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package de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski;
/*
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 de.lmu.ifi.dbs.elki.data.SparseNumberVector;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Manhattan distance function. Optimized for sparse vectors.
*
* @author Erich Schubert
*/
public class SparseManhattanDistanceFunction extends SparseLPNormDistanceFunction {
/**
* Static instance
*/
public static final SparseManhattanDistanceFunction STATIC = new SparseManhattanDistanceFunction();
/**
* Constructor.
*
* @deprecated Use static instance instead.
*/
@Deprecated
public SparseManhattanDistanceFunction() {
super(1.);
}
@Override
public double doubleDistance(SparseNumberVector<?> v1, SparseNumberVector<?> v2) {
// Get the bit masks
double accu = 0.;
int i1 = v1.iter(), i2 = v2.iter();
while(v1.iterValid(i1) && v2.iterValid(i2)) {
final int d1 = v1.iterDim(i1), d2 = v2.iterDim(i2);
if(d1 < d2) {
// In first only
final double val = Math.abs(v1.iterDoubleValue(i1));
accu += val;
i1 = v1.iterAdvance(i1);
}
else if(d2 < d1) {
// In second only
final double val = Math.abs(v2.iterDoubleValue(i2));
accu += val;
i2 = v2.iterAdvance(i2);
}
else {
// Both vectors have a value.
final double val = Math.abs(v1.iterDoubleValue(i1) - v2.iterDoubleValue(i2));
accu += val;
i1 = v1.iterAdvance(i1);
i2 = v2.iterAdvance(i2);
}
}
while(v1.iterValid(i1)) {
// In first only
final double val = Math.abs(v1.iterDoubleValue(i1));
accu += val;
i1 = v1.iterAdvance(i1);
}
while(v2.iterValid(i2)) {
// In second only
final double val = Math.abs(v2.iterDoubleValue(i2));
accu += val;
i2 = v2.iterAdvance(i2);
}
return accu;
}
@Override
public double doubleNorm(SparseNumberVector<?> v1) {
double accu = 0.;
for(int it = v1.iter(); v1.iterValid(it); it = v1.iterAdvance(it)) {
final double val = Math.abs(v1.iterDoubleValue(it));
accu += val;
}
return accu;
}
/**
* Parameterizer
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected SparseManhattanDistanceFunction makeInstance() {
return SparseManhattanDistanceFunction.STATIC;
}
}
}
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