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}