build based on 9b67e32

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.10.2","generation_timestamp":"2024-04-08T02:19:43","documenter_version":"1.3.0"}}
+{"documenter":{"julia_version":"1.10.3","generation_timestamp":"2024-05-03T10:37:44","documenter_version":"1.4.1"}}

dev/api/esn/index.html

@@ -3,7 +3,7 @@
 
 train_data = rand(10, 100)  # 10 features, 100 time steps
 
-esn = ESN(train_data, reservoir = RandSparseReservoir(200), washout = 10)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/222a4362ede1d9b6c23d903d914d4ef0cc7884bb/src/esn/esn.jl#L15-L46">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></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>Returns</strong></p><ul><li>The trained ESN model. Its type and structure depend on <code>training_method</code> and the ESN&#39;s implementation.</li></ul><p><strong>Returns</strong></p><p>The trained ESN model. The exact type and structure of the return value depends on the <code>training_method</code> and the specific ESN implementation.</p><pre><code class="language-julia hljs">using ReservoirComputing
+esn = ESN(train_data, reservoir = RandSparseReservoir(200), washout = 10)</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/9b67e328547877540a86d12d440ac21b1fb743aa/src/esn/esn.jl#L15-L46">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></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>Returns</strong></p><ul><li>The trained ESN model. Its type and structure depend on <code>training_method</code> and the ESN&#39;s implementation.</li></ul><p><strong>Returns</strong></p><p>The trained ESN model. The exact type and structure of the return value depends on the <code>training_method</code> and the specific ESN implementation.</p><pre><code class="language-julia hljs">using ReservoirComputing
 
 # Initialize an ESN instance and target data
 esn = ESN(train_data, reservoir = RandSparseReservoir(200), washout = 10)
@@ -13,4 +13,4 @@
 trained_esn = train(esn, target_data)
 
 # Train the ESN using a custom training method
-trained_esn = train(esn, target_data, training_method = StandardRidge(1.0))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/222a4362ede1d9b6c23d903d914d4ef0cc7884bb/src/esn/esn.jl#L90-L123">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="../esn_drivers/">ESN Drivers »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.3.0 on <span class="colophon-date" title="Monday 8 April 2024 02:19">Monday 8 April 2024</span>. Using Julia version 1.10.2.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+trained_esn = train(esn, target_data, training_method = StandardRidge(1.0))</code></pre></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/9b67e328547877540a86d12d440ac21b1fb743aa/src/esn/esn.jl#L90-L123">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="../esn_drivers/">ESN Drivers »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.1 on <span class="colophon-date" title="Friday 3 May 2024 10:37">Friday 3 May 2024</span>. Using Julia version 1.10.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>