Metadata-Version: 2.1
Name: iwpc
Version: 0.3.11
Summary: An implementation of the divergence framework as described here https://arxiv.org/abs/2405.06397
Author-email: "Jeremy J. H. Wilkinson" <jero.wilkinson@gmail.com>
Project-URL: Homepage, https://bitbucket.org/jjhw3/divergences/src/main/
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: torch
Requires-Dist: lightning
Requires-Dist: matplotlib
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: tensorboard
Requires-Dist: seaborn
Requires-Dist: bokeh

# IWPC #

This package implements the methods described in the research paper https://arxiv.org/abs/2405.06397 for estimating a 
lower bound on the divergence between any two distributions, p and q, using samples from each distribution.

Install using `pip install iwpc`

Please see the package [README](https://bitbucket.org/jjhw3/divergences/src/main/) on bitbucket for more information and
some examples.
