code: def computeRawAnomalyScore(activeColumns, prevPredictedColumns):
    
    nActiveColumns = len(activeColumns)
    if nActiveColumns > 0:
      # Test whether each element of a 1-D array is also present in a second
      # array. Sum to get the total # of columns that are active and were
      # predicted.
      score = numpy.in1d(activeColumns, prevPredictedColumns).sum()
      # Get the percent of active columns that were NOT predicted, that is
      # our anomaly score.
      score = (nActiveColumns - score) / float(nActiveColumns)
    else:
      # There are no active columns.
      score = 0.0
  
    return score
docs: Computes the raw anomaly score.
  
    The raw anomaly score is the fraction of active columns not predicted.
  
    :param activeColumns: array of active column indices
    :param prevPredictedColumns: array of columns indices predicted in prev step
    :returns: anomaly score 0..1 (float)