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.List;
033
034import org.openimaj.ml.linear.kernel.Kernel;
035import org.openimaj.ml.linear.learner.OnlineLearner;
036
037/**
038 *
039 * @param <INDEPENDANT>
040 * @param <DEPENDANT>
041 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
042 */
043public abstract class KernelPerceptron<INDEPENDANT, DEPENDANT> implements OnlineLearner<INDEPENDANT, DEPENDANT>{
044
045        
046        Kernel<INDEPENDANT> kernel;
047        protected int errors;
048        
049        /**
050         * 
051         */
052        public KernelPerceptron() {
053        }
054        
055        /**
056         * @param kernel
057         */
058        public KernelPerceptron(Kernel<INDEPENDANT> kernel) {
059                this.kernel = kernel;
060        }
061        
062        @Override
063        public void process(INDEPENDANT xt, DEPENDANT yt) {
064                DEPENDANT yt_prime = predict(xt);
065                if(!yt_prime.equals(yt)){
066                        update(xt,yt,yt_prime);
067                        this.errors ++;
068                }
069        }
070
071        /**
072         * When there is an error in prediction, update somehow
073         * @param xt
074         * @param yt
075         * @param yt_prime
076         */
077        public abstract void update(INDEPENDANT xt, DEPENDANT yt, DEPENDANT yt_prime) ;
078        
079        /**
080         * @return the vectors that form the support
081         */
082        public abstract List<INDEPENDANT> getSupports();
083        /**
084         * @return the weights of the support vectors
085         */
086        public abstract List<Double> getWeights();
087        
088        /**
089         * @return the bias
090         */
091        public abstract double getBias();
092        
093        /**
094         * @return number of errors made
095         */
096        public int getErrors(){
097                return errors;
098                
099        }
100        
101}