build based on 966d8b5

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.11.2","generation_timestamp":"2025-01-08T19:15:04","documenter_version":"1.8.0"}}
+{"documenter":{"julia_version":"1.11.2","generation_timestamp":"2025-01-08T19:15:20","documenter_version":"1.8.0"}}

dev/api/esn/index.html

@@ -13,7 +13,7 @@
  0.987182   0.898593  0.295241  0.233098      0.789699    0.453692    0.759205
 
 julia> esn = ESN(train_data, 10, 300; washout=10)
-ESN(10 =&gt; 300)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/9e4711f698af3f824a4b6445d98ba593ea9a1640/src/esn/esn.jl#L15-L54">source</a></section></article><h2 id="Training"><a class="docs-heading-anchor" href="#Training">Training</a><a id="Training-1"></a><a class="docs-heading-anchor-permalink" href="#Training" title="Permalink"></a></h2><p>To train an ESN model, you can use the <code>train</code> function. It takes the ESN model, training data, and other optional parameters as input and returns a trained model. Here&#39;s the documentation for the train function:</p><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="ReservoirComputing.train" href="#ReservoirComputing.train"><code>ReservoirComputing.train</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">train(esn::AbstractEchoStateNetwork, target_data, training_method = StandardRidge(0.0))</code></pre><p>Trains an Echo State Network (ESN) using the provided target data and a specified training method.</p><p><strong>Parameters</strong></p><ul><li><code>esn::AbstractEchoStateNetwork</code>: The ESN instance to be trained.</li><li><code>target_data</code>: Supervised training data for the ESN.</li><li><code>training_method</code>: The method for training the ESN (default: <code>StandardRidge(0.0)</code>).</li></ul><p><strong>Example</strong></p><pre><code class="language-julia-repl hljs">julia&gt; train_data = rand(Float32, 10, 100)  # 10 features, 100 time steps
+ESN(10 =&gt; 300)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/966d8b5b39c04b78e47dd4f30a738931de81a152/src/esn/esn.jl#L15-L54">source</a></section></article><h2 id="Training"><a class="docs-heading-anchor" href="#Training">Training</a><a id="Training-1"></a><a class="docs-heading-anchor-permalink" href="#Training" title="Permalink"></a></h2><p>To train an ESN model, you can use the <code>train</code> function. It takes the ESN model, training data, and other optional parameters as input and returns a trained model. Here&#39;s the documentation for the train function:</p><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="ReservoirComputing.train" href="#ReservoirComputing.train"><code>ReservoirComputing.train</code></a> — <span class="docstring-category">Function</span><span class="is-flex-grow-1 docstring-article-toggle-button" title="Collapse docstring"></span></header><section><div><pre><code class="language-julia hljs">train(esn::AbstractEchoStateNetwork, target_data, training_method = StandardRidge(0.0))</code></pre><p>Trains an Echo State Network (ESN) using the provided target data and a specified training method.</p><p><strong>Parameters</strong></p><ul><li><code>esn::AbstractEchoStateNetwork</code>: The ESN instance to be trained.</li><li><code>target_data</code>: Supervised training data for the ESN.</li><li><code>training_method</code>: The method for training the ESN (default: <code>StandardRidge(0.0)</code>).</li></ul><p><strong>Example</strong></p><pre><code class="language-julia-repl hljs">julia&gt; train_data = rand(Float32, 10, 100)  # 10 features, 100 time steps
 10×100 Matrix{Float32}:
  0.11437   0.425367  0.585867   0.34078   …  0.0531493  0.761425  0.883164
  0.301373  0.497806  0.279603   0.802417     0.49873    0.270156  0.333333
@@ -30,4 +30,4 @@
 ESN(10 =&gt; 300)
 
 julia&gt; output_layer = train(esn, rand(Float32, 3, 90))
-OutputLayer successfully trained with output size: 3</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/9e4711f698af3f824a4b6445d98ba593ea9a1640/src/esn/esn.jl#L102-L135">source</a></section></article><p>With these components and variations, you can configure and train ESN models for various time series and sequential data prediction tasks.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../predict/">« Prediction Types</a><a class="docs-footer-nextpage" href="../inits/">ESN Initializers »</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="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.8.0 on <span class="colophon-date" title="Wednesday 8 January 2025 19:15">Wednesday 8 January 2025</span>. Using Julia version 1.11.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+OutputLayer successfully trained with output size: 3</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/966d8b5b39c04b78e47dd4f30a738931de81a152/src/esn/esn.jl#L102-L135">source</a></section></article><p>With these components and variations, you can configure and train ESN models for various time series and sequential data prediction tasks.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../predict/">« Prediction Types</a><a class="docs-footer-nextpage" href="../inits/">ESN Initializers »</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="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.8.0 on <span class="colophon-date" title="Wednesday 8 January 2025 19:15">Wednesday 8 January 2025</span>. Using Julia version 1.11.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>