#region License Information
/* HeuristicLab
* Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;
namespace HeuristicLab.Problems.ParameterOptimization {
[Item("Parameter Optimization Problem", "A base class for other problems for the optimization of a parameter vector.")]
[StorableType("966B9A10-DB66-4D1F-A034-BB010F7620AC")]
public abstract class ParameterOptimizationProblem : SingleObjectiveHeuristicOptimizationProblem, IStorableContent {
public string Filename { get; set; }
private const string ProblemSizeParameterName = "ProblemSize";
private const string BoundsParameterName = "Bounds";
private const string ParameterNamesParameterName = "ParameterNames";
#region parameters
public IFixedValueParameter ProblemSizeParameter {
get { return (IFixedValueParameter)Parameters[ProblemSizeParameterName]; }
}
public IValueParameter BoundsParameter {
get { return (IValueParameter)Parameters[BoundsParameterName]; }
}
public IValueParameter ParameterNamesParameter {
get { return (IValueParameter)Parameters[ParameterNamesParameterName]; }
}
#endregion
#region properties
public int ProblemSize {
get { return ProblemSizeParameter.Value.Value; }
set { ProblemSizeParameter.Value.Value = value; }
}
public DoubleMatrix Bounds {
get { return BoundsParameter.Value; }
set { BoundsParameter.Value = value; }
}
public StringArray ParameterNames {
get { return ParameterNamesParameter.Value; }
set { ParameterNamesParameter.Value = value; }
}
#endregion
[Storable]
protected StdDevStrategyVectorCreator strategyVectorCreator;
[Storable]
protected StdDevStrategyVectorCrossover strategyVectorCrossover;
[Storable]
protected StdDevStrategyVectorManipulator strategyVectorManipulator;
[StorableConstructor]
protected ParameterOptimizationProblem(bool deserializing) : base(deserializing) { }
protected ParameterOptimizationProblem(ParameterOptimizationProblem original, Cloner cloner)
: base(original, cloner) {
strategyVectorCreator = cloner.Clone(original.strategyVectorCreator);
strategyVectorCrossover = cloner.Clone(original.strategyVectorCrossover);
strategyVectorManipulator = cloner.Clone(original.strategyVectorManipulator);
RegisterEventHandlers();
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
RegisterEventHandlers();
}
protected ParameterOptimizationProblem(IParameterVectorEvaluator evaluator)
: base(evaluator, new UniformRandomRealVectorCreator()) {
Parameters.Add(new FixedValueParameter(ProblemSizeParameterName, "The dimension of the parameter vector that is to be optimized.", new IntValue(1)));
Parameters.Add(new ValueParameter(BoundsParameterName, "The bounds for each dimension of the parameter vector. If the number of bounds is smaller than the problem size then the bounds are reused in a cyclic manner.", new DoubleMatrix(new double[,] { { 0, 100 } }, new string[] { "LowerBound", "UpperBound" })));
Parameters.Add(new ValueParameter(ParameterNamesParameterName, "The element names which are used to calculate the quality of a parameter vector.", new StringArray(new string[] { "Parameter0" })));
SolutionCreator.LengthParameter.ActualName = "ProblemSize";
Operators.AddRange(ApplicationManager.Manager.GetInstances());
strategyVectorCreator = new StdDevStrategyVectorCreator();
strategyVectorCreator.LengthParameter.ActualName = ProblemSizeParameter.Name;
strategyVectorCrossover = new StdDevStrategyVectorCrossover();
strategyVectorManipulator = new StdDevStrategyVectorManipulator();
strategyVectorManipulator.LearningRateParameter.Value = new DoubleValue(0.5);
strategyVectorManipulator.GeneralLearningRateParameter.Value = new DoubleValue(0.5);
Operators.Add(strategyVectorCreator);
Operators.Add(strategyVectorCrossover);
Operators.Add(strategyVectorManipulator);
Operators.Add(new BestSolutionAnalyzer());
Operators.Add(new BestSolutionsAnalyzer());
UpdateParameters();
UpdateStrategyVectorBounds();
RegisterEventHandlers();
}
protected override void OnEvaluatorChanged() {
base.OnEvaluatorChanged();
UpdateParameters();
}
private void RegisterEventHandlers() {
Bounds.ToStringChanged += Bounds_ToStringChanged;
ProblemSizeParameter.Value.ValueChanged += ProblemSize_Changed;
ParameterNames.Reset += ParameterNames_Reset;
}
private void UpdateParameters() {
Evaluator.ParameterVectorParameter.ActualName = SolutionCreator.RealVectorParameter.ActualName;
Evaluator.ParameterNamesParameter.ActualName = ParameterNamesParameter.Name;
foreach (var bestSolutionAnalyzer in Operators.OfType()) {
bestSolutionAnalyzer.ParameterVectorParameter.ActualName = SolutionCreator.RealVectorParameter.ActualName;
bestSolutionAnalyzer.ParameterNamesParameter.ActualName = ParameterNamesParameter.Name;
}
Bounds = new DoubleMatrix(ProblemSize, 2);
Bounds.RowNames = ParameterNames;
for (int i = 0; i < Bounds.Rows; i++) {
Bounds[i, 0] = 0.0;
Bounds[i, 1] = 100.0;
}
foreach (var op in Operators.OfType())
op.RealVectorParameter.ActualName = SolutionCreator.RealVectorParameter.ActualName;
}
private void Bounds_ToStringChanged(object sender, EventArgs e) {
if (Bounds.Columns != 2 || Bounds.Rows < 1)
Bounds = new DoubleMatrix(1, 2);
UpdateStrategyVectorBounds();
}
protected virtual void UpdateStrategyVectorBounds() {
DoubleMatrix strategyBounds = (DoubleMatrix)Bounds.Clone();
for (int i = 0; i < strategyBounds.Rows; i++) {
if (strategyBounds[i, 0] < 0) strategyBounds[i, 0] = 0;
strategyBounds[i, 1] = 0.1 * (Bounds[i, 1] - Bounds[i, 0]);
}
strategyVectorCreator.BoundsParameter.Value = strategyBounds;
}
protected virtual void ProblemSize_Changed(object sender, EventArgs e) {
if (ParameterNames.Length != ProblemSize)
((IStringConvertibleArray)ParameterNames).Length = ProblemSize;
for (int i = 0; i < ParameterNames.Length; i++) {
if (string.IsNullOrEmpty(ParameterNames[i])) ParameterNames[i] = "Parameter" + i;
}
strategyVectorManipulator.GeneralLearningRateParameter.Value = new DoubleValue(1.0 / Math.Sqrt(2 * ProblemSize));
strategyVectorManipulator.LearningRateParameter.Value = new DoubleValue(1.0 / Math.Sqrt(2 * Math.Sqrt(ProblemSize)));
}
protected virtual void ParameterNames_Reset(object sender, EventArgs e) {
ProblemSize = ParameterNames.Length;
}
}
}