001/**
002 * Copyright (c) 2011, The University of Southampton and the individual contributors.
003 * All rights reserved.
004 *
005 * Redistribution and use in source and binary forms, with or without modification,
006 * are permitted provided that the following conditions are met:
007 *
008 *   *  Redistributions of source code must retain the above copyright notice,
009 *      this list of conditions and the following disclaimer.
010 *
011 *   *  Redistributions in binary form must reproduce the above copyright notice,
012 *      this list of conditions and the following disclaimer in the documentation
013 *      and/or other materials provided with the distribution.
014 *
015 *   *  Neither the name of the University of Southampton nor the names of its
016 *      contributors may be used to endorse or promote products derived from this
017 *      software without specific prior written permission.
018 *
019 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
020 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
021 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
022 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
023 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
024 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
025 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
026 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
027 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
028 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
029 */
030package org.openimaj.ml.clustering.spectral;
031
032import java.util.Iterator;
033
034import org.openimaj.util.pair.DoubleObjectPair;
035
036import ch.akuhn.matrix.SparseMatrix;
037import ch.akuhn.matrix.Vector;
038import ch.akuhn.matrix.eigenvalues.FewEigenvalues;
039
040/**
041 * Attempts to automatically choose the number of eigen vectors based on the
042 * comparative value of the eigen value with the first eigen value seen.
043 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
044 *
045 */
046public class AbsoluteValueEigenChooser extends EigenChooser{
047
048        private double absoluteGap;
049        private double maxSelect;
050
051        /**
052         * @param absoluteGap the gap between the first and the current value 
053         * @param maxSelect
054         */
055        public AbsoluteValueEigenChooser(double absoluteGap, double maxSelect) {
056                this.absoluteGap = absoluteGap;
057                this.maxSelect = maxSelect;
058        }
059        
060        @Override
061        public int nEigenVectors(Iterator<DoubleObjectPair<Vector>> vals, int totalEigenVectors) {
062                double max = -Double.MAX_VALUE;
063                double[] valids = new double[totalEigenVectors];
064                valids[0] = vals.next().first; // Skip the first item in the calculation of max
065                int i = 1; // start from the second index
066                for (; vals.hasNext();) {
067                        double val = vals.next().first;
068                        if(val < 0) break;
069                        valids[i] = val;
070                        max = Math.max(max, valids[i]);
071                        i++;
072                }
073                int maxindex = i+1;
074                int count = 2; // the first and the second must be included
075                double first = valids[1]; // the second is what we compare against
076                for (int j = 2; j < maxindex; j++) {
077                        double diff = Math.abs(first - valids[j]);
078                        if(diff / max > absoluteGap) 
079                                break;
080                        count++;
081                }
082                return count;
083        }
084
085        @Override
086        public FewEigenvalues prepare(final SparseMatrix laplacian) {
087                int total = laplacian.columnCount();
088                FewEigenvalues eig = FewEigenvalues.of(laplacian);
089                return eig.greatest((int) (total*maxSelect));
090        }
091        
092        @Override
093        public String toString() {
094                return String.format("AbsVal=%2.2f,%2.2f",this.absoluteGap,this.maxSelect);
095        }
096
097}