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small readme.md tweaks
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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. | ||
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PyNomaly is a core library of [deepchecks](https://github.com/deepchecks/deepchecks) and [pysad](https://github.com/selimfirat/pysad). | ||
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[![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) | ||
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Ludwig-Maximilians University Munich - Institute for Informatics; | ||
[LoOP: Local Outlier Probabilities](http://www.dbs.ifi.lmu.de/Publikationen/Papers/LoOP1649.pdf). | ||
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## PyNomaly Seeks Maintainers! :sparkles: | ||
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Love using PyNomaly? Want to develop your open source software (OSS) experience and credentials? | ||
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PyNomaly is looking for maintainers! PyNomaly doesn't need much on a day to day basis, but needs some attention. | ||
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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. | ||
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Interested? Send an email to [[email protected]]([email protected]). | ||
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## Implementation | ||
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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. | ||
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## 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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