def _recomputeRecordFromKNN(self, record):
    """
    returns the classified labeling of 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
def _recomputeRecordFromKNN(self, record):
    """
    returns the classified labeling of 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
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
returns the classified labeling of record
returns the classified labeling of record