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<!DOCTYPE html>
<html lang="en-us">
<head>
<meta charset="UTF-8">
<title>BayesOpt by rmcantin</title>
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<section class="page-header">
<h1 class="project-name">BayesOpt</h1>
<h2 class="project-tagline">BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.</h2>
<a href="https://github.com/rmcantin/bayesopt" class="btn">View on GitHub</a>
<a href="https://github.com/rmcantin/bayesopt/zipball/master" class="btn">Download .zip</a>
<a href="https://github.com/rmcantin/bayesopt/tarball/master" class="btn">Download .tar.gz</a>
</section>

<section class="main-content">
<h1>
<a id="bayesopt-a-bayesian-optimization-library" class="anchor" href="#bayesopt-a-bayesian-optimization-library" aria-hidden="true"><span class="octicon octicon-link"></span></a>BayesOpt: A Bayesian optimization library</h1>

<p>BayesOpt is an efficient implementation of the Bayesian optimization
methodology for nonlinear-optimization, experimental design,
stochastic bandits and hyperparameter tunning. In the literature it is
also called Sequential Kriging Optimization (SKO), Sequential
Model-Based Optimization (SMBO) or Efficient Global Optimization
(EGO).</p>

<p>Bayesian optimization uses a distribution over functions to build a
model of the unknown function for we are looking the extrema, and then
apply some active learning strategy to select the query points that
provides most potential interest or improvement. Thus, it is a
sampling efficient method for nonlinear optimization, design of
experiments or bandits-like problems.</p>

<h2>
<a id="getting-and-installing-bayesopt" class="anchor" href="#getting-and-installing-bayesopt" aria-hidden="true"><span class="octicon octicon-link"></span></a>Getting and installing BayesOpt</h2>

<p>The library can be download from any of this sources:</p>

<ul>
<li>Download: <a href="https://bitbucket.org/rmcantin/bayesopt">https://bitbucket.org/rmcantin/bayesopt</a>
</li>
<li>Mirror: <a href="https://github.com/rmcantin/bayesopt">https://github.com/rmcantin/bayesopt</a>
</li>
<li>Mirror: <a href="http://mloss.org/software/view/453/">http://mloss.org/software/view/453/</a>
</li>
</ul>

<p>You can also get the <em>cutting-edge</em> version from the repositories:</p>

<pre><code>&gt;&gt; hg clone https://bitbucket.org/rmcantin/bayesopt
</code></pre>

<p>or the git mirror:</p>

<pre><code>&gt;&gt; git clone https://github.com/rmcantin/bayesopt
</code></pre>

<p>The install guide and documentation for Windows, Linux and MacOS:</p>

<ul>
<li><a href="http://rmcantin.bitbucket.org/html/">Online docs</a></li>
</ul>

<h2>
<a id="using-bayesopt-for-academic-or-commercial-purposes" class="anchor" href="#using-bayesopt-for-academic-or-commercial-purposes" aria-hidden="true"><span class="octicon octicon-link"></span></a>Using BayesOpt for academic or commercial purposes</h2>

<p>BayesOpt is licensed under the GPL and it is free to use. However,
please consider these recomentations when using BayesOpt:</p>

<ul>
<li>If you use BayesOpt in a work that leads to a <strong>scientific
publication</strong>, we would appreciate it if you would kindly cite BayesOpt
in your manuscript. Cite BayesOpt as:</li>
</ul>

<blockquote>
<p>Ruben Martinez-Cantin, <strong>BayesOpt: A Bayesian Optimization
Library for Nonlinear Optimization, Experimental Design and
Bandits</strong>. Journal of Machine Learning Research, 15(Nov):3735--3739, 2014.</p>
</blockquote>

<p>The paper can be found at <a href="http://jmlr.org/papers/v15/martinezcantin14a.html">http://jmlr.org/papers/v15/martinezcantin14a.html</a>
In addition, if you <strong>use a specific algorithm</strong> (REMBO, GP-Hedge,
etc.), please also cite the corresponding work. The reference for each
specific algorithm can be found in the documentation.</p>

<ul>
<li>Commercial applications may also adquire a <strong>commercial license</strong> or ask for consulting support. Please
contact <a href="mailto:rmcantin@unizar.es">rmcantin@unizar.es</a> for details.</li>
</ul>

<h2>
<a id="getting-involved" class="anchor" href="#getting-involved" aria-hidden="true"><span class="octicon octicon-link"></span></a>Getting involved</h2>

<p>The best place to ask questions and discuss about BayesOpt is the
<a href="https://groups.google.com/forum/#!forum/bayesopt-discussion">bayesopt-discussion mailing
list</a>. Alternatively,
you may directly contact Ruben Martinez-Cantin <a href="mailto:rmcantin@unizar.es">rmcantin@unizar.es</a>.</p>

<p>Please file bug reports or suggestions at: </p>

<ul>
<li>
<a href="https://bitbucket.org/rmcantin/bayesopt/issues">https://bitbucket.org/rmcantin/bayesopt/issues</a> or</li>
<li><a href="https://github.com/rmcantin/bayesopt/issues">https://github.com/rmcantin/bayesopt/issues</a></li>
</ul>

<hr>

<p>Copyright (C) 2011-2015 Ruben Martinez-Cantin <a href="mailto:rmcantin@unizar.es">rmcantin@unizar.es</a></p>

<p>BayesOpt is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the
Free Software Foundation, either version 3 of the License, or (at your
option) any later version.</p>

<p>BayesOpt is distributed 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. See the GNU
General Public License for more details.</p>

<p>You should have received a copy of the GNU General Public License
along with BayesOpt. If not, see <a href="http://www.gnu.org/licenses/">http://www.gnu.org/licenses/</a>.</p>

<hr>

<footer class="site-footer">
<span class="site-footer-owner"><a href="https://github.com/rmcantin/bayesopt">BayesOpt</a> is maintained by <a href="https://github.com/rmcantin">rmcantin</a>.</span>

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</section>

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{"name":"BayesOpt","tagline":"BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.","body":"BayesOpt: A Bayesian optimization library\r\n=========================================\r\n\r\nBayesOpt is an efficient implementation of the Bayesian optimization\r\nmethodology for nonlinear-optimization, experimental design,\r\nstochastic bandits and hyperparameter tunning. In the literature it is\r\nalso called Sequential Kriging Optimization (SKO), Sequential\r\nModel-Based Optimization (SMBO) or Efficient Global Optimization\r\n(EGO).\r\n\r\nBayesian optimization uses a distribution over functions to build a\r\nmodel of the unknown function for we are looking the extrema, and then\r\napply some active learning strategy to select the query points that\r\nprovides most potential interest or improvement. Thus, it is a\r\nsampling efficient method for nonlinear optimization, design of\r\nexperiments or bandits-like problems.\r\n\r\n\r\nGetting and installing BayesOpt\r\n-------------------------------\r\n\r\nThe library can be download from any of this sources:\r\n\r\n- Download: <https://bitbucket.org/rmcantin/bayesopt>\r\n- Mirror: <https://github.com/rmcantin/bayesopt>\r\n- Mirror: <http://mloss.org/software/view/453/>\r\n\r\nYou can also get the *cutting-edge* version from the repositories:\r\n\r\n >> hg clone https://bitbucket.org/rmcantin/bayesopt\r\n\r\nor the git mirror:\r\n\r\n >> git clone https://github.com/rmcantin/bayesopt\r\n\r\nThe install guide and documentation for Windows, Linux and MacOS:\r\n- [Online docs](http://rmcantin.bitbucket.org/html/)\r\n\r\n\r\nUsing BayesOpt for academic or commercial purposes\r\n--------------------------------------------------\r\n\r\nBayesOpt is licensed under the GPL and it is free to use. However,\r\nplease consider these recomentations when using BayesOpt:\r\n\r\n- If you use BayesOpt in a work that leads to a **scientific\r\npublication**, we would appreciate it if you would kindly cite BayesOpt\r\nin your manuscript. Cite BayesOpt as:\r\n\r\n> Ruben Martinez-Cantin, **BayesOpt: A Bayesian Optimization\r\n> Library for Nonlinear Optimization, Experimental Design and\r\n> Bandits**. Journal of Machine Learning Research, 15(Nov):3735--3739, 2014.\r\n\r\nThe paper can be found at http://jmlr.org/papers/v15/martinezcantin14a.html\r\nIn addition, if you **use a specific algorithm** (REMBO, GP-Hedge,\r\netc.), please also cite the corresponding work. The reference for each\r\nspecific algorithm can be found in the documentation.\r\n\r\n- Commercial applications may also adquire a **commercial license** or ask for consulting support. Please\r\ncontact <rmcantin@unizar.es> for details.\r\n\r\nGetting involved\r\n----------------\r\n\r\nThe best place to ask questions and discuss about BayesOpt is the\r\n[bayesopt-discussion mailing\r\nlist](https://groups.google.com/forum/#!forum/bayesopt-discussion). Alternatively,\r\nyou may directly contact Ruben Martinez-Cantin <rmcantin@unizar.es>.\r\n\r\nPlease file bug reports or suggestions at: \r\n\r\n- https://bitbucket.org/rmcantin/bayesopt/issues or\r\n- https://github.com/rmcantin/bayesopt/issues\r\n\r\n----------------------------------------------------------------------\r\n\r\nCopyright (C) 2011-2015 Ruben Martinez-Cantin <rmcantin@unizar.es>\r\n\r\nBayesOpt is free software: you can redistribute it and/or modify it\r\nunder the terms of the GNU General Public License as published by the\r\nFree Software Foundation, either version 3 of the License, or (at your\r\noption) any later version.\r\n\r\nBayesOpt is distributed in the hope that it will be useful, but\r\nWITHOUT ANY WARRANTY; without even the implied warranty of\r\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\r\nGeneral Public License for more details.\r\n\r\nYou should have received a copy of the GNU General Public License\r\nalong with BayesOpt. If not, see <http://www.gnu.org/licenses/>.\r\n\r\n----------------------------------------------------------------------","google":"UA-41494186-1","note":"Don't delete this file! It's used internally to help with page regeneration."}
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