source: branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/Problems/InfillProblem.cs @ 15064

#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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.Algorithms.EGO {
  [StorableClass]
  [Item("InfillProblem", "A problem for finding the most interesing potential new sampling Points by optimizing some InfillCriterion")]
  public sealed class InfillProblem : SingleObjectiveBasicProblem<RealVectorEncoding> {

    public override bool Maximization => true;

    #region ProblemResultNames
    public const string BestInfillSolutionResultName = "BestInfillSolution";
    public const string BestInfillQualityResultName = "BestInfillQuality";
    #endregion

    #region Properties
    [Storable]
    private IInfillCriterion infillCriterion;

    public IInfillCriterion InfillCriterion
    {
      get { return infillCriterion; }
      set
      {
        infillCriterion = value;
        infillCriterion.Encoding = Encoding;
      }
    }
    #endregion

    #region Constructors
    [StorableConstructor]
    private InfillProblem(bool deserializing) : base(deserializing) { }
    private InfillProblem(InfillProblem original, Cloner cloner) : base(original, cloner) {
      infillCriterion = cloner.Clone(original.infillCriterion);
    }
    public InfillProblem() { }
    public override IDeepCloneable Clone(Cloner cloner) { return new InfillProblem(this, cloner); }
    #endregion

    public override double Evaluate(Individual individual, IRandom r) {
      return !InBounds(individual.RealVector(), Encoding.Bounds) ? double.MinValue : InfillCriterion.Evaluate(individual.RealVector());
    }
    public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
      base.Analyze(individuals, qualities, results, random);
      var best = qualities.ArgMax(x => x);
      var newQuality = qualities[best];
      if (!results.ContainsKey(BestInfillQualityResultName)) {
        results.Add(new Result(BestInfillSolutionResultName, (RealVector)individuals[best].RealVector().Clone()));
        results.Add(new Result(BestInfillQualityResultName, new DoubleValue(newQuality)));
        return;
      }
      var qold = results[BestInfillQualityResultName].Value as DoubleValue;
      if (qold == null) throw new ArgumentException("Old best quality is not a double value. Conflicting Analyzers?");
      if (qold.Value >= newQuality) return;
      results[BestInfillSolutionResultName].Value = (RealVector)individuals[best].RealVector().Clone();
      qold.Value = newQuality;
    }
    public override IEnumerable<Individual> GetNeighbors(Individual individual, IRandom random) {
      var bounds = Encoding.Bounds;
      var michalewiczIteration = 0;
      while (true) {
        var neighbour = individual.Copy();
        var r = neighbour.RealVector();
        switch (random.Next(5)) {
          case 0: UniformOnePositionManipulator.Apply(random, r, bounds); break;
          case 1: UniformOnePositionManipulator.Apply(random, r, bounds); break;//FixedNormalAllPositionsManipulator.Apply(random, r, new RealVector(new[] { 0.1 })); break;
          case 2: MichalewiczNonUniformAllPositionsManipulator.Apply(random, r, bounds, new IntValue(michalewiczIteration++), new IntValue(10000), new DoubleValue(5.0)); break;
          case 3: MichalewiczNonUniformOnePositionManipulator.Apply(random, r, bounds, new IntValue(michalewiczIteration++), new IntValue(10000), new DoubleValue(5.0)); break;
          case 4: BreederGeneticAlgorithmManipulator.Apply(random, r, bounds, new DoubleValue(0.1)); break;
          default: throw new NotImplementedException();
        }
        yield return neighbour;
        michalewiczIteration %= 10000;
      }
    }

    public void Initialize(IRegressionSolution model, bool expensiveMaximization) {
      infillCriterion.RegressionSolution = model;
      infillCriterion.ExpensiveMaximization = expensiveMaximization;
      infillCriterion.Encoding = Encoding;
      infillCriterion.Initialize();
    }

    #region helpers
    private static bool InBounds(RealVector r, DoubleMatrix bounds) {
      return !r.Where((t, i) => t < bounds[i % bounds.Rows, 0] || t > bounds[i % bounds.Rows, 1]).Any();
    }
    #endregion

  }
}