package de.lmu.ifi.dbs.elki.datasource.filter.normalization.instancewise;

/*
 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.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.type.SimpleTypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.datasource.filter.normalization.AbstractStreamNormalization;
import de.lmu.ifi.dbs.elki.utilities.Alias;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;

/**
 * Normalize histograms by scaling them to L1 norm 1, then taking the square
 * root in each attribute.
 * 
 * Using Euclidean distance (linear kernel) and this transformation is the same
 * as using Hellinger distance:
 * {@link de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.HellingerDistanceFunction}
 * 
 * @author Erich Schubert
 * 
 * @param <V> vector type
 */
@Alias({ "de.lmu.ifi.dbs.elki.datasource.filter.normalization.HellingerHistogramNormalization" })
public class HellingerHistogramNormalization<V extends NumberVector> extends AbstractStreamNormalization<V> {
  /**
   * Static instance.
   */
  public static final HellingerHistogramNormalization<NumberVector> STATIC = new HellingerHistogramNormalization<>();

  /**
   * Constructor.
   */
  public HellingerHistogramNormalization() {
    super();
  }

  @Override
  protected V filterSingleObject(V featureVector) {
    double[] data = new double[featureVector.getDimensionality()];
    double sum = 0.;
    for(int d = 0; d < data.length; ++d) {
      data[d] = featureVector.doubleValue(d);
      data[d] = data[d] > 0 ? data[d] : -data[d];
      sum += data[d];
    }
    // Normalize and sqrt:
    if(sum > 0.) {
      for(int d = 0; d < data.length; ++d) {
        if(data[d] > 0) {
          data[d] = Math.sqrt(data[d] / sum);
        }
      }
    }
    return factory.newNumberVector(data);
  }

  @Override
  protected SimpleTypeInformation<? super V> getInputTypeRestriction() {
    return TypeUtil.NUMBER_VECTOR_VARIABLE_LENGTH;
  }

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