class_docstr: Base class for KMeans and MiniBatchKMeans
class_docstr_tok: ["Base","class","for","KMeans","and","MiniBatchKMeans"]
docstr: Transform X to a cluster-distance space.
  
  In the new space, each dimension is the distance to the cluster
  centers. Note that even if X is sparse, the array returned by
  `transform` will typically be dense.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      New data to transform.
  
  Returns
  -------
  X_new : ndarray of shape (n_samples, n_clusters)
      X transformed in the new space.
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code: def transform(self, X):
      """Transform X to a cluster-distance space.
  
      In the new space, each dimension is the distance to the cluster
      centers. Note that even if X is sparse, the array returned by
      `transform` will typically be dense.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          New data to transform.
  
      Returns
      -------
      X_new : ndarray of shape (n_samples, n_clusters)
          X transformed in the new space.
      """
      check_is_fitted(self)
  
      X = self._check_test_data(X)
      return self._transform(X)
  
imports: import warnings
  from abc import ABC, abstractmethod
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, ClusterMixin, TransformerMixin, _fit_context
  from ..exceptions import ConvergenceWarning
  from ..metrics.pairwise import _euclidean_distances, euclidean_distances
  from ..utils import check_array, check_random_state
  from ..utils._openmp_helpers import _openmp_effective_n_threads
  from ..utils._param_validation import Interval, StrOptions, validate_params
  from ..utils.extmath import row_norms, stable_cumsum
  from ..utils.fixes import threadpool_info, threadpool_limits
  from ..utils.sparsefuncs import mean_variance_axis
  from ..utils.sparsefuncs_fast import assign_rows_csr
  from ..utils.validation import _check_sample_weight, _is_arraylike_not_scalar, check_is_fitted
  from ._k_means_common import CHUNK_SIZE, _inertia_dense, _inertia_sparse, _is_same_clustering
  from ._k_means_elkan import elkan_iter_chunked_dense, elkan_iter_chunked_sparse, init_bounds_dense, init_bounds_sparse
  from ._k_means_lloyd import lloyd_iter_chunked_dense, lloyd_iter_chunked_sparse
  from ._k_means_minibatch import _minibatch_update_dense, _minibatch_update_sparse
label_desc: Use image node_id 11 for calling the _BaseKMeans obj's underlying member method code with example usage: obj.transform(X) and returns: self