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package de.lmu.ifi.dbs.elki.data.uncertain.uncertainifier;
/*
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.Factory;
import de.lmu.ifi.dbs.elki.data.uncertain.UncertainObject;
import de.lmu.ifi.dbs.elki.data.uncertain.WeightedDiscreteUncertainObject;
import de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.NumberArrayAdapter;
/**
* Class to generate weighted discrete uncertain objects.
*
* This is a second-order generator: it requires the use of another generator to
* sample from (e.g. {@link UniformUncertainifier} or
* {@link SimpleGaussianUncertainifier}).
*
* @author Erich Schubert
* @since 0.7.0
*
* @apiviz.has WeightedDiscreteUncertainObject
*/
public class WeightedDiscreteUncertainifier extends AbstractDiscreteUncertainifier<WeightedDiscreteUncertainObject> {
/**
* Constructor.
*
* @param inner Inner uncertainifier
* @param minQuant Minimum number of samples
* @param maxQuant Maximum number of samples
*/
public WeightedDiscreteUncertainifier(Uncertainifier<?> inner, int minQuant, int maxQuant) {
super(inner, minQuant, maxQuant);
}
@Override
public <A> WeightedDiscreteUncertainObject newFeatureVector(Random rand, A array, NumberArrayAdapter<?, A> adapter) {
UncertainObject uo = inner.newFeatureVector(rand, array, adapter);
final int distributionSize = rand.nextInt((maxQuant - minQuant) + 1) + minQuant;
DoubleVector[] samples = new DoubleVector[distributionSize];
double[] weights = new double[distributionSize];
double wsum = 0.;
for(int i = 0; i < distributionSize; i++) {
samples[i] = uo.drawSample(rand);
double w = rand.nextDouble();
while(w <= 0.) { // Avoid zero weights.
w = rand.nextDouble();
}
weights[i] = w;
wsum += w;
}
// Normalize to a total weight of 1:
assert(wsum > 0.);
for(int i = 0; i < distributionSize; i++) {
weights[i] /= wsum;
}
return new WeightedDiscreteUncertainObject(samples, weights);
}
@Override
public Factory<WeightedDiscreteUncertainObject, ?> getFactory() {
return WeightedDiscreteUncertainObject.FACTORY;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractDiscreteUncertainifier.Parameterizer {
@Override
protected WeightedDiscreteUncertainifier makeInstance() {
return new WeightedDiscreteUncertainifier(inner, minQuant, maxQuant);
}
}
}
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