src/nupic/frameworks/opf/htm_prediction_model_classifier_helper.py
def _deleteRangeFromKNN(self, start=0, end=None):
    """
    This method will remove any stored records within the range from start to
    end. Noninclusive of end.

    parameters
    ------------
    start - integer representing the ROWID of the start of the deletion range,
    end - integer representing the ROWID of the end of the deletion range,
      if None, it will default to end.
    """
    classifier = self.htm_prediction_model._getAnomalyClassifier()
    knn = classifier.getSelf()._knn

    prototype_idx = numpy.array(
      classifier.getSelf().getParameter('categoryRecencyList'))

    if end is None:
      end = prototype_idx.max() + 1

    idsIdxToDelete = numpy.logical_and(prototype_idx >= start,
                                       prototype_idx < end)
    idsToDelete = prototype_idx[idsIdxToDelete]

    nProtos = knn._numPatterns
    knn.removeIds(idsToDelete.tolist())
    assert knn._numPatterns == nProtos - len(idsToDelete)
def _deleteRangeFromKNN(self, start=0, end=None):
    """
    This method will remove any stored records within the range from start to
    end. Noninclusive of end.

    parameters
    ------------
    start - integer representing the ROWID of the start of the deletion range,
    end - integer representing the ROWID of the end of the deletion range,
      if None, it will default to end.
    """
    classifier = self.htm_prediction_model._getAnomalyClassifier()
    knn = classifier.getSelf()._knn

    prototype_idx = numpy.array(
      classifier.getSelf().getParameter('categoryRecencyList'))

    if end is None:
      end = prototype_idx.max() + 1

    idsIdxToDelete = numpy.logical_and(prototype_idx >= start,
                                       prototype_idx < end)
    idsToDelete = prototype_idx[idsIdxToDelete]

    nProtos = knn._numPatterns
    knn.removeIds(idsToDelete.tolist())
    assert knn._numPatterns == nProtos - len(idsToDelete)
def _deleteRangeFromKNN ( self , start = 0 , end = None ) : classifier = self . htm_prediction_model . _getAnomalyClassifier ( ) knn = classifier . getSelf ( ) . _knn prototype_idx = numpy . array ( classifier . getSelf ( ) . getParameter ( 'categoryRecencyList' ) ) if end is None : end = prototype_idx . max ( ) + 1 idsIdxToDelete = numpy . logical_and ( prototype_idx >= start , prototype_idx < end ) idsToDelete = prototype_idx [ idsIdxToDelete ] nProtos = knn . _numPatterns knn . removeIds ( idsToDelete . tolist ( ) ) assert knn . _numPatterns == nProtos - len ( idsToDelete )
This method will remove any stored records within the range from start to
    end. Noninclusive of end.

    parameters
    ------------
    start - integer representing the ROWID of the start of the deletion range,
    end - integer representing the ROWID of the end of the deletion range,
      if None, it will default to end.
This method will remove any stored records within the range from start to end . Noninclusive of end .