source: branches/ProgrammableProblem/HeuristicLab.Problems.Programmable/3.3/MultiObjectiveProblemScript.cs @ 11571

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 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 System.Collections.Generic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.Programmable {
  [Item("Multi-objective Problem Definition Script", "Script that defines the parameter vector and evaluates the solution for a programmable problem.")]
  [StorableClass]
  public class MultiObjectiveProblemScript : ProblemScript, IMultiObjectiveProblemDefinition, IStorableContent {
    public string Filename { get; set; }

    protected override string CodeTemplate {
      get {
        return @"using System;
using System.Linq;
using System.Collections.Generic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.PermutationEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Problems.Programmable;

public class CustomProblemDefinition : ProblemScriptBase, IMultiObjectiveProblemDefinition {
  public bool[] Maximization { get { return new [] { false, false }; } }

  public CustomProblemDefinition() {
    // Define the solution encoding which can also consist of multiple vectors, examples below
    // Encoding = new BinaryEncoding(""b"", length: 5);
    // Encoding = new IntegerEncoding(""i"", lenght: 5, min: 2, max: 14, step: 4);
    // Encoding = new RealEncoding(""r"", length: 5, min: -1.0, max: 1.0);
    // Encoding = new PermutationEncoding(""P"", length: 5, type: PermutationTypes.Absolute);
    // Encoding = new MultiEncoding()
      // .AddBinaryVector(""b"", length: 5)
      // .AddIntegerVector(""i"", length: 5, min: 2, max: 14, step: 4)
      // .AddRealVector(""r"", length: 5, min: -1.0, max: 1.0)
      // .AddPermutation(""P"", length: 5, type: PermutationTypes.Absolute)
    ;
  }

  public override void Initialize() {
    // when the definition is created here you can initialize variables in the variable store
  }

  public double[] Evaluate(IRandom random, Individual individual) {
    var qualities = new [] { 0.0, 0.0 };
    // use vars.yourVariable to access variables in the variable store i.e. yourVariable
    // qualities = new [] { individual.RealVector(""r"").Sum(x => x * x), individual.RealVector(""r"").Sum(x => x * x * x) };
    return qualities;
  }

  public void Analyze(Individual[] individuals, double[][] qualities, ResultCollection results) {
    // write or update results given the range of vectors and resulting qualities
    // use e.g. vars.yourVariable to access variables in the variable store i.e. yourVariable
  }

  public override IEnumerable<Individual> GetNeighbors(IRandom random, Individual individual) {
    // Create new vectors, based on the given one that represent small changes
    // This method is only called from move-based algorithms (LocalSearch, SimulatedAnnealing, etc.)
    while (true) {
      // this is not an infinite loop as only a finite amount of samples will be drawn
      // it is possible to return a concrete amount of neighbors also
      var neighbor = (Individual)individual.Clone();
      //e.g. make a bit flip in a binary parameter
      //var bIndex = random.Next(neighbor.BinaryVector(""b"").Length);
      //neighbor.BinaryVector(""b"")[bIndex] = !neighbor.BinaryVector(""b"")[bIndex];
      yield return neighbor;
    }
  }

  // implement further classes and methods
}";
      }
    }

    [StorableConstructor]
    protected MultiObjectiveProblemScript(bool deserializing) : base(deserializing) { }
    protected MultiObjectiveProblemScript(MultiObjectiveProblemScript original, Cloner cloner)
      : base(original, cloner) { }

    public MultiObjectiveProblemScript() {
      Code = CodeTemplate;
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new MultiObjectiveProblemScript(this, cloner);
    }

    public new IMultiObjectiveProblemDefinition Instance {
      get { return (IMultiObjectiveProblemDefinition)base.Instance; }
      protected set { base.Instance = value; }
    }

    protected override void OnInstanceChanged() {
      OnProblemDefinitionChanged();
      base.OnInstanceChanged();
    }

    bool[] IMultiObjectiveProblemDefinition.Maximization {
      get { return Instance != null ? Instance.Maximization : new bool[0]; }
    }

    IEncoding IProblemDefinition.Encoding {
      get { return Instance != null ? Instance.Encoding : null; }
    }

    double[] IMultiObjectiveProblemDefinition.Evaluate(IRandom random, Individual individual) {
      return Instance.Evaluate(random, individual);
    }

    void IMultiObjectiveProblemDefinition.Analyze(Individual[] individuals, double[][] qualities, ResultCollection results) {
      Instance.Analyze(individuals, qualities, results);
    }

    IEnumerable<Individual> IProblemDefinition.GetNeighbors(IRandom random, Individual individual) {
      return Instance.GetNeighbors(random, individual);
    }

    public event EventHandler ProblemDefinitionChanged;
    private void OnProblemDefinitionChanged() {
      var handler = ProblemDefinitionChanged;
      if (handler != null) handler(this, EventArgs.Empty);
    }
  }
}