GPyOpt

-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: License: BSD 3-clause Description: # GPyOpt

> Gaussian process optimization using > [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]() > - [Tutorial > Notebooks]() > - [Online documentation]() > > [![licence]()]() > [![develstat]()]() > [![covdevel]()]() > [![Research software > impact]()]() > > ### 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]() > "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