code: def _recomputeRecordFromKNN(self, record):
      
      inputs = {
        "categoryIn": [None],
        "bottomUpIn": self._getStateAnomalyVector(record),
      }
  
      outputs = {"categoriesOut": numpy.zeros((1,)),
                 "bestPrototypeIndices":numpy.zeros((1,)),
                 "categoryProbabilitiesOut":numpy.zeros((1,))}
  
      # Only use points before record to classify and after the wait period.
      classifier_indexes = numpy.array(
          self._knnclassifier.getParameter('categoryRecencyList'))
      valid_idx = numpy.where(
          (classifier_indexes >= self.getParameter('trainRecords')) &
          (classifier_indexes < record.ROWID)
        )[0].tolist()
  
      if len(valid_idx) == 0:
        return None
  
      self._knnclassifier.setParameter('inferenceMode', None, True)
      self._knnclassifier.setParameter('learningMode', None, False)
      self._knnclassifier.compute(inputs, outputs)
      self._knnclassifier.setParameter('learningMode', None, True)
  
      classifier_distances = self._knnclassifier.getLatestDistances()
      valid_distances = classifier_distances[valid_idx]
      if valid_distances.min() <= self._classificationMaxDist:
        classifier_indexes_prev = classifier_indexes[valid_idx]
        rowID = classifier_indexes_prev[valid_distances.argmin()]
        indexID = numpy.where(classifier_indexes == rowID)[0][0]
        category = self._knnclassifier.getCategoryList()[indexID]
        return category
      return None
docs: returns the classified labeling of record