Python package for modular Bayesian optimization
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pybo
tests
.coveragerc
.gitignore
.travis.yml
AUTHORS.txt
LICENSE.txt
README.md
setup.py

README.md

pybo

A Python package for modular Bayesian optimization.

This package provides methods for performing optimization of a possibly noise-corrupted function f. In particular this package allows us to place a prior on the possible behavior of f and select points in order to gather information about the function and its maximum.

[Build Status] (https://travis-ci.org/mwhoffman/pybo) [Coverage Status] (https://coveralls.io/r/mwhoffman/pybo)

Installation

The easiest way to install this package is by running

pip install git+https://github.com/mwhoffman/{mwhutils,pygp,pybo}.git

from the command line. Alternatively the packages above can be installed by cloning their repositories and using setup.py directly. Once the package is installed the included demos can be run directly via python. For example, by running

python -m pybo.demos.beginner

A full list of demos can be viewed here.