class_docstr: Weighted Matrix Factorization.
  
  Parameters
  ----------
  name: string, default: 'WMF'
      The name of the recommender model.
  
  k: int, optional, default: 200
      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 AdamOptimizer.
  
  lambda_u: float, optional, default: 0.01
      The regularization parameter for users.
  
  lambda_v: float, optional, default: 0.01
      The regularization parameter for items.
  
  a: float, optional, default: 1
      The confidence of observed ratings.
  
  b: float, optional, default: 0.01
      The confidence of unseen ratings.
  
  batch_size: int, optional, default: 128
      The batch size for SGD.
  
  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).
  
  init_params: dictionary, optional, default: None
      List of initial parameters, e.g., init_params = {'U':U, 'V':V}
  
      U: ndarray, shape (n_users,k)
          The user latent factors, optional initialization via init_params.
          
      V: ndarray, shape (n_items,k)
          The item latent factors, optional initialization via init_params.
  
  seed: int, optional, default: None
      Random seed for weight initialization.
  
  References
  ----------
  * Hu, Y., Koren, Y., & Volinsky, C. (2008, December). Collaborative filtering for implicit feedback datasets.     In 2008 Eighth IEEE International Conference on Data Mining (pp. 263-272).
  
  * Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008, December).     One-class collaborative filtering. In 2008 Eighth IEEE International Conference on Data Mining (pp. 502-511).
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docstr: Getting a valid choice of vector measurement in ANNMixin._measures.
  
  Returns
  -------
  measure: MEASURE_DOT
      Dot product aka. inner product
docstr_tok: ["Getting","a","valid","choice","of","vector","measurement","in","ANNMixin._measures",".","Returns","--","--","--","-","measure",":","MEASURE_DOT","Dot","product","aka",".","inner","product"]
code: def get_vector_measure(self):
      """Getting a valid choice of vector measurement in ANNMixin._measures.
  
      Returns
      -------
      measure: MEASURE_DOT
          Dot product aka. inner product
      """
      return MEASURE_DOT
  
file_path: cornac/cornac/models/wmf/recom_wmf.py
imports: import os
  import numpy
  from tqdm.auto import trange
  from ..recommender import Recommender
  from ..recommender import ANNMixin, MEASURE_DOT
  from ...exception import ScoreException
  from ...utils import get_rng
  from ...utils.init_utils import xavier_uniform
label_desc: Use image node_id 6 for calling the WMF obj's underlying member method code with example usage: obj.get_vector_measure() and returns: MEASURE_DOT