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.demos.ml.linear.data;
031
032import java.util.ArrayList;
033import java.util.List;
034
035import org.openimaj.ml.linear.data.FixedDataGenerator;
036import org.openimaj.ml.linear.kernel.LinearVectorKernel;
037import org.openimaj.ml.linear.learner.perceptron.DoubleArrayKernelPerceptron;
038import org.openimaj.ml.linear.learner.perceptron.MeanCenteredKernelPerceptron;
039import org.openimaj.ml.linear.learner.perceptron.PerceptronClass;
040import org.openimaj.ml.linear.learner.perceptron.ThresholdDoubleArrayKernelPerceptron;
041import org.openimaj.util.pair.IndependentPair;
042
043import cern.colt.Arrays;
044
045/**
046 *
047 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
048 */
049public class WikipediaPerceptronExample {
050
051        /**
052         * @param args
053         */
054        public static void main(String[] args) {
055                thresholded(createData());
056                centered(createData());
057        }
058
059        private static void centered(FixedDataGenerator<double[], PerceptronClass> fdg) {
060                System.out.println("CENTERED");
061                final DoubleArrayKernelPerceptron mkp = new MeanCenteredKernelPerceptron(new LinearVectorKernel());
062                for (int i = 0; i < 10; i++) {
063                        System.out.println("Iteration: " + i);
064                        for (int j = 0; j < 4; j++) {
065                                final IndependentPair<double[], PerceptronClass> v = fdg.generate();
066                                final double[] x = v.firstObject();
067                                final PerceptronClass y = v.secondObject();
068                                final PerceptronClass yestb = mkp.predict(x);
069                                mkp.process(x, y);
070                                final PerceptronClass yesta = mkp.predict(x);
071
072                                System.out.println(String.format("x: %s, y: %s, ypred_b: %s, ypred_a: %s", Arrays.toString(x), y, yestb,
073                                                yesta));
074                                // System.out.println(mkp.getWeights());
075                        }
076                }
077        }
078
079        private static FixedDataGenerator<double[], PerceptronClass> createData() {
080
081                final List<IndependentPair<double[], PerceptronClass>> data = new ArrayList<IndependentPair<double[], PerceptronClass>>();
082                data.add(IndependentPair.pair(new double[] { 1, 0, 0 }, PerceptronClass.TRUE));
083                data.add(IndependentPair.pair(new double[] { 1, 0, 1 }, PerceptronClass.TRUE));
084                data.add(IndependentPair.pair(new double[] { 1, 1, 0 }, PerceptronClass.TRUE));
085                data.add(IndependentPair.pair(new double[] { 1, 1, 1 }, PerceptronClass.FALSE));
086                final FixedDataGenerator<double[], PerceptronClass> fdg = new FixedDataGenerator<double[], PerceptronClass>(data);
087                return fdg;
088        }
089
090        private static void thresholded(
091                        FixedDataGenerator<double[], PerceptronClass> fdg)
092        {
093                System.out.println("Thresholded");
094                final DoubleArrayKernelPerceptron mkp = new ThresholdDoubleArrayKernelPerceptron(new LinearVectorKernel());
095                for (int i = 0; i < 10; i++) {
096                        System.out.println("Iteration: " + i);
097                        for (int j = 0; j < 4; j++) {
098                                final IndependentPair<double[], PerceptronClass> v = fdg.generate();
099                                final double[] x = v.firstObject();
100                                final PerceptronClass y = v.secondObject();
101                                final PerceptronClass yestb = mkp.predict(x);
102                                mkp.process(x, y);
103                                final PerceptronClass yesta = mkp.predict(x);
104
105                                System.out.println(String.format("x: %s, y: %s, ypred_b: %s, ypred_a: %s", Arrays.toString(x), y, yestb,
106                                                yesta));
107                                // System.out.println(mkp.getWeights());
108                        }
109                }
110        }
111
112}