original_docstring:  Special mapping for multi-class classification
  
          It enables label filtering for one vs. REST and one vs. one case.
          Furthermore, the method measures time for the training segments.
          
docstring: Special mapping for multi-class classification
  It enables label filtering for one vs. REST and one vs. one case.
  Furthermore, the method measures time for the training segments.
docstring_tokens: Special mapping for multi - class classification It enables label filtering for one vs . REST and one vs . one case . Furthermore the method measures time for the training segments .
code: def train(self, data, label):
          """ Special mapping for multi-class classification
  
          It enables label filtering for one vs. REST and one vs. one case.
          Furthermore, the method measures time for the training segments.
          """
          # one vs. REST case
          if "REST" in self.classes and not label in self.classes:
              label = "REST"
          # one vs. one case
          if not self.multinomial and len(self.classes) == 2 and \
                  not label in self.classes:
              return
          start_time_stamp = timeit.default_timer()
          super(RegularizedClassifierBase, self).train(data, label)
          stop_time_stamp = timeit.default_timer()
          if not self.classifier_information.has_key("Training_time(classifier)"):
              self.classifier_information["Training_time(classifier)"] = \
                  stop_time_stamp - start_time_stamp
          else:
              self.classifier_information["Training_time(classifier)"] += \
                  stop_time_stamp - start_time_stamp
code_tokens: def train ( self , data , label ) : if "REST" in self . classes and not label in self . classes : label = "REST" if not self . multinomial and len ( self . classes ) == 2 and not label in self . classes : return start_time_stamp = timeit . default_timer ( ) super ( RegularizedClassifierBase , self ) . train ( data , label ) stop_time_stamp = timeit . default_timer ( ) if not self . classifier_information . has_key ( "Training_time(classifier)" ) : self . classifier_information [ "Training_time(classifier)" ] = stop_time_stamp - start_time_stamp else : self . classifier_information [ "Training_time(classifier)" ] += stop_time_stamp - start_time_stamp
short_docstring: Special mapping for multi-class classification
  It enables label filtering for one vs. REST and one vs. one case.
short_docstring_tokens: Special mapping for multi - class classification It enables label filtering for one vs . REST and one vs . one case .
comment: """ Special mapping for multi-class classification
  
          It enables label filtering for one vs. REST and one vs. one case.
          Furthermore, the method measures time for the training segments.
          """$SEP$# one vs. REST case$SEP$# one vs. one case