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.List;
033
034import org.apache.log4j.Logger;
035import org.openimaj.feature.DoubleFV;
036import org.openimaj.feature.FeatureExtractor;
037import org.openimaj.ml.clustering.SimilarityClusterer;
038import org.openimaj.util.function.Function;
039
040import ch.akuhn.matrix.SparseMatrix;
041
042/**
043 * Wraps the functionality of a {@link SimilarityClusterer} around a dataset
044 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
045 * @param <T> 
046 *
047 */
048public abstract class DoubleFVSimilarityFunction<T> implements Function<List<T>,SparseMatrix>{
049        Logger logger = Logger.getLogger(DoubleFVSimilarityFunction.class);
050        protected double[][] feats = null;
051        private FeatureExtractor<DoubleFV, T> extractor;
052        private List<T> data;
053        /**
054         * @param extractor
055         *
056         */
057        public DoubleFVSimilarityFunction(FeatureExtractor<DoubleFV, T> extractor) {
058                this.extractor = extractor;
059        }
060        
061        public SparseMatrix apply(List<T> in) {
062                this.data = in;
063                this.prepareFeats();
064                return this.similarity();
065        };
066        
067        protected void prepareFeats() {
068                if(feats!=null)return;
069                int numInstances = data.size();
070                feats = new double[numInstances][];
071                int index = 0;
072                for (T d : this.data) {
073                        feats[index++] = this.extractor.extractFeature(d).values;
074                }
075        }
076        
077        protected abstract SparseMatrix similarity();
078}