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.StatisticalMoments;
import de.lmu.ifi.dbs.elki.math.statistics.distribution.ExponentiallyModifiedGaussianDistribution;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;

/**
 * Naive distribution estimation using mean and sample variance.
 * 
 * @author Erich Schubert
 * 
 * @apiviz.has ExponentiallyModifiedGaussianDistribution - - estimates
 */
@Reference(authors = "J. Olivier, M. M. Norberg", title = "Positively skewed data: Revisiting the Box-Cox power transformation", booktitle = "International Journal of Psychological Research Vol. 3 No. 1")
public class EMGOlivierNorbergEstimator extends AbstractMOMEstimator<ExponentiallyModifiedGaussianDistribution> {
  /**
   * Static estimator class.
   */
  public static EMGOlivierNorbergEstimator STATIC = new EMGOlivierNorbergEstimator();

  /**
   * Private constructor, use static instance!
   */
  private EMGOlivierNorbergEstimator() {
    // Do not instantiate
  }

  @Override
  public ExponentiallyModifiedGaussianDistribution estimateFromStatisticalMoments(StatisticalMoments moments) {
    // Avoid NaN by disallowing negative kurtosis.
    final double halfsk13 = Math.pow(Math.max(0., moments.getSampleSkewness() * .5), 1. / 3.);
    final double st = moments.getSampleStddev();
    final double mu = moments.getMean() - st * halfsk13;
    // Note: we added "abs" here, to avoid even more NaNs.
    final double si = st * Math.sqrt(Math.abs((1. + halfsk13) * (1. - halfsk13)));
    // One more workaround to ensure finite lambda...
    final double la = (halfsk13 > 0) ? 1 / (st * halfsk13) : 1;
    return new ExponentiallyModifiedGaussianDistribution(mu, si, la);
  }

  @Override
  public Class<? super ExponentiallyModifiedGaussianDistribution> getDistributionClass() {
    return ExponentiallyModifiedGaussianDistribution.class;
  }

  /**
   * Parameterization class.
   * 
   * @author Erich Schubert
   * 
   * @apiviz.exclude
   */
  public static class Parameterizer extends AbstractParameterizer {
    @Override
    protected EMGOlivierNorbergEstimator makeInstance() {
      return STATIC;
    }
  }
}