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package de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator;
/*
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.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.math.statistics.distribution.Distribution;
import de.lmu.ifi.dbs.elki.math.statistics.distribution.UniformDistribution;
import de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.NumberArrayAdapter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Estimate the uniform distribution by computing min and max.
*
* @author Erich Schubert
*
* @apiviz.has UniformDistribution - - estimates
*/
public class UniformMinMaxEstimator implements DistributionEstimator<UniformDistribution> {
/**
* The most naive estimator possible: uses minimum and maximum.
*/
public static final UniformMinMaxEstimator STATIC = new UniformMinMaxEstimator();
/**
* Constructor. Private: use static instance!
*/
private UniformMinMaxEstimator() {
super();
}
@Override
public <A> UniformDistribution estimate(A data, NumberArrayAdapter<?, A> adapter) {
final int len = adapter.size(data);
DoubleMinMax mm = new DoubleMinMax();
for (int i = 0; i < len; i++) {
final double val = adapter.getDouble(data, i);
if (val > Double.NEGATIVE_INFINITY && val < Double.POSITIVE_INFINITY) {
mm.put(val);
}
}
return estimate(mm);
}
/**
* Estimate parameters from minimum and maximum observed.
*
* @param mm Minimum and Maximum
* @return Estimation
*/
public UniformDistribution estimate(DoubleMinMax mm) {
return new UniformDistribution(Math.max(mm.getMin(), -Double.MAX_VALUE), Math.min(mm.getMax(), Double.MAX_VALUE));
}
/**
* Estimate parameters from minimum and maximum observed.
*
* @param min Minimum
* @param max Maximum
* @return Estimation
*/
public Distribution estimate(double min, double max) {
return new UniformDistribution(min, max);
}
@Override
public Class<? super UniformDistribution> getDistributionClass() {
return UniformDistribution.class;
}
@Override
public String toString() {
return this.getClass().getSimpleName();
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected UniformMinMaxEstimator makeInstance() {
return STATIC;
}
}
}
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