class_docstr: Online Non-Negative Matrix Factorization.
  
  `Renbo Zhao et al :"Online Nonnegative Matrix Factorization with Outliers" <https://arxiv.org/abs/1604.02634>`_
class_docstr_tok: ["Online","Non-Negative","Matrix","Factorization",".","`","Renbo","Zhao","et","al",":","''","Online","Nonnegative","Matrix","Factorization","with","Outliers","''","<","https",":","\/\/arxiv.org\/abs\/1604.02634",">","`","_"]
code: def l2_norm(self, v):
      Wt = self._W.T
  
      l2 = 0
  
      for doc, doc_topics in zip(v.T, self._h.T):
          l2 += np.sum(np.square((doc - doc_topics.dot(Wt))))
  
      return np.sqrt(l2)
  
imports: import collections.abc
  import logging
  import numpy
  import scipy.sparse
  from scipy.stats import halfnorm
  from gensim import interfaces
  from gensim import matutils
  from gensim import utils
  from gensim.interfaces import TransformedCorpus
  from gensim.models import basemodel, CoherenceModel
  from gensim.models.nmf_pgd import solve_h
label_desc: Use image node_id 11 for calling the Nmf obj's underlying member method code with example usage: obj.l2_norm(v) and returns: np