package de.lmu.ifi.dbs.elki.math.linearalgebra.pca;

/*
 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 java.util.ArrayList;
import java.util.List;

import de.lmu.ifi.dbs.elki.math.linearalgebra.EigenPair;
import de.lmu.ifi.dbs.elki.math.linearalgebra.SortedEigenPairs;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;

/**
 * The WeakEigenPairFilter sorts the eigenpairs in descending order of their
 * eigenvalues and returns the first eigenpairs who are above the average mark
 * as "strong", the others as "weak".
 * 
 * @author Erich Schubert
 * @since 0.2
 */
@Title("Weak Eigenpair Filter")
@Description("Sorts the eigenpairs in decending order of their eigenvalues and returns those eigenpairs, whose eigenvalue is above the average ('expected') eigenvalue.")
public class WeakEigenPairFilter implements EigenPairFilter {
  /**
   * The default value for walpha.
   */
  public static final double DEFAULT_WALPHA = 0.95;

  /**
   * The noise tolerance level for weak eigenvectors
   */
  private double walpha;

  /**
   * Constructor.
   * 
   * @param walpha
   */
  public WeakEigenPairFilter(double walpha) {
    super();
    this.walpha = walpha;
  }

  /**
   * Filter eigenpairs
   */
  @Override
  public FilteredEigenPairs filter(SortedEigenPairs eigenPairs) {
    // init strong and weak eigenpairs
    List<EigenPair> strongEigenPairs = new ArrayList<>();
    List<EigenPair> weakEigenPairs = new ArrayList<>();

    // determine sum of eigenvalues
    double totalSum = 0;
    for(int i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      totalSum += eigenPair.getEigenvalue();
    }
    double expectEigenvalue = totalSum / eigenPairs.size() * walpha;

    // determine strong and weak eigenpairs
    for(int i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      if(eigenPair.getEigenvalue() > expectEigenvalue) {
        strongEigenPairs.add(eigenPair);
      }
      else {
        weakEigenPairs.add(eigenPair);
      }
    }

    // the code using this method doesn't expect an empty strong set,
    // if we didn't find any strong ones, we make all vectors strong
    if(strongEigenPairs.size() == 0) {
      return new FilteredEigenPairs(new ArrayList<EigenPair>(), weakEigenPairs);
    }
    return new FilteredEigenPairs(weakEigenPairs, strongEigenPairs);
  }

  /**
   * Parameterization class.
   * 
   * @author Erich Schubert
   * 
   * @apiviz.exclude
   */
  public static class Parameterizer extends AbstractParameterizer {
    /**
     * OptionID for the weak alpha value of {@link WeakEigenPairFilter},
     * {@link de.lmu.ifi.dbs.elki.math.linearalgebra.pca.ProgressiveEigenPairFilter}
     * and
     * {@link de.lmu.ifi.dbs.elki.math.linearalgebra.pca.SignificantEigenPairFilter}
     */
    public static final OptionID EIGENPAIR_FILTER_WALPHA = new OptionID("pca.filter.weakalpha", "The minimum strength of the statistically expected variance (1/n) share an eigenvector " + "needs to have to be considered 'strong'.");
    /**
     * The threshold for strong eigenvectors: the strong eigenvectors explain a
     * portion of at least alpha of the total variance.
     */
    private double walpha;

    @Override
    protected void makeOptions(Parameterization config) {
      super.makeOptions(config);
      DoubleParameter walphaP = new DoubleParameter(EIGENPAIR_FILTER_WALPHA, DEFAULT_WALPHA);
      walphaP.addConstraint(CommonConstraints.GREATER_EQUAL_ZERO_DOUBLE);
      if(config.grab(walphaP)) {
        walpha = walphaP.getValue();
      }
    }

    @Override
    protected WeakEigenPairFilter makeInstance() {
      return new WeakEigenPairFilter(walpha);
    }
  }
}
