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    ¿9%et  ã                   @   s–   d Z ddlmZ ddlmZmZmZmZmZm	Z	m
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mZmZmZmZmZmZmZ ddlmZmZmZmZ ddddd	d
dddddddddddddgZdS )a!  
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:

- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
é   )Úconsensus_score)Úadjusted_mutual_info_scoreÚadjusted_rand_scoreÚcompleteness_scoreÚcontingency_matrixÚentropyÚexpected_mutual_informationÚfowlkes_mallows_scoreÚ"homogeneity_completeness_v_measureÚhomogeneity_scoreÚmutual_info_scoreÚnormalized_mutual_info_scoreÚpair_confusion_matrixÚ
rand_scoreÚv_measure_score)Úcalinski_harabasz_scoreÚdavies_bouldin_scoreÚsilhouette_samplesÚsilhouette_scorer   r   r   r   r   r   r   r   r
   r   r   r   r	   r   r   r   r   r   r   N)Ú__doc__Z
_biclusterr   Z_supervisedr   r   r   r   r   r   r	   r
   r   r   r   r   r   r   Z_unsupervisedr   r   r   r   Ú__all__© r   r   ú_/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/sklearn/metrics/cluster/__init__.pyÚ<module>   s.   @í