
    {Kg3                     t    d Z ddlmZ ddlmZmZmZmZmZm	Z	m
Z
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)zEvaluation metrics for cluster analysis results.

- Supervised evaluation uses a ground truth class values for each sample.
- Unsupervised evaluation does use ground truths 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__     d/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/sklearn/metrics/cluster/__init__.py<module>r      s4    (     r   