Ki NSrSSKrSSKJr SSKJr SSKJrJ r "SS\5r g)zY Feature agglomeration. Base classes and functions for performing feature agglomeration. N)issparse)TransformerMixin)check_is_fitted validate_datac$\rSrSrSrSrSrSrg)AgglomerationTransformz@ A class for feature agglomeration via the transform interface. c [U5 [XSS9nUR[R:Xa[ U5(d[R "UR5nURSn[R"[U5Vs/sH-n[R "URXSS245U- PM/ sn5nU$[R"UR5Vs/sH'nURUSS2URU:H4SS9PM) nn[R"U5RnU$s snfs snf)a Transform a new matrix using the built clustering. Parameters ---------- X : array-like of shape (n_samples, n_features) or (n_samples, n_samples) An 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)resetrN)axis) rr pooling_funcnpmeanrbincountlabels_shapearrayrangeuniqueT)selfXsize n_samplesinXls h/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/sklearn/cluster/_feature_agglomeration.py transform AgglomerationTransform.transforms  $ /    ' ;;t||,D IDI)DTUDTqT\\1T73d:DTUB 4<<00A!!!At||q'8$8"9!B0 "B Vs 4E/.Ech[U5 [R"URSS9up#USU4$)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)return_inverse.)rrrr)rrunilinverses rinverse_transform(AgglomerationTransform.inverse_transform:s0  $,,tD gN)__name__ __module__ __qualname____firstlineno____doc__r r&__static_attributes__r)r(rrrs Dr(r) r.numpyr scipy.sparser sklearn.basersklearn.utils.validationrrrr)r(rr4s%!)C 9-9r(