class_docstr: Online Indexable Bayesian Personalized Ranking.
  
  Parameters
  ----------
  k: int, optional, default: 20
      The dimension of the latent factors.
  
  max_iter: int, optional, default: 100
      Maximum number of iterations or the number of epochs for SGD.
  
  learning_rate: float, optional, default: 0.05
      The learning rate for SGD.
  
  lamda: float, optional, default: 0.001
      The regularization parameter.
  
  batch_size: int, optional, default: 100
      The batch size for SGD.
  
  name: string, optional, default: 'IBRP'
      The name of the recommender model.
  
  trainable: boolean, optional, default: True
      When False, the model is not trained and Cornac assumes that the model already         pre-trained (U and V are not None).
      
  verbose: boolean, optional, default: False
      When True, some running logs are displayed.
  
  init_params: dictionary, optional, default: None
      List of initial parameters, e.g., init_params = {'U':U, 'V':V}         please see below the definition of U and V.
  
      U: csc_matrix, shape (n_users,k)
          The user latent factors, optional initialization via init_params.
  
      V: csc_matrix, shape (n_items,k)
          The item latent factors, optional initialization via init_params.
  
  References
  ----------
  * Le, D. D., & Lauw, H. W. (2017, November). Indexable Bayesian personalized ranking for efficient top-k recommendation.      In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 1389-1398). ACM.
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docstr: Getting a matrix of user vectors serving as query for ANN search.
  
  Returns
  -------
  out: numpy.array
      Matrix of user vectors for all users available in the model.
docstr_tok: ["Getting","a","matrix","of","user","vectors","serving","as","query","for","ANN","search",".","Returns","--","--","--","-","out",":","numpy.array","Matrix","of","user","vectors","for","all","users","available","in","the","model","."]
code: def get_user_vectors(self):
      """Getting a matrix of user vectors serving as query for ANN search.
  
      Returns
      -------
      out: numpy.array
          Matrix of user vectors for all users available in the model.
      """
      return self.U
  
file_path: cornac/cornac/models/online_ibpr/recom_online_ibpr.py
imports: import numpy
  from ..recommender import Recommender
  from ..recommender import ANNMixin, MEASURE_DOT
  from ...exception import ScoreException
label_desc: Use image node_id 5 for calling the OnlineIBPR obj's underlying member method code with example usage: obj.get_user_vectors() and returns: self