build based on 7cd0e54

previews/PR308/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.10.3","generation_timestamp":"2024-05-09T17:50:11","documenter_version":"1.2.1"}}
+{"documenter":{"julia_version":"1.10.3","generation_timestamp":"2024-05-09T19:12:02","documenter_version":"1.2.1"}}

previews/PR308/API/Utilities/index.html

@@ -4,8 +4,8 @@
     obs::EnsembleKalmanProcesses.Observations.Observation;
     rng_seed
 ) -> Any
-</code></pre><p>Return a random sample from the observations, for use in the MCMC.</p><ul><li><code>rng</code> - optional RNG object used to pick random sample; defaults to <code>Random.GLOBAL_RNG</code>.</li><li><code>obs</code> - Observation struct with the observations (extract will pick one         of the sample observations to train).</li><li><code>rng_seed</code> - optional kwarg; if provided, used to re-seed <code>rng</code> before sampling.</li></ul></div><a class="docs-sourcelink" target="_blank" href="https://github.com/CliMA/CalibrateEmulateSample.jl/blob/1f33efa3d5ed06475c81fec05a1e30698ee72745/src/Utilities.jl#L54">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="CalibrateEmulateSample.Utilities.get_training_points-Union{Tuple{P}, Tuple{IT}, Tuple{FT}, Tuple{EnsembleKalmanProcesses.EnsembleKalmanProcess{FT, IT, P}, Union{AbstractVector{IT}, IT}}} where {FT, IT, P}" href="#CalibrateEmulateSample.Utilities.get_training_points-Union{Tuple{P}, Tuple{IT}, Tuple{FT}, Tuple{EnsembleKalmanProcesses.EnsembleKalmanProcess{FT, IT, P}, Union{AbstractVector{IT}, IT}}} where {FT, IT, P}"><code>CalibrateEmulateSample.Utilities.get_training_points</code></a> — <span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">get_training_points(
+</code></pre><p>Return a random sample from the observations, for use in the MCMC.</p><ul><li><code>rng</code> - optional RNG object used to pick random sample; defaults to <code>Random.GLOBAL_RNG</code>.</li><li><code>obs</code> - Observation struct with the observations (extract will pick one         of the sample observations to train).</li><li><code>rng_seed</code> - optional kwarg; if provided, used to re-seed <code>rng</code> before sampling.</li></ul></div><a class="docs-sourcelink" target="_blank" href="https://github.com/CliMA/CalibrateEmulateSample.jl/blob/7cd0e546ceda7027a6b9be163141dc2762828b9c/src/Utilities.jl#L54">source</a></section></article><article class="docstring"><header><a class="docstring-article-toggle-button fa-solid fa-chevron-down" href="javascript:;" title="Collapse docstring"></a><a class="docstring-binding" id="CalibrateEmulateSample.Utilities.get_training_points-Union{Tuple{P}, Tuple{IT}, Tuple{FT}, Tuple{EnsembleKalmanProcesses.EnsembleKalmanProcess{FT, IT, P}, Union{AbstractVector{IT}, IT}}} where {FT, IT, P}" href="#CalibrateEmulateSample.Utilities.get_training_points-Union{Tuple{P}, Tuple{IT}, Tuple{FT}, Tuple{EnsembleKalmanProcesses.EnsembleKalmanProcess{FT, IT, P}, Union{AbstractVector{IT}, IT}}} where {FT, IT, P}"><code>CalibrateEmulateSample.Utilities.get_training_points</code></a> — <span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">get_training_points(
     ekp::EnsembleKalmanProcesses.EnsembleKalmanProcess{FT, IT, P},
     train_iterations::Union{AbstractVector{IT}, IT} where IT
 ) -&gt; EnsembleKalmanProcesses.DataContainers.PairedDataContainer
-</code></pre><p>Extract the training points needed to train the Gaussian process regression.</p><ul><li><code>ekp</code> - EnsembleKalmanProcess holding the parameters and the data that were produced during the Ensemble Kalman (EK) process.</li><li><code>train_iterations</code> - Number (or indices) EK layers/iterations to train on.</li></ul></div><a class="docs-sourcelink" target="_blank" href="https://github.com/CliMA/CalibrateEmulateSample.jl/blob/1f33efa3d5ed06475c81fec05a1e30698ee72745/src/Utilities.jl#L17">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../MarkovChainMonteCarlo/">« MarkovChainMonteCarlo</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="auto">Automatic (OS)</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.2.1 on <span class="colophon-date" title="Thursday 9 May 2024 17:50">Thursday 9 May 2024</span>. Using Julia version 1.10.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+</code></pre><p>Extract the training points needed to train the Gaussian process regression.</p><ul><li><code>ekp</code> - EnsembleKalmanProcess holding the parameters and the data that were produced during the Ensemble Kalman (EK) process.</li><li><code>train_iterations</code> - Number (or indices) EK layers/iterations to train on.</li></ul></div><a class="docs-sourcelink" target="_blank" href="https://github.com/CliMA/CalibrateEmulateSample.jl/blob/7cd0e546ceda7027a6b9be163141dc2762828b9c/src/Utilities.jl#L17">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../MarkovChainMonteCarlo/">« MarkovChainMonteCarlo</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="auto">Automatic (OS)</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.2.1 on <span class="colophon-date" title="Thursday 9 May 2024 19:12">Thursday 9 May 2024</span>. Using Julia version 1.10.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

previews/PR308/GaussianProcessEmulator/index.html

@@ -25,4 +25,4 @@
     noise_learn = true )</code></pre><p>When <code>noise_learn</code> is true, an additional white noise kernel is added to the kernel. This white noise is present across all parameter values, including the training data.  The scale parameters of the white noise kernel are learned in <code>optimize_hyperparameters!(emulator)</code>. </p><p>You may not need to learn the noise if you already have a good estimate of the noise from your training data, and if the Gaussian process kernel is well specified.  When <code>noise_learn</code> is false, a small additional regularization is added for stability. The default value is <code>1e-3</code> but this can be chosen through the optional argument <code>alg_reg_noise</code>:</p><pre><code class="language-julia hljs">gauss_proc = GaussianProcess(
     gppackage;
     noise_learn = false,
-    alg_reg_noise = 1e-3 )</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../emulate/">« Emulator</a><a class="docs-footer-nextpage" href="../random_feature_emulator/">Random Features »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="auto">Automatic (OS)</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.2.1 on <span class="colophon-date" title="Thursday 9 May 2024 17:50">Thursday 9 May 2024</span>. Using Julia version 1.10.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+    alg_reg_noise = 1e-3 )</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../emulate/">« Emulator</a><a class="docs-footer-nextpage" href="../random_feature_emulator/">Random Features »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="auto">Automatic (OS)</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.2.1 on <span class="colophon-date" title="Thursday 9 May 2024 19:12">Thursday 9 May 2024</span>. Using Julia version 1.10.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>