build based on a144ae3

dev/api/esn/index.html

@@ -14,4 +14,4 @@
 (esn::ESN)(prediction::AbstractPrediction,
     output_layer::AbstractOutputLayer;
     initial_conditions=output_layer.last_value,
-    last_state=esn.states[:, end])</code></pre><p>Constructor for the Echo State Network model. It requires the reservoir size as the input and the data for the training. It returns a struct ready to be trained with the states already harvested. </p><p>After the training this struct can be used for the prediction following the second function call. This will take as input a prediction type and the output layer from the training. The <code>initial_conditions</code> and <code>last_state</code> parameters can be left as they are, unless there is a specific reason to change them. All the components are detailed in the API documentation. More examples are given in the general documentation.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/8c9745ee77d4d39f584a79af81c483b9b2ff852c/src/esn/echostatenetwork.jl#L49-L72">source</a></section></article><p>In addition to all the components that can be explored in the documentation a couple need a separate introduction. The <code>variation</code> arguments can be</p><article class="docstring"><header><a class="docstring-binding" id="ReservoirComputing.Default" href="#ReservoirComputing.Default"><code>ReservoirComputing.Default</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">Default()</code></pre><p>Sets the type of the ESN as the standard model. No parameters are needed.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/8c9745ee77d4d39f584a79af81c483b9b2ff852c/src/esn/echostatenetwork.jl#L16-L20">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="ReservoirComputing.Hybrid" href="#ReservoirComputing.Hybrid"><code>ReservoirComputing.Hybrid</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">Hybrid(prior_model, u0, tspan, datasize)</code></pre><p>Given the model parameters returns an <code>Hybrid</code> variation of the ESN. This entails a different training and prediction. Construction based on [1].</p><p>[1] Jaideep Pathak et al. &quot;Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model&quot; (2018)</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/8c9745ee77d4d39f584a79af81c483b9b2ff852c/src/esn/echostatenetwork.jl#L31-L39">source</a></section></article><p>These arguments detail more deep variation of the underlying model and they need a separate call. For the moment the most complex is the <code>Hybrid</code> call, but this can and will change in the future. All ESN models can be trained using the following call:</p><article class="docstring"><header><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>Training of the built ESN over the <code>target_data</code>. The default training method is RidgeRegression. The output is an <code>OutputLayer</code> object to be fed at the esn call for the prediction.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/8c9745ee77d4d39f584a79af81c483b9b2ff852c/src/esn/echostatenetwork.jl#L189-L195">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../predict/">« Prediction Types</a><a class="docs-footer-nextpage" href="../esn_layers/">ESN Layers »</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 0.27.24 on <span class="colophon-date" title="Thursday 15 June 2023 19:36">Thursday 15 June 2023</span>. Using Julia version 1.9.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+    last_state=esn.states[:, end])</code></pre><p>Constructor for the Echo State Network model. It requires the reservoir size as the input and the data for the training. It returns a struct ready to be trained with the states already harvested. </p><p>After the training this struct can be used for the prediction following the second function call. This will take as input a prediction type and the output layer from the training. The <code>initial_conditions</code> and <code>last_state</code> parameters can be left as they are, unless there is a specific reason to change them. All the components are detailed in the API documentation. More examples are given in the general documentation.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/a144ae314be866af787909265558477ea5e0e344/src/esn/echostatenetwork.jl#L49-L72">source</a></section></article><p>In addition to all the components that can be explored in the documentation a couple need a separate introduction. The <code>variation</code> arguments can be</p><article class="docstring"><header><a class="docstring-binding" id="ReservoirComputing.Default" href="#ReservoirComputing.Default"><code>ReservoirComputing.Default</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">Default()</code></pre><p>Sets the type of the ESN as the standard model. No parameters are needed.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/a144ae314be866af787909265558477ea5e0e344/src/esn/echostatenetwork.jl#L16-L20">source</a></section></article><article class="docstring"><header><a class="docstring-binding" id="ReservoirComputing.Hybrid" href="#ReservoirComputing.Hybrid"><code>ReservoirComputing.Hybrid</code></a> — <span class="docstring-category">Type</span></header><section><div><pre><code class="language-julia hljs">Hybrid(prior_model, u0, tspan, datasize)</code></pre><p>Given the model parameters returns an <code>Hybrid</code> variation of the ESN. This entails a different training and prediction. Construction based on [1].</p><p>[1] Jaideep Pathak et al. &quot;Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model&quot; (2018)</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/a144ae314be866af787909265558477ea5e0e344/src/esn/echostatenetwork.jl#L31-L39">source</a></section></article><p>These arguments detail more deep variation of the underlying model and they need a separate call. For the moment the most complex is the <code>Hybrid</code> call, but this can and will change in the future. All ESN models can be trained using the following call:</p><article class="docstring"><header><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>Training of the built ESN over the <code>target_data</code>. The default training method is RidgeRegression. The output is an <code>OutputLayer</code> object to be fed at the esn call for the prediction.</p></div><a class="docs-sourcelink" target="_blank" href="https://github.com/SciML/ReservoirComputing.jl/blob/a144ae314be866af787909265558477ea5e0e344/src/esn/echostatenetwork.jl#L189-L195">source</a></section></article></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../predict/">« Prediction Types</a><a class="docs-footer-nextpage" href="../esn_layers/">ESN Layers »</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 11 September 2023 10:00">Monday 11 September 2023</span>. Using Julia version 1.9.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>