original_docstring:  Warm Start Implementation by Mario Michael Krell
  
          The saved status of the algorithm, including the Matrix M, is used
          as a starting point for the iteration.
          Only the problem has to be lifted up one dimension.
          
docstring: Warm Start Implementation by Mario Michael Krell
  The saved status of the algorithm, including the Matrix M, is used
  as a starting point for the iteration.
  Only the problem has to be lifted up one dimension.
docstring_tokens: Warm Start Implementation by Mario Michael Krell The saved status of the algorithm including the Matrix M is used as a starting point for the iteration . Only the problem has to be lifted up one dimension .
code: def incremental_training(self, data, class_label):
          """ Warm Start Implementation by Mario Michael Krell
  
          The saved status of the algorithm, including the Matrix M, is used
          as a starting point for the iteration.
          Only the problem has to be lifted up one dimension.
          """
          raise NotImplementedError(
              "The node %s does not implement incremental training." \
              % self.__class__.__name__)
code_tokens: def incremental_training ( self , data , class_label ) : raise NotImplementedError ( "The node %s does not implement incremental training." % self . __class__ . __name__ )
short_docstring: Warm Start Implementation by Mario Michael Krell
  The saved status of the algorithm, including the Matrix M, is used
  as a starting point for the iteration.
short_docstring_tokens: Warm Start Implementation by Mario Michael Krell The saved status of the algorithm including the Matrix M is used as a starting point for the iteration .
comment: """ Warm Start Implementation by Mario Michael Krell
  
          The saved status of the algorithm, including the Matrix M, is used
          as a starting point for the iteration.
          Only the problem has to be lifted up one dimension.
          """