Metadata-Version: 2.1
Name: bayesian-cut
Version: 0.0.1b0
Summary: An implementation of bayesian cut methods
Home-page: https://github.com/DTUComputeCognitiveSystems/bayesian_cut
Author: Laurent Vermue, Maciej Korzepa, Petr Taborsky, Morten Mørup
Author-email: <lauve@dtu.dk>, <mjko@dtu.dk>, <ptab@dtu.dk>, <mmor@dtu.dk>
License: new BSD
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.5
Description-Content-Type: text/x-rst
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Requires-Dist: scipy (>=1.0.0)
Requires-Dist: scikit-learn (>=0.20.0)
Requires-Dist: networkx (>=2.0)
Requires-Dist: joblib (>=0.12.3)
Provides-Extra: docs
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Provides-Extra: extras
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Requires-Dist: plotly ; extra == 'extras'
Requires-Dist: naturalneighbor ; extra == 'extras'
Requires-Dist: seaborn ; extra == 'extras'
Provides-Extra: tests
Requires-Dist: pytest ; extra == 'tests'

Bayesian Cut Package
====================

The Bayesian Cut Python package provides an easy to use API for the straight-forward application of Bayesian network
cuts using a full Bayesian inference framework based on the Gibbs-Sampler using the degree corrected Stochastic
Blockmodel (dc-SBM) or the Bayesian Cut (BC).
Furthermore it provides modularity, ratio-cut and norm cut based spectral network cut methods.
It also provides a rich visualization library that allow an easy analysis of posterior solution landscapes and network
cuts obtained by the various methods.


