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adds license and readme
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fmfn committed Dec 14, 2014
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9 changes: 9 additions & 0 deletions LICENSE
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The MIT License (MIT)

Copyright (c) 2014 Fernando M. F. Nogueira

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
31 changes: 18 additions & 13 deletions README
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Working Python implementation of global optimization with gaussian processes.
Python implementation of bayesian global optimization with gaussian processes.

—Under (constant) development! (See the wiki for more information.)
This is a constrained global optimization package built upon bayesian inference and
gaussian process, that attempts to find the maximum value of an unknown function in
as few iterations as possible. This technique is particularly suited for optimization
of high cost functions, situations where the balance between exploration and
exploitation is important.

This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important.
Checkout the examples folder for simple scripts indicating how to use this package.

This package was motivated by hyper-parameter optimization of machine leaning algorithms when performing cross validation. Some of the design choices were clearly made with this setting in mind and ultimately a out-of-the-box cross validation optimization object will be implemented (soon).
Disclaimer: This project is under active development, if you find a bug, or anything
that needs correction, please let me know.

Disclaimer: This project is under active development, some of its functionalities and sintaxes are bound to change, sometimes dramatically. If you find a bug, or anything that needs correction, please let me know.
Dependencies:
* Scipy
* Numpy
* Scikit-Learn

Basic dependencies are Scipy, Numpy. Examples dependencies also include matplotlib and sklearn.

References for this implementation can be found in:

http://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
http://arxiv.org/pdf/1012.2599v1.pdf
http://www.gaussianprocess.org/gpml/
https://www.youtube.com/watch?v=vz3D36VXefI&index=10&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6
References:
* http://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf
* http://arxiv.org/pdf/1012.2599v1.pdf
* http://www.gaussianprocess.org/gpml/
* https://www.youtube.com/watch?v=vz3D36VXefI&index=10&list=PLE6Wd9FR--EdyJ5lbFl8UuGjecvVw66F6

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