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package de.lmu.ifi.dbs.elki.data.uncertain;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
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.Random;
import de.lmu.ifi.dbs.elki.data.DoubleVector;
import de.lmu.ifi.dbs.elki.data.FeatureVector;
import de.lmu.ifi.dbs.elki.data.spatial.SpatialComparable;
import de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.ArrayAdapter;
import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
import de.lmu.ifi.dbs.elki.utilities.io.ByteBufferSerializer;
/**
* Gaussian model for uncertain objects, sampled from a 3-sigma bounding box.
*
* This model does not support covariance, but all distributions are
* axis-aligned.
*
* TODO: currently, only a 3 sigma bounding box is supported.
*
* @author Erich Schubert
*/
public class SimpleGaussianContinuousUncertainObject extends AbstractUncertainObject {
/**
* Vector factory.
*/
public static final FeatureVector.Factory<SimpleGaussianContinuousUncertainObject, ?> FACTORY = new Factory();
/**
* Scaling factor from bounding box width to standard deviations. Bounding box
* is 6 standard deviations in width (three on each side)!
*/
private static final double DIV = 1. / 6.;
/**
* Constructor.
*
* @param bounds Bounding box (3 sigma)
*/
public SimpleGaussianContinuousUncertainObject(SpatialComparable bounds) {
super();
this.bounds = bounds;
}
// TODO: move to an abstract superclass?
@Override
public DoubleVector getCenterOfMass() {
final int dim = bounds.getDimensionality();
double[] mean = new double[dim];
for(int d = 0; d < dim; d++) {
mean[d] = (bounds.getMin(d) + bounds.getMax(d)) * .5;
}
return new DoubleVector(mean);
}
@Override
public DoubleVector drawSample(Random rand) {
final int dim = bounds.getDimensionality();
double[] values = new double[dim];
for(int i = 0, maxtries = DEFAULT_TRY_LIMIT; i < dim;) {
final double l = bounds.getMin(i), u = bounds.getMax(i);
final double s = (u - l) * DIV;
assert(s < Double.POSITIVE_INFINITY);
final double v = rand.nextGaussian() * s + (l + u) * .5;
if(v < l || v > u) {
if(--maxtries == 0) {
throw new AbortException("Could not satisfy bounding box!");
}
continue;
}
values[i++] = v; // Success.
}
return new DoubleVector(values);
}
/**
* Factory class for this data type. Not for public use, use
* {@link de.lmu.ifi.dbs.elki.data.uncertain.uncertainifier.Uncertainifier} to
* derive uncertain objects from certain vectors.
*
* TODO: provide serialization functionality.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
private static class Factory implements FeatureVector.Factory<SimpleGaussianContinuousUncertainObject, Number> {
@Override
public <A> SimpleGaussianContinuousUncertainObject newFeatureVector(A array, ArrayAdapter<? extends Number, A> adapter) {
throw new UnsupportedOperationException();
}
@Override
public ByteBufferSerializer<SimpleGaussianContinuousUncertainObject> getDefaultSerializer() {
return null; // No serializer available.
}
@Override
public Class<? super SimpleGaussianContinuousUncertainObject> getRestrictionClass() {
return SimpleGaussianContinuousUncertainObject.class;
}
}
}
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