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small readme.md tweaks #60

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Aug 19, 2024
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16 changes: 4 additions & 12 deletions readme.md
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PyNomaly is a Python 3 implementation of LoOP (Local Outlier Probabilities).
LoOP is a local density based outlier detection method by Kriegel, Kröger, Schubert, and Zimek which provides outlier
scores in the range of [0,1] that are directly interpretable as the probability of a sample being an outlier.
scores in the range of [0,1] that are directly interpretable as the probability of a sample being an outlier.

PyNomaly is a core library of [deepchecks](https://github.com/deepchecks/deepchecks) and [pysad](https://github.com/selimfirat/pysad).

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![PyPi](https://img.shields.io/badge/pypi-0.3.3-blue.svg)](https://pypi.python.org/pypi/PyNomaly/0.3.3)
Expand All @@ -29,24 +31,14 @@ The authors' 2009 paper detailing LoOP's theory, formulation, and application is
Ludwig-Maximilians University Munich - Institute for Informatics;
[LoOP: Local Outlier Probabilities](http://www.dbs.ifi.lmu.de/Publikationen/Papers/LoOP1649.pdf).

## PyNomaly Seeks Maintainers! :sparkles:

Love using PyNomaly? Want to develop your open source software (OSS) experience and credentials?

PyNomaly is looking for maintainers! PyNomaly doesn't need much on a day to day basis, but needs some attention.

On the flip side, the sky is the limit... Have you seen [Mojo](https://docs.modular.com/mojo/notebooks/Matmul.html) and what it can do with matrix multiplication? Would definitely speed things up.

Interested? Send an email to [[email protected]]([email protected]).

## Implementation

This Python 3 implementation uses Numpy and the formulas outlined in
[LoOP: Local Outlier Probabilities](http://www.dbs.ifi.lmu.de/Publikationen/Papers/LoOP1649.pdf)
to calculate the Local Outlier Probability of each sample.

## Dependencies
- Python 3.5 - 3.8
- Python 3.6 - 3.12
- numpy >= 1.16.3
- python-utils >= 2.3.0
- (optional) numba >= 0.45.1
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