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    3‰d¨  ã                   @   s  d Z ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ dd	lm	Z	 dd
lm
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 ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ ddlmZ ddl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adjusted_mutual_info_score)Únormalized_mutual_info_score)Úadjusted_rand_score)Ú
rand_score)Úcompleteness_score)Úcontingency_matrix)Úpair_confusion_matrix)Úexpected_mutual_information)Ú"homogeneity_completeness_v_measure)Úhomogeneity_score)Úmutual_info_score)Úv_measure_score)Úfowlkes_mallows_score)Úentropy)Úsilhouette_samples)Úsilhouette_score)Úcalinski_harabasz_score)Údavies_bouldin_score)Úconsensus_scorer   r   r   r   r   r   r   r	   r
   r   r   r   r   r   r   r   r   r   r   N)Ú__doc__Z_supervisedr   r   r   r   r   r   r   r	   r
   r   r   r   r   r   Z_unsupervisedr   r   r   r   Z
_biclusterr   Ú__all__© r   r   úD/tmp/pip-unpacked-wheel-zrfo1fqw/sklearn/metrics/cluster/__init__.pyÚ<module>   sN   í