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.data; 031 032import java.util.Random; 033 034import no.uib.cipr.matrix.DenseVector; 035import no.uib.cipr.matrix.Vector; 036 037import org.openimaj.math.matrix.GramSchmidtProcess; 038import org.openimaj.ml.linear.learner.perceptron.PerceptronClass; 039import org.openimaj.util.pair.IndependentPair; 040 041/** 042 * 043 * @author Sina Samangooei (ss@ecs.soton.ac.uk) 044 */ 045public class LinearPerceptronDataGenerator implements DataGenerator<double[],PerceptronClass>{ 046 047 048 private Vector origin; 049 private Vector direction; 050 private Random rng; 051 private double range; 052 private int dims; 053 private double prop; 054 private double error = 0; 055 056 057 /** 058 * @param range the range of values 059 * @param dims the number of dimentions 060 * @param prop for both the selection of the origin and selection of the direction of the line of seperate, one dimention is chosen to be limited to the middle of range by this proportion 061 */ 062 public LinearPerceptronDataGenerator(double range, int dims, double prop) { 063 this(range,dims,prop,-1); 064 } 065 066 /** 067 * The range for each dimension 068 * @param range 069 * @param dims 070 * @param prop 071 * @param seed 072 */ 073 public LinearPerceptronDataGenerator(double range, int dims, double prop, int seed) { 074 this.range = range; 075 this.dims = dims; 076 this.prop = prop; 077 if(seed < 0){ 078 this.rng = new Random(); 079 } else { 080 this.rng = new Random(seed); 081 } 082 083 // limited dimention 084 int limitedDim = rng.nextInt(dims); 085 double validRange = this.range * prop; 086 double start = (this.range - validRange)/2.; 087 088 double[] startPoint = new double[dims]; 089 double[] endPoint = new double[dims]; 090 091 double[] originPoint = new double[dims]; 092 093 for (int i = 0; i < endPoint.length; i++) { 094 if(i == limitedDim){ 095 startPoint[i] = 0; 096 endPoint[i] = range; 097 originPoint[i] = start + rng.nextDouble() * validRange; 098 } else { 099 startPoint[i] = start + rng.nextDouble() * validRange; 100 endPoint[i] = start + rng.nextDouble() * validRange; 101 originPoint[i] = rng.nextDouble() * range; 102 } 103 } 104 105 this.direction = (DenseVector) new DenseVector(endPoint).add(-1, new DenseVector(startPoint)); 106 this.origin = new DenseVector(originPoint); 107 108 } 109 110 private double nextRandomValue() { 111 return rng.nextDouble() * range; 112 } 113 114 115 @Override 116 public IndependentPair<double[], PerceptronClass> generate() { 117 double decide = Math.signum(rng.nextDouble() - 0.5); 118 if(decide == 0) decide = 1; 119 PerceptronClass dec = PerceptronClass.fromSign(decide); 120 while(true){ 121 double[] randomPoint = new double[this.dims]; 122 123 for (int i = 0; i < randomPoint.length; i++) { 124 randomPoint[i] = nextRandomValue(); 125 } 126 127 Vector v = new DenseVector(randomPoint); 128 v.add(-1, origin); 129 Vector d = direction.copy(); 130 double dot = v.dot(d); 131 double sgn = Math.signum(dot); 132 if(sgn == 0) sgn = 1; 133 if(rng.nextDouble() <this.error){ 134 sgn = -sgn; 135 } 136 PerceptronClass sgnClass = PerceptronClass.fromSign(sgn); 137 if(sgnClass.equals(dec)) { 138 return IndependentPair.pair(randomPoint, sgnClass); 139 } 140 } 141 } 142 143 public Vector getOrigin() { 144 return this.origin; 145 } 146 147 public Vector getNormDirection() { 148 return this.direction; 149 } 150 151 public Vector[] getPlane() { 152 Vector[] allInclusive = new GramSchmidtProcess().apply(new DenseVector(direction).getData()); 153 Vector[] ret = new Vector[allInclusive.length - 1]; 154 for (int i = 0; i < ret.length; i++) { 155 ret[i] = allInclusive[i+1]; 156 } 157 return ret; 158 } 159 160 public void setError(double d) { 161 this.error = d; 162 } 163 164}