Diverence metric
Project description
Alcraft-Williams Trivial Divergence
There is a google colab to demonstrate this here: https://colab.research.google.com/drive/1NNfjDTaUO6IfAcVu5DDs9UvcEdGY_TDA?usp=sharing
Alcraft-Williams Association
This implements the Alcraft-Williams Association for finding associations in non-linear multi-dimensional data. It has a particular advantage in being able to identify associations in sinusoidal data.
Install
It is installed on PyPi and can installed with
pip install ra-trivial
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
ra-trivial-0.0.4.tar.gz
(25.5 kB
view hashes)
Built Distribution
ra_trivial-0.0.4-py3-none-any.whl
(28.1 kB
view hashes)
Close
Hashes for ra_trivial-0.0.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5a2632d97c78cb4d6cb12a58e854f24102d52930218a83fc79599db8fd76f67e |
|
MD5 | 195c2d005508b241738fa0c14a45b24b |
|
BLAKE2b-256 | 07c74c9a39104ed39912a8c0478e8a788661d40fb7cac1316d4e97576a75b173 |