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| <!DOCTYPE html> | ||
| <html> | ||
| <head> | ||
| <meta http-equiv="content-type" content="text/html; charset=utf-8" /> | ||
| <meta name="keywords" content="Shark Machine Learning" /> | ||
| <meta name="robots" content="all" /> | ||
| <title>Shark Machine Learning Library</title> | ||
| <link rel="shortcut icon" href="shark16.ico"/> | ||
| <link rel="stylesheet" href="frontpage.css"> | ||
| <script> | ||
| function smoothScroll(name) | ||
| { | ||
| var element = document.getElementsByName(name)[0]; | ||
| var initialpos = window.pageYOffset || document.documentElement.scrollTop; | ||
| var targetpos = element.getBoundingClientRect().top + window.scrollY - 50; | ||
| var t = 0; | ||
| for (var i=1; i<=12; i++) setTimeout(function() | ||
| { | ||
| t++; | ||
| var coeff = Math.sin(t * Math.PI / 24); | ||
| window.scrollTo(0, (1.0 - coeff) * initialpos + coeff * targetpos); | ||
| }, 20*i); | ||
| } | ||
| function onSearch(event) | ||
| { | ||
| event = event || window.event; | ||
| if (event.keyCode == 13) | ||
| { | ||
| // submit search to google | ||
| var term = document.getElementById('search').value | ||
| theform = document.createElement("form"); | ||
| theform.method="GET"; | ||
| theform.action="http://www.google.com/search"; | ||
| theinput = document.createElement("input"); | ||
| theinput.type = "text"; | ||
| theinput.name = "q"; | ||
| theinput.value = term + " site:http://shark-ml.org/"; | ||
| theform.appendChild(theinput); | ||
| document.body.appendChild(theform); | ||
| theform.submit(); | ||
| } | ||
| } | ||
| function toggleLegalNotice() | ||
| { | ||
| if (document.getElementById("legal").style.display == "block") | ||
| { | ||
| document.getElementById("legal").style.display = "none"; | ||
| } | ||
| else | ||
| { | ||
| document.getElementById("legal").style.display = "block"; | ||
| setTimeout(smoothScroll("legal"), 1); | ||
| } | ||
| } | ||
| function init() | ||
| { | ||
| document.getElementById('search').addEventListener('keypress', onSearch); | ||
| } | ||
| </script> | ||
| </head> | ||
|
|
||
| <body onload="init()"> | ||
|
|
||
| <!-- header --> | ||
| <div id="header"><div id="header-content"> | ||
| <a href="#section-home" onclick="smoothScroll('section-home'); return false;"><span id="shark">Shark</span></a> | ||
| | ||
| <input id="search" class="menu" type="text" placeholder="search" /> | ||
| | ||
| <a href="#section-news" onclick="smoothScroll('section-news'); return false;"><span class="menu">News</span></a> | ||
| | ||
| <a href="#section-downloads" onclick="smoothScroll('section-downloads'); return false;"><span class="menu">Downloads</span></a> | ||
| | ||
| <a href="#section-getting-started" onclick="smoothScroll('section-getting-started'); return false;"><span class="menu">Quick Start</span></a> | ||
| | ||
| <a href="sphinx_pages/build/html/rest_sources/tutorials/tutorials.html"><span class="menu">Documentation</span></a> | ||
| | ||
| <a href="#section-credit" onclick="smoothScroll('section-credit'); return false;"><span class="menu">Credits</span></a> | ||
| </div></div> | ||
|
|
||
| <div id="spacer"></div> | ||
|
|
||
| <!-- eye-catcher --> | ||
| <div class="section-white"><div class="section-content"> | ||
| <a name="section-home"></a> | ||
| <h1>Shark – Machine Learning</h1> | ||
| <div id="eye-catcher"></div> | ||
| <b>Shark is a fast, modular, feature-rich open-source C++ machine learning library.</b> | ||
| </p> | ||
| <p> | ||
| It provides methods for linear and nonlinear optimization, kernel-based learning | ||
| algorithms, neural networks, and various other machine learning techniques. | ||
| It serves as a powerful toolbox for real world applications as well as for research. | ||
| Shark works on Windows, MacOS X, and Linux. It comes with extensive documentation. | ||
| Shark is licensed under the GNU Lesser General Public License. | ||
| </p> | ||
| </div></div> | ||
|
|
||
| <!-- news --> | ||
| <div class="section-colored"><div class="section-content"> | ||
| <a name="section-news"></a> | ||
| <h2>News</h2> | ||
| <h3>Shark 4.0 Released</h3> | ||
| <p><span class="date">Juni 9, 2018:</span> We are happy to announce the official release of Shark 4.0.0</p> | ||
| <h3>Shark 3.1 Released</h3> | ||
| <p><span class="date">March 1, 2016:</span> We are happy to announce the official release of Shark 3.1.0.</p> | ||
| <h3>Shark 3.0 Released</h3> | ||
| <p><span class="date">October 27, 2015:</span> We are happy to announce the official release of Shark 3.0.0.</p> | ||
|
|
||
| <h3>Shark moves to GitHub</h3> | ||
| <p><span class="date">October 9, 2015:</span> Shark moved to GitHub. Please update your repositories, see the downloads page for more details.</p> | ||
|
|
||
| <h3>Shark goes LGPL</h3> | ||
| <p>As of <span class="date">January 2014</span>, Shark is distributed under the permissive | ||
| <a href="http://www.gnu.org/copyleft/lesser.html">GNU Lesser General Public License</a>.</p> | ||
| <p><img src="lgplv3-147x51.png"/></p> | ||
|
|
||
| </div></div> | ||
|
|
||
| <!-- downloads --> | ||
| <div class="section-white"><div class="section-content"> | ||
| <a name="section-downloads"></a> | ||
| <h2>Downloads</h2> | ||
| <p> | ||
| The current stable version is Shark 4.0, released 9-6-2018. | ||
| </p> | ||
| <div class="note"> | ||
| Shark 4 is largely incompatible with earlier versions of Shark. We have made interfaces cleaner and support now computation with floating point precision. | ||
| There is also experimental support for GPU compations which is expanded in future minor releases. | ||
| </div> | ||
| <h3>Source Packages</h3> | ||
| <p> | ||
| We have two source packages available:<br/> | ||
| <a class="download" href="https://github.com/Shark-ML/Shark/archive/v4.0.0.zip"><img class="downloadicon" src="download.png"> Shark-4.0.0.zip</a> | ||
| <a class="download" href="https://github.com/Shark-ML/Shark/archive/v4.0.0.tar.gz"><img class="downloadicon" src="download.png"> Shark-4.0.0.tar.gz</a> | ||
| </p> | ||
| <h3>Shark Repository</h3> | ||
| <p> | ||
| Get the current Shark repository snapshot: | ||
| <div id="git-clone"> | ||
| git clone https://github.com/Shark-ML/Shark/ | ||
| </div> | ||
| </p> | ||
| <p> | ||
| <table class="status"> | ||
| <tr> | ||
| <th>OS</th> | ||
| <th>coverage</th> | ||
| <th>status</th> | ||
| <th>service</th> | ||
| </tr> | ||
| <tr> | ||
| <td>Linux and Mac OS</td><td>library and unit tests</td> | ||
| <td><img src="https://travis-ci.org/Shark-ML/Shark.svg?branch=master" alt="build results Linux and Mac OS"></td> | ||
| <td><a href="https://travis-ci.org/Shark-ML">travis-ci</a></td> | ||
| </tr> | ||
| <tr> | ||
| <td>Windows</td><td>library only, no unit tests</td> | ||
| <td><img src="https://ci.appveyor.com/api/projects/status/github/shark-ml/shark?branch=master&svg=true" alt="build results Windows"></td> | ||
| <td><a href="http://www.appveyor.com/">appveyor</a></td> | ||
| </tr> | ||
| </table> | ||
| </p> | ||
| </div></div> | ||
|
|
||
| <!-- quick start --> | ||
| <div class="section-colored"><div class="section-content"> | ||
| <a name="section-getting-started"></a> | ||
| <h2>Quick Start</h2> | ||
| <p> | ||
| Let's demonstrate basic use of Shark with very few lines of code. | ||
| This is C++, so we start with includes. | ||
| </p> | ||
| <div class="code"><span class="preproc">#include <shark/Data/Download.h> | ||
| #include <shark/Algorithms/Trainers/LDA.h> | ||
| #include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h></span> | ||
| <span class="keyword">using namespace</span> shark;</div> | ||
| <p> | ||
| Let's load some data for learning. | ||
| </p> | ||
| <div class="code">ClassificationDataset traindata; | ||
| downloadCsvData(traindata, | ||
| <span class="string">"www.shark-ml.org/data/quickstart-train.csv"</span>, | ||
| LAST_COLUMN, | ||
| <span class="string">' '</span>); | ||
| </div> | ||
| <p> | ||
| The next step is to create a predictive model. Here we use a simple linear classifier. | ||
| </p> | ||
| <div class="code">LinearClassifier<> classifier;</div> | ||
| <p> | ||
| The core step of learning is to train the model on data using a trainer. | ||
| In Shark, the trainer is not glued to the model. Instead it is a separate object. | ||
| Here, good old Linear Discriminant Analysis (LDA) suits our needs. | ||
| </p> | ||
| <div class="code">LDA lda; | ||
| lda.train(classifier, traindata);</div> | ||
| <p> | ||
| Congrats! We have a readily trained classifier. | ||
| Let's try it out by applying it to new data. | ||
| </p> | ||
| <div class="code">ClassificationDataset testdata; | ||
| downloadCsvData(testdata, | ||
| <span class="string">"www.shark-ml.org/data/quickstart-test.csv"</span>, | ||
| LAST_COLUMN, | ||
| <span class="string">' '</span>); | ||
| ZeroOneLoss<> loss; | ||
| <span class="keyword">double</span> error = loss(testdata.labels(), classifier(testdata.inputs()));</div> | ||
| <p> | ||
| Further reading: | ||
| <ul> | ||
| <li><a href="sphinx_pages/build/html/rest_sources/installation.html">Installation Guide</a></li> | ||
| <li><a href="sphinx_pages/build/html/rest_sources/tutorials/tutorials.html">Tutorials</a></li> | ||
| </ul> | ||
| </p> | ||
| </div></div> | ||
|
|
||
| <!-- Why Shark? --> | ||
| <div class="section-white"><div class="section-content"> | ||
| <a name="section-why-shark"></a> | ||
| <h2>Why Shark?</h2> | ||
|
|
||
| <h3>Speed and flexibility</h3> | ||
| <p> | ||
| Shark provides an excellent trade-off between flexibility and | ||
| ease-of-use on the one hand, and computational efficiency on the other. | ||
| </p> | ||
|
|
||
| <h3>One for all</h3> | ||
| <p> | ||
| Shark offers numerous algorithms from various machine learning and computational intelligence domains in a way that they can be easily combined and extended. | ||
| </p> | ||
|
|
||
| <h3>Unique features</h3> | ||
| <p> | ||
| Shark comes with a lot of powerful algorithms that are to our best knowledge not implemented in any other library, for example in the domains of model selection and training of binary and multi-class SVMs, or evolutionary single- and multi-objective optimization. | ||
| </p> | ||
| </div></div> | ||
|
|
||
| <!-- credits and copyright --> | ||
| <div class="section-colored"><div class="section-content"> | ||
| <a name="section-credit"></a> | ||
| <h2>Credits and Copyright</h2> | ||
| <h3>Citing Shark</h3> | ||
| <p> | ||
| We kindly ask you to cite Shark in academic work as: | ||
| </p> | ||
| <p style='font-family: monospace'> | ||
| Christian Igel, Verena Heidrich-Meisner, and Tobias Glasmachers. | ||
| Shark. | ||
| Journal of Machine Learning Research 9, pp. 993-996, 2008. | ||
| <p> | ||
| The article's bibtex entry reads: | ||
| </p> | ||
| <p> | ||
| <pre style='font-family: monospace'> | ||
| @Article{shark08, | ||
| author = {Christian Igel and Verena Heidrich-Meisner and Tobias Glasmachers}, | ||
| title = {Shark}, | ||
| journal = {Journal of Machine Learning Research}, | ||
| year = {2008}, | ||
| volume = {9}, | ||
| pages = {993--996} | ||
| } | ||
| </pre> | ||
| </p> | ||
|
|
||
| <h3>License</h3> | ||
| <p> | ||
| The Shark library is made available under the | ||
| <a href="http://www.gnu.org/copyleft/lesser.html">GNU Lesser General Public License</a>. | ||
| </p> | ||
| <p> | ||
| <img src="lgplv3-147x51.png"> | ||
| </p> | ||
|
|
||
|
|
||
| <h3>Hosting institutions</h3> | ||
|
|
||
| <p> | ||
| The Shark machine learning library is jointly maintained by researchers from | ||
| <ul> | ||
| <li><a target="_blank" href="http://www.ini.rub.de/">Institut für Neuroinformatik (INI), Ruhr-Universität Bochum, Germany</a></li> | ||
| <li><a target="_blank" href="http://www.diku.dk/">Department of Computer Science (DIKU), University of Copenhagen, Denmark</a></li> | ||
| </ul> | ||
| </p> | ||
|
|
||
| </div></div> | ||
|
|
||
| <!-- footer --> | ||
| <div id="footer"><div id="footer-content"> | ||
| <span class="legal-notice" onclick="toggleLegalNotice();">legal notice</span> | ||
| <div id="legal" name="legal"> | ||
| <p> | ||
| Copyright (C) 2015-2018 Shark development team. | ||
| </p> | ||
| <p> | ||
| This website is provided in the hope that it will be useful, | ||
| but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. | ||
| </p> | ||
| <p> | ||
| The website contains links to external content. The Shark developers | ||
| have no control over the contents of externally linked websites. | ||
| Therefore they explicitly disclaim responsibility for the contents of | ||
| externally linked pages. | ||
| </p> | ||
| </div> | ||
| </div></div> | ||
|
|
||
| </body> | ||
| Wait for it ... upadte ahead ... come back mid June! | ||
| </html> |