class_docstr: Hierarchical Poisson Factorization.
  
  Parameters
  ----------
  k: int, optional, default: 5
      The dimension of the latent factors.
  
  max_iter: int, optional, default: 100
      Maximum number of iterations.
  
  name: string, optional, default: 'HPF'
      The name of the recommender model.
  
  trainable: boolean, optional, default: True
      When False, the model is not trained and Cornac assumes that the model is already         pre-trained (Theta and Beta are not None). 
      
  verbose: boolean, optional, default: False
      When True, some running logs are displayed.
      
  hierarchical: boolean, optional, default: True
      When False, PF is used instead of HPF.
      
  seed: int, optional, default: None
      Random seed for parameters initialization.
  
  init_params: dict, optional, default: None
      Initial parameters of the model.
      
      Theta: ndarray, shape (n_users, k)
          The expected user latent factors.
      
      Beta: ndarray, shape (n_items, k)
          The expected item latent factors.
  
      G_s: ndarray, shape (n_users, k)
          This represents "shape" parameters of Gamma distribution over Theta.
  
      G_r: ndarray, shape (n_users, k)
          This represents "rate" parameters of Gamma distribution over Theta.
          
      L_s: ndarray, shape (n_items, k)
          This represents "shape" parameters of Gamma distribution over Beta.
      
      L_r: ndarray, shape (n_items, k)
          This represents "rate" parameters of Gamma distribution over Beta.
      
  References
  ----------
  * Gopalan, Prem, Jake M. Hofman, and David M. Blei. Scalable Recommendation with     Hierarchical Poisson Factorization. In UAI, pp. 326-335. 2015.
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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.Theta
  
file_path: cornac/cornac/models/hpf/recom_hpf.py
imports: import numpy
  import scipy.sparse
  from cornac.models.hpf import hpf
  from ..recommender import Recommender
  from ..recommender import ANNMixin, MEASURE_DOT
  from ...exception import ScoreException
label_desc: Use image node_id 5 for calling the HPF obj's underlying member method code with example usage: obj.get_user_vectors() and returns: self