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.linear.learner.perceptron;
031
032import java.util.ArrayList;
033import java.util.List;
034
035import org.openimaj.math.matrix.MeanVector;
036import org.openimaj.ml.linear.kernel.VectorKernel;
037
038/**
039 *
040 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
041 */
042public class MeanCenteredKernelPerceptron extends DoubleArrayKernelPerceptron{
043        MeanVector mv = new MeanVector();
044        
045        /**
046         * @param k
047         */
048        public MeanCenteredKernelPerceptron(VectorKernel k) {
049                super(k);
050        }
051        
052        @Override
053        public void update(double[] xt, PerceptronClass yt, PerceptronClass yt_prime) {
054                mv.update(xt);
055                super.update(xt, yt, yt_prime);
056        }
057        @Override
058        public double[] correct(double[] in) {
059                return center(in);
060        }
061        
062        @Override
063        public List<double[]> getSupports() {
064                List<double[]> pre = super.getSupports();
065                List<double[]> ret = new ArrayList<double[]>();
066                for (double[] ds : pre) {
067                        ret.add(correct(ds));
068                }
069                return ret;
070        }
071        
072        private double[] center(double[] xt) {
073                double[] mvec = mv.vec();
074                double[] ret = new double[xt.length];
075                if(mvec == null) return ret;
076                
077                for (int i = 0; i < mvec.length; i++) {
078                        ret[i] = xt[i] - mvec[i];
079                }
080                return ret;
081        }
082
083        public double[] getMean() {
084                return this.mv.vec();
085        }
086        
087}