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84 lines
4.4 KiB
HTML
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<title>GPy by SheffieldML</title>
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<h1 class="header">GPy</h1>
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<p class="header">a Gaussian processes framework in python</p>
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<ul>
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<li><a class="buttons tutorial" href="http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb">Tutorials </a></li>
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<li class="download"><a class="buttons" href="https://github.com/SheffieldML/GPy/zipball/master">Download ZIP</a></li>
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<li><a class="buttons github" href="https://github.com/SheffieldML/GPy">View On GitHub</a></li>
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<p class="header">This project is maintained by <a class="header name" href="https://github.com/SheffieldML">SheffieldML</a></p>
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<section>
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<h1>GPy</h1>
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<p>GPy is a Gaussian Process (GP) framework written in python, from the Sheffield machine learning group. </p>
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<p>Gaussian processes underpin range of modern machine learning algorithms. In GPy, we've used python to implement a range of machine learning algorithms based on GPs. </p>
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<p>GPy is available under the BSD 3-clause license. We'd love to incorporate your changes, so fork us on <a href="https://github.com/SheffieldML/GPy">github</a>!</p>
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<h2>New release!</h2>
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<p> After a long series of changes, we're pleased to announce the GPy 0.6 is ready for use. Users can download the code from github or install with pip.</p>. There are lots of changes, we've tried to highlight important ones in the <a href=./changelog06.md>changelog</a>.
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<h2>Installation</h2>
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<p>Installation instructions along side the source code can be found on the <a href="https://github.com/SheffieldML/GPy">GPy project github page</a>. The instructions vary slightly with the target OS but essentially revolve around installing from the python package index, <a href="https://pypi.python.org/pypi/GPy/">PyPI</a></p>
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<h2>Tutorials</h2>
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A series of tutorials on working with GPy can be found here:</p>
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<ul>
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<li><a href="http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb">Online tutorials</a></li>
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</ul>
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These are in the ipython notebook format: the code, plots and text can be read online. If you wish to run the examples locally they can be <a href="https://github.com/SheffieldML/notebook/tree/master/GPy">downloaded</a> and run with the <a href="http://ipython.org/notebook.html">ipython notebook</a>.
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<h2>User mailing list</h2>
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<p>
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If you have any questions about the project, or require any help working with the code please make sure you are signed up the the
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</p>
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<ul>
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<li><a href="https://lists.shef.ac.uk/sympa/subscribe/gpy-users">GPy user mailing list</a> </li>
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</ul>
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<h2>Documentation</h2>
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<p>You can read the online documentation for GPy here:</p>
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<ul>
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<li><a href="https://gpy.readthedocs.org/en/latest/">Online documentation</a></li>
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</ul>
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These documents are automatically compiled from the docstrings defined in the code: we recommend new users take a look at the <a href="http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb">tutorials</a> first.
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<h2>Tests</h2>
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<p>As the code is under active development we try to ensure that the code is well tested using Continuous Integreation. We'd love to here of any bugs you have found so please do submit issues through our <a href="https://github.com/SheffieldML/GPy/issues">GPy issues page</a>. Continuous integration status is currently:<br><a href="https://travis-ci.org/SheffieldML/GPy"><img src="https://travis-ci.org/SheffieldML/GPy.png" alt="CI status"></a></br></p>
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