class_docstr: Social recommendation using Probabilistic Matrix Factorization.
  
  Parameters
  ----------
  k: int, optional, default: 5
      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: 0.001
      The learning rate for SGD_RMSProp.
  
  gamma: float, optional, default: 0.9
      The weight for previous/current gradient in RMSProp.
  
  lambda_c: float, optional, default: 10
      The parameter balancing the information from the user-item rating matrix and the user social network.
  
  lambda_reg: float, optional, default: 0.001
      The regularization parameter.
      
  weight_link: boolean, optional, default: True
      When true the social network links are weighted according to eq. (4) in the original paper.
  
  name: string, optional, default: 'SoRec'
      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, V and Z 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, 'Z':Z}.
  
      U: a ndarray of shape (n_users, k)
          Containing the user latent factors.
          
      V: a ndarray of shape (n_items, k)
          Containing the item latent factors.
          
      Z: a ndarray of shape (n_users, k)
          Containing the social network latent factors.
  
  seed: int, optional, default: None
      Random seed for parameters initialization.
  
  References
  ----------
  * H. Ma, H. Yang, M. R. Lyu, and I. King. SoRec:Social recommendation using probabilistic matrix factorization.      CIKM ’08, pages 931–940, Napa Valley, USA, 2008.
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/sorec/recom_sorec.py
imports: import numpy
  import scipy
  from ..recommender import Recommender
  from ..recommender import ANNMixin, MEASURE_DOT
  from ...utils.common import sigmoid
  from ...utils.common import scale
  from ...exception import ScoreException
label_desc: Use image node_id 5 for calling the SoRec obj's underlying member method code with example usage: obj.get_user_vectors() and returns: self