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package de.lmu.ifi.dbs.elki.distance.distancefunction.subspace;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2013
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 java.util.BitSet;
import de.lmu.ifi.dbs.elki.data.FeatureVector;
import de.lmu.ifi.dbs.elki.distance.distancefunction.AbstractPrimitiveDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDoubleDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
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.constraints.GreaterEqualConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.ListEachConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntListParameter;
/**
* Provides a distance function that computes the distance (which is a double
* distance) between feature vectors only in specified dimensions.
*
* @author Elke Achtert
* @param <V> the type of FeatureVector to compute the distances in between
*/
public abstract class AbstractDimensionsSelectingDoubleDistanceFunction<V extends FeatureVector<?>> extends AbstractPrimitiveDistanceFunction<V, DoubleDistance> implements PrimitiveDoubleDistanceFunction<V>, DimensionSelectingSubspaceDistanceFunction<V, DoubleDistance> {
/**
* Dimensions parameter.
*/
public static final OptionID DIMS_ID = new OptionID("distance.dims", "a comma separated array of integer values, where 0 <= d_i < the dimensionality of the feature space specifying the dimensions to be considered for distance computation. If this parameter is not set, no dimensions will be considered, i.e. the distance between two objects is always 0.");
/**
* The dimensions to be considered for distance computation.
*/
protected BitSet dimensions;
/**
* Constructor.
*
* @param dimensions
*/
public AbstractDimensionsSelectingDoubleDistanceFunction(BitSet dimensions) {
super();
this.dimensions = dimensions;
}
@Override
public DoubleDistance distance(V o1, V o2) {
return new DoubleDistance(doubleDistance(o1, o2));
}
@Override
public BitSet getSelectedDimensions() {
return dimensions;
}
@Override
public void setSelectedDimensions(BitSet dimensions) {
this.dimensions.clear();
this.dimensions.or(dimensions);
}
@Override
public DoubleDistance getDistanceFactory() {
return DoubleDistance.FACTORY;
}
@Override
public boolean equals(Object obj) {
if (obj == null) {
return false;
}
if (!this.getClass().equals(obj.getClass())) {
return false;
}
return this.dimensions.equals(((AbstractDimensionsSelectingDoubleDistanceFunction<?>) obj).dimensions);
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public abstract static class Parameterizer extends AbstractParameterizer {
protected BitSet dimensions = null;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
dimensions = new BitSet();
final IntListParameter dimsP = new IntListParameter(DIMS_ID);
dimsP.addConstraint(new ListEachConstraint<Integer>(new GreaterEqualConstraint(0)));
dimsP.setOptional(true);
if (config.grab(dimsP)) {
for (int d : dimsP.getValue()) {
dimensions.set(d);
}
}
}
}
}
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