
    h$,ft                     t    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 g dZy)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.
   )consensus_score)adjusted_mutual_info_scoreadjusted_rand_scorecompleteness_scorecontingency_matrixentropyexpected_mutual_informationfowlkes_mallows_score"homogeneity_completeness_v_measurehomogeneity_scoremutual_info_scorenormalized_mutual_info_scorepair_confusion_matrix
rand_scorev_measure_score)calinski_harabasz_scoredavies_bouldin_scoresilhouette_samplessilhouette_score)r   r   r   r   r   r   r   r	   r   r   r   r   r
   r   r   r   r   r   r   N)__doc__
_biclusterr   _supervisedr   r   r   r   r   r	   r
   r   r   r   r   r   r   r   _unsupervisedr   r   r   r   __all__     @lib/python3.12/site-packages/sklearn/metrics/cluster/__init__.py<module>r      s4    (     r   