Metadata-Version: 1.1
Name: GPyOpt
Version: 0.1.3
Summary: The Bayesian Optimization Toolbox
Home-page: http://sheffieldml.github.com/GPyOpt/
Author: -Javier Gonzalez
-Zhenwen Dai
-see also GPy authors

Author-email: j.h.gonzalez@sheffield.ac.uk
License: BSD 3-clause
Description: GPyOpt
        ======
        
        Gaussian process optimization using [GPy](http://sheffieldml.github.io/GPy/). Performs global optimization with different acquisition functions. Among other functionalities, it is possible to use GPyOpt to optimize physical experiments (sequentially or in batches) and tune the parameters of Machine Learning algorithms. It is able to handle large data sets via sparse Gaussian process models. 
        
        * [GPyOpt homepage](http://sheffieldml.github.io/GPyOpt/)
        * [Tutorial Notebooks](http://nbviewer.ipython.org/github/SheffieldML/GPyOpt/blob/master/manual/index.ipynb)
        * [Users Mailing list](https://lists.shef.ac.uk/sympa/info/gpyopt-users)
        * [Online documentation](http://pythonhosted.org/GPyOpt)
        
        ### Citation
        
            @Misc{gpy2014,
              author =   {The GPyOpt authors},
              title =    {{GPy}: A Bayesian Optimization framework in python},
              howpublished = {\url{http://github.com/SheffieldML/GPyOpt}},
              year = {2015}
            }
        
        Getting started
        ===============
        
        Installing with pip
        -------------------
        The simplest way to install GPyOpt is using pip. ubuntu users can do:
        
            sudo apt-get install python-pip
            pip install gpyopt
        
        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. Clone the repository in GitHub and add it to your $PYTHONPATH.
        
            git clone git@github.com:SheffieldML/GPyOpt.git ~/SheffieldML
            echo 'PYTHONPATH=$PYTHONPATH:~/SheffieldML' >> ~/.bashrc
        
        Dependencies:
        ------------------------
          - GPy
          - numpy
          - scipy
          - DIRECT (optional)
          - cma (optional)
          - pyDOE (optional)
        
        Funding Acknowledgements
        ========================
        * [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"
        
        * See GPy funding Acknowledgements
        
        
        
        
        
        
        
Keywords: machine-learning gaussian-processes kernels optimization
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
