class_docstr: Metric class for convergence evaluation. 
class_docstr_tok: ["Metric","class","for","convergence","evaluation","."]
docstr: Get the sum of each element in the difference matrix between each pair of topics in two topic models.
  
  A small difference between the partially trained models produced by subsequent training iterations can indicate
  that the model has stopped significantly improving and has therefore converged to a local or global optimum.
  
  Parameters
  ----------
  **kwargs
      Key word arguments to override the object's internal attributes.
      Two models of type :class:`~gensim.models.ldamodel.LdaModel`
      are expected using the keys `model` and `other_model`.
  
  Returns
  -------
  float
      The sum of the difference matrix between two trained topic models (usually the same model after two
      subsequent training iterations).
docstr_tok: ["Get","the","sum","of","each","element","in","the","difference","matrix","between","each","pair","of","topics","in","two","topic","models",".","A","small","difference","between","the","partially","trained","models","produced","by","subsequent","training","iterations","can","indicate","that","the","model","has","stopped","significantly","improving","and","has","therefore","converged","to","a","local","or","global","optimum",".","Parameters","--","--","--","--","--","*","*","kwargs","Key","word","arguments","to","override","the","object","'s","internal","attributes",".","Two","models","of","type",":","class",":","`","~gensim.models.ldamodel.LdaModel","`","are","expected","using","the","keys","`","model","`","and","`","other_model","`",".","Returns","--","--","--","-","float","The","sum","of","the","difference","matrix","between","two","trained","topic","models","(","usually","the","same","model","after","two","subsequent","training","iterations",")","."]
code: def get_value(self, **kwargs):
      """Get the sum of each element in the difference matrix between each pair of topics in two topic models.
  
      A small difference between the partially trained models produced by subsequent training iterations can indicate
      that the model has stopped significantly improving and has therefore converged to a local or global optimum.
  
      Parameters
      ----------
      **kwargs
          Key word arguments to override the object's internal attributes.
          Two models of type :class:`~gensim.models.ldamodel.LdaModel`
          are expected using the keys `model` and `other_model`.
  
      Returns
      -------
      float
          The sum of the difference matrix between two trained topic models (usually the same model after two
          subsequent training iterations).
  
      """
      super(ConvergenceMetric, self).set_parameters(**kwargs)
      diff_diagonal, _ = self.model.diff(
          self.other_model,
          self.distance,
          self.num_words,
          self.n_ann_terms,
          self.diagonal,
          self.annotation,
          self.normed,
      )
      return np.sum(diff_diagonal)
  
imports: import gensim
  import logging
  import copy
  import sys
  import numpy
label_desc: Use image node_id 2 for calling the ConvergenceMetric obj's underlying member method code with example usage: obj.get_value() and returns: np