U 3dW @sDdZddlZddlmZddlmZddlmZGdddeZ dS) zY Feature agglomeration. Base classes and functions for performing feature agglomeration. N)TransformerMixin)check_is_fitted)issparsec@s eZdZdZddZddZdS)AgglomerationTransformzH A class for feature agglomeration via the transform interface. cstjddjtjkrbtsbtjjd}t fddt |D}n(fddt 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_).0iXselfsizeJ/tmp/pip-unpacked-wheel-zrfo1fqw/sklearn/cluster/_feature_agglomeration.py /sz4AgglomerationTransform.transform..cs,g|]$}jddj|kfddqS)N)Zaxis) pooling_funcr )r l)rrrrr2s) rZ_validate_datarrZmeanrr r shapearrayrangeuniqueT)rrZ n_samplesZnXrr r transforms     z AgglomerationTransform.transformcCs(t|tj|jdd\}}|d|fS)a Inverse the transformation and return a vector of size `n_features`. Parameters ---------- Xred : array-like of shape (n_samples, n_clusters) or (n_clusters,) The values to be assigned to each cluster of samples. 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)Zreturn_inverse.)rrrr )rZXredZunilZinverserrrinverse_transform9sz(AgglomerationTransform.inverse_transformN)__name__ __module__ __qualname____doc__rrrrrrrs"r) r!ZnumpyrbaserZutils.validationrZ scipy.sparserrrrrrs