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The Bayesian Optimization Toolbox

Project description

-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.

[![licence](]( [![develstat](]( [![covdevel](]( [![Research software impact](](

### Citation

` @Misc{gpyopt2016, author = {The GPyOpt authors}, title = {{GPyOpt}: A Bayesian Optimization framework in python}, howpublished = {\url{}}, 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 cd GPyOpt python 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

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

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