A toolbox for practical applications of information theory.
Project description
UNITE Toolbox
Unified diagnostic evaluation of scientific models based on information theory
The UNITE Toolbox is a Python library for incorporating Information Theory into data analysis and modeling workflows. The toolbox collects different methods of estimating information-theoretic quantities in one easy-to-use Python package. Currently, UNITE includes functions to calculate entropy $H(X)$, Kullback-Leibler divergence $D_{KL}(p||q)$, and mutual information $I(X; Y)$, using three methods:
- Kernel density-based estimation (KDE)
- Binning using histograms
- k-nearest neighbor-based estimation (k-NN)
Installation
Although the code is still highly experimental and in very active development,
a release version is available on PyPI and can be installed using pip
.
pip install unite_toolbox
Alternatively, the latest updates can be installed directly from this repository
pip install git+https://github.com/manuel-alvarez-chaves/unite_toolbox
Check the pyproject.toml
for requirements.
How-to
In the documentation please find tutorials on the general usage of the toolbox and some applications.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for unite_toolbox-0.1.9-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e14c70ab9250f3717d601ba2c695d528bb75e2f2bc233c523b699f18ee57905a |
|
MD5 | 2a755a70c201832c72161b149d4a2ed4 |
|
BLAKE2b-256 | 6bc6a70d5a38d471d97c071fcc79c271e0e73cc3564ee1e237ad2204b1ae3292 |