[3336] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[5445] | 3 | * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[3336] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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[4068] | 22 | using System;
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[4722] | 23 | using HeuristicLab.Common;
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[3336] | 24 | using HeuristicLab.Core;
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[4068] | 25 | using HeuristicLab.Data;
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[3336] | 26 | using HeuristicLab.Operators;
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| 27 | using HeuristicLab.Optimization;
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| 28 | using HeuristicLab.Parameters;
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[4068] | 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[3336] | 30 | using HeuristicLab.Random;
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| 31 |
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| 32 | namespace HeuristicLab.Encodings.RealVectorEncoding {
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| 33 | /// <summary>
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| 34 | /// Mutates the endogenous strategy parameters.
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| 35 | /// </summary>
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[3450] | 36 | [Item("StdDevStrategyVectorManipulator", "Mutates the endogenous strategy parameters.")]
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| 37 | [StorableClass]
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| 38 | public class StdDevStrategyVectorManipulator : SingleSuccessorOperator, IStochasticOperator, IRealVectorStdDevStrategyParameterManipulator {
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[3520] | 39 | public override bool CanChangeName {
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| 40 | get { return false; }
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| 41 | }
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[3336] | 42 | public ILookupParameter<IRandom> RandomParameter {
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| 43 | get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
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| 44 | }
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| 45 | public ILookupParameter<RealVector> StrategyParameterParameter {
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| 46 | get { return (ILookupParameter<RealVector>)Parameters["StrategyParameter"]; }
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| 47 | }
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| 48 | public IValueLookupParameter<DoubleValue> GeneralLearningRateParameter {
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| 49 | get { return (IValueLookupParameter<DoubleValue>)Parameters["GeneralLearningRate"]; }
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| 50 | }
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| 51 | public IValueLookupParameter<DoubleValue> LearningRateParameter {
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| 52 | get { return (IValueLookupParameter<DoubleValue>)Parameters["LearningRate"]; }
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| 53 | }
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| 54 | public IValueLookupParameter<DoubleMatrix> BoundsParameter {
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| 55 | get { return (IValueLookupParameter<DoubleMatrix>)Parameters["Bounds"]; }
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| 56 | }
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[4722] | 57 |
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| 58 | [StorableConstructor]
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| 59 | protected StdDevStrategyVectorManipulator(bool deserializing) : base(deserializing) { }
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| 60 | protected StdDevStrategyVectorManipulator(StdDevStrategyVectorManipulator original, Cloner cloner) : base(original, cloner) { }
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[3336] | 61 | /// <summary>
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| 62 | /// Initializes a new instance of <see cref="StrategyVectorManipulator"/> with four
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| 63 | /// parameters (<c>Random</c>, <c>StrategyVector</c>, <c>GeneralLearningRate</c> and
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| 64 | /// <c>LearningRate</c>).
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| 65 | /// </summary>
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[3450] | 66 | public StdDevStrategyVectorManipulator()
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[3336] | 67 | : base() {
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| 68 | Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator to use."));
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| 69 | Parameters.Add(new LookupParameter<RealVector>("StrategyParameter", "The strategy parameter to manipulate."));
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| 70 | Parameters.Add(new ValueLookupParameter<DoubleValue>("GeneralLearningRate", "The general learning rate (tau0)."));
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| 71 | Parameters.Add(new ValueLookupParameter<DoubleValue>("LearningRate", "The learning rate (tau)."));
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| 72 | Parameters.Add(new ValueLookupParameter<DoubleMatrix>("Bounds", "A 2 column matrix specifying the lower and upper bound for each dimension. If there are less rows than dimension the bounds vector is cycled.", new DoubleMatrix(new double[,] { { 0, 5 } })));
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| 73 | }
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| 74 |
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[4722] | 75 | public override IDeepCloneable Clone(Cloner cloner) {
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| 76 | return new StdDevStrategyVectorManipulator(this, cloner);
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| 77 | }
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| 78 |
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[3336] | 79 | /// <summary>
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| 80 | /// Mutates the endogenous strategy parameters.
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| 81 | /// </summary>
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| 82 | /// <param name="random">The random number generator to use.</param>
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| 83 | /// <param name="vector">The strategy vector to manipulate.</param>
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| 84 | /// <param name="generalLearningRate">The general learning rate dampens the mutation over all dimensions.</param>
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| 85 | /// <param name="learningRate">The learning rate dampens the mutation in each dimension.</param>
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[4545] | 86 | /// <param name="bounds">The minimal and maximal value for each component, bounds are cycled if the length of bounds is smaller than the length of vector</param>
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[3336] | 87 | public static void Apply(IRandom random, RealVector vector, double generalLearningRate, double learningRate, DoubleMatrix bounds) {
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| 88 | NormalDistributedRandom N = new NormalDistributedRandom(random, 0.0, 1.0);
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| 89 | double generalMultiplier = Math.Exp(generalLearningRate * N.NextDouble());
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| 90 | for (int i = 0; i < vector.Length; i++) {
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[5381] | 91 | double change = vector[i] * generalMultiplier * Math.Exp(learningRate * N.NextDouble());
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[3336] | 92 | if (bounds != null) {
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| 93 | double min = bounds[i % bounds.Rows, 0], max = bounds[i % bounds.Rows, 1];
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[5381] | 94 | if (min == max) vector[i] = min;
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| 95 | else {
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| 96 | while (change < min || change > max)
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| 97 | change = vector[i] * generalMultiplier * Math.Exp(learningRate * N.NextDouble());
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| 98 | vector[i] = change;
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| 99 | }
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[3336] | 100 | }
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| 101 | }
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| 102 | }
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| 103 | /// <summary>
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| 104 | /// Mutates the endogenous strategy parameters.
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| 105 | /// </summary>
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| 106 | /// <remarks>Calls <see cref="OperatorBase.Apply"/> of base class <see cref="OperatorBase"/>.</remarks>
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| 107 | /// <inheritdoc select="returns"/>
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| 108 | public override IOperation Apply() {
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| 109 | RealVector strategyParams = StrategyParameterParameter.ActualValue;
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| 110 | if (strategyParams != null) { // only apply if there is a strategy vector
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| 111 | IRandom random = RandomParameter.ActualValue;
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| 112 | double tau0 = GeneralLearningRateParameter.ActualValue.Value;
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| 113 | double tau = LearningRateParameter.ActualValue.Value;
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| 114 | Apply(random, strategyParams, tau0, tau, BoundsParameter.ActualValue);
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| 115 | }
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| 116 | return base.Apply();
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| 117 | }
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| 118 | }
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| 119 | }
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