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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.MeanVariance;
import de.lmu.ifi.dbs.elki.math.statistics.distribution.WeibullDistribution;
import de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.NumberArrayAdapter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
/**
* Naive parameter estimation via least squares.
*
* TODO: this doesn't seem to work very well yet. Buggy?
*
* TODO: the naming is misleading: while it uses some method of moments, it
* doesn't use "the" statistical moments.
*
* @author Erich Schubert
* @since 0.6.0
*
* @apiviz.has WeibullDistribution - - estimates
*/
public class WeibullLogMOMEstimator implements DistributionEstimator<WeibullDistribution> {
/**
* The naive least-squares estimator.
*/
public static final WeibullLogMOMEstimator STATIC = new WeibullLogMOMEstimator();
/**
* Constructor. Private: use static instance!
*/
private WeibullLogMOMEstimator() {
super();
}
@Override
public <A> WeibullDistribution estimate(A data, NumberArrayAdapter<?, A> adapter) {
double beta1 = 0.0, beta3 = 0.0;
MeanVariance mvlogx = new MeanVariance();
int size = adapter.size(data);
double size1 = size + 1.;
for (int i = 0; i < size; i++) {
final double val = adapter.getDouble(data, i);
if (!(val > 0)) {
throw new ArithmeticException("Cannot least squares fit weibull to a data set which includes non-positive values: " + val);
}
final double yi = Math.log(-Math.log((size - i) / size1));
final double logxi = Math.log(val);
beta1 += yi * logxi;
beta3 += yi;
mvlogx.put(logxi);
}
double k = (beta1 / size - beta3 / size * mvlogx.getMean()) / mvlogx.getSampleVariance();
double lambda = 1. / Math.exp(beta3 / size - k * mvlogx.getMean());
return new WeibullDistribution(k, lambda);
}
@Override
public Class<? super WeibullDistribution> getDistributionClass() {
return WeibullDistribution.class;
}
@Override
public String toString() {
return this.getClass().getSimpleName();
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected WeibullLogMOMEstimator makeInstance() {
return STATIC;
}
}
}
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