Metadata-Version: 2.0
Name: GPy
Version: 0.8.8
Summary: The Gaussian Process Toolbox
Home-page: http://sheffieldml.github.com/GPy/
Author: See contributors.
Requires-Dist: numpy (>=1.7)
Requires-Dist: scipy (>=0.16)
Requires-Dist: six
Provides-Extra: docs
Requires-Dist: matplotlib (>=1.3); extra == 'docs'
Requires-Dist: Sphinx; extra == 'docs'
Requires-Dist: IPython; extra == 'docs'

Author-email: gpy.authors@gmail.com
License: BSD 3-clause
Description: # GPy
        
        
        A Gaussian processes framework in Python.
        
        * [GPy homepage](http://sheffieldml.github.io/GPy/)
        * [Tutorial notebooks](http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb)
        * [User mailing list](https://lists.shef.ac.uk/sympa/subscribe/gpy-users)
        * [Online documentation](https://gpy.readthedocs.org/en/latest/)
        * [Unit tests (Travis-CI)](https://travis-ci.org/SheffieldML/GPy)
        
        Continuous integration status: ![CI status](https://travis-ci.org/SheffieldML/GPy.png)
        
        ### Citation
        
            @Misc{gpy2014,
              author =   {{The GPy authors}},
              title =    {{GPy}: A Gaussian process framework in python},
              howpublished = {\url{http://github.com/SheffieldML/GPy}},
              year = {2012--2015}
            }
        
        ### Pronounciation
        
        We like to pronounce it 'Gee-pie'.
        
        ### Getting started: installing with pip
        
        We are now requiring the newest version (0.16) of 
        [scipy](http://www.scipy.org/) and thus, we strongly recommend using 
        the  [anaconda python distribution](http://continuum.io/downloads).
        With anaconda you can install GPy by the following:
        
            conda update scipy
            pip install gpy
            
        We've also had luck with [enthought](http://www.enthought.com), 
        although enthought currently (as of 8th Sep. 2015) does not support scipy 0.16.
        
        If you'd like to install from source, or want to contribute to the project (e.g. by sending pull requests via github), read on.
        
        ### Python 3 Compatibility
        Work is underway to make GPy run on Python 3.
        
        * All tests in the testsuite now run on Python3. 
        
        To see this for yourself, in Ubuntu 14.04, you can do
        
            git clone https://github.com/mikecroucher/GPy.git
            cd GPy
            git checkout devel
            python3 setup.py build_ext --inplace
            nosetests3 GPy/testing
        
        nosetests3 is Ubuntu's way of reffering to the Python 3 version of nosetests. You install it with 
        
            sudo apt-get install python3-nose
        
        The command `python3 setup.py build_ext --inplace` builds the Cython extensions. IF it doesn't work, you may need to install this:
        
            sudo apt-get install python3-dev
        
        * Test coverage is less than 100% so it is expected that there is still more work to be done. We need more tests and examples to try out.
        * All weave functions not covered by the test suite are *simply commented out*. Can add equivalents later as test functions become available
        * A set of benchmarks would be useful! 
        
        
        
        ### Ubuntu hackers
        
        > Note: Right now the Ubuntu package index does not include scipy 0.16.0, and thus, cannot
        > be used for GPy. We hope this gets fixed soon.
        
        For the most part, the developers are using ubuntu. To install the required packages:
        
            sudo apt-get install python-numpy python-scipy python-matplotlib
        
        clone this git repository and add it to your path:
        
            git clone git@github.com:SheffieldML/GPy.git ~/SheffieldML
            echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.bashrc
        
        
         
        ### OSX
        
        
        We were working hard to make pre-built distributions ready. 
        You can now install GPy via pip on MacOSX using 
        [anaconda python distribution](http://continuum.io/downloads):
        
            conda update scipy
            pip install gpy
        
        If this does not work, then you need to build GPy yourself, 
        using the [development toolkits](https://developer.apple.com/xcode/). 
        Download/clone GPy and run the build process:
        
            conda update scipy
            git clone git@github.com:SheffieldML/GPy.git ~/GPy
            cd ~/GPy
            python setup.py install
        
        If you do not wish to build the C extensions (10 times speedup),
        you can run the pure python installations, by just adding GPy
        to your python path.
        
           echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.profile
        
        
        
        ### Compiling documentation:
        
        
        The documentation is stored in doc/ and is compiled with the Sphinx Python documentation generator, and is written in the reStructuredText format.
        
        The Sphinx documentation is available here: http://sphinx-doc.org/latest/contents.html
        
        
        ##### Installing dependencies:
        
        
        To compile the documentation, first ensure that Sphinx is installed. On Debian-based systems, this can be achieved as follows:
        
            sudo apt-get install python-pip
            sudo pip install sphinx
        
        A LaTeX distribution is also required to compile the equations. Note that the extra packages are necessary to install the unicode packages. To compile the equations to PNG format for use in HTML pages, the package *dvipng* must be installed. IPython is also required. On Debian-based systems, this can be achieved as follows:
        
            sudo apt-get install texlive texlive-latex-extra texlive-base texlive-recommended
            sudo apt-get install dvipng
            sudo apt-get install ipython
        
        
        #### Compiling documentation:
        
        
        The documentation can be compiled as follows:
        
            cd doc
            make html
        
        The HTML files are then stored in doc/_build/
        
        
        ## Running unit tests:
        
        
        Ensure nose is installed via pip:
        
            pip install nose
        
        Run nosetests from the root directory of the repository:
        
            nosetests -v GPy/testing
        
        or from within IPython
        
            import GPy; GPy.tests()
        
        
        
        ## Funding Acknowledgements
        
        
        Current support for the GPy software is coming through the following projects. 
        
        * [EU FP7-PEOPLE Project Ref 316861](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/mlpm/) "MLPM2012: Machine Learning for Personalized Medicine"
        
        * MRC Special Training Fellowship "Bayesian models of expression in the transcriptome for clinical RNA-seq"
        
        *  [EU FP7-ICT Project Ref 612139](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/wysiwyd/) "WYSIWYD: What You Say is What You Did"
        
        Previous support for the GPy software came from the following projects:
        * [BBSRC Project No BB/K011197/1](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/recombinant/) "Linking recombinant gene sequence to protein product manufacturability using CHO cell genomic resources"
        * [EU FP7-KBBE Project Ref 289434](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/biopredyn/) "From Data to Models: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications"
        * [BBSRC Project No BB/H018123/2](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/iterative/) "An iterative pipeline of computational modelling and experimental design for uncovering gene regulatory networks in vertebrates"
        * [Erasysbio](http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/projects/synergy/) "SYNERGY: Systems approach to gene regulation biology through nuclear receptors"
        
Keywords: machine-learning gaussian-processes kernels
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
