def computeRawAnomalyScore(activeColumns, prevPredictedColumns):
 nActiveColumns = len(activeColumns)
 if (nActiveColumns > 0):
  score = numpy.in1d(activeColumns, prevPredictedColumns).sum()
  score = ((nActiveColumns - score) / float(nActiveColumns))
 else:
  score = 0.0
 return score

def computeRawAnomalyScore ( activeColumns , prevPredictedColumns ) : nActiveColumns = len ( activeColumns ) if ( nActiveColumns > 0 ) : score = numpy . in1d ( activeColumns , prevPredictedColumns ) . sum ( ) score = ( ( nActiveColumns - score ) / float ( nActiveColumns ) ) else : score = 0.0 return score
computes the raw anomaly score .