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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) 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 de.lmu.ifi.dbs.elki.math.statistics.distribution.LogGammaDistribution;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Robust parameter estimation for the LogGamma distribution.
*
* A modified algorithm for LogGamma distributions.
*
* @author Erich Schubert
* @since 0.6.0
*
* @apiviz.has LogGammaDistribution - - estimates
*/
public class LogGammaLogMADEstimator extends AbstractLogMADEstimator<LogGammaDistribution> {
/**
* Static estimator, more robust to outliers by using the median.
*/
public static final LogGammaLogMADEstimator STATIC = new LogGammaLogMADEstimator();
/**
* Private constructor.
*/
private LogGammaLogMADEstimator() {
// Do not instantiate - use static class
}
@Override
public LogGammaDistribution estimateFromLogMedianMAD(double median, double mad, double shift) {
if (median < Double.MIN_NORMAL) {
throw new ArithmeticException("Cannot estimate Gamma parameters on a distribution with zero median.");
}
if (mad < Double.MIN_NORMAL) {
throw new ArithmeticException("Cannot estimate Gamma parameters on a distribution with zero MAD.");
}
final double theta = median / (mad * mad);
final double k = median * theta;
if (!(k > 0.) || !(theta > 0.)) {
throw new ArithmeticException("LogGamma estimation produced non-positive parameter values: k=" + k + " theta=" + theta);
}
return new LogGammaDistribution(k, theta, shift - 1);
}
@Override
public Class<? super LogGammaDistribution> getDistributionClass() {
return LogGammaDistribution.class;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected LogGammaLogMADEstimator makeInstance() {
return STATIC;
}
}
}
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