class_docstr: Collaborative Context Poisson Factorization.
  
  Parameters
  ----------
  k: int, optional, default: 100
      The dimension of the latent factors.
  
  max_iter: int, optional, default: 100
      Maximum number of iterations for variational C2PF.
  
  variant: string, optional, default: 'c2pf'
      C2pf's variant: c2pf: 'c2pf', 'tc2pf' (tied-c2pf) or 'rc2pf' (reduced-c2pf).         Please refer to the original paper for details.
  
  name: string, optional, default: None
      The name of the recommender model. If None,         then "variant" is used as the default name of the 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).
  
  Item_context: See "cornac/examples/c2pf_example.py" in the GitHub repo for an example of how to use         cornac's graph modality to load and provide "item context" for C2PF.
  
  init_params: dict, 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, 'L2_s':L2_s, 'L2_r':L2_r, 'L3_s':L3_s, 'L3_r': L3_r}
  
      Theta: ndarray, shape (n_users, k)
          The expected user latent factors.
  
      Beta: ndarray, shape (n_items, k)
          The expected item latent factors.
  
      Xi: ndarray, shape (n_items, k)
          The expected context item latent factors multiplied by context effects Kappa.
          
      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. 
      
      L2_s: ndarray, shape (n_items, k)
          Represent the "shape" parameters of Gamma distribution over Xi.
          
      L2_r: ndarray, shape (n_items, k)
          Represent the "rate" parameters of Gamma distribution over Xi.
           
      L3_s: ndarray
          Represent the "shape" parameters of Gamma distribution over Kappa.
          
      L3_r: ndarray 
          Represent the "rate" parameters of Gamma distribution over Kappa.
  
  References
  ----------
  * Salah, Aghiles, and Hady W. Lauw. A Bayesian Latent Variable Model of User Preferences with Item Context.     In IJCAI, pp. 2667-2674. 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.
docstr_tok: ["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.
      """
      if self.variant == "rc2pf":
          item_vectors = np.concatenate((self.Beta, self.Xi), axis=1)
      else:
          item_vectors = self.Beta
      return item_vectors
  
file_path: cornac/cornac/models/c2pf/recom_c2pf.py
imports: import numpy
  import scipy.sparse
  from cornac.models.c2pf import c2pf
  from ..recommender import Recommender
  from ..recommender import ANNMixin, MEASURE_DOT
label_desc: Use image node_id 6 for calling the C2PF obj's underlying member method code with example usage: obj.get_item_vectors() and returns: item_vectors