package de.lmu.ifi.dbs.elki.utilities.scaling.outlier;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import de.lmu.ifi.dbs.elki.database.ids.DBID;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
import de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Scaling that can map arbitrary values to a probability in the range of [0:1],
* by assuming a Gamma distribution on the data and evaluating the Gamma CDF.
*
* @author Erich Schubert
*/
@Reference(authors = "H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek", title = "Interpreting and Unifying Outlier Scores", booktitle = "Proc. 11th SIAM International Conference on Data Mining (SDM), Mesa, AZ, 2011", url = "http://siam.omnibooksonline.com/2011datamining/data/papers/018.pdf")
public class MinusLogGammaScaling extends OutlierGammaScaling {
/**
* Maximum value seen
*/
double max = 0;
/**
* Minimum value (after log step, so maximum again)
*/
double mlogmax;
/**
* Constructor.
*/
public MinusLogGammaScaling() {
super(false);
}
@Override
protected double preScale(double score) {
assert (max > 0) : "prepare() was not run prior to using the scaling function.";
return -Math.log(score / max) / mlogmax;
}
@Override
public void prepare(OutlierResult or) {
meta = or.getOutlierMeta();
// Determine Minimum and Maximum.
DoubleMinMax mm = new DoubleMinMax();
for(DBID id : or.getScores().iterDBIDs()) {
double score = or.getScores().get(id);
if(!Double.isNaN(score) && !Double.isInfinite(score)) {
mm.put(score);
}
}
max = mm.getMax();
mlogmax = -Math.log(mm.getMin() / max);
// with the prescaling, do Gamma Scaling.
MeanVariance mv = new MeanVariance();
for(DBID id : or.getScores().iterDBIDs()) {
double score = or.getScores().get(id);
score = preScale(score);
if(!Double.isNaN(score) && !Double.isInfinite(score)) {
mv.put(score);
}
}
final double mean = mv.getMean();
final double var = mv.getSampleVariance();
k = (mean * mean) / var;
theta = var / mean;
atmean = GammaDistribution.regularizedGammaP(k, mean / theta);
// logger.warning("Mean:"+mean+" Var:"+var+" Theta: "+theta+" k: "+k+" valatmean"+atmean);
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected MinusLogGammaScaling makeInstance() {
return new MinusLogGammaScaling();
}
}
}