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Feature/onlinedoc #8

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Oct 22, 2023
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Feature/onlinedoc #8

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e1937a7
New: add online document
tiskw Oct 22, 2023
d813119
Update: improve accessibility of the pages
tiskw Oct 22, 2023
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -136,7 +136,7 @@ Notes
them. (This bug information was reported by Mr. Katsuya Terahata @ Toyota Research Institute
Advanced Development. Thank you so much for the reporting!)
- Application of RFF to the Gaussian process model is not straightforward.
See [this document](./documents/rff_for_gaussian_process.pdf) for mathematical details.
See [this document](./articles/rff_for_gaussian_process.pdf) for mathematical details.


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159 changes: 159 additions & 0 deletions docs/api_reference.html
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<!doctype html>
<html lang="en" data-bs-theme="light">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta name="description" content="User's manual for RFFLearn, a library for random Fourier feature based ML models">
<meta name="keywords" content="random Fourier features,rff,fast machine learning,Tetsuya Ishikawa">
<meta name="robots" content="index,follow" />
<meta name="author" content="Tetsuya Ishikawa, tiskw111@gmail.com">
<link rel="icon" href="./images/logo-short.svg">

<title>RFFLearn: User's Manual - API Reference</title>

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<body>

<!-- Header -->
<header>
<div class="container mx-auto"><div class="row">
<p class="text-center"><img src="./images/logo_long.svg" style="width:640px;" alt="rfflearn_logo"></p>
</div></div>
</header>

<main>

<!-- -->
<section class="container py-3">
<h1 class="py-2 border-bottom" id="setting_up">API Reference</h1>
<p>The RFFLearn library consists of the several sub modules. <!--
-->Click the module name to see the details of each module.</p>

<div class="row align-items-center justify-content-center"><div class="col-8"><table class="table table-hover">
<thead><tr>
<th scope="col">Sub module name</th>
<th scope="col">Description</th>
</tr></thead>
<tbody><tr>
<td><a href="#rfflearn_cpu">rfflearn.cpu</a></td>
<td>Regressors and classifiers of random Fourier features on CPU.</td>
</tr><tr>
<td><a href="#rfflearn_gpu">rfflearn.gpu</a></td>
<td>Regressors and classifiers of random Fourier features on GPU.</td>
</tr><tr>
<td><a href="#rfflearn_explainer">rfflearn.explainer</a></td>
<td>Classes and functions to get model explanation for RFF-based models.</td>
</tr><tr>
<td><a href="#rfflearn_tuner">rfflearn.tuner</a></td>
<td>Classes and functions for automatic hyperparameter tuning.</td>
</tr></tbody>
</table></div></div>
</section>

<section class="container py-3">
<h2 class="py-2" id="rfflearn_cpu">rfflearn.cpu</h2>
<p>Sub module for machine learning algorithm of random Fourier feature runnable on CPU. <!--
-->This sub module contains machine learning algorithm (e.g. regressors, classifiers) which is designed to be run on CPU. <!--
-->Interfaces of classes and functions in this module have quite close interfaces with scikit-learn library. <!--
-->Most (but not all) of the classes uses scikit-learn as a back end.</p>

<div class="row align-items-center justify-content-center"><div class="col-8"><table class="table table-hover">
<thead><tr>
<th scope="col">Class name</th>
<th scope="col">Description</th>
</tr></thead>
<tbody><tr>
<td><a href="./api_reference_RFFCCA.html">rfflearn.cpu.RFFCCA</a></td>
<td>Canonical correlation analysis with random Fourier features.</td>
</tr><tr>
<td><a href="./api_reference_RFFGPC.html">rfflearn.cpu.RFFGPC</a></td>
<td>Gaussian process classification with random Fourier features.</td>
</tr><tr>
<td><a href="./api_reference_RFFGPR.html">rfflearn.cpu.RFFGPR</a></td>
<td>Gaussian process regression with random Fourier features.</td>
</tr><tr>
<td><a href="./api_reference_RFFPCA.html">rfflearn.cpu.RFFPCA</a></td>
<td>Principal component analysis with random Fourier features.</td>
</tr><tr>
<td><a href="./api_reference_RFFRegression.html">rfflearn.cpu.RFFRegression</a></td>
<td>Regression with random Fourier features.</td>
</tr><tr>
<td><a href="./api_reference_RFFSVC.html">rfflearn.cpu.RFFSVC</a></td>
<td>Support vector classification with random Fourier features.</td>
</tr><tr>
<td><a href="./api_reference_RFFSVR.html">rfflearn.cpu.RFFSVR</a></td>
<td>Support vector regression with random Fourier features.</td>
</tr><tr>
<td><a href="./api_reference_RFFBatchSVC.html">rfflearn.cpu.RFFBatchSVC</a></td>
<td>Batch learning version of <code>rfflearn.cpu.RFFSVC</code>.</td>
</tr><tr>
<td><a href="#">rfflearn.cpu.ORF*</a></td>
<td>ORF (orthogonal random features) version of estimators. For example, <code>rfflearn.cpu.ORFSVC</code> <!--
-->is a support vector classifier with ORF. The arguments of constructor and member functions are the same as RFF version, <!--
-->so please see the document of <code>rfflearn.cpu.RFF*</code> for the details of the usage of each class.</td>
</tr><tr>
<td><a href="#">rfflearn.cpu.QRF*</a></td>
<td>QRF (quasi random features) version of estimators. For example, <code>rfflearn.cpu.QRFSVC</code> <!--
-->is a support vector classifier with QRF. The arguments of constructor and member functions are the same as RFF version, <!--
-->so please see the document of <code>rfflearn.cpu.RFF*</code> for the details of the usage of each class.</td>
</tr></tbody>
</table></div></div>
</section>

<section class="container py-3">
<h2 class="py-2" id="rfflearn_gpu">rfflearn.gpu</h2>
<p>Sub module for regressors and classifiers of random Fourier feature runnable on GPU. <!--
-->This module is designed to have the same interface as <code>rfflearn.cpu</code>. <!--
-->See the <a href="#rfflearn_cpu">API reference of rfflearn.cpu</a> for the details of this module.</p>
</section>

<section class="container py-3">
<h2 class="py-2" id="rfflearn_explainer">rfflearn.explainer</h2>
<p>TBD</p>
</section>

<section class="container py-3">
<h2 class="py-2" id="rfflearn_tuner">rfflearn.tuner</h2>
<p>TBD</p>
</section>

</main>

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