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.math.matrix.similarity.processor;
031
032import org.openimaj.math.matrix.similarity.SimilarityMatrix;
033
034/**
035 * Normalise and optionally invert a {@link SimilarityMatrix}.
036 * 
037 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
038 *
039 */
040public class NormaliseData implements SimilarityMatrixProcessor {
041        boolean invert = false;
042        
043        /**
044         * Default constructor. 
045         */
046        public NormaliseData() {
047                this(false);
048        }
049        
050        /**
051         * Default constructor. 
052         * @param invert invert the resultant matrix
053         */
054        public NormaliseData(boolean invert) {
055                this.invert = invert;
056        }
057        
058        @Override
059        public void process(SimilarityMatrix matrix) {
060                final int rows = matrix.getRowDimension();
061                final int cols = matrix.getColumnDimension();
062                final double[][] data = matrix.getArray(); 
063                
064                double max = -Double.MAX_VALUE;
065                double min = Double.MAX_VALUE;
066                
067                for (int r=0; r<rows; r++) {
068                        for (int c=0; c<cols; c++) {
069                                if (data[r][c] < min) {
070                                        min = data[r][c];
071                                }
072                                if (data[r][c] > max) {
073                                        max = data[r][c];
074                                }
075                        }
076                }
077                
078                for (int r=0; r<rows; r++) {
079                        for (int c=0; c<cols; c++) {
080                                double norm = (data[r][c] - min) / (max - min);
081                                
082                                if (invert)
083                                        norm = 1 - norm;
084                                
085                                data[r][c] = norm;
086                        }
087                }
088        }
089}