This package offers tools to analyze the unimodality of data sampled from multivariate distributions lying in the Euclidean Space.
- The
mud-pod
test: A multivariate unimodality test. - The
dip-means
clustering algorithm: A wrapper ofk-means
that also detects the numbers of clusters.
To install mudpod
, you can use pdm
, which is a modern packaging tool that manages your Python packages without the need for creating a virtualenv in a traditional sense.
Ensure you have pdm
installed on your system. If not, install it using the following command:
curl -sSL https://raw.githubusercontent.com/pdm-project/pdm/main/install-pdm.py | python3 -
Please run:
pdm install -G core
Note: If you want to run the tests or the experiments, please install the additional dependencies, i.e., test
and exps
, respectively, using the following command:
pdm install -G GROUP_NAME
If you find this code useful in your research, please cite:
@misc{kolyvakis2023multivariate,
title={A Multivariate Unimodality Test Harnenssing the Dip Statistic of Mahalanobis Distances Over Random Projections},
author={Prodromos Kolyvakis and Aristidis Likas},
year={2023},
eprint={2311.16614},
archivePrefix={arXiv},
primaryClass={stat.ME}
}
This project is licensed under the GNU Affero General Public License v3.0 - see the LICENSE file for details.