class_docstr: Probabilistic Collaborative Representation Learning.
  
  Parameters
  ----------
  k: int, optional, default: 100
      The dimension of the latent factors.
      
  z_dims: Numpy 1d array, optional, default: [300]
      The dimensions of the hidden intermdiate layers 'z' in the order         [dim(z_L), ...,dim(z_1)], please refer to Figure 1 in the orginal paper for more details.
  
  max_iter: int, optional, default: 300
      Maximum number of iterations (number of epochs) for variational PCRL.
      
  batch_size: int, optional, default: 300
      The batch size for SGD.
      
  learning_rate: float, optional, default: 0.001
      The learning rate for SGD.
  
  aux_info: see "cornac/examples/pcrl_example.py" in the GitHub repo for an example of how to use         cornac's graph modality provide item auxiliary data (e.g., context, text, etc.) for PCRL.
  
  name: string, optional, default: 'PCRL'
      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 (Theta, Beta and Xi are not None). 
      
  w_determinist: boolean, optional, default: True
      When True, determinist wheights "W" are used for the generator network,         otherwise "W" is stochastic as in the original paper.
  
  init_params: dictionary, optional, default: None
      List of initial parameters, e.g., init_params = {'G_s':G_s, 'G_r':G_r, 'L_s':L_s, 'L_r':L_r}.
      
      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)
          Represent the "shape" parameters of Gamma distribution over Theta.
          
      G_r: ndarray, shape (n_users, k)
          Represent the "rate" parameters of Gamma distribution over Theta. 
      
      L_s: ndarray, shape (n_items, k)
          Represent the "shape" parameters of Gamma distribution over Beta.
          
      L_r: ndarray, shape (n_items, k)
          Represent the "rate" parameters of Gamma distribution over Beta. 
      
  References
  ----------
  * Salah, Aghiles, and Hady W. Lauw. Probabilistic Collaborative Representation Learning for Personalized Item Recommendation.     In UAI 2018.
docstr: Getting a matrix of item vectors used for building the index for ANN search.
  
  Returns
  -------
  out: numpy.array
      Matrix of item vectors for all items available in the model.
code: def get_item_vectors(self):
      """Getting a matrix of item vectors used for building the index for ANN search.
  
      Returns
      -------
      out: numpy.array
          Matrix of item vectors for all items available in the model.
      """
      return self.Beta
  
file_path: cornac/cornac/models/pcrl/recom_pcrl.py
imports: import numpy
  from ..recommender import Recommender
  from ..recommender import ANNMixin, MEASURE_DOT
label_desc: Use image node_id 6 for calling the PCRL obj's underlying member method code with example usage: obj.get_item_vectors() and returns: self