o iz @sHdZddlZddlmZddlmZddlmZm Z GdddeZ dS) zY Feature agglomeration. Base classes and functions for performing feature agglomeration. N)issparse)TransformerMixin)check_is_fitted validate_datac@s eZdZdZddZddZdS)AgglomerationTransformzH A class for feature agglomeration via the transform interface. csttddjtjkr2ts2tjjd}t fddt |D}|Sfddt jD}t |j }|S)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)resetrc s*g|]}tj|ddfqS)N)npbincountlabels_).0iXselfsizeu/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/sklearn/cluster/_feature_agglomeration.py 0s*z4AgglomerationTransform.transform..cs,g|]}jddj|kfddqS)N)Zaxis) pooling_funcr )r l)rrrrr3s) rrrr meanrr r shapearrayrangeuniqueT)rrZ n_samplesZnXrrr transforms    z AgglomerationTransform.transformcCs(t|tj|jdd\}}|d|fS)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. Returns ------- X_original : ndarray of shape (n_samples, n_features) or (n_features,) A vector of size `n_samples` with the values of `X` assigned to each of the cluster of samples. T)Zreturn_inverse.)rr rr )rrZunilZinverserrrinverse_transform:s z(AgglomerationTransform.inverse_transformN)__name__ __module__ __qualname____doc__rrrrrrrs "r) r#numpyr Z scipy.sparserbaserZutils.validationrrrrrrrs