package de.lmu.ifi.dbs.elki.utilities.scaling.outlier;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures

Copyright (C) 2011
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.database.ids.DBID;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.math.MathUtil;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;

/**
 * Scaling that can map arbitrary values to a probability in the range of [0:1],
 * by assuming a Gamma distribution on the data and evaluating the Gamma CDF.
 * 
 * @author Erich Schubert
 */
@Reference(authors = "H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek", title = "Interpreting and Unifying Outlier Scores", booktitle = "Proc. 11th SIAM International Conference on Data Mining (SDM), Mesa, AZ, 2011", url = "http://www.dbs.ifi.lmu.de/~zimek/publications/SDM2011/SDM11-outlier-preprint.pdf")
public class MinusLogGammaScaling extends OutlierGammaScaling {
  /**
   * Maximum value seen
   */
  double max = 0;

  /**
   * Minimum value (after log step, so maximum again)
   */
  double mlogmax;

  /**
   * Constructor.
   */
  public MinusLogGammaScaling() {
    super(false);
  }

  @Override
  protected double preScale(double score) {
    assert (max > 0) : "prepare() was not run prior to using the scaling function.";
    return -Math.log(score / max) / mlogmax;
  }

  @Override
  public void prepare(OutlierResult or) {
    meta = or.getOutlierMeta();
    // Determine Minimum and Maximum.
    DoubleMinMax mm = new DoubleMinMax();
    for(DBID id : or.getScores().iterDBIDs()) {
      double score = or.getScores().get(id);
      if(!Double.isNaN(score) && !Double.isInfinite(score)) {
        mm.put(score);
      }
    }
    max = mm.getMax();
    mlogmax = -Math.log(mm.getMin() / max);
    // with the prescaling, do Gamma Scaling.
    MeanVariance mv = new MeanVariance();
    for(DBID id : or.getScores().iterDBIDs()) {
      double score = or.getScores().get(id);
      score = preScale(score);
      if(!Double.isNaN(score) && !Double.isInfinite(score)) {
        mv.put(score);
      }
    }
    final double mean = mv.getMean();
    final double var = mv.getSampleVariance();
    k = (mean * mean) / var;
    theta = var / mean;
    atmean = MathUtil.regularizedGammaP(k, mean / theta);
    // logger.warning("Mean:"+mean+" Var:"+var+" Theta: "+theta+" k: "+k+" valatmean"+atmean);
  }

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