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.Arrays;
034import java.util.HashMap;
035import java.util.List;
036import java.util.Map;
037
038import org.openimaj.ml.linear.kernel.VectorKernel;
039import org.openimaj.util.pair.IndependentPair;
040
041/**
042 * An implementation of a simple {@link KernelPerceptron} which works with
043 * double arrays.
044 *
045 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
046 */
047public class DoubleArrayKernelPerceptron extends KernelPerceptron<double[], PerceptronClass> {
048
049        class WrappedDouble {
050                private double[] d;
051
052                public WrappedDouble(double[] d) {
053                        this.d = d;
054                }
055
056                @Override
057                public boolean equals(Object obj) {
058                        if (obj instanceof WrappedDouble) {
059                                final WrappedDouble that = (WrappedDouble) obj;
060                                return Arrays.equals(d, that.d);
061                        }
062                        return false;
063                }
064
065                @Override
066                public int hashCode() {
067                        return Arrays.hashCode(d);
068                }
069        }
070
071        protected List<double[]> supports = new ArrayList<double[]>();
072        protected List<Double> weights = new ArrayList<Double>();
073
074        Map<WrappedDouble, Integer> index = new HashMap<WrappedDouble, Integer>();
075
076        /**
077         * @param k
078         *            the kernel
079         */
080        public DoubleArrayKernelPerceptron(VectorKernel k) {
081                super(k);
082        }
083
084        double[] correct(double[] in) {
085                return in.clone();
086        }
087
088        protected double mapping(double[] in) {
089                double ret = getBias();
090                in = correct(in);
091                for (int i = 0; i < supports.size(); i++) {
092                        final double alpha = this.weights.get(i);
093                        final double[] x_i = correct(this.supports.get(i));
094                        ret += alpha * kernel.apply(IndependentPair.pair(x_i, in));
095
096                }
097                return ret;
098        }
099
100        @Override
101        public PerceptronClass predict(double[] x) {
102                return PerceptronClass.fromSign(Math.signum(mapping(x)));
103        }
104
105        @Override
106        public void update(double[] xt, PerceptronClass yt, PerceptronClass yt_prime) {
107                final WrappedDouble d = new WrappedDouble(xt);
108                final double updateAmount = this.getUpdateRate() * yt.v();
109                if (!this.index.containsKey(d)) {
110                        this.index.put(d, this.supports.size());
111                        this.supports.add(xt);
112                        this.weights.add(updateAmount);
113                } else {
114                        final int index = this.index.get(d);
115                        this.weights.set(index, this.weights.get(index) + updateAmount);
116                }
117        }
118
119        double getUpdateRate() {
120                return 1;
121        }
122
123        @Override
124        public List<double[]> getSupports() {
125                return this.supports;
126        }
127
128        @Override
129        public List<Double> getWeights() {
130                return this.weights;
131        }
132
133        @Override
134        public double getBias() {
135                double bias = 0;
136                for (final double d : this.weights) {
137                        bias += d;
138                }
139                return bias;
140        }
141
142}