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package de.lmu.ifi.dbs.elki.algorithm.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.algorithm.AbstractAlgorithm;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.database.relation.RelationUtil;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.math.MathUtil;
import de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
import de.lmu.ifi.dbs.elki.result.outlier.BasicOutlierScoreMeta;
import de.lmu.ifi.dbs.elki.result.outlier.InvertedOutlierScoreMeta;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Flag;
/**
* Outlier have smallest GMOD_PROB: the outlier scores is the
* <em>probability density</em> of the assumed distribution.
*
* @author Lisa Reichert
*
* @param <V> Vector type
*/
@Title("Gaussian Model Outlier Detection")
@Description("Fit a multivariate gaussian model onto the data, and use the PDF to compute an outlier score.")
public class GaussianModel<V extends NumberVector<?>> extends AbstractAlgorithm<OutlierResult> implements OutlierAlgorithm {
/**
* The logger for this class.
*/
private static final Logging LOG = Logging.getLogger(GaussianModel.class);
/**
* OptionID for inversion flag.
*/
public static final OptionID INVERT_ID = new OptionID("gaussod.invert", "Invert the value range to [0:1], with 1 being outliers instead of 0.");
/**
* Small value to increment diagonally of a matrix in order to avoid
* singularity before building the inverse.
*/
private static final double SINGULARITY_CHEAT = 1E-9;
/**
* Invert the result
*/
private boolean invert = false;
/**
* Constructor with actual parameters.
*
* @param invert inversion flag.
*/
public GaussianModel(boolean invert) {
super();
this.invert = invert;
}
/**
* Run the algorithm
*
* @param relation Data relation
* @return Outlier result
*/
public OutlierResult run(Relation<V> relation) {
DoubleMinMax mm = new DoubleMinMax();
// resulting scores
WritableDoubleDataStore oscores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT);
// Compute mean and covariance Matrix
CovarianceMatrix temp = CovarianceMatrix.make(relation);
Vector mean = temp.getMeanVector(relation).getColumnVector();
// debugFine(mean.toString());
Matrix covarianceMatrix = temp.destroyToNaiveMatrix();
// debugFine(covarianceMatrix.toString());
Matrix covarianceTransposed = covarianceMatrix.cheatToAvoidSingularity(SINGULARITY_CHEAT).inverse();
// Normalization factors for Gaussian PDF
final double fakt = (1.0 / (Math.sqrt(Math.pow(MathUtil.TWOPI, RelationUtil.dimensionality(relation)) * covarianceMatrix.det())));
// for each object compute Mahalanobis distance
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
Vector x = relation.get(iditer).getColumnVector().minusEquals(mean);
// Gaussian PDF
final double mDist = x.transposeTimesTimes(covarianceTransposed, x);
final double prob = fakt * Math.exp(-mDist / 2.0);
mm.put(prob);
oscores.putDouble(iditer, prob);
}
final OutlierScoreMeta meta;
if(invert) {
double max = mm.getMax() != 0 ? mm.getMax() : 1.;
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
oscores.putDouble(iditer, (max - oscores.doubleValue(iditer)) / max);
}
meta = new BasicOutlierScoreMeta(0.0, 1.0);
}
else {
meta = new InvertedOutlierScoreMeta(mm.getMin(), mm.getMax(), 0.0, Double.POSITIVE_INFINITY);
}
Relation<Double> res = new MaterializedRelation<Double>("Gaussian Model Outlier Score", "gaussian-model-outlier", TypeUtil.DOUBLE, oscores, relation.getDBIDs());
return new OutlierResult(meta, res);
}
@Override
public TypeInformation[] getInputTypeRestriction() {
return TypeUtil.array(TypeUtil.NUMBER_VECTOR_FIELD);
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<V extends NumberVector<?>> extends AbstractParameterizer {
protected boolean invert = false;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final Flag flag = new Flag(INVERT_ID);
if(config.grab(flag)) {
invert = flag.getValue();
}
}
@Override
protected GaussianModel<V> makeInstance() {
return new GaussianModel<V>(invert);
}
}
}
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