#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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 HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Optimization; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using System; using System.Collections.Generic; using System.Linq; using System.Threading; namespace HeuristicLab.Analysis.FitnessLandscape { [Item("Random Walk Calculator", "Calculates characteristics from a random walk.")] [StorableClass] public class RandomWalkCalculator : NamedItem, ICharacteristicCalculator { [Storable] private IProblem problem; public IProblem Problem { get { return problem; } set { if (problem == value) return; problem = value; var soProblem = problem as ISingleObjectiveHeuristicOptimizationProblem; walker.Problem = soProblem; } } [Storable] private RandomWalk walker; [StorableConstructor] private RandomWalkCalculator(bool deserializing) : base(deserializing) { } private RandomWalkCalculator(RandomWalkCalculator original, Cloner cloner) : base(original, cloner) { problem = cloner.Clone(original.problem); walker = cloner.Clone(original.walker); characteristics = cloner.Clone(original.characteristics); } public RandomWalkCalculator() { Name = ItemName; Description = ItemDescription; walker = new RandomWalk(); characteristics = new CheckedItemList( new[] { "AutoCorrelation1", "CorrelationLength", "InformationContent", "PartialInformationContent", "DensityBasinInformation", "InformationStability", "Diversity", "Regularity", "TotalEntropy", "PeakInformationContent", "PeakDensityBasinInformation" }.Select(x => new StringValue(x))); characteristics.SetItemCheckedState(1, false); characteristics.SetItemCheckedState(5, false); } public override IDeepCloneable Clone(Cloner cloner) { return new RandomWalkCalculator(this, cloner); } private CheckedItemList characteristics; public ReadOnlyCheckedItemList Characteristics { get { return characteristics.AsReadOnly(); } } public bool CanCalculate() { return Problem is ISingleObjectiveHeuristicOptimizationProblem && Problem.Operators.Any(x => x is IManipulator); } public IEnumerable Calculate() { walker.Prepare(true); using (var waitHandle = new AutoResetEvent(false)) { EventHandler evHandle = (sender, e) => { if (walker.ExecutionState == ExecutionState.Paused || walker.ExecutionState == ExecutionState.Stopped) waitHandle.Set(); }; walker.ExecutionStateChanged += evHandle; walker.Start(); waitHandle.WaitOne(); walker.ExecutionStateChanged -= evHandle; } foreach (var p in characteristics.CheckedItems) { yield return new Result("RandomWalk." + walker.MutatorParameter.Value.Name + "." + p.Value.Value, walker.Results[p.Value.Value].Value); } walker.Prepare(true); } public void CollectParameterValues(IDictionary values) { walker.CollectParameterValues(values); } public IKeyedItemCollection Parameters { get { return ((IParameterizedItem)walker).Parameters; } } } }