build based on f68a73b

dev/concrete_features/index.html

@@ -18,9 +18,9 @@
 plot!([plot, ]x::AbstractVector, f::FiniteGP; ribbon_scale=1, kwargs...)</code></pre><p>Plot the predictive mean for the projection <code>f</code> of a Gaussian process and a ribbon of <code>ribbon_scale</code> standard deviations around it versus <code>x</code>.</p><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>Make sure to load <a href="https://github.com/JuliaPlots/Plots.jl">Plots.jl</a> before you use this function.</p></div></div><p><strong>Examples</strong></p><p>Plot the mean and a ribbon of 3 standard deviations:</p><pre><code class="language-julia hljs">using Plots
 
 gp = GP(SqExponentialKernel())
-plot(gp(rand(5)); ribbon_scale=3)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/6becdc266bf58ae93a68db9977367acf51671e78/src/util/plotting.jl#L18-L39">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="RecipesBase.plot-Tuple{AbstractGPs.FiniteGP}" href="#RecipesBase.plot-Tuple{AbstractGPs.FiniteGP}"><code>RecipesBase.plot</code></a> — <span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">plot(f::FiniteGP; kwargs...)
-plot!([plot, ]f::FiniteGP; kwargs...)</code></pre><p>Plot the predictive mean and a ribbon around it for the projection <code>f</code> of a Gaussian process versus <code>f.x</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/6becdc266bf58ae93a68db9977367acf51671e78/src/util/plotting.jl#L48-L54">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="RecipesBase.plot-Tuple{AbstractVector, AbstractGPs.AbstractGP}" href="#RecipesBase.plot-Tuple{AbstractVector, AbstractGPs.AbstractGP}"><code>RecipesBase.plot</code></a> — <span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">plot(x::AbstractVector, gp::AbstractGP; kwargs...)
-plot!([plot, ]x::AbstractVector, gp::AbstractGP; kwargs...)</code></pre><p>Plot the predictive mean and a ribbon around it for the projection <code>gp(x)</code> of the Gaussian process <code>gp</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/6becdc266bf58ae93a68db9977367acf51671e78/src/util/plotting.jl#L61-L67">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="AbstractGPs.sampleplot" href="#AbstractGPs.sampleplot"><code>AbstractGPs.sampleplot</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">sampleplot([x::AbstractVector=f.x, ]f::FiniteGP; samples=1, kwargs...)</code></pre><p>Plot samples from the projection <code>f</code> of a Gaussian process versus <code>x</code>.</p><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>Make sure to load <a href="https://github.com/JuliaPlots/Plots.jl">Plots.jl</a> before you use this function.</p></div></div><p>When plotting multiple samples, these are treated as a <em>single</em> series (i.e., only a single entry will be added to the legend when providing a <code>label</code>).</p><p><strong>Example</strong></p><pre><code class="language-julia hljs">using Plots
+plot(gp(rand(5)); ribbon_scale=3)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/f68a73b4a1db7067406afc7d35b9a828ee332d3f/src/util/plotting.jl#L18-L39">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="RecipesBase.plot-Tuple{AbstractGPs.FiniteGP}" href="#RecipesBase.plot-Tuple{AbstractGPs.FiniteGP}"><code>RecipesBase.plot</code></a> — <span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">plot(f::FiniteGP; kwargs...)
+plot!([plot, ]f::FiniteGP; kwargs...)</code></pre><p>Plot the predictive mean and a ribbon around it for the projection <code>f</code> of a Gaussian process versus <code>f.x</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/f68a73b4a1db7067406afc7d35b9a828ee332d3f/src/util/plotting.jl#L48-L54">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="RecipesBase.plot-Tuple{AbstractVector, AbstractGPs.AbstractGP}" href="#RecipesBase.plot-Tuple{AbstractVector, AbstractGPs.AbstractGP}"><code>RecipesBase.plot</code></a> — <span class="docstring-category">Method</span></header><section><div><pre><code class="language-julia hljs">plot(x::AbstractVector, gp::AbstractGP; kwargs...)
+plot!([plot, ]x::AbstractVector, gp::AbstractGP; kwargs...)</code></pre><p>Plot the predictive mean and a ribbon around it for the projection <code>gp(x)</code> of the Gaussian process <code>gp</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/f68a73b4a1db7067406afc7d35b9a828ee332d3f/src/util/plotting.jl#L61-L67">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="AbstractGPs.sampleplot" href="#AbstractGPs.sampleplot"><code>AbstractGPs.sampleplot</code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia hljs">sampleplot([x::AbstractVector=f.x, ]f::FiniteGP; samples=1, kwargs...)</code></pre><p>Plot samples from the projection <code>f</code> of a Gaussian process versus <code>x</code>.</p><div class="admonition is-info"><header class="admonition-header">Note</header><div class="admonition-body"><p>Make sure to load <a href="https://github.com/JuliaPlots/Plots.jl">Plots.jl</a> before you use this function.</p></div></div><p>When plotting multiple samples, these are treated as a <em>single</em> series (i.e., only a single entry will be added to the legend when providing a <code>label</code>).</p><p><strong>Example</strong></p><pre><code class="language-julia hljs">using Plots
 
 gp = GP(SqExponentialKernel())
-sampleplot(gp(rand(5)); samples=10, linealpha=1.0)</code></pre><p>The given example plots 10 samples from the projection of the GP <code>gp</code>. The <code>linealpha</code> is modified from default of 0.35 to 1.</p><hr/><pre><code class="nohighlight hljs">sampleplot(x::AbstractVector, gp::AbstractGP; samples=1, kwargs...)</code></pre><p>Plot samples from the finite projection <code>gp(x, 1e-9)</code> versus <code>x</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/6becdc266bf58ae93a68db9977367acf51671e78/src/util/plotting.jl#L76-L103">source</a></section></article><h3 id="Makie.jl"><a class="docs-heading-anchor" href="#Makie.jl">Makie.jl</a><a id="Makie.jl-1"></a><a class="docs-heading-anchor-permalink" href="#Makie.jl" title="Permalink"></a></h3><p>You can use the Julia package <a href="https://github.com/JuliaGaussianProcesses/AbstractGPsMakie.jl">AbstractGPsMakie.jl</a> to plot Gaussian processes with <a href="https://github.com/JuliaPlots/Makie.jl">Makie.jl</a>.</p><p><video src="https://juliagaussianprocesses.github.io/AbstractGPsMakie.jl/stable/posterior_animation.mp4" controls="true" title="posterior animation"><a href="https://juliagaussianprocesses.github.io/AbstractGPsMakie.jl/stable/posterior_animation.mp4">posterior animation</a></video></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../api/">« The Main APIs</a><a class="docs-footer-nextpage" href="../examples/0-intro-1d/">Intro to AbstractGPs: one-dimensional regression »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Monday 25 September 2023 19:22">Monday 25 September 2023</span>. Using Julia version 1.9.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+sampleplot(gp(rand(5)); samples=10, linealpha=1.0)</code></pre><p>The given example plots 10 samples from the projection of the GP <code>gp</code>. The <code>linealpha</code> is modified from default of 0.35 to 1.</p><hr/><pre><code class="nohighlight hljs">sampleplot(x::AbstractVector, gp::AbstractGP; samples=1, kwargs...)</code></pre><p>Plot samples from the finite projection <code>gp(x, 1e-9)</code> versus <code>x</code>.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/JuliaGaussianProcesses/AbstractGPs.jl/blob/f68a73b4a1db7067406afc7d35b9a828ee332d3f/src/util/plotting.jl#L76-L103">source</a></section></article><h3 id="Makie.jl"><a class="docs-heading-anchor" href="#Makie.jl">Makie.jl</a><a id="Makie.jl-1"></a><a class="docs-heading-anchor-permalink" href="#Makie.jl" title="Permalink"></a></h3><p>You can use the Julia package <a href="https://github.com/JuliaGaussianProcesses/AbstractGPsMakie.jl">AbstractGPsMakie.jl</a> to plot Gaussian processes with <a href="https://github.com/JuliaPlots/Makie.jl">Makie.jl</a>.</p><p><video src="https://juliagaussianprocesses.github.io/AbstractGPsMakie.jl/stable/posterior_animation.mp4" controls="true" title="posterior animation"><a href="https://juliagaussianprocesses.github.io/AbstractGPsMakie.jl/stable/posterior_animation.mp4">posterior animation</a></video></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../api/">« The Main APIs</a><a class="docs-footer-nextpage" href="../examples/0-intro-1d/">Intro to AbstractGPs: one-dimensional regression »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 0.27.25 on <span class="colophon-date" title="Sunday 5 November 2023 11:36">Sunday 5 November 2023</span>. Using Julia version 1.9.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>