source: branches/2780_SAPBA/HeuristicLab.Algorithms.SAPBA/Strategies/InfillStrategy.cs @ 16386

#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.Linq;
using HeuristicLab.Algorithms.EGO;
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;

namespace HeuristicLab.Algorithms.SAPBA {
  [StorableClass]
  public class InfillStrategy : StrategyBase {
    #region Parameternames
    public const string NoGenerationsParameterName = "Number of generations";
    public const string NoIndividualsParameterName = "Number of individuals";
    public const string InfillCriterionParameterName = "InfillCriterion";
    #endregion
    #region Paramterproperties
    public IFixedValueParameter<IntValue> NoGenerationsParameter => Parameters[NoGenerationsParameterName] as IFixedValueParameter<IntValue>;
    public IFixedValueParameter<IntValue> NoIndividualsParameter => Parameters[NoIndividualsParameterName] as IFixedValueParameter<IntValue>;
    public IConstrainedValueParameter<IInfillCriterion> InfillCriterionParameter => Parameters[InfillCriterionParameterName] as IConstrainedValueParameter<IInfillCriterion>;
    #endregion
    #region Properties
    public IntValue NoGenerations => NoGenerationsParameter.Value;
    public IntValue NoIndividuals => NoIndividualsParameter.Value;
    public IInfillCriterion InfillCriterion => InfillCriterionParameter.Value;
    [Storable]
    public int Generations;
    #endregion

    #region Constructors
    [StorableConstructor]
    protected InfillStrategy(bool deserializing) : base(deserializing) { }
    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      AttachListeners();
    }
    protected InfillStrategy(InfillStrategy original, Cloner cloner) : base(original, cloner) {
      Generations = original.Generations;
      AttachListeners();
    }
    public InfillStrategy() {
      var critera = new ItemSet<IInfillCriterion> { new ExpectedImprovement(), new AugmentedExpectedImprovement(), new ExpectedQuality(), new ExpectedQuantileImprovement(), new MinimalQuantileCriterium(), new PluginExpectedImprovement() };
      Parameters.Add(new FixedValueParameter<IntValue>(NoGenerationsParameterName, "The number of generations before a new model is constructed", new IntValue(3)));
      Parameters.Add(new FixedValueParameter<IntValue>(NoIndividualsParameterName, "The number of individuals that are sampled each generation ", new IntValue(3)));
      Parameters.Add(new ConstrainedValueParameter<IInfillCriterion>(InfillCriterionParameterName, "The infill criterion used to cheaply evaluate points.", critera, critera.First()));
      AttachListeners();
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new InfillStrategy(this, cloner);
    }
    #endregion

    protected override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, ResultCollection globalResults, IRandom random) { }
    protected override void ProcessPopulation(Individual[] individuals, double[] qualities, IRandom random) {
      if (RegressionSolution != null && Generations < NoGenerations.Value) Generations++;
      else {
        //Select NoIndividuals best samples
        var samples = individuals
          .Zip(qualities, (individual, d) => new Tuple<Individual, double>(individual, d))
          .OrderBy(t => Problem.Maximization ? -t.Item2 : t.Item2)
          .Take(NoIndividuals.Value)
          .Select(t => t.Item1.RealVector());
        foreach (var indi in samples) EvaluateSample(indi, random);
        BuildRegressionSolution(random);
        Generations = 0;
      }
    }
    protected override void Initialize() {
      Generations = 0;
    }

    #region events
    private void AttachListeners() {
      ModelChanged += OnModelChanged;
    }
    private void OnModelChanged(object sender, EventArgs e) {
      InfillCriterion.Encoding = Problem?.Encoding as RealVectorEncoding;
      InfillCriterion.RegressionSolution = RegressionSolution;
      InfillCriterion.ExpensiveMaximization = Problem?.Maximization ?? false;
      if (RegressionSolution != null && InfillCriterion.Encoding != null)
        InfillCriterion.Initialize();
    }
    #endregion

    protected override double Estimate(RealVector point, IRandom random) {
      return InfillCriterion.Maximization() != Maximization() ? -InfillCriterion.Evaluate(point) : InfillCriterion.Evaluate(point);
    }
  }
}