
    {Kg                     b    d Z ddl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  G d	 d
e      Zy)zY
Feature agglomeration. Base classes and functions for performing feature
agglomeration.
    N)issparse   )TransformerMixin)metadata_routing)"_deprecate_Xt_in_inverse_transform)check_is_fittedc                   @    e Zd ZdZdej
                  iZd ZddddZy)AgglomerationTransformzH
    A class for feature agglomeration via the transform interface.
    Xtc                    t        |        | j                  |d      }| j                  t        j                  k(  rt        |      st        j                  | j                        }|j                  d   }t        j                  t        |      D cg c],  }t        j                  | j                  ||ddf         |z  . c}      }|S t        j                  | j                        D cg c])  }| j                  |dd| j                  |k(  f   d      + }}t        j                  |      j                  }|S c c}w c c}w )a  
        Transform a new matrix using the built clustering.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_features) or                 (n_samples, n_samples)
            A M by N array of M observations in N dimensions or a length
            M array of M one-dimensional observations.

        Returns
        -------
        Y : ndarray of shape (n_samples, n_clusters) or (n_clusters,)
            The pooled values for each feature cluster.
        F)resetr   N   )axis)r   _validate_datapooling_funcnpmeanr   bincountlabels_shapearrayrangeuniqueT)selfXsize	n_samplesinXls          j/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/sklearn/cluster/_feature_agglomeration.py	transformz AgglomerationTransform.transform    s     	/';;t||,D
IDI)DTUDTqT\\1QT73d:DTUB 	 4<<00A !!!At||q'8$8"9!B0   "B	 Vs   1D=,.EN)r   c                    t        ||      }t        |        t        j                  | j                  d      \  }}|d|f   S )a  
        Inverse the transformation and return a vector of size `n_features`.

        Parameters
        ----------
        X : array-like of shape (n_samples, n_clusters) or (n_clusters,)
            The values to be assigned to each cluster of samples.

        Xt : array-like of shape (n_samples, n_clusters) or (n_clusters,)
            The values to be assigned to each cluster of samples.

            .. deprecated:: 1.5
                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.

        Returns
        -------
        X : ndarray of shape (n_samples, n_features) or (n_features,)
            A vector of size `n_samples` with the values of `Xred` assigned to
            each of the cluster of samples.
        T)return_inverse.)r   r   r   r   r   )r   r   r   unilinverses        r"   inverse_transformz(AgglomerationTransform.inverse_transformB   s>    * /q"5		$,,tDgg    )N)	__name__
__module____qualname____doc__r   UNUSED<_AgglomerationTransform__metadata_request__inverse_transformr#   r(    r)   r"   r
   r
      s-     .23C3J3J,K) Dd r)   r
   )r-   numpyr   scipy.sparser   baser   utilsr   utils.deprecationr   utils.validationr   r
   r0   r)   r"   <module>r7      s-     ! # $ B .F- Fr)   