source: branches/LearningClassifierSystems/HeuristicLab.Algorithms.LearningClassifierSystems/3.3/LearningClassifierSystem.cs @ 8941

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.CombinedIntegerVectorEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Optimization.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Random;

namespace HeuristicLab.Algorithms.LearningClassifierSystems {
  /// <summary>
  /// A learning classifier system.
  /// </summary>
  [Item("Learning Classifier System", "A genetic algorithm.")]
  [Creatable("Algorithms")]
  [StorableClass]
  public class LearningClassifierSystem : HeuristicOptimizationEngineAlgorithm, IStorableContent {
    public string Filename { get; set; }

    #region Problem Properties
    public override Type ProblemType {
      get { return typeof(ISingleObjectiveHeuristicOptimizationProblem); }
    }
    public new ISingleObjectiveHeuristicOptimizationProblem Problem {
      get { return (ISingleObjectiveHeuristicOptimizationProblem)base.Problem; }
      set { base.Problem = value; }
    }
    #endregion

    #region Parameter Properties
    private ValueParameter<IntValue> SeedParameter {
      get { return (ValueParameter<IntValue>)Parameters["Seed"]; }
    }
    private ValueParameter<BoolValue> SetSeedRandomlyParameter {
      get { return (ValueParameter<BoolValue>)Parameters["SetSeedRandomly"]; }
    }
    private ValueParameter<IntValue> PopulationSizeParameter {
      get { return (ValueParameter<IntValue>)Parameters["PopulationSize"]; }
    }
    //for test purpose
    public ValueParameter<ICombinedIntegerVectorCreator> SolutionCreatorParameter {
      get { return (ValueParameter<ICombinedIntegerVectorCreator>)Parameters["SolutionCreator"]; }
    }
    public ValueParameter<IntValue> Length {
      get { return (ValueParameter<IntValue>)Parameters["Length"]; }
    }
    public ValueParameter<IntValue> ActionPartLength {
      get { return (ValueParameter<IntValue>)Parameters["ActionPartLength"]; }
    }
    public ValueParameter<IntMatrix> Bounds {
      get { return (ValueParameter<IntMatrix>)Parameters["Bounds"]; }
    }
    #endregion

    #region Properties
    public IntValue Seed {
      get { return SeedParameter.Value; }
      set { SeedParameter.Value = value; }
    }
    public BoolValue SetSeedRandomly {
      get { return SetSeedRandomlyParameter.Value; }
      set { SetSeedRandomlyParameter.Value = value; }
    }
    public IntValue PopulationSize {
      get { return PopulationSizeParameter.Value; }
      set { PopulationSizeParameter.Value = value; }
    }

    #endregion

    public LearningClassifierSystem()
      : base() {
      #region Create parameters
      Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
      Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
      //for test purpose
      Parameters.Add(new ValueParameter<ICombinedIntegerVectorCreator>("SolutionCreator", "The operator to create a solution.", new UniformRandomCombinedIntegerVectorCreator()));
      Parameters.Add(new ValueParameter<IntValue>("Length", "The operator to create a solution.", new IntValue(3)));
      Parameters.Add(new ValueParameter<IntValue>("ActionPartLength", "The operator to create a solution.", new IntValue(1)));
      int[,] elements = new int[,] { { 0, 3 }, { 0, 3 }, { 0, 2 } };
      Parameters.Add(new ValueParameter<IntMatrix>("Bounds", "The operator to create a solution.", new IntMatrix(elements)));
      #endregion

      #region Create operators
      RandomCreator randomCreator = new RandomCreator();
      SolutionsCreator solutionsCreator = new SolutionsCreator();
      UniformSubScopesProcessor uniformSubScopeProcessor = new UniformSubScopesProcessor();
      UniformRandomizer uniformRandomizer = new UniformRandomizer();
      //ResultsCollector resultsCollector = new ResultsCollector();
      LearningClassifierSystemMainLoop mainLoop = new LearningClassifierSystemMainLoop();

      randomCreator.RandomParameter.ActualName = "Random";
      randomCreator.SeedParameter.ActualName = SeedParameter.Name;
      randomCreator.SeedParameter.Value = null;
      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
      randomCreator.SetSeedRandomlyParameter.Value = null;

      SolutionCreatorParameter.Value.ActionPartLengthParameter.ActualName = ActionPartLength.Name;
      SolutionCreatorParameter.Value.LengthParameter.ActualName = Length.Name;
      SolutionCreatorParameter.Value.BoundsParameter.ActualName = Bounds.Name;

      solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
      solutionsCreator.SolutionCreatorParameter.ActualName = SolutionCreatorParameter.Name;

      uniformSubScopeProcessor.Parallel = new BoolValue(true);

      uniformRandomizer.RandomParameter.ActualName = randomCreator.RandomParameter.ActualName;
      uniformRandomizer.MinParameter.Value = new DoubleValue(0);
      uniformRandomizer.MaxParameter.Value = new DoubleValue(100);
      uniformRandomizer.ValueParameter.ActualName = "Fitness";

      #endregion

      #region Create operator graph
      OperatorGraph.InitialOperator = randomCreator;
      randomCreator.Successor = solutionsCreator;
      solutionsCreator.Successor = uniformSubScopeProcessor;
      uniformSubScopeProcessor.Operator = uniformRandomizer;
      uniformSubScopeProcessor.Successor = mainLoop;
      #endregion
    }
    protected LearningClassifierSystem(LearningClassifierSystem original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new LearningClassifierSystem(this, cloner);
    }
    [StorableConstructor]
    private LearningClassifierSystem(bool deserializing) : base(deserializing) { }
  }
}