package de.lmu.ifi.dbs.elki.datasource.filter.normalization;
/*
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 java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import de.lmu.ifi.dbs.elki.data.IntegerVector;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.type.SimpleTypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.data.type.VectorFieldTypeInformation;
import de.lmu.ifi.dbs.elki.datasource.bundle.MultipleObjectsBundle;
import de.lmu.ifi.dbs.elki.datasource.filter.ObjectFilter;
import de.lmu.ifi.dbs.elki.utilities.pairs.DoubleIntPair;
/**
* Normalize vectors according to their rank in the attributes.
*
* Note: ranks are multiplied by 2, to be able to give ties an integer rank.
* (e.g. first two records are tied at "1" then, followed by the next on "4")
*
* @author Erich Schubert
*/
public class RankTieNormalization implements ObjectFilter {
/**
* Constructor.
*/
public RankTieNormalization() {
super();
}
@Override
public MultipleObjectsBundle filter(MultipleObjectsBundle objects) {
final int len = objects.dataLength();
MultipleObjectsBundle bundle = new MultipleObjectsBundle();
for(int r = 0; r < objects.metaLength(); r++) {
final SimpleTypeInformation<?> type = objects.meta(r);
final List<?> column = objects.getColumn(r);
if(!TypeUtil.NUMBER_VECTOR_FIELD.isAssignableFromType(type)) {
bundle.appendColumn(type, column);
continue;
}
@SuppressWarnings("unchecked")
final List<? extends NumberVector<?, ?>> castColumn = (List<? extends NumberVector<?, ?>>) column;
// Get the replacement type information
final int dim = ((VectorFieldTypeInformation<?>) type).dimensionality();
final VectorFieldTypeInformation<IntegerVector> outType = new VectorFieldTypeInformation<IntegerVector>(IntegerVector.class, dim, IntegerVector.STATIC);
// Output vectors
int[][] posvecs = new int[len][dim];
// Sort for each dimension
// TODO: an int[] array would be enough, if we could use a comparator...
DoubleIntPair[] sorter = new DoubleIntPair[len];
for(int i = 0; i < sorter.length; i++) {
sorter[i] = new DoubleIntPair(Double.NaN, -1);
}
for(int d = 1; d <= dim; d++) {
// fill array
for(int i = 0; i < sorter.length; i++) {
sorter[i].first = castColumn.get(i).doubleValue(d);
sorter[i].second = i;
}
// Sort
Arrays.sort(sorter);
// Transfer positions to output vectors
for(int sta = 0; sta < sorter.length;) {
// Compute ties
int end = sta + 1;
while(end < sorter.length && sorter[sta].first == sorter[end].first) {
end++;
}
final int pos = (sta + end - 1);
for(int i = sta; i < end; i++) {
posvecs[sorter[i].second][d - 1] = pos;
}
sta = end;
}
}
// Prepare output data
final List<IntegerVector> outColumn = new ArrayList<IntegerVector>(len);
for(int i = 0; i < len; i++) {
outColumn.add(new IntegerVector(posvecs[i]));
}
bundle.appendColumn(outType, outColumn);
}
return bundle;
}
}