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package de.lmu.ifi.dbs.elki.math.scales;
/*
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.NumberVector;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.database.relation.RelationUtil;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
/**
* Scales helper class. Currently, this will just compute a linear scale for
* each axis. It is planned to add functionality to include some analysis to be
* able to automatically choose log scales when appropriate.
*
* @author Erich Schubert
*
* @apiviz.has LinearScale oneway - - computes
*/
public final class Scales {
/**
* Fake constructor.
*/
private Scales() {
// Do not instantiate.
}
/**
* Compute a linear scale for each dimension.
*
* @param <O> vector type
* @param db Database
* @return Scales, indexed starting with 0 (like Vector, not database
* objects!)
*/
public static <O extends NumberVector<? extends Number>> LinearScale[] calcScales(Relation<O> db) {
if (db == null) {
throw new AbortException("No database was given to Scales.calcScales.");
}
int dim = RelationUtil.dimensionality(db);
DoubleMinMax[] minmax = DoubleMinMax.newArray(dim);
LinearScale[] scales = new LinearScale[dim];
// analyze data
for (DBIDIter iditer = db.iterDBIDs(); iditer.valid(); iditer.advance()) {
O v = db.get(iditer);
for (int d = 0; d < dim; d++) {
minmax[d].put(v.doubleValue(d));
}
}
// generate scales
for (int d = 0; d < dim; d++) {
scales[d] = new LinearScale(minmax[d].getMin(), minmax[d].getMax());
}
return scales;
}
}
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