GPyOpt 1.2.6
The Bayesian Optimization Toolbox

-Alan Saul
-Andreas Damianou
-Andrei Paleyes
-Fela Winkelmolen
-Huibin Shen
-James Hensman
-Javier Gonzalez
-Jordan Massiah
-Josh Fass
-Neil Lawrence
-Rasmus Berg Palm
-Rodolphe Jenatton
-Simon Kamronn
-Zhenwen Dai
-see also GPy and GPyOpt contributors in GitHub
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)
        * [Online documentation](http://gpyopt.readthedocs.io/)
        
        [![licence](https://img.shields.io/badge/licence-BSD-blue.svg)](http://opensource.org/licenses/BSD-3-Clause)  [![develstat](https://travis-ci.org/SheffieldML/GPyOpt.svg?branch=master)](https://travis-ci.org/SheffieldML/GPyOpt) [![covdevel](http://codecov.io/github/SheffieldML/GPyOpt/coverage.svg?branch=master)](http://codecov.io/github/SheffieldML/GPyOpt?branch=master) [![Research software impact](http://depsy.org/api/package/pypi/GPyOpt/badge.svg)](http://depsy.org/package/python/GPyOpt)
        
        ### Citation
        
        ```
        @Misc{gpyopt2016,
        author = {The GPyOpt authors},
        title = {{GPyOpt}: A Bayesian Optimization framework in python},
        howpublished = {\url{http://github.com/SheffieldML/GPyOpt}},
        year = {2016}
        }
        ```
        
        ## Getting started
        
        ### Installing with pip
        
        The simplest way to install GPyOpt is using pip. ubuntu users can do:
        
        ```bash
        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.
        
        ```bash
        git clone https://github.com/SheffieldML/GPyOpt.git
        cd GPyOpt
        python setup.py develop
        ```
        
        ## Dependencies:
        
          - GPy
          - paramz
          - numpy
          - scipy
          - matplotlib
          - DIRECT (optional)
          - cma (optional)
          - pyDOE (optional)
          - sobol_seq (optional)
        
        You can install dependencies by running:
        ```
        pip install -r requirements.txt
        ```
        
        
        ##  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
Description-Content-Type: text/markdown
Provides-Extra: optimizer
Provides-Extra: docs