class_docstr: Compatibility layer from River to scikit-learn for clustering.
  
  Parameters
  ----------
  river_estimator
class_docstr_tok: ["Compatibility","layer","from","River","to","scikit-learn","for","clustering",".","Parameters","--","--","--","--","--","river_estimator"]
docstr: Predicts the target of an entire dataset contained in memory.
  
  Parameters
  ----------
  X
      array-like of shape (n_samples, n_features).
  
  Returns
  -------
  Transformed output.
docstr_tok: ["Predicts","the","target","of","an","entire","dataset","contained","in","memory",".","Parameters","--","--","--","--","--","X","array-like","of","shape","(","n_samples",",","n_features",")",".","Returns","--","--","--","-","Transformed","output","."]
code: def predict(self, X):
      """Predicts the target of an entire dataset contained in memory.
  
      Parameters
      ----------
      X
          array-like of shape (n_samples, n_features).
  
      Returns
      -------
      Transformed output.
  
      """
  
      # Check the fit method has been called
      utils.validation.check_is_fitted(self, attributes="instance_")
  
      # Check the input
      X = utils.check_array(X, **SKLEARN_INPUT_X_PARAMS)
  
      # Call predict_proba_one for each observation
      y_pred = np.empty(len(X), dtype=np.int32)
      for i, (x, _) in enumerate(STREAM_METHODS[type(X)](X)):
          y_pred[i] = self.instance_.predict_one(x)
  
      return y_pred
  
imports: from __future__ import annotations
  import copy
  import typing
  import numpy
  from sklearn import base
  from sklearn import pipeline, preprocessing, utils
  from river import base, compose, stream
label_desc: Use image node_id 5 for calling the River2SKLClusterer obj's underlying member method code with example usage: obj.predict(X) and returns: y_pred