Examples with ScalingFunction de.lmu.ifi.dbs.elki.utilities.scaling.ScalingFunction used on opensource projects

Example 1 with ScalingFunction

use of de.lmu.ifi.dbs.elki.utilities.scaling.ScalingFunction in project elki by elki-project.

the class JudgeOutlierScores method computeScore.

/**
 * Evaluate a single outlier score result.
 *
 * @param ids Inlier IDs
 * @param outlierIds Outlier IDs
 * @param or Outlier Result to evaluate
 * @return Outlier score result
 * @throws IllegalStateException
 */
protected ScoreResult computeScore(DBIDs ids, DBIDs outlierIds, OutlierResult or) throws IllegalStateException {
    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?
        LOG.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 (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
        double result = or.getScores().doubleValue(iter);
        result = innerScaling.getScaled(scaling.getScaled(result));
        posscore += (1.0 - result);
    }
    for (DBIDIter iter = outlierIds.iter(); iter.valid(); iter.advance()) {
        double result = or.getScores().doubleValue(iter);
        result = innerScaling.getScaled(scaling.getScaled(result));
        negscore += result;
    }
    posscore /= ids.size();
    negscore /= outlierIds.size();
    LOG.verbose("Scores: " + posscore + " " + negscore);
    ArrayList<double[]> s = new ArrayList<>(1);
    s.add(new double[] { (posscore + negscore) * .5, posscore, negscore });
    return new ScoreResult(s);
}