semvec/pypi-packages huggingface dataset:
coresets 0.2.0
Coreset generation for k-Means and (Bayesian) Gaussian mixture models
License: BSD-3-Clause
Requires Python: >=3.8,<4.0

Coresets
--------
This library contains the implementation coreset generation for k-Means and (Bayesian) Gaussian mixture models.
It also offers the extended versions of the corresponding algorithms that support weighted data sets.

To get started, take a look at:
>examples/intro.ipynb

(this is a fork of https://github.com/zalanborsos/coresets, intended to fix installation issues + publish to pypi)

Setup
-------
1. Install [poetry](https://python-poetry.org/docs/).
2.
```shell
poetry build
poetry install
```

Running tests
-------------
In project root run:
```shell
poetry run pytest
```


References
---------
The implementation of the library is based on the following works:
>Bachem, O., Lucic, M., & Krause, A. (2017). Practical coreset constructions for machine learning. arXiv preprint arXiv:1703.06476.

> Bachem, O., Lucic, M., & Krause, A. (2017). Scalable and distributed clustering via lightweight coresets. arXiv preprint arXiv:1702.08248.

>Lucic, M., Faulkner, M., Krause, A., & Feldman, D. (2018). Training Gaussian Mixture Models at Scale via Coresets. Journal of Machine Learning Research, 18, Art-No.

> Borsos, Z., Bachem, O., & Krause, A. Variational Inference for DPGMM with Coresets. (2017). Advances in Approximate Bayesian Inference


Publishing a new version
------------------------

```shell
rm -rf build dist
poetry build
rename -v 's/manylinux_2_\d+/manylinux1/' dist/*.whl  # Rename the wheel to manylinux1 as we don't use advanced LIBC feats
poetry publish
```