class_docstr: An implementation of `"TENE" <https://ieeexplore.ieee.org/document/8545577>`_
  from the ICPR '18 paper "Enhanced Network Embedding with Text Information". The
  procedure jointly factorizes the adjacency and node feature matrices using alternating
  least squares.
  
  Args:
      dimensions (int): Number of embedding dimensions. Default is 32.
      lower_control (float): Embedding score minimal value. Default is 10**-15.
      alpha (float): Adjacency matrix regularization coefficient. Default is 0.1.
      beta (float): Feature matrix regularization coefficient. Default is 0.1.
      iterations (int): ALS iterations. Default is 200.
      seed (int): Random seed value. Default is 42.
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docstr: Update features.
docstr_tok: ["Update","features","."]
code: def _update_U(self):
      """
      Update features.
      """
      enum = self._X.T.dot(self._M) + self.alpha * self._Q.dot(self._C)
      denom = self._U.dot(
          (self._M.T.dot(self._M) + self.alpha * self._C.T.dot(self._C))
      )
      self._U = np.multiply(self._U, enum / denom)
      self._U[self._U < self.lower_control] = self.lower_control
  
file_path: karateclub/karateclub/node_embedding/attributed/tene.py
imports: import numpy
  import networkx
  from typing import Union
  from scipy import sparse
  from scipy.sparse import coo_matrix
  from karateclub.estimator import Estimator
label_desc: Use image node_id 6 for calling the TENE obj's underlying member method code with example usage: obj._update_U() without return types