Metadata-Version: 1.0
Name: pyGPs
Version: 1.3.1
Summary: Gaussian Processes for Regression and Classification
Home-page: https://github.com/marionmari/pyGPs
Author: ['Marion Neumann', 'Shan Huang', 'Daniel Marthaler', 'Kristian Kersting']
Author-email: ['marion.neumann@uni-bonn.de.com', 'shan.huang@iais.fraunhofer.de', 'dan.marthaler@gmail.com', 'kristian.kersting@cs.tu-dortmund.de']
License: COPYRIGHT.txt
Description: ================================================================================
            Marion Neumann [marion dot neumann at uni-bonn dot de]
            Daniel Marthaler [dan dot marthaler at gmail dot com]
            Shan Huang [schan dot huang at gmail dot com]
            Kristian Kersting [kristian dot kersting at cs dot tu-dortmund dot de]
        
            This file is part of pyGPs.
            The software package is released under the BSD 2-Clause (FreeBSD) License.
        
            Copyright (c) by
            Marion Neumann, Daniel Marthaler, Shan Huang & Kristian Kersting, 18/02/2014
        ================================================================================
        
        pyGPs is a library containing code for Gaussian Process (GP) Regression and Classification.
        
        Here is the online documentation: [ONLINE documentation](http://www-ai.cs.uni-dortmund.de/weblab/static/api_docs/pyGPs/).
        
        pyGPs is an object-oriented implementation of GPs. Its functionality follows roughly the gpml matlab implementation by Carl Edward Rasmussen and Hannes Nickisch (Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2013-01-21).
        
        Standard GP regression and (binary) classification as well as FITC (sparse GPs) inference is implemented.
        For a list of implemented covariance, mean, likelihood, and inference functions see list_of_functions.txt. 
        The current implementation is optimized and tested, however, the work on this library is still in progress. We appreciate any feedback.
        
        A comprehensive introduction to functionalities and demonstrations can be found in the *doc* folder; just open /doc/build/html/index.html in your browser to get to the html documentation of the whole package. 
        
        Further, pyGPs includes implementations of
        - minimize.py implemented in python by Roland Memisevic 2008, following minimize.m which is copyright (C) 1999 - 2006, Carl Edward Rasmussen
        - scg.py (Copyright (c) Ian T Nabney (1996-2001))
        - brentmin.py (Copyright (c) by Hannes Nickisch 2010-01-10.)
        
        
        Installing pyGPs
        ------------------
        Download the archive and extract it to any local directory.
        
        You can either add the local directory to your PYTHONPATH:
        
            export PYTHONPATH=$PYTHONPATH:/path/to/local/directory/../parent_folder_of_pyGPs
        
        or install the package using setup.py:
        
            python setup.py install
        
        Requirements
        --------------
        - python 2.6 or 2.7
        - scipy (v0.13.0 or later), numpy, and matplotlib: open-source packages for scientific computing using the Python programming language. 
        
        
        Acknowledgements
        --------------
        The following persons helped to improve this software: Roman Garnett, Maciej Kurek, Hannes Nickisch, Zhao Xu, and Alejandro Molina.
        
        This work is partly supported by the Fraunhofer ATTRACT fellowship STREAM.
        
        
Platform: UNKNOWN
