if(scaling instanceof OutlierScalingFunction) {
OutlierScalingFunction oscaling = (OutlierScalingFunction) scaling;
oscaling.prepare(or);
}
final ScalingFunction innerScaling;
// If we have useful (finite) min/max, use these for binning.
double min = scaling.getMin();
double max = scaling.getMax();
if(Double.isInfinite(min) || Double.isNaN(min) || Double.isInfinite(max) || Double.isNaN(max)) {
innerScaling = new IdentityScaling();
// TODO: does the outlier score give us this guarantee?
logger.warning("JudgeOutlierScores expects values between 0.0 and 1.0, but we don't have such a guarantee by the scaling function: min:" + min + " max:" + max);
}
else {
if(min == 0.0 && max == 1.0) {
innerScaling = new IdentityScaling();
}
else {
innerScaling = new LinearScaling(1.0 / (max - min), -min);
}
}
double posscore = 0.0;
double negscore = 0.0;
// fill histogram with values of each object
for(DBID id : ids) {
double result = or.getScores().get(id);
result = innerScaling.getScaled(scaling.getScaled(result));
posscore += (1.0 - result);
}
for(DBID id : outlierIds) {
double result = or.getScores().get(id);
result = innerScaling.getScaled(scaling.getScaled(result));
negscore += result;
}
posscore /= ids.size();
negscore /= outlierIds.size();