Metadata-Version: 2.1
Name: Concentration-Free-Outlier-Factor
Version: 0.1.1
Summary: Calculate the Concentration Free Outlier Factor score, based on Angiulli's work
Home-page: https://github.com/luk-f/pyCFOF
Author: Lucas Foulon
Author-email: lucas.foulon@gmail.com
License: UNKNOWN
Description: [![PyPI](https://github.com/luk-f/pyCFOF/actions/workflows/python-publish.yml/badge.svg)](https://github.com/luk-f/pyCFOF/actions/workflows/python-publish.yml)
        
        # pyCFOF
        
        ## Pour commencer
        
        ### Installation
        
        Lancer `pip install -r requirements.txt` ou `python3 -m pip install -r requirements.txt`.
        
        Ou à partir du dépôt `pip install Concentration-Free-Outlier-Factor`.
        
        ### Utilisation
        
            >>> from pyCFOF import ConcentrationFreeOutlierFactor as CFOF
            >>> X = [[-1.1], [0.2], [101.1], [0.3]]
            >>> cfof = CFOF(n_neighbors=len(X), rho=[0.1])
            >>> cfof.fit_predict(X)
            array([[ 1],
                   [ 1],
                   [-1],
                   [ 1]])
            >>> cfof.outlier_factor_
            array([[0.75],
                   [0.5 ],
                   [1.  ],
                   [0.5 ]])
        
        ## Remerciements
        
        Développements des travaux de :
         - Fabrizio Angiulli, [CFOF: A Concentration Free Measure for Anomaly Detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 14(1):Article 4, 2020](https://dl.acm.org/doi/abs/10.1145/3362158)
        
        
        Utilisation de :
         - [Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011](https://scikit-learn.org/stable/index.html)
        
Platform: UNKNOWN
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
