original_docstring:  Manipulation of training set for updating the svm 
docstring: Manipulation of training set for updating the svm
docstring_tokens: Manipulation of training set for updating the svm
code: def _inc_train(self, data, class_label=None):
          """ Manipulation of training set for updating the svm """
          #######################################################################
          if not self.classifier_information.has_key("Inc_iterations"):
              self.classifier_information["Inc_iterations"] = 1
          else:
              self.classifier_information["Inc_iterations"] += 1
  
          if self.u_retrain:
              class_label = self._execute(data).label
  
          start_time_stamp = timeit.default_timer()
          #######################################################################
  
          self.adapt_training_set(data, class_label)
  
          #######################################################################
          stop_time_stamp = timeit.default_timer()
          if not self.classifier_information.has_key("Retraining_time(classifier)"):
              self.classifier_information["Retraining_time(classifier)"] = \
                  stop_time_stamp - start_time_stamp
          else:
              self.classifier_information["Retraining_time(classifier)"] += \
                  stop_time_stamp - start_time_stamp
          #######################################################################
code_tokens: def _inc_train ( self , data , class_label = None ) : if not self . classifier_information . has_key ( "Inc_iterations" ) : self . classifier_information [ "Inc_iterations" ] = 1 else : self . classifier_information [ "Inc_iterations" ] += 1 if self . u_retrain : class_label = self . _execute ( data ) . label start_time_stamp = timeit . default_timer ( ) self . adapt_training_set ( data , class_label ) stop_time_stamp = timeit . default_timer ( ) if not self . classifier_information . has_key ( "Retraining_time(classifier)" ) : self . classifier_information [ "Retraining_time(classifier)" ] = stop_time_stamp - start_time_stamp else : self . classifier_information [ "Retraining_time(classifier)" ] += stop_time_stamp - start_time_stamp
short_docstring: Manipulation of training set for updating the svm
short_docstring_tokens: Manipulation of training set for updating the svm
comment: """ Manipulation of training set for updating the svm """$SEP$#######################################################################$SEP$#######################################################################$SEP$#######################################################################$SEP$#######################################################################