-next-line no-unused-expressions
- arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
- push = { apply: arr.length ?
-
- // Leverage slice if possible
- function( target, els ) {
- pushNative.apply( target, slice.call( els ) );
- } :
-
- // Support: IE<9
- // Otherwise append directly
- function( target, els ) {
- var j = target.length,
- i = 0;
-
- // Can't trust NodeList.length
- while ( ( target[ j++ ] = els[ i++ ] ) ) {}
- target.length = j - 1;
- }
- };
-}
-
-function Sizzle( selector, context, results, seed ) {
- var m, i, elem, nid, match, groups, newSelector,
- newContext = context && context.ownerDocument,
-
- // nodeType defaults to 9, since context defaults to document
- nodeType = context ? context.nodeType : 9;
-
- results = results || [];
-
- // Return early from calls with invalid selector or context
- if ( typeof selector !== "string" || !selector ||
- nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
- return results;
- }
-
- // Try to shortcut find operations (as opposed to filters) in HTML documents
- if ( !seed ) {
- setDocument( context );
- context = context || document;
-
- if ( documentIsHTML ) {
-
- // If the selector is sufficiently simple, try using a "get*By*" DOM method
- // (excepting DocumentFragment context, where the methods don't exist)
- if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
- // ID selector
- if ( ( m = match[ 1 ] ) ) {
-
- // Document context
- if ( nodeType === 9 ) {
- if ( ( elem = context.getElementById( m ) ) ) {
-
- // Support: IE, Opera, Webkit
- // TODO: identify versions
- // getElementById can match elements by name instead of ID
- if ( elem.id === m ) {
- results.push( elem );
- return results;
- }
- } else {
- return results;
- }
-
- // Element context
- } else {
-
- // Support: IE, Opera, Webkit
- // TODO: identify versions
- // getElementById can match elements by name instead of ID
- if ( newContext && ( elem = newContext.getElementById( m ) ) &&
- contains( context, elem ) &&
- elem.id === m ) {
-
- results.push( elem );
- return results;
- }
- }
-
- // Type selector
- } else if ( match[ 2 ] ) {
- push.apply( results, context.getElementsByTagName( selector ) );
- return results;
-
- // Class selector
- } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
- context.getElementsByClassName ) {
-
- push.apply( results, context.getElementsByClassName( m ) );
- return results;
- }
- }
-
- // Take advantage of querySelectorAll
- if ( support.qsa &&
- !nonnativeSelectorCache[ selector + " " ] &&
- ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
- // Support: IE 8 only
- // Exclude object elements
- ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
- newSelector = selector;
- newContext = context;
-
- // qSA considers elements outside a scoping root when evaluating child or
- // descendant combinators, which is not what we want.
- // In such cases, we work around the behavior by prefixing every selector in the
- // list with an ID selector referencing the scope context.
- // The technique has to be used as well when a leading combinator is used
- // as such selectors are not recognized by querySelectorAll.
- // Thanks to Andrew Dupont for this technique.
- if ( nodeType === 1 &&
- ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
- // Expand context for sibling selectors
- newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
- context;
-
- // We can use :scope instead of the ID hack if the browser
- // supports it & if we're not changing the context.
- if ( newContext !== context || !support.scope ) {
-
- // Capture the context ID, setting it first if necessary
- if ( ( nid = context.getAttribute( "id" ) ) ) {
- nid = nid.replace( rcssescape, fcssescape );
- } else {
- context.setAttribute( "id", ( nid = expando ) );
- }
- }
-
- // Prefix every selector in the list
- groups = tokenize( selector );
- i = groups.length;
- while ( i-- ) {
- groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
- toSelector( groups[ i ] );
- }
- newSelector = groups.join( "," );
- }
-
- try {
- push.apply( results,
- newContext.querySelectorAll( newSelector )
- );
- return results;
- } catch ( qsaError ) {
- nonnativeSelectorCache( selector, true );
- } finally {
- if ( nid === expando ) {
- context.removeAttribute( "id" );
- }
- }
- }
- }
- }
-
- // All others
- return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- * deleting the oldest entry
- */
-function createCache() {
- var keys = [];
-
- function cache( key, value ) {
-
- // Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
- if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
- // Only keep the most recent entries
- delete cache[ keys.shift() ];
- }
- return ( cache[ key + " " ] = value );
- }
- return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
- fn[ expando ] = true;
- return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
- var el = document.createElement( "fieldset" );
-
- try {
- return !!fn( el );
- } catch ( e ) {
- return false;
- } finally {
-
- // Remove from its parent by default
- if ( el.parentNode ) {
- el.parentNode.removeChild( el );
- }
-
- // release memory in IE
- el = null;
- }
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
- var arr = attrs.split( "|" ),
- i = arr.length;
-
- while ( i-- ) {
- Expr.attrHandle[ arr[ i ] ] = handler;
- }
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
- var cur = b && a,
- diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
- a.sourceIndex - b.sourceIndex;
-
- // Use IE sourceIndex if available on both nodes
- if ( diff ) {
- return diff;
- }
-
- // Check if b follows a
- if ( cur ) {
- while ( ( cur = cur.nextSibling ) ) {
- if ( cur === b ) {
- return -1;
- }
- }
- }
-
- return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
- return function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return name === "input" && elem.type === type;
- };
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
- return function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return ( name === "input" || name === "button" ) && elem.type === type;
- };
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
- // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
- return function( elem ) {
-
- // Only certain elements can match :enabled or :disabled
- // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
- // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
- if ( "form" in elem ) {
-
- // Check for inherited disabledness on relevant non-disabled elements:
- // * listed form-associated elements in a disabled fieldset
- // https://html.spec.whatwg.org/multipage/forms.html#category-listed
- // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
- // * option elements in a disabled optgroup
- // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
- // All such elements have a "form" property.
- if ( elem.parentNode && elem.disabled === false ) {
-
- // Option elements defer to a parent optgroup if present
- if ( "label" in elem ) {
- if ( "label" in elem.parentNode ) {
- return elem.parentNode.disabled === disabled;
- } else {
- return elem.disabled === disabled;
- }
- }
-
- // Support: IE 6 - 11
- // Use the isDisabled shortcut property to check for disabled fieldset ancestors
- return elem.isDisabled === disabled ||
-
- // Where there is no isDisabled, check manually
- /* jshint -W018 */
- elem.isDisabled !== !disabled &&
- inDisabledFieldset( elem ) === disabled;
- }
-
- return elem.disabled === disabled;
-
- // Try to winnow out elements that can't be disabled before trusting the disabled property.
- // Some victims get caught in our net (label, legend, menu, track), but it shouldn't
- // even exist on them, let alone have a boolean value.
- } else if ( "label" in elem ) {
- return elem.disabled === disabled;
- }
-
- // Remaining elements are neither :enabled nor :disabled
- return false;
- };
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
- return markFunction( function( argument ) {
- argument = +argument;
- return markFunction( function( seed, matches ) {
- var j,
- matchIndexes = fn( [], seed.length, argument ),
- i = matchIndexes.length;
-
- // Match elements found at the specified indexes
- while ( i-- ) {
- if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
- seed[ j ] = !( matches[ j ] = seed[ j ] );
- }
- }
- } );
- } );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
- return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
- var namespace = elem && elem.namespaceURI,
- docElem = elem && ( elem.ownerDocument || elem ).documentElement;
-
- // Support: IE <=8
- // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
- // https://bugs.jquery.com/ticket/4833
- return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
- var hasCompare, subWindow,
- doc = node ? node.ownerDocument || node : preferredDoc;
-
- // Return early if doc is invalid or already selected
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
- return document;
- }
-
- // Update global variables
- document = doc;
- docElem = document.documentElement;
- documentIsHTML = !isXML( document );
-
- // Support: IE 9 - 11+, Edge 12 - 18+
- // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( preferredDoc != document &&
- ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
- // Support: IE 11, Edge
- if ( subWindow.addEventListener ) {
- subWindow.addEventListener( "unload", unloadHandler, false );
-
- // Support: IE 9 - 10 only
- } else if ( subWindow.attachEvent ) {
- subWindow.attachEvent( "onunload", unloadHandler );
- }
- }
-
- // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
- // Safari 4 - 5 only, Opera <=11.6 - 12.x only
- // IE/Edge & older browsers don't support the :scope pseudo-class.
- // Support: Safari 6.0 only
- // Safari 6.0 supports :scope but it's an alias of :root there.
- support.scope = assert( function( el ) {
- docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
- return typeof el.querySelectorAll !== "undefined" &&
- !el.querySelectorAll( ":scope fieldset div" ).length;
- } );
-
- /* Attributes
- ---------------------------------------------------------------------- */
-
- // Support: IE<8
- // Verify that getAttribute really returns attributes and not properties
- // (excepting IE8 booleans)
- support.attributes = assert( function( el ) {
- el.className = "i";
- return !el.getAttribute( "className" );
- } );
-
- /* getElement(s)By*
- ---------------------------------------------------------------------- */
-
- // Check if getElementsByTagName("*") returns only elements
- support.getElementsByTagName = assert( function( el ) {
- el.appendChild( document.createComment( "" ) );
- return !el.getElementsByTagName( "*" ).length;
- } );
-
- // Support: IE<9
- support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
- // Support: IE<10
- // Check if getElementById returns elements by name
- // The broken getElementById methods don't pick up programmatically-set names,
- // so use a roundabout getElementsByName test
- support.getById = assert( function( el ) {
- docElem.appendChild( el ).id = expando;
- return !document.getElementsByName || !document.getElementsByName( expando ).length;
- } );
-
- // ID filter and find
- if ( support.getById ) {
- Expr.filter[ "ID" ] = function( id ) {
- var attrId = id.replace( runescape, funescape );
- return function( elem ) {
- return elem.getAttribute( "id" ) === attrId;
- };
- };
- Expr.find[ "ID" ] = function( id, context ) {
- if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
- var elem = context.getElementById( id );
- return elem ? [ elem ] : [];
- }
- };
- } else {
- Expr.filter[ "ID" ] = function( id ) {
- var attrId = id.replace( runescape, funescape );
- return function( elem ) {
- var node = typeof elem.getAttributeNode !== "undefined" &&
- elem.getAttributeNode( "id" );
- return node && node.value === attrId;
- };
- };
-
- // Support: IE 6 - 7 only
- // getElementById is not reliable as a find shortcut
- Expr.find[ "ID" ] = function( id, context ) {
- if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
- var node, i, elems,
- elem = context.getElementById( id );
-
- if ( elem ) {
-
- // Verify the id attribute
- node = elem.getAttributeNode( "id" );
- if ( node && node.value === id ) {
- return [ elem ];
- }
-
- // Fall back on getElementsByName
- elems = context.getElementsByName( id );
- i = 0;
- while ( ( elem = elems[ i++ ] ) ) {
- node = elem.getAttributeNode( "id" );
- if ( node && node.value === id ) {
- return [ elem ];
- }
- }
- }
-
- return [];
- }
- };
- }
-
- // Tag
- Expr.find[ "TAG" ] = support.getElementsByTagName ?
- function( tag, context ) {
- if ( typeof context.getElementsByTagName !== "undefined" ) {
- return context.getElementsByTagName( tag );
-
- // DocumentFragment nodes don't have gEBTN
- } else if ( support.qsa ) {
- return context.querySelectorAll( tag );
- }
- } :
-
- function( tag, context ) {
- var elem,
- tmp = [],
- i = 0,
-
- // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
- results = context.getElementsByTagName( tag );
-
- // Filter out possible comments
- if ( tag === "*" ) {
- while ( ( elem = results[ i++ ] ) ) {
- if ( elem.nodeType === 1 ) {
- tmp.push( elem );
- }
- }
-
- return tmp;
- }
- return results;
- };
-
- // Class
- Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
- if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
- return context.getElementsByClassName( className );
- }
- };
-
- /* QSA/matchesSelector
- ---------------------------------------------------------------------- */
-
- // QSA and matchesSelector support
-
- // matchesSelector(:active) reports false when true (IE9/Opera 11.5)
- rbuggyMatches = [];
-
- // qSa(:focus) reports false when true (Chrome 21)
- // We allow this because of a bug in IE8/9 that throws an error
- // whenever `document.activeElement` is accessed on an iframe
- // So, we allow :focus to pass through QSA all the time to avoid the IE error
- // See https://bugs.jquery.com/ticket/13378
- rbuggyQSA = [];
-
- if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
- // Build QSA regex
- // Regex strategy adopted from Diego Perini
- assert( function( el ) {
-
- var input;
-
- // Select is set to empty string on purpose
- // This is to test IE's treatment of not explicitly
- // setting a boolean content attribute,
- // since its presence should be enough
- // https://bugs.jquery.com/ticket/12359
- docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
- "<select id='" + expando + "-\r\\' msallowcapture=''>" +
- "<option selected=''></option></select>";
-
- // Support: IE8, Opera 11-12.16
- // Nothing should be selected when empty strings follow ^= or $= or *=
- // The test attribute must be unknown in Opera but "safe" for WinRT
- // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
- if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
- rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
- }
-
- // Support: IE8
- // Boolean attributes and "value" are not treated correctly
- if ( !el.querySelectorAll( "[selected]" ).length ) {
- rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
- }
-
- // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
- if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
- rbuggyQSA.push( "~=" );
- }
-
- // Support: IE 11+, Edge 15 - 18+
- // IE 11/Edge don't find elements on a `[name='']` query in some cases.
- // Adding a temporary attribute to the document before the selection works
- // around the issue.
- // Interestingly, IE 10 & older don't seem to have the issue.
- input = document.createElement( "input" );
- input.setAttribute( "name", "" );
- el.appendChild( input );
- if ( !el.querySelectorAll( "[name='']" ).length ) {
- rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
- whitespace + "*(?:''|\"\")" );
- }
-
- // Webkit/Opera - :checked should return selected option elements
- // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
- // IE8 throws error here and will not see later tests
- if ( !el.querySelectorAll( ":checked" ).length ) {
- rbuggyQSA.push( ":checked" );
- }
-
- // Support: Safari 8+, iOS 8+
- // https://bugs.webkit.org/show_bug.cgi?id=136851
- // In-page `selector#id sibling-combinator selector` fails
- if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
- rbuggyQSA.push( ".#.+[+~]" );
- }
-
- // Support: Firefox <=3.6 - 5 only
- // Old Firefox doesn't throw on a badly-escaped identifier.
- el.querySelectorAll( "\\\f" );
- rbuggyQSA.push( "[\\r\\n\\f]" );
- } );
-
- assert( function( el ) {
- el.innerHTML = "<a href='' disabled='disabled'></a>" +
- "<select disabled='disabled'><option/></select>";
-
- // Support: Windows 8 Native Apps
- // The type and name attributes are restricted during .innerHTML assignment
- var input = document.createElement( "input" );
- input.setAttribute( "type", "hidden" );
- el.appendChild( input ).setAttribute( "name", "D" );
-
- // Support: IE8
- // Enforce case-sensitivity of name attribute
- if ( el.querySelectorAll( "[name=d]" ).length ) {
- rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
- }
-
- // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
- // IE8 throws error here and will not see later tests
- if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
- rbuggyQSA.push( ":enabled", ":disabled" );
- }
-
- // Support: IE9-11+
- // IE's :disabled selector does not pick up the children of disabled fieldsets
- docElem.appendChild( el ).disabled = true;
- if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
- rbuggyQSA.push( ":enabled", ":disabled" );
- }
-
- // Support: Opera 10 - 11 only
- // Opera 10-11 does not throw on post-comma invalid pseudos
- el.querySelectorAll( "*,:x" );
- rbuggyQSA.push( ",.*:" );
- } );
- }
-
- if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
- docElem.webkitMatchesSelector ||
- docElem.mozMatchesSelector ||
- docElem.oMatchesSelector ||
- docElem.msMatchesSelector ) ) ) ) {
-
- assert( function( el ) {
-
- // Check to see if it's possible to do matchesSelector
- // on a disconnected node (IE 9)
- support.disconnectedMatch = matches.call( el, "*" );
-
- // This should fail with an exception
- // Gecko does not error, returns false instead
- matches.call( el, "[s!='']:x" );
- rbuggyMatches.push( "!=", pseudos );
- } );
- }
-
- rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
- rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
- /* Contains
- ---------------------------------------------------------------------- */
- hasCompare = rnative.test( docElem.compareDocumentPosition );
-
- // Element contains another
- // Purposefully self-exclusive
- // As in, an element does not contain itself
- contains = hasCompare || rnative.test( docElem.contains ) ?
- function( a, b ) {
- var adown = a.nodeType === 9 ? a.documentElement : a,
- bup = b && b.parentNode;
- return a === bup || !!( bup && bup.nodeType === 1 && (
- adown.contains ?
- adown.contains( bup ) :
- a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
- ) );
- } :
- function( a, b ) {
- if ( b ) {
- while ( ( b = b.parentNode ) ) {
- if ( b === a ) {
- return true;
- }
- }
- }
- return false;
- };
-
- /* Sorting
- ---------------------------------------------------------------------- */
-
- // Document order sorting
- sortOrder = hasCompare ?
- function( a, b ) {
-
- // Flag for duplicate removal
- if ( a === b ) {
- hasDuplicate = true;
- return 0;
- }
-
- // Sort on method existence if only one input has compareDocumentPosition
- var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
- if ( compare ) {
- return compare;
- }
-
- // Calculate position if both inputs belong to the same document
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
- a.compareDocumentPosition( b ) :
-
- // Otherwise we know they are disconnected
- 1;
-
- // Disconnected nodes
- if ( compare & 1 ||
- ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
- // Choose the first element that is related to our preferred document
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( a == document || a.ownerDocument == preferredDoc &&
- contains( preferredDoc, a ) ) {
- return -1;
- }
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( b == document || b.ownerDocument == preferredDoc &&
- contains( preferredDoc, b ) ) {
- return 1;
- }
-
- // Maintain original order
- return sortInput ?
- ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
- 0;
- }
-
- return compare & 4 ? -1 : 1;
- } :
- function( a, b ) {
-
- // Exit early if the nodes are identical
- if ( a === b ) {
- hasDuplicate = true;
- return 0;
- }
-
- var cur,
- i = 0,
- aup = a.parentNode,
- bup = b.parentNode,
- ap = [ a ],
- bp = [ b ];
-
- // Parentless nodes are either documents or disconnected
- if ( !aup || !bup ) {
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- /* eslint-disable eqeqeq */
- return a == document ? -1 :
- b == document ? 1 :
- /* eslint-enable eqeqeq */
- aup ? -1 :
- bup ? 1 :
- sortInput ?
- ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
- 0;
-
- // If the nodes are siblings, we can do a quick check
- } else if ( aup === bup ) {
- return siblingCheck( a, b );
- }
-
- // Otherwise we need full lists of their ancestors for comparison
- cur = a;
- while ( ( cur = cur.parentNode ) ) {
- ap.unshift( cur );
- }
- cur = b;
- while ( ( cur = cur.parentNode ) ) {
- bp.unshift( cur );
- }
-
- // Walk down the tree looking for a discrepancy
- while ( ap[ i ] === bp[ i ] ) {
- i++;
- }
-
- return i ?
-
- // Do a sibling check if the nodes have a common ancestor
- siblingCheck( ap[ i ], bp[ i ] ) :
-
- // Otherwise nodes in our document sort first
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- /* eslint-disable eqeqeq */
- ap[ i ] == preferredDoc ? -1 :
- bp[ i ] == preferredDoc ? 1 :
- /* eslint-enable eqeqeq */
- 0;
- };
-
- return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
- return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
- setDocument( elem );
-
- if ( support.matchesSelector && documentIsHTML &&
- !nonnativeSelectorCache[ expr + " " ] &&
- ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
- ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) {
-
- try {
- var ret = matches.call( elem, expr );
-
- // IE 9's matchesSelector returns false on disconnected nodes
- if ( ret || support.disconnectedMatch ||
-
- // As well, disconnected nodes are said to be in a document
- // fragment in IE 9
- elem.document && elem.document.nodeType !== 11 ) {
- return ret;
- }
- } catch ( e ) {
- nonnativeSelectorCache( expr, true );
- }
- }
-
- return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
- // Set document vars if needed
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( ( context.ownerDocument || context ) != document ) {
- setDocument( context );
- }
- return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
- // Set document vars if needed
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( ( elem.ownerDocument || elem ) != document ) {
- setDocument( elem );
- }
-
- var fn = Expr.attrHandle[ name.toLowerCase() ],
-
- // Don't get fooled by Object.prototype properties (jQuery #13807)
- val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
- fn( elem, name, !documentIsHTML ) :
- undefined;
-
- return val !== undefined ?
- val :
- support.attributes || !documentIsHTML ?
- elem.getAttribute( name ) :
- ( val = elem.getAttributeNode( name ) ) && val.specified ?
- val.value :
- null;
-};
-
-Sizzle.escape = function( sel ) {
- return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
- throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
- var elem,
- duplicates = [],
- j = 0,
- i = 0;
-
- // Unless we *know* we can detect duplicates, assume their presence
- hasDuplicate = !support.detectDuplicates;
- sortInput = !support.sortStable && results.slice( 0 );
- results.sort( sortOrder );
-
- if ( hasDuplicate ) {
- while ( ( elem = results[ i++ ] ) ) {
- if ( elem === results[ i ] ) {
- j = duplicates.push( i );
- }
- }
- while ( j-- ) {
- results.splice( duplicates[ j ], 1 );
- }
- }
-
- // Clear input after sorting to release objects
- // See https://github.com/jquery/sizzle/pull/225
- sortInput = null;
-
- return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
- var node,
- ret = "",
- i = 0,
- nodeType = elem.nodeType;
-
- if ( !nodeType ) {
-
- // If no nodeType, this is expected to be an array
- while ( ( node = elem[ i++ ] ) ) {
-
- // Do not traverse comment nodes
- ret += getText( node );
- }
- } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
- // Use textContent for elements
- // innerText usage removed for consistency of new lines (jQuery #11153)
- if ( typeof elem.textContent === "string" ) {
- return elem.textContent;
- } else {
-
- // Traverse its children
- for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
- ret += getText( elem );
- }
- }
- } else if ( nodeType === 3 || nodeType === 4 ) {
- return elem.nodeValue;
- }
-
- // Do not include comment or processing instruction nodes
-
- return ret;
-};
-
-Expr = Sizzle.selectors = {
-
- // Can be adjusted by the user
- cacheLength: 50,
-
- createPseudo: markFunction,
-
- match: matchExpr,
-
- attrHandle: {},
-
- find: {},
-
- relative: {
- ">": { dir: "parentNode", first: true },
- " ": { dir: "parentNode" },
- "+": { dir: "previousSibling", first: true },
- "~": { dir: "previousSibling" }
- },
-
- preFilter: {
- "ATTR": function( match ) {
- match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
- // Move the given value to match[3] whether quoted or unquoted
- match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
- match[ 5 ] || "" ).replace( runescape, funescape );
-
- if ( match[ 2 ] === "~=" ) {
- match[ 3 ] = " " + match[ 3 ] + " ";
- }
-
- return match.slice( 0, 4 );
- },
-
- "CHILD": function( match ) {
-
- /* matches from matchExpr["CHILD"]
- 1 type (only|nth|...)
- 2 what (child|of-type)
- 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
- 4 xn-component of xn+y argument ([+-]?\d*n|)
- 5 sign of xn-component
- 6 x of xn-component
- 7 sign of y-component
- 8 y of y-component
- */
- match[ 1 ] = match[ 1 ].toLowerCase();
-
- if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
- // nth-* requires argument
- if ( !match[ 3 ] ) {
- Sizzle.error( match[ 0 ] );
- }
-
- // numeric x and y parameters for Expr.filter.CHILD
- // remember that false/true cast respectively to 0/1
- match[ 4 ] = +( match[ 4 ] ?
- match[ 5 ] + ( match[ 6 ] || 1 ) :
- 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
- match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
- // other types prohibit arguments
- } else if ( match[ 3 ] ) {
- Sizzle.error( match[ 0 ] );
- }
-
- return match;
- },
-
- "PSEUDO": function( match ) {
- var excess,
- unquoted = !match[ 6 ] && match[ 2 ];
-
- if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
- return null;
- }
-
- // Accept quoted arguments as-is
- if ( match[ 3 ] ) {
- match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
- // Strip excess characters from unquoted arguments
- } else if ( unquoted && rpseudo.test( unquoted ) &&
-
- // Get excess from tokenize (recursively)
- ( excess = tokenize( unquoted, true ) ) &&
-
- // advance to the next closing parenthesis
- ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
- // excess is a negative index
- match[ 0 ] = match[ 0 ].slice( 0, excess );
- match[ 2 ] = unquoted.slice( 0, excess );
- }
-
- // Return only captures needed by the pseudo filter method (type and argument)
- return match.slice( 0, 3 );
- }
- },
-
- filter: {
-
- "TAG": function( nodeNameSelector ) {
- var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
- return nodeNameSelector === "*" ?
- function() {
- return true;
- } :
- function( elem ) {
- return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
- };
- },
-
- "CLASS": function( className ) {
- var pattern = classCache[ className + " " ];
-
- return pattern ||
- ( pattern = new RegExp( "(^|" + whitespace +
- ")" + className + "(" + whitespace + "|$)" ) ) && classCache(
- className, function( elem ) {
- return pattern.test(
- typeof elem.className === "string" && elem.className ||
- typeof elem.getAttribute !== "undefined" &&
- elem.getAttribute( "class" ) ||
- ""
- );
- } );
- },
-
- "ATTR": function( name, operator, check ) {
- return function( elem ) {
- var result = Sizzle.attr( elem, name );
-
- if ( result == null ) {
- return operator === "!=";
- }
- if ( !operator ) {
- return true;
- }
-
- result += "";
-
- /* eslint-disable max-len */
-
- return operator === "=" ? result === check :
- operator === "!=" ? result !== check :
- operator === "^=" ? check && result.indexOf( check ) === 0 :
- operator === "*=" ? check && result.indexOf( check ) > -1 :
- operator === "$=" ? check && result.slice( -check.length ) === check :
- operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
- operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
- false;
- /* eslint-enable max-len */
-
- };
- },
-
- "CHILD": function( type, what, _argument, first, last ) {
- var simple = type.slice( 0, 3 ) !== "nth",
- forward = type.slice( -4 ) !== "last",
- ofType = what === "of-type";
-
- return first === 1 && last === 0 ?
-
- // Shortcut for :nth-*(n)
- function( elem ) {
- return !!elem.parentNode;
- } :
-
- function( elem, _context, xml ) {
- var cache, uniqueCache, outerCache, node, nodeIndex, start,
- dir = simple !== forward ? "nextSibling" : "previousSibling",
- parent = elem.parentNode,
- name = ofType && elem.nodeName.toLowerCase(),
- useCache = !xml && !ofType,
- diff = false;
-
- if ( parent ) {
-
- // :(first|last|only)-(child|of-type)
- if ( simple ) {
- while ( dir ) {
- node = elem;
- while ( ( node = node[ dir ] ) ) {
- if ( ofType ?
- node.nodeName.toLowerCase() === name :
- node.nodeType === 1 ) {
-
- return false;
- }
- }
-
- // Reverse direction for :only-* (if we haven't yet done so)
- start = dir = type === "only" && !start && "nextSibling";
- }
- return true;
- }
-
- start = [ forward ? parent.firstChild : parent.lastChild ];
-
- // non-xml :nth-child(...) stores cache data on `parent`
- if ( forward && useCache ) {
-
- // Seek `elem` from a previously-cached index
-
- // ...in a gzip-friendly way
- node = parent;
- outerCache = node[ expando ] || ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- cache = uniqueCache[ type ] || [];
- nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
- diff = nodeIndex && cache[ 2 ];
- node = nodeIndex && parent.childNodes[ nodeIndex ];
-
- while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
- // Fallback to seeking `elem` from the start
- ( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
- // When found, cache indexes on `parent` and break
- if ( node.nodeType === 1 && ++diff && node === elem ) {
- uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
- break;
- }
- }
-
- } else {
-
- // Use previously-cached element index if available
- if ( useCache ) {
-
- // ...in a gzip-friendly way
- node = elem;
- outerCache = node[ expando ] || ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- cache = uniqueCache[ type ] || [];
- nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
- diff = nodeIndex;
- }
-
- // xml :nth-child(...)
- // or :nth-last-child(...) or :nth(-last)?-of-type(...)
- if ( diff === false ) {
-
- // Use the same loop as above to seek `elem` from the start
- while ( ( node = ++nodeIndex && node && node[ dir ] ||
- ( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
- if ( ( ofType ?
- node.nodeName.toLowerCase() === name :
- node.nodeType === 1 ) &&
- ++diff ) {
-
- // Cache the index of each encountered element
- if ( useCache ) {
- outerCache = node[ expando ] ||
- ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- uniqueCache[ type ] = [ dirruns, diff ];
- }
-
- if ( node === elem ) {
- break;
- }
- }
- }
- }
- }
-
- // Incorporate the offset, then check against cycle size
- diff -= last;
- return diff === first || ( diff % first === 0 && diff / first >= 0 );
- }
- };
- },
-
- "PSEUDO": function( pseudo, argument ) {
-
- // pseudo-class names are case-insensitive
- // http://www.w3.org/TR/selectors/#pseudo-classes
- // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
- // Remember that setFilters inherits from pseudos
- var args,
- fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
- Sizzle.error( "unsupported pseudo: " + pseudo );
-
- // The user may use createPseudo to indicate that
- // arguments are needed to create the filter function
- // just as Sizzle does
- if ( fn[ expando ] ) {
- return fn( argument );
- }
-
- // But maintain support for old signatures
- if ( fn.length > 1 ) {
- args = [ pseudo, pseudo, "", argument ];
- return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
- markFunction( function( seed, matches ) {
- var idx,
- matched = fn( seed, argument ),
- i = matched.length;
- while ( i-- ) {
- idx = indexOf( seed, matched[ i ] );
- seed[ idx ] = !( matches[ idx ] = matched[ i ] );
- }
- } ) :
- function( elem ) {
- return fn( elem, 0, args );
- };
- }
-
- return fn;
- }
- },
-
- pseudos: {
-
- // Potentially complex pseudos
- "not": markFunction( function( selector ) {
-
- // Trim the selector passed to compile
- // to avoid treating leading and trailing
- // spaces as combinators
- var input = [],
- results = [],
- matcher = compile( selector.replace( rtrim, "$1" ) );
-
- return matcher[ expando ] ?
- markFunction( function( seed, matches, _context, xml ) {
- var elem,
- unmatched = matcher( seed, null, xml, [] ),
- i = seed.length;
-
- // Match elements unmatched by `matcher`
- while ( i-- ) {
- if ( ( elem = unmatched[ i ] ) ) {
- seed[ i ] = !( matches[ i ] = elem );
- }
- }
- } ) :
- function( elem, _context, xml ) {
- input[ 0 ] = elem;
- matcher( input, null, xml, results );
-
- // Don't keep the element (issue #299)
- input[ 0 ] = null;
- return !results.pop();
- };
- } ),
-
- "has": markFunction( function( selector ) {
- return function( elem ) {
- return Sizzle( selector, elem ).length > 0;
- };
- } ),
-
- "contains": markFunction( function( text ) {
- text = text.replace( runescape, funescape );
- return function( elem ) {
- return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
- };
- } ),
-
- // "Whether an element is represented by a :lang() selector
- // is based solely on the element's language value
- // being equal to the identifier C,
- // or beginning with the identifier C immediately followed by "-".
- // The matching of C against the element's language value is performed case-insensitively.
- // The identifier C does not have to be a valid language name."
- // http://www.w3.org/TR/selectors/#lang-pseudo
- "lang": markFunction( function( lang ) {
-
- // lang value must be a valid identifier
- if ( !ridentifier.test( lang || "" ) ) {
- Sizzle.error( "unsupported lang: " + lang );
- }
- lang = lang.replace( runescape, funescape ).toLowerCase();
- return function( elem ) {
- var elemLang;
- do {
- if ( ( elemLang = documentIsHTML ?
- elem.lang :
- elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
- elemLang = elemLang.toLowerCase();
- return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
- }
- } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
- return false;
- };
- } ),
-
- // Miscellaneous
- "target": function( elem ) {
- var hash = window.location && window.location.hash;
- return hash && hash.slice( 1 ) === elem.id;
- },
-
- "root": function( elem ) {
- return elem === docElem;
- },
-
- "focus": function( elem ) {
- return elem === document.activeElement &&
- ( !document.hasFocus || document.hasFocus() ) &&
- !!( elem.type || elem.href || ~elem.tabIndex );
- },
-
- // Boolean properties
- "enabled": createDisabledPseudo( false ),
- "disabled": createDisabledPseudo( true ),
-
- "checked": function( elem ) {
-
- // In CSS3, :checked should return both checked and selected elements
- // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
- var nodeName = elem.nodeName.toLowerCase();
- return ( nodeName === "input" && !!elem.checked ) ||
- ( nodeName === "option" && !!elem.selected );
- },
-
- "selected": function( elem ) {
-
- // Accessing this property makes selected-by-default
- // options in Safari work properly
- if ( elem.parentNode ) {
- // eslint-disable-next-line no-unused-expressions
- elem.parentNode.selectedIndex;
- }
-
- return elem.selected === true;
- },
-
- // Contents
- "empty": function( elem ) {
-
- // http://www.w3.org/TR/selectors/#empty-pseudo
- // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
- // but not by others (comment: 8; processing instruction: 7; etc.)
- // nodeType < 6 works because attributes (2) do not appear as children
- for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
- if ( elem.nodeType < 6 ) {
- return false;
- }
- }
- return true;
- },
-
- "parent": function( elem ) {
- return !Expr.pseudos[ "empty" ]( elem );
- },
-
- // Element/input types
- "header": function( elem ) {
- return rheader.test( elem.nodeName );
- },
-
- "input": function( elem ) {
- return rinputs.test( elem.nodeName );
- },
-
- "button": function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return name === "input" && elem.type === "button" || name === "button";
- },
-
- "text": function( elem ) {
- var attr;
- return elem.nodeName.toLowerCase() === "input" &&
- elem.type === "text" &&
-
- // Support: IE<8
- // New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
- ( ( attr = elem.getAttribute( "type" ) ) == null ||
- attr.toLowerCase() === "text" );
- },
-
- // Position-in-collection
- "first": createPositionalPseudo( function() {
- return [ 0 ];
- } ),
-
- "last": createPositionalPseudo( function( _matchIndexes, length ) {
- return [ length - 1 ];
- } ),
-
- "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
- return [ argument < 0 ? argument + length : argument ];
- } ),
-
- "even": createPositionalPseudo( function( matchIndexes, length ) {
- var i = 0;
- for ( ; i < length; i += 2 ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "odd": createPositionalPseudo( function( matchIndexes, length ) {
- var i = 1;
- for ( ; i < length; i += 2 ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
- var i = argument < 0 ?
- argument + length :
- argument > length ?
- length :
- argument;
- for ( ; --i >= 0; ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
- var i = argument < 0 ? argument + length : argument;
- for ( ; ++i < length; ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } )
- }
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
- Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
- Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
- var matched, match, tokens, type,
- soFar, groups, preFilters,
- cached = tokenCache[ selector + " " ];
-
- if ( cached ) {
- return parseOnly ? 0 : cached.slice( 0 );
- }
-
- soFar = selector;
- groups = [];
- preFilters = Expr.preFilter;
-
- while ( soFar ) {
-
- // Comma and first run
- if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
- if ( match ) {
-
- // Don't consume trailing commas as valid
- soFar = soFar.slice( match[ 0 ].length ) || soFar;
- }
- groups.push( ( tokens = [] ) );
- }
-
- matched = false;
-
- // Combinators
- if ( ( match = rcombinators.exec( soFar ) ) ) {
- matched = match.shift();
- tokens.push( {
- value: matched,
-
- // Cast descendant combinators to space
- type: match[ 0 ].replace( rtrim, " " )
- } );
- soFar = soFar.slice( matched.length );
- }
-
- // Filters
- for ( type in Expr.filter ) {
- if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
- ( match = preFilters[ type ]( match ) ) ) ) {
- matched = match.shift();
- tokens.push( {
- value: matched,
- type: type,
- matches: match
- } );
- soFar = soFar.slice( matched.length );
- }
- }
-
- if ( !matched ) {
- break;
- }
- }
-
- // Return the length of the invalid excess
- // if we're just parsing
- // Otherwise, throw an error or return tokens
- return parseOnly ?
- soFar.length :
- soFar ?
- Sizzle.error( selector ) :
-
- // Cache the tokens
- tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
- var i = 0,
- len = tokens.length,
- selector = "";
- for ( ; i < len; i++ ) {
- selector += tokens[ i ].value;
- }
- return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
- var dir = combinator.dir,
- skip = combinator.next,
- key = skip || dir,
- checkNonElements = base && key === "parentNode",
- doneName = done++;
-
- return combinator.first ?
-
- // Check against closest ancestor/preceding element
- function( elem, context, xml ) {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- return matcher( elem, context, xml );
- }
- }
- return false;
- } :
-
- // Check against all ancestor/preceding elements
- function( elem, context, xml ) {
- var oldCache, uniqueCache, outerCache,
- newCache = [ dirruns, doneName ];
-
- // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
- if ( xml ) {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- if ( matcher( elem, context, xml ) ) {
- return true;
- }
- }
- }
- } else {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ elem.uniqueID ] ||
- ( outerCache[ elem.uniqueID ] = {} );
-
- if ( skip && skip === elem.nodeName.toLowerCase() ) {
- elem = elem[ dir ] || elem;
- } else if ( ( oldCache = uniqueCache[ key ] ) &&
- oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
- // Assign to newCache so results back-propagate to previous elements
- return ( newCache[ 2 ] = oldCache[ 2 ] );
- } else {
-
- // Reuse newcache so results back-propagate to previous elements
- uniqueCache[ key ] = newCache;
-
- // A match means we're done; a fail means we have to keep checking
- if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
- return true;
- }
- }
- }
- }
- }
- return false;
- };
-}
-
-function elementMatcher( matchers ) {
- return matchers.length > 1 ?
- function( elem, context, xml ) {
- var i = matchers.length;
- while ( i-- ) {
- if ( !matchers[ i ]( elem, context, xml ) ) {
- return false;
- }
- }
- return true;
- } :
- matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
- var i = 0,
- len = contexts.length;
- for ( ; i < len; i++ ) {
- Sizzle( selector, contexts[ i ], results );
- }
- return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
- var elem,
- newUnmatched = [],
- i = 0,
- len = unmatched.length,
- mapped = map != null;
-
- for ( ; i < len; i++ ) {
- if ( ( elem = unmatched[ i ] ) ) {
- if ( !filter || filter( elem, context, xml ) ) {
- newUnmatched.push( elem );
- if ( mapped ) {
- map.push( i );
- }
- }
- }
- }
-
- return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
- if ( postFilter && !postFilter[ expando ] ) {
- postFilter = setMatcher( postFilter );
- }
- if ( postFinder && !postFinder[ expando ] ) {
- postFinder = setMatcher( postFinder, postSelector );
- }
- return markFunction( function( seed, results, context, xml ) {
- var temp, i, elem,
- preMap = [],
- postMap = [],
- preexisting = results.length,
-
- // Get initial elements from seed or context
- elems = seed || multipleContexts(
- selector || "*",
- context.nodeType ? [ context ] : context,
- []
- ),
-
- // Prefilter to get matcher input, preserving a map for seed-results synchronization
- matcherIn = preFilter && ( seed || !selector ) ?
- condense( elems, preMap, preFilter, context, xml ) :
- elems,
-
- matcherOut = matcher ?
-
- // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
- postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
- // ...intermediate processing is necessary
- [] :
-
- // ...otherwise use results directly
- results :
- matcherIn;
-
- // Find primary matches
- if ( matcher ) {
- matcher( matcherIn, matcherOut, context, xml );
- }
-
- // Apply postFilter
- if ( postFilter ) {
- temp = condense( matcherOut, postMap );
- postFilter( temp, [], context, xml );
-
- // Un-match failing elements by moving them back to matcherIn
- i = temp.length;
- while ( i-- ) {
- if ( ( elem = temp[ i ] ) ) {
- matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
- }
- }
- }
-
- if ( seed ) {
- if ( postFinder || preFilter ) {
- if ( postFinder ) {
-
- // Get the final matcherOut by condensing this intermediate into postFinder contexts
- temp = [];
- i = matcherOut.length;
- while ( i-- ) {
- if ( ( elem = matcherOut[ i ] ) ) {
-
- // Restore matcherIn since elem is not yet a final match
- temp.push( ( matcherIn[ i ] = elem ) );
- }
- }
- postFinder( null, ( matcherOut = [] ), temp, xml );
- }
-
- // Move matched elements from seed to results to keep them synchronized
- i = matcherOut.length;
- while ( i-- ) {
- if ( ( elem = matcherOut[ i ] ) &&
- ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
- seed[ temp ] = !( results[ temp ] = elem );
- }
- }
- }
-
- // Add elements to results, through postFinder if defined
- } else {
- matcherOut = condense(
- matcherOut === results ?
- matcherOut.splice( preexisting, matcherOut.length ) :
- matcherOut
- );
- if ( postFinder ) {
- postFinder( null, results, matcherOut, xml );
- } else {
- push.apply( results, matcherOut );
- }
- }
- } );
-}
-
-function matcherFromTokens( tokens ) {
- var checkContext, matcher, j,
- len = tokens.length,
- leadingRelative = Expr.relative[ tokens[ 0 ].type ],
- implicitRelative = leadingRelative || Expr.relative[ " " ],
- i = leadingRelative ? 1 : 0,
-
- // The foundational matcher ensures that elements are reachable from top-level context(s)
- matchContext = addCombinator( function( elem ) {
- return elem === checkContext;
- }, implicitRelative, true ),
- matchAnyContext = addCombinator( function( elem ) {
- return indexOf( checkContext, elem ) > -1;
- }, implicitRelative, true ),
- matchers = [ function( elem, context, xml ) {
- var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
- ( checkContext = context ).nodeType ?
- matchContext( elem, context, xml ) :
- matchAnyContext( elem, context, xml ) );
-
- // Avoid hanging onto element (issue #299)
- checkContext = null;
- return ret;
- } ];
-
- for ( ; i < len; i++ ) {
- if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
- matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
- } else {
- matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
- // Return special upon seeing a positional matcher
- if ( matcher[ expando ] ) {
-
- // Find the next relative operator (if any) for proper handling
- j = ++i;
- for ( ; j < len; j++ ) {
- if ( Expr.relative[ tokens[ j ].type ] ) {
- break;
- }
- }
- return setMatcher(
- i > 1 && elementMatcher( matchers ),
- i > 1 && toSelector(
-
- // If the preceding token was a descendant combinator, insert an implicit any-element `*`
- tokens
- .slice( 0, i - 1 )
- .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
- ).replace( rtrim, "$1" ),
- matcher,
- i < j && matcherFromTokens( tokens.slice( i, j ) ),
- j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
- j < len && toSelector( tokens )
- );
- }
- matchers.push( matcher );
- }
- }
-
- return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
- var bySet = setMatchers.length > 0,
- byElement = elementMatchers.length > 0,
- superMatcher = function( seed, context, xml, results, outermost ) {
- var elem, j, matcher,
- matchedCount = 0,
- i = "0",
- unmatched = seed && [],
- setMatched = [],
- contextBackup = outermostContext,
-
- // We must always have either seed elements or outermost context
- elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
- // Use integer dirruns iff this is the outermost matcher
- dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
- len = elems.length;
-
- if ( outermost ) {
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- outermostContext = context == document || context || outermost;
- }
-
- // Add elements passing elementMatchers directly to results
- // Support: IE<9, Safari
- // Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
- for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
- if ( byElement && elem ) {
- j = 0;
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( !context && elem.ownerDocument != document ) {
- setDocument( elem );
- xml = !documentIsHTML;
- }
- while ( ( matcher = elementMatchers[ j++ ] ) ) {
- if ( matcher( elem, context || document, xml ) ) {
- results.push( elem );
- break;
- }
- }
- if ( outermost ) {
- dirruns = dirrunsUnique;
- }
- }
-
- // Track unmatched elements for set filters
- if ( bySet ) {
-
- // They will have gone through all possible matchers
- if ( ( elem = !matcher && elem ) ) {
- matchedCount--;
- }
-
- // Lengthen the array for every element, matched or not
- if ( seed ) {
- unmatched.push( elem );
- }
- }
- }
-
- // `i` is now the count of elements visited above, and adding it to `matchedCount`
- // makes the latter nonnegative.
- matchedCount += i;
-
- // Apply set filters to unmatched elements
- // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
- // equals `i`), unless we didn't visit _any_ elements in the above loop because we have
- // no element matchers and no seed.
- // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
- // case, which will result in a "00" `matchedCount` that differs from `i` but is also
- // numerically zero.
- if ( bySet && i !== matchedCount ) {
- j = 0;
- while ( ( matcher = setMatchers[ j++ ] ) ) {
- matcher( unmatched, setMatched, context, xml );
- }
-
- if ( seed ) {
-
- // Reintegrate element matches to eliminate the need for sorting
- if ( matchedCount > 0 ) {
- while ( i-- ) {
- if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
- setMatched[ i ] = pop.call( results );
- }
- }
- }
-
- // Discard index placeholder values to get only actual matches
- setMatched = condense( setMatched );
- }
-
- // Add matches to results
- push.apply( results, setMatched );
-
- // Seedless set matches succeeding multiple successful matchers stipulate sorting
- if ( outermost && !seed && setMatched.length > 0 &&
- ( matchedCount + setMatchers.length ) > 1 ) {
-
- Sizzle.uniqueSort( results );
- }
- }
-
- // Override manipulation of globals by nested matchers
- if ( outermost ) {
- dirruns = dirrunsUnique;
- outermostContext = contextBackup;
- }
-
- return unmatched;
- };
-
- return bySet ?
- markFunction( superMatcher ) :
- superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
- var i,
- setMatchers = [],
- elementMatchers = [],
- cached = compilerCache[ selector + " " ];
-
- if ( !cached ) {
-
- // Generate a function of recursive functions that can be used to check each element
- if ( !match ) {
- match = tokenize( selector );
- }
- i = match.length;
- while ( i-- ) {
- cached = matcherFromTokens( match[ i ] );
- if ( cached[ expando ] ) {
- setMatchers.push( cached );
- } else {
- elementMatchers.push( cached );
- }
- }
-
- // Cache the compiled function
- cached = compilerCache(
- selector,
- matcherFromGroupMatchers( elementMatchers, setMatchers )
- );
-
- // Save selector and tokenization
- cached.selector = selector;
- }
- return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- * selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- * selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
- var i, tokens, token, type, find,
- compiled = typeof selector === "function" && selector,
- match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
- results = results || [];
-
- // Try to minimize operations if there is only one selector in the list and no seed
- // (the latter of which guarantees us context)
- if ( match.length === 1 ) {
-
- // Reduce context if the leading compound selector is an ID
- tokens = match[ 0 ] = match[ 0 ].slice( 0 );
- if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
- context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
- context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
- .replace( runescape, funescape ), context ) || [] )[ 0 ];
- if ( !context ) {
- return results;
-
- // Precompiled matchers will still verify ancestry, so step up a level
- } else if ( compiled ) {
- context = context.parentNode;
- }
-
- selector = selector.slice( tokens.shift().value.length );
- }
-
- // Fetch a seed set for right-to-left matching
- i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
- while ( i-- ) {
- token = tokens[ i ];
-
- // Abort if we hit a combinator
- if ( Expr.relative[ ( type = token.type ) ] ) {
- break;
- }
- if ( ( find = Expr.find[ type ] ) ) {
-
- // Search, expanding context for leading sibling combinators
- if ( ( seed = find(
- token.matches[ 0 ].replace( runescape, funescape ),
- rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
- context
- ) ) ) {
-
- // If seed is empty or no tokens remain, we can return early
- tokens.splice( i, 1 );
- selector = seed.length && toSelector( tokens );
- if ( !selector ) {
- push.apply( results, seed );
- return results;
- }
-
- break;
- }
- }
- }
- }
-
- // Compile and execute a filtering function if one is not provided
- // Provide `match` to avoid retokenization if we modified the selector above
- ( compiled || compile( selector, match ) )(
- seed,
- context,
- !documentIsHTML,
- results,
- !context || rsibling.test( selector ) && testContext( context.parentNode ) || context
- );
- return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
- // Should return 1, but returns 4 (following)
- return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
- el.innerHTML = "<a href='#'></a>";
- return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
- addHandle( "type|href|height|width", function( elem, name, isXML ) {
- if ( !isXML ) {
- return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
- }
- } );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
- el.innerHTML = "<input/>";
- el.firstChild.setAttribute( "value", "" );
- return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
- addHandle( "value", function( elem, _name, isXML ) {
- if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
- return elem.defaultValue;
- }
- } );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
- return el.getAttribute( "disabled" ) == null;
-} ) ) {
- addHandle( booleans, function( elem, name, isXML ) {
- var val;
- if ( !isXML ) {
- return elem[ name ] === true ? name.toLowerCase() :
- ( val = elem.getAttributeNode( name ) ) && val.specified ?
- val.value :
- null;
- }
- } );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
- var matched = [],
- truncate = until !== undefined;
-
- while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
- if ( elem.nodeType === 1 ) {
- if ( truncate && jQuery( elem ).is( until ) ) {
- break;
- }
- matched.push( elem );
- }
- }
- return matched;
-};
-
-
-var siblings = function( n, elem ) {
- var matched = [];
-
- for ( ; n; n = n.nextSibling ) {
- if ( n.nodeType === 1 && n !== elem ) {
- matched.push( n );
- }
- }
-
- return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
- return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-}
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
- if ( isFunction( qualifier ) ) {
- return jQuery.grep( elements, function( elem, i ) {
- return !!qualifier.call( elem, i, elem ) !== not;
- } );
- }
-
- // Single element
- if ( qualifier.nodeType ) {
- return jQuery.grep( elements, function( elem ) {
- return ( elem === qualifier ) !== not;
- } );
- }
-
- // Arraylike of elements (jQuery, arguments, Array)
- if ( typeof qualifier !== "string" ) {
- return jQuery.grep( elements, function( elem ) {
- return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
- } );
- }
-
- // Filtered directly for both simple and complex selectors
- return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
- var elem = elems[ 0 ];
-
- if ( not ) {
- expr = ":not(" + expr + ")";
- }
-
- if ( elems.length === 1 && elem.nodeType === 1 ) {
- return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
- }
-
- return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
- return elem.nodeType === 1;
- } ) );
-};
-
-jQuery.fn.extend( {
- find: function( selector ) {
- var i, ret,
- len = this.length,
- self = this;
-
- if ( typeof selector !== "string" ) {
- return this.pushStack( jQuery( selector ).filter( function() {
- for ( i = 0; i < len; i++ ) {
- if ( jQuery.contains( self[ i ], this ) ) {
- return true;
- }
- }
- } ) );
- }
-
- ret = this.pushStack( [] );
-
- for ( i = 0; i < len; i++ ) {
- jQuery.find( selector, self[ i ], ret );
- }
-
- return len > 1 ? jQuery.uniqueSort( ret ) : ret;
- },
- filter: function( selector ) {
- return this.pushStack( winnow( this, selector || [], false ) );
- },
- not: function( selector ) {
- return this.pushStack( winnow( this, selector || [], true ) );
- },
- is: function( selector ) {
- return !!winnow(
- this,
-
- // If this is a positional/relative selector, check membership in the returned set
- // so $("p:first").is("p:last") won't return true for a doc with two "p".
- typeof selector === "string" && rneedsContext.test( selector ) ?
- jQuery( selector ) :
- selector || [],
- false
- ).length;
- }
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
- // A simple way to check for HTML strings
- // Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
- // Strict HTML recognition (#11290: must start with <)
- // Shortcut simple #id case for speed
- rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
- init = jQuery.fn.init = function( selector, context, root ) {
- var match, elem;
-
- // HANDLE: $(""), $(null), $(undefined), $(false)
- if ( !selector ) {
- return this;
- }
-
- // Method init() accepts an alternate rootjQuery
- // so migrate can support jQuery.sub (gh-2101)
- root = root || rootjQuery;
-
- // Handle HTML strings
- if ( typeof selector === "string" ) {
- if ( selector[ 0 ] === "<" &&
- selector[ selector.length - 1 ] === ">" &&
- selector.length >= 3 ) {
-
- // Assume that strings that start and end with <> are HTML and skip the regex check
- match = [ null, selector, null ];
-
- } else {
- match = rquickExpr.exec( selector );
- }
-
- // Match html or make sure no context is specified for #id
- if ( match && ( match[ 1 ] || !context ) ) {
-
- // HANDLE: $(html) -> $(array)
- if ( match[ 1 ] ) {
- context = context instanceof jQuery ? context[ 0 ] : context;
-
- // Option to run scripts is true for back-compat
- // Intentionally let the error be thrown if parseHTML is not present
- jQuery.merge( this, jQuery.parseHTML(
- match[ 1 ],
- context && context.nodeType ? context.ownerDocument || context : document,
- true
- ) );
-
- // HANDLE: $(html, props)
- if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
- for ( match in context ) {
-
- // Properties of context are called as methods if possible
- if ( isFunction( this[ match ] ) ) {
- this[ match ]( context[ match ] );
-
- // ...and otherwise set as attributes
- } else {
- this.attr( match, context[ match ] );
- }
- }
- }
-
- return this;
-
- // HANDLE: $(#id)
- } else {
- elem = document.getElementById( match[ 2 ] );
-
- if ( elem ) {
-
- // Inject the element directly into the jQuery object
- this[ 0 ] = elem;
- this.length = 1;
- }
- return this;
- }
-
- // HANDLE: $(expr, $(...))
- } else if ( !context || context.jquery ) {
- return ( context || root ).find( selector );
-
- // HANDLE: $(expr, context)
- // (which is just equivalent to: $(context).find(expr)
- } else {
- return this.constructor( context ).find( selector );
- }
-
- // HANDLE: $(DOMElement)
- } else if ( selector.nodeType ) {
- this[ 0 ] = selector;
- this.length = 1;
- return this;
-
- // HANDLE: $(function)
- // Shortcut for document ready
- } else if ( isFunction( selector ) ) {
- return root.ready !== undefined ?
- root.ready( selector ) :
-
- // Execute immediately if ready is not present
- selector( jQuery );
- }
-
- return jQuery.makeArray( selector, this );
- };
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
- // Methods guaranteed to produce a unique set when starting from a unique set
- guaranteedUnique = {
- children: true,
- contents: true,
- next: true,
- prev: true
- };
-
-jQuery.fn.extend( {
- has: function( target ) {
- var targets = jQuery( target, this ),
- l = targets.length;
-
- return this.filter( function() {
- var i = 0;
- for ( ; i < l; i++ ) {
- if ( jQuery.contains( this, targets[ i ] ) ) {
- return true;
- }
- }
- } );
- },
-
- closest: function( selectors, context ) {
- var cur,
- i = 0,
- l = this.length,
- matched = [],
- targets = typeof selectors !== "string" && jQuery( selectors );
-
- // Positional selectors never match, since there's no _selection_ context
- if ( !rneedsContext.test( selectors ) ) {
- for ( ; i < l; i++ ) {
- for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
- // Always skip document fragments
- if ( cur.nodeType < 11 && ( targets ?
- targets.index( cur ) > -1 :
-
- // Don't pass non-elements to Sizzle
- cur.nodeType === 1 &&
- jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
- matched.push( cur );
- break;
- }
- }
- }
- }
-
- return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
- },
-
- // Determine the position of an element within the set
- index: function( elem ) {
-
- // No argument, return index in parent
- if ( !elem ) {
- return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
- }
-
- // Index in selector
- if ( typeof elem === "string" ) {
- return indexOf.call( jQuery( elem ), this[ 0 ] );
- }
-
- // Locate the position of the desired element
- return indexOf.call( this,
-
- // If it receives a jQuery object, the first element is used
- elem.jquery ? elem[ 0 ] : elem
- );
- },
-
- add: function( selector, context ) {
- return this.pushStack(
- jQuery.uniqueSort(
- jQuery.merge( this.get(), jQuery( selector, context ) )
- )
- );
- },
-
- addBack: function( selector ) {
- return this.add( selector == null ?
- this.prevObject : this.prevObject.filter( selector )
- );
- }
-} );
-
-function sibling( cur, dir ) {
- while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
- return cur;
-}
-
-jQuery.each( {
- parent: function( elem ) {
- var parent = elem.parentNode;
- return parent && parent.nodeType !== 11 ? parent : null;
- },
- parents: function( elem ) {
- return dir( elem, "parentNode" );
- },
- parentsUntil: function( elem, _i, until ) {
- return dir( elem, "parentNode", until );
- },
- next: function( elem ) {
- return sibling( elem, "nextSibling" );
- },
- prev: function( elem ) {
- return sibling( elem, "previousSibling" );
- },
- nextAll: function( elem ) {
- return dir( elem, "nextSibling" );
- },
- prevAll: function( elem ) {
- return dir( elem, "previousSibling" );
- },
- nextUntil: function( elem, _i, until ) {
- return dir( elem, "nextSibling", until );
- },
- prevUntil: function( elem, _i, until ) {
- return dir( elem, "previousSibling", until );
- },
- siblings: function( elem ) {
- return siblings( ( elem.parentNode || {} ).firstChild, elem );
- },
- children: function( elem ) {
- return siblings( elem.firstChild );
- },
- contents: function( elem ) {
- if ( elem.contentDocument != null &&
-
- // Support: IE 11+
- // <object> elements with no `data` attribute has an object
- // `contentDocument` with a `null` prototype.
- getProto( elem.contentDocument ) ) {
-
- return elem.contentDocument;
- }
-
- // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
- // Treat the template element as a regular one in browsers that
- // don't support it.
- if ( nodeName( elem, "template" ) ) {
- elem = elem.content || elem;
- }
-
- return jQuery.merge( [], elem.childNodes );
- }
-}, function( name, fn ) {
- jQuery.fn[ name ] = function( until, selector ) {
- var matched = jQuery.map( this, fn, until );
-
- if ( name.slice( -5 ) !== "Until" ) {
- selector = until;
- }
-
- if ( selector && typeof selector === "string" ) {
- matched = jQuery.filter( selector, matched );
- }
-
- if ( this.length > 1 ) {
-
- // Remove duplicates
- if ( !guaranteedUnique[ name ] ) {
- jQuery.uniqueSort( matched );
- }
-
- // Reverse order for parents* and prev-derivatives
- if ( rparentsprev.test( name ) ) {
- matched.reverse();
- }
- }
-
- return this.pushStack( matched );
- };
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
- var object = {};
- jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
- object[ flag ] = true;
- } );
- return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- * options: an optional list of space-separated options that will change how
- * the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- * once: will ensure the callback list can only be fired once (like a Deferred)
- *
- * memory: will keep track of previous values and will call any callback added
- * after the list has been fired right away with the latest "memorized"
- * values (like a Deferred)
- *
- * unique: will ensure a callback can only be added once (no duplicate in the list)
- *
- * stopOnFalse: interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
- // Convert options from String-formatted to Object-formatted if needed
- // (we check in cache first)
- options = typeof options === "string" ?
- createOptions( options ) :
- jQuery.extend( {}, options );
-
- var // Flag to know if list is currently firing
- firing,
-
- // Last fire value for non-forgettable lists
- memory,
-
- // Flag to know if list was already fired
- fired,
-
- // Flag to prevent firing
- locked,
-
- // Actual callback list
- list = [],
-
- // Queue of execution data for repeatable lists
- queue = [],
-
- // Index of currently firing callback (modified by add/remove as needed)
- firingIndex = -1,
-
- // Fire callbacks
- fire = function() {
-
- // Enforce single-firing
- locked = locked || options.once;
-
- // Execute callbacks for all pending executions,
- // respecting firingIndex overrides and runtime changes
- fired = firing = true;
- for ( ; queue.length; firingIndex = -1 ) {
- memory = queue.shift();
- while ( ++firingIndex < list.length ) {
-
- // Run callback and check for early termination
- if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
- options.stopOnFalse ) {
-
- // Jump to end and forget the data so .add doesn't re-fire
- firingIndex = list.length;
- memory = false;
- }
- }
- }
-
- // Forget the data if we're done with it
- if ( !options.memory ) {
- memory = false;
- }
-
- firing = false;
-
- // Clean up if we're done firing for good
- if ( locked ) {
-
- // Keep an empty list if we have data for future add calls
- if ( memory ) {
- list = [];
-
- // Otherwise, this object is spent
- } else {
- list = "";
- }
- }
- },
-
- // Actual Callbacks object
- self = {
-
- // Add a callback or a collection of callbacks to the list
- add: function() {
- if ( list ) {
-
- // If we have memory from a past run, we should fire after adding
- if ( memory && !firing ) {
- firingIndex = list.length - 1;
- queue.push( memory );
- }
-
- ( function add( args ) {
- jQuery.each( args, function( _, arg ) {
- if ( isFunction( arg ) ) {
- if ( !options.unique || !self.has( arg ) ) {
- list.push( arg );
- }
- } else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
- // Inspect recursively
- add( arg );
- }
- } );
- } )( arguments );
-
- if ( memory && !firing ) {
- fire();
- }
- }
- return this;
- },
-
- // Remove a callback from the list
- remove: function() {
- jQuery.each( arguments, function( _, arg ) {
- var index;
- while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
- list.splice( index, 1 );
-
- // Handle firing indexes
- if ( index <= firingIndex ) {
- firingIndex--;
- }
- }
- } );
- return this;
- },
-
- // Check if a given callback is in the list.
- // If no argument is given, return whether or not list has callbacks attached.
- has: function( fn ) {
- return fn ?
- jQuery.inArray( fn, list ) > -1 :
- list.length > 0;
- },
-
- // Remove all callbacks from the list
- empty: function() {
- if ( list ) {
- list = [];
- }
- return this;
- },
-
- // Disable .fire and .add
- // Abort any current/pending executions
- // Clear all callbacks and values
- disable: function() {
- locked = queue = [];
- list = memory = "";
- return this;
- },
- disabled: function() {
- return !list;
- },
-
- // Disable .fire
- // Also disable .add unless we have memory (since it would have no effect)
- // Abort any pending executions
- lock: function() {
- locked = queue = [];
- if ( !memory && !firing ) {
- list = memory = "";
- }
- return this;
- },
- locked: function() {
- return !!locked;
- },
-
- // Call all callbacks with the given context and arguments
- fireWith: function( context, args ) {
- if ( !locked ) {
- args = args || [];
- args = [ context, args.slice ? args.slice() : args ];
- queue.push( args );
- if ( !firing ) {
- fire();
- }
- }
- return this;
- },
-
- // Call all the callbacks with the given arguments
- fire: function() {
- self.fireWith( this, arguments );
- return this;
- },
-
- // To know if the callbacks have already been called at least once
- fired: function() {
- return !!fired;
- }
- };
-
- return self;
-};
-
-
-function Identity( v ) {
- return v;
-}
-function Thrower( ex ) {
- throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
- var method;
-
- try {
-
- // Check for promise aspect first to privilege synchronous behavior
- if ( value && isFunction( ( method = value.promise ) ) ) {
- method.call( value ).done( resolve ).fail( reject );
-
- // Other thenables
- } else if ( value && isFunction( ( method = value.then ) ) ) {
- method.call( value, resolve, reject );
-
- // Other non-thenables
- } else {
-
- // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
- // * false: [ value ].slice( 0 ) => resolve( value )
- // * true: [ value ].slice( 1 ) => resolve()
- resolve.apply( undefined, [ value ].slice( noValue ) );
- }
-
- // For Promises/A+, convert exceptions into rejections
- // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
- // Deferred#then to conditionally suppress rejection.
- } catch ( value ) {
-
- // Support: Android 4.0 only
- // Strict mode functions invoked without .call/.apply get global-object context
- reject.apply( undefined, [ value ] );
- }
-}
-
-jQuery.extend( {
-
- Deferred: function( func ) {
- var tuples = [
-
- // action, add listener, callbacks,
- // ... .then handlers, argument index, [final state]
- [ "notify", "progress", jQuery.Callbacks( "memory" ),
- jQuery.Callbacks( "memory" ), 2 ],
- [ "resolve", "done", jQuery.Callbacks( "once memory" ),
- jQuery.Callbacks( "once memory" ), 0, "resolved" ],
- [ "reject", "fail", jQuery.Callbacks( "once memory" ),
- jQuery.Callbacks( "once memory" ), 1, "rejected" ]
- ],
- state = "pending",
- promise = {
- state: function() {
- return state;
- },
- always: function() {
- deferred.done( arguments ).fail( arguments );
- return this;
- },
- "catch": function( fn ) {
- return promise.then( null, fn );
- },
-
- // Keep pipe for back-compat
- pipe: function( /* fnDone, fnFail, fnProgress */ ) {
- var fns = arguments;
-
- return jQuery.Deferred( function( newDefer ) {
- jQuery.each( tuples, function( _i, tuple ) {
-
- // Map tuples (progress, done, fail) to arguments (done, fail, progress)
- var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
- // deferred.progress(function() { bind to newDefer or newDefer.notify })
- // deferred.done(function() { bind to newDefer or newDefer.resolve })
- // deferred.fail(function() { bind to newDefer or newDefer.reject })
- deferred[ tuple[ 1 ] ]( function() {
- var returned = fn && fn.apply( this, arguments );
- if ( returned && isFunction( returned.promise ) ) {
- returned.promise()
- .progress( newDefer.notify )
- .done( newDefer.resolve )
- .fail( newDefer.reject );
- } else {
- newDefer[ tuple[ 0 ] + "With" ](
- this,
- fn ? [ returned ] : arguments
- );
- }
- } );
- } );
- fns = null;
- } ).promise();
- },
- then: function( onFulfilled, onRejected, onProgress ) {
- var maxDepth = 0;
- function resolve( depth, deferred, handler, special ) {
- return function() {
- var that = this,
- args = arguments,
- mightThrow = function() {
- var returned, then;
-
- // Support: Promises/A+ section 2.3.3.3.3
- // https://promisesaplus.com/#point-59
- // Ignore double-resolution attempts
- if ( depth < maxDepth ) {
- return;
- }
-
- returned = handler.apply( that, args );
-
- // Support: Promises/A+ section 2.3.1
- // https://promisesaplus.com/#point-48
- if ( returned === deferred.promise() ) {
- throw new TypeError( "Thenable self-resolution" );
- }
-
- // Support: Promises/A+ sections 2.3.3.1, 3.5
- // https://promisesaplus.com/#point-54
- // https://promisesaplus.com/#point-75
- // Retrieve `then` only once
- then = returned &&
-
- // Support: Promises/A+ section 2.3.4
- // https://promisesaplus.com/#point-64
- // Only check objects and functions for thenability
- ( typeof returned === "object" ||
- typeof returned === "function" ) &&
- returned.then;
-
- // Handle a returned thenable
- if ( isFunction( then ) ) {
-
- // Special processors (notify) just wait for resolution
- if ( special ) {
- then.call(
- returned,
- resolve( maxDepth, deferred, Identity, special ),
- resolve( maxDepth, deferred, Thrower, special )
- );
-
- // Normal processors (resolve) also hook into progress
- } else {
-
- // ...and disregard older resolution values
- maxDepth++;
-
- then.call(
- returned,
- resolve( maxDepth, deferred, Identity, special ),
- resolve( maxDepth, deferred, Thrower, special ),
- resolve( maxDepth, deferred, Identity,
- deferred.notifyWith )
- );
- }
-
- // Handle all other returned values
- } else {
-
- // Only substitute handlers pass on context
- // and multiple values (non-spec behavior)
- if ( handler !== Identity ) {
- that = undefined;
- args = [ returned ];
- }
-
- // Process the value(s)
- // Default process is resolve
- ( special || deferred.resolveWith )( that, args );
- }
- },
-
- // Only normal processors (resolve) catch and reject exceptions
- process = special ?
- mightThrow :
- function() {
- try {
- mightThrow();
- } catch ( e ) {
-
- if ( jQuery.Deferred.exceptionHook ) {
- jQuery.Deferred.exceptionHook( e,
- process.stackTrace );
- }
-
- // Support: Promises/A+ section 2.3.3.3.4.1
- // https://promisesaplus.com/#point-61
- // Ignore post-resolution exceptions
- if ( depth + 1 >= maxDepth ) {
-
- // Only substitute handlers pass on context
- // and multiple values (non-spec behavior)
- if ( handler !== Thrower ) {
- that = undefined;
- args = [ e ];
- }
-
- deferred.rejectWith( that, args );
- }
- }
- };
-
- // Support: Promises/A+ section 2.3.3.3.1
- // https://promisesaplus.com/#point-57
- // Re-resolve promises immediately to dodge false rejection from
- // subsequent errors
- if ( depth ) {
- process();
- } else {
-
- // Call an optional hook to record the stack, in case of exception
- // since it's otherwise lost when execution goes async
- if ( jQuery.Deferred.getStackHook ) {
- process.stackTrace = jQuery.Deferred.getStackHook();
- }
- window.setTimeout( process );
- }
- };
- }
-
- return jQuery.Deferred( function( newDefer ) {
-
- // progress_handlers.add( ... )
- tuples[ 0 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onProgress ) ?
- onProgress :
- Identity,
- newDefer.notifyWith
- )
- );
-
- // fulfilled_handlers.add( ... )
- tuples[ 1 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onFulfilled ) ?
- onFulfilled :
- Identity
- )
- );
-
- // rejected_handlers.add( ... )
- tuples[ 2 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onRejected ) ?
- onRejected :
- Thrower
- )
- );
- } ).promise();
- },
-
- // Get a promise for this deferred
- // If obj is provided, the promise aspect is added to the object
- promise: function( obj ) {
- return obj != null ? jQuery.extend( obj, promise ) : promise;
- }
- },
- deferred = {};
-
- // Add list-specific methods
- jQuery.each( tuples, function( i, tuple ) {
- var list = tuple[ 2 ],
- stateString = tuple[ 5 ];
-
- // promise.progress = list.add
- // promise.done = list.add
- // promise.fail = list.add
- promise[ tuple[ 1 ] ] = list.add;
-
- // Handle state
- if ( stateString ) {
- list.add(
- function() {
-
- // state = "resolved" (i.e., fulfilled)
- // state = "rejected"
- state = stateString;
- },
-
- // rejected_callbacks.disable
- // fulfilled_callbacks.disable
- tuples[ 3 - i ][ 2 ].disable,
-
- // rejected_handlers.disable
- // fulfilled_handlers.disable
- tuples[ 3 - i ][ 3 ].disable,
-
- // progress_callbacks.lock
- tuples[ 0 ][ 2 ].lock,
-
- // progress_handlers.lock
- tuples[ 0 ][ 3 ].lock
- );
- }
-
- // progress_handlers.fire
- // fulfilled_handlers.fire
- // rejected_handlers.fire
- list.add( tuple[ 3 ].fire );
-
- // deferred.notify = function() { deferred.notifyWith(...) }
- // deferred.resolve = function() { deferred.resolveWith(...) }
- // deferred.reject = function() { deferred.rejectWith(...) }
- deferred[ tuple[ 0 ] ] = function() {
- deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
- return this;
- };
-
- // deferred.notifyWith = list.fireWith
- // deferred.resolveWith = list.fireWith
- // deferred.rejectWith = list.fireWith
- deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
- } );
-
- // Make the deferred a promise
- promise.promise( deferred );
-
- // Call given func if any
- if ( func ) {
- func.call( deferred, deferred );
- }
-
- // All done!
- return deferred;
- },
-
- // Deferred helper
- when: function( singleValue ) {
- var
-
- // count of uncompleted subordinates
- remaining = arguments.length,
-
- // count of unprocessed arguments
- i = remaining,
-
- // subordinate fulfillment data
- resolveContexts = Array( i ),
- resolveValues = slice.call( arguments ),
-
- // the primary Deferred
- primary = jQuery.Deferred(),
-
- // subordinate callback factory
- updateFunc = function( i ) {
- return function( value ) {
- resolveContexts[ i ] = this;
- resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
- if ( !( --remaining ) ) {
- primary.resolveWith( resolveContexts, resolveValues );
- }
- };
- };
-
- // Single- and empty arguments are adopted like Promise.resolve
- if ( remaining <= 1 ) {
- adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
- !remaining );
-
- // Use .then() to unwrap secondary thenables (cf. gh-3000)
- if ( primary.state() === "pending" ||
- isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
- return primary.then();
- }
- }
-
- // Multiple arguments are aggregated like Promise.all array elements
- while ( i-- ) {
- adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
- }
-
- return primary.promise();
- }
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
- // Support: IE 8 - 9 only
- // Console exists when dev tools are open, which can happen at any time
- if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
- window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
- }
-};
-
-
-
-
-jQuery.readyException = function( error ) {
- window.setTimeout( function() {
- throw error;
- } );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
- readyList
- .then( fn )
-
- // Wrap jQuery.readyException in a function so that the lookup
- // happens at the time of error handling instead of callback
- // registration.
- .catch( function( error ) {
- jQuery.readyException( error );
- } );
-
- return this;
-};
-
-jQuery.extend( {
-
- // Is the DOM ready to be used? Set to true once it occurs.
- isReady: false,
-
- // A counter to track how many items to wait for before
- // the ready event fires. See #6781
- readyWait: 1,
-
- // Handle when the DOM is ready
- ready: function( wait ) {
-
- // Abort if there are pending holds or we're already ready
- if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
- return;
- }
-
- // Remember that the DOM is ready
- jQuery.isReady = true;
-
- // If a normal DOM Ready event fired, decrement, and wait if need be
- if ( wait !== true && --jQuery.readyWait > 0 ) {
- return;
- }
-
- // If there are functions bound, to execute
- readyList.resolveWith( document, [ jQuery ] );
- }
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
- document.removeEventListener( "DOMContentLoaded", completed );
- window.removeEventListener( "load", completed );
- jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
- ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
- // Handle it asynchronously to allow scripts the opportunity to delay ready
- window.setTimeout( jQuery.ready );
-
-} else {
-
- // Use the handy event callback
- document.addEventListener( "DOMContentLoaded", completed );
-
- // A fallback to window.onload, that will always work
- window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
- var i = 0,
- len = elems.length,
- bulk = key == null;
-
- // Sets many values
- if ( toType( key ) === "object" ) {
- chainable = true;
- for ( i in key ) {
- access( elems, fn, i, key[ i ], true, emptyGet, raw );
- }
-
- // Sets one value
- } else if ( value !== undefined ) {
- chainable = true;
-
- if ( !isFunction( value ) ) {
- raw = true;
- }
-
- if ( bulk ) {
-
- // Bulk operations run against the entire set
- if ( raw ) {
- fn.call( elems, value );
- fn = null;
-
- // ...except when executing function values
- } else {
- bulk = fn;
- fn = function( elem, _key, value ) {
- return bulk.call( jQuery( elem ), value );
- };
- }
- }
-
- if ( fn ) {
- for ( ; i < len; i++ ) {
- fn(
- elems[ i ], key, raw ?
- value :
- value.call( elems[ i ], i, fn( elems[ i ], key ) )
- );
- }
- }
- }
-
- if ( chainable ) {
- return elems;
- }
-
- // Gets
- if ( bulk ) {
- return fn.call( elems );
- }
-
- return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
- rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
- return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
- return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
- // Accepts only:
- // - Node
- // - Node.ELEMENT_NODE
- // - Node.DOCUMENT_NODE
- // - Object
- // - Any
- return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
- this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
- cache: function( owner ) {
-
- // Check if the owner object already has a cache
- var value = owner[ this.expando ];
-
- // If not, create one
- if ( !value ) {
- value = {};
-
- // We can accept data for non-element nodes in modern browsers,
- // but we should not, see #8335.
- // Always return an empty object.
- if ( acceptData( owner ) ) {
-
- // If it is a node unlikely to be stringify-ed or looped over
- // use plain assignment
- if ( owner.nodeType ) {
- owner[ this.expando ] = value;
-
- // Otherwise secure it in a non-enumerable property
- // configurable must be true to allow the property to be
- // deleted when data is removed
- } else {
- Object.defineProperty( owner, this.expando, {
- value: value,
- configurable: true
- } );
- }
- }
- }
-
- return value;
- },
- set: function( owner, data, value ) {
- var prop,
- cache = this.cache( owner );
-
- // Handle: [ owner, key, value ] args
- // Always use camelCase key (gh-2257)
- if ( typeof data === "string" ) {
- cache[ camelCase( data ) ] = value;
-
- // Handle: [ owner, { properties } ] args
- } else {
-
- // Copy the properties one-by-one to the cache object
- for ( prop in data ) {
- cache[ camelCase( prop ) ] = data[ prop ];
- }
- }
- return cache;
- },
- get: function( owner, key ) {
- return key === undefined ?
- this.cache( owner ) :
-
- // Always use camelCase key (gh-2257)
- owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
- },
- access: function( owner, key, value ) {
-
- // In cases where either:
- //
- // 1. No key was specified
- // 2. A string key was specified, but no value provided
- //
- // Take the "read" path and allow the get method to determine
- // which value to return, respectively either:
- //
- // 1. The entire cache object
- // 2. The data stored at the key
- //
- if ( key === undefined ||
- ( ( key && typeof key === "string" ) && value === undefined ) ) {
-
- return this.get( owner, key );
- }
-
- // When the key is not a string, or both a key and value
- // are specified, set or extend (existing objects) with either:
- //
- // 1. An object of properties
- // 2. A key and value
- //
- this.set( owner, key, value );
-
- // Since the "set" path can have two possible entry points
- // return the expected data based on which path was taken[*]
- return value !== undefined ? value : key;
- },
- remove: function( owner, key ) {
- var i,
- cache = owner[ this.expando ];
-
- if ( cache === undefined ) {
- return;
- }
-
- if ( key !== undefined ) {
-
- // Support array or space separated string of keys
- if ( Array.isArray( key ) ) {
-
- // If key is an array of keys...
- // We always set camelCase keys, so remove that.
- key = key.map( camelCase );
- } else {
- key = camelCase( key );
-
- // If a key with the spaces exists, use it.
- // Otherwise, create an array by matching non-whitespace
- key = key in cache ?
- [ key ] :
- ( key.match( rnothtmlwhite ) || [] );
- }
-
- i = key.length;
-
- while ( i-- ) {
- delete cache[ key[ i ] ];
- }
- }
-
- // Remove the expando if there's no more data
- if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
- // Support: Chrome <=35 - 45
- // Webkit & Blink performance suffers when deleting properties
- // from DOM nodes, so set to undefined instead
- // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
- if ( owner.nodeType ) {
- owner[ this.expando ] = undefined;
- } else {
- delete owner[ this.expando ];
- }
- }
- },
- hasData: function( owner ) {
- var cache = owner[ this.expando ];
- return cache !== undefined && !jQuery.isEmptyObject( cache );
- }
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-// Implementation Summary
-//
-// 1. Enforce API surface and semantic compatibility with 1.9.x branch
-// 2. Improve the module's maintainability by reducing the storage
-// paths to a single mechanism.
-// 3. Use the same single mechanism to support "private" and "user" data.
-// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-// 5. Avoid exposing implementation details on user objects (eg. expando properties)
-// 6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
- rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
- if ( data === "true" ) {
- return true;
- }
-
- if ( data === "false" ) {
- return false;
- }
-
- if ( data === "null" ) {
- return null;
- }
-
- // Only convert to a number if it doesn't change the string
- if ( data === +data + "" ) {
- return +data;
- }
-
- if ( rbrace.test( data ) ) {
- return JSON.parse( data );
- }
-
- return data;
-}
-
-function dataAttr( elem, key, data ) {
- var name;
-
- // If nothing was found internally, try to fetch any
- // data from the HTML5 data-* attribute
- if ( data === undefined && elem.nodeType === 1 ) {
- name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
- data = elem.getAttribute( name );
-
- if ( typeof data === "string" ) {
- try {
- data = getData( data );
- } catch ( e ) {}
-
- // Make sure we set the data so it isn't changed later
- dataUser.set( elem, key, data );
- } else {
- data = undefined;
- }
- }
- return data;
-}
-
-jQuery.extend( {
- hasData: function( elem ) {
- return dataUser.hasData( elem ) || dataPriv.hasData( elem );
- },
-
- data: function( elem, name, data ) {
- return dataUser.access( elem, name, data );
- },
-
- removeData: function( elem, name ) {
- dataUser.remove( elem, name );
- },
-
- // TODO: Now that all calls to _data and _removeData have been replaced
- // with direct calls to dataPriv methods, these can be deprecated.
- _data: function( elem, name, data ) {
- return dataPriv.access( elem, name, data );
- },
-
- _removeData: function( elem, name ) {
- dataPriv.remove( elem, name );
- }
-} );
-
-jQuery.fn.extend( {
- data: function( key, value ) {
- var i, name, data,
- elem = this[ 0 ],
- attrs = elem && elem.attributes;
-
- // Gets all values
- if ( key === undefined ) {
- if ( this.length ) {
- data = dataUser.get( elem );
-
- if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
- i = attrs.length;
- while ( i-- ) {
-
- // Support: IE 11 only
- // The attrs elements can be null (#14894)
- if ( attrs[ i ] ) {
- name = attrs[ i ].name;
- if ( name.indexOf( "data-" ) === 0 ) {
- name = camelCase( name.slice( 5 ) );
- dataAttr( elem, name, data[ name ] );
- }
- }
- }
- dataPriv.set( elem, "hasDataAttrs", true );
- }
- }
-
- return data;
- }
-
- // Sets multiple values
- if ( typeof key === "object" ) {
- return this.each( function() {
- dataUser.set( this, key );
- } );
- }
-
- return access( this, function( value ) {
- var data;
-
- // The calling jQuery object (element matches) is not empty
- // (and therefore has an element appears at this[ 0 ]) and the
- // `value` parameter was not undefined. An empty jQuery object
- // will result in `undefined` for elem = this[ 0 ] which will
- // throw an exception if an attempt to read a data cache is made.
- if ( elem && value === undefined ) {
-
- // Attempt to get data from the cache
- // The key will always be camelCased in Data
- data = dataUser.get( elem, key );
- if ( data !== undefined ) {
- return data;
- }
-
- // Attempt to "discover" the data in
- // HTML5 custom data-* attrs
- data = dataAttr( elem, key );
- if ( data !== undefined ) {
- return data;
- }
-
- // We tried really hard, but the data doesn't exist.
- return;
- }
-
- // Set the data...
- this.each( function() {
-
- // We always store the camelCased key
- dataUser.set( this, key, value );
- } );
- }, null, value, arguments.length > 1, null, true );
- },
-
- removeData: function( key ) {
- return this.each( function() {
- dataUser.remove( this, key );
- } );
- }
-} );
-
-
-jQuery.extend( {
- queue: function( elem, type, data ) {
- var queue;
-
- if ( elem ) {
- type = ( type || "fx" ) + "queue";
- queue = dataPriv.get( elem, type );
-
- // Speed up dequeue by getting out quickly if this is just a lookup
- if ( data ) {
- if ( !queue || Array.isArray( data ) ) {
- queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
- } else {
- queue.push( data );
- }
- }
- return queue || [];
- }
- },
-
- dequeue: function( elem, type ) {
- type = type || "fx";
-
- var queue = jQuery.queue( elem, type ),
- startLength = queue.length,
- fn = queue.shift(),
- hooks = jQuery._queueHooks( elem, type ),
- next = function() {
- jQuery.dequeue( elem, type );
- };
-
- // If the fx queue is dequeued, always remove the progress sentinel
- if ( fn === "inprogress" ) {
- fn = queue.shift();
- startLength--;
- }
-
- if ( fn ) {
-
- // Add a progress sentinel to prevent the fx queue from being
- // automatically dequeued
- if ( type === "fx" ) {
- queue.unshift( "inprogress" );
- }
-
- // Clear up the last queue stop function
- delete hooks.stop;
- fn.call( elem, next, hooks );
- }
-
- if ( !startLength && hooks ) {
- hooks.empty.fire();
- }
- },
-
- // Not public - generate a queueHooks object, or return the current one
- _queueHooks: function( elem, type ) {
- var key = type + "queueHooks";
- return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
- empty: jQuery.Callbacks( "once memory" ).add( function() {
- dataPriv.remove( elem, [ type + "queue", key ] );
- } )
- } );
- }
-} );
-
-jQuery.fn.extend( {
- queue: function( type, data ) {
- var setter = 2;
-
- if ( typeof type !== "string" ) {
- data = type;
- type = "fx";
- setter--;
- }
-
- if ( arguments.length < setter ) {
- return jQuery.queue( this[ 0 ], type );
- }
-
- return data === undefined ?
- this :
- this.each( function() {
- var queue = jQuery.queue( this, type, data );
-
- // Ensure a hooks for this queue
- jQuery._queueHooks( this, type );
-
- if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
- jQuery.dequeue( this, type );
- }
- } );
- },
- dequeue: function( type ) {
- return this.each( function() {
- jQuery.dequeue( this, type );
- } );
- },
- clearQueue: function( type ) {
- return this.queue( type || "fx", [] );
- },
-
- // Get a promise resolved when queues of a certain type
- // are emptied (fx is the type by default)
- promise: function( type, obj ) {
- var tmp,
- count = 1,
- defer = jQuery.Deferred(),
- elements = this,
- i = this.length,
- resolve = function() {
- if ( !( --count ) ) {
- defer.resolveWith( elements, [ elements ] );
- }
- };
-
- if ( typeof type !== "string" ) {
- obj = type;
- type = undefined;
- }
- type = type || "fx";
-
- while ( i-- ) {
- tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
- if ( tmp && tmp.empty ) {
- count++;
- tmp.empty.add( resolve );
- }
- }
- resolve();
- return defer.promise( obj );
- }
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
- var isAttached = function( elem ) {
- return jQuery.contains( elem.ownerDocument, elem );
- },
- composed = { composed: true };
-
- // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
- // Check attachment across shadow DOM boundaries when possible (gh-3504)
- // Support: iOS 10.0-10.2 only
- // Early iOS 10 versions support `attachShadow` but not `getRootNode`,
- // leading to errors. We need to check for `getRootNode`.
- if ( documentElement.getRootNode ) {
- isAttached = function( elem ) {
- return jQuery.contains( elem.ownerDocument, elem ) ||
- elem.getRootNode( composed ) === elem.ownerDocument;
- };
- }
-var isHiddenWithinTree = function( elem, el ) {
-
- // isHiddenWithinTree might be called from jQuery#filter function;
- // in that case, element will be second argument
- elem = el || elem;
-
- // Inline style trumps all
- return elem.style.display === "none" ||
- elem.style.display === "" &&
-
- // Otherwise, check computed style
- // Support: Firefox <=43 - 45
- // Disconnected elements can have computed display: none, so first confirm that elem is
- // in the document.
- isAttached( elem ) &&
-
- jQuery.css( elem, "display" ) === "none";
- };
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
- var adjusted, scale,
- maxIterations = 20,
- currentValue = tween ?
- function() {
- return tween.cur();
- } :
- function() {
- return jQuery.css( elem, prop, "" );
- },
- initial = currentValue(),
- unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
- // Starting value computation is required for potential unit mismatches
- initialInUnit = elem.nodeType &&
- ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
- rcssNum.exec( jQuery.css( elem, prop ) );
-
- if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
- // Support: Firefox <=54
- // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
- initial = initial / 2;
-
- // Trust units reported by jQuery.css
- unit = unit || initialInUnit[ 3 ];
-
- // Iteratively approximate from a nonzero starting point
- initialInUnit = +initial || 1;
-
- while ( maxIterations-- ) {
-
- // Evaluate and update our best guess (doubling guesses that zero out).
- // Finish if the scale equals or crosses 1 (making the old*new product non-positive).
- jQuery.style( elem, prop, initialInUnit + unit );
- if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
- maxIterations = 0;
- }
- initialInUnit = initialInUnit / scale;
-
- }
-
- initialInUnit = initialInUnit * 2;
- jQuery.style( elem, prop, initialInUnit + unit );
-
- // Make sure we update the tween properties later on
- valueParts = valueParts || [];
- }
-
- if ( valueParts ) {
- initialInUnit = +initialInUnit || +initial || 0;
-
- // Apply relative offset (+=/-=) if specified
- adjusted = valueParts[ 1 ] ?
- initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
- +valueParts[ 2 ];
- if ( tween ) {
- tween.unit = unit;
- tween.start = initialInUnit;
- tween.end = adjusted;
- }
- }
- return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
- var temp,
- doc = elem.ownerDocument,
- nodeName = elem.nodeName,
- display = defaultDisplayMap[ nodeName ];
-
- if ( display ) {
- return display;
- }
-
- temp = doc.body.appendChild( doc.createElement( nodeName ) );
- display = jQuery.css( temp, "display" );
-
- temp.parentNode.removeChild( temp );
-
- if ( display === "none" ) {
- display = "block";
- }
- defaultDisplayMap[ nodeName ] = display;
-
- return display;
-}
-
-function showHide( elements, show ) {
- var display, elem,
- values = [],
- index = 0,
- length = elements.length;
-
- // Determine new display value for elements that need to change
- for ( ; index < length; index++ ) {
- elem = elements[ index ];
- if ( !elem.style ) {
- continue;
- }
-
- display = elem.style.display;
- if ( show ) {
-
- // Since we force visibility upon cascade-hidden elements, an immediate (and slow)
- // check is required in this first loop unless we have a nonempty display value (either
- // inline or about-to-be-restored)
- if ( display === "none" ) {
- values[ index ] = dataPriv.get( elem, "display" ) || null;
- if ( !values[ index ] ) {
- elem.style.display = "";
- }
- }
- if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
- values[ index ] = getDefaultDisplay( elem );
- }
- } else {
- if ( display !== "none" ) {
- values[ index ] = "none";
-
- // Remember what we're overwriting
- dataPriv.set( elem, "display", display );
- }
- }
- }
-
- // Set the display of the elements in a second loop to avoid constant reflow
- for ( index = 0; index < length; index++ ) {
- if ( values[ index ] != null ) {
- elements[ index ].style.display = values[ index ];
- }
- }
-
- return elements;
-}
-
-jQuery.fn.extend( {
- show: function() {
- return showHide( this, true );
- },
- hide: function() {
- return showHide( this );
- },
- toggle: function( state ) {
- if ( typeof state === "boolean" ) {
- return state ? this.show() : this.hide();
- }
-
- return this.each( function() {
- if ( isHiddenWithinTree( this ) ) {
- jQuery( this ).show();
- } else {
- jQuery( this ).hide();
- }
- } );
- }
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
- var fragment = document.createDocumentFragment(),
- div = fragment.appendChild( document.createElement( "div" ) ),
- input = document.createElement( "input" );
-
- // Support: Android 4.0 - 4.3 only
- // Check state lost if the name is set (#11217)
- // Support: Windows Web Apps (WWA)
- // `name` and `type` must use .setAttribute for WWA (#14901)
- input.setAttribute( "type", "radio" );
- input.setAttribute( "checked", "checked" );
- input.setAttribute( "name", "t" );
-
- div.appendChild( input );
-
- // Support: Android <=4.1 only
- // Older WebKit doesn't clone checked state correctly in fragments
- support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
- // Support: IE <=11 only
- // Make sure textarea (and checkbox) defaultValue is properly cloned
- div.innerHTML = "<textarea>x</textarea>";
- support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
- // Support: IE <=9 only
- // IE <=9 replaces <option> tags with their contents when inserted outside of
- // the select element.
- div.innerHTML = "<option></option>";
- support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
- // XHTML parsers do not magically insert elements in the
- // same way that tag soup parsers do. So we cannot shorten
- // this by omitting <tbody> or other required elements.
- thead: [ 1, "<table>", "</table>" ],
- col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
- tr: [ 2, "<table><tbody>", "</tbody></table>" ],
- td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
- _default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
- wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
- // Support: IE <=9 - 11 only
- // Use typeof to avoid zero-argument method invocation on host objects (#15151)
- var ret;
-
- if ( typeof context.getElementsByTagName !== "undefined" ) {
- ret = context.getElementsByTagName( tag || "*" );
-
- } else if ( typeof context.querySelectorAll !== "undefined" ) {
- ret = context.querySelectorAll( tag || "*" );
-
- } else {
- ret = [];
- }
-
- if ( tag === undefined || tag && nodeName( context, tag ) ) {
- return jQuery.merge( [ context ], ret );
- }
-
- return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
- var i = 0,
- l = elems.length;
-
- for ( ; i < l; i++ ) {
- dataPriv.set(
- elems[ i ],
- "globalEval",
- !refElements || dataPriv.get( refElements[ i ], "globalEval" )
- );
- }
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
- var elem, tmp, tag, wrap, attached, j,
- fragment = context.createDocumentFragment(),
- nodes = [],
- i = 0,
- l = elems.length;
-
- for ( ; i < l; i++ ) {
- elem = elems[ i ];
-
- if ( elem || elem === 0 ) {
-
- // Add nodes directly
- if ( toType( elem ) === "object" ) {
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
- // Convert non-html into a text node
- } else if ( !rhtml.test( elem ) ) {
- nodes.push( context.createTextNode( elem ) );
-
- // Convert html into DOM nodes
- } else {
- tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
- // Deserialize a standard representation
- tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
- wrap = wrapMap[ tag ] || wrapMap._default;
- tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
- // Descend through wrappers to the right content
- j = wrap[ 0 ];
- while ( j-- ) {
- tmp = tmp.lastChild;
- }
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( nodes, tmp.childNodes );
-
- // Remember the top-level container
- tmp = fragment.firstChild;
-
- // Ensure the created nodes are orphaned (#12392)
- tmp.textContent = "";
- }
- }
- }
-
- // Remove wrapper from fragment
- fragment.textContent = "";
-
- i = 0;
- while ( ( elem = nodes[ i++ ] ) ) {
-
- // Skip elements already in the context collection (trac-4087)
- if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
- if ( ignored ) {
- ignored.push( elem );
- }
- continue;
- }
-
- attached = isAttached( elem );
-
- // Append to fragment
- tmp = getAll( fragment.appendChild( elem ), "script" );
-
- // Preserve script evaluation history
- if ( attached ) {
- setGlobalEval( tmp );
- }
-
- // Capture executables
- if ( scripts ) {
- j = 0;
- while ( ( elem = tmp[ j++ ] ) ) {
- if ( rscriptType.test( elem.type || "" ) ) {
- scripts.push( elem );
- }
- }
- }
- }
-
- return fragment;
-}
-
-
-var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
- return true;
-}
-
-function returnFalse() {
- return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
- return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
- try {
- return document.activeElement;
- } catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
- var origFn, type;
-
- // Types can be a map of types/handlers
- if ( typeof types === "object" ) {
-
- // ( types-Object, selector, data )
- if ( typeof selector !== "string" ) {
-
- // ( types-Object, data )
- data = data || selector;
- selector = undefined;
- }
- for ( type in types ) {
- on( elem, type, selector, data, types[ type ], one );
- }
- return elem;
- }
-
- if ( data == null && fn == null ) {
-
- // ( types, fn )
- fn = selector;
- data = selector = undefined;
- } else if ( fn == null ) {
- if ( typeof selector === "string" ) {
-
- // ( types, selector, fn )
- fn = data;
- data = undefined;
- } else {
-
- // ( types, data, fn )
- fn = data;
- data = selector;
- selector = undefined;
- }
- }
- if ( fn === false ) {
- fn = returnFalse;
- } else if ( !fn ) {
- return elem;
- }
-
- if ( one === 1 ) {
- origFn = fn;
- fn = function( event ) {
-
- // Can use an empty set, since event contains the info
- jQuery().off( event );
- return origFn.apply( this, arguments );
- };
-
- // Use same guid so caller can remove using origFn
- fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
- }
- return elem.each( function() {
- jQuery.event.add( this, types, fn, data, selector );
- } );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
- global: {},
-
- add: function( elem, types, handler, data, selector ) {
-
- var handleObjIn, eventHandle, tmp,
- events, t, handleObj,
- special, handlers, type, namespaces, origType,
- elemData = dataPriv.get( elem );
-
- // Only attach events to objects that accept data
- if ( !acceptData( elem ) ) {
- return;
- }
-
- // Caller can pass in an object of custom data in lieu of the handler
- if ( handler.handler ) {
- handleObjIn = handler;
- handler = handleObjIn.handler;
- selector = handleObjIn.selector;
- }
-
- // Ensure that invalid selectors throw exceptions at attach time
- // Evaluate against documentElement in case elem is a non-element node (e.g., document)
- if ( selector ) {
- jQuery.find.matchesSelector( documentElement, selector );
- }
-
- // Make sure that the handler has a unique ID, used to find/remove it later
- if ( !handler.guid ) {
- handler.guid = jQuery.guid++;
- }
-
- // Init the element's event structure and main handler, if this is the first
- if ( !( events = elemData.events ) ) {
- events = elemData.events = Object.create( null );
- }
- if ( !( eventHandle = elemData.handle ) ) {
- eventHandle = elemData.handle = function( e ) {
-
- // Discard the second event of a jQuery.event.trigger() and
- // when an event is called after a page has unloaded
- return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
- jQuery.event.dispatch.apply( elem, arguments ) : undefined;
- };
- }
-
- // Handle multiple events separated by a space
- types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
- t = types.length;
- while ( t-- ) {
- tmp = rtypenamespace.exec( types[ t ] ) || [];
- type = origType = tmp[ 1 ];
- namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
- // There *must* be a type, no attaching namespace-only handlers
- if ( !type ) {
- continue;
- }
-
- // If event changes its type, use the special event handlers for the changed type
- special = jQuery.event.special[ type ] || {};
-
- // If selector defined, determine special event api type, otherwise given type
- type = ( selector ? special.delegateType : special.bindType ) || type;
-
- // Update special based on newly reset type
- special = jQuery.event.special[ type ] || {};
-
- // handleObj is passed to all event handlers
- handleObj = jQuery.extend( {
- type: type,
- origType: origType,
- data: data,
- handler: handler,
- guid: handler.guid,
- selector: selector,
- needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
- namespace: namespaces.join( "." )
- }, handleObjIn );
-
- // Init the event handler queue if we're the first
- if ( !( handlers = events[ type ] ) ) {
- handlers = events[ type ] = [];
- handlers.delegateCount = 0;
-
- // Only use addEventListener if the special events handler returns false
- if ( !special.setup ||
- special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
- if ( elem.addEventListener ) {
- elem.addEventListener( type, eventHandle );
- }
- }
- }
-
- if ( special.add ) {
- special.add.call( elem, handleObj );
-
- if ( !handleObj.handler.guid ) {
- handleObj.handler.guid = handler.guid;
- }
- }
-
- // Add to the element's handler list, delegates in front
- if ( selector ) {
- handlers.splice( handlers.delegateCount++, 0, handleObj );
- } else {
- handlers.push( handleObj );
- }
-
- // Keep track of which events have ever been used, for event optimization
- jQuery.event.global[ type ] = true;
- }
-
- },
-
- // Detach an event or set of events from an element
- remove: function( elem, types, handler, selector, mappedTypes ) {
-
- var j, origCount, tmp,
- events, t, handleObj,
- special, handlers, type, namespaces, origType,
- elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
- if ( !elemData || !( events = elemData.events ) ) {
- return;
- }
-
- // Once for each type.namespace in types; type may be omitted
- types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
- t = types.length;
- while ( t-- ) {
- tmp = rtypenamespace.exec( types[ t ] ) || [];
- type = origType = tmp[ 1 ];
- namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
- // Unbind all events (on this namespace, if provided) for the element
- if ( !type ) {
- for ( type in events ) {
- jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
- }
- continue;
- }
-
- special = jQuery.event.special[ type ] || {};
- type = ( selector ? special.delegateType : special.bindType ) || type;
- handlers = events[ type ] || [];
- tmp = tmp[ 2 ] &&
- new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
- // Remove matching events
- origCount = j = handlers.length;
- while ( j-- ) {
- handleObj = handlers[ j ];
-
- if ( ( mappedTypes || origType === handleObj.origType ) &&
- ( !handler || handler.guid === handleObj.guid ) &&
- ( !tmp || tmp.test( handleObj.namespace ) ) &&
- ( !selector || selector === handleObj.selector ||
- selector === "**" && handleObj.selector ) ) {
- handlers.splice( j, 1 );
-
- if ( handleObj.selector ) {
- handlers.delegateCount--;
- }
- if ( special.remove ) {
- special.remove.call( elem, handleObj );
- }
- }
- }
-
- // Remove generic event handler if we removed something and no more handlers exist
- // (avoids potential for endless recursion during removal of special event handlers)
- if ( origCount && !handlers.length ) {
- if ( !special.teardown ||
- special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
- jQuery.removeEvent( elem, type, elemData.handle );
- }
-
- delete events[ type ];
- }
- }
-
- // Remove data and the expando if it's no longer used
- if ( jQuery.isEmptyObject( events ) ) {
- dataPriv.remove( elem, "handle events" );
- }
- },
-
- dispatch: function( nativeEvent ) {
-
- var i, j, ret, matched, handleObj, handlerQueue,
- args = new Array( arguments.length ),
-
- // Make a writable jQuery.Event from the native event object
- event = jQuery.event.fix( nativeEvent ),
-
- handlers = (
- dataPriv.get( this, "events" ) || Object.create( null )
- )[ event.type ] || [],
- special = jQuery.event.special[ event.type ] || {};
-
- // Use the fix-ed jQuery.Event rather than the (read-only) native event
- args[ 0 ] = event;
-
- for ( i = 1; i < arguments.length; i++ ) {
- args[ i ] = arguments[ i ];
- }
-
- event.delegateTarget = this;
-
- // Call the preDispatch hook for the mapped type, and let it bail if desired
- if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
- return;
- }
-
- // Determine handlers
- handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
- // Run delegates first; they may want to stop propagation beneath us
- i = 0;
- while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
- event.currentTarget = matched.elem;
-
- j = 0;
- while ( ( handleObj = matched.handlers[ j++ ] ) &&
- !event.isImmediatePropagationStopped() ) {
-
- // If the event is namespaced, then each handler is only invoked if it is
- // specially universal or its namespaces are a superset of the event's.
- if ( !event.rnamespace || handleObj.namespace === false ||
- event.rnamespace.test( handleObj.namespace ) ) {
-
- event.handleObj = handleObj;
- event.data = handleObj.data;
-
- ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
- handleObj.handler ).apply( matched.elem, args );
-
- if ( ret !== undefined ) {
- if ( ( event.result = ret ) === false ) {
- event.preventDefault();
- event.stopPropagation();
- }
- }
- }
- }
- }
-
- // Call the postDispatch hook for the mapped type
- if ( special.postDispatch ) {
- special.postDispatch.call( this, event );
- }
-
- return event.result;
- },
-
- handlers: function( event, handlers ) {
- var i, handleObj, sel, matchedHandlers, matchedSelectors,
- handlerQueue = [],
- delegateCount = handlers.delegateCount,
- cur = event.target;
-
- // Find delegate handlers
- if ( delegateCount &&
-
- // Support: IE <=9
- // Black-hole SVG <use> instance trees (trac-13180)
- cur.nodeType &&
-
- // Support: Firefox <=42
- // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
- // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
- // Support: IE 11 only
- // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
- !( event.type === "click" && event.button >= 1 ) ) {
-
- for ( ; cur !== this; cur = cur.parentNode || this ) {
-
- // Don't check non-elements (#13208)
- // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
- if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
- matchedHandlers = [];
- matchedSelectors = {};
- for ( i = 0; i < delegateCount; i++ ) {
- handleObj = handlers[ i ];
-
- // Don't conflict with Object.prototype properties (#13203)
- sel = handleObj.selector + " ";
-
- if ( matchedSelectors[ sel ] === undefined ) {
- matchedSelectors[ sel ] = handleObj.needsContext ?
- jQuery( sel, this ).index( cur ) > -1 :
- jQuery.find( sel, this, null, [ cur ] ).length;
- }
- if ( matchedSelectors[ sel ] ) {
- matchedHandlers.push( handleObj );
- }
- }
- if ( matchedHandlers.length ) {
- handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
- }
- }
- }
- }
-
- // Add the remaining (directly-bound) handlers
- cur = this;
- if ( delegateCount < handlers.length ) {
- handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
- }
-
- return handlerQueue;
- },
-
- addProp: function( name, hook ) {
- Object.defineProperty( jQuery.Event.prototype, name, {
- enumerable: true,
- configurable: true,
-
- get: isFunction( hook ) ?
- function() {
- if ( this.originalEvent ) {
- return hook( this.originalEvent );
- }
- } :
- function() {
- if ( this.originalEvent ) {
- return this.originalEvent[ name ];
- }
- },
-
- set: function( value ) {
- Object.defineProperty( this, name, {
- enumerable: true,
- configurable: true,
- writable: true,
- value: value
- } );
- }
- } );
- },
-
- fix: function( originalEvent ) {
- return originalEvent[ jQuery.expando ] ?
- originalEvent :
- new jQuery.Event( originalEvent );
- },
-
- special: {
- load: {
-
- // Prevent triggered image.load events from bubbling to window.load
- noBubble: true
- },
- click: {
-
- // Utilize native event to ensure correct state for checkable inputs
- setup: function( data ) {
-
- // For mutual compressibility with _default, replace `this` access with a local var.
- // `|| data` is dead code meant only to preserve the variable through minification.
- var el = this || data;
-
- // Claim the first handler
- if ( rcheckableType.test( el.type ) &&
- el.click && nodeName( el, "input" ) ) {
-
- // dataPriv.set( el, "click", ... )
- leverageNative( el, "click", returnTrue );
- }
-
- // Return false to allow normal processing in the caller
- return false;
- },
- trigger: function( data ) {
-
- // For mutual compressibility with _default, replace `this` access with a local var.
- // `|| data` is dead code meant only to preserve the variable through minification.
- var el = this || data;
-
- // Force setup before triggering a click
- if ( rcheckableType.test( el.type ) &&
- el.click && nodeName( el, "input" ) ) {
-
- leverageNative( el, "click" );
- }
-
- // Return non-false to allow normal event-path propagation
- return true;
- },
-
- // For cross-browser consistency, suppress native .click() on links
- // Also prevent it if we're currently inside a leveraged native-event stack
- _default: function( event ) {
- var target = event.target;
- return rcheckableType.test( target.type ) &&
- target.click && nodeName( target, "input" ) &&
- dataPriv.get( target, "click" ) ||
- nodeName( target, "a" );
- }
- },
-
- beforeunload: {
- postDispatch: function( event ) {
-
- // Support: Firefox 20+
- // Firefox doesn't alert if the returnValue field is not set.
- if ( event.result !== undefined && event.originalEvent ) {
- event.originalEvent.returnValue = event.result;
- }
- }
- }
- }
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
- // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
- if ( !expectSync ) {
- if ( dataPriv.get( el, type ) === undefined ) {
- jQuery.event.add( el, type, returnTrue );
- }
- return;
- }
-
- // Register the controller as a special universal handler for all event namespaces
- dataPriv.set( el, type, false );
- jQuery.event.add( el, type, {
- namespace: false,
- handler: function( event ) {
- var notAsync, result,
- saved = dataPriv.get( this, type );
-
- if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
- // Interrupt processing of the outer synthetic .trigger()ed event
- // Saved data should be false in such cases, but might be a leftover capture object
- // from an async native handler (gh-4350)
- if ( !saved.length ) {
-
- // Store arguments for use when handling the inner native event
- // There will always be at least one argument (an event object), so this array
- // will not be confused with a leftover capture object.
- saved = slice.call( arguments );
- dataPriv.set( this, type, saved );
-
- // Trigger the native event and capture its result
- // Support: IE <=9 - 11+
- // focus() and blur() are asynchronous
- notAsync = expectSync( this, type );
- this[ type ]();
- result = dataPriv.get( this, type );
- if ( saved !== result || notAsync ) {
- dataPriv.set( this, type, false );
- } else {
- result = {};
- }
- if ( saved !== result ) {
-
- // Cancel the outer synthetic event
- event.stopImmediatePropagation();
- event.preventDefault();
-
- // Support: Chrome 86+
- // In Chrome, if an element having a focusout handler is blurred by
- // clicking outside of it, it invokes the handler synchronously. If
- // that handler calls `.remove()` on the element, the data is cleared,
- // leaving `result` undefined. We need to guard against this.
- return result && result.value;
- }
-
- // If this is an inner synthetic event for an event with a bubbling surrogate
- // (focus or blur), assume that the surrogate already propagated from triggering the
- // native event and prevent that from happening again here.
- // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
- // bubbling surrogate propagates *after* the non-bubbling base), but that seems
- // less bad than duplication.
- } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
- event.stopPropagation();
- }
-
- // If this is a native event triggered above, everything is now in order
- // Fire an inner synthetic event with the original arguments
- } else if ( saved.length ) {
-
- // ...and capture the result
- dataPriv.set( this, type, {
- value: jQuery.event.trigger(
-
- // Support: IE <=9 - 11+
- // Extend with the prototype to reset the above stopImmediatePropagation()
- jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
- saved.slice( 1 ),
- this
- )
- } );
-
- // Abort handling of the native event
- event.stopImmediatePropagation();
- }
- }
- } );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
- // This "if" is needed for plain objects
- if ( elem.removeEventListener ) {
- elem.removeEventListener( type, handle );
- }
-};
-
-jQuery.Event = function( src, props ) {
-
- // Allow instantiation without the 'new' keyword
- if ( !( this instanceof jQuery.Event ) ) {
- return new jQuery.Event( src, props );
- }
-
- // Event object
- if ( src && src.type ) {
- this.originalEvent = src;
- this.type = src.type;
-
- // Events bubbling up the document may have been marked as prevented
- // by a handler lower down the tree; reflect the correct value.
- this.isDefaultPrevented = src.defaultPrevented ||
- src.defaultPrevented === undefined &&
-
- // Support: Android <=2.3 only
- src.returnValue === false ?
- returnTrue :
- returnFalse;
-
- // Create target properties
- // Support: Safari <=6 - 7 only
- // Target should not be a text node (#504, #13143)
- this.target = ( src.target && src.target.nodeType === 3 ) ?
- src.target.parentNode :
- src.target;
-
- this.currentTarget = src.currentTarget;
- this.relatedTarget = src.relatedTarget;
-
- // Event type
- } else {
- this.type = src;
- }
-
- // Put explicitly provided properties onto the event object
- if ( props ) {
- jQuery.extend( this, props );
- }
-
- // Create a timestamp if incoming event doesn't have one
- this.timeStamp = src && src.timeStamp || Date.now();
-
- // Mark it as fixed
- this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
- constructor: jQuery.Event,
- isDefaultPrevented: returnFalse,
- isPropagationStopped: returnFalse,
- isImmediatePropagationStopped: returnFalse,
- isSimulated: false,
-
- preventDefault: function() {
- var e = this.originalEvent;
-
- this.isDefaultPrevented = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.preventDefault();
- }
- },
- stopPropagation: function() {
- var e = this.originalEvent;
-
- this.isPropagationStopped = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.stopPropagation();
- }
- },
- stopImmediatePropagation: function() {
- var e = this.originalEvent;
-
- this.isImmediatePropagationStopped = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.stopImmediatePropagation();
- }
-
- this.stopPropagation();
- }
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
- altKey: true,
- bubbles: true,
- cancelable: true,
- changedTouches: true,
- ctrlKey: true,
- detail: true,
- eventPhase: true,
- metaKey: true,
- pageX: true,
- pageY: true,
- shiftKey: true,
- view: true,
- "char": true,
- code: true,
- charCode: true,
- key: true,
- keyCode: true,
- button: true,
- buttons: true,
- clientX: true,
- clientY: true,
- offsetX: true,
- offsetY: true,
- pointerId: true,
- pointerType: true,
- screenX: true,
- screenY: true,
- targetTouches: true,
- toElement: true,
- touches: true,
- which: true
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
- jQuery.event.special[ type ] = {
-
- // Utilize native event if possible so blur/focus sequence is correct
- setup: function() {
-
- // Claim the first handler
- // dataPriv.set( this, "focus", ... )
- // dataPriv.set( this, "blur", ... )
- leverageNative( this, type, expectSync );
-
- // Return false to allow normal processing in the caller
- return false;
- },
- trigger: function() {
-
- // Force setup before trigger
- leverageNative( this, type );
-
- // Return non-false to allow normal event-path propagation
- return true;
- },
-
- // Suppress native focus or blur as it's already being fired
- // in leverageNative.
- _default: function() {
- return true;
- },
-
- delegateType: delegateType
- };
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
- mouseenter: "mouseover",
- mouseleave: "mouseout",
- pointerenter: "pointerover",
- pointerleave: "pointerout"
-}, function( orig, fix ) {
- jQuery.event.special[ orig ] = {
- delegateType: fix,
- bindType: fix,
-
- handle: function( event ) {
- var ret,
- target = this,
- related = event.relatedTarget,
- handleObj = event.handleObj;
-
- // For mouseenter/leave call the handler if related is outside the target.
- // NB: No relatedTarget if the mouse left/entered the browser window
- if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
- event.type = handleObj.origType;
- ret = handleObj.handler.apply( this, arguments );
- event.type = fix;
- }
- return ret;
- }
- };
-} );
-
-jQuery.fn.extend( {
-
- on: function( types, selector, data, fn ) {
- return on( this, types, selector, data, fn );
- },
- one: function( types, selector, data, fn ) {
- return on( this, types, selector, data, fn, 1 );
- },
- off: function( types, selector, fn ) {
- var handleObj, type;
- if ( types && types.preventDefault && types.handleObj ) {
-
- // ( event ) dispatched jQuery.Event
- handleObj = types.handleObj;
- jQuery( types.delegateTarget ).off(
- handleObj.namespace ?
- handleObj.origType + "." + handleObj.namespace :
- handleObj.origType,
- handleObj.selector,
- handleObj.handler
- );
- return this;
- }
- if ( typeof types === "object" ) {
-
- // ( types-object [, selector] )
- for ( type in types ) {
- this.off( type, selector, types[ type ] );
- }
- return this;
- }
- if ( selector === false || typeof selector === "function" ) {
-
- // ( types [, fn] )
- fn = selector;
- selector = undefined;
- }
- if ( fn === false ) {
- fn = returnFalse;
- }
- return this.each( function() {
- jQuery.event.remove( this, types, fn, selector );
- } );
- }
-} );
-
-
-var
-
- // Support: IE <=10 - 11, Edge 12 - 13 only
- // In IE/Edge using regex groups here causes severe slowdowns.
- // See https://connect.microsoft.com/IE/feedback/details/1736512/
- rnoInnerhtml = /<script|<style|<link/i,
-
- // checked="checked" or checked
- rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
- rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
- if ( nodeName( elem, "table" ) &&
- nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
- return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
- }
-
- return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
- elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
- return elem;
-}
-function restoreScript( elem ) {
- if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
- elem.type = elem.type.slice( 5 );
- } else {
- elem.removeAttribute( "type" );
- }
-
- return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
- var i, l, type, pdataOld, udataOld, udataCur, events;
-
- if ( dest.nodeType !== 1 ) {
- return;
- }
-
- // 1. Copy private data: events, handlers, etc.
- if ( dataPriv.hasData( src ) ) {
- pdataOld = dataPriv.get( src );
- events = pdataOld.events;
-
- if ( events ) {
- dataPriv.remove( dest, "handle events" );
-
- for ( type in events ) {
- for ( i = 0, l = events[ type ].length; i < l; i++ ) {
- jQuery.event.add( dest, type, events[ type ][ i ] );
- }
- }
- }
- }
-
- // 2. Copy user data
- if ( dataUser.hasData( src ) ) {
- udataOld = dataUser.access( src );
- udataCur = jQuery.extend( {}, udataOld );
-
- dataUser.set( dest, udataCur );
- }
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
- var nodeName = dest.nodeName.toLowerCase();
-
- // Fails to persist the checked state of a cloned checkbox or radio button.
- if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
- dest.checked = src.checked;
-
- // Fails to return the selected option to the default selected state when cloning options
- } else if ( nodeName === "input" || nodeName === "textarea" ) {
- dest.defaultValue = src.defaultValue;
- }
-}
-
-function domManip( collection, args, callback, ignored ) {
-
- // Flatten any nested arrays
- args = flat( args );
-
- var fragment, first, scripts, hasScripts, node, doc,
- i = 0,
- l = collection.length,
- iNoClone = l - 1,
- value = args[ 0 ],
- valueIsFunction = isFunction( value );
-
- // We can't cloneNode fragments that contain checked, in WebKit
- if ( valueIsFunction ||
- ( l > 1 && typeof value === "string" &&
- !support.checkClone && rchecked.test( value ) ) ) {
- return collection.each( function( index ) {
- var self = collection.eq( index );
- if ( valueIsFunction ) {
- args[ 0 ] = value.call( this, index, self.html() );
- }
- domManip( self, args, callback, ignored );
- } );
- }
-
- if ( l ) {
- fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
- first = fragment.firstChild;
-
- if ( fragment.childNodes.length === 1 ) {
- fragment = first;
- }
-
- // Require either new content or an interest in ignored elements to invoke the callback
- if ( first || ignored ) {
- scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
- hasScripts = scripts.length;
-
- // Use the original fragment for the last item
- // instead of the first because it can end up
- // being emptied incorrectly in certain situations (#8070).
- for ( ; i < l; i++ ) {
- node = fragment;
-
- if ( i !== iNoClone ) {
- node = jQuery.clone( node, true, true );
-
- // Keep references to cloned scripts for later restoration
- if ( hasScripts ) {
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( scripts, getAll( node, "script" ) );
- }
- }
-
- callback.call( collection[ i ], node, i );
- }
-
- if ( hasScripts ) {
- doc = scripts[ scripts.length - 1 ].ownerDocument;
-
- // Reenable scripts
- jQuery.map( scripts, restoreScript );
-
- // Evaluate executable scripts on first document insertion
- for ( i = 0; i < hasScripts; i++ ) {
- node = scripts[ i ];
- if ( rscriptType.test( node.type || "" ) &&
- !dataPriv.access( node, "globalEval" ) &&
- jQuery.contains( doc, node ) ) {
-
- if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) {
-
- // Optional AJAX dependency, but won't run scripts if not present
- if ( jQuery._evalUrl && !node.noModule ) {
- jQuery._evalUrl( node.src, {
- nonce: node.nonce || node.getAttribute( "nonce" )
- }, doc );
- }
- } else {
- DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
- }
- }
- }
- }
- }
- }
-
- return collection;
-}
-
-function remove( elem, selector, keepData ) {
- var node,
- nodes = selector ? jQuery.filter( selector, elem ) : elem,
- i = 0;
-
- for ( ; ( node = nodes[ i ] ) != null; i++ ) {
- if ( !keepData && node.nodeType === 1 ) {
- jQuery.cleanData( getAll( node ) );
- }
-
- if ( node.parentNode ) {
- if ( keepData && isAttached( node ) ) {
- setGlobalEval( getAll( node, "script" ) );
- }
- node.parentNode.removeChild( node );
- }
- }
-
- return elem;
-}
-
-jQuery.extend( {
- htmlPrefilter: function( html ) {
- return html;
- },
-
- clone: function( elem, dataAndEvents, deepDataAndEvents ) {
- var i, l, srcElements, destElements,
- clone = elem.cloneNode( true ),
- inPage = isAttached( elem );
-
- // Fix IE cloning issues
- if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
- !jQuery.isXMLDoc( elem ) ) {
-
- // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
- destElements = getAll( clone );
- srcElements = getAll( elem );
-
- for ( i = 0, l = srcElements.length; i < l; i++ ) {
- fixInput( srcElements[ i ], destElements[ i ] );
- }
- }
-
- // Copy the events from the original to the clone
- if ( dataAndEvents ) {
- if ( deepDataAndEvents ) {
- srcElements = srcElements || getAll( elem );
- destElements = destElements || getAll( clone );
-
- for ( i = 0, l = srcElements.length; i < l; i++ ) {
- cloneCopyEvent( srcElements[ i ], destElements[ i ] );
- }
- } else {
- cloneCopyEvent( elem, clone );
- }
- }
-
- // Preserve script evaluation history
- destElements = getAll( clone, "script" );
- if ( destElements.length > 0 ) {
- setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
- }
-
- // Return the cloned set
- return clone;
- },
-
- cleanData: function( elems ) {
- var data, elem, type,
- special = jQuery.event.special,
- i = 0;
-
- for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
- if ( acceptData( elem ) ) {
- if ( ( data = elem[ dataPriv.expando ] ) ) {
- if ( data.events ) {
- for ( type in data.events ) {
- if ( special[ type ] ) {
- jQuery.event.remove( elem, type );
-
- // This is a shortcut to avoid jQuery.event.remove's overhead
- } else {
- jQuery.removeEvent( elem, type, data.handle );
- }
- }
- }
-
- // Support: Chrome <=35 - 45+
- // Assign undefined instead of using delete, see Data#remove
- elem[ dataPriv.expando ] = undefined;
- }
- if ( elem[ dataUser.expando ] ) {
-
- // Support: Chrome <=35 - 45+
- // Assign undefined instead of using delete, see Data#remove
- elem[ dataUser.expando ] = undefined;
- }
- }
- }
- }
-} );
-
-jQuery.fn.extend( {
- detach: function( selector ) {
- return remove( this, selector, true );
- },
-
- remove: function( selector ) {
- return remove( this, selector );
- },
-
- text: function( value ) {
- return access( this, function( value ) {
- return value === undefined ?
- jQuery.text( this ) :
- this.empty().each( function() {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- this.textContent = value;
- }
- } );
- }, null, value, arguments.length );
- },
-
- append: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- var target = manipulationTarget( this, elem );
- target.appendChild( elem );
- }
- } );
- },
-
- prepend: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- var target = manipulationTarget( this, elem );
- target.insertBefore( elem, target.firstChild );
- }
- } );
- },
-
- before: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.parentNode ) {
- this.parentNode.insertBefore( elem, this );
- }
- } );
- },
-
- after: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.parentNode ) {
- this.parentNode.insertBefore( elem, this.nextSibling );
- }
- } );
- },
-
- empty: function() {
- var elem,
- i = 0;
-
- for ( ; ( elem = this[ i ] ) != null; i++ ) {
- if ( elem.nodeType === 1 ) {
-
- // Prevent memory leaks
- jQuery.cleanData( getAll( elem, false ) );
-
- // Remove any remaining nodes
- elem.textContent = "";
- }
- }
-
- return this;
- },
-
- clone: function( dataAndEvents, deepDataAndEvents ) {
- dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
- deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
- return this.map( function() {
- return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
- } );
- },
-
- html: function( value ) {
- return access( this, function( value ) {
- var elem = this[ 0 ] || {},
- i = 0,
- l = this.length;
-
- if ( value === undefined && elem.nodeType === 1 ) {
- return elem.innerHTML;
- }
-
- // See if we can take a shortcut and just use innerHTML
- if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
- !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
- value = jQuery.htmlPrefilter( value );
-
- try {
- for ( ; i < l; i++ ) {
- elem = this[ i ] || {};
-
- // Remove element nodes and prevent memory leaks
- if ( elem.nodeType === 1 ) {
- jQuery.cleanData( getAll( elem, false ) );
- elem.innerHTML = value;
- }
- }
-
- elem = 0;
-
- // If using innerHTML throws an exception, use the fallback method
- } catch ( e ) {}
- }
-
- if ( elem ) {
- this.empty().append( value );
- }
- }, null, value, arguments.length );
- },
-
- replaceWith: function() {
- var ignored = [];
-
- // Make the changes, replacing each non-ignored context element with the new content
- return domManip( this, arguments, function( elem ) {
- var parent = this.parentNode;
-
- if ( jQuery.inArray( this, ignored ) < 0 ) {
- jQuery.cleanData( getAll( this ) );
- if ( parent ) {
- parent.replaceChild( elem, this );
- }
- }
-
- // Force callback invocation
- }, ignored );
- }
-} );
-
-jQuery.each( {
- appendTo: "append",
- prependTo: "prepend",
- insertBefore: "before",
- insertAfter: "after",
- replaceAll: "replaceWith"
-}, function( name, original ) {
- jQuery.fn[ name ] = function( selector ) {
- var elems,
- ret = [],
- insert = jQuery( selector ),
- last = insert.length - 1,
- i = 0;
-
- for ( ; i <= last; i++ ) {
- elems = i === last ? this : this.clone( true );
- jQuery( insert[ i ] )[ original ]( elems );
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // .get() because push.apply(_, arraylike) throws on ancient WebKit
- push.apply( ret, elems.get() );
- }
-
- return this.pushStack( ret );
- };
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
- // Support: IE <=11 only, Firefox <=30 (#15098, #14150)
- // IE throws on elements created in popups
- // FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
- var view = elem.ownerDocument.defaultView;
-
- if ( !view || !view.opener ) {
- view = window;
- }
-
- return view.getComputedStyle( elem );
- };
-
-var swap = function( elem, options, callback ) {
- var ret, name,
- old = {};
-
- // Remember the old values, and insert the new ones
- for ( name in options ) {
- old[ name ] = elem.style[ name ];
- elem.style[ name ] = options[ name ];
- }
-
- ret = callback.call( elem );
-
- // Revert the old values
- for ( name in options ) {
- elem.style[ name ] = old[ name ];
- }
-
- return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
- // Executing both pixelPosition & boxSizingReliable tests require only one layout
- // so they're executed at the same time to save the second computation.
- function computeStyleTests() {
-
- // This is a singleton, we need to execute it only once
- if ( !div ) {
- return;
- }
-
- container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
- "margin-top:1px;padding:0;border:0";
- div.style.cssText =
- "position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
- "margin:auto;border:1px;padding:1px;" +
- "width:60%;top:1%";
- documentElement.appendChild( container ).appendChild( div );
-
- var divStyle = window.getComputedStyle( div );
- pixelPositionVal = divStyle.top !== "1%";
-
- // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
- reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
- // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
- // Some styles come back with percentage values, even though they shouldn't
- div.style.right = "60%";
- pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
- // Support: IE 9 - 11 only
- // Detect misreporting of content dimensions for box-sizing:border-box elements
- boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
- // Support: IE 9 only
- // Detect overflow:scroll screwiness (gh-3699)
- // Support: Chrome <=64
- // Don't get tricked when zoom affects offsetWidth (gh-4029)
- div.style.position = "absolute";
- scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
- documentElement.removeChild( container );
-
- // Nullify the div so it wouldn't be stored in the memory and
- // it will also be a sign that checks already performed
- div = null;
- }
-
- function roundPixelMeasures( measure ) {
- return Math.round( parseFloat( measure ) );
- }
-
- var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
- reliableTrDimensionsVal, reliableMarginLeftVal,
- container = document.createElement( "div" ),
- div = document.createElement( "div" );
-
- // Finish early in limited (non-browser) environments
- if ( !div.style ) {
- return;
- }
-
- // Support: IE <=9 - 11 only
- // Style of cloned element affects source element cloned (#8908)
- div.style.backgroundClip = "content-box";
- div.cloneNode( true ).style.backgroundClip = "";
- support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
- jQuery.extend( support, {
- boxSizingReliable: function() {
- computeStyleTests();
- return boxSizingReliableVal;
- },
- pixelBoxStyles: function() {
- computeStyleTests();
- return pixelBoxStylesVal;
- },
- pixelPosition: function() {
- computeStyleTests();
- return pixelPositionVal;
- },
- reliableMarginLeft: function() {
- computeStyleTests();
- return reliableMarginLeftVal;
- },
- scrollboxSize: function() {
- computeStyleTests();
- return scrollboxSizeVal;
- },
-
- // Support: IE 9 - 11+, Edge 15 - 18+
- // IE/Edge misreport `getComputedStyle` of table rows with width/height
- // set in CSS while `offset*` properties report correct values.
- // Behavior in IE 9 is more subtle than in newer versions & it passes
- // some versions of this test; make sure not to make it pass there!
- //
- // Support: Firefox 70+
- // Only Firefox includes border widths
- // in computed dimensions. (gh-4529)
- reliableTrDimensions: function() {
- var table, tr, trChild, trStyle;
- if ( reliableTrDimensionsVal == null ) {
- table = document.createElement( "table" );
- tr = document.createElement( "tr" );
- trChild = document.createElement( "div" );
-
- table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
- tr.style.cssText = "border:1px solid";
-
- // Support: Chrome 86+
- // Height set through cssText does not get applied.
- // Computed height then comes back as 0.
- tr.style.height = "1px";
- trChild.style.height = "9px";
-
- // Support: Android 8 Chrome 86+
- // In our bodyBackground.html iframe,
- // display for all div elements is set to "inline",
- // which causes a problem only in Android 8 Chrome 86.
- // Ensuring the div is display: block
- // gets around this issue.
- trChild.style.display = "block";
-
- documentElement
- .appendChild( table )
- .appendChild( tr )
- .appendChild( trChild );
-
- trStyle = window.getComputedStyle( tr );
- reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
- parseInt( trStyle.borderTopWidth, 10 ) +
- parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
-
- documentElement.removeChild( table );
- }
- return reliableTrDimensionsVal;
- }
- } );
-} )();
-
-
-function curCSS( elem, name, computed ) {
- var width, minWidth, maxWidth, ret,
-
- // Support: Firefox 51+
- // Retrieving style before computed somehow
- // fixes an issue with getting wrong values
- // on detached elements
- style = elem.style;
-
- computed = computed || getStyles( elem );
-
- // getPropertyValue is needed for:
- // .css('filter') (IE 9 only, #12537)
- // .css('--customProperty) (#3144)
- if ( computed ) {
- ret = computed.getPropertyValue( name ) || computed[ name ];
-
- if ( ret === "" && !isAttached( elem ) ) {
- ret = jQuery.style( elem, name );
- }
-
- // A tribute to the "awesome hack by Dean Edwards"
- // Android Browser returns percentage for some values,
- // but width seems to be reliably pixels.
- // This is against the CSSOM draft spec:
- // https://drafts.csswg.org/cssom/#resolved-values
- if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
- // Remember the original values
- width = style.width;
- minWidth = style.minWidth;
- maxWidth = style.maxWidth;
-
- // Put in the new values to get a computed value out
- style.minWidth = style.maxWidth = style.width = ret;
- ret = computed.width;
-
- // Revert the changed values
- style.width = width;
- style.minWidth = minWidth;
- style.maxWidth = maxWidth;
- }
- }
-
- return ret !== undefined ?
-
- // Support: IE <=9 - 11 only
- // IE returns zIndex value as an integer.
- ret + "" :
- ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
- // Define the hook, we'll check on the first run if it's really needed.
- return {
- get: function() {
- if ( conditionFn() ) {
-
- // Hook not needed (or it's not possible to use it due
- // to missing dependency), remove it.
- delete this.get;
- return;
- }
-
- // Hook needed; redefine it so that the support test is not executed again.
- return ( this.get = hookFn ).apply( this, arguments );
- }
- };
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
- emptyStyle = document.createElement( "div" ).style,
- vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
- // Check for vendor prefixed names
- var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
- i = cssPrefixes.length;
-
- while ( i-- ) {
- name = cssPrefixes[ i ] + capName;
- if ( name in emptyStyle ) {
- return name;
- }
- }
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
- var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
- if ( final ) {
- return final;
- }
- if ( name in emptyStyle ) {
- return name;
- }
- return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
- // Swappable if display is none or starts with table
- // except "table", "table-cell", or "table-caption"
- // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
- rdisplayswap = /^(none|table(?!-c[ea]).+)/,
- rcustomProp = /^--/,
- cssShow = { position: "absolute", visibility: "hidden", display: "block" },
- cssNormalTransform = {
- letterSpacing: "0",
- fontWeight: "400"
- };
-
-function setPositiveNumber( _elem, value, subtract ) {
-
- // Any relative (+/-) values have already been
- // normalized at this point
- var matches = rcssNum.exec( value );
- return matches ?
-
- // Guard against undefined "subtract", e.g., when used as in cssHooks
- Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
- value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
- var i = dimension === "width" ? 1 : 0,
- extra = 0,
- delta = 0;
-
- // Adjustment may not be necessary
- if ( box === ( isBorderBox ? "border" : "content" ) ) {
- return 0;
- }
-
- for ( ; i < 4; i += 2 ) {
-
- // Both box models exclude margin
- if ( box === "margin" ) {
- delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
- }
-
- // If we get here with a content-box, we're seeking "padding" or "border" or "margin"
- if ( !isBorderBox ) {
-
- // Add padding
- delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
- // For "border" or "margin", add border
- if ( box !== "padding" ) {
- delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
- // But still keep track of it otherwise
- } else {
- extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
- }
-
- // If we get here with a border-box (content + padding + border), we're seeking "content" or
- // "padding" or "margin"
- } else {
-
- // For "content", subtract padding
- if ( box === "content" ) {
- delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
- }
-
- // For "content" or "padding", subtract border
- if ( box !== "margin" ) {
- delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
- }
- }
- }
-
- // Account for positive content-box scroll gutter when requested by providing computedVal
- if ( !isBorderBox && computedVal >= 0 ) {
-
- // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
- // Assuming integer scroll gutter, subtract the rest and round down
- delta += Math.max( 0, Math.ceil(
- elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
- computedVal -
- delta -
- extra -
- 0.5
-
- // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
- // Use an explicit zero to avoid NaN (gh-3964)
- ) ) || 0;
- }
-
- return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
- // Start with computed style
- var styles = getStyles( elem ),
-
- // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
- // Fake content-box until we know it's needed to know the true value.
- boxSizingNeeded = !support.boxSizingReliable() || extra,
- isBorderBox = boxSizingNeeded &&
- jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
- valueIsBorderBox = isBorderBox,
-
- val = curCSS( elem, dimension, styles ),
- offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
- // Support: Firefox <=54
- // Return a confounding non-pixel value or feign ignorance, as appropriate.
- if ( rnumnonpx.test( val ) ) {
- if ( !extra ) {
- return val;
- }
- val = "auto";
- }
-
-
- // Support: IE 9 - 11 only
- // Use offsetWidth/offsetHeight for when box sizing is unreliable.
- // In those cases, the computed value can be trusted to be border-box.
- if ( ( !support.boxSizingReliable() && isBorderBox ||
-
- // Support: IE 10 - 11+, Edge 15 - 18+
- // IE/Edge misreport `getComputedStyle` of table rows with width/height
- // set in CSS while `offset*` properties report correct values.
- // Interestingly, in some cases IE 9 doesn't suffer from this issue.
- !support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
- // Fall back to offsetWidth/offsetHeight when value is "auto"
- // This happens for inline elements with no explicit setting (gh-3571)
- val === "auto" ||
-
- // Support: Android <=4.1 - 4.3 only
- // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
- !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
- // Make sure the element is visible & connected
- elem.getClientRects().length ) {
-
- isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
- // Where available, offsetWidth/offsetHeight approximate border box dimensions.
- // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
- // retrieved value as a content box dimension.
- valueIsBorderBox = offsetProp in elem;
- if ( valueIsBorderBox ) {
- val = elem[ offsetProp ];
- }
- }
-
- // Normalize "" and auto
- val = parseFloat( val ) || 0;
-
- // Adjust for the element's box model
- return ( val +
- boxModelAdjustment(
- elem,
- dimension,
- extra || ( isBorderBox ? "border" : "content" ),
- valueIsBorderBox,
- styles,
-
- // Provide the current computed size to request scroll gutter calculation (gh-3589)
- val
- )
- ) + "px";
-}
-
-jQuery.extend( {
-
- // Add in style property hooks for overriding the default
- // behavior of getting and setting a style property
- cssHooks: {
- opacity: {
- get: function( elem, computed ) {
- if ( computed ) {
-
- // We should always get a number back from opacity
- var ret = curCSS( elem, "opacity" );
- return ret === "" ? "1" : ret;
- }
- }
- }
- },
-
- // Don't automatically add "px" to these possibly-unitless properties
- cssNumber: {
- "animationIterationCount": true,
- "columnCount": true,
- "fillOpacity": true,
- "flexGrow": true,
- "flexShrink": true,
- "fontWeight": true,
- "gridArea": true,
- "gridColumn": true,
- "gridColumnEnd": true,
- "gridColumnStart": true,
- "gridRow": true,
- "gridRowEnd": true,
- "gridRowStart": true,
- "lineHeight": true,
- "opacity": true,
- "order": true,
- "orphans": true,
- "widows": true,
- "zIndex": true,
- "zoom": true
- },
-
- // Add in properties whose names you wish to fix before
- // setting or getting the value
- cssProps: {},
-
- // Get and set the style property on a DOM Node
- style: function( elem, name, value, extra ) {
-
- // Don't set styles on text and comment nodes
- if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
- return;
- }
-
- // Make sure that we're working with the right name
- var ret, type, hooks,
- origName = camelCase( name ),
- isCustomProp = rcustomProp.test( name ),
- style = elem.style;
-
- // Make sure that we're working with the right name. We don't
- // want to query the value if it is a CSS custom property
- // since they are user-defined.
- if ( !isCustomProp ) {
- name = finalPropName( origName );
- }
-
- // Gets hook for the prefixed version, then unprefixed version
- hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
- // Check if we're setting a value
- if ( value !== undefined ) {
- type = typeof value;
-
- // Convert "+=" or "-=" to relative numbers (#7345)
- if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
- value = adjustCSS( elem, name, ret );
-
- // Fixes bug #9237
- type = "number";
- }
-
- // Make sure that null and NaN values aren't set (#7116)
- if ( value == null || value !== value ) {
- return;
- }
-
- // If a number was passed in, add the unit (except for certain CSS properties)
- // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
- // "px" to a few hardcoded values.
- if ( type === "number" && !isCustomProp ) {
- value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
- }
-
- // background-* props affect original clone's values
- if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
- style[ name ] = "inherit";
- }
-
- // If a hook was provided, use that value, otherwise just set the specified value
- if ( !hooks || !( "set" in hooks ) ||
- ( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
- if ( isCustomProp ) {
- style.setProperty( name, value );
- } else {
- style[ name ] = value;
- }
- }
-
- } else {
-
- // If a hook was provided get the non-computed value from there
- if ( hooks && "get" in hooks &&
- ( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
- return ret;
- }
-
- // Otherwise just get the value from the style object
- return style[ name ];
- }
- },
-
- css: function( elem, name, extra, styles ) {
- var val, num, hooks,
- origName = camelCase( name ),
- isCustomProp = rcustomProp.test( name );
-
- // Make sure that we're working with the right name. We don't
- // want to modify the value if it is a CSS custom property
- // since they are user-defined.
- if ( !isCustomProp ) {
- name = finalPropName( origName );
- }
-
- // Try prefixed name followed by the unprefixed name
- hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
- // If a hook was provided get the computed value from there
- if ( hooks && "get" in hooks ) {
- val = hooks.get( elem, true, extra );
- }
-
- // Otherwise, if a way to get the computed value exists, use that
- if ( val === undefined ) {
- val = curCSS( elem, name, styles );
- }
-
- // Convert "normal" to computed value
- if ( val === "normal" && name in cssNormalTransform ) {
- val = cssNormalTransform[ name ];
- }
-
- // Make numeric if forced or a qualifier was provided and val looks numeric
- if ( extra === "" || extra ) {
- num = parseFloat( val );
- return extra === true || isFinite( num ) ? num || 0 : val;
- }
-
- return val;
- }
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
- jQuery.cssHooks[ dimension ] = {
- get: function( elem, computed, extra ) {
- if ( computed ) {
-
- // Certain elements can have dimension info if we invisibly show them
- // but it must have a current display style that would benefit
- return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
- // Support: Safari 8+
- // Table columns in Safari have non-zero offsetWidth & zero
- // getBoundingClientRect().width unless display is changed.
- // Support: IE <=11 only
- // Running getBoundingClientRect on a disconnected node
- // in IE throws an error.
- ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
- swap( elem, cssShow, function() {
- return getWidthOrHeight( elem, dimension, extra );
- } ) :
- getWidthOrHeight( elem, dimension, extra );
- }
- },
-
- set: function( elem, value, extra ) {
- var matches,
- styles = getStyles( elem ),
-
- // Only read styles.position if the test has a chance to fail
- // to avoid forcing a reflow.
- scrollboxSizeBuggy = !support.scrollboxSize() &&
- styles.position === "absolute",
-
- // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
- boxSizingNeeded = scrollboxSizeBuggy || extra,
- isBorderBox = boxSizingNeeded &&
- jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
- subtract = extra ?
- boxModelAdjustment(
- elem,
- dimension,
- extra,
- isBorderBox,
- styles
- ) :
- 0;
-
- // Account for unreliable border-box dimensions by comparing offset* to computed and
- // faking a content-box to get border and padding (gh-3699)
- if ( isBorderBox && scrollboxSizeBuggy ) {
- subtract -= Math.ceil(
- elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
- parseFloat( styles[ dimension ] ) -
- boxModelAdjustment( elem, dimension, "border", false, styles ) -
- 0.5
- );
- }
-
- // Convert to pixels if value adjustment is needed
- if ( subtract && ( matches = rcssNum.exec( value ) ) &&
- ( matches[ 3 ] || "px" ) !== "px" ) {
-
- elem.style[ dimension ] = value;
- value = jQuery.css( elem, dimension );
- }
-
- return setPositiveNumber( elem, value, subtract );
- }
- };
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
- function( elem, computed ) {
- if ( computed ) {
- return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
- elem.getBoundingClientRect().left -
- swap( elem, { marginLeft: 0 }, function() {
- return elem.getBoundingClientRect().left;
- } )
- ) + "px";
- }
- }
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
- margin: "",
- padding: "",
- border: "Width"
-}, function( prefix, suffix ) {
- jQuery.cssHooks[ prefix + suffix ] = {
- expand: function( value ) {
- var i = 0,
- expanded = {},
-
- // Assumes a single number if not a string
- parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
- for ( ; i < 4; i++ ) {
- expanded[ prefix + cssExpand[ i ] + suffix ] =
- parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
- }
-
- return expanded;
- }
- };
-
- if ( prefix !== "margin" ) {
- jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
- }
-} );
-
-jQuery.fn.extend( {
- css: function( name, value ) {
- return access( this, function( elem, name, value ) {
- var styles, len,
- map = {},
- i = 0;
-
- if ( Array.isArray( name ) ) {
- styles = getStyles( elem );
- len = name.length;
-
- for ( ; i < len; i++ ) {
- map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
- }
-
- return map;
- }
-
- return value !== undefined ?
- jQuery.style( elem, name, value ) :
- jQuery.css( elem, name );
- }, name, value, arguments.length > 1 );
- }
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
- return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
- constructor: Tween,
- init: function( elem, options, prop, end, easing, unit ) {
- this.elem = elem;
- this.prop = prop;
- this.easing = easing || jQuery.easing._default;
- this.options = options;
- this.start = this.now = this.cur();
- this.end = end;
- this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
- },
- cur: function() {
- var hooks = Tween.propHooks[ this.prop ];
-
- return hooks && hooks.get ?
- hooks.get( this ) :
- Tween.propHooks._default.get( this );
- },
- run: function( percent ) {
- var eased,
- hooks = Tween.propHooks[ this.prop ];
-
- if ( this.options.duration ) {
- this.pos = eased = jQuery.easing[ this.easing ](
- percent, this.options.duration * percent, 0, 1, this.options.duration
- );
- } else {
- this.pos = eased = percent;
- }
- this.now = ( this.end - this.start ) * eased + this.start;
-
- if ( this.options.step ) {
- this.options.step.call( this.elem, this.now, this );
- }
-
- if ( hooks && hooks.set ) {
- hooks.set( this );
- } else {
- Tween.propHooks._default.set( this );
- }
- return this;
- }
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
- _default: {
- get: function( tween ) {
- var result;
-
- // Use a property on the element directly when it is not a DOM element,
- // or when there is no matching style property that exists.
- if ( tween.elem.nodeType !== 1 ||
- tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
- return tween.elem[ tween.prop ];
- }
-
- // Passing an empty string as a 3rd parameter to .css will automatically
- // attempt a parseFloat and fallback to a string if the parse fails.
- // Simple values such as "10px" are parsed to Float;
- // complex values such as "rotate(1rad)" are returned as-is.
- result = jQuery.css( tween.elem, tween.prop, "" );
-
- // Empty strings, null, undefined and "auto" are converted to 0.
- return !result || result === "auto" ? 0 : result;
- },
- set: function( tween ) {
-
- // Use step hook for back compat.
- // Use cssHook if its there.
- // Use .style if available and use plain properties where available.
- if ( jQuery.fx.step[ tween.prop ] ) {
- jQuery.fx.step[ tween.prop ]( tween );
- } else if ( tween.elem.nodeType === 1 && (
- jQuery.cssHooks[ tween.prop ] ||
- tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
- jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
- } else {
- tween.elem[ tween.prop ] = tween.now;
- }
- }
- }
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
- set: function( tween ) {
- if ( tween.elem.nodeType && tween.elem.parentNode ) {
- tween.elem[ tween.prop ] = tween.now;
- }
- }
-};
-
-jQuery.easing = {
- linear: function( p ) {
- return p;
- },
- swing: function( p ) {
- return 0.5 - Math.cos( p * Math.PI ) / 2;
- },
- _default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
- fxNow, inProgress,
- rfxtypes = /^(?:toggle|show|hide)$/,
- rrun = /queueHooks$/;
-
-function schedule() {
- if ( inProgress ) {
- if ( document.hidden === false && window.requestAnimationFrame ) {
- window.requestAnimationFrame( schedule );
- } else {
- window.setTimeout( schedule, jQuery.fx.interval );
- }
-
- jQuery.fx.tick();
- }
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
- window.setTimeout( function() {
- fxNow = undefined;
- } );
- return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
- var which,
- i = 0,
- attrs = { height: type };
-
- // If we include width, step value is 1 to do all cssExpand values,
- // otherwise step value is 2 to skip over Left and Right
- includeWidth = includeWidth ? 1 : 0;
- for ( ; i < 4; i += 2 - includeWidth ) {
- which = cssExpand[ i ];
- attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
- }
-
- if ( includeWidth ) {
- attrs.opacity = attrs.width = type;
- }
-
- return attrs;
-}
-
-function createTween( value, prop, animation ) {
- var tween,
- collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
- index = 0,
- length = collection.length;
- for ( ; index < length; index++ ) {
- if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
- // We're done with this property
- return tween;
- }
- }
-}
-
-function defaultPrefilter( elem, props, opts ) {
- var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
- isBox = "width" in props || "height" in props,
- anim = this,
- orig = {},
- style = elem.style,
- hidden = elem.nodeType && isHiddenWithinTree( elem ),
- dataShow = dataPriv.get( elem, "fxshow" );
-
- // Queue-skipping animations hijack the fx hooks
- if ( !opts.queue ) {
- hooks = jQuery._queueHooks( elem, "fx" );
- if ( hooks.unqueued == null ) {
- hooks.unqueued = 0;
- oldfire = hooks.empty.fire;
- hooks.empty.fire = function() {
- if ( !hooks.unqueued ) {
- oldfire();
- }
- };
- }
- hooks.unqueued++;
-
- anim.always( function() {
-
- // Ensure the complete handler is called before this completes
- anim.always( function() {
- hooks.unqueued--;
- if ( !jQuery.queue( elem, "fx" ).length ) {
- hooks.empty.fire();
- }
- } );
- } );
- }
-
- // Detect show/hide animations
- for ( prop in props ) {
- value = props[ prop ];
- if ( rfxtypes.test( value ) ) {
- delete props[ prop ];
- toggle = toggle || value === "toggle";
- if ( value === ( hidden ? "hide" : "show" ) ) {
-
- // Pretend to be hidden if this is a "show" and
- // there is still data from a stopped show/hide
- if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
- hidden = true;
-
- // Ignore all other no-op show/hide data
- } else {
- continue;
- }
- }
- orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
- }
- }
-
- // Bail out if this is a no-op like .hide().hide()
- propTween = !jQuery.isEmptyObject( props );
- if ( !propTween && jQuery.isEmptyObject( orig ) ) {
- return;
- }
-
- // Restrict "overflow" and "display" styles during box animations
- if ( isBox && elem.nodeType === 1 ) {
-
- // Support: IE <=9 - 11, Edge 12 - 15
- // Record all 3 overflow attributes because IE does not infer the shorthand
- // from identically-valued overflowX and overflowY and Edge just mirrors
- // the overflowX value there.
- opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
- // Identify a display type, preferring old show/hide data over the CSS cascade
- restoreDisplay = dataShow && dataShow.display;
- if ( restoreDisplay == null ) {
- restoreDisplay = dataPriv.get( elem, "display" );
- }
- display = jQuery.css( elem, "display" );
- if ( display === "none" ) {
- if ( restoreDisplay ) {
- display = restoreDisplay;
- } else {
-
- // Get nonempty value(s) by temporarily forcing visibility
- showHide( [ elem ], true );
- restoreDisplay = elem.style.display || restoreDisplay;
- display = jQuery.css( elem, "display" );
- showHide( [ elem ] );
- }
- }
-
- // Animate inline elements as inline-block
- if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
- if ( jQuery.css( elem, "float" ) === "none" ) {
-
- // Restore the original display value at the end of pure show/hide animations
- if ( !propTween ) {
- anim.done( function() {
- style.display = restoreDisplay;
- } );
- if ( restoreDisplay == null ) {
- display = style.display;
- restoreDisplay = display === "none" ? "" : display;
- }
- }
- style.display = "inline-block";
- }
- }
- }
-
- if ( opts.overflow ) {
- style.overflow = "hidden";
- anim.always( function() {
- style.overflow = opts.overflow[ 0 ];
- style.overflowX = opts.overflow[ 1 ];
- style.overflowY = opts.overflow[ 2 ];
- } );
- }
-
- // Implement show/hide animations
- propTween = false;
- for ( prop in orig ) {
-
- // General show/hide setup for this element animation
- if ( !propTween ) {
- if ( dataShow ) {
- if ( "hidden" in dataShow ) {
- hidden = dataShow.hidden;
- }
- } else {
- dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
- }
-
- // Store hidden/visible for toggle so `.stop().toggle()` "reverses"
- if ( toggle ) {
- dataShow.hidden = !hidden;
- }
-
- // Show elements before animating them
- if ( hidden ) {
- showHide( [ elem ], true );
- }
-
- /* eslint-disable no-loop-func */
-
- anim.done( function() {
-
- /* eslint-enable no-loop-func */
-
- // The final step of a "hide" animation is actually hiding the element
- if ( !hidden ) {
- showHide( [ elem ] );
- }
- dataPriv.remove( elem, "fxshow" );
- for ( prop in orig ) {
- jQuery.style( elem, prop, orig[ prop ] );
- }
- } );
- }
-
- // Per-property setup
- propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
- if ( !( prop in dataShow ) ) {
- dataShow[ prop ] = propTween.start;
- if ( hidden ) {
- propTween.end = propTween.start;
- propTween.start = 0;
- }
- }
- }
-}
-
-function propFilter( props, specialEasing ) {
- var index, name, easing, value, hooks;
-
- // camelCase, specialEasing and expand cssHook pass
- for ( index in props ) {
- name = camelCase( index );
- easing = specialEasing[ name ];
- value = props[ index ];
- if ( Array.isArray( value ) ) {
- easing = value[ 1 ];
- value = props[ index ] = value[ 0 ];
- }
-
- if ( index !== name ) {
- props[ name ] = value;
- delete props[ index ];
- }
-
- hooks = jQuery.cssHooks[ name ];
- if ( hooks && "expand" in hooks ) {
- value = hooks.expand( value );
- delete props[ name ];
-
- // Not quite $.extend, this won't overwrite existing keys.
- // Reusing 'index' because we have the correct "name"
- for ( index in value ) {
- if ( !( index in props ) ) {
- props[ index ] = value[ index ];
- specialEasing[ index ] = easing;
- }
- }
- } else {
- specialEasing[ name ] = easing;
- }
- }
-}
-
-function Animation( elem, properties, options ) {
- var result,
- stopped,
- index = 0,
- length = Animation.prefilters.length,
- deferred = jQuery.Deferred().always( function() {
-
- // Don't match elem in the :animated selector
- delete tick.elem;
- } ),
- tick = function() {
- if ( stopped ) {
- return false;
- }
- var currentTime = fxNow || createFxNow(),
- remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
- // Support: Android 2.3 only
- // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
- temp = remaining / animation.duration || 0,
- percent = 1 - temp,
- index = 0,
- length = animation.tweens.length;
-
- for ( ; index < length; index++ ) {
- animation.tweens[ index ].run( percent );
- }
-
- deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
- // If there's more to do, yield
- if ( percent < 1 && length ) {
- return remaining;
- }
-
- // If this was an empty animation, synthesize a final progress notification
- if ( !length ) {
- deferred.notifyWith( elem, [ animation, 1, 0 ] );
- }
-
- // Resolve the animation and report its conclusion
- deferred.resolveWith( elem, [ animation ] );
- return false;
- },
- animation = deferred.promise( {
- elem: elem,
- props: jQuery.extend( {}, properties ),
- opts: jQuery.extend( true, {
- specialEasing: {},
- easing: jQuery.easing._default
- }, options ),
- originalProperties: properties,
- originalOptions: options,
- startTime: fxNow || createFxNow(),
- duration: options.duration,
- tweens: [],
- createTween: function( prop, end ) {
- var tween = jQuery.Tween( elem, animation.opts, prop, end,
- animation.opts.specialEasing[ prop ] || animation.opts.easing );
- animation.tweens.push( tween );
- return tween;
- },
- stop: function( gotoEnd ) {
- var index = 0,
-
- // If we are going to the end, we want to run all the tweens
- // otherwise we skip this part
- length = gotoEnd ? animation.tweens.length : 0;
- if ( stopped ) {
- return this;
- }
- stopped = true;
- for ( ; index < length; index++ ) {
- animation.tweens[ index ].run( 1 );
- }
-
- // Resolve when we played the last frame; otherwise, reject
- if ( gotoEnd ) {
- deferred.notifyWith( elem, [ animation, 1, 0 ] );
- deferred.resolveWith( elem, [ animation, gotoEnd ] );
- } else {
- deferred.rejectWith( elem, [ animation, gotoEnd ] );
- }
- return this;
- }
- } ),
- props = animation.props;
-
- propFilter( props, animation.opts.specialEasing );
-
- for ( ; index < length; index++ ) {
- result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
- if ( result ) {
- if ( isFunction( result.stop ) ) {
- jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
- result.stop.bind( result );
- }
- return result;
- }
- }
-
- jQuery.map( props, createTween, animation );
-
- if ( isFunction( animation.opts.start ) ) {
- animation.opts.start.call( elem, animation );
- }
-
- // Attach callbacks from options
- animation
- .progress( animation.opts.progress )
- .done( animation.opts.done, animation.opts.complete )
- .fail( animation.opts.fail )
- .always( animation.opts.always );
-
- jQuery.fx.timer(
- jQuery.extend( tick, {
- elem: elem,
- anim: animation,
- queue: animation.opts.queue
- } )
- );
-
- return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
- tweeners: {
- "*": [ function( prop, value ) {
- var tween = this.createTween( prop, value );
- adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
- return tween;
- } ]
- },
-
- tweener: function( props, callback ) {
- if ( isFunction( props ) ) {
- callback = props;
- props = [ "*" ];
- } else {
- props = props.match( rnothtmlwhite );
- }
-
- var prop,
- index = 0,
- length = props.length;
-
- for ( ; index < length; index++ ) {
- prop = props[ index ];
- Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
- Animation.tweeners[ prop ].unshift( callback );
- }
- },
-
- prefilters: [ defaultPrefilter ],
-
- prefilter: function( callback, prepend ) {
- if ( prepend ) {
- Animation.prefilters.unshift( callback );
- } else {
- Animation.prefilters.push( callback );
- }
- }
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
- var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
- complete: fn || !fn && easing ||
- isFunction( speed ) && speed,
- duration: speed,
- easing: fn && easing || easing && !isFunction( easing ) && easing
- };
-
- // Go to the end state if fx are off
- if ( jQuery.fx.off ) {
- opt.duration = 0;
-
- } else {
- if ( typeof opt.duration !== "number" ) {
- if ( opt.duration in jQuery.fx.speeds ) {
- opt.duration = jQuery.fx.speeds[ opt.duration ];
-
- } else {
- opt.duration = jQuery.fx.speeds._default;
- }
- }
- }
-
- // Normalize opt.queue - true/undefined/null -> "fx"
- if ( opt.queue == null || opt.queue === true ) {
- opt.queue = "fx";
- }
-
- // Queueing
- opt.old = opt.complete;
-
- opt.complete = function() {
- if ( isFunction( opt.old ) ) {
- opt.old.call( this );
- }
-
- if ( opt.queue ) {
- jQuery.dequeue( this, opt.queue );
- }
- };
-
- return opt;
-};
-
-jQuery.fn.extend( {
- fadeTo: function( speed, to, easing, callback ) {
-
- // Show any hidden elements after setting opacity to 0
- return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
- // Animate to the value specified
- .end().animate( { opacity: to }, speed, easing, callback );
- },
- animate: function( prop, speed, easing, callback ) {
- var empty = jQuery.isEmptyObject( prop ),
- optall = jQuery.speed( speed, easing, callback ),
- doAnimation = function() {
-
- // Operate on a copy of prop so per-property easing won't be lost
- var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
- // Empty animations, or finishing resolves immediately
- if ( empty || dataPriv.get( this, "finish" ) ) {
- anim.stop( true );
- }
- };
-
- doAnimation.finish = doAnimation;
-
- return empty || optall.queue === false ?
- this.each( doAnimation ) :
- this.queue( optall.queue, doAnimation );
- },
- stop: function( type, clearQueue, gotoEnd ) {
- var stopQueue = function( hooks ) {
- var stop = hooks.stop;
- delete hooks.stop;
- stop( gotoEnd );
- };
-
- if ( typeof type !== "string" ) {
- gotoEnd = clearQueue;
- clearQueue = type;
- type = undefined;
- }
- if ( clearQueue ) {
- this.queue( type || "fx", [] );
- }
-
- return this.each( function() {
- var dequeue = true,
- index = type != null && type + "queueHooks",
- timers = jQuery.timers,
- data = dataPriv.get( this );
-
- if ( index ) {
- if ( data[ index ] && data[ index ].stop ) {
- stopQueue( data[ index ] );
- }
- } else {
- for ( index in data ) {
- if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
- stopQueue( data[ index ] );
- }
- }
- }
-
- for ( index = timers.length; index--; ) {
- if ( timers[ index ].elem === this &&
- ( type == null || timers[ index ].queue === type ) ) {
-
- timers[ index ].anim.stop( gotoEnd );
- dequeue = false;
- timers.splice( index, 1 );
- }
- }
-
- // Start the next in the queue if the last step wasn't forced.
- // Timers currently will call their complete callbacks, which
- // will dequeue but only if they were gotoEnd.
- if ( dequeue || !gotoEnd ) {
- jQuery.dequeue( this, type );
- }
- } );
- },
- finish: function( type ) {
- if ( type !== false ) {
- type = type || "fx";
- }
- return this.each( function() {
- var index,
- data = dataPriv.get( this ),
- queue = data[ type + "queue" ],
- hooks = data[ type + "queueHooks" ],
- timers = jQuery.timers,
- length = queue ? queue.length : 0;
-
- // Enable finishing flag on private data
- data.finish = true;
-
- // Empty the queue first
- jQuery.queue( this, type, [] );
-
- if ( hooks && hooks.stop ) {
- hooks.stop.call( this, true );
- }
-
- // Look for any active animations, and finish them
- for ( index = timers.length; index--; ) {
- if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
- timers[ index ].anim.stop( true );
- timers.splice( index, 1 );
- }
- }
-
- // Look for any animations in the old queue and finish them
- for ( index = 0; index < length; index++ ) {
- if ( queue[ index ] && queue[ index ].finish ) {
- queue[ index ].finish.call( this );
- }
- }
-
- // Turn off finishing flag
- delete data.finish;
- } );
- }
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
- var cssFn = jQuery.fn[ name ];
- jQuery.fn[ name ] = function( speed, easing, callback ) {
- return speed == null || typeof speed === "boolean" ?
- cssFn.apply( this, arguments ) :
- this.animate( genFx( name, true ), speed, easing, callback );
- };
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
- slideDown: genFx( "show" ),
- slideUp: genFx( "hide" ),
- slideToggle: genFx( "toggle" ),
- fadeIn: { opacity: "show" },
- fadeOut: { opacity: "hide" },
- fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
- jQuery.fn[ name ] = function( speed, easing, callback ) {
- return this.animate( props, speed, easing, callback );
- };
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
- var timer,
- i = 0,
- timers = jQuery.timers;
-
- fxNow = Date.now();
-
- for ( ; i < timers.length; i++ ) {
- timer = timers[ i ];
-
- // Run the timer and safely remove it when done (allowing for external removal)
- if ( !timer() && timers[ i ] === timer ) {
- timers.splice( i--, 1 );
- }
- }
-
- if ( !timers.length ) {
- jQuery.fx.stop();
- }
- fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
- jQuery.timers.push( timer );
- jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
- if ( inProgress ) {
- return;
- }
-
- inProgress = true;
- schedule();
-};
-
-jQuery.fx.stop = function() {
- inProgress = null;
-};
-
-jQuery.fx.speeds = {
- slow: 600,
- fast: 200,
-
- // Default speed
- _default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
- time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
- type = type || "fx";
-
- return this.queue( type, function( next, hooks ) {
- var timeout = window.setTimeout( next, time );
- hooks.stop = function() {
- window.clearTimeout( timeout );
- };
- } );
-};
-
-
-( function() {
- var input = document.createElement( "input" ),
- select = document.createElement( "select" ),
- opt = select.appendChild( document.createElement( "option" ) );
-
- input.type = "checkbox";
-
- // Support: Android <=4.3 only
- // Default value for a checkbox should be "on"
- support.checkOn = input.value !== "";
-
- // Support: IE <=11 only
- // Must access selectedIndex to make default options select
- support.optSelected = opt.selected;
-
- // Support: IE <=11 only
- // An input loses its value after becoming a radio
- input = document.createElement( "input" );
- input.value = "t";
- input.type = "radio";
- support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
- attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
- attr: function( name, value ) {
- return access( this, jQuery.attr, name, value, arguments.length > 1 );
- },
-
- removeAttr: function( name ) {
- return this.each( function() {
- jQuery.removeAttr( this, name );
- } );
- }
-} );
-
-jQuery.extend( {
- attr: function( elem, name, value ) {
- var ret, hooks,
- nType = elem.nodeType;
-
- // Don't get/set attributes on text, comment and attribute nodes
- if ( nType === 3 || nType === 8 || nType === 2 ) {
- return;
- }
-
- // Fallback to prop when attributes are not supported
- if ( typeof elem.getAttribute === "undefined" ) {
- return jQuery.prop( elem, name, value );
- }
-
- // Attribute hooks are determined by the lowercase version
- // Grab necessary hook if one is defined
- if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
- hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
- ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
- }
-
- if ( value !== undefined ) {
- if ( value === null ) {
- jQuery.removeAttr( elem, name );
- return;
- }
-
- if ( hooks && "set" in hooks &&
- ( ret = hooks.set( elem, value, name ) ) !== undefined ) {
- return ret;
- }
-
- elem.setAttribute( name, value + "" );
- return value;
- }
-
- if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
- return ret;
- }
-
- ret = jQuery.find.attr( elem, name );
-
- // Non-existent attributes return null, we normalize to undefined
- return ret == null ? undefined : ret;
- },
-
- attrHooks: {
- type: {
- set: function( elem, value ) {
- if ( !support.radioValue && value === "radio" &&
- nodeName( elem, "input" ) ) {
- var val = elem.value;
- elem.setAttribute( "type", value );
- if ( val ) {
- elem.value = val;
- }
- return value;
- }
- }
- }
- },
-
- removeAttr: function( elem, value ) {
- var name,
- i = 0,
-
- // Attribute names can contain non-HTML whitespace characters
- // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
- attrNames = value && value.match( rnothtmlwhite );
-
- if ( attrNames && elem.nodeType === 1 ) {
- while ( ( name = attrNames[ i++ ] ) ) {
- elem.removeAttribute( name );
- }
- }
- }
-} );
-
-// Hooks for boolean attributes
-boolHook = {
- set: function( elem, value, name ) {
- if ( value === false ) {
-
- // Remove boolean attributes when set to false
- jQuery.removeAttr( elem, name );
- } else {
- elem.setAttribute( name, name );
- }
- return name;
- }
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
- var getter = attrHandle[ name ] || jQuery.find.attr;
-
- attrHandle[ name ] = function( elem, name, isXML ) {
- var ret, handle,
- lowercaseName = name.toLowerCase();
-
- if ( !isXML ) {
-
- // Avoid an infinite loop by temporarily removing this function from the getter
- handle = attrHandle[ lowercaseName ];
- attrHandle[ lowercaseName ] = ret;
- ret = getter( elem, name, isXML ) != null ?
- lowercaseName :
- null;
- attrHandle[ lowercaseName ] = handle;
- }
- return ret;
- };
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
- rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
- prop: function( name, value ) {
- return access( this, jQuery.prop, name, value, arguments.length > 1 );
- },
-
- removeProp: function( name ) {
- return this.each( function() {
- delete this[ jQuery.propFix[ name ] || name ];
- } );
- }
-} );
-
-jQuery.extend( {
- prop: function( elem, name, value ) {
- var ret, hooks,
- nType = elem.nodeType;
-
- // Don't get/set properties on text, comment and attribute nodes
- if ( nType === 3 || nType === 8 || nType === 2 ) {
- return;
- }
-
- if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
- // Fix name and attach hooks
- name = jQuery.propFix[ name ] || name;
- hooks = jQuery.propHooks[ name ];
- }
-
- if ( value !== undefined ) {
- if ( hooks && "set" in hooks &&
- ( ret = hooks.set( elem, value, name ) ) !== undefined ) {
- return ret;
- }
-
- return ( elem[ name ] = value );
- }
-
- if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
- return ret;
- }
-
- return elem[ name ];
- },
-
- propHooks: {
- tabIndex: {
- get: function( elem ) {
-
- // Support: IE <=9 - 11 only
- // elem.tabIndex doesn't always return the
- // correct value when it hasn't been explicitly set
- // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
- // Use proper attribute retrieval(#12072)
- var tabindex = jQuery.find.attr( elem, "tabindex" );
-
- if ( tabindex ) {
- return parseInt( tabindex, 10 );
- }
-
- if (
- rfocusable.test( elem.nodeName ) ||
- rclickable.test( elem.nodeName ) &&
- elem.href
- ) {
- return 0;
- }
-
- return -1;
- }
- }
- },
-
- propFix: {
- "for": "htmlFor",
- "class": "className"
- }
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
- jQuery.propHooks.selected = {
- get: function( elem ) {
-
- /* eslint no-unused-expressions: "off" */
-
- var parent = elem.parentNode;
- if ( parent && parent.parentNode ) {
- parent.parentNode.selectedIndex;
- }
- return null;
- },
- set: function( elem ) {
-
- /* eslint no-unused-expressions: "off" */
-
- var parent = elem.parentNode;
- if ( parent ) {
- parent.selectedIndex;
-
- if ( parent.parentNode ) {
- parent.parentNode.selectedIndex;
- }
- }
- }
- };
-}
-
-jQuery.each( [
- "tabIndex",
- "readOnly",
- "maxLength",
- "cellSpacing",
- "cellPadding",
- "rowSpan",
- "colSpan",
- "useMap",
- "frameBorder",
- "contentEditable"
-], function() {
- jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
- // Strip and collapse whitespace according to HTML spec
- // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
- function stripAndCollapse( value ) {
- var tokens = value.match( rnothtmlwhite ) || [];
- return tokens.join( " " );
- }
-
-
-function getClass( elem ) {
- return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
- if ( Array.isArray( value ) ) {
- return value;
- }
- if ( typeof value === "string" ) {
- return value.match( rnothtmlwhite ) || [];
- }
- return [];
-}
-
-jQuery.fn.extend( {
- addClass: function( value ) {
- var classes, elem, cur, curValue, clazz, j, finalValue,
- i = 0;
-
- if ( isFunction( value ) ) {
- return this.each( function( j ) {
- jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
- } );
- }
-
- classes = classesToArray( value );
-
- if ( classes.length ) {
- while ( ( elem = this[ i++ ] ) ) {
- curValue = getClass( elem );
- cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
- if ( cur ) {
- j = 0;
- while ( ( clazz = classes[ j++ ] ) ) {
- if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
- cur += clazz + " ";
- }
- }
-
- // Only assign if different to avoid unneeded rendering.
- finalValue = stripAndCollapse( cur );
- if ( curValue !== finalValue ) {
- elem.setAttribute( "class", finalValue );
- }
- }
- }
- }
-
- return this;
- },
-
- removeClass: function( value ) {
- var classes, elem, cur, curValue, clazz, j, finalValue,
- i = 0;
-
- if ( isFunction( value ) ) {
- return this.each( function( j ) {
- jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
- } );
- }
-
- if ( !arguments.length ) {
- return this.attr( "class", "" );
- }
-
- classes = classesToArray( value );
-
- if ( classes.length ) {
- while ( ( elem = this[ i++ ] ) ) {
- curValue = getClass( elem );
-
- // This expression is here for better compressibility (see addClass)
- cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
- if ( cur ) {
- j = 0;
- while ( ( clazz = classes[ j++ ] ) ) {
-
- // Remove *all* instances
- while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
- cur = cur.replace( " " + clazz + " ", " " );
- }
- }
-
- // Only assign if different to avoid unneeded rendering.
- finalValue = stripAndCollapse( cur );
- if ( curValue !== finalValue ) {
- elem.setAttribute( "class", finalValue );
- }
- }
- }
- }
-
- return this;
- },
-
- toggleClass: function( value, stateVal ) {
- var type = typeof value,
- isValidValue = type === "string" || Array.isArray( value );
-
- if ( typeof stateVal === "boolean" && isValidValue ) {
- return stateVal ? this.addClass( value ) : this.removeClass( value );
- }
-
- if ( isFunction( value ) ) {
- return this.each( function( i ) {
- jQuery( this ).toggleClass(
- value.call( this, i, getClass( this ), stateVal ),
- stateVal
- );
- } );
- }
-
- return this.each( function() {
- var className, i, self, classNames;
-
- if ( isValidValue ) {
-
- // Toggle individual class names
- i = 0;
- self = jQuery( this );
- classNames = classesToArray( value );
-
- while ( ( className = classNames[ i++ ] ) ) {
-
- // Check each className given, space separated list
- if ( self.hasClass( className ) ) {
- self.removeClass( className );
- } else {
- self.addClass( className );
- }
- }
-
- // Toggle whole class name
- } else if ( value === undefined || type === "boolean" ) {
- className = getClass( this );
- if ( className ) {
-
- // Store className if set
- dataPriv.set( this, "__className__", className );
- }
-
- // If the element has a class name or if we're passed `false`,
- // then remove the whole classname (if there was one, the above saved it).
- // Otherwise bring back whatever was previously saved (if anything),
- // falling back to the empty string if nothing was stored.
- if ( this.setAttribute ) {
- this.setAttribute( "class",
- className || value === false ?
- "" :
- dataPriv.get( this, "__className__" ) || ""
- );
- }
- }
- } );
- },
-
- hasClass: function( selector ) {
- var className, elem,
- i = 0;
-
- className = " " + selector + " ";
- while ( ( elem = this[ i++ ] ) ) {
- if ( elem.nodeType === 1 &&
- ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
- return true;
- }
- }
-
- return false;
- }
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
- val: function( value ) {
- var hooks, ret, valueIsFunction,
- elem = this[ 0 ];
-
- if ( !arguments.length ) {
- if ( elem ) {
- hooks = jQuery.valHooks[ elem.type ] ||
- jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
- if ( hooks &&
- "get" in hooks &&
- ( ret = hooks.get( elem, "value" ) ) !== undefined
- ) {
- return ret;
- }
-
- ret = elem.value;
-
- // Handle most common string cases
- if ( typeof ret === "string" ) {
- return ret.replace( rreturn, "" );
- }
-
- // Handle cases where value is null/undef or number
- return ret == null ? "" : ret;
- }
-
- return;
- }
-
- valueIsFunction = isFunction( value );
-
- return this.each( function( i ) {
- var val;
-
- if ( this.nodeType !== 1 ) {
- return;
- }
-
- if ( valueIsFunction ) {
- val = value.call( this, i, jQuery( this ).val() );
- } else {
- val = value;
- }
-
- // Treat null/undefined as ""; convert numbers to string
- if ( val == null ) {
- val = "";
-
- } else if ( typeof val === "number" ) {
- val += "";
-
- } else if ( Array.isArray( val ) ) {
- val = jQuery.map( val, function( value ) {
- return value == null ? "" : value + "";
- } );
- }
-
- hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
- // If set returns undefined, fall back to normal setting
- if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
- this.value = val;
- }
- } );
- }
-} );
-
-jQuery.extend( {
- valHooks: {
- option: {
- get: function( elem ) {
-
- var val = jQuery.find.attr( elem, "value" );
- return val != null ?
- val :
-
- // Support: IE <=10 - 11 only
- // option.text throws exceptions (#14686, #14858)
- // Strip and collapse whitespace
- // https://html.spec.whatwg.org/#strip-and-collapse-whitespace
- stripAndCollapse( jQuery.text( elem ) );
- }
- },
- select: {
- get: function( elem ) {
- var value, option, i,
- options = elem.options,
- index = elem.selectedIndex,
- one = elem.type === "select-one",
- values = one ? null : [],
- max = one ? index + 1 : options.length;
-
- if ( index < 0 ) {
- i = max;
-
- } else {
- i = one ? index : 0;
- }
-
- // Loop through all the selected options
- for ( ; i < max; i++ ) {
- option = options[ i ];
-
- // Support: IE <=9 only
- // IE8-9 doesn't update selected after form reset (#2551)
- if ( ( option.selected || i === index ) &&
-
- // Don't return options that are disabled or in a disabled optgroup
- !option.disabled &&
- ( !option.parentNode.disabled ||
- !nodeName( option.parentNode, "optgroup" ) ) ) {
-
- // Get the specific value for the option
- value = jQuery( option ).val();
-
- // We don't need an array for one selects
- if ( one ) {
- return value;
- }
-
- // Multi-Selects return an array
- values.push( value );
- }
- }
-
- return values;
- },
-
- set: function( elem, value ) {
- var optionSet, option,
- options = elem.options,
- values = jQuery.makeArray( value ),
- i = options.length;
-
- while ( i-- ) {
- option = options[ i ];
-
- /* eslint-disable no-cond-assign */
-
- if ( option.selected =
- jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
- ) {
- optionSet = true;
- }
-
- /* eslint-enable no-cond-assign */
- }
-
- // Force browsers to behave consistently when non-matching value is set
- if ( !optionSet ) {
- elem.selectedIndex = -1;
- }
- return values;
- }
- }
- }
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
- jQuery.valHooks[ this ] = {
- set: function( elem, value ) {
- if ( Array.isArray( value ) ) {
- return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
- }
- }
- };
- if ( !support.checkOn ) {
- jQuery.valHooks[ this ].get = function( elem ) {
- return elem.getAttribute( "value" ) === null ? "on" : elem.value;
- };
- }
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
- stopPropagationCallback = function( e ) {
- e.stopPropagation();
- };
-
-jQuery.extend( jQuery.event, {
-
- trigger: function( event, data, elem, onlyHandlers ) {
-
- var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
- eventPath = [ elem || document ],
- type = hasOwn.call( event, "type" ) ? event.type : event,
- namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
- cur = lastElement = tmp = elem = elem || document;
-
- // Don't do events on text and comment nodes
- if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
- return;
- }
-
- // focus/blur morphs to focusin/out; ensure we're not firing them right now
- if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
- return;
- }
-
- if ( type.indexOf( "." ) > -1 ) {
-
- // Namespaced trigger; create a regexp to match event type in handle()
- namespaces = type.split( "." );
- type = namespaces.shift();
- namespaces.sort();
- }
- ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
- // Caller can pass in a jQuery.Event object, Object, or just an event type string
- event = event[ jQuery.expando ] ?
- event :
- new jQuery.Event( type, typeof event === "object" && event );
-
- // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
- event.isTrigger = onlyHandlers ? 2 : 3;
- event.namespace = namespaces.join( "." );
- event.rnamespace = event.namespace ?
- new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
- null;
-
- // Clean up the event in case it is being reused
- event.result = undefined;
- if ( !event.target ) {
- event.target = elem;
- }
-
- // Clone any incoming data and prepend the event, creating the handler arg list
- data = data == null ?
- [ event ] :
- jQuery.makeArray( data, [ event ] );
-
- // Allow special events to draw outside the lines
- special = jQuery.event.special[ type ] || {};
- if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
- return;
- }
-
- // Determine event propagation path in advance, per W3C events spec (#9951)
- // Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
- if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
- bubbleType = special.delegateType || type;
- if ( !rfocusMorph.test( bubbleType + type ) ) {
- cur = cur.parentNode;
- }
- for ( ; cur; cur = cur.parentNode ) {
- eventPath.push( cur );
- tmp = cur;
- }
-
- // Only add window if we got to document (e.g., not plain obj or detached DOM)
- if ( tmp === ( elem.ownerDocument || document ) ) {
- eventPath.push( tmp.defaultView || tmp.parentWindow || window );
- }
- }
-
- // Fire handlers on the event path
- i = 0;
- while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
- lastElement = cur;
- event.type = i > 1 ?
- bubbleType :
- special.bindType || type;
-
- // jQuery handler
- handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
- dataPriv.get( cur, "handle" );
- if ( handle ) {
- handle.apply( cur, data );
- }
-
- // Native handler
- handle = ontype && cur[ ontype ];
- if ( handle && handle.apply && acceptData( cur ) ) {
- event.result = handle.apply( cur, data );
- if ( event.result === false ) {
- event.preventDefault();
- }
- }
- }
- event.type = type;
-
- // If nobody prevented the default action, do it now
- if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
- if ( ( !special._default ||
- special._default.apply( eventPath.pop(), data ) === false ) &&
- acceptData( elem ) ) {
-
- // Call a native DOM method on the target with the same name as the event.
- // Don't do default actions on window, that's where global variables be (#6170)
- if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
- // Don't re-trigger an onFOO event when we call its FOO() method
- tmp = elem[ ontype ];
-
- if ( tmp ) {
- elem[ ontype ] = null;
- }
-
- // Prevent re-triggering of the same event, since we already bubbled it above
- jQuery.event.triggered = type;
-
- if ( event.isPropagationStopped() ) {
- lastElement.addEventListener( type, stopPropagationCallback );
- }
-
- elem[ type ]();
-
- if ( event.isPropagationStopped() ) {
- lastElement.removeEventListener( type, stopPropagationCallback );
- }
-
- jQuery.event.triggered = undefined;
-
- if ( tmp ) {
- elem[ ontype ] = tmp;
- }
- }
- }
- }
-
- return event.result;
- },
-
- // Piggyback on a donor event to simulate a different one
- // Used only for `focus(in | out)` events
- simulate: function( type, elem, event ) {
- var e = jQuery.extend(
- new jQuery.Event(),
- event,
- {
- type: type,
- isSimulated: true
- }
- );
-
- jQuery.event.trigger( e, null, elem );
- }
-
-} );
-
-jQuery.fn.extend( {
-
- trigger: function( type, data ) {
- return this.each( function() {
- jQuery.event.trigger( type, data, this );
- } );
- },
- triggerHandler: function( type, data ) {
- var elem = this[ 0 ];
- if ( elem ) {
- return jQuery.event.trigger( type, data, elem, true );
- }
- }
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
- jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
- // Attach a single capturing handler on the document while someone wants focusin/focusout
- var handler = function( event ) {
- jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
- };
-
- jQuery.event.special[ fix ] = {
- setup: function() {
-
- // Handle: regular nodes (via `this.ownerDocument`), window
- // (via `this.document`) & document (via `this`).
- var doc = this.ownerDocument || this.document || this,
- attaches = dataPriv.access( doc, fix );
-
- if ( !attaches ) {
- doc.addEventListener( orig, handler, true );
- }
- dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
- },
- teardown: function() {
- var doc = this.ownerDocument || this.document || this,
- attaches = dataPriv.access( doc, fix ) - 1;
-
- if ( !attaches ) {
- doc.removeEventListener( orig, handler, true );
- dataPriv.remove( doc, fix );
-
- } else {
- dataPriv.access( doc, fix, attaches );
- }
- }
- };
- } );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
- var xml, parserErrorElem;
- if ( !data || typeof data !== "string" ) {
- return null;
- }
-
- // Support: IE 9 - 11 only
- // IE throws on parseFromString with invalid input.
- try {
- xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
- } catch ( e ) {}
-
- parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
- if ( !xml || parserErrorElem ) {
- jQuery.error( "Invalid XML: " + (
- parserErrorElem ?
- jQuery.map( parserErrorElem.childNodes, function( el ) {
- return el.textContent;
- } ).join( "\n" ) :
- data
- ) );
- }
- return xml;
-};
-
-
-var
- rbracket = /\[\]$/,
- rCRLF = /\r?\n/g,
- rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
- rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
- var name;
-
- if ( Array.isArray( obj ) ) {
-
- // Serialize array item.
- jQuery.each( obj, function( i, v ) {
- if ( traditional || rbracket.test( prefix ) ) {
-
- // Treat each array item as a scalar.
- add( prefix, v );
-
- } else {
-
- // Item is non-scalar (array or object), encode its numeric index.
- buildParams(
- prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
- v,
- traditional,
- add
- );
- }
- } );
-
- } else if ( !traditional && toType( obj ) === "object" ) {
-
- // Serialize object item.
- for ( name in obj ) {
- buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
- }
-
- } else {
-
- // Serialize scalar item.
- add( prefix, obj );
- }
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
- var prefix,
- s = [],
- add = function( key, valueOrFunction ) {
-
- // If value is a function, invoke it and use its return value
- var value = isFunction( valueOrFunction ) ?
- valueOrFunction() :
- valueOrFunction;
-
- s[ s.length ] = encodeURIComponent( key ) + "=" +
- encodeURIComponent( value == null ? "" : value );
- };
-
- if ( a == null ) {
- return "";
- }
-
- // If an array was passed in, assume that it is an array of form elements.
- if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
- // Serialize the form elements
- jQuery.each( a, function() {
- add( this.name, this.value );
- } );
-
- } else {
-
- // If traditional, encode the "old" way (the way 1.3.2 or older
- // did it), otherwise encode params recursively.
- for ( prefix in a ) {
- buildParams( prefix, a[ prefix ], traditional, add );
- }
- }
-
- // Return the resulting serialization
- return s.join( "&" );
-};
-
-jQuery.fn.extend( {
- serialize: function() {
- return jQuery.param( this.serializeArray() );
- },
- serializeArray: function() {
- return this.map( function() {
-
- // Can add propHook for "elements" to filter or add form elements
- var elements = jQuery.prop( this, "elements" );
- return elements ? jQuery.makeArray( elements ) : this;
- } ).filter( function() {
- var type = this.type;
-
- // Use .is( ":disabled" ) so that fieldset[disabled] works
- return this.name && !jQuery( this ).is( ":disabled" ) &&
- rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
- ( this.checked || !rcheckableType.test( type ) );
- } ).map( function( _i, elem ) {
- var val = jQuery( this ).val();
-
- if ( val == null ) {
- return null;
- }
-
- if ( Array.isArray( val ) ) {
- return jQuery.map( val, function( val ) {
- return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
- } );
- }
-
- return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
- } ).get();
- }
-} );
-
-
-var
- r20 = /%20/g,
- rhash = /#.*$/,
- rantiCache = /([?&])_=[^&]*/,
- rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
- // #7653, #8125, #8152: local protocol detection
- rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
- rnoContent = /^(?:GET|HEAD)$/,
- rprotocol = /^\/\//,
-
- /* Prefilters
- * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
- * 2) These are called:
- * - BEFORE asking for a transport
- * - AFTER param serialization (s.data is a string if s.processData is true)
- * 3) key is the dataType
- * 4) the catchall symbol "*" can be used
- * 5) execution will start with transport dataType and THEN continue down to "*" if needed
- */
- prefilters = {},
-
- /* Transports bindings
- * 1) key is the dataType
- * 2) the catchall symbol "*" can be used
- * 3) selection will start with transport dataType and THEN go to "*" if needed
- */
- transports = {},
-
- // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
- allTypes = "*/".concat( "*" ),
-
- // Anchor tag for parsing the document origin
- originAnchor = document.createElement( "a" );
-
-originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
- // dataTypeExpression is optional and defaults to "*"
- return function( dataTypeExpression, func ) {
-
- if ( typeof dataTypeExpression !== "string" ) {
- func = dataTypeExpression;
- dataTypeExpression = "*";
- }
-
- var dataType,
- i = 0,
- dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
- if ( isFunction( func ) ) {
-
- // For each dataType in the dataTypeExpression
- while ( ( dataType = dataTypes[ i++ ] ) ) {
-
- // Prepend if requested
- if ( dataType[ 0 ] === "+" ) {
- dataType = dataType.slice( 1 ) || "*";
- ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
- // Otherwise append
- } else {
- ( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
- }
- }
- }
- };
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
- var inspected = {},
- seekingTransport = ( structure === transports );
-
- function inspect( dataType ) {
- var selected;
- inspected[ dataType ] = true;
- jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
- var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
- if ( typeof dataTypeOrTransport === "string" &&
- !seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
- options.dataTypes.unshift( dataTypeOrTransport );
- inspect( dataTypeOrTransport );
- return false;
- } else if ( seekingTransport ) {
- return !( selected = dataTypeOrTransport );
- }
- } );
- return selected;
- }
-
- return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
- var key, deep,
- flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
- for ( key in src ) {
- if ( src[ key ] !== undefined ) {
- ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
- }
- }
- if ( deep ) {
- jQuery.extend( true, target, deep );
- }
-
- return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
- var ct, type, finalDataType, firstDataType,
- contents = s.contents,
- dataTypes = s.dataTypes;
-
- // Remove auto dataType and get content-type in the process
- while ( dataTypes[ 0 ] === "*" ) {
- dataTypes.shift();
- if ( ct === undefined ) {
- ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
- }
- }
-
- // Check if we're dealing with a known content-type
- if ( ct ) {
- for ( type in contents ) {
- if ( contents[ type ] && contents[ type ].test( ct ) ) {
- dataTypes.unshift( type );
- break;
- }
- }
- }
-
- // Check to see if we have a response for the expected dataType
- if ( dataTypes[ 0 ] in responses ) {
- finalDataType = dataTypes[ 0 ];
- } else {
-
- // Try convertible dataTypes
- for ( type in responses ) {
- if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
- finalDataType = type;
- break;
- }
- if ( !firstDataType ) {
- firstDataType = type;
- }
- }
-
- // Or just use first one
- finalDataType = finalDataType || firstDataType;
- }
-
- // If we found a dataType
- // We add the dataType to the list if needed
- // and return the corresponding response
- if ( finalDataType ) {
- if ( finalDataType !== dataTypes[ 0 ] ) {
- dataTypes.unshift( finalDataType );
- }
- return responses[ finalDataType ];
- }
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
- var conv2, current, conv, tmp, prev,
- converters = {},
-
- // Work with a copy of dataTypes in case we need to modify it for conversion
- dataTypes = s.dataTypes.slice();
-
- // Create converters map with lowercased keys
- if ( dataTypes[ 1 ] ) {
- for ( conv in s.converters ) {
- converters[ conv.toLowerCase() ] = s.converters[ conv ];
- }
- }
-
- current = dataTypes.shift();
-
- // Convert to each sequential dataType
- while ( current ) {
-
- if ( s.responseFields[ current ] ) {
- jqXHR[ s.responseFields[ current ] ] = response;
- }
-
- // Apply the dataFilter if provided
- if ( !prev && isSuccess && s.dataFilter ) {
- response = s.dataFilter( response, s.dataType );
- }
-
- prev = current;
- current = dataTypes.shift();
-
- if ( current ) {
-
- // There's only work to do if current dataType is non-auto
- if ( current === "*" ) {
-
- current = prev;
-
- // Convert response if prev dataType is non-auto and differs from current
- } else if ( prev !== "*" && prev !== current ) {
-
- // Seek a direct converter
- conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
- // If none found, seek a pair
- if ( !conv ) {
- for ( conv2 in converters ) {
-
- // If conv2 outputs current
- tmp = conv2.split( " " );
- if ( tmp[ 1 ] === current ) {
-
- // If prev can be converted to accepted input
- conv = converters[ prev + " " + tmp[ 0 ] ] ||
- converters[ "* " + tmp[ 0 ] ];
- if ( conv ) {
-
- // Condense equivalence converters
- if ( conv === true ) {
- conv = converters[ conv2 ];
-
- // Otherwise, insert the intermediate dataType
- } else if ( converters[ conv2 ] !== true ) {
- current = tmp[ 0 ];
- dataTypes.unshift( tmp[ 1 ] );
- }
- break;
- }
- }
- }
- }
-
- // Apply converter (if not an equivalence)
- if ( conv !== true ) {
-
- // Unless errors are allowed to bubble, catch and return them
- if ( conv && s.throws ) {
- response = conv( response );
- } else {
- try {
- response = conv( response );
- } catch ( e ) {
- return {
- state: "parsererror",
- error: conv ? e : "No conversion from " + prev + " to " + current
- };
- }
- }
- }
- }
- }
- }
-
- return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
- // Counter for holding the number of active queries
- active: 0,
-
- // Last-Modified header cache for next request
- lastModified: {},
- etag: {},
-
- ajaxSettings: {
- url: location.href,
- type: "GET",
- isLocal: rlocalProtocol.test( location.protocol ),
- global: true,
- processData: true,
- async: true,
- contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
- /*
- timeout: 0,
- data: null,
- dataType: null,
- username: null,
- password: null,
- cache: null,
- throws: false,
- traditional: false,
- headers: {},
- */
-
- accepts: {
- "*": allTypes,
- text: "text/plain",
- html: "text/html",
- xml: "application/xml, text/xml",
- json: "application/json, text/javascript"
- },
-
- contents: {
- xml: /\bxml\b/,
- html: /\bhtml/,
- json: /\bjson\b/
- },
-
- responseFields: {
- xml: "responseXML",
- text: "responseText",
- json: "responseJSON"
- },
-
- // Data converters
- // Keys separate source (or catchall "*") and destination types with a single space
- converters: {
-
- // Convert anything to text
- "* text": String,
-
- // Text to html (true = no transformation)
- "text html": true,
-
- // Evaluate text as a json expression
- "text json": JSON.parse,
-
- // Parse text as xml
- "text xml": jQuery.parseXML
- },
-
- // For options that shouldn't be deep extended:
- // you can add your own custom options here if
- // and when you create one that shouldn't be
- // deep extended (see ajaxExtend)
- flatOptions: {
- url: true,
- context: true
- }
- },
-
- // Creates a full fledged settings object into target
- // with both ajaxSettings and settings fields.
- // If target is omitted, writes into ajaxSettings.
- ajaxSetup: function( target, settings ) {
- return settings ?
-
- // Building a settings object
- ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
- // Extending ajaxSettings
- ajaxExtend( jQuery.ajaxSettings, target );
- },
-
- ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
- ajaxTransport: addToPrefiltersOrTransports( transports ),
-
- // Main method
- ajax: function( url, options ) {
-
- // If url is an object, simulate pre-1.5 signature
- if ( typeof url === "object" ) {
- options = url;
- url = undefined;
- }
-
- // Force options to be an object
- options = options || {};
-
- var transport,
-
- // URL without anti-cache param
- cacheURL,
-
- // Response headers
- responseHeadersString,
- responseHeaders,
-
- // timeout handle
- timeoutTimer,
-
- // Url cleanup var
- urlAnchor,
-
- // Request state (becomes false upon send and true upon completion)
- completed,
-
- // To know if global events are to be dispatched
- fireGlobals,
-
- // Loop variable
- i,
-
- // uncached part of the url
- uncached,
-
- // Create the final options object
- s = jQuery.ajaxSetup( {}, options ),
-
- // Callbacks context
- callbackContext = s.context || s,
-
- // Context for global events is callbackContext if it is a DOM node or jQuery collection
- globalEventContext = s.context &&
- ( callbackContext.nodeType || callbackContext.jquery ) ?
- jQuery( callbackContext ) :
- jQuery.event,
-
- // Deferreds
- deferred = jQuery.Deferred(),
- completeDeferred = jQuery.Callbacks( "once memory" ),
-
- // Status-dependent callbacks
- statusCode = s.statusCode || {},
-
- // Headers (they are sent all at once)
- requestHeaders = {},
- requestHeadersNames = {},
-
- // Default abort message
- strAbort = "canceled",
-
- // Fake xhr
- jqXHR = {
- readyState: 0,
-
- // Builds headers hashtable if needed
- getResponseHeader: function( key ) {
- var match;
- if ( completed ) {
- if ( !responseHeaders ) {
- responseHeaders = {};
- while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
- responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
- ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
- .concat( match[ 2 ] );
- }
- }
- match = responseHeaders[ key.toLowerCase() + " " ];
- }
- return match == null ? null : match.join( ", " );
- },
-
- // Raw string
- getAllResponseHeaders: function() {
- return completed ? responseHeadersString : null;
- },
-
- // Caches the header
- setRequestHeader: function( name, value ) {
- if ( completed == null ) {
- name = requestHeadersNames[ name.toLowerCase() ] =
- requestHeadersNames[ name.toLowerCase() ] || name;
- requestHeaders[ name ] = value;
- }
- return this;
- },
-
- // Overrides response content-type header
- overrideMimeType: function( type ) {
- if ( completed == null ) {
- s.mimeType = type;
- }
- return this;
- },
-
- // Status-dependent callbacks
- statusCode: function( map ) {
- var code;
- if ( map ) {
- if ( completed ) {
-
- // Execute the appropriate callbacks
- jqXHR.always( map[ jqXHR.status ] );
- } else {
-
- // Lazy-add the new callbacks in a way that preserves old ones
- for ( code in map ) {
- statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
- }
- }
- }
- return this;
- },
-
- // Cancel the request
- abort: function( statusText ) {
- var finalText = statusText || strAbort;
- if ( transport ) {
- transport.abort( finalText );
- }
- done( 0, finalText );
- return this;
- }
- };
-
- // Attach deferreds
- deferred.promise( jqXHR );
-
- // Add protocol if not provided (prefilters might expect it)
- // Handle falsy url in the settings object (#10093: consistency with old signature)
- // We also use the url parameter if available
- s.url = ( ( url || s.url || location.href ) + "" )
- .replace( rprotocol, location.protocol + "//" );
-
- // Alias method option to type as per ticket #12004
- s.type = options.method || options.type || s.method || s.type;
-
- // Extract dataTypes list
- s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
- // A cross-domain request is in order when the origin doesn't match the current origin.
- if ( s.crossDomain == null ) {
- urlAnchor = document.createElement( "a" );
-
- // Support: IE <=8 - 11, Edge 12 - 15
- // IE throws exception on accessing the href property if url is malformed,
- // e.g. http://example.com:80x/
- try {
- urlAnchor.href = s.url;
-
- // Support: IE <=8 - 11 only
- // Anchor's host property isn't correctly set when s.url is relative
- urlAnchor.href = urlAnchor.href;
- s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
- urlAnchor.protocol + "//" + urlAnchor.host;
- } catch ( e ) {
-
- // If there is an error parsing the URL, assume it is crossDomain,
- // it can be rejected by the transport if it is invalid
- s.crossDomain = true;
- }
- }
-
- // Convert data if not already a string
- if ( s.data && s.processData && typeof s.data !== "string" ) {
- s.data = jQuery.param( s.data, s.traditional );
- }
-
- // Apply prefilters
- inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
- // If request was aborted inside a prefilter, stop there
- if ( completed ) {
- return jqXHR;
- }
-
- // We can fire global events as of now if asked to
- // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
- fireGlobals = jQuery.event && s.global;
-
- // Watch for a new set of requests
- if ( fireGlobals && jQuery.active++ === 0 ) {
- jQuery.event.trigger( "ajaxStart" );
- }
-
- // Uppercase the type
- s.type = s.type.toUpperCase();
-
- // Determine if request has content
- s.hasContent = !rnoContent.test( s.type );
-
- // Save the URL in case we're toying with the If-Modified-Since
- // and/or If-None-Match header later on
- // Remove hash to simplify url manipulation
- cacheURL = s.url.replace( rhash, "" );
-
- // More options handling for requests with no content
- if ( !s.hasContent ) {
-
- // Remember the hash so we can put it back
- uncached = s.url.slice( cacheURL.length );
-
- // If data is available and should be processed, append data to url
- if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
- cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
- // #9682: remove data so that it's not used in an eventual retry
- delete s.data;
- }
-
- // Add or update anti-cache param if needed
- if ( s.cache === false ) {
- cacheURL = cacheURL.replace( rantiCache, "$1" );
- uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
- uncached;
- }
-
- // Put hash and anti-cache on the URL that will be requested (gh-1732)
- s.url = cacheURL + uncached;
-
- // Change '%20' to '+' if this is encoded form body content (gh-2658)
- } else if ( s.data && s.processData &&
- ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
- s.data = s.data.replace( r20, "+" );
- }
-
- // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
- if ( s.ifModified ) {
- if ( jQuery.lastModified[ cacheURL ] ) {
- jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
- }
- if ( jQuery.etag[ cacheURL ] ) {
- jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
- }
- }
-
- // Set the correct header, if data is being sent
- if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
- jqXHR.setRequestHeader( "Content-Type", s.contentType );
- }
-
- // Set the Accepts header for the server, depending on the dataType
- jqXHR.setRequestHeader(
- "Accept",
- s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
- s.accepts[ s.dataTypes[ 0 ] ] +
- ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
- s.accepts[ "*" ]
- );
-
- // Check for headers option
- for ( i in s.headers ) {
- jqXHR.setRequestHeader( i, s.headers[ i ] );
- }
-
- // Allow custom headers/mimetypes and early abort
- if ( s.beforeSend &&
- ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
- // Abort if not done already and return
- return jqXHR.abort();
- }
-
- // Aborting is no longer a cancellation
- strAbort = "abort";
-
- // Install callbacks on deferreds
- completeDeferred.add( s.complete );
- jqXHR.done( s.success );
- jqXHR.fail( s.error );
-
- // Get transport
- transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
- // If no transport, we auto-abort
- if ( !transport ) {
- done( -1, "No Transport" );
- } else {
- jqXHR.readyState = 1;
-
- // Send global event
- if ( fireGlobals ) {
- globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
- }
-
- // If request was aborted inside ajaxSend, stop there
- if ( completed ) {
- return jqXHR;
- }
-
- // Timeout
- if ( s.async && s.timeout > 0 ) {
- timeoutTimer = window.setTimeout( function() {
- jqXHR.abort( "timeout" );
- }, s.timeout );
- }
-
- try {
- completed = false;
- transport.send( requestHeaders, done );
- } catch ( e ) {
-
- // Rethrow post-completion exceptions
- if ( completed ) {
- throw e;
- }
-
- // Propagate others as results
- done( -1, e );
- }
- }
-
- // Callback for when everything is done
- function done( status, nativeStatusText, responses, headers ) {
- var isSuccess, success, error, response, modified,
- statusText = nativeStatusText;
-
- // Ignore repeat invocations
- if ( completed ) {
- return;
- }
-
- completed = true;
-
- // Clear timeout if it exists
- if ( timeoutTimer ) {
- window.clearTimeout( timeoutTimer );
- }
-
- // Dereference transport for early garbage collection
- // (no matter how long the jqXHR object will be used)
- transport = undefined;
-
- // Cache response headers
- responseHeadersString = headers || "";
-
- // Set readyState
- jqXHR.readyState = status > 0 ? 4 : 0;
-
- // Determine if successful
- isSuccess = status >= 200 && status < 300 || status === 304;
-
- // Get response data
- if ( responses ) {
- response = ajaxHandleResponses( s, jqXHR, responses );
- }
-
- // Use a noop converter for missing script but not if jsonp
- if ( !isSuccess &&
- jQuery.inArray( "script", s.dataTypes ) > -1 &&
- jQuery.inArray( "json", s.dataTypes ) < 0 ) {
- s.converters[ "text script" ] = function() {};
- }
-
- // Convert no matter what (that way responseXXX fields are always set)
- response = ajaxConvert( s, response, jqXHR, isSuccess );
-
- // If successful, handle type chaining
- if ( isSuccess ) {
-
- // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
- if ( s.ifModified ) {
- modified = jqXHR.getResponseHeader( "Last-Modified" );
- if ( modified ) {
- jQuery.lastModified[ cacheURL ] = modified;
- }
- modified = jqXHR.getResponseHeader( "etag" );
- if ( modified ) {
- jQuery.etag[ cacheURL ] = modified;
- }
- }
-
- // if no content
- if ( status === 204 || s.type === "HEAD" ) {
- statusText = "nocontent";
-
- // if not modified
- } else if ( status === 304 ) {
- statusText = "notmodified";
-
- // If we have data, let's convert it
- } else {
- statusText = response.state;
- success = response.data;
- error = response.error;
- isSuccess = !error;
- }
- } else {
-
- // Extract error from statusText and normalize for non-aborts
- error = statusText;
- if ( status || !statusText ) {
- statusText = "error";
- if ( status < 0 ) {
- status = 0;
- }
- }
- }
-
- // Set data for the fake xhr object
- jqXHR.status = status;
- jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
- // Success/Error
- if ( isSuccess ) {
- deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
- } else {
- deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
- }
-
- // Status-dependent callbacks
- jqXHR.statusCode( statusCode );
- statusCode = undefined;
-
- if ( fireGlobals ) {
- globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
- [ jqXHR, s, isSuccess ? success : error ] );
- }
-
- // Complete
- completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
- if ( fireGlobals ) {
- globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
- // Handle the global AJAX counter
- if ( !( --jQuery.active ) ) {
- jQuery.event.trigger( "ajaxStop" );
- }
- }
- }
-
- return jqXHR;
- },
-
- getJSON: function( url, data, callback ) {
- return jQuery.get( url, data, callback, "json" );
- },
-
- getScript: function( url, callback ) {
- return jQuery.get( url, undefined, callback, "script" );
- }
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
- jQuery[ method ] = function( url, data, callback, type ) {
-
- // Shift arguments if data argument was omitted
- if ( isFunction( data ) ) {
- type = type || callback;
- callback = data;
- data = undefined;
- }
-
- // The url can be an options object (which then must have .url)
- return jQuery.ajax( jQuery.extend( {
- url: url,
- type: method,
- dataType: type,
- data: data,
- success: callback
- }, jQuery.isPlainObject( url ) && url ) );
- };
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
- var i;
- for ( i in s.headers ) {
- if ( i.toLowerCase() === "content-type" ) {
- s.contentType = s.headers[ i ] || "";
- }
- }
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
- return jQuery.ajax( {
- url: url,
-
- // Make this explicit, since user can override this through ajaxSetup (#11264)
- type: "GET",
- dataType: "script",
- cache: true,
- async: false,
- global: false,
-
- // Only evaluate the response if it is successful (gh-4126)
- // dataFilter is not invoked for failure responses, so using it instead
- // of the default converter is kludgy but it works.
- converters: {
- "text script": function() {}
- },
- dataFilter: function( response ) {
- jQuery.globalEval( response, options, doc );
- }
- } );
-};
-
-
-jQuery.fn.extend( {
- wrapAll: function( html ) {
- var wrap;
-
- if ( this[ 0 ] ) {
- if ( isFunction( html ) ) {
- html = html.call( this[ 0 ] );
- }
-
- // The elements to wrap the target around
- wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
- if ( this[ 0 ].parentNode ) {
- wrap.insertBefore( this[ 0 ] );
- }
-
- wrap.map( function() {
- var elem = this;
-
- while ( elem.firstElementChild ) {
- elem = elem.firstElementChild;
- }
-
- return elem;
- } ).append( this );
- }
-
- return this;
- },
-
- wrapInner: function( html ) {
- if ( isFunction( html ) ) {
- return this.each( function( i ) {
- jQuery( this ).wrapInner( html.call( this, i ) );
- } );
- }
-
- return this.each( function() {
- var self = jQuery( this ),
- contents = self.contents();
-
- if ( contents.length ) {
- contents.wrapAll( html );
-
- } else {
- self.append( html );
- }
- } );
- },
-
- wrap: function( html ) {
- var htmlIsFunction = isFunction( html );
-
- return this.each( function( i ) {
- jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
- } );
- },
-
- unwrap: function( selector ) {
- this.parent( selector ).not( "body" ).each( function() {
- jQuery( this ).replaceWith( this.childNodes );
- } );
- return this;
- }
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
- return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
- return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
- try {
- return new window.XMLHttpRequest();
- } catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
- // File protocol always yields status code 0, assume 200
- 0: 200,
-
- // Support: IE <=9 only
- // #1450: sometimes IE returns 1223 when it should be 204
- 1223: 204
- },
- xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
- var callback, errorCallback;
-
- // Cross domain only allowed if supported through XMLHttpRequest
- if ( support.cors || xhrSupported && !options.crossDomain ) {
- return {
- send: function( headers, complete ) {
- var i,
- xhr = options.xhr();
-
- xhr.open(
- options.type,
- options.url,
- options.async,
- options.username,
- options.password
- );
-
- // Apply custom fields if provided
- if ( options.xhrFields ) {
- for ( i in options.xhrFields ) {
- xhr[ i ] = options.xhrFields[ i ];
- }
- }
-
- // Override mime type if needed
- if ( options.mimeType && xhr.overrideMimeType ) {
- xhr.overrideMimeType( options.mimeType );
- }
-
- // X-Requested-With header
- // For cross-domain requests, seeing as conditions for a preflight are
- // akin to a jigsaw puzzle, we simply never set it to be sure.
- // (it can always be set on a per-request basis or even using ajaxSetup)
- // For same-domain requests, won't change header if already provided.
- if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
- headers[ "X-Requested-With" ] = "XMLHttpRequest";
- }
-
- // Set headers
- for ( i in headers ) {
- xhr.setRequestHeader( i, headers[ i ] );
- }
-
- // Callback
- callback = function( type ) {
- return function() {
- if ( callback ) {
- callback = errorCallback = xhr.onload =
- xhr.onerror = xhr.onabort = xhr.ontimeout =
- xhr.onreadystatechange = null;
-
- if ( type === "abort" ) {
- xhr.abort();
- } else if ( type === "error" ) {
-
- // Support: IE <=9 only
- // On a manual native abort, IE9 throws
- // errors on any property access that is not readyState
- if ( typeof xhr.status !== "number" ) {
- complete( 0, "error" );
- } else {
- complete(
-
- // File: protocol always yields status 0; see #8605, #14207
- xhr.status,
- xhr.statusText
- );
- }
- } else {
- complete(
- xhrSuccessStatus[ xhr.status ] || xhr.status,
- xhr.statusText,
-
- // Support: IE <=9 only
- // IE9 has no XHR2 but throws on binary (trac-11426)
- // For XHR2 non-text, let the caller handle it (gh-2498)
- ( xhr.responseType || "text" ) !== "text" ||
- typeof xhr.responseText !== "string" ?
- { binary: xhr.response } :
- { text: xhr.responseText },
- xhr.getAllResponseHeaders()
- );
- }
- }
- };
- };
-
- // Listen to events
- xhr.onload = callback();
- errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
- // Support: IE 9 only
- // Use onreadystatechange to replace onabort
- // to handle uncaught aborts
- if ( xhr.onabort !== undefined ) {
- xhr.onabort = errorCallback;
- } else {
- xhr.onreadystatechange = function() {
-
- // Check readyState before timeout as it changes
- if ( xhr.readyState === 4 ) {
-
- // Allow onerror to be called first,
- // but that will not handle a native abort
- // Also, save errorCallback to a variable
- // as xhr.onerror cannot be accessed
- window.setTimeout( function() {
- if ( callback ) {
- errorCallback();
- }
- } );
- }
- };
- }
-
- // Create the abort callback
- callback = callback( "abort" );
-
- try {
-
- // Do send the request (this may raise an exception)
- xhr.send( options.hasContent && options.data || null );
- } catch ( e ) {
-
- // #14683: Only rethrow if this hasn't been notified as an error yet
- if ( callback ) {
- throw e;
- }
- }
- },
-
- abort: function() {
- if ( callback ) {
- callback();
- }
- }
- };
- }
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
- if ( s.crossDomain ) {
- s.contents.script = false;
- }
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
- accepts: {
- script: "text/javascript, application/javascript, " +
- "application/ecmascript, application/x-ecmascript"
- },
- contents: {
- script: /\b(?:java|ecma)script\b/
- },
- converters: {
- "text script": function( text ) {
- jQuery.globalEval( text );
- return text;
- }
- }
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
- if ( s.cache === undefined ) {
- s.cache = false;
- }
- if ( s.crossDomain ) {
- s.type = "GET";
- }
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
- // This transport only deals with cross domain or forced-by-attrs requests
- if ( s.crossDomain || s.scriptAttrs ) {
- var script, callback;
- return {
- send: function( _, complete ) {
- script = jQuery( "<script>" )
- .attr( s.scriptAttrs || {} )
- .prop( { charset: s.scriptCharset, src: s.url } )
- .on( "load error", callback = function( evt ) {
- script.remove();
- callback = null;
- if ( evt ) {
- complete( evt.type === "error" ? 404 : 200, evt.type );
- }
- } );
-
- // Use native DOM manipulation to avoid our domManip AJAX trickery
- document.head.appendChild( script[ 0 ] );
- },
- abort: function() {
- if ( callback ) {
- callback();
- }
- }
- };
- }
-} );
-
-
-
-
-var oldCallbacks = [],
- rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
- jsonp: "callback",
- jsonpCallback: function() {
- var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
- this[ callback ] = true;
- return callback;
- }
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
- var callbackName, overwritten, responseContainer,
- jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
- "url" :
- typeof s.data === "string" &&
- ( s.contentType || "" )
- .indexOf( "application/x-www-form-urlencoded" ) === 0 &&
- rjsonp.test( s.data ) && "data"
- );
-
- // Handle iff the expected data type is "jsonp" or we have a parameter to set
- if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
- // Get callback name, remembering preexisting value associated with it
- callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
- s.jsonpCallback() :
- s.jsonpCallback;
-
- // Insert callback into url or form data
- if ( jsonProp ) {
- s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
- } else if ( s.jsonp !== false ) {
- s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
- }
-
- // Use data converter to retrieve json after script execution
- s.converters[ "script json" ] = function() {
- if ( !responseContainer ) {
- jQuery.error( callbackName + " was not called" );
- }
- return responseContainer[ 0 ];
- };
-
- // Force json dataType
- s.dataTypes[ 0 ] = "json";
-
- // Install callback
- overwritten = window[ callbackName ];
- window[ callbackName ] = function() {
- responseContainer = arguments;
- };
-
- // Clean-up function (fires after converters)
- jqXHR.always( function() {
-
- // If previous value didn't exist - remove it
- if ( overwritten === undefined ) {
- jQuery( window ).removeProp( callbackName );
-
- // Otherwise restore preexisting value
- } else {
- window[ callbackName ] = overwritten;
- }
-
- // Save back as free
- if ( s[ callbackName ] ) {
-
- // Make sure that re-using the options doesn't screw things around
- s.jsonpCallback = originalSettings.jsonpCallback;
-
- // Save the callback name for future use
- oldCallbacks.push( callbackName );
- }
-
- // Call if it was a function and we have a response
- if ( responseContainer && isFunction( overwritten ) ) {
- overwritten( responseContainer[ 0 ] );
- }
-
- responseContainer = overwritten = undefined;
- } );
-
- // Delegate to script
- return "script";
- }
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
- var body = document.implementation.createHTMLDocument( "" ).body;
- body.innerHTML = "<form></form><form></form>";
- return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
- if ( typeof data !== "string" ) {
- return [];
- }
- if ( typeof context === "boolean" ) {
- keepScripts = context;
- context = false;
- }
-
- var base, parsed, scripts;
-
- if ( !context ) {
-
- // Stop scripts or inline event handlers from being executed immediately
- // by using document.implementation
- if ( support.createHTMLDocument ) {
- context = document.implementation.createHTMLDocument( "" );
-
- // Set the base href for the created document
- // so any parsed elements with URLs
- // are based on the document's URL (gh-2965)
- base = context.createElement( "base" );
- base.href = document.location.href;
- context.head.appendChild( base );
- } else {
- context = document;
- }
- }
-
- parsed = rsingleTag.exec( data );
- scripts = !keepScripts && [];
-
- // Single tag
- if ( parsed ) {
- return [ context.createElement( parsed[ 1 ] ) ];
- }
-
- parsed = buildFragment( [ data ], context, scripts );
-
- if ( scripts && scripts.length ) {
- jQuery( scripts ).remove();
- }
-
- return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
- var selector, type, response,
- self = this,
- off = url.indexOf( " " );
-
- if ( off > -1 ) {
- selector = stripAndCollapse( url.slice( off ) );
- url = url.slice( 0, off );
- }
-
- // If it's a function
- if ( isFunction( params ) ) {
-
- // We assume that it's the callback
- callback = params;
- params = undefined;
-
- // Otherwise, build a param string
- } else if ( params && typeof params === "object" ) {
- type = "POST";
- }
-
- // If we have elements to modify, make the request
- if ( self.length > 0 ) {
- jQuery.ajax( {
- url: url,
-
- // If "type" variable is undefined, then "GET" method will be used.
- // Make value of this field explicit since
- // user can override it through ajaxSetup method
- type: type || "GET",
- dataType: "html",
- data: params
- } ).done( function( responseText ) {
-
- // Save response for use in complete callback
- response = arguments;
-
- self.html( selector ?
-
- // If a selector was specified, locate the right elements in a dummy div
- // Exclude scripts to avoid IE 'Permission Denied' errors
- jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
- // Otherwise use the full result
- responseText );
-
- // If the request succeeds, this function gets "data", "status", "jqXHR"
- // but they are ignored because response was set above.
- // If it fails, this function gets "jqXHR", "status", "error"
- } ).always( callback && function( jqXHR, status ) {
- self.each( function() {
- callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
- } );
- } );
- }
-
- return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
- return jQuery.grep( jQuery.timers, function( fn ) {
- return elem === fn.elem;
- } ).length;
-};
-
-
-
-
-jQuery.offset = {
- setOffset: function( elem, options, i ) {
- var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
- position = jQuery.css( elem, "position" ),
- curElem = jQuery( elem ),
- props = {};
-
- // Set position first, in-case top/left are set even on static elem
- if ( position === "static" ) {
- elem.style.position = "relative";
- }
-
- curOffset = curElem.offset();
- curCSSTop = jQuery.css( elem, "top" );
- curCSSLeft = jQuery.css( elem, "left" );
- calculatePosition = ( position === "absolute" || position === "fixed" ) &&
- ( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
- // Need to be able to calculate position if either
- // top or left is auto and position is either absolute or fixed
- if ( calculatePosition ) {
- curPosition = curElem.position();
- curTop = curPosition.top;
- curLeft = curPosition.left;
-
- } else {
- curTop = parseFloat( curCSSTop ) || 0;
- curLeft = parseFloat( curCSSLeft ) || 0;
- }
-
- if ( isFunction( options ) ) {
-
- // Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
- options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
- }
-
- if ( options.top != null ) {
- props.top = ( options.top - curOffset.top ) + curTop;
- }
- if ( options.left != null ) {
- props.left = ( options.left - curOffset.left ) + curLeft;
- }
-
- if ( "using" in options ) {
- options.using.call( elem, props );
-
- } else {
- curElem.css( props );
- }
- }
-};
-
-jQuery.fn.extend( {
-
- // offset() relates an element's border box to the document origin
- offset: function( options ) {
-
- // Preserve chaining for setter
- if ( arguments.length ) {
- return options === undefined ?
- this :
- this.each( function( i ) {
- jQuery.offset.setOffset( this, options, i );
- } );
- }
-
- var rect, win,
- elem = this[ 0 ];
-
- if ( !elem ) {
- return;
- }
-
- // Return zeros for disconnected and hidden (display: none) elements (gh-2310)
- // Support: IE <=11 only
- // Running getBoundingClientRect on a
- // disconnected node in IE throws an error
- if ( !elem.getClientRects().length ) {
- return { top: 0, left: 0 };
- }
-
- // Get document-relative position by adding viewport scroll to viewport-relative gBCR
- rect = elem.getBoundingClientRect();
- win = elem.ownerDocument.defaultView;
- return {
- top: rect.top + win.pageYOffset,
- left: rect.left + win.pageXOffset
- };
- },
-
- // position() relates an element's margin box to its offset parent's padding box
- // This corresponds to the behavior of CSS absolute positioning
- position: function() {
- if ( !this[ 0 ] ) {
- return;
- }
-
- var offsetParent, offset, doc,
- elem = this[ 0 ],
- parentOffset = { top: 0, left: 0 };
-
- // position:fixed elements are offset from the viewport, which itself always has zero offset
- if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
- // Assume position:fixed implies availability of getBoundingClientRect
- offset = elem.getBoundingClientRect();
-
- } else {
- offset = this.offset();
-
- // Account for the *real* offset parent, which can be the document or its root element
- // when a statically positioned element is identified
- doc = elem.ownerDocument;
- offsetParent = elem.offsetParent || doc.documentElement;
- while ( offsetParent &&
- ( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
- jQuery.css( offsetParent, "position" ) === "static" ) {
-
- offsetParent = offsetParent.parentNode;
- }
- if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
- // Incorporate borders into its offset, since they are outside its content origin
- parentOffset = jQuery( offsetParent ).offset();
- parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
- parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
- }
- }
-
- // Subtract parent offsets and element margins
- return {
- top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
- left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
- };
- },
-
- // This method will return documentElement in the following cases:
- // 1) For the element inside the iframe without offsetParent, this method will return
- // documentElement of the parent window
- // 2) For the hidden or detached element
- // 3) For body or html element, i.e. in case of the html node - it will return itself
- //
- // but those exceptions were never presented as a real life use-cases
- // and might be considered as more preferable results.
- //
- // This logic, however, is not guaranteed and can change at any point in the future
- offsetParent: function() {
- return this.map( function() {
- var offsetParent = this.offsetParent;
-
- while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
- offsetParent = offsetParent.offsetParent;
- }
-
- return offsetParent || documentElement;
- } );
- }
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
- var top = "pageYOffset" === prop;
-
- jQuery.fn[ method ] = function( val ) {
- return access( this, function( elem, method, val ) {
-
- // Coalesce documents and windows
- var win;
- if ( isWindow( elem ) ) {
- win = elem;
- } else if ( elem.nodeType === 9 ) {
- win = elem.defaultView;
- }
-
- if ( val === undefined ) {
- return win ? win[ prop ] : elem[ method ];
- }
-
- if ( win ) {
- win.scrollTo(
- !top ? val : win.pageXOffset,
- top ? val : win.pageYOffset
- );
-
- } else {
- elem[ method ] = val;
- }
- }, method, val, arguments.length );
- };
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
- jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
- function( elem, computed ) {
- if ( computed ) {
- computed = curCSS( elem, prop );
-
- // If curCSS returns percentage, fallback to offset
- return rnumnonpx.test( computed ) ?
- jQuery( elem ).position()[ prop ] + "px" :
- computed;
- }
- }
- );
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
- jQuery.each( {
- padding: "inner" + name,
- content: type,
- "": "outer" + name
- }, function( defaultExtra, funcName ) {
-
- // Margin is only for outerHeight, outerWidth
- jQuery.fn[ funcName ] = function( margin, value ) {
- var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
- extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
- return access( this, function( elem, type, value ) {
- var doc;
-
- if ( isWindow( elem ) ) {
-
- // $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
- return funcName.indexOf( "outer" ) === 0 ?
- elem[ "inner" + name ] :
- elem.document.documentElement[ "client" + name ];
- }
-
- // Get document width or height
- if ( elem.nodeType === 9 ) {
- doc = elem.documentElement;
-
- // Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
- // whichever is greatest
- return Math.max(
- elem.body[ "scroll" + name ], doc[ "scroll" + name ],
- elem.body[ "offset" + name ], doc[ "offset" + name ],
- doc[ "client" + name ]
- );
- }
-
- return value === undefined ?
-
- // Get width or height on the element, requesting but not forcing parseFloat
- jQuery.css( elem, type, extra ) :
-
- // Set width or height on the element
- jQuery.style( elem, type, value, extra );
- }, type, chainable ? margin : undefined, chainable );
- };
- } );
-} );
-
-
-jQuery.each( [
- "ajaxStart",
- "ajaxStop",
- "ajaxComplete",
- "ajaxError",
- "ajaxSuccess",
- "ajaxSend"
-], function( _i, type ) {
- jQuery.fn[ type ] = function( fn ) {
- return this.on( type, fn );
- };
-} );
-
-
-
-
-jQuery.fn.extend( {
-
- bind: function( types, data, fn ) {
- return this.on( types, null, data, fn );
- },
- unbind: function( types, fn ) {
- return this.off( types, null, fn );
- },
-
- delegate: function( selector, types, data, fn ) {
- return this.on( types, selector, data, fn );
- },
- undelegate: function( selector, types, fn ) {
-
- // ( namespace ) or ( selector, types [, fn] )
- return arguments.length === 1 ?
- this.off( selector, "**" ) :
- this.off( types, selector || "**", fn );
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-
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-
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-
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-
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- if ( deep && window.jQuery === jQuery ) {
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-
- return jQuery;
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-
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- window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/public/_static/jquery.js b/public/_static/jquery.js
deleted file mode 100644
index c4c6022f2..000000000
--- a/public/_static/jquery.js
+++ /dev/null
@@ -1,2 +0,0 @@
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diff --git a/public/_static/language_data.js b/public/_static/language_data.js
deleted file mode 100644
index 367b8ed81..000000000
--- a/public/_static/language_data.js
+++ /dev/null
@@ -1,199 +0,0 @@
-/*
- * language_data.js
- * ~~~~~~~~~~~~~~~~
- *
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- *
- * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
-
-
-/* Non-minified version is copied as a separate JS file, if available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
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- ational: 'ate',
- tional: 'tion',
- enci: 'ence',
- anci: 'ance',
- izer: 'ize',
- bli: 'ble',
- alli: 'al',
- entli: 'ent',
- eli: 'e',
- ousli: 'ous',
- ization: 'ize',
- ation: 'ate',
- ator: 'ate',
- alism: 'al',
- iveness: 'ive',
- fulness: 'ful',
- ousness: 'ous',
- aliti: 'al',
- iviti: 'ive',
- biliti: 'ble',
- logi: 'log'
- };
-
- var step3list = {
- icate: 'ic',
- ative: '',
- alize: 'al',
- iciti: 'ic',
- ical: 'ic',
- ful: '',
- ness: ''
- };
-
- var c = "[^aeiou]"; // consonant
- var v = "[aeiouy]"; // vowel
- var C = c + "[^aeiouy]*"; // consonant sequence
- var V = v + "[aeiou]*"; // vowel sequence
-
- var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
- var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
- var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
- var s_v = "^(" + C + ")?" + v; // vowel in stem
-
- this.stemWord = function (w) {
- var stem;
- var suffix;
- var firstch;
- var origword = w;
-
- if (w.length < 3)
- return w;
-
- var re;
- var re2;
- var re3;
- var re4;
-
- firstch = w.substr(0,1);
- if (firstch == "y")
- w = firstch.toUpperCase() + w.substr(1);
-
- // Step 1a
- re = /^(.+?)(ss|i)es$/;
- re2 = /^(.+?)([^s])s$/;
-
- if (re.test(w))
- w = w.replace(re,"$1$2");
- else if (re2.test(w))
- w = w.replace(re2,"$1$2");
-
- // Step 1b
- re = /^(.+?)eed$/;
- re2 = /^(.+?)(ed|ing)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- re = new RegExp(mgr0);
- if (re.test(fp[1])) {
- re = /.$/;
- w = w.replace(re,"");
- }
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1];
- re2 = new RegExp(s_v);
- if (re2.test(stem)) {
- w = stem;
- re2 = /(at|bl|iz)$/;
- re3 = new RegExp("([^aeiouylsz])\\1$");
- re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re2.test(w))
- w = w + "e";
- else if (re3.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
- else if (re4.test(w))
- w = w + "e";
- }
- }
-
- // Step 1c
- re = /^(.+?)y$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(s_v);
- if (re.test(stem))
- w = stem + "i";
- }
-
- // Step 2
- re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step2list[suffix];
- }
-
- // Step 3
- re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step3list[suffix];
- }
-
- // Step 4
- re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
- re2 = /^(.+?)(s|t)(ion)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- if (re.test(stem))
- w = stem;
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1] + fp[2];
- re2 = new RegExp(mgr1);
- if (re2.test(stem))
- w = stem;
- }
-
- // Step 5
- re = /^(.+?)e$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- re2 = new RegExp(meq1);
- re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
- w = stem;
- }
- re = /ll$/;
- re2 = new RegExp(mgr1);
- if (re.test(w) && re2.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
-
- // and turn initial Y back to y
- if (firstch == "y")
- w = firstch.toLowerCase() + w.substr(1);
- return w;
- }
-}
-
diff --git a/public/_static/pygments.css b/public/_static/pygments.css
deleted file mode 100644
index d9a83a7ba..000000000
--- a/public/_static/pygments.css
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index e69de29bb..000000000
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deleted file mode 100644
index 32e7c05be..000000000
--- a/public/_static/scripts/furo.js
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index 1632189c7..000000000
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false,\n nestedClass: \"active\",\n\n // Offset & reflow\n offset: 0,\n reflow: false,\n\n // Event support\n events: true,\n };\n\n //\n // Methods\n //\n\n /**\n * Merge two or more objects together.\n * @param {Object} objects The objects to merge together\n * @returns {Object} Merged values of defaults and options\n */\n var extend = function () {\n var merged = {};\n Array.prototype.forEach.call(arguments, function (obj) {\n for (var key in obj) {\n if (!obj.hasOwnProperty(key)) return;\n merged[key] = obj[key];\n }\n });\n return merged;\n };\n\n /**\n * Emit a custom event\n * @param {String} type The event type\n * @param {Node} elem The element to attach the event to\n * @param {Object} detail Any details to pass along with the event\n */\n var emitEvent = function (type, elem, detail) {\n // Make sure events are enabled\n if (!detail.settings.events) return;\n\n // Create a new event\n var event = new CustomEvent(type, {\n bubbles: true,\n cancelable: true,\n detail: detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the 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function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n 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globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader() {\n if (Math.floor(header.getBoundingClientRect().top) == 0) {\n header.classList.add(\"scrolled\");\n } else {\n header.classList.remove(\"scrolled\");\n }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n if (positionY < GO_TO_TOP_OFFSET) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n } else {\n if (positionY < lastScrollTop) {\n document.documentElement.classList.add(\"show-back-to-top\");\n } else if (positionY > lastScrollTop) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n }\n }\n lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n if (tocScroll === null) {\n return;\n }\n\n // top of page.\n if (positionY == 0) {\n tocScroll.scrollTo(0, 0);\n } else if (\n // bottom of page.\n Math.ceil(positionY) >=\n Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n ) {\n tocScroll.scrollTo(0, tocScroll.scrollHeight);\n } else {\n // somewhere in the middle.\n const current = document.querySelector(\".scroll-current\");\n if (current == null) {\n return;\n }\n\n // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n // // scroll the currently \"active\" heading in toc, into view.\n // const rect = current.getBoundingClientRect();\n // if (0 > rect.top) {\n // current.scrollIntoView(true); // the argument is \"alignTop\"\n // } else if (rect.bottom > window.innerHeight) {\n // current.scrollIntoView(false);\n // }\n }\n}\n\nfunction scrollHandler(positionY) {\n scrollHandlerForHeader();\n scrollHandlerForBackToTop(positionY);\n scrollHandlerForTOC(positionY);\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Theme Toggle\n////////////////////////////////////////////////////////////////////////////////\nfunction setTheme(mode) {\n if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n mode = \"auto\";\n }\n\n document.body.dataset.theme = mode;\n localStorage.setItem(\"theme\", mode);\n console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n if (prefersDark) {\n // Auto (dark) -> Light -> Dark\n if (currentTheme === \"auto\") {\n setTheme(\"light\");\n } else if (currentTheme == \"light\") {\n setTheme(\"dark\");\n } else {\n setTheme(\"auto\");\n }\n } else {\n // Auto (light) -> Dark -> Light\n if (currentTheme === \"auto\") {\n setTheme(\"dark\");\n } else if (currentTheme == \"dark\") {\n setTheme(\"light\");\n } else {\n setTheme(\"auto\");\n }\n }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n let last_known_scroll_position = 0;\n let ticking = false;\n\n window.addEventListener(\"scroll\", function (e) {\n last_known_scroll_position = window.scrollY;\n\n if (!ticking) {\n window.requestAnimationFrame(function () {\n scrollHandler(last_known_scroll_position);\n ticking = false;\n });\n\n ticking = true;\n }\n });\n window.scroll();\n}\n\nfunction setupScrollSpy() {\n if (tocScroll === null) {\n return;\n }\n\n // Scrollspy -- highlight table on contents, based on scroll\n new Gumshoe(\".toc-tree a\", {\n reflow: true,\n recursive: true,\n navClass: \"scroll-current\",\n offset: () => {\n let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n return header.getBoundingClientRect().height + 0.5 * rem + 1;\n },\n });\n}\n\nfunction setupTheme() {\n // Attach event handlers for toggling themes\n const buttons = document.getElementsByClassName(\"theme-toggle\");\n Array.from(buttons).forEach((btn) => {\n btn.addEventListener(\"click\", cycleThemeOnce);\n });\n}\n\nfunction setup() {\n setupTheme();\n setupScrollHandler();\n setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n document.body.parentNode.classList.remove(\"no-js\");\n\n header = document.querySelector(\"header\");\n tocScroll = document.querySelector(\".toc-scroll\");\n\n setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","GO_TO_TOP_OFFSET","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","floor","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""}
\ No newline at end of file
diff --git a/public/_static/searchtools.js b/public/_static/searchtools.js
deleted file mode 100644
index 92da3f8b2..000000000
--- a/public/_static/searchtools.js
+++ /dev/null
@@ -1,619 +0,0 @@
-/*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
- var Scorer = {
- // Implement the following function to further tweak the score for each result
- // The function takes a result array [docname, title, anchor, descr, score, filename]
- // and returns the new score.
- /*
- score: result => {
- const [docname, title, anchor, descr, score, filename] = result
- return score
- },
- */
-
- // query matches the full name of an object
- objNameMatch: 11,
- // or matches in the last dotted part of the object name
- objPartialMatch: 6,
- // Additive scores depending on the priority of the object
- objPrio: {
- 0: 15, // used to be importantResults
- 1: 5, // used to be objectResults
- 2: -5, // used to be unimportantResults
- },
- // Used when the priority is not in the mapping.
- objPrioDefault: 0,
-
- // query found in title
- title: 15,
- partialTitle: 7,
- // query found in terms
- term: 5,
- partialTerm: 2,
- };
-}
-
-const _removeChildren = (element) => {
- while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
- string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, searchTerms, highlightTerms) => {
- const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
- const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
- const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
- const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
- const contentRoot = document.documentElement.dataset.content_root;
-
- const [docName, title, anchor, descr, score, _filename] = item;
-
- let listItem = document.createElement("li");
- let requestUrl;
- let linkUrl;
- if (docBuilder === "dirhtml") {
- // dirhtml builder
- let dirname = docName + "/";
- if (dirname.match(/\/index\/$/))
- dirname = dirname.substring(0, dirname.length - 6);
- else if (dirname === "index/") dirname = "";
- requestUrl = contentRoot + dirname;
- linkUrl = requestUrl;
- } else {
- // normal html builders
- requestUrl = contentRoot + docName + docFileSuffix;
- linkUrl = docName + docLinkSuffix;
- }
- let linkEl = listItem.appendChild(document.createElement("a"));
- linkEl.href = linkUrl + anchor;
- linkEl.dataset.score = score;
- linkEl.innerHTML = title;
- if (descr) {
- listItem.appendChild(document.createElement("span")).innerHTML =
- " (" + descr + ")";
- // highlight search terms in the description
- if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
- highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
- }
- else if (showSearchSummary)
- fetch(requestUrl)
- .then((responseData) => responseData.text())
- .then((data) => {
- if (data)
- listItem.appendChild(
- Search.makeSearchSummary(data, searchTerms, anchor)
- );
- // highlight search terms in the summary
- if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
- highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
- });
- Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
- Search.stopPulse();
- Search.title.innerText = _("Search Results");
- if (!resultCount)
- Search.status.innerText = Documentation.gettext(
- "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
- );
- else
- Search.status.innerText = _(
- "Search finished, found ${resultCount} page(s) matching the search query."
- ).replace('${resultCount}', resultCount);
-};
-const _displayNextItem = (
- results,
- resultCount,
- searchTerms,
- highlightTerms,
-) => {
- // results left, load the summary and display it
- // this is intended to be dynamic (don't sub resultsCount)
- if (results.length) {
- _displayItem(results.pop(), searchTerms, highlightTerms);
- setTimeout(
- () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
- 5
- );
- }
- // search finished, update title and status message
- else _finishSearch(resultCount);
-};
-// Helper function used by query() to order search results.
-// Each input is an array of [docname, title, anchor, descr, score, filename].
-// Order the results by score (in opposite order of appearance, since the
-// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
-const _orderResultsByScoreThenName = (a, b) => {
- const leftScore = a[4];
- const rightScore = b[4];
- if (leftScore === rightScore) {
- // same score: sort alphabetically
- const leftTitle = a[1].toLowerCase();
- const rightTitle = b[1].toLowerCase();
- if (leftTitle === rightTitle) return 0;
- return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
- }
- return leftScore > rightScore ? 1 : -1;
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
- var splitQuery = (query) => query
- .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
- .filter(term => term) // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
- _index: null,
- _queued_query: null,
- _pulse_status: -1,
-
- htmlToText: (htmlString, anchor) => {
- const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
- for (const removalQuery of [".headerlinks", "script", "style"]) {
- htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
- }
- if (anchor) {
- const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
- if (anchorContent) return anchorContent.textContent;
-
- console.warn(
- `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
- );
- }
-
- // if anchor not specified or not found, fall back to main content
- const docContent = htmlElement.querySelector('[role="main"]');
- if (docContent) return docContent.textContent;
-
- console.warn(
- "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
- );
- return "";
- },
-
- init: () => {
- const query = new URLSearchParams(window.location.search).get("q");
- document
- .querySelectorAll('input[name="q"]')
- .forEach((el) => (el.value = query));
- if (query) Search.performSearch(query);
- },
-
- loadIndex: (url) =>
- (document.body.appendChild(document.createElement("script")).src = url),
-
- setIndex: (index) => {
- Search._index = index;
- if (Search._queued_query !== null) {
- const query = Search._queued_query;
- Search._queued_query = null;
- Search.query(query);
- }
- },
-
- hasIndex: () => Search._index !== null,
-
- deferQuery: (query) => (Search._queued_query = query),
-
- stopPulse: () => (Search._pulse_status = -1),
-
- startPulse: () => {
- if (Search._pulse_status >= 0) return;
-
- const pulse = () => {
- Search._pulse_status = (Search._pulse_status + 1) % 4;
- Search.dots.innerText = ".".repeat(Search._pulse_status);
- if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
- };
- pulse();
- },
-
- /**
- * perform a search for something (or wait until index is loaded)
- */
- performSearch: (query) => {
- // create the required interface elements
- const searchText = document.createElement("h2");
- searchText.textContent = _("Searching");
- const searchSummary = document.createElement("p");
- searchSummary.classList.add("search-summary");
- searchSummary.innerText = "";
- const searchList = document.createElement("ul");
- searchList.classList.add("search");
-
- const out = document.getElementById("search-results");
- Search.title = out.appendChild(searchText);
- Search.dots = Search.title.appendChild(document.createElement("span"));
- Search.status = out.appendChild(searchSummary);
- Search.output = out.appendChild(searchList);
-
- const searchProgress = document.getElementById("search-progress");
- // Some themes don't use the search progress node
- if (searchProgress) {
- searchProgress.innerText = _("Preparing search...");
- }
- Search.startPulse();
-
- // index already loaded, the browser was quick!
- if (Search.hasIndex()) Search.query(query);
- else Search.deferQuery(query);
- },
-
- _parseQuery: (query) => {
- // stem the search terms and add them to the correct list
- const stemmer = new Stemmer();
- const searchTerms = new Set();
- const excludedTerms = new Set();
- const highlightTerms = new Set();
- const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
- splitQuery(query.trim()).forEach((queryTerm) => {
- const queryTermLower = queryTerm.toLowerCase();
-
- // maybe skip this "word"
- // stopwords array is from language_data.js
- if (
- stopwords.indexOf(queryTermLower) !== -1 ||
- queryTerm.match(/^\d+$/)
- )
- return;
-
- // stem the word
- let word = stemmer.stemWord(queryTermLower);
- // select the correct list
- if (word[0] === "-") excludedTerms.add(word.substr(1));
- else {
- searchTerms.add(word);
- highlightTerms.add(queryTermLower);
- }
- });
-
- if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
- localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
- }
-
- // console.debug("SEARCH: searching for:");
- // console.info("required: ", [...searchTerms]);
- // console.info("excluded: ", [...excludedTerms]);
-
- return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
- },
-
- /**
- * execute search (requires search index to be loaded)
- */
- _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
- const allTitles = Search._index.alltitles;
- const indexEntries = Search._index.indexentries;
-
- // Collect multiple result groups to be sorted separately and then ordered.
- // Each is an array of [docname, title, anchor, descr, score, filename].
- const normalResults = [];
- const nonMainIndexResults = [];
-
- _removeChildren(document.getElementById("search-progress"));
-
- const queryLower = query.toLowerCase().trim();
- for (const [title, foundTitles] of Object.entries(allTitles)) {
- if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
- for (const [file, id] of foundTitles) {
- let score = Math.round(100 * queryLower.length / title.length)
- normalResults.push([
- docNames[file],
- titles[file] !== title ? `${titles[file]} > ${title}` : title,
- id !== null ? "#" + id : "",
- null,
- score,
- filenames[file],
- ]);
- }
- }
- }
-
- // search for explicit entries in index directives
- for (const [entry, foundEntries] of Object.entries(indexEntries)) {
- if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
- for (const [file, id, isMain] of foundEntries) {
- const score = Math.round(100 * queryLower.length / entry.length);
- const result = [
- docNames[file],
- titles[file],
- id ? "#" + id : "",
- null,
- score,
- filenames[file],
- ];
- if (isMain) {
- normalResults.push(result);
- } else {
- nonMainIndexResults.push(result);
- }
- }
- }
- }
-
- // lookup as object
- objectTerms.forEach((term) =>
- normalResults.push(...Search.performObjectSearch(term, objectTerms))
- );
-
- // lookup as search terms in fulltext
- normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
- // let the scorer override scores with a custom scoring function
- if (Scorer.score) {
- normalResults.forEach((item) => (item[4] = Scorer.score(item)));
- nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
- }
-
- // Sort each group of results by score and then alphabetically by name.
- normalResults.sort(_orderResultsByScoreThenName);
- nonMainIndexResults.sort(_orderResultsByScoreThenName);
-
- // Combine the result groups in (reverse) order.
- // Non-main index entries are typically arbitrary cross-references,
- // so display them after other results.
- let results = [...nonMainIndexResults, ...normalResults];
-
- // remove duplicate search results
- // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
- let seen = new Set();
- results = results.reverse().reduce((acc, result) => {
- let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
- if (!seen.has(resultStr)) {
- acc.push(result);
- seen.add(resultStr);
- }
- return acc;
- }, []);
-
- return results.reverse();
- },
-
- query: (query) => {
- const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
- const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
-
- // for debugging
- //Search.lastresults = results.slice(); // a copy
- // console.info("search results:", Search.lastresults);
-
- // print the results
- _displayNextItem(results, results.length, searchTerms, highlightTerms);
- },
-
- /**
- * search for object names
- */
- performObjectSearch: (object, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const objects = Search._index.objects;
- const objNames = Search._index.objnames;
- const titles = Search._index.titles;
-
- const results = [];
-
- const objectSearchCallback = (prefix, match) => {
- const name = match[4]
- const fullname = (prefix ? prefix + "." : "") + name;
- const fullnameLower = fullname.toLowerCase();
- if (fullnameLower.indexOf(object) < 0) return;
-
- let score = 0;
- const parts = fullnameLower.split(".");
-
- // check for different match types: exact matches of full name or
- // "last name" (i.e. last dotted part)
- if (fullnameLower === object || parts.slice(-1)[0] === object)
- score += Scorer.objNameMatch;
- else if (parts.slice(-1)[0].indexOf(object) > -1)
- score += Scorer.objPartialMatch; // matches in last name
-
- const objName = objNames[match[1]][2];
- const title = titles[match[0]];
-
- // If more than one term searched for, we require other words to be
- // found in the name/title/description
- const otherTerms = new Set(objectTerms);
- otherTerms.delete(object);
- if (otherTerms.size > 0) {
- const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
- if (
- [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
- )
- return;
- }
-
- let anchor = match[3];
- if (anchor === "") anchor = fullname;
- else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
- const descr = objName + _(", in ") + title;
-
- // add custom score for some objects according to scorer
- if (Scorer.objPrio.hasOwnProperty(match[2]))
- score += Scorer.objPrio[match[2]];
- else score += Scorer.objPrioDefault;
-
- results.push([
- docNames[match[0]],
- fullname,
- "#" + anchor,
- descr,
- score,
- filenames[match[0]],
- ]);
- };
- Object.keys(objects).forEach((prefix) =>
- objects[prefix].forEach((array) =>
- objectSearchCallback(prefix, array)
- )
- );
- return results;
- },
-
- /**
- * search for full-text terms in the index
- */
- performTermsSearch: (searchTerms, excludedTerms) => {
- // prepare search
- const terms = Search._index.terms;
- const titleTerms = Search._index.titleterms;
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
-
- const scoreMap = new Map();
- const fileMap = new Map();
-
- // perform the search on the required terms
- searchTerms.forEach((word) => {
- const files = [];
- const arr = [
- { files: terms[word], score: Scorer.term },
- { files: titleTerms[word], score: Scorer.title },
- ];
- // add support for partial matches
- if (word.length > 2) {
- const escapedWord = _escapeRegExp(word);
- if (!terms.hasOwnProperty(word)) {
- Object.keys(terms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: terms[term], score: Scorer.partialTerm });
- });
- }
- if (!titleTerms.hasOwnProperty(word)) {
- Object.keys(titleTerms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
- });
- }
- }
-
- // no match but word was a required one
- if (arr.every((record) => record.files === undefined)) return;
-
- // found search word in contents
- arr.forEach((record) => {
- if (record.files === undefined) return;
-
- let recordFiles = record.files;
- if (recordFiles.length === undefined) recordFiles = [recordFiles];
- files.push(...recordFiles);
-
- // set score for the word in each file
- recordFiles.forEach((file) => {
- if (!scoreMap.has(file)) scoreMap.set(file, {});
- scoreMap.get(file)[word] = record.score;
- });
- });
-
- // create the mapping
- files.forEach((file) => {
- if (!fileMap.has(file)) fileMap.set(file, [word]);
- else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
- });
- });
-
- // now check if the files don't contain excluded terms
- const results = [];
- for (const [file, wordList] of fileMap) {
- // check if all requirements are matched
-
- // as search terms with length < 3 are discarded
- const filteredTermCount = [...searchTerms].filter(
- (term) => term.length > 2
- ).length;
- if (
- wordList.length !== searchTerms.size &&
- wordList.length !== filteredTermCount
- )
- continue;
-
- // ensure that none of the excluded terms is in the search result
- if (
- [...excludedTerms].some(
- (term) =>
- terms[term] === file ||
- titleTerms[term] === file ||
- (terms[term] || []).includes(file) ||
- (titleTerms[term] || []).includes(file)
- )
- )
- break;
-
- // select one (max) score for the file.
- const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
- // add result to the result list
- results.push([
- docNames[file],
- titles[file],
- "",
- null,
- score,
- filenames[file],
- ]);
- }
- return results;
- },
-
- /**
- * helper function to return a node containing the
- * search summary for a given text. keywords is a list
- * of stemmed words.
- */
- makeSearchSummary: (htmlText, keywords, anchor) => {
- const text = Search.htmlToText(htmlText, anchor);
- if (text === "") return null;
-
- const textLower = text.toLowerCase();
- const actualStartPosition = [...keywords]
- .map((k) => textLower.indexOf(k.toLowerCase()))
- .filter((i) => i > -1)
- .slice(-1)[0];
- const startWithContext = Math.max(actualStartPosition - 120, 0);
-
- const top = startWithContext === 0 ? "" : "...";
- const tail = startWithContext + 240 < text.length ? "..." : "";
-
- let summary = document.createElement("p");
- summary.classList.add("context");
- summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
-
- return summary;
- },
-};
-
-_ready(Search.init);
diff --git a/public/_static/skeleton.css b/public/_static/skeleton.css
deleted file mode 100644
index 467c878c6..000000000
--- a/public/_static/skeleton.css
+++ /dev/null
@@ -1,296 +0,0 @@
-/* Some sane resets. */
-html {
- height: 100%;
-}
-
-body {
- margin: 0;
- min-height: 100%;
-}
-
-/* All the flexbox magic! */
-body,
-.sb-announcement,
-.sb-content,
-.sb-main,
-.sb-container,
-.sb-container__inner,
-.sb-article-container,
-.sb-footer-content,
-.sb-header,
-.sb-header-secondary,
-.sb-footer {
- display: flex;
-}
-
-/* These order things vertically */
-body,
-.sb-main,
-.sb-article-container {
- flex-direction: column;
-}
-
-/* Put elements in the center */
-.sb-header,
-.sb-header-secondary,
-.sb-container,
-.sb-content,
-.sb-footer,
-.sb-footer-content {
- justify-content: center;
-}
-/* Put elements at the ends */
-.sb-article-container {
- justify-content: space-between;
-}
-
-/* These elements grow. */
-.sb-main,
-.sb-content,
-.sb-container,
-article {
- flex-grow: 1;
-}
-
-/* Because padding making this wider is not fun */
-article {
- box-sizing: border-box;
-}
-
-/* The announcements element should never be wider than the page. */
-.sb-announcement {
- max-width: 100%;
-}
-
-.sb-sidebar-primary,
-.sb-sidebar-secondary {
- flex-shrink: 0;
- width: 17rem;
-}
-
-.sb-announcement__inner {
- justify-content: center;
-
- box-sizing: border-box;
- height: 3rem;
-
- overflow-x: auto;
- white-space: nowrap;
-}
-
-/* Sidebars, with checkbox-based toggle */
-.sb-sidebar-primary,
-.sb-sidebar-secondary {
- position: fixed;
- height: 100%;
- top: 0;
-}
-
-.sb-sidebar-primary {
- left: -17rem;
- transition: left 250ms ease-in-out;
-}
-.sb-sidebar-secondary {
- right: -17rem;
- transition: right 250ms ease-in-out;
-}
-
-.sb-sidebar-toggle {
- display: none;
-}
-.sb-sidebar-overlay {
- position: fixed;
- top: 0;
- width: 0;
- height: 0;
-
- transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease;
-
- opacity: 0;
- background-color: rgba(0, 0, 0, 0.54);
-}
-
-#sb-sidebar-toggle--primary:checked
- ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"],
-#sb-sidebar-toggle--secondary:checked
- ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] {
- width: 100%;
- height: 100%;
- opacity: 1;
- transition: width 0ms ease, height 0ms ease, opacity 250ms ease;
-}
-
-#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary {
- left: 0;
-}
-#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary {
- right: 0;
-}
-
-/* Full-width mode */
-.drop-secondary-sidebar-for-full-width-content
- .hide-when-secondary-sidebar-shown {
- display: none !important;
-}
-.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary {
- display: none !important;
-}
-
-/* Mobile views */
-.sb-page-width {
- width: 100%;
-}
-
-.sb-article-container,
-.sb-footer-content__inner,
-.drop-secondary-sidebar-for-full-width-content .sb-article,
-.drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 100vw;
-}
-
-.sb-article,
-.match-content-width {
- padding: 0 1rem;
- box-sizing: border-box;
-}
-
-@media (min-width: 32rem) {
- .sb-article,
- .match-content-width {
- padding: 0 2rem;
- }
-}
-
-/* Tablet views */
-@media (min-width: 42rem) {
- .sb-article-container {
- width: auto;
- }
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 42rem;
- }
- .sb-article,
- .match-content-width {
- width: 42rem;
- }
-}
-@media (min-width: 46rem) {
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 46rem;
- }
- .sb-article,
- .match-content-width {
- width: 46rem;
- }
-}
-@media (min-width: 50rem) {
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 50rem;
- }
- .sb-article,
- .match-content-width {
- width: 50rem;
- }
-}
-
-/* Tablet views */
-@media (min-width: 59rem) {
- .sb-sidebar-secondary {
- position: static;
- }
- .hide-when-secondary-sidebar-shown {
- display: none !important;
- }
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 59rem;
- }
- .sb-article,
- .match-content-width {
- width: 42rem;
- }
-}
-@media (min-width: 63rem) {
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 63rem;
- }
- .sb-article,
- .match-content-width {
- width: 46rem;
- }
-}
-@media (min-width: 67rem) {
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 67rem;
- }
- .sb-article,
- .match-content-width {
- width: 50rem;
- }
-}
-
-/* Desktop views */
-@media (min-width: 76rem) {
- .sb-sidebar-primary {
- position: static;
- }
- .hide-when-primary-sidebar-shown {
- display: none !important;
- }
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 59rem;
- }
- .sb-article,
- .match-content-width {
- width: 42rem;
- }
-}
-
-/* Full desktop views */
-@media (min-width: 80rem) {
- .sb-article,
- .match-content-width {
- width: 46rem;
- }
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 63rem;
- }
-}
-
-@media (min-width: 84rem) {
- .sb-article,
- .match-content-width {
- width: 50rem;
- }
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 67rem;
- }
-}
-
-@media (min-width: 88rem) {
- .sb-footer-content__inner,
- .drop-secondary-sidebar-for-full-width-content .sb-article,
- .drop-secondary-sidebar-for-full-width-content .match-content-width {
- width: 67rem;
- }
- .sb-page-width {
- width: 88rem;
- }
-}
diff --git a/public/_static/sphinx_highlight.js b/public/_static/sphinx_highlight.js
deleted file mode 100644
index 8a96c69a1..000000000
--- a/public/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,154 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
- if (node.nodeType === Node.TEXT_NODE) {
- const val = node.nodeValue;
- const parent = node.parentNode;
- const pos = val.toLowerCase().indexOf(text);
- if (
- pos >= 0 &&
- !parent.classList.contains(className) &&
- !parent.classList.contains("nohighlight")
- ) {
- let span;
-
- const closestNode = parent.closest("body, svg, foreignObject");
- const isInSVG = closestNode && closestNode.matches("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.classList.add(className);
- }
-
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- const rest = document.createTextNode(val.substr(pos + text.length));
- parent.insertBefore(
- span,
- parent.insertBefore(
- rest,
- node.nextSibling
- )
- );
- node.nodeValue = val.substr(0, pos);
- /* There may be more occurrences of search term in this node. So call this
- * function recursively on the remaining fragment.
- */
- _highlight(rest, addItems, text, className);
-
- if (isInSVG) {
- const rect = document.createElementNS(
- "http://www.w3.org/2000/svg",
- "rect"
- );
- const bbox = parent.getBBox();
- rect.x.baseVal.value = bbox.x;
- rect.y.baseVal.value = bbox.y;
- rect.width.baseVal.value = bbox.width;
- rect.height.baseVal.value = bbox.height;
- rect.setAttribute("class", className);
- addItems.push({ parent: parent, target: rect });
- }
- }
- } else if (node.matches && !node.matches("button, select, textarea")) {
- node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
- }
-};
-const _highlightText = (thisNode, text, className) => {
- let addItems = [];
- _highlight(thisNode, addItems, text, className);
- addItems.forEach((obj) =>
- obj.parent.insertAdjacentElement("beforebegin", obj.target)
- );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-
- /**
- * highlight the search words provided in localstorage in the text
- */
- highlightSearchWords: () => {
- if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
-
- // get and clear terms from localstorage
- const url = new URL(window.location);
- const highlight =
- localStorage.getItem("sphinx_highlight_terms")
- || url.searchParams.get("highlight")
- || "";
- localStorage.removeItem("sphinx_highlight_terms")
- url.searchParams.delete("highlight");
- window.history.replaceState({}, "", url);
-
- // get individual terms from highlight string
- const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
- if (terms.length === 0) return; // nothing to do
-
- // There should never be more than one element matching "div.body"
- const divBody = document.querySelectorAll("div.body");
- const body = divBody.length ? divBody[0] : document.querySelector("body");
- window.setTimeout(() => {
- terms.forEach((term) => _highlightText(body, term, "highlighted"));
- }, 10);
-
- const searchBox = document.getElementById("searchbox");
- if (searchBox === null) return;
- searchBox.appendChild(
- document
- .createRange()
- .createContextualFragment(
- '<p class="highlight-link">' +
- '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
- _("Hide Search Matches") +
- "</a></p>"
- )
- );
- },
-
- /**
- * helper function to hide the search marks again
- */
- hideSearchWords: () => {
- document
- .querySelectorAll("#searchbox .highlight-link")
- .forEach((el) => el.remove());
- document
- .querySelectorAll("span.highlighted")
- .forEach((el) => el.classList.remove("highlighted"));
- localStorage.removeItem("sphinx_highlight_terms")
- },
-
- initEscapeListener: () => {
- // only install a listener if it is really needed
- if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
- document.addEventListener("keydown", (event) => {
- // bail for input elements
- if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
- // bail with special keys
- if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
- if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
- SphinxHighlight.hideSearchWords();
- event.preventDefault();
- }
- });
- },
-};
-
-_ready(() => {
- /* Do not call highlightSearchWords() when we are on the search page.
- * It will highlight words from the *previous* search query.
- */
- if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
- SphinxHighlight.initEscapeListener();
-});
diff --git a/public/_static/styles/furo-extensions.css b/public/_static/styles/furo-extensions.css
deleted file mode 100644
index bc447f228..000000000
--- a/public/_static/styles/furo-extensions.css
+++ /dev/null
@@ -1,2 +0,0 @@
-#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout a{color:var(--color-foreground-secondary)!important}#furo-readthedocs-versions{background:transparent;display:block;position:static;width:100%}#furo-readthedocs-versions .rst-versions{background:#1a1c1e}#furo-readthedocs-versions .rst-current-version{background:var(--color-sidebar-item-background);cursor:unset}#furo-readthedocs-versions .rst-current-version:hover{background:var(--color-sidebar-item-background)}#furo-readthedocs-versions .rst-current-version .fa-book{color:var(--color-foreground-primary)}#furo-readthedocs-versions>.rst-other-versions{padding:0}#furo-readthedocs-versions>.rst-other-versions small{opacity:1}#furo-readthedocs-versions .injected .rst-versions{position:unset}#furo-readthedocs-versions:focus-within,#furo-readthedocs-versions:hover{box-shadow:0 0 0 1px var(--color-sidebar-background-border)}#furo-readthedocs-versions:focus-within .rst-current-version,#furo-readthedocs-versions:hover .rst-current-version{background:#1a1c1e;font-size:inherit;height:auto;line-height:inherit;padding:12px;text-align:right}#furo-readthedocs-versions:focus-within .rst-current-version .fa-book,#furo-readthedocs-versions:hover .rst-current-version .fa-book{color:#fff;float:left}#furo-readthedocs-versions:focus-within .fa-caret-down,#furo-readthedocs-versions:hover .fa-caret-down{display:none}#furo-readthedocs-versions:focus-within .injected,#furo-readthedocs-versions:focus-within .rst-current-version,#furo-readthedocs-versions:focus-within .rst-other-versions,#furo-readthedocs-versions:hover .injected,#furo-readthedocs-versions:hover .rst-current-version,#furo-readthedocs-versions:hover .rst-other-versions{display:block}#furo-readthedocs-versions:focus-within>.rst-current-version,#furo-readthedocs-versions:hover>.rst-current-version{display:none}.highlight:hover button.copybtn{color:var(--color-code-foreground)}.highlight button.copybtn{align-items:center;background-color:var(--color-code-background);border:none;color:var(--color-background-item);cursor:pointer;height:1.25em;opacity:1;right:.5rem;top:.625rem;transition:color .3s,opacity .3s;width:1.25em}.highlight button.copybtn:hover{background-color:var(--color-code-background);color:var(--color-brand-content)}.highlight button.copybtn:after{background-color:transparent;color:var(--color-code-foreground);display:none}.highlight button.copybtn.success{color:#22863a;transition:color 0ms}.highlight button.copybtn.success:after{display:block}.highlight button.copybtn svg{padding:0}body{--sd-color-primary:var(--color-brand-primary);--sd-color-primary-highlight:var(--color-brand-content);--sd-color-primary-text:var(--color-background-primary);--sd-color-shadow:rgba(0,0,0,.05);--sd-color-card-border:var(--color-card-border);--sd-color-card-border-hover:var(--color-brand-content);--sd-color-card-background:var(--color-card-background);--sd-color-card-text:var(--color-foreground-primary);--sd-color-card-header:var(--color-card-marginals-background);--sd-color-card-footer:var(--color-card-marginals-background);--sd-color-tabs-label-active:var(--color-brand-content);--sd-color-tabs-label-hover:var(--color-foreground-muted);--sd-color-tabs-label-inactive:var(--color-foreground-muted);--sd-color-tabs-underline-active:var(--color-brand-content);--sd-color-tabs-underline-hover:var(--color-foreground-border);--sd-color-tabs-underline-inactive:var(--color-background-border);--sd-color-tabs-overline:var(--color-background-border);--sd-color-tabs-underline:var(--color-background-border)}.sd-tab-content{box-shadow:0 -2px var(--sd-color-tabs-overline),0 1px var(--sd-color-tabs-underline)}.sd-card{box-shadow:0 .1rem .25rem var(--sd-color-shadow),0 0 .0625rem rgba(0,0,0,.1)}.sd-shadow-sm{box-shadow:0 .1rem .25rem var(--sd-color-shadow),0 0 .0625rem rgba(0,0,0,.1)!important}.sd-shadow-md{box-shadow:0 .3rem .75rem var(--sd-color-shadow),0 0 .0625rem rgba(0,0,0,.1)!important}.sd-shadow-lg{box-shadow:0 .6rem 1.5rem var(--sd-color-shadow),0 0 .0625rem rgba(0,0,0,.1)!important}.sd-card-hover:hover{transform:none}.sd-cards-carousel{gap:.25rem;padding:.25rem}body{--tabs--label-text:var(--color-foreground-muted);--tabs--label-text--hover:var(--color-foreground-muted);--tabs--label-text--active:var(--color-brand-content);--tabs--label-text--active--hover:var(--color-brand-content);--tabs--label-background:transparent;--tabs--label-background--hover:transparent;--tabs--label-background--active:transparent;--tabs--label-background--active--hover:transparent;--tabs--padding-x:0.25em;--tabs--margin-x:1em;--tabs--border:var(--color-background-border);--tabs--label-border:transparent;--tabs--label-border--hover:var(--color-foreground-muted);--tabs--label-border--active:var(--color-brand-content);--tabs--label-border--active--hover:var(--color-brand-content)}[role=main] .container{max-width:none;padding-left:0;padding-right:0}.shadow.docutils{border:none;box-shadow:0 .2rem .5rem rgba(0,0,0,.05),0 0 .0625rem rgba(0,0,0,.1)!important}.sphinx-bs .card{background-color:var(--color-background-secondary);color:var(--color-foreground)}
-/*# sourceMappingURL=furo-extensions.css.map*/
\ No newline at end of file
diff --git a/public/_static/styles/furo-extensions.css.map b/public/_static/styles/furo-extensions.css.map
deleted file mode 100644
index 9ba5637f9..000000000
--- a/public/_static/styles/furo-extensions.css.map
+++ /dev/null
@@ -1 +0,0 @@
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This file contains the styles used for tweaking how ReadTheDoc's embedded\n// contents would show up inside the theme.\n\n#furo-sidebar-ad-placement\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n .ethical-sidebar\n // Remove the border and box-shadow.\n border: none\n box-shadow: none\n // Manage the background colors.\n background: var(--color-background-secondary)\n &:hover\n background: var(--color-background-hover)\n // Ensure the text is legible.\n a\n color: var(--color-foreground-primary)\n\n .ethical-callout a\n color: var(--color-foreground-secondary) !important\n\n#furo-readthedocs-versions\n position: static\n width: 100%\n background: transparent\n display: block\n\n // Make the background color fit with the theme's aesthetic.\n .rst-versions\n background: rgb(26, 28, 30)\n\n .rst-current-version\n cursor: unset\n background: var(--color-sidebar-item-background)\n &:hover\n background: var(--color-sidebar-item-background)\n .fa-book\n color: var(--color-foreground-primary)\n\n > .rst-other-versions\n padding: 0\n small\n opacity: 1\n\n .injected\n .rst-versions\n position: unset\n\n &:hover,\n &:focus-within\n box-shadow: 0 0 0 1px var(--color-sidebar-background-border)\n\n .rst-current-version\n // Undo the tweaks done in RTD's CSS\n font-size: inherit\n line-height: inherit\n height: auto\n text-align: right\n padding: 12px\n\n // Match the rest of the body\n background: #1a1c1e\n\n .fa-book\n float: left\n color: white\n\n .fa-caret-down\n display: none\n\n .rst-current-version,\n .rst-other-versions,\n .injected\n display: block\n\n > .rst-current-version\n display: none\n",".highlight\n &:hover button.copybtn\n color: var(--color-code-foreground)\n\n button.copybtn\n // Make it visible\n opacity: 1\n\n // Align things correctly\n align-items: center\n\n height: 1.25em\n width: 1.25em\n\n top: 0.625rem // $code-spacing-vertical\n right: 0.5rem\n\n // Make it look better\n color: var(--color-background-item)\n background-color: var(--color-code-background)\n border: none\n\n // Change to cursor to make it obvious that you can click on it\n cursor: pointer\n\n // Transition smoothly, for aesthetics\n transition: color 300ms, opacity 300ms\n\n &:hover\n color: var(--color-brand-content)\n background-color: var(--color-code-background)\n\n &::after\n display: none\n color: var(--color-code-foreground)\n background-color: transparent\n\n &.success\n transition: color 0ms\n color: #22863a\n &::after\n display: block\n\n svg\n padding: 0\n","body\n // Colors\n --sd-color-primary: var(--color-brand-primary)\n --sd-color-primary-highlight: var(--color-brand-content)\n --sd-color-primary-text: var(--color-background-primary)\n\n // Shadows\n --sd-color-shadow: rgba(0, 0, 0, 0.05)\n\n // Cards\n --sd-color-card-border: var(--color-card-border)\n --sd-color-card-border-hover: var(--color-brand-content)\n --sd-color-card-background: var(--color-card-background)\n 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diff --git a/public/_static/styles/furo.css b/public/_static/styles/furo.css
deleted file mode 100644
index 3d29a218f..000000000
--- a/public/_static/styles/furo.css
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Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto\n display: block\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"currentColor\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><circle cx=\"10\" cy=\"10\" r=\"7\" /><line x1=\"21\" y1=\"21\" x2=\"15\" y2=\"15\" /></svg>'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z\"/></svg>'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M4 5h16v2H4V5m0 4h16v2H4V9m0 4h16v2H4v-2m0 4h10v2H4v-2z\"/></svg>'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 9h-2V7h2m0 10h-2v-6h2m-1-9A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10A10 10 0 0012 2z\"/></svg>'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M17.55 11.2c-.23-.3-.5-.56-.76-.82-.65-.6-1.4-1.03-2.03-1.66C13.3 7.26 13 4.85 13.91 3c-.91.23-1.75.75-2.45 1.32-2.54 2.08-3.54 5.75-2.34 8.9.04.1.08.2.08.33 0 .22-.15.42-.35.5-.22.1-.46.04-.64-.12a.83.83 0 01-.15-.17c-1.1-1.43-1.28-3.48-.53-5.12C5.89 10 5 12.3 5.14 14.47c.04.5.1 1 .27 1.5.14.6.4 1.2.72 1.73 1.04 1.73 2.87 2.97 4.84 3.22 2.1.27 4.35-.12 5.96-1.6 1.8-1.66 2.45-4.32 1.5-6.6l-.13-.26c-.2-.46-.47-.87-.8-1.25l.05-.01m-3.1 6.3c-.28.24-.73.5-1.08.6-1.1.4-2.2-.16-2.87-.82 1.19-.28 1.89-1.16 2.09-2.05.17-.8-.14-1.46-.27-2.23-.12-.74-.1-1.37.18-2.06.17.38.37.76.6 1.06.76 1 1.95 1.44 2.2 2.8.04.14.06.28.06.43.03.82-.32 1.72-.92 2.27h.01z\"/></svg>'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M15.07 11.25l-.9.92C13.45 12.89 13 13.5 13 15h-2v-.5c0-1.11.45-2.11 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41a2 2 0 00-2-2 2 2 0 00-2 2H8a4 4 0 014-4 4 4 0 014 4 3.2 3.2 0 01-.93 2.25M13 19h-2v-2h2M12 2A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10c0-5.53-4.5-10-10-10z\"/></svg>'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 14h-2v-4h2m0 8h-2v-2h2M1 21h22L12 2 1 21z\"/></svg>'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M12 2c5.53 0 10 4.47 10 10s-4.47 10-10 10S2 17.53 2 12 6.47 2 12 2m3.59 5L12 10.59 8.41 7 7 8.41 10.59 12 7 15.59 8.41 17 12 13.41 15.59 17 17 15.59 13.41 12 17 8.41 15.59 7z\"/></svg>'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M11.5 20l4.86-9.73H13V4l-5 9.73h3.5V20M12 2c2.75 0 5.1 1 7.05 2.95C21 6.9 22 9.25 22 12s-1 5.1-2.95 7.05C17.1 21 14.75 22 12 22s-5.1-1-7.05-2.95C3 17.1 2 14.75 2 12s1-5.1 2.95-7.05C6.9 3 9.25 2 12 2z\"/></svg>')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #646776; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2962ff;\n --color-brand-content: #2a5adf;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link-underline--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #ffffffcc; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #2b8cee;\n --color-brand-content: #368ce2;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n vertical-align: middle\n\n.theme-toggle\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n vertical-align: middle\n height: 1rem\n width: 1rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n float: left\n padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1rem\n width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page svg\n color: inherit\n height: 1rem\n width: 1rem\n\n.sidebar-toggle\n position: absolute\n display: none\n// <debugging things>\n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// </debugging things>\n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n .theme-toggle-header\n display: block\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: 1.25rem\n width: 1.25rem\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: var(--header-height)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Center the page, and accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n .content\n margin-left: auto\n margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n .content\n padding: 0 $content-padding--small\n // Don't float sidebars to the right.\n article aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 3.5rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\n.versionmodified\n font-style: italic\ndiv.versionadded, div.versionchanged, div.deprecated\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > p,\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 0.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(0.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(0.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,<svg width=\"12\" height=\"12\" xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"%23607D8B\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><path d=\"M11 7h-5a2 2 0 0 0 -2 2v9a2 2 0 0 0 2 2h9a2 2 0 0 0 2 -2v-5\" /><line x1=\"10\" y1=\"14\" x2=\"20\" y2=\"4\" /><polyline points=\"15 4 20 4 20 9\" /></svg>')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the <label>\n li.has-children\n > .reference\n padding-right: var(--sidebar-expander-width)\n\n // Colorize the top-level list items and icon.\n .toctree-l1\n & > .reference,\n & > label .icon\n color: var(--color-sidebar-link-text--top-level)\n\n // Color changes on hover\n label\n background: var(--color-sidebar-item-expander-background)\n &:hover\n background: var(--color-sidebar-item-expander-background--hover)\n\n .current > .reference\n background: var(--color-sidebar-item-background--current)\n &:hover\n background: var(--color-sidebar-item-background--hover)\n\n.toctree-checkbox\n position: absolute\n display: none\n\n////////////////////////////////////////////////////////////////////////////////\n// Togglable expand/collapse\n////////////////////////////////////////////////////////////////////////////////\n.toctree-checkbox\n ~ ul\n display: none\n\n ~ label .icon svg\n transform: rotate(90deg)\n\n.toctree-checkbox:checked\n ~ ul\n display: block\n\n ~ label .icon svg\n transform: rotate(-90deg)\n","// This file contains the styles for the contents of the right sidebar, which\n// contains the table of contents for the current page.\n.toc-title-container\n padding: var(--toc-title-padding)\n padding-top: var(--toc-spacing-vertical)\n\n.toc-title\n color: var(--color-toc-title-text)\n font-size: var(--toc-title-font-size)\n padding-left: var(--toc-spacing-horizontal)\n text-transform: uppercase\n\n// If the ToC is not present, hide these elements coz they're not relevant.\n.no-toc\n display: none\n\n.toc-tree-container\n padding-bottom: var(--toc-spacing-vertical)\n\n.toc-tree\n font-size: var(--toc-font-size)\n line-height: 1.3\n border-left: 1px solid var(--color-background-border)\n\n padding-left: calc(var(--toc-spacing-horizontal) - var(--toc-item-spacing-horizontal))\n\n // Hide the first \"top level\" bullet.\n > ul > li:first-child\n padding-top: 0\n & > ul\n padding-left: 0\n & > a\n display: none\n\n ul\n list-style-type: none\n margin-top: 0\n margin-bottom: 0\n padding-left: var(--toc-item-spacing-horizontal)\n li\n padding-top: var(--toc-item-spacing-vertical)\n\n &.scroll-current >.reference\n color: var(--color-toc-item-text--active)\n font-weight: bold\n\n .reference\n color: var(--color-toc-item-text)\n text-decoration: none\n overflow-wrap: anywhere\n\n.toc-scroll\n max-height: 100vh\n overflow-y: scroll\n\n// Be very annoying when someone includes the table of contents\n.contents:not(.this-will-duplicate-information-and-it-is-still-useful-here)\n color: var(--color-problematic)\n background: rgba(255, 0, 0, 0.25)\n &::before\n content: \"ERROR: Adding a table of contents in Furo-based documentation is unnecessary, and does not work well with existing styling.Add a 'this-will-duplicate-information-and-it-is-still-useful-here' class, if you want an escape hatch.\"\n","// Shameful hacks, to work around bugs.\n\n// MyST parser doesn't correctly generate classes, to align table contents.\n// https://github.com/executablebooks/MyST-Parser/issues/412\n.text-align\\:left > p\n text-align: left\n\n.text-align\\:center > p\n text-align: center\n\n.text-align\\:right > p\n text-align: right\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/public/_static/underscore-1.13.1.js b/public/_static/underscore-1.13.1.js
deleted file mode 100644
index ffd77af96..000000000
--- a/public/_static/underscore-1.13.1.js
+++ /dev/null
@@ -1,2042 +0,0 @@
-(function (global, factory) {
- typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
- typeof define === 'function' && define.amd ? define('underscore', factory) :
- (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
- var current = global._;
- var exports = global._ = factory();
- exports.noConflict = function () { global._ = current; return exports; };
- }()));
-}(this, (function () {
- // Underscore.js 1.13.1
- // https://underscorejs.org
- // (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
- // Underscore may be freely distributed under the MIT license.
-
- // Current version.
- var VERSION = '1.13.1';
-
- // Establish the root object, `window` (`self`) in the browser, `global`
- // on the server, or `this` in some virtual machines. We use `self`
- // instead of `window` for `WebWorker` support.
- var root = typeof self == 'object' && self.self === self && self ||
- typeof global == 'object' && global.global === global && global ||
- Function('return this')() ||
- {};
-
- // Save bytes in the minified (but not gzipped) version:
- var ArrayProto = Array.prototype, ObjProto = Object.prototype;
- var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
-
- // Create quick reference variables for speed access to core prototypes.
- var push = ArrayProto.push,
- slice = ArrayProto.slice,
- toString = ObjProto.toString,
- hasOwnProperty = ObjProto.hasOwnProperty;
-
- // Modern feature detection.
- var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
- supportsDataView = typeof DataView !== 'undefined';
-
- // All **ECMAScript 5+** native function implementations that we hope to use
- // are declared here.
- var nativeIsArray = Array.isArray,
- nativeKeys = Object.keys,
- nativeCreate = Object.create,
- nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
- // Create references to these builtin functions because we override them.
- var _isNaN = isNaN,
- _isFinite = isFinite;
-
- // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
- var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
- var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
- 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
- // The largest integer that can be represented exactly.
- var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
- // Some functions take a variable number of arguments, or a few expected
- // arguments at the beginning and then a variable number of values to operate
- // on. This helper accumulates all remaining arguments past the function’s
- // argument length (or an explicit `startIndex`), into an array that becomes
- // the last argument. Similar to ES6’s "rest parameter".
- function restArguments(func, startIndex) {
- startIndex = startIndex == null ? func.length - 1 : +startIndex;
- return function() {
- var length = Math.max(arguments.length - startIndex, 0),
- rest = Array(length),
- index = 0;
- for (; index < length; index++) {
- rest[index] = arguments[index + startIndex];
- }
- switch (startIndex) {
- case 0: return func.call(this, rest);
- case 1: return func.call(this, arguments[0], rest);
- case 2: return func.call(this, arguments[0], arguments[1], rest);
- }
- var args = Array(startIndex + 1);
- for (index = 0; index < startIndex; index++) {
- args[index] = arguments[index];
- }
- args[startIndex] = rest;
- return func.apply(this, args);
- };
- }
-
- // Is a given variable an object?
- function isObject(obj) {
- var type = typeof obj;
- return type === 'function' || type === 'object' && !!obj;
- }
-
- // Is a given value equal to null?
- function isNull(obj) {
- return obj === null;
- }
-
- // Is a given variable undefined?
- function isUndefined(obj) {
- return obj === void 0;
- }
-
- // Is a given value a boolean?
- function isBoolean(obj) {
- return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
- }
-
- // Is a given value a DOM element?
- function isElement(obj) {
- return !!(obj && obj.nodeType === 1);
- }
-
- // Internal function for creating a `toString`-based type tester.
- function tagTester(name) {
- var tag = '[object ' + name + ']';
- return function(obj) {
- return toString.call(obj) === tag;
- };
- }
-
- var isString = tagTester('String');
-
- var isNumber = tagTester('Number');
-
- var isDate = tagTester('Date');
-
- var isRegExp = tagTester('RegExp');
-
- var isError = tagTester('Error');
-
- var isSymbol = tagTester('Symbol');
-
- var isArrayBuffer = tagTester('ArrayBuffer');
-
- var isFunction = tagTester('Function');
-
- // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
- // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
- var nodelist = root.document && root.document.childNodes;
- if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
- isFunction = function(obj) {
- return typeof obj == 'function' || false;
- };
- }
-
- var isFunction$1 = isFunction;
-
- var hasObjectTag = tagTester('Object');
-
- // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
- // In IE 11, the most common among them, this problem also applies to
- // `Map`, `WeakMap` and `Set`.
- var hasStringTagBug = (
- supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
- ),
- isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
- var isDataView = tagTester('DataView');
-
- // In IE 10 - Edge 13, we need a different heuristic
- // to determine whether an object is a `DataView`.
- function ie10IsDataView(obj) {
- return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
- }
-
- var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
- // Is a given value an array?
- // Delegates to ECMA5's native `Array.isArray`.
- var isArray = nativeIsArray || tagTester('Array');
-
- // Internal function to check whether `key` is an own property name of `obj`.
- function has$1(obj, key) {
- return obj != null && hasOwnProperty.call(obj, key);
- }
-
- var isArguments = tagTester('Arguments');
-
- // Define a fallback version of the method in browsers (ahem, IE < 9), where
- // there isn't any inspectable "Arguments" type.
- (function() {
- if (!isArguments(arguments)) {
- isArguments = function(obj) {
- return has$1(obj, 'callee');
- };
- }
- }());
-
- var isArguments$1 = isArguments;
-
- // Is a given object a finite number?
- function isFinite$1(obj) {
- return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
- }
-
- // Is the given value `NaN`?
- function isNaN$1(obj) {
- return isNumber(obj) && _isNaN(obj);
- }
-
- // Predicate-generating function. Often useful outside of Underscore.
- function constant(value) {
- return function() {
- return value;
- };
- }
-
- // Common internal logic for `isArrayLike` and `isBufferLike`.
- function createSizePropertyCheck(getSizeProperty) {
- return function(collection) {
- var sizeProperty = getSizeProperty(collection);
- return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
- }
- }
-
- // Internal helper to generate a function to obtain property `key` from `obj`.
- function shallowProperty(key) {
- return function(obj) {
- return obj == null ? void 0 : obj[key];
- };
- }
-
- // Internal helper to obtain the `byteLength` property of an object.
- var getByteLength = shallowProperty('byteLength');
-
- // Internal helper to determine whether we should spend extensive checks against
- // `ArrayBuffer` et al.
- var isBufferLike = createSizePropertyCheck(getByteLength);
-
- // Is a given value a typed array?
- var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
- function isTypedArray(obj) {
- // `ArrayBuffer.isView` is the most future-proof, so use it when available.
- // Otherwise, fall back on the above regular expression.
- return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
- isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
- }
-
- var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
- // Internal helper to obtain the `length` property of an object.
- var getLength = shallowProperty('length');
-
- // Internal helper to create a simple lookup structure.
- // `collectNonEnumProps` used to depend on `_.contains`, but this led to
- // circular imports. `emulatedSet` is a one-off solution that only works for
- // arrays of strings.
- function emulatedSet(keys) {
- var hash = {};
- for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
- return {
- contains: function(key) { return hash[key]; },
- push: function(key) {
- hash[key] = true;
- return keys.push(key);
- }
- };
- }
-
- // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
- // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
- // needed.
- function collectNonEnumProps(obj, keys) {
- keys = emulatedSet(keys);
- var nonEnumIdx = nonEnumerableProps.length;
- var constructor = obj.constructor;
- var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
- // Constructor is a special case.
- var prop = 'constructor';
- if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
- while (nonEnumIdx--) {
- prop = nonEnumerableProps[nonEnumIdx];
- if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
- keys.push(prop);
- }
- }
- }
-
- // Retrieve the names of an object's own properties.
- // Delegates to **ECMAScript 5**'s native `Object.keys`.
- function keys(obj) {
- if (!isObject(obj)) return [];
- if (nativeKeys) return nativeKeys(obj);
- var keys = [];
- for (var key in obj) if (has$1(obj, key)) keys.push(key);
- // Ahem, IE < 9.
- if (hasEnumBug) collectNonEnumProps(obj, keys);
- return keys;
- }
-
- // Is a given array, string, or object empty?
- // An "empty" object has no enumerable own-properties.
- function isEmpty(obj) {
- if (obj == null) return true;
- // Skip the more expensive `toString`-based type checks if `obj` has no
- // `.length`.
- var length = getLength(obj);
- if (typeof length == 'number' && (
- isArray(obj) || isString(obj) || isArguments$1(obj)
- )) return length === 0;
- return getLength(keys(obj)) === 0;
- }
-
- // Returns whether an object has a given set of `key:value` pairs.
- function isMatch(object, attrs) {
- var _keys = keys(attrs), length = _keys.length;
- if (object == null) return !length;
- var obj = Object(object);
- for (var i = 0; i < length; i++) {
- var key = _keys[i];
- if (attrs[key] !== obj[key] || !(key in obj)) return false;
- }
- return true;
- }
-
- // If Underscore is called as a function, it returns a wrapped object that can
- // be used OO-style. This wrapper holds altered versions of all functions added
- // through `_.mixin`. Wrapped objects may be chained.
- function _$1(obj) {
- if (obj instanceof _$1) return obj;
- if (!(this instanceof _$1)) return new _$1(obj);
- this._wrapped = obj;
- }
-
- _$1.VERSION = VERSION;
-
- // Extracts the result from a wrapped and chained object.
- _$1.prototype.value = function() {
- return this._wrapped;
- };
-
- // Provide unwrapping proxies for some methods used in engine operations
- // such as arithmetic and JSON stringification.
- _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
-
- _$1.prototype.toString = function() {
- return String(this._wrapped);
- };
-
- // Internal function to wrap or shallow-copy an ArrayBuffer,
- // typed array or DataView to a new view, reusing the buffer.
- function toBufferView(bufferSource) {
- return new Uint8Array(
- bufferSource.buffer || bufferSource,
- bufferSource.byteOffset || 0,
- getByteLength(bufferSource)
- );
- }
-
- // We use this string twice, so give it a name for minification.
- var tagDataView = '[object DataView]';
-
- // Internal recursive comparison function for `_.isEqual`.
- function eq(a, b, aStack, bStack) {
- // Identical objects are equal. `0 === -0`, but they aren't identical.
- // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
- if (a === b) return a !== 0 || 1 / a === 1 / b;
- // `null` or `undefined` only equal to itself (strict comparison).
- if (a == null || b == null) return false;
- // `NaN`s are equivalent, but non-reflexive.
- if (a !== a) return b !== b;
- // Exhaust primitive checks
- var type = typeof a;
- if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
- return deepEq(a, b, aStack, bStack);
- }
-
- // Internal recursive comparison function for `_.isEqual`.
- function deepEq(a, b, aStack, bStack) {
- // Unwrap any wrapped objects.
- if (a instanceof _$1) a = a._wrapped;
- if (b instanceof _$1) b = b._wrapped;
- // Compare `[[Class]]` names.
- var className = toString.call(a);
- if (className !== toString.call(b)) return false;
- // Work around a bug in IE 10 - Edge 13.
- if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
- if (!isDataView$1(b)) return false;
- className = tagDataView;
- }
- switch (className) {
- // These types are compared by value.
- case '[object RegExp]':
- // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
- case '[object String]':
- // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
- // equivalent to `new String("5")`.
- return '' + a === '' + b;
- case '[object Number]':
- // `NaN`s are equivalent, but non-reflexive.
- // Object(NaN) is equivalent to NaN.
- if (+a !== +a) return +b !== +b;
- // An `egal` comparison is performed for other numeric values.
- return +a === 0 ? 1 / +a === 1 / b : +a === +b;
- case '[object Date]':
- case '[object Boolean]':
- // Coerce dates and booleans to numeric primitive values. Dates are compared by their
- // millisecond representations. Note that invalid dates with millisecond representations
- // of `NaN` are not equivalent.
- return +a === +b;
- case '[object Symbol]':
- return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
- case '[object ArrayBuffer]':
- case tagDataView:
- // Coerce to typed array so we can fall through.
- return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
- }
-
- var areArrays = className === '[object Array]';
- if (!areArrays && isTypedArray$1(a)) {
- var byteLength = getByteLength(a);
- if (byteLength !== getByteLength(b)) return false;
- if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
- areArrays = true;
- }
- if (!areArrays) {
- if (typeof a != 'object' || typeof b != 'object') return false;
-
- // Objects with different constructors are not equivalent, but `Object`s or `Array`s
- // from different frames are.
- var aCtor = a.constructor, bCtor = b.constructor;
- if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
- isFunction$1(bCtor) && bCtor instanceof bCtor)
- && ('constructor' in a && 'constructor' in b)) {
- return false;
- }
- }
- // Assume equality for cyclic structures. The algorithm for detecting cyclic
- // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
- // Initializing stack of traversed objects.
- // It's done here since we only need them for objects and arrays comparison.
- aStack = aStack || [];
- bStack = bStack || [];
- var length = aStack.length;
- while (length--) {
- // Linear search. Performance is inversely proportional to the number of
- // unique nested structures.
- if (aStack[length] === a) return bStack[length] === b;
- }
-
- // Add the first object to the stack of traversed objects.
- aStack.push(a);
- bStack.push(b);
-
- // Recursively compare objects and arrays.
- if (areArrays) {
- // Compare array lengths to determine if a deep comparison is necessary.
- length = a.length;
- if (length !== b.length) return false;
- // Deep compare the contents, ignoring non-numeric properties.
- while (length--) {
- if (!eq(a[length], b[length], aStack, bStack)) return false;
- }
- } else {
- // Deep compare objects.
- var _keys = keys(a), key;
- length = _keys.length;
- // Ensure that both objects contain the same number of properties before comparing deep equality.
- if (keys(b).length !== length) return false;
- while (length--) {
- // Deep compare each member
- key = _keys[length];
- if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
- }
- }
- // Remove the first object from the stack of traversed objects.
- aStack.pop();
- bStack.pop();
- return true;
- }
-
- // Perform a deep comparison to check if two objects are equal.
- function isEqual(a, b) {
- return eq(a, b);
- }
-
- // Retrieve all the enumerable property names of an object.
- function allKeys(obj) {
- if (!isObject(obj)) return [];
- var keys = [];
- for (var key in obj) keys.push(key);
- // Ahem, IE < 9.
- if (hasEnumBug) collectNonEnumProps(obj, keys);
- return keys;
- }
-
- // Since the regular `Object.prototype.toString` type tests don't work for
- // some types in IE 11, we use a fingerprinting heuristic instead, based
- // on the methods. It's not great, but it's the best we got.
- // The fingerprint method lists are defined below.
- function ie11fingerprint(methods) {
- var length = getLength(methods);
- return function(obj) {
- if (obj == null) return false;
- // `Map`, `WeakMap` and `Set` have no enumerable keys.
- var keys = allKeys(obj);
- if (getLength(keys)) return false;
- for (var i = 0; i < length; i++) {
- if (!isFunction$1(obj[methods[i]])) return false;
- }
- // If we are testing against `WeakMap`, we need to ensure that
- // `obj` doesn't have a `forEach` method in order to distinguish
- // it from a regular `Map`.
- return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
- };
- }
-
- // In the interest of compact minification, we write
- // each string in the fingerprints only once.
- var forEachName = 'forEach',
- hasName = 'has',
- commonInit = ['clear', 'delete'],
- mapTail = ['get', hasName, 'set'];
-
- // `Map`, `WeakMap` and `Set` each have slightly different
- // combinations of the above sublists.
- var mapMethods = commonInit.concat(forEachName, mapTail),
- weakMapMethods = commonInit.concat(mapTail),
- setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
- var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
- var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
- var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
- var isWeakSet = tagTester('WeakSet');
-
- // Retrieve the values of an object's properties.
- function values(obj) {
- var _keys = keys(obj);
- var length = _keys.length;
- var values = Array(length);
- for (var i = 0; i < length; i++) {
- values[i] = obj[_keys[i]];
- }
- return values;
- }
-
- // Convert an object into a list of `[key, value]` pairs.
- // The opposite of `_.object` with one argument.
- function pairs(obj) {
- var _keys = keys(obj);
- var length = _keys.length;
- var pairs = Array(length);
- for (var i = 0; i < length; i++) {
- pairs[i] = [_keys[i], obj[_keys[i]]];
- }
- return pairs;
- }
-
- // Invert the keys and values of an object. The values must be serializable.
- function invert(obj) {
- var result = {};
- var _keys = keys(obj);
- for (var i = 0, length = _keys.length; i < length; i++) {
- result[obj[_keys[i]]] = _keys[i];
- }
- return result;
- }
-
- // Return a sorted list of the function names available on the object.
- function functions(obj) {
- var names = [];
- for (var key in obj) {
- if (isFunction$1(obj[key])) names.push(key);
- }
- return names.sort();
- }
-
- // An internal function for creating assigner functions.
- function createAssigner(keysFunc, defaults) {
- return function(obj) {
- var length = arguments.length;
- if (defaults) obj = Object(obj);
- if (length < 2 || obj == null) return obj;
- for (var index = 1; index < length; index++) {
- var source = arguments[index],
- keys = keysFunc(source),
- l = keys.length;
- for (var i = 0; i < l; i++) {
- var key = keys[i];
- if (!defaults || obj[key] === void 0) obj[key] = source[key];
- }
- }
- return obj;
- };
- }
-
- // Extend a given object with all the properties in passed-in object(s).
- var extend = createAssigner(allKeys);
-
- // Assigns a given object with all the own properties in the passed-in
- // object(s).
- // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
- var extendOwn = createAssigner(keys);
-
- // Fill in a given object with default properties.
- var defaults = createAssigner(allKeys, true);
-
- // Create a naked function reference for surrogate-prototype-swapping.
- function ctor() {
- return function(){};
- }
-
- // An internal function for creating a new object that inherits from another.
- function baseCreate(prototype) {
- if (!isObject(prototype)) return {};
- if (nativeCreate) return nativeCreate(prototype);
- var Ctor = ctor();
- Ctor.prototype = prototype;
- var result = new Ctor;
- Ctor.prototype = null;
- return result;
- }
-
- // Creates an object that inherits from the given prototype object.
- // If additional properties are provided then they will be added to the
- // created object.
- function create(prototype, props) {
- var result = baseCreate(prototype);
- if (props) extendOwn(result, props);
- return result;
- }
-
- // Create a (shallow-cloned) duplicate of an object.
- function clone(obj) {
- if (!isObject(obj)) return obj;
- return isArray(obj) ? obj.slice() : extend({}, obj);
- }
-
- // Invokes `interceptor` with the `obj` and then returns `obj`.
- // The primary purpose of this method is to "tap into" a method chain, in
- // order to perform operations on intermediate results within the chain.
- function tap(obj, interceptor) {
- interceptor(obj);
- return obj;
- }
-
- // Normalize a (deep) property `path` to array.
- // Like `_.iteratee`, this function can be customized.
- function toPath$1(path) {
- return isArray(path) ? path : [path];
- }
- _$1.toPath = toPath$1;
-
- // Internal wrapper for `_.toPath` to enable minification.
- // Similar to `cb` for `_.iteratee`.
- function toPath(path) {
- return _$1.toPath(path);
- }
-
- // Internal function to obtain a nested property in `obj` along `path`.
- function deepGet(obj, path) {
- var length = path.length;
- for (var i = 0; i < length; i++) {
- if (obj == null) return void 0;
- obj = obj[path[i]];
- }
- return length ? obj : void 0;
- }
-
- // Get the value of the (deep) property on `path` from `object`.
- // If any property in `path` does not exist or if the value is
- // `undefined`, return `defaultValue` instead.
- // The `path` is normalized through `_.toPath`.
- function get(object, path, defaultValue) {
- var value = deepGet(object, toPath(path));
- return isUndefined(value) ? defaultValue : value;
- }
-
- // Shortcut function for checking if an object has a given property directly on
- // itself (in other words, not on a prototype). Unlike the internal `has`
- // function, this public version can also traverse nested properties.
- function has(obj, path) {
- path = toPath(path);
- var length = path.length;
- for (var i = 0; i < length; i++) {
- var key = path[i];
- if (!has$1(obj, key)) return false;
- obj = obj[key];
- }
- return !!length;
- }
-
- // Keep the identity function around for default iteratees.
- function identity(value) {
- return value;
- }
-
- // Returns a predicate for checking whether an object has a given set of
- // `key:value` pairs.
- function matcher(attrs) {
- attrs = extendOwn({}, attrs);
- return function(obj) {
- return isMatch(obj, attrs);
- };
- }
-
- // Creates a function that, when passed an object, will traverse that object’s
- // properties down the given `path`, specified as an array of keys or indices.
- function property(path) {
- path = toPath(path);
- return function(obj) {
- return deepGet(obj, path);
- };
- }
-
- // Internal function that returns an efficient (for current engines) version
- // of the passed-in callback, to be repeatedly applied in other Underscore
- // functions.
- function optimizeCb(func, context, argCount) {
- if (context === void 0) return func;
- switch (argCount == null ? 3 : argCount) {
- case 1: return function(value) {
- return func.call(context, value);
- };
- // The 2-argument case is omitted because we’re not using it.
- case 3: return function(value, index, collection) {
- return func.call(context, value, index, collection);
- };
- case 4: return function(accumulator, value, index, collection) {
- return func.call(context, accumulator, value, index, collection);
- };
- }
- return function() {
- return func.apply(context, arguments);
- };
- }
-
- // An internal function to generate callbacks that can be applied to each
- // element in a collection, returning the desired result — either `_.identity`,
- // an arbitrary callback, a property matcher, or a property accessor.
- function baseIteratee(value, context, argCount) {
- if (value == null) return identity;
- if (isFunction$1(value)) return optimizeCb(value, context, argCount);
- if (isObject(value) && !isArray(value)) return matcher(value);
- return property(value);
- }
-
- // External wrapper for our callback generator. Users may customize
- // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
- // This abstraction hides the internal-only `argCount` argument.
- function iteratee(value, context) {
- return baseIteratee(value, context, Infinity);
- }
- _$1.iteratee = iteratee;
-
- // The function we call internally to generate a callback. It invokes
- // `_.iteratee` if overridden, otherwise `baseIteratee`.
- function cb(value, context, argCount) {
- if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
- return baseIteratee(value, context, argCount);
- }
-
- // Returns the results of applying the `iteratee` to each element of `obj`.
- // In contrast to `_.map` it returns an object.
- function mapObject(obj, iteratee, context) {
- iteratee = cb(iteratee, context);
- var _keys = keys(obj),
- length = _keys.length,
- results = {};
- for (var index = 0; index < length; index++) {
- var currentKey = _keys[index];
- results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
- }
- return results;
- }
-
- // Predicate-generating function. Often useful outside of Underscore.
- function noop(){}
-
- // Generates a function for a given object that returns a given property.
- function propertyOf(obj) {
- if (obj == null) return noop;
- return function(path) {
- return get(obj, path);
- };
- }
-
- // Run a function **n** times.
- function times(n, iteratee, context) {
- var accum = Array(Math.max(0, n));
- iteratee = optimizeCb(iteratee, context, 1);
- for (var i = 0; i < n; i++) accum[i] = iteratee(i);
- return accum;
- }
-
- // Return a random integer between `min` and `max` (inclusive).
- function random(min, max) {
- if (max == null) {
- max = min;
- min = 0;
- }
- return min + Math.floor(Math.random() * (max - min + 1));
- }
-
- // A (possibly faster) way to get the current timestamp as an integer.
- var now = Date.now || function() {
- return new Date().getTime();
- };
-
- // Internal helper to generate functions for escaping and unescaping strings
- // to/from HTML interpolation.
- function createEscaper(map) {
- var escaper = function(match) {
- return map[match];
- };
- // Regexes for identifying a key that needs to be escaped.
- var source = '(?:' + keys(map).join('|') + ')';
- var testRegexp = RegExp(source);
- var replaceRegexp = RegExp(source, 'g');
- return function(string) {
- string = string == null ? '' : '' + string;
- return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
- };
- }
-
- // Internal list of HTML entities for escaping.
- var escapeMap = {
- '&': '&',
- '<': '<',
- '>': '>',
- '"': '"',
- "'": ''',
- '`': '`'
- };
-
- // Function for escaping strings to HTML interpolation.
- var _escape = createEscaper(escapeMap);
-
- // Internal list of HTML entities for unescaping.
- var unescapeMap = invert(escapeMap);
-
- // Function for unescaping strings from HTML interpolation.
- var _unescape = createEscaper(unescapeMap);
-
- // By default, Underscore uses ERB-style template delimiters. Change the
- // following template settings to use alternative delimiters.
- var templateSettings = _$1.templateSettings = {
- evaluate: /<%([\s\S]+?)%>/g,
- interpolate: /<%=([\s\S]+?)%>/g,
- escape: /<%-([\s\S]+?)%>/g
- };
-
- // When customizing `_.templateSettings`, if you don't want to define an
- // interpolation, evaluation or escaping regex, we need one that is
- // guaranteed not to match.
- var noMatch = /(.)^/;
-
- // Certain characters need to be escaped so that they can be put into a
- // string literal.
- var escapes = {
- "'": "'",
- '\\': '\\',
- '\r': 'r',
- '\n': 'n',
- '\u2028': 'u2028',
- '\u2029': 'u2029'
- };
-
- var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
- function escapeChar(match) {
- return '\\' + escapes[match];
- }
-
- // In order to prevent third-party code injection through
- // `_.templateSettings.variable`, we test it against the following regular
- // expression. It is intentionally a bit more liberal than just matching valid
- // identifiers, but still prevents possible loopholes through defaults or
- // destructuring assignment.
- var bareIdentifier = /^\s*(\w|\$)+\s*$/;
-
- // JavaScript micro-templating, similar to John Resig's implementation.
- // Underscore templating handles arbitrary delimiters, preserves whitespace,
- // and correctly escapes quotes within interpolated code.
- // NB: `oldSettings` only exists for backwards compatibility.
- function template(text, settings, oldSettings) {
- if (!settings && oldSettings) settings = oldSettings;
- settings = defaults({}, settings, _$1.templateSettings);
-
- // Combine delimiters into one regular expression via alternation.
- var matcher = RegExp([
- (settings.escape || noMatch).source,
- (settings.interpolate || noMatch).source,
- (settings.evaluate || noMatch).source
- ].join('|') + '|$', 'g');
-
- // Compile the template source, escaping string literals appropriately.
- var index = 0;
- var source = "__p+='";
- text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
- source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
- index = offset + match.length;
-
- if (escape) {
- source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
- } else if (interpolate) {
- source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
- } else if (evaluate) {
- source += "';\n" + evaluate + "\n__p+='";
- }
-
- // Adobe VMs need the match returned to produce the correct offset.
- return match;
- });
- source += "';\n";
-
- var argument = settings.variable;
- if (argument) {
- // Insure against third-party code injection. (CVE-2021-23358)
- if (!bareIdentifier.test(argument)) throw new Error(
- 'variable is not a bare identifier: ' + argument
- );
- } else {
- // If a variable is not specified, place data values in local scope.
- source = 'with(obj||{}){\n' + source + '}\n';
- argument = 'obj';
- }
-
- source = "var __t,__p='',__j=Array.prototype.join," +
- "print=function(){__p+=__j.call(arguments,'');};\n" +
- source + 'return __p;\n';
-
- var render;
- try {
- render = new Function(argument, '_', source);
- } catch (e) {
- e.source = source;
- throw e;
- }
-
- var template = function(data) {
- return render.call(this, data, _$1);
- };
-
- // Provide the compiled source as a convenience for precompilation.
- template.source = 'function(' + argument + '){\n' + source + '}';
-
- return template;
- }
-
- // Traverses the children of `obj` along `path`. If a child is a function, it
- // is invoked with its parent as context. Returns the value of the final
- // child, or `fallback` if any child is undefined.
- function result(obj, path, fallback) {
- path = toPath(path);
- var length = path.length;
- if (!length) {
- return isFunction$1(fallback) ? fallback.call(obj) : fallback;
- }
- for (var i = 0; i < length; i++) {
- var prop = obj == null ? void 0 : obj[path[i]];
- if (prop === void 0) {
- prop = fallback;
- i = length; // Ensure we don't continue iterating.
- }
- obj = isFunction$1(prop) ? prop.call(obj) : prop;
- }
- return obj;
- }
-
- // Generate a unique integer id (unique within the entire client session).
- // Useful for temporary DOM ids.
- var idCounter = 0;
- function uniqueId(prefix) {
- var id = ++idCounter + '';
- return prefix ? prefix + id : id;
- }
-
- // Start chaining a wrapped Underscore object.
- function chain(obj) {
- var instance = _$1(obj);
- instance._chain = true;
- return instance;
- }
-
- // Internal function to execute `sourceFunc` bound to `context` with optional
- // `args`. Determines whether to execute a function as a constructor or as a
- // normal function.
- function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
- if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
- var self = baseCreate(sourceFunc.prototype);
- var result = sourceFunc.apply(self, args);
- if (isObject(result)) return result;
- return self;
- }
-
- // Partially apply a function by creating a version that has had some of its
- // arguments pre-filled, without changing its dynamic `this` context. `_` acts
- // as a placeholder by default, allowing any combination of arguments to be
- // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
- var partial = restArguments(function(func, boundArgs) {
- var placeholder = partial.placeholder;
- var bound = function() {
- var position = 0, length = boundArgs.length;
- var args = Array(length);
- for (var i = 0; i < length; i++) {
- args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
- }
- while (position < arguments.length) args.push(arguments[position++]);
- return executeBound(func, bound, this, this, args);
- };
- return bound;
- });
-
- partial.placeholder = _$1;
-
- // Create a function bound to a given object (assigning `this`, and arguments,
- // optionally).
- var bind = restArguments(function(func, context, args) {
- if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
- var bound = restArguments(function(callArgs) {
- return executeBound(func, bound, context, this, args.concat(callArgs));
- });
- return bound;
- });
-
- // Internal helper for collection methods to determine whether a collection
- // should be iterated as an array or as an object.
- // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
- // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
- var isArrayLike = createSizePropertyCheck(getLength);
-
- // Internal implementation of a recursive `flatten` function.
- function flatten$1(input, depth, strict, output) {
- output = output || [];
- if (!depth && depth !== 0) {
- depth = Infinity;
- } else if (depth <= 0) {
- return output.concat(input);
- }
- var idx = output.length;
- for (var i = 0, length = getLength(input); i < length; i++) {
- var value = input[i];
- if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
- // Flatten current level of array or arguments object.
- if (depth > 1) {
- flatten$1(value, depth - 1, strict, output);
- idx = output.length;
- } else {
- var j = 0, len = value.length;
- while (j < len) output[idx++] = value[j++];
- }
- } else if (!strict) {
- output[idx++] = value;
- }
- }
- return output;
- }
-
- // Bind a number of an object's methods to that object. Remaining arguments
- // are the method names to be bound. Useful for ensuring that all callbacks
- // defined on an object belong to it.
- var bindAll = restArguments(function(obj, keys) {
- keys = flatten$1(keys, false, false);
- var index = keys.length;
- if (index < 1) throw new Error('bindAll must be passed function names');
- while (index--) {
- var key = keys[index];
- obj[key] = bind(obj[key], obj);
- }
- return obj;
- });
-
- // Memoize an expensive function by storing its results.
- function memoize(func, hasher) {
- var memoize = function(key) {
- var cache = memoize.cache;
- var address = '' + (hasher ? hasher.apply(this, arguments) : key);
- if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
- return cache[address];
- };
- memoize.cache = {};
- return memoize;
- }
-
- // Delays a function for the given number of milliseconds, and then calls
- // it with the arguments supplied.
- var delay = restArguments(function(func, wait, args) {
- return setTimeout(function() {
- return func.apply(null, args);
- }, wait);
- });
-
- // Defers a function, scheduling it to run after the current call stack has
- // cleared.
- var defer = partial(delay, _$1, 1);
-
- // Returns a function, that, when invoked, will only be triggered at most once
- // during a given window of time. Normally, the throttled function will run
- // as much as it can, without ever going more than once per `wait` duration;
- // but if you'd like to disable the execution on the leading edge, pass
- // `{leading: false}`. To disable execution on the trailing edge, ditto.
- function throttle(func, wait, options) {
- var timeout, context, args, result;
- var previous = 0;
- if (!options) options = {};
-
- var later = function() {
- previous = options.leading === false ? 0 : now();
- timeout = null;
- result = func.apply(context, args);
- if (!timeout) context = args = null;
- };
-
- var throttled = function() {
- var _now = now();
- if (!previous && options.leading === false) previous = _now;
- var remaining = wait - (_now - previous);
- context = this;
- args = arguments;
- if (remaining <= 0 || remaining > wait) {
- if (timeout) {
- clearTimeout(timeout);
- timeout = null;
- }
- previous = _now;
- result = func.apply(context, args);
- if (!timeout) context = args = null;
- } else if (!timeout && options.trailing !== false) {
- timeout = setTimeout(later, remaining);
- }
- return result;
- };
-
- throttled.cancel = function() {
- clearTimeout(timeout);
- previous = 0;
- timeout = context = args = null;
- };
-
- return throttled;
- }
-
- // When a sequence of calls of the returned function ends, the argument
- // function is triggered. The end of a sequence is defined by the `wait`
- // parameter. If `immediate` is passed, the argument function will be
- // triggered at the beginning of the sequence instead of at the end.
- function debounce(func, wait, immediate) {
- var timeout, previous, args, result, context;
-
- var later = function() {
- var passed = now() - previous;
- if (wait > passed) {
- timeout = setTimeout(later, wait - passed);
- } else {
- timeout = null;
- if (!immediate) result = func.apply(context, args);
- // This check is needed because `func` can recursively invoke `debounced`.
- if (!timeout) args = context = null;
- }
- };
-
- var debounced = restArguments(function(_args) {
- context = this;
- args = _args;
- previous = now();
- if (!timeout) {
- timeout = setTimeout(later, wait);
- if (immediate) result = func.apply(context, args);
- }
- return result;
- });
-
- debounced.cancel = function() {
- clearTimeout(timeout);
- timeout = args = context = null;
- };
-
- return debounced;
- }
-
- // Returns the first function passed as an argument to the second,
- // allowing you to adjust arguments, run code before and after, and
- // conditionally execute the original function.
- function wrap(func, wrapper) {
- return partial(wrapper, func);
- }
-
- // Returns a negated version of the passed-in predicate.
- function negate(predicate) {
- return function() {
- return !predicate.apply(this, arguments);
- };
- }
-
- // Returns a function that is the composition of a list of functions, each
- // consuming the return value of the function that follows.
- function compose() {
- var args = arguments;
- var start = args.length - 1;
- return function() {
- var i = start;
- var result = args[start].apply(this, arguments);
- while (i--) result = args[i].call(this, result);
- return result;
- };
- }
-
- // Returns a function that will only be executed on and after the Nth call.
- function after(times, func) {
- return function() {
- if (--times < 1) {
- return func.apply(this, arguments);
- }
- };
- }
-
- // Returns a function that will only be executed up to (but not including) the
- // Nth call.
- function before(times, func) {
- var memo;
- return function() {
- if (--times > 0) {
- memo = func.apply(this, arguments);
- }
- if (times <= 1) func = null;
- return memo;
- };
- }
-
- // Returns a function that will be executed at most one time, no matter how
- // often you call it. Useful for lazy initialization.
- var once = partial(before, 2);
-
- // Returns the first key on an object that passes a truth test.
- function findKey(obj, predicate, context) {
- predicate = cb(predicate, context);
- var _keys = keys(obj), key;
- for (var i = 0, length = _keys.length; i < length; i++) {
- key = _keys[i];
- if (predicate(obj[key], key, obj)) return key;
- }
- }
-
- // Internal function to generate `_.findIndex` and `_.findLastIndex`.
- function createPredicateIndexFinder(dir) {
- return function(array, predicate, context) {
- predicate = cb(predicate, context);
- var length = getLength(array);
- var index = dir > 0 ? 0 : length - 1;
- for (; index >= 0 && index < length; index += dir) {
- if (predicate(array[index], index, array)) return index;
- }
- return -1;
- };
- }
-
- // Returns the first index on an array-like that passes a truth test.
- var findIndex = createPredicateIndexFinder(1);
-
- // Returns the last index on an array-like that passes a truth test.
- var findLastIndex = createPredicateIndexFinder(-1);
-
- // Use a comparator function to figure out the smallest index at which
- // an object should be inserted so as to maintain order. Uses binary search.
- function sortedIndex(array, obj, iteratee, context) {
- iteratee = cb(iteratee, context, 1);
- var value = iteratee(obj);
- var low = 0, high = getLength(array);
- while (low < high) {
- var mid = Math.floor((low + high) / 2);
- if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
- }
- return low;
- }
-
- // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
- function createIndexFinder(dir, predicateFind, sortedIndex) {
- return function(array, item, idx) {
- var i = 0, length = getLength(array);
- if (typeof idx == 'number') {
- if (dir > 0) {
- i = idx >= 0 ? idx : Math.max(idx + length, i);
- } else {
- length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
- }
- } else if (sortedIndex && idx && length) {
- idx = sortedIndex(array, item);
- return array[idx] === item ? idx : -1;
- }
- if (item !== item) {
- idx = predicateFind(slice.call(array, i, length), isNaN$1);
- return idx >= 0 ? idx + i : -1;
- }
- for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
- if (array[idx] === item) return idx;
- }
- return -1;
- };
- }
-
- // Return the position of the first occurrence of an item in an array,
- // or -1 if the item is not included in the array.
- // If the array is large and already in sort order, pass `true`
- // for **isSorted** to use binary search.
- var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
- // Return the position of the last occurrence of an item in an array,
- // or -1 if the item is not included in the array.
- var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
- // Return the first value which passes a truth test.
- function find(obj, predicate, context) {
- var keyFinder = isArrayLike(obj) ? findIndex : findKey;
- var key = keyFinder(obj, predicate, context);
- if (key !== void 0 && key !== -1) return obj[key];
- }
-
- // Convenience version of a common use case of `_.find`: getting the first
- // object containing specific `key:value` pairs.
- function findWhere(obj, attrs) {
- return find(obj, matcher(attrs));
- }
-
- // The cornerstone for collection functions, an `each`
- // implementation, aka `forEach`.
- // Handles raw objects in addition to array-likes. Treats all
- // sparse array-likes as if they were dense.
- function each(obj, iteratee, context) {
- iteratee = optimizeCb(iteratee, context);
- var i, length;
- if (isArrayLike(obj)) {
- for (i = 0, length = obj.length; i < length; i++) {
- iteratee(obj[i], i, obj);
- }
- } else {
- var _keys = keys(obj);
- for (i = 0, length = _keys.length; i < length; i++) {
- iteratee(obj[_keys[i]], _keys[i], obj);
- }
- }
- return obj;
- }
-
- // Return the results of applying the iteratee to each element.
- function map(obj, iteratee, context) {
- iteratee = cb(iteratee, context);
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length,
- results = Array(length);
- for (var index = 0; index < length; index++) {
- var currentKey = _keys ? _keys[index] : index;
- results[index] = iteratee(obj[currentKey], currentKey, obj);
- }
- return results;
- }
-
- // Internal helper to create a reducing function, iterating left or right.
- function createReduce(dir) {
- // Wrap code that reassigns argument variables in a separate function than
- // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
- var reducer = function(obj, iteratee, memo, initial) {
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length,
- index = dir > 0 ? 0 : length - 1;
- if (!initial) {
- memo = obj[_keys ? _keys[index] : index];
- index += dir;
- }
- for (; index >= 0 && index < length; index += dir) {
- var currentKey = _keys ? _keys[index] : index;
- memo = iteratee(memo, obj[currentKey], currentKey, obj);
- }
- return memo;
- };
-
- return function(obj, iteratee, memo, context) {
- var initial = arguments.length >= 3;
- return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
- };
- }
-
- // **Reduce** builds up a single result from a list of values, aka `inject`,
- // or `foldl`.
- var reduce = createReduce(1);
-
- // The right-associative version of reduce, also known as `foldr`.
- var reduceRight = createReduce(-1);
-
- // Return all the elements that pass a truth test.
- function filter(obj, predicate, context) {
- var results = [];
- predicate = cb(predicate, context);
- each(obj, function(value, index, list) {
- if (predicate(value, index, list)) results.push(value);
- });
- return results;
- }
-
- // Return all the elements for which a truth test fails.
- function reject(obj, predicate, context) {
- return filter(obj, negate(cb(predicate)), context);
- }
-
- // Determine whether all of the elements pass a truth test.
- function every(obj, predicate, context) {
- predicate = cb(predicate, context);
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length;
- for (var index = 0; index < length; index++) {
- var currentKey = _keys ? _keys[index] : index;
- if (!predicate(obj[currentKey], currentKey, obj)) return false;
- }
- return true;
- }
-
- // Determine if at least one element in the object passes a truth test.
- function some(obj, predicate, context) {
- predicate = cb(predicate, context);
- var _keys = !isArrayLike(obj) && keys(obj),
- length = (_keys || obj).length;
- for (var index = 0; index < length; index++) {
- var currentKey = _keys ? _keys[index] : index;
- if (predicate(obj[currentKey], currentKey, obj)) return true;
- }
- return false;
- }
-
- // Determine if the array or object contains a given item (using `===`).
- function contains(obj, item, fromIndex, guard) {
- if (!isArrayLike(obj)) obj = values(obj);
- if (typeof fromIndex != 'number' || guard) fromIndex = 0;
- return indexOf(obj, item, fromIndex) >= 0;
- }
-
- // Invoke a method (with arguments) on every item in a collection.
- var invoke = restArguments(function(obj, path, args) {
- var contextPath, func;
- if (isFunction$1(path)) {
- func = path;
- } else {
- path = toPath(path);
- contextPath = path.slice(0, -1);
- path = path[path.length - 1];
- }
- return map(obj, function(context) {
- var method = func;
- if (!method) {
- if (contextPath && contextPath.length) {
- context = deepGet(context, contextPath);
- }
- if (context == null) return void 0;
- method = context[path];
- }
- return method == null ? method : method.apply(context, args);
- });
- });
-
- // Convenience version of a common use case of `_.map`: fetching a property.
- function pluck(obj, key) {
- return map(obj, property(key));
- }
-
- // Convenience version of a common use case of `_.filter`: selecting only
- // objects containing specific `key:value` pairs.
- function where(obj, attrs) {
- return filter(obj, matcher(attrs));
- }
-
- // Return the maximum element (or element-based computation).
- function max(obj, iteratee, context) {
- var result = -Infinity, lastComputed = -Infinity,
- value, computed;
- if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
- obj = isArrayLike(obj) ? obj : values(obj);
- for (var i = 0, length = obj.length; i < length; i++) {
- value = obj[i];
- if (value != null && value > result) {
- result = value;
- }
- }
- } else {
- iteratee = cb(iteratee, context);
- each(obj, function(v, index, list) {
- computed = iteratee(v, index, list);
- if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
- result = v;
- lastComputed = computed;
- }
- });
- }
- return result;
- }
-
- // Return the minimum element (or element-based computation).
- function min(obj, iteratee, context) {
- var result = Infinity, lastComputed = Infinity,
- value, computed;
- if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
- obj = isArrayLike(obj) ? obj : values(obj);
- for (var i = 0, length = obj.length; i < length; i++) {
- value = obj[i];
- if (value != null && value < result) {
- result = value;
- }
- }
- } else {
- iteratee = cb(iteratee, context);
- each(obj, function(v, index, list) {
- computed = iteratee(v, index, list);
- if (computed < lastComputed || computed === Infinity && result === Infinity) {
- result = v;
- lastComputed = computed;
- }
- });
- }
- return result;
- }
-
- // Sample **n** random values from a collection using the modern version of the
- // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
- // If **n** is not specified, returns a single random element.
- // The internal `guard` argument allows it to work with `_.map`.
- function sample(obj, n, guard) {
- if (n == null || guard) {
- if (!isArrayLike(obj)) obj = values(obj);
- return obj[random(obj.length - 1)];
- }
- var sample = isArrayLike(obj) ? clone(obj) : values(obj);
- var length = getLength(sample);
- n = Math.max(Math.min(n, length), 0);
- var last = length - 1;
- for (var index = 0; index < n; index++) {
- var rand = random(index, last);
- var temp = sample[index];
- sample[index] = sample[rand];
- sample[rand] = temp;
- }
- return sample.slice(0, n);
- }
-
- // Shuffle a collection.
- function shuffle(obj) {
- return sample(obj, Infinity);
- }
-
- // Sort the object's values by a criterion produced by an iteratee.
- function sortBy(obj, iteratee, context) {
- var index = 0;
- iteratee = cb(iteratee, context);
- return pluck(map(obj, function(value, key, list) {
- return {
- value: value,
- index: index++,
- criteria: iteratee(value, key, list)
- };
- }).sort(function(left, right) {
- var a = left.criteria;
- var b = right.criteria;
- if (a !== b) {
- if (a > b || a === void 0) return 1;
- if (a < b || b === void 0) return -1;
- }
- return left.index - right.index;
- }), 'value');
- }
-
- // An internal function used for aggregate "group by" operations.
- function group(behavior, partition) {
- return function(obj, iteratee, context) {
- var result = partition ? [[], []] : {};
- iteratee = cb(iteratee, context);
- each(obj, function(value, index) {
- var key = iteratee(value, index, obj);
- behavior(result, value, key);
- });
- return result;
- };
- }
-
- // Groups the object's values by a criterion. Pass either a string attribute
- // to group by, or a function that returns the criterion.
- var groupBy = group(function(result, value, key) {
- if (has$1(result, key)) result[key].push(value); else result[key] = [value];
- });
-
- // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
- // when you know that your index values will be unique.
- var indexBy = group(function(result, value, key) {
- result[key] = value;
- });
-
- // Counts instances of an object that group by a certain criterion. Pass
- // either a string attribute to count by, or a function that returns the
- // criterion.
- var countBy = group(function(result, value, key) {
- if (has$1(result, key)) result[key]++; else result[key] = 1;
- });
-
- // Split a collection into two arrays: one whose elements all pass the given
- // truth test, and one whose elements all do not pass the truth test.
- var partition = group(function(result, value, pass) {
- result[pass ? 0 : 1].push(value);
- }, true);
-
- // Safely create a real, live array from anything iterable.
- var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
- function toArray(obj) {
- if (!obj) return [];
- if (isArray(obj)) return slice.call(obj);
- if (isString(obj)) {
- // Keep surrogate pair characters together.
- return obj.match(reStrSymbol);
- }
- if (isArrayLike(obj)) return map(obj, identity);
- return values(obj);
- }
-
- // Return the number of elements in a collection.
- function size(obj) {
- if (obj == null) return 0;
- return isArrayLike(obj) ? obj.length : keys(obj).length;
- }
-
- // Internal `_.pick` helper function to determine whether `key` is an enumerable
- // property name of `obj`.
- function keyInObj(value, key, obj) {
- return key in obj;
- }
-
- // Return a copy of the object only containing the allowed properties.
- var pick = restArguments(function(obj, keys) {
- var result = {}, iteratee = keys[0];
- if (obj == null) return result;
- if (isFunction$1(iteratee)) {
- if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
- keys = allKeys(obj);
- } else {
- iteratee = keyInObj;
- keys = flatten$1(keys, false, false);
- obj = Object(obj);
- }
- for (var i = 0, length = keys.length; i < length; i++) {
- var key = keys[i];
- var value = obj[key];
- if (iteratee(value, key, obj)) result[key] = value;
- }
- return result;
- });
-
- // Return a copy of the object without the disallowed properties.
- var omit = restArguments(function(obj, keys) {
- var iteratee = keys[0], context;
- if (isFunction$1(iteratee)) {
- iteratee = negate(iteratee);
- if (keys.length > 1) context = keys[1];
- } else {
- keys = map(flatten$1(keys, false, false), String);
- iteratee = function(value, key) {
- return !contains(keys, key);
- };
- }
- return pick(obj, iteratee, context);
- });
-
- // Returns everything but the last entry of the array. Especially useful on
- // the arguments object. Passing **n** will return all the values in
- // the array, excluding the last N.
- function initial(array, n, guard) {
- return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
- }
-
- // Get the first element of an array. Passing **n** will return the first N
- // values in the array. The **guard** check allows it to work with `_.map`.
- function first(array, n, guard) {
- if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
- if (n == null || guard) return array[0];
- return initial(array, array.length - n);
- }
-
- // Returns everything but the first entry of the `array`. Especially useful on
- // the `arguments` object. Passing an **n** will return the rest N values in the
- // `array`.
- function rest(array, n, guard) {
- return slice.call(array, n == null || guard ? 1 : n);
- }
-
- // Get the last element of an array. Passing **n** will return the last N
- // values in the array.
- function last(array, n, guard) {
- if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
- if (n == null || guard) return array[array.length - 1];
- return rest(array, Math.max(0, array.length - n));
- }
-
- // Trim out all falsy values from an array.
- function compact(array) {
- return filter(array, Boolean);
- }
-
- // Flatten out an array, either recursively (by default), or up to `depth`.
- // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
- function flatten(array, depth) {
- return flatten$1(array, depth, false);
- }
-
- // Take the difference between one array and a number of other arrays.
- // Only the elements present in just the first array will remain.
- var difference = restArguments(function(array, rest) {
- rest = flatten$1(rest, true, true);
- return filter(array, function(value){
- return !contains(rest, value);
- });
- });
-
- // Return a version of the array that does not contain the specified value(s).
- var without = restArguments(function(array, otherArrays) {
- return difference(array, otherArrays);
- });
-
- // Produce a duplicate-free version of the array. If the array has already
- // been sorted, you have the option of using a faster algorithm.
- // The faster algorithm will not work with an iteratee if the iteratee
- // is not a one-to-one function, so providing an iteratee will disable
- // the faster algorithm.
- function uniq(array, isSorted, iteratee, context) {
- if (!isBoolean(isSorted)) {
- context = iteratee;
- iteratee = isSorted;
- isSorted = false;
- }
- if (iteratee != null) iteratee = cb(iteratee, context);
- var result = [];
- var seen = [];
- for (var i = 0, length = getLength(array); i < length; i++) {
- var value = array[i],
- computed = iteratee ? iteratee(value, i, array) : value;
- if (isSorted && !iteratee) {
- if (!i || seen !== computed) result.push(value);
- seen = computed;
- } else if (iteratee) {
- if (!contains(seen, computed)) {
- seen.push(computed);
- result.push(value);
- }
- } else if (!contains(result, value)) {
- result.push(value);
- }
- }
- return result;
- }
-
- // Produce an array that contains the union: each distinct element from all of
- // the passed-in arrays.
- var union = restArguments(function(arrays) {
- return uniq(flatten$1(arrays, true, true));
- });
-
- // Produce an array that contains every item shared between all the
- // passed-in arrays.
- function intersection(array) {
- var result = [];
- var argsLength = arguments.length;
- for (var i = 0, length = getLength(array); i < length; i++) {
- var item = array[i];
- if (contains(result, item)) continue;
- var j;
- for (j = 1; j < argsLength; j++) {
- if (!contains(arguments[j], item)) break;
- }
- if (j === argsLength) result.push(item);
- }
- return result;
- }
-
- // Complement of zip. Unzip accepts an array of arrays and groups
- // each array's elements on shared indices.
- function unzip(array) {
- var length = array && max(array, getLength).length || 0;
- var result = Array(length);
-
- for (var index = 0; index < length; index++) {
- result[index] = pluck(array, index);
- }
- return result;
- }
-
- // Zip together multiple lists into a single array -- elements that share
- // an index go together.
- var zip = restArguments(unzip);
-
- // Converts lists into objects. Pass either a single array of `[key, value]`
- // pairs, or two parallel arrays of the same length -- one of keys, and one of
- // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
- function object(list, values) {
- var result = {};
- for (var i = 0, length = getLength(list); i < length; i++) {
- if (values) {
- result[list[i]] = values[i];
- } else {
- result[list[i][0]] = list[i][1];
- }
- }
- return result;
- }
-
- // Generate an integer Array containing an arithmetic progression. A port of
- // the native Python `range()` function. See
- // [the Python documentation](https://docs.python.org/library/functions.html#range).
- function range(start, stop, step) {
- if (stop == null) {
- stop = start || 0;
- start = 0;
- }
- if (!step) {
- step = stop < start ? -1 : 1;
- }
-
- var length = Math.max(Math.ceil((stop - start) / step), 0);
- var range = Array(length);
-
- for (var idx = 0; idx < length; idx++, start += step) {
- range[idx] = start;
- }
-
- return range;
- }
-
- // Chunk a single array into multiple arrays, each containing `count` or fewer
- // items.
- function chunk(array, count) {
- if (count == null || count < 1) return [];
- var result = [];
- var i = 0, length = array.length;
- while (i < length) {
- result.push(slice.call(array, i, i += count));
- }
- return result;
- }
-
- // Helper function to continue chaining intermediate results.
- function chainResult(instance, obj) {
- return instance._chain ? _$1(obj).chain() : obj;
- }
-
- // Add your own custom functions to the Underscore object.
- function mixin(obj) {
- each(functions(obj), function(name) {
- var func = _$1[name] = obj[name];
- _$1.prototype[name] = function() {
- var args = [this._wrapped];
- push.apply(args, arguments);
- return chainResult(this, func.apply(_$1, args));
- };
- });
- return _$1;
- }
-
- // Add all mutator `Array` functions to the wrapper.
- each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
- var method = ArrayProto[name];
- _$1.prototype[name] = function() {
- var obj = this._wrapped;
- if (obj != null) {
- method.apply(obj, arguments);
- if ((name === 'shift' || name === 'splice') && obj.length === 0) {
- delete obj[0];
- }
- }
- return chainResult(this, obj);
- };
- });
-
- // Add all accessor `Array` functions to the wrapper.
- each(['concat', 'join', 'slice'], function(name) {
- var method = ArrayProto[name];
- _$1.prototype[name] = function() {
- var obj = this._wrapped;
- if (obj != null) obj = method.apply(obj, arguments);
- return chainResult(this, obj);
- };
- });
-
- // Named Exports
-
- var allExports = {
- __proto__: null,
- VERSION: VERSION,
- restArguments: restArguments,
- isObject: isObject,
- isNull: isNull,
- isUndefined: isUndefined,
- isBoolean: isBoolean,
- isElement: isElement,
- isString: isString,
- isNumber: isNumber,
- isDate: isDate,
- isRegExp: isRegExp,
- isError: isError,
- isSymbol: isSymbol,
- isArrayBuffer: isArrayBuffer,
- isDataView: isDataView$1,
- isArray: isArray,
- isFunction: isFunction$1,
- isArguments: isArguments$1,
- isFinite: isFinite$1,
- isNaN: isNaN$1,
- isTypedArray: isTypedArray$1,
- isEmpty: isEmpty,
- isMatch: isMatch,
- isEqual: isEqual,
- isMap: isMap,
- isWeakMap: isWeakMap,
- isSet: isSet,
- isWeakSet: isWeakSet,
- keys: keys,
- allKeys: allKeys,
- values: values,
- pairs: pairs,
- invert: invert,
- functions: functions,
- methods: functions,
- extend: extend,
- extendOwn: extendOwn,
- assign: extendOwn,
- defaults: defaults,
- create: create,
- clone: clone,
- tap: tap,
- get: get,
- has: has,
- mapObject: mapObject,
- identity: identity,
- constant: constant,
- noop: noop,
- toPath: toPath$1,
- property: property,
- propertyOf: propertyOf,
- matcher: matcher,
- matches: matcher,
- times: times,
- random: random,
- now: now,
- escape: _escape,
- unescape: _unescape,
- templateSettings: templateSettings,
- template: template,
- result: result,
- uniqueId: uniqueId,
- chain: chain,
- iteratee: iteratee,
- partial: partial,
- bind: bind,
- bindAll: bindAll,
- memoize: memoize,
- delay: delay,
- defer: defer,
- throttle: throttle,
- debounce: debounce,
- wrap: wrap,
- negate: negate,
- compose: compose,
- after: after,
- before: before,
- once: once,
- findKey: findKey,
- findIndex: findIndex,
- findLastIndex: findLastIndex,
- sortedIndex: sortedIndex,
- indexOf: indexOf,
- lastIndexOf: lastIndexOf,
- find: find,
- detect: find,
- findWhere: findWhere,
- each: each,
- forEach: each,
- map: map,
- collect: map,
- reduce: reduce,
- foldl: reduce,
- inject: reduce,
- reduceRight: reduceRight,
- foldr: reduceRight,
- filter: filter,
- select: filter,
- reject: reject,
- every: every,
- all: every,
- some: some,
- any: some,
- contains: contains,
- includes: contains,
- include: contains,
- invoke: invoke,
- pluck: pluck,
- where: where,
- max: max,
- min: min,
- shuffle: shuffle,
- sample: sample,
- sortBy: sortBy,
- groupBy: groupBy,
- indexBy: indexBy,
- countBy: countBy,
- partition: partition,
- toArray: toArray,
- size: size,
- pick: pick,
- omit: omit,
- first: first,
- head: first,
- take: first,
- initial: initial,
- last: last,
- rest: rest,
- tail: rest,
- drop: rest,
- compact: compact,
- flatten: flatten,
- without: without,
- uniq: uniq,
- unique: uniq,
- union: union,
- intersection: intersection,
- difference: difference,
- unzip: unzip,
- transpose: unzip,
- zip: zip,
- object: object,
- range: range,
- chunk: chunk,
- mixin: mixin,
- 'default': _$1
- };
-
- // Default Export
-
- // Add all of the Underscore functions to the wrapper object.
- var _ = mixin(allExports);
- // Legacy Node.js API.
- _._ = _;
-
- return _;
-
-})));
-//# sourceMappingURL=underscore-umd.js.map
diff --git a/public/_static/underscore.js b/public/_static/underscore.js
deleted file mode 100644
index cf177d428..000000000
--- a/public/_static/underscore.js
+++ /dev/null
@@ -1,6 +0,0 @@
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- <section id="active-learning-iteratively-expanding-the-training-set">
-<h1>Active learning: iteratively expanding the training set<a class="headerlink" href="#active-learning-iteratively-expanding-the-training-set" title="Link to this heading">¶</a></h1>
-<p>Active learning (AL), also called sequential training, is the iterative choice of additional training samples after the initial training of a surrogate model.
-The new samples can be chosen in an explorative manner or by exploiting available data and properties of the surrogate.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>The active learning methods are currently being reworked.
-This should not change the function call <code class="docutils literal notranslate"><span class="pre">Engine.train_sequential()</span></code>, but will change the associated class structures.</p>
-</div>
-<a class="reference internal image-reference" href="_images/active_learning_reduced.png"><img alt="UML diagram for the classes and functions used in active learning in BayesValidRox." src="_images/active_learning_reduced.png" style="width: 550px;" /></a>
-<p>In BayesValidRox AL is realized by additional properties of the <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.exp_designs.ExpDesigns</span></code></a> and <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code></a> classes without any changes to the surrogate model.</p>
-<section id="exploration-exploitation-and-tradeoff">
-<h2>Exploration, exploitation and tradeoff<a class="headerlink" href="#exploration-exploitation-and-tradeoff" title="Link to this heading">¶</a></h2>
-<p><strong>Exploration</strong> methods choose the new samples in a space-filling manner, while <strong>exploitation methods</strong> make use of available data or properties of the surrogate models, such as the estimated surrogate standard deviation.
-Exploration methods in BayesValidRox include random or latin-hypercube sampling, voronoi sampling, choice based on leave-one-out cross validation or dual-annealing.
-Exploitation can be set to Bayesian designs, such as Bayesian3 Active Learning, or variance-based designs.</p>
-<p>The tradeoff between exploration and exploitation is defined by <strong>tradeoff-schemes</strong>, such as an equal split, epsilon-decreaseing or adaptive schemes.</p>
-</section>
-<section id="example">
-<h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
-<p>We take the engine from <a class="reference internal" href="surrogate_description.html"><span class="doc">Training surrogate models</span></a> and change the settings to perform sequential training.</p>
-<p>This mainly changes the experimental design.
-For this example we start with the 10 initial samples from <a class="reference internal" href="surrogate_description.html"><span class="doc">Training surrogate models</span></a> and increase them iteratively to the number of samples given in <code class="docutils literal notranslate"><span class="pre">n_max_samples</span></code>.
-The parameter <code class="docutils literal notranslate"><span class="pre">n_new_samples</span></code> sets the number of new samples that are chosen in each iteration, while <code class="docutils literal notranslate"><span class="pre">mod_LOO_threshold</span></code> sets an additional stopping condition.</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">n_max_samples</span> <span class="o">=</span> <span class="mi">14</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">mod_LOO_threshold</span> <span class="o">=</span> <span class="mf">1e-16</span>
-</pre></div>
-</div>
-<p>Here we do not set a <code class="docutils literal notranslate"><span class="pre">tradeoff_scheme</span></code>.
-This will result in all samples being chosen based on the exploration weights.</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">tradeoff_scheme</span> <span class="o">=</span> <span class="kc">None</span>
-</pre></div>
-</div>
-<p>As the proposed samples come from the exploration method, we still need to define this.</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">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">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>For the exploitation method we use a variance-based method, as no data is given.</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">exploit_method</span> <span class="o">=</span> <span class="s1">'VarOptDesign'</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">'EIGF'</span>
-</pre></div>
-</div>
-<p>Once all properties are set, we can assemble the engine and start it.
-This time we use <code class="docutils literal notranslate"><span class="pre">train_sequential</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">MetaMod</span><span class="p">,</span> <span class="n">Model</span><span class="p">,</span> <span class="n">ExpDesign</span><span class="p">)</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_sequential</span><span class="p">()</span>
-</pre></div>
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-<li><a class="reference internal" href="#">Active learning: iteratively expanding the training set</a><ul>
-<li><a class="reference internal" href="#exploration-exploitation-and-tradeoff">Exploration, exploitation and tradeoff</a></li>
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- <article role="main">
- <section id="example-analytical-function">
-<h1>Example: Analytical function<a class="headerlink" href="#example-analytical-function" title="Link to this heading">¶</a></h1>
-<p>This example shows a surrogate-assisted Bayesian calibration of a time dependent analytical function.
-A detailed explanation of this example is provided in the <a class="reference internal" href="tutorial.html"><span class="doc">TUTORIAL</span></a>.</p>
-<section id="model-and-data">
-<h2>Model and Data<a class="headerlink" href="#model-and-data" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id1">
-<table class="docutils align-default" id="id1">
-<caption><span class="caption-text">Pylink model</span><a class="headerlink" href="#id1" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Model type</p></td>
-<td><p>Function</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of input parameters</p></td>
-<td><p>Set to either 2 or 10</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of output parameters</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-odd"><td><p>Time- or space- dependency</p></td>
-<td><p>Time-dependent output, 10 steps in time</p></td>
-</tr>
-<tr class="row-even"><td><p>MC reference</p></td>
-<td><p>Yes</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id2">
-<table class="docutils align-default" id="id2">
-<caption><span class="caption-text">Priors</span><a class="headerlink" href="#id2" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Parameter</p></th>
-<th class="head"><p>Distribution</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>0-10</p></td>
-<td><p>Uniform in (-5,5)</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id3">
-<table class="docutils align-default" id="id3">
-<caption><span class="caption-text">Discrepancy</span><a class="headerlink" href="#id3" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Distribution type</p></td>
-<td><p>Gaussian</p></td>
-</tr>
-<tr class="row-odd"><td><p>Characteristic value</p></td>
-<td><p>variance: data^2</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="surrogate">
-<h2>Surrogate<a class="headerlink" href="#surrogate" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id4">
-<table class="docutils align-default" id="id4">
-<caption><span class="caption-text">MetaModel settings</span><a class="headerlink" href="#id4" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>surrogate-type</p></td>
-<td><p>aPCE</p></td>
-</tr>
-<tr class="row-odd"><td><p>associated model</p></td>
-<td><p>‘analytical function’</p></td>
-</tr>
-<tr class="row-even"><td><p>degree choices</p></td>
-<td><p>max degree 12, q-norm truncation 0.85 or 0.5 depending on number of inputs</p></td>
-</tr>
-<tr class="row-odd"><td><p>regression</p></td>
-<td><p>FastARD with bootstrapping</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id5">
-<table class="docutils align-default" id="id5">
-<caption><span class="caption-text">Training choices</span><a class="headerlink" href="#id5" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Static sampling method</p></td>
-<td><p>latin-hypercube</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of static samples</p></td>
-<td><p>3*#inputparams</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of total samples</p></td>
-<td><p>150</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of samples per AL iteration</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-even"><td><p>AL tradeoff scheme</p></td>
-<td><p>None</p></td>
-</tr>
-<tr class="row-odd"><td><p>AL exploration method</p></td>
-<td><p>random, n_candidates=1000, n_cand_groups=4</p></td>
-</tr>
-<tr class="row-even"><td><p>AL exploitation method</p></td>
-<td><p>Bayesian Active Design (BAL) with DKL</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-</section>
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- <div class="toc-tree">
- <ul>
-<li><a class="reference internal" href="#">Example: Analytical function</a><ul>
-<li><a class="reference internal" href="#model-and-data">Model and Data</a></li>
-<li><a class="reference internal" href="#surrogate">Surrogate</a></li>
-</ul>
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diff --git a/public/analyticalfunction.rst b/public/analyticalfunction.rst
deleted file mode 100644
index d0e56b071..000000000
--- a/public/analyticalfunction.rst
+++ /dev/null
@@ -1,84 +0,0 @@
-Example: Analytical function
-****************************
-This example shows a surrogate-assisted Bayesian calibration of a time dependent analytical function.
-A detailed explanation of this example is provided in the :any:`tutorial`.
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function
- * - Number of input parameters
- - Set to either 2 or 10
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - Time-dependent output, 10 steps in time
- * - MC reference
- - Yes
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - 0-10
- - Uniform in (-5,5)
-
-.. list-table:: Discrepancy
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Distribution type
- - Gaussian
- * - Characteristic value
- - variance: data^2
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - 'analytical function'
- * - degree choices
- - max degree 12, q-norm truncation 0.85 or 0.5 depending on number of inputs
- * - regression
- - FastARD with bootstrapping
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 3*#inputparams
- * - Number of total samples
- - 150
- * - Number of samples per AL iteration
- - 1
- * - AL tradeoff scheme
- - None
- * - AL exploration method
- - random, n_candidates=1000, n_cand_groups=4
- * - AL exploitation method
- - Bayesian Active Design (BAL) with DKL
\ No newline at end of file
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-<h1>API<a class="headerlink" href="#api" title="Link to this heading">¶</a></h1>
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diff --git a/public/api.rst b/public/api.rst
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-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.meta_model_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
----------------
-
-.. autosummary::
- :toctree: generated
-
- 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
diff --git a/public/bayes_description.html b/public/bayes_description.html
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- <section id="bayesian-inference">
-<h1>Bayesian inference<a class="headerlink" href="#bayesian-inference" title="Link to this heading">¶</a></h1>
-<div class="twocol docutils container">
-<div class="leftside docutils container">
-<p>With Bayesian inference we ask the question ‘how does our understanding of the inputs change given some observation of the outputs of the model?’, i.e. we perform an updating step of the prior distributions to posterior, based on some observations.
-Bayesvalidrox provides a dedicated class to perform this task, <a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.bayes_inference.BayesInference</span></code></a>, which infers the posterior via <code class="docutils literal notranslate"><span class="pre">rejection-sampling</span></code> or <code class="docutils literal notranslate"><span class="pre">MCMC</span></code>.
-The likelihood in rejection sampling is estimated with the help of <code class="docutils literal notranslate"><span class="pre">bootstrapping</span></code>.
-MCMC-specific parameters are to be given as a dictionary called <code class="docutils literal notranslate"><span class="pre">mcmc_params</span></code> and can include</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">init_samples</span></code>: initial samples</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">n_steps</span></code>: number of steps</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">n_walkers</span></code>: number of walkers</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">n_burn</span></code>: length of the burn-in</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">moves</span></code>: function to use for the moves, e.g. taken from <code class="docutils literal notranslate"><span class="pre">emcee</span></code></p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">multiprocessing</span></code>: setting for multiprocessing</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">verbose</span></code>: verbosity</p></li>
-</ul>
-</div>
-<div class="rightside docutils container">
-<a class="reference internal image-reference" href="_images/bayesian_validation.png"><img alt="UML diagram for classes related to Bayesian inference." src="_images/bayesian_validation.png" style="width: 300px;" /></a>
-</div>
-</div>
-<p>The observation should be set as <code class="docutils literal notranslate"><span class="pre">Model.observations</span></code> in the <code class="docutils literal notranslate"><span class="pre">Engine</span></code>, and an estimation of its uncertainty can be provided as a <a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html#bayesvalidrox.bayes_inference.discrepancy.Discrepancy" title="bayesvalidrox.bayes_inference.discrepancy.Discrepancy"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.discrepancy.Discrepancy</span></code></a> object.</p>
-<section id="example">
-<h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
-<p>For this example we need to add the following imports.</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">Discrepancy</span><span class="p">,</span> <span class="n">BayesInference</span>
-</pre></div>
-</div>
-<p>In order to run Bayesian inference we first need to provide an observation.
-For this example we take an evaluation of the model on some chosen sample and add the resulting values as <code class="docutils literal notranslate"><span class="pre">Model.observations</span></code>.
-As this expects a 1D-array for each output key, we need to change the format slightly.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">true_sample</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">2</span><span class="p">]]</span>
-<span class="gp">>>> </span><span class="n">observation</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">run_model_parallel</span><span class="p">(</span><span class="n">true_sample</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">observations</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="gp">>>> </span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">observation</span><span class="p">:</span>
-<span class="gp">>>> </span> <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">'x_values'</span><span class="p">:</span>
-<span class="gp">>>> </span> <span class="n">Model</span><span class="o">.</span><span class="n">observations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">=</span><span class="n">observation</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-<span class="gp">>>> </span> <span class="k">else</span><span class="p">:</span>
-<span class="gp">>>> </span> <span class="n">Model</span><span class="o">.</span><span class="n">observations</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">=</span><span class="n">observation</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>Next we define the uncertainty on the observation with the class <a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.Discrepancy.html#bayesvalidrox.bayes_inference.discrepancy.Discrepancy" title="bayesvalidrox.bayes_inference.discrepancy.Discrepancy"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.discrepancy.Discrepancy</span></code></a>.
-For this example we set the uncertainty to be zero-mean gaussian and dependent on the values in the observation, i.e. larger values have a larger uncertainty associated with them.
-The <code class="docutils literal notranslate"><span class="pre">parameters</span></code> contain the variance for each point in the observation.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>For models with only a single uncertain input parameter, numerical issues can appear when the discrepancy is set only depending on the observed data.
-To resolve this, a small value can be added to the variance of the discrepancy.</p>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">obsData</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">Model</span><span class="o">.</span><span class="n">observations</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</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="p">)</span>
-<span class="gp">>>> </span><span class="n">DiscrepancyOpts</span> <span class="o">=</span> <span class="n">Discrepancy</span><span class="p">(</span><span class="s1">''</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">DiscrepancyOpts</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="s1">'Gaussian'</span>
-<span class="gp">>>> </span><span class="n">DiscrepancyOpts</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">obsData</span><span class="o">**</span><span class="mi">2</span><span class="o">+</span><span class="mf">0.01</span>
-</pre></div>
-</div>
-<p>Now we can initialize an object of class <a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.bayes_inference.BayesInference</span></code></a> with all the wanted properties.
-This object has to be given our <code class="docutils literal notranslate"><span class="pre">Engine</span></code>.
-If it should use the surrogate during inference, set <code class="docutils literal notranslate"><span class="pre">emulator</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code>, otherwise the model will be evaluated directly.
-We also set the defined <code class="docutils literal notranslate"><span class="pre">Discrepancy</span></code>. and set <code class="docutils literal notranslate"><span class="pre">post_plot_pred</span></code> if posterior predictions should be visualized.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">BayesObj</span> <span class="o">=</span> <span class="n">BayesInference</span><span class="p">(</span><span class="n">Engine_</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">emulator</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="gp">>>> </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">Discrepancy</span> <span class="o">=</span> <span class="n">DiscrepancyOpts</span>
-<span class="gp">>>> </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">plot_post_pred</span> <span class="o">=</span> <span class="kc">True</span>
-</pre></div>
-</div>
-<p>In order to run with rejection sampling, we set the <code class="docutils literal notranslate"><span class="pre">inference_method</span></code> accordingly and add properties for <code class="docutils literal notranslate"><span class="pre">bootstrap</span></code>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">inference_method</span> <span class="o">=</span> <span class="s1">'rejection'</span>
-<span class="gp">>>> </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">bootstrap</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="gp">>>> </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">n_bootstrap_itrs</span> <span class="o">=</span> <span class="mi">500</span>
-<span class="gp">>>> </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">bootstrap_noise</span> <span class="o">=</span> <span class="mi">2</span>
-</pre></div>
-</div>
-<p>If the sampling should be done with MCMC, then this is set as the <code class="docutils literal notranslate"><span class="pre">inference_method</span></code> and additional properties are given in <code class="docutils literal notranslate"><span class="pre">mcmc_params</span></code>.
-For this example we use the python package <code class="docutils literal notranslate"><span class="pre">emcee</span></code> to define the MCMC moves.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">inference_method</span> <span class="o">=</span> <span class="s1">'MCMC'</span>
-<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">emcee</span>
-<span class="gp">>>> </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">mcmc_params</span> <span class="o">=</span> <span class="p">{</span>
-<span class="gp">>>> </span> <span class="s1">'n_steps'</span><span class="p">:</span> <span class="mf">1e4</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s1">'n_walkers'</span><span class="p">:</span> <span class="mi">30</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s1">'moves'</span><span class="p">:</span> <span class="n">emcee</span><span class="o">.</span><span class="n">moves</span><span class="o">.</span><span class="n">KDEMove</span><span class="p">(),</span>
-<span class="gp">>>> </span> <span class="s1">'multiprocessing'</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s1">'verbose'</span><span class="p">:</span> <span class="kc">False</span>
-<span class="gp">>>> </span> <span class="p">}</span>
-</pre></div>
-</div>
-<p>Then we run the inference.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">BayesObj</span><span class="o">.</span><span class="n">create_inference</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>If the output directory <code class="docutils literal notranslate"><span class="pre">BayesObj.out_dir</span></code> is not set otherwise, the outputs are written into the folder <code class="docutils literal notranslate"><span class="pre">Outputs_Bayes_model_Calib</span></code>.
-This folder includes the posterior distribution of the input parameters, as well as the predictions resulting from the mean of the posterior.
-For inference with MCMC, chain diagnostics are also written out in the console.</p>
-<div class="twocol docutils container">
-<div class="leftside docutils container">
-<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="o">---------------</span><span class="n">Posterior</span> <span class="n">diagnostics</span><span class="o">---------------</span>
-<span class="n">Mean</span> <span class="n">auto</span><span class="o">-</span><span class="n">correlation</span> <span class="n">time</span><span class="p">:</span> <span class="mf">2.057</span>
-<span class="n">Thin</span><span class="p">:</span> <span class="mi">1</span>
-<span class="n">Burn</span><span class="o">-</span><span class="ow">in</span><span class="p">:</span> <span class="mi">4</span>
-<span class="n">Flat</span> <span class="n">chain</span> <span class="n">shape</span><span class="p">:</span> <span class="p">(</span><span class="mi">13380</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-<span class="n">Mean</span> <span class="n">acceptance</span> <span class="n">fraction</span><span class="o">*</span><span class="p">:</span> <span class="mf">0.752</span>
-<span class="n">Gelman</span><span class="o">-</span><span class="n">Rubin</span> <span class="n">Test</span><span class="o">**</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.001</span><span class="p">]</span>
-
-<span class="o">*</span> <span class="n">This</span> <span class="n">value</span> <span class="n">must</span> <span class="n">lay</span> <span class="n">between</span> <span class="mf">0.234</span> <span class="ow">and</span> <span class="mf">0.5</span><span class="o">.</span>
-<span class="o">**</span> <span class="n">These</span> <span class="n">values</span> <span class="n">must</span> <span class="n">be</span> <span class="n">smaller</span> <span class="n">than</span> <span class="mf">1.1</span><span class="o">.</span>
-<span class="o">--------------------------------------------------</span>
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- <article role="main">
- <section id="example-beam">
-<h1>Example: beam<a class="headerlink" href="#example-beam" title="Link to this heading">¶</a></h1>
-<p>This example shows how a surrogate for a beam deflection model can be created and
-illustrates how a model with an executable and an
-input file can be linked with the bayesvalidrox package.</p>
-<p>The surrogate is trained without active learning and no inference is performed, though reference data is available.</p>
-<section id="model-and-data">
-<h2>Model and Data<a class="headerlink" href="#model-and-data" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id1">
-<table class="docutils align-default" id="id1">
-<caption><span class="caption-text">Pylink model</span><a class="headerlink" href="#id1" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Model type</p></td>
-<td><p>Runs model via given shell command and parser</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of input parameters</p></td>
-<td><p>4</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of output parameters</p></td>
-<td><p>1: deflection [m]</p></td>
-</tr>
-<tr class="row-odd"><td><p>Time- or space- dependency</p></td>
-<td><p>??</p></td>
-</tr>
-<tr class="row-even"><td><p>MC reference</p></td>
-<td><p>Yes</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id2">
-<table class="docutils align-default" id="id2">
-<caption><span class="caption-text">Priors</span><a class="headerlink" href="#id2" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Parameter</p></th>
-<th class="head"><p>Distribution</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Beam width</p></td>
-<td><p>lognormal</p></td>
-</tr>
-<tr class="row-odd"><td><p>Beam height</p></td>
-<td><p>lognormal</p></td>
-</tr>
-<tr class="row-even"><td><p>Youngs modulus</p></td>
-<td><p>lognormal</p></td>
-</tr>
-<tr class="row-odd"><td><p>Uniform load</p></td>
-<td><p>lognormal</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id3">
-<table class="docutils align-default" id="id3">
-<caption><span class="caption-text">Discrepancy</span><a class="headerlink" href="#id3" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Distribution type</p></td>
-<td><p>Gaussian</p></td>
-</tr>
-<tr class="row-odd"><td><p>Characteristic value</p></td>
-<td><p>variance: data^2</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="surrogate">
-<h2>Surrogate<a class="headerlink" href="#surrogate" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id4">
-<table class="docutils align-default" id="id4">
-<caption><span class="caption-text">MetaModel settings</span><a class="headerlink" href="#id4" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>surrogate-type</p></td>
-<td><p>PCE</p></td>
-</tr>
-<tr class="row-odd"><td><p>associated model</p></td>
-<td><p>‘Beam9points’</p></td>
-</tr>
-<tr class="row-even"><td><p>degree choices</p></td>
-<td><p>max degree 6, q-norm truncation 0.75</p></td>
-</tr>
-<tr class="row-odd"><td><p>regression</p></td>
-<td><p>FastARD</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id5">
-<table class="docutils align-default" id="id5">
-<caption><span class="caption-text">Training choices</span><a class="headerlink" href="#id5" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Static sampling method</p></td>
-<td><p>latin-hypercube</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of static samples</p></td>
-<td><p>100</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of total samples</p></td>
-<td><p>100</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-</section>
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- <ul>
-<li><a class="reference internal" href="#">Example: beam</a><ul>
-<li><a class="reference internal" href="#model-and-data">Model and Data</a></li>
-<li><a class="reference internal" href="#surrogate">Surrogate</a></li>
-</ul>
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diff --git a/public/beam.rst b/public/beam.rst
deleted file mode 100644
index f64f800e0..000000000
--- a/public/beam.rst
+++ /dev/null
@@ -1,88 +0,0 @@
-Example: beam
-*************
-This example shows how a surrogate for a beam deflection model can be created and
-illustrates how a model with an executable and an
-input file can be linked with the bayesvalidrox package.
-
-The surrogate is trained without active learning and no inference is performed, though reference data is available.
-
-.. warning::
- This example does not run through without issues at the moment.
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Runs model via given shell command and parser
- * - Number of input parameters
- - 4
- * - Number of output parameters
- - 1: deflection [m]
- * - Time- or space- dependency
- - ??
- * - MC reference
- - Yes
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - Beam width
- - lognormal
- * - Beam height
- - lognormal
- * - Youngs modulus
- - lognormal
- * - Uniform load
- - lognormal
-
-.. list-table:: Discrepancy
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Distribution type
- - Gaussian
- * - Characteristic value
- - variance: data^2
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - PCE
- * - associated model
- - 'Beam9points'
- * - degree choices
- - max degree 6, q-norm truncation 0.75
- * - regression
- - FastARD
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 100
- * - Number of total samples
- - 100
\ No newline at end of file
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- <section id="bayesian-multi-model-comparison">
-<h1>Bayesian multi-model comparison<a class="headerlink" href="#bayesian-multi-model-comparison" title="Link to this heading">¶</a></h1>
-<div class="twocol docutils container">
-<div class="leftside docutils container">
-<p>Bayesvalidrox provides three distinct methods to compare sets of models against each other given some observation of the outputs, Bayes’ Factors, model weights and confusion matrices.
-These are contained within the class <a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison</span></code></a> and can be called one-at-a-time with their respective functions, or consecutively with the function <code class="docutils literal notranslate"><span class="pre">model_comparison_all()</span></code>.</p>
-</div>
-<div class="rightside docutils container">
-<a class="reference internal image-reference" href="_images/bayesian_model_comparison.png"><img alt="UML diagram for classes related to Bayesian multi-model comparison." src="_images/bayesian_model_comparison.png" style="width: 400px;" /></a>
-</div>
-</div>
-<section id="example">
-<h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
-<p>To perform model comparison, we first need to define the set of competing models.
-For this, we create an additional model in the file <code class="docutils literal notranslate"><span class="pre">model2.py</span></code> based on the example model from <a class="reference internal" href="model_description.html"><span class="doc">Models</span></a>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">model2</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">x_values</span><span class="p">):</span>
-<span class="gp">>>> </span> <span class="n">poly</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">x_values</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-<span class="gp">>>> </span> <span class="n">outputs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'A'</span><span class="p">:</span> <span class="n">poly</span><span class="p">,</span> <span class="s1">'x_values'</span><span class="p">:</span> <span class="n">x_values</span><span class="p">}</span>
-<span class="gp">>>> </span> <span class="k">return</span> <span class="n">outputs</span>
-</pre></div>
-</div>
-<p>Then we can build another surrogate for this model, following the same code as for the surrogate in <a class="reference internal" href="surrogate_description.html"><span class="doc">Training surrogate models</span></a>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">Model2</span> <span class="o">=</span> <span class="n">PyLinkForwardModel</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="n">Model2</span><span class="o">.</span><span class="n">link_type</span> <span class="o">=</span> <span class="s1">'Function'</span>
-<span class="gp">>>> </span><span class="n">Model2</span><span class="o">.</span><span class="n">py_file</span> <span class="o">=</span> <span class="s1">'model2'</span>
-<span class="gp">>>> </span><span class="n">Model2</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'model2'</span>
-<span class="gp">>>> </span><span class="n">Model2</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">'A'</span><span class="p">]</span>
-<span class="gp">>>> </span><span class="n">Model2</span><span class="o">.</span><span class="n">func_args</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'x_values'</span><span class="p">:</span> <span class="n">x_values</span><span class="p">}</span>
-<span class="gp">>>> </span><span class="n">Model2</span><span class="o">.</span><span class="n">store</span> <span class="o">=</span> <span class="kc">False</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaMod2</span> <span class="o">=</span> <span class="n">MetaModel</span><span class="p">(</span><span class="n">Inputs</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">MetaMod2</span><span class="o">.</span><span class="n">meta_model_type</span> <span class="o">=</span> <span class="s1">'aPCE'</span>
-<span class="gp">>>> </span><span class="n">MetaMod2</span><span class="o">.</span><span class="n">pce_reg_method</span> <span class="o">=</span> <span class="s1">'FastARD'</span>
-<span class="gp">>>> </span><span class="n">MetaMod2</span><span class="o">.</span><span class="n">pce_deg</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="gp">>>> </span><span class="n">MetaMod2</span><span class="o">.</span><span class="n">pce_q_norm</span> <span class="o">=</span> <span class="mi">1</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ExpDesign2</span> <span class="o">=</span> <span class="n">ExpDesigns</span><span class="p">(</span><span class="n">Inputs</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">ExpDesign2</span><span class="o">.</span><span class="n">n_init_samples</span> <span class="o">=</span> <span class="mi">30</span>
-<span class="gp">>>> </span><span class="n">ExpDesign2</span><span class="o">.</span><span class="n">sampling_method</span> <span class="o">=</span> <span class="s1">'random'</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">Engine_2</span> <span class="o">=</span> <span class="n">Engine</span><span class="p">(</span><span class="n">MetaMod2</span><span class="p">,</span> <span class="n">Model2</span><span class="p">,</span> <span class="n">ExpDesign2</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">Engine_2</span><span class="o">.</span><span class="n">train_normal</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>To perform model comparison we use the class <a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison.html#bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison" title="bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.bayes_model_comparison.BayesModelComparison</span></code></a>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span>>>> from bayesvalidrox import BayesModelComparison`
-</pre></div>
-</div>
-<p>We collect the engines that should be compared in a dictionary, and assign them names.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">meta_models</span> <span class="o">=</span> <span class="p">{</span>
-<span class="gp">>>> </span> <span class="s2">"linear"</span><span class="p">:</span> <span class="n">Engine_</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s2">"degthree"</span><span class="p">:</span> <span class="n">Engine_2</span>
-<span class="gp">>>> </span> <span class="p">}</span>
-</pre></div>
-</div>
-<p>Then we create an object of class <code class="docutils literal notranslate"><span class="pre">BayesModelComparison</span></code>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">BayesOpts</span> <span class="o">=</span> <span class="n">BayesModelComparison</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>As the comparison uses the class <a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.bayes_inference.BayesInference</span></code></a>, we can also set the properties for this class as well.
-These are collected in a dictionary and given to the function calls that perform the model comparison.
-In this example we use the following settings.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">opts_bootstrap</span> <span class="o">=</span> <span class="p">{</span>
-<span class="gp">>>> </span> <span class="s2">"bootstrap"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s2">"n_samples"</span><span class="p">:</span> <span class="mi">100</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s2">"Discrepancy"</span><span class="p">:</span> <span class="n">DiscrepancyOpts</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s2">"emulator"</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s2">"plot_post_pred"</span><span class="p">:</span> <span class="kc">False</span>
-<span class="gp">>>> </span> <span class="p">}</span>
-</pre></div>
-</div>
-<p>Now we can run the full model comparison.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">output_dict</span> <span class="o">=</span> <span class="n">BayesOpts</span><span class="o">.</span><span class="n">model_comparison_all</span><span class="p">(</span><span class="n">meta_models</span><span class="p">,</span> <span class="n">opts_bootstrap</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The created plots are saved in the folder <code class="docutils literal notranslate"><span class="pre">Outputs_Comparison</span></code>.</p>
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- <article role="main">
- <section id="example-borehole">
-<h1>Example: borehole<a class="headerlink" href="#example-borehole" title="Link to this heading">¶</a></h1>
-<p>This test deals with the surrogate modeling of a Borehole function.
-You will see how to check the quality of your regression model and perform sensitivity analysis via Sobol Indices</p>
-<blockquote>
-<div><p>BOREHOLE FUNCTION</p>
-<dl class="simple">
-<dt>Authors: Sonja Surjanovic, Simon Fraser University</dt><dd><p>Derek Bingham, Simon Fraser University</p>
-</dd>
-</dl>
-<p>Questions/Comments: Please email Derek Bingham at <a class="reference external" href="mailto:dbingham%40stat.sfu.ca">dbingham<span>@</span>stat<span>.</span>sfu<span>.</span>ca</a>.</p>
-<p>Copyright 2013. Derek Bingham, Simon Fraser University.</p>
-<p>THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY
-FOR THE USE OF THIS SOFTWARE. If software is modified to produce
-derivative works, such modified software should be clearly marked.
-Additionally, this program is free software; you can redistribute it
-and/or modify it under the terms of the GNU General Public License as
-published by the Free Software Foundation; version 2.0 of the License.
-Accordingly, this program is distributed in the hope that it will be
-useful, but WITHOUT ANY WARRANTY; without even the implied warranty
-of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-General Public License for more details.</p>
-<dl class="simple">
-<dt>For function details and reference information, see:</dt><dd><p><a class="reference external" href="https://www.sfu.ca/~ssurjano/ishigami.html">https://www.sfu.ca/~ssurjano/ishigami.html</a></p>
-</dd>
-</dl>
-</div></blockquote>
-<p>no reference data given
-Surrogate with AL - OMP for regression and Space-filling sequential exploitaiton scheme (no data)</p>
-<section id="model-and-data">
-<h2>Model and Data<a class="headerlink" href="#model-and-data" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id1">
-<table class="docutils align-default" id="id1">
-<caption><span class="caption-text">Pylink model</span><a class="headerlink" href="#id1" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Model type</p></td>
-<td><p>Function</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of input parameters</p></td>
-<td><p>8</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of output parameters</p></td>
-<td><p>1: flow rate [m$^3$/yr]</p></td>
-</tr>
-<tr class="row-odd"><td><p>Time- or space- dependency</p></td>
-<td><p>??</p></td>
-</tr>
-<tr class="row-even"><td><p>MC reference</p></td>
-<td><p>No</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id2">
-<table class="docutils align-default" id="id2">
-<caption><span class="caption-text">Priors</span><a class="headerlink" href="#id2" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Parameter</p></th>
-<th class="head"><p>Distribution</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>r_w</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-odd"><td><p>L</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-even"><td><p>K_w</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-odd"><td><p>T_u</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-even"><td><p>T_l</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-odd"><td><p>H_u</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-even"><td><p>H_l</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-odd"><td><p>r</p></td>
-<td><p>lognormal</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="surrogate">
-<h2>Surrogate<a class="headerlink" href="#surrogate" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id3">
-<table class="docutils align-default" id="id3">
-<caption><span class="caption-text">MetaModel settings</span><a class="headerlink" href="#id3" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>surrogate-type</p></td>
-<td><p>aPCE</p></td>
-</tr>
-<tr class="row-odd"><td><p>associated model</p></td>
-<td><p>‘borehole’</p></td>
-</tr>
-<tr class="row-even"><td><p>degree choices</p></td>
-<td><p>max degree 5, q-norm truncation 1.0</p></td>
-</tr>
-<tr class="row-odd"><td><p>regression</p></td>
-<td><p>OMP (Orthogonal matching pursuit)</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id4">
-<table class="docutils align-default" id="id4">
-<caption><span class="caption-text">Training choices</span><a class="headerlink" href="#id4" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Static sampling method</p></td>
-<td><p>latin-hypercube</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of static samples</p></td>
-<td><p>50</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of total samples</p></td>
-<td><p>300</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of samples per AL iteration</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-even"><td><p>AL tradeoff scheme</p></td>
-<td><p>None</p></td>
-</tr>
-<tr class="row-odd"><td><p>AL exploration method</p></td>
-<td><p>latin-hypercube, n_candidates=5000, n_cand_groups=4</p></td>
-</tr>
-<tr class="row-even"><td><p>AL exploitation method</p></td>
-<td><p>space-filling</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-</section>
-
- </article>
- </div>
- <footer>
-
- <div class="related-pages">
- <a class="next-page" href="ishigami.html">
- <div class="page-info">
- <div class="context">
- <span>Next</span>
- </div>
- <div class="title">Example: ishigami</div>
- </div>
- <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
- </a>
- <a class="prev-page" href="beam.html">
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- <div class="left-details">
- <div class="copyright">
- Copyright © 2023, Farid Mohammadi, Rebecca Kohlhaas
- </div>
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- <a href="https://github.com/pradyunsg/furo">Furo</a>
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- On this page
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- <ul>
-<li><a class="reference internal" href="#">Example: borehole</a><ul>
-<li><a class="reference internal" href="#model-and-data">Model and Data</a></li>
-<li><a class="reference internal" href="#surrogate">Surrogate</a></li>
-</ul>
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diff --git a/public/borehole.rst b/public/borehole.rst
deleted file mode 100644
index fba221448..000000000
--- a/public/borehole.rst
+++ /dev/null
@@ -1,117 +0,0 @@
-Example: borehole
-*****************
-This test deals with the surrogate modeling of a Borehole function.
-You will see how to check the quality of your regression model and perform sensitivity analysis via Sobol Indices
-
-
- BOREHOLE FUNCTION
-
- Authors: Sonja Surjanovic, Simon Fraser University
- Derek Bingham, Simon Fraser University
-
- Questions/Comments: Please email Derek Bingham at dbingham@stat.sfu.ca.
-
- Copyright 2013. Derek Bingham, Simon Fraser University.
-
- THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY
- FOR THE USE OF THIS SOFTWARE. If software is modified to produce
- derivative works, such modified software should be clearly marked.
- Additionally, this program is free software; you can redistribute it
- and/or modify it under the terms of the GNU General Public License as
- published by the Free Software Foundation; version 2.0 of the License.
- Accordingly, this program is distributed in the hope that it will be
- useful, but WITHOUT ANY WARRANTY; without even the implied warranty
- of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- General Public License for more details.
-
- For function details and reference information, see:
- https://www.sfu.ca/~ssurjano/ishigami.html
-
-no reference data given
-Surrogate with AL - OMP for regression and Space-filling sequential exploitaiton scheme (no data)
-
-.. warning::
- Still some error with saving the sobol indices
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function
- * - Number of input parameters
- - 8
- * - Number of output parameters
- - 1: flow rate [m$^3$/yr]
- * - Time- or space- dependency
- - ??
- * - MC reference
- - No
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - r_w
- - uniform
- * - L
- - uniform
- * - K_w
- - uniform
- * - T_u
- - uniform
- * - T_l
- - uniform
- * - H_u
- - uniform
- * - H_l
- - uniform
- * - r
- - lognormal
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - 'borehole'
- * - degree choices
- - max degree 5, q-norm truncation 1.0
- * - regression
- - OMP (Orthogonal matching pursuit)
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 50
- * - Number of total samples
- - 300
- * - Number of samples per AL iteration
- - 1
- * - AL tradeoff scheme
- - None
- * - AL exploration method
- - latin-hypercube, n_candidates=5000, n_cand_groups=4
- * - AL exploitation method
- - space-filling
\ No newline at end of file
diff --git a/public/conf.py b/public/conf.py
deleted file mode 100644
index b56918dd0..000000000
--- a/public/conf.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# For the full list of built-in configuration values, see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-sys.path.insert(0, os.path.abspath('../../'))
-sys.path.insert(0, os.path.abspath('../../src/'))
-#sys.path.insert(0, os.path.abspath('../../src/bayesvalidrox'))
-#sys.path.insert(0, os.path.abspath('../../src/bayesvalidrox/surrogate_models'))
-#sys.path.insert(0, os.path.abspath('../../src/bayesvalidrox/pylink'))
-#sys.path.insert(0, os.path.abspath('../../src/bayesvalidrox/post_processing'))
-#sys.path.insert(0, os.path.abspath('../../src/bayesvalidrox/bayes_inference'))
-
-# -- Project information -----------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
-
-project = 'bayesvalidrox'
-copyright = '2023, Farid Mohammadi, Rebecca Kohlhaas'
-author = 'Farid Mohammadi, Rebecca Kohlhaas'
-release = '0.0.5'
-
-# -- General configuration ---------------------------------------------------
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
- 'sphinx.ext.doctest',
- 'sphinx.ext.autodoc',
- 'sphinx.ext.autosummary',
- 'm2r2'
-]
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = []
-
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages. See the documentation for
-# a list of builtin themes.
-#
-html_theme = 'furo'
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
\ No newline at end of file
diff --git a/public/examples.html b/public/examples.html
deleted file mode 100644
index 396ff2984..000000000
--- a/public/examples.html
+++ /dev/null
@@ -1,415 +0,0 @@
-<!doctype html>
-<html class="no-js" lang="en" data-content_root="./">
- <head><meta charset="utf-8"/>
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-<link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" /><link rel="next" title="Example: Analytical function" href="analyticalfunction.html" /><link rel="prev" title="TUTORIAL" href="tutorial.html" />
-
- <!-- Generated with Sphinx 7.3.7 and Furo 2023.09.10 -->
- <title>EXAMPLES - bayesvalidrox 1.0.0 documentation</title>
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-<h1>EXAMPLES<a class="headerlink" href="#examples" title="Link to this heading">¶</a></h1>
-<p>Within the git repository of <strong>bayesvalidrox</strong> a set of examples can be found.
-Here we provide short introductions into the used models and what each example can teach about the functionality of the package.</p>
-<p>The following examples are provided:</p>
-<div class="toctree-wrapper compound">
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-<li class="toctree-l1"><a class="reference internal" href="analyticalfunction.html">Analytical function</a></li>
-<li class="toctree-l1"><a class="reference internal" href="beam.html">Beam</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="modelcomparison.html">Model comparison</a></li>
-<li class="toctree-l1"><a class="reference internal" href="ohaganfunction.html">OHagan-function</a></li>
-<li class="toctree-l1"><a class="reference internal" href="pollution.html">Pollution</a></li>
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diff --git a/public/examples.rst b/public/examples.rst
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+++ /dev/null
@@ -1,17 +0,0 @@
-EXAMPLES
-********
-Within the git repository of **bayesvalidrox** a set of examples can be found.
-Here we provide short introductions into the used models and what each example can teach about the functionality of the package.
-
-The following examples are provided:
-
-.. toctree::
- :maxdepth: 1
-
- Analytical function <analyticalfunction>
- Beam <beam>
- Borehole <borehole>
- Ishigami <ishigami>
- Model comparison <modelcomparison>
- OHagan-function <ohaganfunction>
- Pollution <pollution>
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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>
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">BayesInference</span></code>(engine[, discrepancy, ...])</p></td>
-<td><p>A class to perform Bayesian Analysis.</p></td>
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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">
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">Discrepancy</span></code>([InputDisc, disc_type, parameters])</p></td>
-<td><p>Discrepancy class for Bayesian inference method.</p></td>
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-<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">
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gelman_rubin</span></code>(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>
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-<p class="rubric">Classes</p>
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">MCMC</span></code>(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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-<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>
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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>
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-<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>
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">gamma_mean</span></code>(a, b)</p></td>
-<td><p>Computes mean of gamma distribution</p></td>
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">BayesianLinearRegression</span></code>(n_iter, tol, ...)</p></td>
-<td><p>Superclass for Empirical Bayes and Variational Bayes implementations of Bayesian Linear Regression Model</p></td>
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-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">EBLinearRegression</span></code>([n_iter, tol, optimizer, ...])</p></td>
-<td><p>Bayesian Regression with type II maximum likelihood (Empirical Bayes)</p></td>
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">VBLinearRegression</span></code>([n_iter, tol, ...])</p></td>
-<td><p>Implements Bayesian Linear Regression using mean-field approximation.</p></td>
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-<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">
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">hellinger_distance</span></code>(P, Q)</p></td>
-<td><p>Hellinger distance between two continuous distributions.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">logpdf</span></code>(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><code class="xref py py-obj docutils literal notranslate"><span class="pre">subdomain</span></code>(Bounds, n_new_samples)</p></td>
-<td><p>Divides a domain defined by Bounds into subdomains.</p></td>
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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><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_rec_rule</span></code>(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><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_rec_rule_arbitrary</span></code>(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><code class="xref py py-obj docutils literal notranslate"><span class="pre">eval_univ_basis</span></code>(x, max_deg, poly_types[, ...])</p></td>
-<td><p>Evaluates univariate regressors along input directions.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">poly_rec_coeffs</span></code>(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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-<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><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_ranges</span></code>(theta, ranges)</p></td>
-<td><p>This function checks if theta lies in the given ranges.</p></td>
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">ExpDesigns</span></code>(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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-<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>
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-<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><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_truncate</span></code>(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><code class="xref py py-obj docutils literal notranslate"><span class="pre">glexindex</span></code>(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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-<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>
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">Input</span></code>()</p></td>
-<td><p>A class to define the uncertain input parameters.</p></td>
-</tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">Marginal</span></code>()</p></td>
-<td><p>An object containing the specifications of the marginals for each uncertain parameter.</p></td>
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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><code class="xref py py-obj docutils literal notranslate"><span class="pre">corr</span></code>(x, y)</p></td>
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-<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><code class="xref py py-obj docutils literal notranslate"><span class="pre">OrthogonalMatchingPursuit</span></code>([fit_intercept, ...])</p></td>
-<td><p>Regression with Orthogonal Matching Pursuit [1].</p></td>
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-<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>
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-<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>
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-<span id="bayesvalidrox-surrogate-models-reg-fast-laplace"></span><h1>bayesvalidrox.surrogate_models.reg_fast_laplace<a class="headerlink" href="#module-bayesvalidrox.surrogate_models.reg_fast_laplace" title="Link to this heading">¶</a></h1>
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-<td><p>Sparse regression with Bayesian Compressive Sensing as described in Alg.</p></td>
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-<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><code class="xref py py-obj docutils literal notranslate"><span class="pre">corr_loocv_error</span></code>(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><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_psi</span></code>(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><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_process_emulator</span></code>(X, y[, nug_term, ...])</p></td>
-<td><p>Fits a Gaussian Process Emulator to the target given the training</p></td>
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-<p class="rubric">Classes</p>
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-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">MetaModel</span></code>(input_obj[, meta_model_type, ...])</p></td>
-<td><p>Meta (surrogate) model</p></td>
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-<li class="toctree-l4 has-children"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.html">bayesvalidrox.surrogate_models.sequential_design</a><input class="toctree-checkbox" id="toctree-checkbox-28" name="toctree-checkbox-28" role="switch" type="checkbox"/><label for="toctree-checkbox-28"><div class="visually-hidden">Toggle navigation of bayesvalidrox.surrogate_models.sequential_design</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.sequential_design.logpdf.html">bayesvalidrox.surrogate_models.sequential_design.logpdf</a></li>
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-<li class="toctree-l5"><a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.html">bayesvalidrox.surrogate_models.sequential_design.SequentialDesign</a></li>
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-<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-29" name="toctree-checkbox-29" role="switch" type="checkbox"/><label for="toctree-checkbox-29"><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>
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-<section class="genindex-section">
- <h1 id="index">Index</h1>
- <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.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.__init__">(bayesvalidrox.surrogate_models.sequential_design.SequentialDesign 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="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.html#module-bayesvalidrox.bayes_inference.bayes_inference">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.bayes_inference.bayes_model_comparison
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.html#module-bayesvalidrox.bayes_inference.bayes_model_comparison">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.bayes_inference.discrepancy
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.html#module-bayesvalidrox.bayes_inference.discrepancy">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.bayes_inference.mcmc
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.html#module-bayesvalidrox.bayes_inference.mcmc">module</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="_autosummary/bayesvalidrox.post_processing.post_processing.html#module-bayesvalidrox.post_processing.post_processing">module</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="_autosummary/bayesvalidrox.pylink.pylink.html#module-bayesvalidrox.pylink.pylink">module</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="_autosummary/bayesvalidrox.surrogate_models.adaptPlot.html#module-bayesvalidrox.surrogate_models.adaptPlot">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.apoly_construction
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction">module</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li>
- bayesvalidrox.surrogate_models.bayes_linear
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.engine
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.eval_rec_rule
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.html#module-bayesvalidrox.surrogate_models.eval_rec_rule">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.exp_designs
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.exploration
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.glexindex
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex">module</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="_autosummary/bayesvalidrox.surrogate_models.inputs.html#module-bayesvalidrox.surrogate_models.inputs">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.orthogonal_matching_pursuit
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.orthogonal_matching_pursuit.html#module-bayesvalidrox.surrogate_models.orthogonal_matching_pursuit">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.reg_fast_ard
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html#module-bayesvalidrox.surrogate_models.reg_fast_ard">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.reg_fast_laplace
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_laplace.html#module-bayesvalidrox.surrogate_models.reg_fast_laplace">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.sequential_design
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.html#module-bayesvalidrox.surrogate_models.sequential_design">module</a>
-</li>
- </ul></li>
- <li>
- bayesvalidrox.surrogate_models.surrogate_models
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models">module</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.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.choose_next_sample">choose_next_sample() (bayesvalidrox.surrogate_models.sequential_design.SequentialDesign 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.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.dual_annealing">dual_annealing() (bayesvalidrox.surrogate_models.sequential_design.SequentialDesign 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>
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.hellinger_distance.html#bayesvalidrox.surrogate_models.sequential_design.hellinger_distance">(in module bayesvalidrox.surrogate_models.sequential_design)</a>
-</li>
- </ul></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>
-
- <ul>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.logpdf.html#bayesvalidrox.surrogate_models.sequential_design.logpdf">(in module bayesvalidrox.surrogate_models.sequential_design)</a>
-</li>
- </ul></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>
-
-<section id="M" class="genindex-section">
- <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="_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>
-</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>
-</li>
- <li><a href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.html#module-bayesvalidrox.bayes_inference.discrepancy">bayesvalidrox.bayes_inference.discrepancy</a>
-</li>
- <li><a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.html#module-bayesvalidrox.bayes_inference.mcmc">bayesvalidrox.bayes_inference.mcmc</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>
-</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>
-</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>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction">bayesvalidrox.surrogate_models.apoly_construction</a>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear">bayesvalidrox.surrogate_models.bayes_linear</a>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.engine.html#module-bayesvalidrox.surrogate_models.engine">bayesvalidrox.surrogate_models.engine</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>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs">bayesvalidrox.surrogate_models.exp_designs</a>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration">bayesvalidrox.surrogate_models.exploration</a>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex">bayesvalidrox.surrogate_models.glexindex</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>
-</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>
-</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>
-</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>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.html#module-bayesvalidrox.surrogate_models.sequential_design">bayesvalidrox.surrogate_models.sequential_design</a>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.html#module-bayesvalidrox.surrogate_models.surrogate_models">bayesvalidrox.surrogate_models.surrogate_models</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.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.plot_samples">plot_samples() (bayesvalidrox.surrogate_models.exp_designs.ExpDesigns 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>
- </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="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel.run_command">run_command() (bayesvalidrox.pylink.pylink.PyLinkForwardModel method)</a>
-</li>
- <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="_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="_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="_autosummary/bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.run_util_func">run_util_func() (bayesvalidrox.surrogate_models.sequential_design.SequentialDesign method)</a>
-</li>
- </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="_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>
- </ul></li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign">SequentialDesign (class in bayesvalidrox.surrogate_models.sequential_design)</a>
-</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="_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="_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="_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>
- </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="_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>
- </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="_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="_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="_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.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.start_seqdesign">start_seqdesign() (bayesvalidrox.surrogate_models.sequential_design.SequentialDesign method)</a>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.subdomain.html#bayesvalidrox.surrogate_models.sequential_design.subdomain">subdomain() (in module bayesvalidrox.surrogate_models.sequential_design)</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.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.tradeoff_weights">tradeoff_weights() (bayesvalidrox.surrogate_models.sequential_design.SequentialDesign 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.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.util_AlphOptDesign">util_AlphOptDesign() (bayesvalidrox.surrogate_models.sequential_design.SequentialDesign method)</a>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.util_BayesianActiveDesign">util_BayesianActiveDesign() (bayesvalidrox.surrogate_models.sequential_design.SequentialDesign method)</a>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.util_BayesianDesign">util_BayesianDesign() (bayesvalidrox.surrogate_models.sequential_design.SequentialDesign method)</a>
-</li>
- <li><a href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.html#bayesvalidrox.surrogate_models.sequential_design.SequentialDesign.util_VarBasedDesign">util_VarBasedDesign() (bayesvalidrox.surrogate_models.sequential_design.SequentialDesign 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>
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- <section id="surrogate-assisted-bayesian-validation-of-computational-models">
-<h1>Surrogate-assisted Bayesian validation of computational models<a class="headerlink" href="#surrogate-assisted-bayesian-validation-of-computational-models" title="Link to this heading">¶</a></h1>
-<p><strong>BayesValidRox</strong> is an open-source python package that provides methods for surrogate modeling, Bayesian inference and model comparison.</p>
-<a class="reference internal image-reference" href="_images/balance.png"><img alt="Weighting model results against data with associated uncertainty, costs and sparsity" src="_images/balance.png" style="width: 600px;" /></a>
-<p>An introductory tutorial to the overall workflow with <strong>bayesvalidrox</strong> is provided in <a class="reference internal" href="tutorial.html"><span class="doc">TUTORIAL</span></a> and descriptions of the available examples can be found in <a class="reference internal" href="examples.html"><span class="doc">EXAMPLES</span></a>.
-The functionality and options for the different classes is described more in-depth in <a class="reference internal" href="packagedescription.html"><span class="doc">USER GUIDE</span></a> and a list of all the classes and functions is provided in <a class="reference internal" href="api.html"><span class="doc">API</span></a>.</p>
-<section id="links">
-<h2>Links<a class="headerlink" href="#links" title="Link to this heading">¶</a></h2>
-<ul class="simple">
-<li><p>GitHub: <a class="reference external" href="https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/tree/master?ref_type=heads">https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/tree/master?ref_type=heads</a></p></li>
-<li><p>PyPI: <a class="reference external" href="https://pypi.org/project/bayesvalidrox/">https://pypi.org/project/bayesvalidrox/</a></p></li>
-<li><p>Documentation:</p></li>
-</ul>
-</section>
-<section id="installation">
-<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
-<p>This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0.0 on, use pip to install:</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>bayesvalidrox
-</pre></div>
-</div>
-</section>
-<section id="quickstart">
-<h2>Quickstart<a class="headerlink" href="#quickstart" title="Link to this heading">¶</a></h2>
-<p>Here we show a minimal example to get started on working with BayesValidRox.
-The <a class="reference internal" href="packagedescription.html"><span class="doc">USER GUIDE</span></a> goes into more detail on the available options and proposed workflow.</p>
-<p>The central functionalities of BayesValidRox all depend on building an object of class <code class="docutils literal notranslate"><span class="pre">Engine</span></code> that includes an interface to a model and a definition of an input space and sampling option in the form of an <code class="docutils literal notranslate"><span class="pre">ExpDesigns</span></code> object.
-It can contain and build a surrogate model of class <code class="docutils literal notranslate"><span class="pre">MetaModel</span></code>, but also functions without one.</p>
-<p>We import the needed classes in our main file <code class="docutils literal notranslate"><span class="pre">main.py</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">PyLinkForwardModel</span><span class="p">,</span> <span class="n">InputSpace</span><span class="p">,</span> <span class="n">ExpDesigns</span><span class="p">,</span> <span class="n">Engine</span><span class="p">,</span> <span class="n">MetaModel</span>
-</pre></div>
-</div>
-<p>Here we use a simple linear model.
-This is defined in another python file in the same folder, here we call it <code class="docutils literal notranslate"><span class="pre">model.py</span></code>.
-This file contains a python function that expects samples of two parameter and returns a linear combination of them.
-For a detailed description of the expected output format see <a class="reference internal" href="model_description.html"><span class="doc">Models</span></a>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">model</span><span class="p">(</span><span class="n">samples</span><span class="p">):</span>
-<span class="gp">>>> </span><span class="k">return</span> <span class="p">{</span><span class="s1">'Z'</span><span class="p">:</span><span class="n">samples</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">samples</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="s1">'x_values'</span><span class="p">:[</span><span class="mi">0</span><span class="p">]}</span>
-</pre></div>
-</div>
-<p>With this we can create the interface to the model in <code class="docutils literal notranslate"><span class="pre">main.py</span></code>.</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">PyLinkForwardModel</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">link_type</span> <span class="o">=</span> <span class="s1">'Function'</span>
-<span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">py_file</span> <span class="o">=</span> <span class="s1">'model'</span>
-<span class="gp">>>> </span><span class="n">model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'linear model'</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>We specify marginal distributions on the inputs in an object of class <code class="docutils literal notranslate"><span class="pre">InputSpace</span></code> and use this to build the experimental design.</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">InputSpace</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">'input0'</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">'unif'</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="mi">0</span><span class="p">,</span><span class="mi">1</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">1</span><span class="p">]</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'input1'</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">1</span><span class="p">]</span><span class="o">.</span><span class="n">dist_type</span> <span class="o">=</span> <span class="s1">'unif'</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">1</span><span class="p">]</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">expdes</span> <span class="o">=</span> <span class="n">ExpDesigns</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">expdes</span><span class="o">.</span><span class="n">sampling_method</span> <span class="o">=</span> <span class="s1">'random'</span>
-</pre></div>
-</div>
-<p>If we do not want to build a surrogate model, we can define the engine from these objects.</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="kc">None</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">expdes</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>If we want to build a surrogate model, we create and object of class <code class="docutils literal notranslate"><span class="pre">MetaModel</span></code> and set its properties.
-Here we build an arbitrary Polynomial Chaos Expansion and train it on samples given by the experimental design and the model.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">metamodel</span> <span class="o">=</span> <span class="n">MetaModel</span><span class="p">(</span><span class="n">Inputs</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">metamodel</span><span class="o">.</span><span class="n">meta_model_type</span> <span class="o">=</span> <span class="s1">'aPCE'</span>
-<span class="gp">>>> </span><span class="n">metamodel</span><span class="o">.</span><span class="n">pce_reg_method</span> <span class="o">=</span> <span class="s1">'FastARD'</span>
-<span class="gp">>>> </span><span class="n">metamodel</span><span class="o">.</span><span class="n">pce_deg</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="gp">>>> </span><span class="n">MetaMod</span><span class="o">.</span><span class="n">pce_q_norm</span> <span class="o">=</span> <span class="mf">0.85</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">expdes</span><span class="o">.</span><span class="n">n_init_samples</span> <span class="o">=</span> <span class="mi">10</span>
-</pre></div>
-</div>
-<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">metamodel</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">expdes</span><span class="p">)</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>The engine with the trained metamodel can now be used for postprocessing, Bayesian inference, of Bayesian model comparison.</p>
-</section>
-<section id="license">
-<h2>License<a class="headerlink" href="#license" title="Link to this heading">¶</a></h2>
-<p>BayesValidRox is licensed under the MIT <a class="reference external" href="https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/master/LICENCE.md">license</a>.</p>
-</section>
-<section id="contribution">
-<h2>Contribution<a class="headerlink" href="#contribution" title="Link to this heading">¶</a></h2>
-<p>We would be happy for you to contribute to <strong>BayesValidRox</strong>.
-This can include e.g. reporting issues, proposing new features, working on features, or support with the documentation.
-If you want to contibute, check out our <a class="reference external" href="https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/docs/sphinx_new/CONTRIBUTING.md?ref_type=heads">contribution</a> guidelines.
-You can contact us on the <a class="reference external" href="https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox">gitlab</a> page.</p>
-</section>
-<section id="further-contents">
-<h2>Further contents<a class="headerlink" href="#further-contents" title="Link to this heading">¶</a></h2>
-<div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="packagedescription.html">USER GUIDE</a></li>
-<li class="toctree-l1"><a class="reference internal" href="tutorial.html">TUTORIAL</a></li>
-<li class="toctree-l1"><a class="reference internal" href="examples.html">EXAMPLES</a></li>
-<li class="toctree-l1"><a class="reference internal" href="api.html">API</a></li>
-</ul>
-</div>
-</section>
-</section>
-<section id="indices-and-tables">
-<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Link to this heading">¶</a></h1>
-<ul class="simple">
-<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
-<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
-<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
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-<li><a class="reference internal" href="#installation">Installation</a></li>
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-.. bayesvalidrox documentation master file, created by
- sphinx-quickstart on Wed Dec 13 11:14:59 2023.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
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-Surrogate-assisted Bayesian validation of computational models
-==============================================================
-**bayesvalidrox** is an open-source python package that provides methods for surrogate modeling, Bayesian inference and model comparison.
-
-
-An introductory tutorial to the overall workflow with **bayesvalidrox** is provided in :any:`tutorial` and descriptions of the available examples can be found in :any:`examples`.
-The functionality and options for the different classes is described more in-depth in :any:`packagedescription` and a list of all the classes and functions is provided in :any:`api`.
-
-
-Contents
---------
-
-.. toctree::
- :maxdepth: 1
-
- tutorial
- examples
- packagedescription
- api
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
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- <section id="priors-input-space-and-experimental-design">
-<h1>Priors, input space and experimental design<a class="headerlink" href="#priors-input-space-and-experimental-design" title="Link to this heading">¶</a></h1>
-<p>The surrogate models, as used in BayesValidRox, consider model formulations where at least one of the input parameters is associated with uncertainty.
-This uncertainty can be described as probability distributions over possible values for the parameter.</p>
-<div class="twocol docutils container">
-<div class="leftside docutils container">
-<p>Four classes contained in bayesvalidrox are associated with describing uncertain inputs: <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html#bayesvalidrox.surrogate_models.inputs.Marginal" title="bayesvalidrox.surrogate_models.inputs.Marginal"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Marginal</span></code></a>, <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input" title="bayesvalidrox.surrogate_models.inputs.Input"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Input</span></code></a>, <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace" title="bayesvalidrox.surrogate_models.input_space.InputSpace"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.input_space.InputSpace</span></code></a> and <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.exp_designs.ExpDesigns</span></code></a>.</p>
-<p>Uncertain parameters are specified via their marginal distributions in <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Marginal.html#bayesvalidrox.surrogate_models.inputs.Marginal" title="bayesvalidrox.surrogate_models.inputs.Marginal"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Marginal</span></code></a> objects as either distribution types with associated parameters, or via a set of realizations.
-Supported distribution types include <code class="docutils literal notranslate"><span class="pre">unif</span></code>, <code class="docutils literal notranslate"><span class="pre">norm</span></code>, <code class="docutils literal notranslate"><span class="pre">gamma</span></code>, <code class="docutils literal notranslate"><span class="pre">beta</span></code>, <code class="docutils literal notranslate"><span class="pre">lognorm</span></code>, <code class="docutils literal notranslate"><span class="pre">expon</span></code> and <code class="docutils literal notranslate"><span class="pre">weibull</span></code>.</p>
-<p>All marginals contained in an <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input" title="bayesvalidrox.surrogate_models.inputs.Input"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Input</span></code></a> object should be defined in the same manner, mixing definitions via distribution type and sampels is not supported.</p>
-<p>If <code class="docutils literal notranslate"><span class="pre">rosenblatt</span></code> is set as <code class="docutils literal notranslate"><span class="pre">True</span></code>, then a Rosenblatt transform will be applied for training the surrogate.</p>
-</div>
-<div class="rightside docutils container">
-<a class="reference internal image-reference" href="_images/input_classes.png"><img alt="UML diagram for input-related classes in bayesvalidrox" src="_images/input_classes.png" style="width: 300px;" /></a>
-</div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>When using a polynomial-type surrogate setting <code class="docutils literal notranslate"><span class="pre">rosenblatt</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code> results in all hermite polynomials.
-If all uncertain parameters are specified as samples, the corresponding polynomials are automatically calculated following an aPCE.</p>
-<p>#TODO Recheck this second point!</p>
-</div>
-<p>The <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace" title="bayesvalidrox.surrogate_models.input_space.InputSpace"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.input_space.InputSpace</span></code></a> checks the validity of the given marginals and builds the input space.
-This includes sampling from the distributions and applying the Rosenblatt transform or an isoprobabilistic transformation.</p>
-<p>The <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.input_space.InputSpace.html#bayesvalidrox.surrogate_models.input_space.InputSpace" title="bayesvalidrox.surrogate_models.input_space.InputSpace"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.input_space.InputSpace</span></code></a> is used mainly in the surrogate model.
-The class <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.exp_designs.ExpDesigns</span></code></a> additionally contains methods and attributes related to sampling from the input space for static and iterative training of the surrogate model.
-Supported sampling methods include <code class="docutils literal notranslate"><span class="pre">random</span></code>, <code class="docutils literal notranslate"><span class="pre">latin-hypercube</span></code>, <code class="docutils literal notranslate"><span class="pre">sobol</span></code>, <code class="docutils literal notranslate"><span class="pre">halton</span></code>, <code class="docutils literal notranslate"><span class="pre">hammersley</span></code>, <code class="docutils literal notranslate"><span class="pre">chebyshev(FT)</span></code>, <code class="docutils literal notranslate"><span class="pre">grid(FT)</span></code> and <code class="docutils literal notranslate"><span class="pre">user</span></code> for user-defined sampling.</p>
-<p>The options for iterative metamodel training are detailed in <a class="reference internal" href="surrogate_description.html"><span class="doc">Training surrogate models</span></a>.</p>
-<section id="example">
-<h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
-<p>In practice, only the classes <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.inputs.Input.html#bayesvalidrox.surrogate_models.inputs.Input" title="bayesvalidrox.surrogate_models.inputs.Input"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.inputs.Input</span></code></a> and <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.ExpDesigns.html#bayesvalidrox.surrogate_models.exp_designs.ExpDesigns" title="bayesvalidrox.surrogate_models.exp_designs.ExpDesigns"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.exp_designs.ExpDesigns</span></code></a> are directly used.</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">Input</span><span class="p">,</span> <span class="n">ExpDesign</span>
-</pre></div>
-</div>
-<p>Marginals of uncertain parameters can be directly added and defined on an <code class="docutils literal notranslate"><span class="pre">Input</span></code> object.</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">Input</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>If they are defined via distribution types, the <code class="docutils literal notranslate"><span class="pre">name</span></code>, <code class="docutils literal notranslate"><span class="pre">dist_type</span></code> and <code class="docutils literal notranslate"><span class="pre">parameters</span></code> for the distribution should be set.</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">'$X$'</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">'unif'</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 they are given via data, only <code class="docutils literal notranslate"><span class="pre">name</span></code> and <code class="docutils literal notranslate"><span class="pre">input_data</span></code> are relevant.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">inputParams</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span><span class="mi">100</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">'$X$'</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="n">inputParams</span>
-</pre></div>
-</div>
-<p>An experimental design can be constructed based on these inputs.</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">ExpDesigns</span><span class="p">(</span><span class="n">Inputs</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Samples of the marginals can be created by specifying a sampling method and generating the wanted number of samples.</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">sampling_method</span> <span class="o">=</span> <span class="s1">'latin_hypercube'</span>
-<span class="gp">>>> </span><span class="n">samples</span> <span class="o">=</span> <span class="n">ExpDesign</span><span class="o">.</span><span class="n">generate_samples</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The generated samples can be visualized against their marginal distributions.</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">plot_samples</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The results will be saved in the folder <code class="docutils literal notranslate"><span class="pre">Outputs_Priors</span></code>.</p>
-<a class="reference internal image-reference" href="_images/prior_$X$.png"><img alt="Generated samples against their marginal distribution" src="_images/prior_%24X%24.png" style="width: 400px;" /></a>
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- <article role="main">
- <section id="example-ishigami">
-<h1>Example: ishigami<a class="headerlink" href="#example-ishigami" title="Link to this heading">¶</a></h1>
-<p>This example deals with the surrogate modeling of a Ishigami function.
-You will see how to check the quality of your regression model and perform
-sensitivity analysis via Sobol Indices.</p>
-<blockquote>
-<div><blockquote>
-<div><p>ISHIGAMI FUNCTION</p>
-</div></blockquote>
-<dl class="simple">
-<dt>Authors: Sonja Surjanovic, Simon Fraser University</dt><dd><p>Derek Bingham, Simon Fraser University</p>
-</dd>
-</dl>
-<p>Questions/Comments: Please email Derek Bingham at <a class="reference external" href="mailto:dbingham%40stat.sfu.ca">dbingham<span>@</span>stat<span>.</span>sfu<span>.</span>ca</a>.</p>
-<p>Copyright 2013. Derek Bingham, Simon Fraser University.</p>
-<p>THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY
-FOR THE USE OF THIS SOFTWARE. If software is modified to produce
-derivative works, such modified software should be clearly marked.
-Additionally, this program is free software; you can redistribute it
-and/or modify it under the terms of the GNU General Public License as
-published by the Free Software Foundation; version 2.0 of the License.
-Accordingly, this program is distributed in the hope that it will be
-useful, but WITHOUT ANY WARRANTY; without even the implied warranty
-of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-General Public License for more details.</p>
-<dl class="simple">
-<dt>For function details and reference information, see:</dt><dd><p><a class="reference external" href="https://www.sfu.ca/~ssurjano/ishigami.html">https://www.sfu.ca/~ssurjano/ishigami.html</a></p>
-</dd>
-</dl>
-</div></blockquote>
-<p>No reference data is given for this example, the surrogate is trained with BCS as the regression method and no active learning.</p>
-<section id="model-and-data">
-<h2>Model and Data<a class="headerlink" href="#model-and-data" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id1">
-<table class="docutils align-default" id="id1">
-<caption><span class="caption-text">Pylink model</span><a class="headerlink" href="#id1" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Model type</p></td>
-<td><p>Function</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of input parameters</p></td>
-<td><p>3</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of output parameters</p></td>
-<td><p>1: flow rate [m$^3$/yr]</p></td>
-</tr>
-<tr class="row-odd"><td><p>Time- or space- dependency</p></td>
-<td><p>??</p></td>
-</tr>
-<tr class="row-even"><td><p>MC reference</p></td>
-<td><p>No</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id2">
-<table class="docutils align-default" id="id2">
-<caption><span class="caption-text">Priors</span><a class="headerlink" href="#id2" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Parameter</p></th>
-<th class="head"><p>Distribution</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>X_1</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-odd"><td><p>X_2</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-even"><td><p>X_3</p></td>
-<td><p>uniform</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="surrogate">
-<h2>Surrogate<a class="headerlink" href="#surrogate" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id3">
-<table class="docutils align-default" id="id3">
-<caption><span class="caption-text">MetaModel settings</span><a class="headerlink" href="#id3" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>surrogate-type</p></td>
-<td><p>aPCE</p></td>
-</tr>
-<tr class="row-odd"><td><p>associated model</p></td>
-<td><p>‘Ishigami’</p></td>
-</tr>
-<tr class="row-even"><td><p>degree choices</p></td>
-<td><p>max degree 14, q-norm truncation 1.0</p></td>
-</tr>
-<tr class="row-odd"><td><p>regression</p></td>
-<td><p>BCS (Bayesian Compressive Sensing)</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id4">
-<table class="docutils align-default" id="id4">
-<caption><span class="caption-text">Training choices</span><a class="headerlink" href="#id4" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Static sampling method</p></td>
-<td><p>latin-hypercube</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of static samples</p></td>
-<td><p>200</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of total samples</p></td>
-<td><p>200</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-</section>
-
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- <ul>
-<li><a class="reference internal" href="#">Example: ishigami</a><ul>
-<li><a class="reference internal" href="#model-and-data">Model and Data</a></li>
-<li><a class="reference internal" href="#surrogate">Surrogate</a></li>
-</ul>
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diff --git a/public/ishigami.rst b/public/ishigami.rst
deleted file mode 100644
index 8bb5639db..000000000
--- a/public/ishigami.rst
+++ /dev/null
@@ -1,98 +0,0 @@
-Example: ishigami
-*****************
-This example deals with the surrogate modeling of a Ishigami function.
-You will see how to check the quality of your regression model and perform
-sensitivity analysis via Sobol Indices.
-
- ISHIGAMI FUNCTION
-
- Authors: Sonja Surjanovic, Simon Fraser University
- Derek Bingham, Simon Fraser University
-
- Questions/Comments: Please email Derek Bingham at dbingham@stat.sfu.ca.
-
- Copyright 2013. Derek Bingham, Simon Fraser University.
-
- THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY
- FOR THE USE OF THIS SOFTWARE. If software is modified to produce
- derivative works, such modified software should be clearly marked.
- Additionally, this program is free software; you can redistribute it
- and/or modify it under the terms of the GNU General Public License as
- published by the Free Software Foundation; version 2.0 of the License.
- Accordingly, this program is distributed in the hope that it will be
- useful, but WITHOUT ANY WARRANTY; without even the implied warranty
- of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- General Public License for more details.
-
- For function details and reference information, see:
- https://www.sfu.ca/~ssurjano/ishigami.html
-
-No reference data is given for this example, the surrogate is trained with BCS as the regression method and no active learning.
-
-.. warning::
- Results in error messages due to regression method in 0.0.5 - will be fixed in next release
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function
- * - Number of input parameters
- - 3
- * - Number of output parameters
- - 1: flow rate [m$^3$/yr]
- * - Time- or space- dependency
- - ??
- * - MC reference
- - No
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - X_1
- - uniform
- * - X_2
- - uniform
- * - X_3
- - uniform
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - 'Ishigami'
- * - degree choices
- - max degree 14, q-norm truncation 1.0
- * - regression
- - BCS (Bayesian Compressive Sensing)
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 200
- * - Number of total samples
- - 200
\ No newline at end of file
diff --git a/public/model_description.html b/public/model_description.html
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- <article role="main">
- <section id="models">
-<h1>Models<a class="headerlink" href="#models" title="Link to this heading">¶</a></h1>
-<div class="twocol docutils container">
-<div class="leftside docutils container">
-<p>BayesValidRox gives options to create interfaces for a variety of models with the class <a class="reference internal" href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.pylink.pylink.PyLinkForwardModel</span></code></a>.
-Its main function is to run the model on given samples and to read in and contain MC references and observations.</p>
-<p>Models can be defined via python functions, shell commands or as general executables.
-This allows for the use of BayesValidRox with a broad range of models and easy extension to models that are defined with e.g. UM-Bridge.</p>
-</div>
-<div class="rightside docutils container">
-<a class="reference internal image-reference" href="_images/model.png"><img alt="UML diagram for the bayesvalidrox class :any:`bayesvalidrox.pylink.pylink.PyLinkForwardModel`." src="_images/model.png" style="width: 150px;" /></a>
-</div>
-</div>
-<section id="example">
-<h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
-<p>For this example we use a very simple model that maps the uncertain parameter <span class="math notranslate nohighlight">\(X\)</span> from the example in <a class="reference internal" href="input_description.html"><span class="doc">Priors, input space and experimental design</span></a> to two outputs, the original value and its square.
-We define this model as a function <code class="docutils literal notranslate"><span class="pre">model</span></code> in a new file <code class="docutils literal notranslate"><span class="pre">model.py</span></code>.
-This function takes a single realization of the uncertain parameter as a 2-dimensional <code class="docutils literal notranslate"><span class="pre">np.array</span></code> and returns a dictionary of model results.
-Here we use the key <code class="docutils literal notranslate"><span class="pre">A</span></code> for the sample values and <code class="docutils literal notranslate"><span class="pre">B</span></code> for their squares.
-Under the key <code class="docutils literal notranslate"><span class="pre">x_values</span></code> a list should be given that is of the same length as each output of the model for a single input.
-The values in this list can denote e.g. timesteps and are used in postprocessing as labels of the x-axis.
-If we want to set the <code class="docutils literal notranslate"><span class="pre">x_values</span></code> outside of the model, it can also be given as an additional parameter</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="k">def</span> <span class="nf">model</span><span class="p">(</span><span class="n">samples</span><span class="p">,</span> <span class="n">x_values</span><span class="p">):</span>
-<span class="gp">>>> </span> <span class="n">sample</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">x_values</span>
-<span class="gp">>>> </span> <span class="n">square</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">x_values</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-<span class="gp">>>> </span> <span class="n">outputs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'A'</span><span class="p">:</span> <span class="n">sample</span><span class="p">,</span> <span class="s1">'B'</span><span class="p">:</span> <span class="n">square</span><span class="p">,</span> <span class="s1">'x_values'</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">]}</span>
-<span class="gp">>>> </span> <span class="k">return</span> <span class="n">outputs</span>
-</pre></div>
-</div>
-<p>In the main python file we can create an object of <a class="reference internal" href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.pylink.pylink.PyLinkForwardModel</span></code></a> that links to this model.</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">PyLinkForwardModel</span>
-</pre></div>
-</div>
-<p>We create an object of this class and set the <code class="docutils literal notranslate"><span class="pre">link_type</span></code> as ‘Function’ to indicate that this is a function in a python file.
-<code class="docutils literal notranslate"><span class="pre">py_file</span></code> should be set to the filename of the model and <code class="docutils literal notranslate"><span class="pre">name</span></code> to the name of the function.
-Lastly we list the keys of the outputs that we are interested in.</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">PyLinkForwardModel</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">link_type</span> <span class="o">=</span> <span class="s1">'Function'</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">py_file</span> <span class="o">=</span> <span class="s1">'model'</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'model'</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">'A'</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>Any parameters to the model function, that are not the samples, can be set via the <code class="docutils literal notranslate"><span class="pre">func_args</span></code> argument.
-In this case we define <code class="docutils literal notranslate"><span class="pre">x_values</span></code> as a <code class="docutils literal notranslate"><span class="pre">np.array</span></code> and include it.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</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="mf">0.1</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">func_args</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'x_values'</span><span class="p">:</span><span class="n">x_values</span><span class="p">}</span>
-</pre></div>
-</div>
-<p>With this we have completed an interface to our model.
-We can now evaluate this model on the samples created in the input example.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">output</span><span class="p">,</span> <span class="n">samples</span> <span class="o">=</span> <span class="n">Model</span><span class="o">.</span><span class="n">run_model_parallel</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Other types of model links can be found in <a class="reference internal" href="examples.html"><span class="doc">EXAMPLES</span></a>, e.g. <a class="reference internal" href="beam.html"><span class="doc">Example: beam</span></a> uses a shell command and parser.</p>
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- <article role="main">
- <section id="example-model-comparison">
-<h1>Example: model comparison<a class="headerlink" href="#example-model-comparison" title="Link to this heading">¶</a></h1>
-<p>This example shows the multi-model comparison.
-You will see how to perform a multi-model comparison</p>
-<p>Provided are three models, a linear models with 2 input parameters, a nonlinear model with 2 input parameters
-and a nonlinear model with 4 input parameters.
-The data to base the comparison on is given in an extra file.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>A detailed explanation of this example will be provided in future as part of the tutorial.</p>
-</div>
-<section id="model-1-l2-model">
-<h2>Model 1: L2_model<a class="headerlink" href="#model-1-l2-model" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id1">
-<table class="docutils align-default" id="id1">
-<caption><span class="caption-text">Pylink model1</span><a class="headerlink" href="#id1" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Model type</p></td>
-<td><p>Function (linear)</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of input parameters</p></td>
-<td><p>2</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of output parameters</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-odd"><td><p>Time- or space- dependency</p></td>
-<td><p>space-dependency</p></td>
-</tr>
-<tr class="row-even"><td><p>MC reference</p></td>
-<td><p>No</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id2">
-<table class="docutils align-default" id="id2">
-<caption><span class="caption-text">Priors1</span><a class="headerlink" href="#id2" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Parameter</p></th>
-<th class="head"><p>Distribution</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>0-2</p></td>
-<td><p>given as correlated samples</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="model-1-nl2-model">
-<h2>Model 1: NL2_model<a class="headerlink" href="#model-1-nl2-model" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id3">
-<table class="docutils align-default" id="id3">
-<caption><span class="caption-text">Pylink model1</span><a class="headerlink" href="#id3" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Model type</p></td>
-<td><p>Function (exponential)</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of input parameters</p></td>
-<td><p>2</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of output parameters</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-odd"><td><p>Time- or space- dependency</p></td>
-<td><p>space-dependency</p></td>
-</tr>
-<tr class="row-even"><td><p>MC reference</p></td>
-<td><p>No</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id4">
-<table class="docutils align-default" id="id4">
-<caption><span class="caption-text">Priors1</span><a class="headerlink" href="#id4" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Parameter</p></th>
-<th class="head"><p>Distribution</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>0-2</p></td>
-<td><p>given as correlated samples</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="model-1-nl4-model">
-<h2>Model 1: NL4_model<a class="headerlink" href="#model-1-nl4-model" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id5">
-<table class="docutils align-default" id="id5">
-<caption><span class="caption-text">Pylink model1</span><a class="headerlink" href="#id5" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Model type</p></td>
-<td><p>Function (cosine)</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of input parameters</p></td>
-<td><p>4</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of output parameters</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-odd"><td><p>Time- or space- dependency</p></td>
-<td><p>space-dependency</p></td>
-</tr>
-<tr class="row-even"><td><p>MC reference</p></td>
-<td><p>No</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id6">
-<table class="docutils align-default" id="id6">
-<caption><span class="caption-text">Priors1</span><a class="headerlink" href="#id6" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Parameter</p></th>
-<th class="head"><p>Distribution</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>0-4</p></td>
-<td><p>given as correlated samples</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="surrogates-1-3">
-<h2>Surrogates 1-3<a class="headerlink" href="#surrogates-1-3" title="Link to this heading">¶</a></h2>
-<p>All surrogates share the same setup and only differ in the given model.</p>
-<div class="table-wrapper colwidths-given docutils container" id="id7">
-<table class="docutils align-default" id="id7">
-<caption><span class="caption-text">MetaModel settings</span><a class="headerlink" href="#id7" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>surrogate-type</p></td>
-<td><p>aPCE</p></td>
-</tr>
-<tr class="row-odd"><td><p>associated model</p></td>
-<td><p>see lists above</p></td>
-</tr>
-<tr class="row-even"><td><p>degree choices</p></td>
-<td><p>1-12, q-norm truncation 1.0</p></td>
-</tr>
-<tr class="row-odd"><td><p>regression</p></td>
-<td><p>OMP (Orthogonal matching pursuit)</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id8">
-<table class="docutils align-default" id="id8">
-<caption><span class="caption-text">Training choices</span><a class="headerlink" href="#id8" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Static sampling method</p></td>
-<td><p>latin-hypercube</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of static samples</p></td>
-<td><p>100</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of total samples</p></td>
-<td><p>100</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-</section>
-
- </article>
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- <ul>
-<li><a class="reference internal" href="#">Example: model comparison</a><ul>
-<li><a class="reference internal" href="#model-1-l2-model">Model 1: L2_model</a></li>
-<li><a class="reference internal" href="#model-1-nl2-model">Model 1: NL2_model</a></li>
-<li><a class="reference internal" href="#model-1-nl4-model">Model 1: NL4_model</a></li>
-<li><a class="reference internal" href="#surrogates-1-3">Surrogates 1-3</a></li>
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diff --git a/public/modelcomparison.rst b/public/modelcomparison.rst
deleted file mode 100644
index c89ea1f9c..000000000
--- a/public/modelcomparison.rst
+++ /dev/null
@@ -1,131 +0,0 @@
-Example: model comparison
-*************************
-This example shows the multi-model comparison.
-You will see how to perform a multi-model comparison
-
-Provided are three models, a linear models with 2 input parameters, a nonlinear model with 2 input parameters
-and a nonlinear model with 4 input parameters.
-The data to base the comparison on is given in an extra file.
-
-.. note::
- A detailed explanation of this example will be provided in future as part of the tutorial.
-
-Model 1: L2_model
-=================
-
-.. list-table:: Pylink model1
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function (linear)
- * - Number of input parameters
- - 2
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - space-dependency
- * - MC reference
- - No
-
-.. list-table:: Priors1
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - 0-2
- - given as correlated samples
-
-Model 1: NL2_model
-==================
-
-.. list-table:: Pylink model1
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function (exponential)
- * - Number of input parameters
- - 2
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - space-dependency
- * - MC reference
- - No
-
-.. list-table:: Priors1
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - 0-2
- - given as correlated samples
-
-Model 1: NL4_model
-==================
-
-.. list-table:: Pylink model1
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function (cosine)
- * - Number of input parameters
- - 4
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - space-dependency
- * - MC reference
- - No
-
-.. list-table:: Priors1
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - 0-4
- - given as correlated samples
-
-Surrogates 1-3
-==============
-All surrogates share the same setup and only differ in the given model.
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - see lists above
- * - degree choices
- - 1-12, q-norm truncation 1.0
- * - regression
- - OMP (Orthogonal matching pursuit)
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 100
- * - Number of total samples
- - 100
\ No newline at end of file
diff --git a/public/objects.inv b/public/objects.inv
deleted file mode 100644
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- <section id="example-ohagan-function">
-<h1>Example: OHagan-function<a class="headerlink" href="#example-ohagan-function" title="Link to this heading">¶</a></h1>
-<p>This example deals with the surrogate modeling of O’Hagan function with 15
-parameters.
-You will see how to check the quality of your regression model and perform sensitivity analysis via Sobol Indices</p>
-<blockquote>
-<div><p>Oakley & O’Hagan (2004) Function</p>
-<p>This function’s a-coefficients are chosen so that 5 of the input variables
-contribute significantly to the output variance, 5 have a much smaller
-effect, and the remaining 5 have almost no effect on the output variance.</p>
-<p>O’Hagan, 2004, Probabilistic sensitivity analysis of complex models: a
-Bayesian approach J. R. Statist. Soc. B (2004) 66, Part 3, pp. 751-769.</p>
-</div></blockquote>
-<p>This example trains a surrogate with AL.
-FastARD is set as the regression type and the space-filling sequential exploitaiton scheme is chosen as no data is given.</p>
-<section id="model-and-data">
-<h2>Model and Data<a class="headerlink" href="#model-and-data" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id1">
-<table class="docutils align-default" id="id1">
-<caption><span class="caption-text">Pylink model</span><a class="headerlink" href="#id1" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Model type</p></td>
-<td><p>Function</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of input parameters</p></td>
-<td><p>15</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of output parameters</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-odd"><td><p>Time- or space- dependency</p></td>
-<td><p>??</p></td>
-</tr>
-<tr class="row-even"><td><p>MC reference</p></td>
-<td><p>No</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id2">
-<table class="docutils align-default" id="id2">
-<caption><span class="caption-text">Priors</span><a class="headerlink" href="#id2" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Parameter</p></th>
-<th class="head"><p>Distribution</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>0-14</p></td>
-<td><p>gaussian</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="surrogate">
-<h2>Surrogate<a class="headerlink" href="#surrogate" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id3">
-<table class="docutils align-default" id="id3">
-<caption><span class="caption-text">MetaModel settings</span><a class="headerlink" href="#id3" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>surrogate-type</p></td>
-<td><p>aPCE</p></td>
-</tr>
-<tr class="row-odd"><td><p>associated model</p></td>
-<td><p>‘OHagan’</p></td>
-</tr>
-<tr class="row-even"><td><p>degree choices</p></td>
-<td><p>max degree 7, q-norm truncation 0.65</p></td>
-</tr>
-<tr class="row-odd"><td><p>regression</p></td>
-<td><p>FastARD</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id4">
-<table class="docutils align-default" id="id4">
-<caption><span class="caption-text">Training choices</span><a class="headerlink" href="#id4" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Static sampling method</p></td>
-<td><p>latin-hypercube</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of static samples</p></td>
-<td><p>100</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of total samples</p></td>
-<td><p>500</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of samples per AL iteration</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-even"><td><p>AL tradeoff scheme</p></td>
-<td><p>None</p></td>
-</tr>
-<tr class="row-odd"><td><p>AL exploration method</p></td>
-<td><p>latin-hypercube, n_candidates=10000, n_cand_groups=4</p></td>
-</tr>
-<tr class="row-even"><td><p>AL exploitation method</p></td>
-<td><p>space-filling</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-</section>
-
- </article>
- </div>
- <footer>
-
- <div class="related-pages">
- <a class="next-page" href="pollution.html">
- <div class="page-info">
- <div class="context">
- <span>Next</span>
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- </div>
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-
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- </a>
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- <div class="copyright">
- Copyright © 2023, Farid Mohammadi, Rebecca Kohlhaas
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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="#">Example: OHagan-function</a><ul>
-<li><a class="reference internal" href="#model-and-data">Model and Data</a></li>
-<li><a class="reference internal" href="#surrogate">Surrogate</a></li>
-</ul>
-</li>
-</ul>
-
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diff --git a/public/ohaganfunction.rst b/public/ohaganfunction.rst
deleted file mode 100644
index 2b3949128..000000000
--- a/public/ohaganfunction.rst
+++ /dev/null
@@ -1,88 +0,0 @@
-Example: OHagan-function
-************************
-This example deals with the surrogate modeling of O'Hagan function with 15
-parameters.
-You will see how to check the quality of your regression model and perform sensitivity analysis via Sobol Indices
-
-
- Oakley & O'Hagan (2004) Function
-
- This function's a-coefficients are chosen so that 5 of the input variables
- contribute significantly to the output variance, 5 have a much smaller
- effect, and the remaining 5 have almost no effect on the output variance.
-
- O'Hagan, 2004, Probabilistic sensitivity analysis of complex models: a
- Bayesian approach J. R. Statist. Soc. B (2004) 66, Part 3, pp. 751-769.
-
-This example trains a surrogate with AL.
-FastARD is set as the regression type and the space-filling sequential exploitaiton scheme is chosen as no data is given.
-
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function
- * - Number of input parameters
- - 15
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - ??
- * - MC reference
- - No
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - 0-14
- - gaussian
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - 'OHagan'
- * - degree choices
- - max degree 7, q-norm truncation 0.65
- * - regression
- - FastARD
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 100
- * - Number of total samples
- - 500
- * - Number of samples per AL iteration
- - 1
- * - AL tradeoff scheme
- - None
- * - AL exploration method
- - latin-hypercube, n_candidates=10000, n_cand_groups=4
- * - AL exploitation method
- - space-filling
\ No newline at end of file
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- <section id="user-guide">
-<h1>USER GUIDE<a class="headerlink" href="#user-guide" title="Link to this heading">¶</a></h1>
-<section id="installation">
-<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">¶</a></h2>
-<p>BayesValidRox provides functionalities for describing uncertain parameters, building surrogate models based on model outputs and evaluating them with Bayesian validation methods.</p>
-<p>This package runs under Python 3.9 for versions <1.0.0 and 3.9+ from version 1.0.0 on.
-It can be installed with pip, best practice is to do so inside a virtual environment.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>-m<span class="w"> </span>venv<span class="w"> </span>bayes_env
-<span class="nb">cd</span><span class="w"> </span>bayes_env
-<span class="nb">source</span><span class="w"> </span>bin/activate
-</pre></div>
-</div>
-<p>Here replace <code class="docutils literal notranslate"><span class="pre">bayes_env</span></code> with your preferred name.
-Then install the latest release of BayesValidRox inside the venv.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>bayesvalidrox
-</pre></div>
-</div>
-<p>The current master can be installed by cloning the repository.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox.git
-<span class="nb">cd</span><span class="w"> </span>bayesvalidrox
-pip<span class="w"> </span>install<span class="w"> </span>.
-</pre></div>
-</div>
-<section id="overview">
-<h3>Overview<a class="headerlink" href="#overview" title="Link to this heading">¶</a></h3>
-<p>This package is split into multiple aspects corresponding to its folder structure.</p>
-<a class="reference internal image-reference" href="_images/folder_structure.png"><img alt="Folder structure of **bayesvalidrox**" src="_images/folder_structure.png" style="width: 600px;" /></a>
-<p>The folder <code class="docutils literal notranslate"><span class="pre">surrogate_models</span></code> contains all the functions and classes that are necessary in order to create and train the surrogate model.
-This includes</p>
-<ul class="simple">
-<li><p>defining the input marginals</p></li>
-<li><p>setting properties of the sampling in an experimental design</p></li>
-<li><p>choosing the surrogate model and its properties</p></li>
-<li><p>training the surrogate model on model evaluations in a straightforward manner or iteratively with active learning</p></li>
-</ul>
-<p>The computational model is linked via a <code class="docutils literal notranslate"><span class="pre">pylink</span></code> interface.</p>
-<p>Multiple post-processing options are available, including the calculation of Sobol’ indices, checking the accuracy of the surrogate model and visualizations of the moments of the surrogate.</p>
-<p>Bayesian inference can be performed with rejection sampling or MCMC, while taking into account the estimated uncertainty of the data that the (surrogate) model is compared to.
-If multiple (surrogate) models are given, they can be compared against each other with pairwise Bayes Factors, model weights or a justifiability analysis.</p>
-<p>The next pages lead through the topics given in BayesValidRox and describe the available classes and give brief examples for their use.</p>
-<div class="toctree-wrapper compound">
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="input_description.html">Priors, input space and experimental design</a></li>
-<li class="toctree-l1"><a class="reference internal" href="model_description.html">Models</a></li>
-<li class="toctree-l1"><a class="reference internal" href="surrogate_description.html">Training surrogate models</a></li>
-<li class="toctree-l1"><a class="reference internal" href="al_description.html">Active learning: iteratively expanding the training set</a></li>
-<li class="toctree-l1"><a class="reference internal" href="post_description.html">Postprocessing</a></li>
-<li class="toctree-l1"><a class="reference internal" href="bayes_description.html">Bayesian inference</a></li>
-<li class="toctree-l1"><a class="reference internal" href="bmc_description.html">Bayesian multi-model comparison</a></li>
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diff --git a/public/packagedescription.rst b/public/packagedescription.rst
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-PACKAGE DESCRIPTION
-*******************
-The package **bayesvalidrox** is split into multiple topics corresponding to the folder structure of the package.
-The folder `surrogate_models` contains all the functions and classes to set up the experimental design with priors and create and train the surrogate model.
-The computational model is linked via *pylink*.
-Postprocessing, Bayesian inference and Bayesian model comparison each also get their own section.
-
-
-TODO: add image to show folder/file interconnection
-
-Priors, experimental design and surrogates
-==========================================
-The package **bayesvalidrox**
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- <article role="main">
- <section id="example-pollution">
-<h1>Example: pollution<a class="headerlink" href="#example-pollution" title="Link to this heading">¶</a></h1>
-<p>This test shows a surrogate-assisted Bayesian calibration of a time dependent
-pollution function. Here, the noise will be jointly inferred with the input parameters.</p>
-<blockquote>
-<div><p>ENVIRONMENTAL MODEL FUNCTION</p>
-<dl class="simple">
-<dt>Authors: Sonja Surjanovic, Simon Fraser University</dt><dd><p>Derek Bingham, Simon Fraser University</p>
-</dd>
-</dl>
-<p>Questions/Comments: Please email Derek Bingham at <a class="reference external" href="mailto:dbingham%40stat.sfu.ca">dbingham<span>@</span>stat<span>.</span>sfu<span>.</span>ca</a>.</p>
-<p>Copyright 2013. Derek Bingham, Simon Fraser University.</p>
-<p>THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY
-FOR THE USE OF THIS SOFTWARE. If software is modified to produce
-derivative works, such modified software should be clearly marked.
-Additionally, this program is free software; you can redistribute it
-and/or modify it under the terms of the GNU General Public License as
-published by the Free Software Foundation; version 2.0 of the License.
-Accordingly, this program is distributed in the hope that it will be
-useful, but WITHOUT ANY WARRANTY; without even the implied warranty
-of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-General Public License for more details.</p>
-<dl class="simple">
-<dt>For function details and reference information, see:</dt><dd><p><a class="reference external" href="http://www.sfu.ca/~ssurjano/">http://www.sfu.ca/~ssurjano/</a></p>
-</dd>
-</dl>
-</div></blockquote>
-<p>This example trains a surrogate and performs Bayesian Inference based on the given data.
-Active Learning can be activated and will then be performed with Variance Optimal Design based on Entropy and an epsilon-decreasing tradeoff scheme.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This example contains two <code class="docutils literal notranslate"><span class="pre">test_*</span></code> files that can be run.</p>
-</div>
-<section id="model-and-data">
-<h2>Model and Data<a class="headerlink" href="#model-and-data" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id1">
-<table class="docutils align-default" id="id1">
-<caption><span class="caption-text">Pylink model</span><a class="headerlink" href="#id1" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Model type</p></td>
-<td><p>Function</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of input parameters</p></td>
-<td><p>4</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of output parameters</p></td>
-<td><p>1</p></td>
-</tr>
-<tr class="row-odd"><td><p>Time- or space- dependency</p></td>
-<td><p>Yes, ??</p></td>
-</tr>
-<tr class="row-even"><td><p>MC reference</p></td>
-<td><p>No</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id2">
-<table class="docutils align-default" id="id2">
-<caption><span class="caption-text">Priors</span><a class="headerlink" href="#id2" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Parameter</p></th>
-<th class="head"><p>Distribution</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>M</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-odd"><td><p>D</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-even"><td><p>L</p></td>
-<td><p>uniform</p></td>
-</tr>
-<tr class="row-odd"><td><p>tau</p></td>
-<td><p>uniform</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-<section id="surrogate">
-<h2>Surrogate<a class="headerlink" href="#surrogate" title="Link to this heading">¶</a></h2>
-<div class="table-wrapper colwidths-given docutils container" id="id3">
-<table class="docutils align-default" id="id3">
-<caption><span class="caption-text">MetaModel settings</span><a class="headerlink" href="#id3" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>surrogate-type</p></td>
-<td><p>aPCE</p></td>
-</tr>
-<tr class="row-odd"><td><p>associated model</p></td>
-<td><p>‘pollution’</p></td>
-</tr>
-<tr class="row-even"><td><p>degree choices</p></td>
-<td><p>max degree 8, q-norm truncation 1.0</p></td>
-</tr>
-<tr class="row-odd"><td><p>regression</p></td>
-<td><p>BCS (Bayesian Compressive Sensing) with <code class="docutils literal notranslate"><span class="pre">'fast'</span></code> bootstrap</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-<div class="table-wrapper colwidths-given docutils container" id="id4">
-<table class="docutils align-default" id="id4">
-<caption><span class="caption-text">Training choices</span><a class="headerlink" href="#id4" title="Link to this table">¶</a></caption>
-<colgroup>
-<col style="width: 50%" />
-<col style="width: 50%" />
-</colgroup>
-<thead>
-<tr class="row-odd"><th class="head"><p>Property</p></th>
-<th class="head"><p>Setting</p></th>
-</tr>
-</thead>
-<tbody>
-<tr class="row-even"><td><p>Static sampling method</p></td>
-<td><p>latin-hypercube</p></td>
-</tr>
-<tr class="row-odd"><td><p>Number of static samples</p></td>
-<td><p>150</p></td>
-</tr>
-<tr class="row-even"><td><p>Number of total samples</p></td>
-<td><p>150</p></td>
-</tr>
-</tbody>
-</table>
-</div>
-</section>
-</section>
-
- </article>
- </div>
- <footer>
-
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- On this page
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- <div class="toc-tree">
- <ul>
-<li><a class="reference internal" href="#">Example: pollution</a><ul>
-<li><a class="reference internal" href="#model-and-data">Model and Data</a></li>
-<li><a class="reference internal" href="#surrogate">Surrogate</a></li>
-</ul>
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diff --git a/public/pollution.rst b/public/pollution.rst
deleted file mode 100644
index 77cb7e217..000000000
--- a/public/pollution.rst
+++ /dev/null
@@ -1,100 +0,0 @@
-Example: pollution
-******************
-This test shows a surrogate-assisted Bayesian calibration of a time dependent
-pollution function. Here, the noise will be jointly inferred with the input parameters.
-
- ENVIRONMENTAL MODEL FUNCTION
-
- Authors: Sonja Surjanovic, Simon Fraser University
- Derek Bingham, Simon Fraser University
-
- Questions/Comments: Please email Derek Bingham at dbingham@stat.sfu.ca.
-
- Copyright 2013. Derek Bingham, Simon Fraser University.
-
- THERE IS NO WARRANTY, EXPRESS OR IMPLIED. WE DO NOT ASSUME ANY LIABILITY
- FOR THE USE OF THIS SOFTWARE. If software is modified to produce
- derivative works, such modified software should be clearly marked.
- Additionally, this program is free software; you can redistribute it
- and/or modify it under the terms of the GNU General Public License as
- published by the Free Software Foundation; version 2.0 of the License.
- Accordingly, this program is distributed in the hope that it will be
- useful, but WITHOUT ANY WARRANTY; without even the implied warranty
- of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- General Public License for more details.
-
- For function details and reference information, see:
- http://www.sfu.ca/~ssurjano/
-
-This example trains a surrogate and performs Bayesian Inference based on the given data.
-Active Learning can be activated and will then be performed with Variance Optimal Design based on Entropy and an epsilon-decreasing tradeoff scheme.
-
-.. note::
- This example contains two ``test_*`` files that can be run.
-
-Model and Data
-==============
-
-.. list-table:: Pylink model
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Model type
- - Function
- * - Number of input parameters
- - 4
- * - Number of output parameters
- - 1
- * - Time- or space- dependency
- - Yes, ??
- * - MC reference
- - No
-
-.. list-table:: Priors
- :widths: 30 30
- :header-rows: 1
-
- * - Parameter
- - Distribution
- * - M
- - uniform
- * - D
- - uniform
- * - L
- - uniform
- * - tau
- - uniform
-
-Surrogate
-=========
-
-.. list-table:: MetaModel settings
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - surrogate-type
- - aPCE
- * - associated model
- - 'pollution'
- * - degree choices
- - max degree 8, q-norm truncation 1.0
- * - regression
- - BCS (Bayesian Compressive Sensing) with ``'fast'`` bootstrap
-
-
-.. list-table:: Training choices
- :widths: 30 30
- :header-rows: 1
-
- * - Property
- - Setting
- * - Static sampling method
- - latin-hypercube
- * - Number of static samples
- - 150
- * - Number of total samples
- - 150
\ No newline at end of file
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- <section id="postprocessing">
-<h1>Postprocessing<a class="headerlink" href="#postprocessing" title="Link to this heading">¶</a></h1>
-<div class="twocol docutils container">
-<div class="leftside docutils container">
-<p>Postprocessing refers to evaluations and checks performed on a model to get an understanding of its properties and estimate its quality.
-The BayesValidRox class <a class="reference internal" href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing" title="bayesvalidrox.post_processing.post_processing.PostProcessing"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.post_processing.post_processing.PostProcessing</span></code></a> includes functions that are applicable to all types of surrogate model.</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">valid_metamodel</span></code>: visualizing some metamodel runs against the corresponding model runs</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">check_accuracy</span></code>: computing the RMSE error of the surrogate model</p></li>
-</ul>
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-<div class="rightside docutils container">
-<a class="reference internal image-reference" href="_images/postprocessing.png"><img alt="UML diagram for the classes and functions used in active learning in BayesValidRox." src="_images/postprocessing.png" style="width: 300px;" /></a>
-</div>
-</div>
-<p>Postprocessing methods that are specific to a type of surrogate or training include</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">plot_moments</span></code>: visualizing the moments of a polynomial chaos expansion (PCE, aPCE)</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">sobol_indices</span></code>: calculating the Sobol’ and Total Sobol’ indices (PCE, aPCE)</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">plot_seq_design_diagnostics</span></code>: visualizing diagnostics that were evaluated during sequential training (surrogate trained with <code class="docutils literal notranslate"><span class="pre">train_sequential</span></code>)</p></li>
-</ul>
-<section id="example">
-<h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
-<p>We want to compare out trained surrogate from <a class="reference internal" href="al_description.html"><span class="doc">Active learning: iteratively expanding the training set</span></a> against its original model.</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>
-</pre></div>
-</div>
-<p>The postprocessing object expects the full engine. This allows it to perform both sampling, running the model and running the surrogate model.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">PostProc</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>To gain an understanding of the approximation quality we use both <code class="docutils literal notranslate"><span class="pre">valid_metamodel</span></code> and <code class="docutils literal notranslate"><span class="pre">check_accuracy</span></code>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">PostProc</span><span class="o">.</span><span class="n">valid_metamodel</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">PostProc</span><span class="o">.</span><span class="n">check_accuracy</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The visual comparison of model and metamodel results is saved in the folder <code class="docutils literal notranslate"><span class="pre">Outputs_PostProcessing_calib</span></code>, while the accuracy check is given as console output.</p>
-<div class="twocol docutils container">
-<div class="leftside docutils container">
-<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="go">>>>>> Errors of A <<<<<</span>
-
-<span class="go">Index | RMSE | Validation Error</span>
-<span class="go">-----------------------------------</span>
-<span class="go">1 | 1.513e-07 | 2.224e-15</span>
-
-<span class="go">>>>>> Errors of B <<<<<</span>
-
-<span class="go">Index | RMSE | Validation Error</span>
-<span class="go">-----------------------------------</span>
-<span class="go">1 | 5.045e-05 | 2.406e-11</span>
-</pre></div>
-</div>
-</div>
-<div class="rightside docutils container">
-<a class="reference internal image-reference" href="_images/Model_vs_PCEModel_y_1.pdf"><img alt="Visual comparison of model and metamodel outputs" src="_images/Model_vs_PCEModel_y_1.pdf" style="width: 400px;" /></a>
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- <td></td><td><strong>b</strong></td><td></td>
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- <td>
- <a href="_autosummary/bayesvalidrox.html#module-bayesvalidrox"><code class="xref">bayesvalidrox</code></a></td><td>
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- <td></td>
- <td>   
- <a href="_autosummary/bayesvalidrox.bayes_inference.html#module-bayesvalidrox.bayes_inference"><code class="xref">bayesvalidrox.bayes_inference</code></a></td><td>
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- <td>   
- <a href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.html#module-bayesvalidrox.bayes_inference.bayes_inference"><code class="xref">bayesvalidrox.bayes_inference.bayes_inference</code></a></td><td>
- <em></em></td>
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- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="_autosummary/bayesvalidrox.bayes_inference.bayes_model_comparison.html#module-bayesvalidrox.bayes_inference.bayes_model_comparison"><code class="xref">bayesvalidrox.bayes_inference.bayes_model_comparison</code></a></td><td>
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- <td>   
- <a href="_autosummary/bayesvalidrox.bayes_inference.discrepancy.html#module-bayesvalidrox.bayes_inference.discrepancy"><code class="xref">bayesvalidrox.bayes_inference.discrepancy</code></a></td><td>
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- <a href="_autosummary/bayesvalidrox.bayes_inference.mcmc.html#module-bayesvalidrox.bayes_inference.mcmc"><code class="xref">bayesvalidrox.bayes_inference.mcmc</code></a></td><td>
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- <td>   
- <a href="_autosummary/bayesvalidrox.post_processing.post_processing.html#module-bayesvalidrox.post_processing.post_processing"><code class="xref">bayesvalidrox.post_processing.post_processing</code></a></td><td>
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- <a href="_autosummary/bayesvalidrox.pylink.html#module-bayesvalidrox.pylink"><code class="xref">bayesvalidrox.pylink</code></a></td><td>
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- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.html#module-bayesvalidrox.surrogate_models"><code class="xref">bayesvalidrox.surrogate_models</code></a></td><td>
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- <td></td>
- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.adaptPlot.html#module-bayesvalidrox.surrogate_models.adaptPlot"><code class="xref">bayesvalidrox.surrogate_models.adaptPlot</code></a></td><td>
- <em></em></td>
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- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.apoly_construction.html#module-bayesvalidrox.surrogate_models.apoly_construction"><code class="xref">bayesvalidrox.surrogate_models.apoly_construction</code></a></td><td>
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- <a href="_autosummary/bayesvalidrox.surrogate_models.bayes_linear.html#module-bayesvalidrox.surrogate_models.bayes_linear"><code class="xref">bayesvalidrox.surrogate_models.bayes_linear</code></a></td><td>
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- <td>   
- <a href="_autosummary/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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- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.eval_rec_rule.html#module-bayesvalidrox.surrogate_models.eval_rec_rule"><code class="xref">bayesvalidrox.surrogate_models.eval_rec_rule</code></a></td><td>
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- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.exp_designs.html#module-bayesvalidrox.surrogate_models.exp_designs"><code class="xref">bayesvalidrox.surrogate_models.exp_designs</code></a></td><td>
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- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.exploration.html#module-bayesvalidrox.surrogate_models.exploration"><code class="xref">bayesvalidrox.surrogate_models.exploration</code></a></td><td>
- <em></em></td>
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- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.glexindex.html#module-bayesvalidrox.surrogate_models.glexindex"><code class="xref">bayesvalidrox.surrogate_models.glexindex</code></a></td><td>
- <em></em></td>
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- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.input_space.html#module-bayesvalidrox.surrogate_models.input_space"><code class="xref">bayesvalidrox.surrogate_models.input_space</code></a></td><td>
- <em></em></td>
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- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.inputs.html#module-bayesvalidrox.surrogate_models.inputs"><code class="xref">bayesvalidrox.surrogate_models.inputs</code></a></td><td>
- <em></em></td>
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- <td></td>
- <td>   
- <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>
- <em></em></td>
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- <td></td>
- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.reg_fast_ard.html#module-bayesvalidrox.surrogate_models.reg_fast_ard"><code class="xref">bayesvalidrox.surrogate_models.reg_fast_ard</code></a></td><td>
- <em></em></td>
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- <tr class="cg-1">
- <td></td>
- <td>   
- <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>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <a href="_autosummary/bayesvalidrox.surrogate_models.sequential_design.html#module-bayesvalidrox.surrogate_models.sequential_design"><code class="xref">bayesvalidrox.surrogate_models.sequential_design</code></a></td><td>
- <em></em></td>
- </tr>
- <tr class="cg-1">
- <td></td>
- <td>   
- <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>
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- <section id="training-surrogate-models">
-<h1>Training surrogate models<a class="headerlink" href="#training-surrogate-models" title="Link to this heading">¶</a></h1>
-<p>Surrogate models, also called metamodels, are models that are built on evaluations of full models with the goal to capture the full behaviour, but reduce the cost of evaluations.</p>
-<p>The surrogate models are trained on datasets <span class="math notranslate nohighlight">\(\mathcal{D}=(x_i, y_i)_{i=1,\dots,M)\)</span> that consist of <span class="math notranslate nohighlight">\(M\)</span> samples of the uncertain parameters and the corresponding model outputs.
-We call this dataset the training data, with training samples <span class="math notranslate nohighlight">\((x_i)_{i=1,\dots,M)\)</span>.</p>
-<p>BayesValidRox creates surrogate models as objects of the class <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</span></code></a>.
-Training is performed by the class <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code></a>.</p>
-<a class="reference internal image-reference" href="_images/metamod_training_reduced.png"><img alt="UML diagram for metamodel-related classes in bayesvalidrox" src="_images/metamod_training_reduced.png" style="width: 800px;" /></a>
-<section id="metamodel-options">
-<h2>MetaModel options<a class="headerlink" href="#metamodel-options" title="Link to this heading">¶</a></h2>
-<p>In BayesValidRox two types of surrogate model are available, Polynomial Chaos Expansion (PCE) and Gaussian Processes (GP).
-The Polynomial Chaos Expansion (PCE) and its variant the arbitrary Polynomial Chaos Expansion (aPC) build polynomials from the given distributions of uncertain inputs.
-Gaussian processes (GP) give kernel-based representations of the model results.</p>
-<p>We provide a broad range of regression methods for useage with PCE-surrogates that can be set by the parameter <code class="docutils literal notranslate"><span class="pre">MetaModel.pce_reg_method</span></code>.
-These include Ordinary Least Squares (<code class="docutils literal notranslate"><span class="pre">ols</span></code>), Bayesian Ridge Regression (<code class="docutils literal notranslate"><span class="pre">brr</span></code>), Least angle regression (<code class="docutils literal notranslate"><span class="pre">lars</span></code>), Bayesian ARD Regression (<code class="docutils literal notranslate"><span class="pre">ard</span></code>), Fast Bayesian ARD Regression (<code class="docutils literal notranslate"><span class="pre">fastard</span></code>), Variational Bayesian Learning (<code class="docutils literal notranslate"><span class="pre">vbl</span></code>) and Emperical Bayesian Learning (<code class="docutils literal notranslate"><span class="pre">ebl</span></code>).
-Depending on the chosen regression method, the surrogate outputs a mean approximation and an associated standard deviation.</p>
-<p>Dimensionality reduction can be performed on outputs with Principal Component Analysis (PCA).
-PCA is applied on the set of surrogates built for the <code class="docutils literal notranslate"><span class="pre">x_values</span></code> defined in the model.</p>
-<p>If bootstrapping is used, multiple surrogates will be created based on bootstrapped training data, and jointly evaluated.
-The final outputs will then be the mean and standard deviation of their approximations.</p>
-</section>
-<section id="training-with-the-engine">
-<h2>Training with the engine<a class="headerlink" href="#training-with-the-engine" title="Link to this heading">¶</a></h2>
-<p>For training a surrogate model we use an object of class <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code></a>.
-This needs to be given three things: the metamodel itself, the model that the metamodel should replace and the experimental design that matches the uncertain inputs for the model and metamodel.</p>
-<p>The standard method of training the surrogate is performed by the function <code class="docutils literal notranslate"><span class="pre">train_normal()</span></code>.
-Other available training methods in BayesValidRox are presented in <a class="reference internal" href="al_description.html"><span class="doc">Active learning: iteratively expanding the training set</span></a>.</p>
-<div class="twocol docutils container">
-<div class="leftside docutils container">
-<p>For training the engine performs three main steps.</p>
-<ol class="arabic simple">
-<li><p>Generating training samples from the experimental design.</p></li>
-<li><p>Evaluating the model on the training samples.</p></li>
-<li><p>Fitting the surrogate to the training dataset.</p></li>
-</ol>
-</div>
-<div class="rightside docutils container">
-<a class="reference internal image-reference" href="_images/engine_train_normal.png"><img alt="Diagram of main steps in ``Engine.train_normal()``" src="_images/engine_train_normal.png" style="width: 800px;" /></a>
-</div>
-</div>
-</section>
-<section id="example">
-<h2>Example<a class="headerlink" href="#example" title="Link to this heading">¶</a></h2>
-<p>We now build a surrogate model for the simple model from <a class="reference internal" href="model_description.html"><span class="doc">Models</span></a> using the experimental design from <a class="reference internal" href="input_description.html"><span class="doc">Priors, input space and experimental design</span></a>.
-For this we need the classes <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</span></code></a> and <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code></a>.</p>
-<p>>> from bayesvalidrox import MetaModel, Engine</p>
-<p>First we set up the surrogate model and tell it to consider the uncertain parameters defined in <code class="docutils literal notranslate"><span class="pre">Inputs</span></code> as its input parameters.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaMod</span> <span class="o">=</span> <span class="n">MetaModel</span><span class="p">(</span><span class="n">Inputs</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Then we specify what type of surrogate we want and its properties.
-Here we use an aPCE with maximal polynomial degree 3 and want to use FastARD as the regression method.
-We set the value of the q-norm truncation scheme to 0.9.
-This combination will give us a sparse aPCE.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">MetaMod</span><span class="o">.</span><span class="n">meta_model_type</span> <span class="o">=</span> <span class="s1">'aPCE'</span>
-<span class="gp">>>> </span><span class="n">MetaMod</span><span class="o">.</span><span class="n">pce_reg_method</span> <span class="o">=</span> <span class="s1">'FastARD'</span>
-<span class="gp">>>> </span><span class="n">MetaMod</span><span class="o">.</span><span class="n">pce_deg</span> <span class="o">=</span> <span class="mi">3</span>
-<span class="gp">>>> </span><span class="n">MetaMod</span><span class="o">.</span><span class="n">pce_q_norm</span> <span class="o">=</span> <span class="mf">0.85</span>
-</pre></div>
-</div>
-<p>Before we start the actual training we set <code class="docutils literal notranslate"><span class="pre">n_init_samples</span></code> to our wanted number of training samples.</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">n_init_samples</span> <span class="o">=</span> <span class="mi">10</span>
-</pre></div>
-</div>
-<p>Like this the experimental design will generate 10 samples according to our previously set sampling method.
-Alternatively we can set the samples that we generated in <a class="reference internal" href="input_description.html"><span class="doc">Priors, input space and experimental design</span></a> as the training samples.
-For this the sampling method should be set to ‘user’ and our samples given as <code class="docutils literal notranslate"><span class="pre">X</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">sampling_method</span> <span class="o">=</span> <span class="s1">'user'</span>
-<span class="gp">>>> </span><span class="n">ExpDesign</span><span class="o">.</span><span class="n">root_samples</span> <span class="o">=</span> <span class="n">samples</span>
-</pre></div>
-</div>
-<p>Now we create an engine object with the model, experimental design and surrogate model.
-With the function <code class="docutils literal notranslate"><span class="pre">start_engine</span></code> the engine performs its preparations for training.</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">MetaMod</span><span class="p">,</span> <span class="n">Model</span><span class="p">,</span> <span class="n">ExpDesign</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">Engine_</span><span class="o">.</span><span class="n">start_engine</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>Then we train the surrogate model.</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">train_normal</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>We can evaluate the trained surrogate model in two ways, via the engine, or directly.
-The evaluations return the mean approximation of the surrogate and its associated standard deviation.
-Evaluation via the surrogate model can make use of the sampling in the experimental design,</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mean</span><span class="p">,</span> <span class="n">stdev</span> <span class="o">=</span> <span class="n">Engine_</span><span class="o">.</span><span class="n">eval_metamodel</span><span class="p">(</span><span class="n">nsamples</span> <span class="o">=</span> <span class="mi">10</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>while for direct evaluation the exact set of samples has to be given.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mean</span><span class="p">,</span> <span class="n">stdev</span> <span class="o">=</span> <span class="n">Engine_</span><span class="o">.</span><span class="n">MetaModel</span><span class="o">.</span><span class="n">eval_metamodel</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
-</pre></div>
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- <section id="tutorial">
-<h1>TUTORIAL<a class="headerlink" href="#tutorial" title="Link to this heading">¶</a></h1>
-<p>Here we provide an introductory tutorial for the general workflow with <strong>bayesvalidrox</strong>.
-This tutorial follows along the example <a class="reference internal" href="analyticalfunction.html"><span class="doc">Example: Analytical function</span></a> to explain the basic functionality of working with <strong>bayesvalidrox</strong> surrogates, postprocessing and the Bayesian classes.
-The full code can be found in <code class="xref py py-mod docutils literal notranslate"><span class="pre">examples.analytical-function.test_analytical_function</span></code>.</p>
-<section id="import-necessary-libraries">
-<h2>Import necessary libraries<a class="headerlink" href="#import-necessary-libraries" title="Link to this heading">¶</a></h2>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">sys</span>
-<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">joblib</span>
-<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">IFrame</span>
-</pre></div>
-</div>
-</section>
-<section id="define-the-model-with-pylinkforwardmodel">
-<h2>Define the model with PyLinkForwardModel<a class="headerlink" href="#define-the-model-with-pylinkforwardmodel" title="Link to this heading">¶</a></h2>
-<p>We use the <a class="reference internal" href="_autosummary/bayesvalidrox.pylink.pylink.PyLinkForwardModel.html#bayesvalidrox.pylink.pylink.PyLinkForwardModel" title="bayesvalidrox.pylink.pylink.PyLinkForwardModel"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.pylink.pylink.PyLinkForwardModel</span></code></a> object to define the model.</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">PyLinkForwardModel</span>
-<span class="gp">>>> </span><span class="n">Model</span> <span class="o">=</span> <span class="n">PyLinkForwardModel</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>Since the analytical function is implmented as a python function in a separate file, we can set the type of the link to <code class="docutils literal notranslate"><span class="pre">Function</span></code> and only need to pass it’s name (without the <code class="docutils literal notranslate"><span class="pre">.py</span></code> extension) to the object variable <code class="docutils literal notranslate"><span class="pre">py_file</span></code>.
-Note that the function name in the python script should match that of the script.</p>
-<p>The name variable takes any user defined string.</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">link_type</span> <span class="o">=</span> <span class="s1">'Function'</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">py_file</span> <span class="o">=</span> <span class="s1">'analytical_function'</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'AnalyticFunc'</span>
-</pre></div>
-</div>
-<p>The model output names are defined as a list of strings.
-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>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>
-<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">mc_reference</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">mc_reference</span><span class="p">[</span><span class="s1">'Time [s]'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mf">1.</span><span class="p">)</span> <span class="o">/</span> <span class="mi">9</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">mc_reference</span><span class="p">[</span><span class="s1">'mean'</span><span class="p">]</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/mean_2.npy"</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">mc_reference</span><span class="p">[</span><span class="s1">'std'</span><span class="p">]</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/std_2.npy"</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Some of the training methods and evaluations make use of given data.
-For this we choose the model results for the parameter values X=[0,0].</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">observations</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">observations</span><span class="p">[</span><span class="s1">'Time [s]'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mf">1.</span><span class="p">)</span> <span class="o">/</span> <span class="mi">9</span>
-<span class="gp">>>> </span><span class="n">Model</span><span class="o">.</span><span class="n">observations</span><span class="p">[</span><span class="s1">'Z'</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">([</span><span class="mf">2.</span><span class="p">],</span> <span class="mi">10</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="define-probabilistic-input-model">
-<h2>Define probabilistic input model<a class="headerlink" href="#define-probabilistic-input-model" title="Link to this heading">¶</a></h2>
-<p>Import and instantiate the input object</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">Input</span>
-<span class="gp">>>> </span><span class="n">Inputs</span> <span class="o">=</span> <span class="n">Input</span><span class="p">()</span>
-</pre></div>
-</div>
-<p>Now, we define the distribution of the model inputs.
-<strong>bayesvalidrox</strong> accepts the definition in two ways: by defining the distribution directly or by passing available data.
-The latter is handy when little information is available on the parameters or they do not follow any typical distributions.
-Here we show both options, the associated example <a class="reference internal" href="analyticalfunction.html"><span class="doc">Example: Analytical function</span></a> read the input parameters from a numpy file in the <code class="docutils literal notranslate"><span class="pre">data/</span></code> directory.</p>
-<p><strong>Option I:</strong> Define distribution directy with their name, type and parameters</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># First parameter</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">'unif'</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>
-<span class="gp">>>></span>
-<span class="gp">>>> </span><span class="c1"># Second parameter</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">1</span><span class="p">]</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'$X_2$'</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">1</span><span class="p">]</span><span class="o">.</span><span class="n">dist_type</span> <span class="o">=</span> <span class="s1">'unif'</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">1</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><strong>Option II:</strong> Pass available data for input parameters</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">inputParams</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="s1">'data/InputParameters_2.npy'</span><span class="p">)</span>
-<span class="gp">>>></span>
-<span class="gp">>>> </span><span class="c1"># First parameter</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="n">inputParams</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
-<span class="gp">>>></span>
-<span class="gp">>>> </span><span class="c1"># Second parameter</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">1</span><span class="p">]</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'$X_2$'</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">1</span><span class="p">]</span><span class="o">.</span><span class="n">input_data</span> <span class="o">=</span> <span class="n">inputParams</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-<section id="define-surrogate-meta-model">
-<h2>Define surrogate (meta) model<a class="headerlink" href="#define-surrogate-meta-model" title="Link to this heading">¶</a></h2>
-<p>In <strong>bayesvalidrox</strong> a metamodel is an object of class <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.surrogate_models.MetaModel.html#bayesvalidrox.surrogate_models.surrogate_models.MetaModel" title="bayesvalidrox.surrogate_models.surrogate_models.MetaModel"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.surrogate_models.MetaModel</span></code></a>.
-This object also accepts the inputs we defined in the previous section as an argument.</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">MetaModel</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="n">Inputs</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>In this example, we use the arbitrary Polynomial Chaos Expansion (<code class="docutils literal notranslate"><span class="pre">'aPCE'</span></code>) as our <code class="docutils literal notranslate"><span class="pre">meta_model_type</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">meta_model_type</span> <span class="o">=</span> <span class="s1">'aPCE'</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>In <strong>bayesvalidrox</strong> two implementations of the Polynomial Chaos Expansion are available, <code class="docutils literal notranslate"><span class="pre">'PCE'</span></code> and <code class="docutils literal notranslate"><span class="pre">'aPCE'</span></code>.
-The tag <code class="docutils literal notranslate"><span class="pre">'PCE'</span></code> uses the generalized Polynomial Chaos Expansion by Xiu & Karniadakis (<a class="reference external" href="https://epubs.siam.org/doi/10.1137/S1064827501387826/">2002</a>).
-while using <code class="docutils literal notranslate"><span class="pre">'aPCE'</span></code> results in its arbitrary extension by Oladyshkin & Nowak (<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0951832012000853?via%3Dihub/">2012</a>).</p>
-</div>
-<p>In <strong>bayesvalidrox</strong> the calculation of the polynomial coefficient is by standard done as described in Blatman & Sudret (<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0021999110006856/">2011</a>).</p>
-<p>In addition the regression method to be used at the core of this calculation has to be set.
-Here we choose <code class="docutils literal notranslate"><span class="pre">'FastARD'</span></code>, as it also induces sparsity in the expansion.</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">pce_reg_method</span> <span class="o">=</span> <span class="s1">'FastARD'</span>
-</pre></div>
-</div>
-<p>The polynomial degrees to consider can be either set as a range, or a single value to indicate the highest allowed degree.</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">pce_deg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">9</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>The truncation shceme of the expansion is given as the q-norm, with a value between 0 and 1.
-A value of 1 results in standard truncation of the expansion, while smaller values make the expansion prefer less combined terms.</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">pce_q_norm</span> <span class="o">=</span> <span class="mf">0.75</span>
-</pre></div>
-</div>
-</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 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">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-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 <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code></a>.</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>
-<section id="sequential-training">
-<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 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">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 in the dedicated page of the <a class="reference internal" href="packagedescription.html"><span class="doc">USER GUIDE</span></a>.
-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">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">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">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, 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">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 with standard deviations that are as large as the values themselves.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">obsData</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">Model</span><span class="o">.</span><span class="n">observations</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</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="p">)</span>
-<span class="gp">>>> </span><span class="n">DiscrepancyOpts</span> <span class="o">=</span> <span class="n">Discrepancy</span><span class="p">(</span><span class="s1">''</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">DiscrepancyOpts</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="s1">'Gaussian'</span>
-<span class="gp">>>> </span><span class="n">DiscrepancyOpts</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">obsData</span><span class="o">**</span><span class="mi">2</span>
-<span class="gp">>>> </span><span class="n">MetaModelOpts</span><span class="o">.</span><span class="n">Discrepancy</span> <span class="o">=</span> <span class="n">DiscrepancyOpts</span>
-</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">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">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 again performed by the class <a class="reference internal" href="_autosummary/bayesvalidrox.surrogate_models.engine.Engine.html#bayesvalidrox.surrogate_models.engine.Engine" title="bayesvalidrox.surrogate_models.engine.Engine"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.surrogate_models.engine.Engine</span></code></a>, but using the function <code class="docutils literal notranslate"><span class="pre">train_sequential</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">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>
-<section id="post-processing">
-<h2>Post-processing<a class="headerlink" href="#post-processing" title="Link to this heading">¶</a></h2>
-<p>The available post-processing methods for <strong>bayesvalidrox</strong> are given in the class <a class="reference internal" href="_autosummary/bayesvalidrox.post_processing.post_processing.PostProcessing.html#bayesvalidrox.post_processing.post_processing.PostProcessing" title="bayesvalidrox.post_processing.post_processing.PostProcessing"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.post_processing.post_processing.PostProcessing</span></code></a>.
-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">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">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>
-</div>
-<p>The method <code class="docutils literal notranslate"><span class="pre">valid_metamodel</span></code> allows for visual comparison between the model and surrogate for samples from the prior parameter distribution.
-The samples can be drawn randomly, or set by the user.</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">valid_metamodel</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Another way to check the accuracy of the meta model is to use the method <code class="docutils literal notranslate"><span class="pre">accuracyCheckMetaModel</span></code> to show the Root Mean Square Error (RMSE) and the validation error.</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">accuracyCheckMetaModel</span><span class="p">(</span><span class="n">nSamples</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>Global sensitivity analysis can be performed on the surrogate via the Total Sobol Indices.
-These are especially cheap to compute for PCE as they can exploit the properties of the calculated coefficients.
-The method <code class="docutils literal notranslate"><span class="pre">sobolIndicesPCE</span></code> returns a dictionary that contains the total sobol indices and stores their plots.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">total_sobol</span> <span class="o">=</span> <span class="n">PostPCE</span><span class="o">.</span><span class="n">sobolIndicesPCE</span><span class="p">()</span>
-</pre></div>
-</div>
-</section>
-<section id="bayesian-inference">
-<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 <a class="reference internal" href="_autosummary/bayesvalidrox.bayes_inference.bayes_inference.BayesInference.html#bayesvalidrox.bayes_inference.bayes_inference.BayesInference" title="bayesvalidrox.bayes_inference.bayes_inference.BayesInference"><code class="xref any py py-class docutils literal notranslate"><span class="pre">bayesvalidrox.bayes_inference.bayes_inference.BayesInference</span></code></a>.</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">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.
-Some posterior predictions will be plotted by setting <code class="docutils literal notranslate"><span class="pre">plot_post_pred</span></code>.
-More options for Bayesian inference are listed at <a class="reference internal" href="bayes_description.html"><span class="doc">Bayesian inference</span></a>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Setting <code class="docutils literal notranslate"><span class="pre">emulator</span> <span class="pre">=</span> <span class="pre">False</span></code> means that the inference is based on actual model runs and not the surrogate.
-This can also be achieved by initializing the Engine without a surrogate object.</p>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">emulator</span> <span class="o">=</span> <span class="kc">True</span>
-<span class="gp">>>> </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">plot_post_pred</span> <span class="o">=</span> <span class="kc">True</span>
-</pre></div>
-</div>
-<p>We use MCMC to approximate the posterior distribution, rejection sampling is also available.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">emcee</span>
-<span class="gp">>>> </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">inference_method</span> <span class="o">=</span> <span class="s2">"MCMC"</span>
-<span class="gp">>>> </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">mcmc_params</span> <span class="o">=</span> <span class="p">{</span>
-<span class="gp">>>> </span> <span class="s1">'n_steps'</span><span class="p">:</span> <span class="mf">1e5</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s1">'n_walkers'</span><span class="p">:</span> <span class="mi">30</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s1">'moves'</span><span class="p">:</span> <span class="n">emcee</span><span class="o">.</span><span class="n">moves</span><span class="o">.</span><span class="n">KDEMove</span><span class="p">(),</span>
-<span class="gp">>>> </span> <span class="s1">'multiprocessing'</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s1">'verbose'</span><span class="p">:</span> <span class="kc">False</span>
-<span class="gp">>>> </span> <span class="p">}</span>
-</pre></div>
-</div>
-</section>
-<section id="define-the-data-uncertainty">
-<h2>Define the data uncertainty<a class="headerlink" href="#define-the-data-uncertainty" title="Link to this heading">¶</a></h2>
-<p>The estimated uncertainty of the data is used for the Bayesian Inference.
-<strong>bayesvalidrox</strong> provides three option to add this.
-For this tutorial we assume the uncertainty of the data to be distributed according to a Gaussian distribution around the known data with a standard deviation that is as large as the values in the data.</p>
-<p><strong>Option I:</strong> Set error directly for all data</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">measurement_error</span> <span class="o">=</span> <span class="n">obsData</span>
-</pre></div>
-</div>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>This option will become deprecated.</p>
-</div>
-<p><strong>Option II:</strong> Set discrepancy distributions all at once</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">DiscrepancyOpts</span> <span class="o">=</span> <span class="n">Discrepancy</span><span class="p">(</span><span class="s1">''</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">DiscrepancyOpts</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="s1">'Gaussian'</span>
-<span class="gp">>>> </span><span class="n">DiscrepancyOpts</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="n">obsData</span><span class="o">**</span><span class="mi">2</span>
-<span class="gp">>>> </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">Discrepancy</span> <span class="o">=</span> <span class="n">DiscrepancyOpts</span>
-</pre></div>
-</div>
-<p><strong>Option III:</strong> Set discrepancy distributions for each parameter at a time</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">bias_inputs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Z'</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mf">1.</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">9</span><span class="p">}</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">DiscOutputOpts</span> <span class="o">=</span> <span class="n">Input</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="n">DiscOutputOpts</span><span class="o">.</span><span class="n">add_marginals</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="n">DiscOutputOpts</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">Nnme</span> <span class="o">=</span> <span class="s1">'$\sigma^2_{\epsilon}$'</span>
-<span class="gp">>>> </span><span class="n">DiscOutputOpts</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">DiscOutputOpts</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="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">]</span>
-<span class="gp">>>> </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">Discrepancy</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'known'</span><span class="p">:</span> <span class="n">DiscrepancyOpts</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s1">'infer'</span><span class="p">:</span> <span class="n">Discrepancy</span><span class="p">(</span><span class="n">DiscOutputOpts</span><span class="p">)}</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">DiscOutputOpts</span> <span class="o">=</span> <span class="n">Input</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="n">DiscOutputOpts</span><span class="o">.</span><span class="n">add_marginals</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="n">DiscOutputOpts</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">'$\lambda$'</span>
-<span class="gp">>>> </span><span class="n">DiscOutputOpts</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">DiscOutputOpts</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="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-</pre></div>
-</div>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">DiscOutputOpts</span><span class="o">.</span><span class="n">add_marginals</span><span class="p">()</span>
-<span class="gp">>>> </span><span class="n">DiscOutputOpts</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">Name</span> <span class="o">=</span> <span class="s1">'$\sigma_f$'</span>
-<span class="gp">>>> </span><span class="n">DiscOutputOpts</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">1</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">DiscOutputOpts</span><span class="o">.</span><span class="n">Marginals</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1e-4</span><span class="p">]</span>
-<span class="gp">>>> </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">Discrepancy</span> <span class="o">=</span> <span class="n">Discrepancy</span><span class="p">(</span><span class="n">DiscOutputOpts</span><span class="p">)</span>
-<span class="gp">>>> </span><span class="n">BayesOpts</span><span class="o">.</span><span class="n">Discrepancy</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'known'</span><span class="p">:</span> <span class="n">DiscrepancyOpts</span><span class="p">,</span>
-<span class="gp">>>> </span> <span class="s1">'infer'</span><span class="p">:</span> <span class="n">Discrepancy</span><span class="p">(</span><span class="n">DiscOutputOpts</span><span class="p">)}</span>
-</pre></div>
-</div>
-<p>With the method <code class="docutils literal notranslate"><span class="pre">create_inference</span></code> we start the calibration/inference and save the results in a a``.pkl`` file.
-This also creates and saves multiple plots in the folder <code class="docutils literal notranslate"><span class="pre">Output_Bayes_*modelname</span></code>.
-The saved plots include a histogram of the BME of the surrogate and TOM, the posterior distribution with its most likely values and
-plots of posterior predictions if wanted.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">Bayes_PCE</span> <span class="o">=</span> <span class="n">BayesOpts</span><span class="o">.</span><span class="n">create_inference</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">'Bayes_</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">Bayes_PCE</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>
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- <ul>
-<li><a class="reference internal" href="#">TUTORIAL</a><ul>
-<li><a class="reference internal" href="#import-necessary-libraries">Import necessary libraries</a></li>
-<li><a class="reference internal" href="#define-the-model-with-pylinkforwardmodel">Define the model with PyLinkForwardModel</a></li>
-<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-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>
-<li><a class="reference internal" href="#define-the-data-uncertainty">Define the data uncertainty</a></li>
-</ul>
-</li>
-</ul>
-
- </div>
- </div>
- </div>
-
-
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-TUTORIAL
-********
-Here we provide an introductory tutorial for the general workflow with **bayesvalidrox**.
-This tutorial follows along the example :any:`analyticalfunction` to explain the basic functionality of working with **bayesvalidrox** surrogates, postprocessing and the Bayesian classes.
-The full code can be found in :py:mod:`examples.analytical-function.test_analytical_function`.
-
-Import necessary libraries
---------------------------
->>> import numpy as np
->>> import pandas as pd
->>> import sys
->>> import joblib
->>> from IPython.display import IFrame
-
-Define the model with PyLinkForwardModel
-----------------------------------------
-We use the :py:class:`bayesvalidrox.pylink.pylink.PyLinkForwardModel` object to define the model.
-
->>> from bayesvalidrox import PyLinkForwardModel
->>> Model = PyLinkForwardModel()
-
-Since the analytical function is implmented as a python function in a separate file, we can set the type of the link to ``Function`` and only need to pass it's name (without the ``.py`` extension) to the object variable ``py_file``.
-Note that the function name in the python script should match that of the script.
-
-The name variable takes any user defined string.
-
->>> Model.link_type = 'Function'
->>> Model.py_file = 'analytical_function'
->>> Model.name = 'AnalyticFunc'
-
-The model output names are defined as a list of strings.
-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.
-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``.
-
->>> Model.mc_reference = {}
->>> Model.mc_reference['Time [s]'] = np.arange(0, 10, 1.) / 9
->>> Model.mc_reference['mean'] = np.load(f"data/mean_2.npy")
->>> Model.mc_reference['std'] = np.load(f"data/std_2.npy")
-
-Some of the training methods and evaluations make use of given data.
-For this we choose the model results for the parameter values X=[0,0].
-
->>> Model.observations = {}
->>> Model.observations['Time [s]'] = np.arange(0, 10, 1.) / 9
->>> Model.observations['Z'] = np.repeat([2.], 10)
-
-
-Define probabilistic input model
---------------------------------
-Import and instantiate the input object
-
->>> from bayesvalidrox import Input
->>> Inputs = Input()
-
-Now, we define the distribution of the model inputs.
-**bayesvalidrox** accepts the definition in two ways: by defining the distribution directly or by passing available data.
-The latter is handy when little information is available on the parameters or they do not follow any typical distributions.
-Here we show both options, the associated example :any:`analyticalfunction` read the input parameters from a numpy file in the ``data/`` directory.
-
-**Option I:** Define distribution directy with their name, type and parameters
-
->>> # First parameter
->>> Inputs.add_marginals()
->>> Inputs.Marginals[0].name = '$X_1$'
->>> Inputs.Marginals[0].dist_type = 'unif'
->>> Inputs.Marginals[0].parameters = [-5, 5]
->>>
->>> # Second parameter
->>> Inputs.add_marginals()
->>> Inputs.Marginals[1].name = '$X_2$'
->>> Inputs.Marginals[1].dist_type = 'unif'
->>> Inputs.Marginals[1].parameters = [-5, 5]
-
-**Option II:** Pass available data for input parameters
-
->>> inputParams = np.load('data/InputParameters_2.npy')
->>>
->>> # First parameter
->>> Inputs.add_marginals()
->>> Inputs.Marginals[0].name = '$X_1$'
->>> Inputs.Marginals[0].input_data = inputParams[:, 0]
->>>
->>> # Second parameter
->>> Inputs.add_marginals()
->>> Inputs.Marginals[1].name = '$X_2$'
->>> Inputs.Marginals[1].input_data = inputParams[:, 1]
-
-
-Define surrogate (meta) model
------------------------------
-In **bayesvalidrox** a metamodel is an object of class :py:class:`bayesvalidrox.surrogate_models.surrogate_models.MetaModel`.
-This object also accepts the inputs we defined in the previous section as an argument.
-
-
->>> from bayesvalidrox import MetaModel
->>> MetaModelOpts = MetaModel(Inputs)
-
-In this example, we use the arbitrary Polynomial Chaos Expansion (``'aPCE'``) as our ``meta_model_type``.
-
->>> MetaModelOpts.meta_model_type = 'aPCE'
-
-.. _2002: https://epubs.siam.org/doi/10.1137/S1064827501387826/
-
-.. note::
- In **bayesvalidrox** two implementations of the Polynomial Chaos Expansion are available, ``'PCE'`` and ``'aPCE'``.
- The tag ``'PCE'`` uses the generalized Polynomial Chaos Expansion by Xiu & Karniadakis (2002_).
- while using ``'aPCE'`` results in its arbitrary extension by Oladyshkin & Nowak (2012_).
-
-.. _2012: https://www.sciencedirect.com/science/article/pii/S0951832012000853?via%3Dihub/
-
-
-In **bayesvalidrox** the calculation of the polynomial coefficient is by standard done as described in Blatman & Sudret (2011_).
-
-.. _2011: https://www.sciencedirect.com/science/article/pii/S0021999110006856/
-
-In addition the regression method to be used at the core of this calculation has to be set.
-Here we choose ``'FastARD'``, as it also induces sparsity in the expansion.
-
->>> MetaModelOpts.pce_reg_method = 'FastARD'
-
-The polynomial degrees to consider can be either set as a range, or a single value to indicate the highest allowed degree.
-
->>> MetaModelOpts.pce_deg = np.arange(9)
-
-The truncation shceme of the expansion is given as the q-norm, with a value between 0 and 1.
-A value of 1 results in standard truncation of the expansion, while smaller values make the expansion prefer less combined terms.
-
->>> MetaModelOpts.pce_q_norm = 0.75
-
-
-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.
-
->>> MetaModelOpts.add_ExpDesign()
->>> MetaModelOpts.ExpDesign.Method = 'normal'
->>> MetaModelOpts.ExpDesign.n_init_samples = 100
->>> MetaModelOpts.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.
-
->>> PCEModel = MetaModelOpts.create_metamodel(Model)
->>> with open(f'PCEModel_{Model.name}.pkl', 'wb') as output:
->>> joblib.dump(PCEModel, 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'``.
-
->>> MetaModelOpts.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'
-
-The options for sequential training are listed at ...........
-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.
-
->>> MetaModelOpts.ExpDesign.n_new_samples = 1
->>> MetaModelOpts.ExpDesign.n_max_samples = 150
->>> MetaModelOpts.ExpDesign.mod_LOO_threshold = 1e-16
->>>
->>> MetaModelOpts.ExpDesign.tradeoff_scheme = None
->>> MetaModelOpts.ExpDesign.explore_method = 'random'
->>>
->>> # Use when 'Voronoi' or 'random' or 'latin_hypercube' chosen
->>> MetaModelOpts.ExpDesign.n_canddidate = 1000
->>> MetaModelOpts.ExpDesign.n_cand_groups = 4
-
-
-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.
-
-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'
-
-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.
-
->>> obsData = pd.DataFrame(Model.observations, columns=Model.Output.names)
->>> DiscrepancyOpts = Discrepancy('')
->>> DiscrepancyOpts.type = 'Gaussian'
->>> DiscrepancyOpts.parameters = obsData**2
->>> MetaModelOpts.Discrepancy = DiscrepancyOpts
-
-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
-
-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]}
-
-The sequential training is performed by the class :py:class:`bayesvalidrox.surrogate_models.meta_model_engine.MetaModelEngine`.
-
->>> meta_model_engine = MetaModelEngine(MetaModelOpts)
->>> meta_model_engine.run()
->>> PCEModel = meta_model_engine.MetaModel
-
-
-
-Post-processing
----------------
-The available post-processing methods for **bayesvalidrox** are given in the class :py:class:`bayesvalidrox.post_processing.post_processing.PostProcessing`.
-All results created by this class are automatically stored in an output-folder ``Outputs_PostProcessing_*modelname``.
-
->>> from bayesvalidrox import PostProcessing
->>> PostPCE = PostProcessing(PCEModel)
-
-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.
-This method generates a plot and stores it in the output folder.
-
->>> PostPCE.plot_moments()
-
-The method ``valid_metamodel`` allows for visual comparison between the model and surrogate for samples from the prior parameter distribution.
-The samples can be drawn randomly, or set by the user.
-
->>> PostPCE.valid_metamodel(n_samples=3)
-
-Another way to check the accuracy of the meta model is to use the method ``accuracyCheckMetaModel`` to show the Root Mean Square Error (RMSE) and the validation error.
-
->>> PostPCE.accuracyCheckMetaModel(nSamples=200)
-
-Global sensitivity analysis can be performed on the surrogate via the Total Sobol Indices.
-These are especially cheap to compute for PCE as they can exploit the properties of the calculated coefficients.
-The method ``sobolIndicesPCE`` returns a dictionary that contains the total sobol indices and stores their plots.
-
->>> total_sobol = PostPCE.sobolIndicesPCE()
-
-
-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)
-
-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``.
-More options for Bayesian inference are listed at .....
-
-.. note::
- Setting ``emulator = False`` means that the inference is based on actual model runs and not the surrogate.
- However, there might still be some bugs here,
-
->>> BayesOpts.emulator = True
->>> BayesOpts.plot_post_pred = True
-
-We use MCMC to approximate the posterior distribution, rejection sampling is also available.
-
->>> import emcee
->>> BayesOpts.inference_method = "MCMC"
->>> BayesOpts.mcmc_params = {
->>> 'n_steps': 1e5,
->>> 'n_walkers': 30,
->>> 'moves': emcee.moves.KDEMove(),
->>> 'multiprocessing': False,
->>> 'verbose': False
->>> }
-
-Define the data uncertainty
----------------------------
-The estimated uncertainty of the data is used for the Bayesian Inference.
-**bayesvalidrox** provides three option to add this.
-For this tutorial we assume the uncertainty of the data to be distributed according to a Gaussian distribution around the known data with a standard deviation that is as large as the values in the data.
-
-**Option I:** Set error directly for all data
-
->>> BayesOpts.measurement_error = obsData
-
-.. warning::
- This option might be deprecated quite soon or not work at all?
-
-**Option II:** Set discrepancy distributions all at once
-
->>> DiscrepancyOpts = Discrepancy('')
->>> DiscrepancyOpts.type = 'Gaussian'
->>> DiscrepancyOpts.parameters = obsData**2
->>> BayesOpts.Discrepancy = DiscrepancyOpts
-
-**Option III:** Set discrepancy distributions for each parameter at a time
-
->>> BayesOpts.bias_inputs = {'Z':np.arange(0, 10, 1.).reshape(-1,1) / 9}
-
->>> DiscOutputOpts = Input()
->>> DiscOutputOpts.add_marginals()
->>> DiscOutputOpts.Marginals[0].Nnme = '$\sigma^2_{\epsilon}$'
->>> DiscOutputOpts.Marginals[0].dist_type = 'uniform'
->>> DiscOutputOpts.Marginals[0].parameters = [0, 10]
->>> BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
->>> 'infer': Discrepancy(DiscOutputOpts)}
-
->>> DiscOutputOpts = Input()
->>> DiscOutputOpts.add_marginals()
->>> DiscOutputOpts.Marginals[0].name = '$\lambda$'
->>> DiscOutputOpts.Marginals[0].dist_type = 'uniform'
->>> DiscOutputOpts.Marginals[0].parameters = [0, 1]
-
->>> DiscOutputOpts.add_marginals()
->>> DiscOutputOpts.Marginals[1].Name = '$\sigma_f$'
->>> DiscOutputOpts.Marginals[1].dist_type = 'uniform'
->>> DiscOutputOpts.Marginals[1].parameters = [0, 1e-4]
->>> BayesOpts.Discrepancy = Discrepancy(DiscOutputOpts)
->>> BayesOpts.Discrepancy = {'known': DiscrepancyOpts,
->>> 'infer': Discrepancy(DiscOutputOpts)}
-
-With the method ``create_inference`` we start the calibration/inference and save the results in a a``.pkl`` file.
-This also creates and saves multiple plots in the folder ``Output_Bayes_*modelname``.
-The saved plots include a histogram of the BME of the surrogate and TOM, the posterior distribution with its most likely values and
-plots of posterior predictions if wanted.
-
->>> Bayes_PCE = BayesOpts.create_inference()
->>> with open(f'Bayes_{Model.name}.pkl', 'wb') as output:
->>> joblib.dump(Bayes_PCE, output, 2)
--
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