dpmmlearn

Dirichlet process mixture model in Python with scikit-learn like API.

These details have not been verified by PyPI

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

Project description

dpmmlearn is a algorithms for Dirichlet Process Mixture Model.

The required dependencies to use dpmmlearn are,

You also need matplotlib, seaborn to run the demo and pytest to run the tests.

pip install dpmmlearn

We have posted a usage example in the github’s demo folder.

This code is licensed under MIT License.

python setup.py test

These details have not been verified by PyPI

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

This version

0.0.1b1 pre-release

Sep 13, 2021

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Uploaded Sep 13, 2021 Source

Uploaded Sep 13, 2021 Python 3

Hashes for dpmmlearn-0.0.1b1.tar.gz
Algorithm	Hash digest
SHA256	`ac1b5c71140c765728394c0b2c9c69699e7c817961a21152690abbdb6c97869c`
MD5	`a3f1c0c9398dd9d11afd0cc7c8e53e85`
BLAKE2b-256	`b22cef85711db649e1afb7580b47a66b8810aa8dbef914fca76ba189df7f1b40`

Hashes for dpmmlearn-0.0.1b1-py3-none-any.whl
Algorithm	Hash digest
SHA256	`3a862b70139e2c1d9f9eb2f83ce0f701b59db2808f5fe3b2ebdba8e3630e004d`
MD5	`c230c77cdd7c447a09d21e3a677814df`
BLAKE2b-256	`53a73a4c8ef6783228475afebd345d3e42840a4523bbca8aa4174794ed7aa83d`