Metadata-Version: 2.1
Name: mmd-critic
Version: 0.1.1
Summary: Python package for the MMD Critic method
Home-page: https://github.com/PhysBoom/mmd_critic
Author: Matthew Chak
Author-email: mchak@calpoly.edu
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: numexpr

# mmd-critic

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/yourusername/mmd-critic/blob/main/LICENSE)

A Python package for implementing the Maximum Mean Discrepancy Critic (MMD-Critic) method. This method is commonly used to find prototypes and criticisms (outliers, roughly speaking) in datasets.

## Installation

You can install the package via pip:

```bash
pip install mmd-critic
```

## Usage

```python
from mmd_critic import MMDCritic
from mmd_critic.kernels import RBFKernel

critic = MMDCritic(X, RBFKernel(sigma=1), criticism_kernel=RBFKernel(2), labels=y)

protos, proto_labels = critic.select_prototypes(50)
criticisms, criticism_labels = critic.select_criticisms(10, protos)
```

Note that the labels and criticism_kernel are optional arguments which are None by default. If `criticism_kernel`
is none, then the prototype kernel will be used for criticisms. If labels are none, then returned labels will be None.

See more in the [examples](https://github.com/PhysBoom/mmd_critic/tree/main/examples)

## More Info

Read my [article](https://medium.com/@physboom/the-mmd-critic-method-explained-c6a77f2dbf18) for more info on the MMD critic method. I also encourage you to read the original [paper](https://papers.nips.cc/paper_files/paper/2016/hash/5680522b8e2bb01943234bce7bf84534-Abstract.html).

## Acknowledgements

The implementation here is based on Been Kim's [original implementation](https://github.com/BeenKim/MMD-critic/tree/master) and [paper](https://papers.nips.cc/paper_files/paper/2016/hash/5680522b8e2bb01943234bce7bf84534-Abstract.html)
