source: branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMSucsessMap.cs @ 17021

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2019 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 HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using System.Collections.Generic;
using System.Linq;

namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
  [Item("SucsessMap", "A map of models of models of models")]
  [StorableType("3880BA82-4CB0-4838-A17A-823E91BC046C")]
  public class EMMSucsessMap : EMMMapBase<ISymbolicExpressionTree> {
    [Storable]
    public List<double> Probabilities { get; private set; }
    [Storable]
    public List<List<double>> SucsessStatistics { get; private set; }
    #region conctructors
    [StorableConstructor]
    protected EMMSucsessMap(StorableConstructorFlag _) : base(_) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new EMMSucsessMap(this, cloner);
    }
    public EMMSucsessMap() : base() {
      ModelSet = new List<ISymbolicExpressionTree>();
      SucsessStatistics = new List<List<double>>();
    }
    public EMMSucsessMap(EMMSucsessMap original, Cloner cloner) : base(original, cloner) {
      SucsessStatistics = original.SucsessStatistics.Select(x => x.ToList()).ToList();
    }
    #endregion
    #region MapCreation
    override public void CreateMap(IRandom random, int k) {

      Probabilities = new List<double>();
      Map.Clear();
      Map.Add(new List<int>());
      MapSizeCheck(ModelSet.Count);
      ApplySucsessMapCreationAlgorithm(random, CalculateDistances(), Map, Probabilities, SucsessStatistics);
    }
    public static void ApplySucsessMapCreationAlgorithm(IRandom random, double[,] distances, List<List<int>> map, List<double> probabilities, List<List<double>> sucsessStatistics) {
      int mapSize = distances.GetLength(0);
      for (int t = 0; t < mapSize; t++) {
        map[t].Add(t);
        probabilities.Add(1.0 / ((double)(mapSize))); // uniform distribution as start point
      }
    }
    public override void MapUpDate(Dictionary<ISymbolicExpressionTree, double> population) {
      SucsessStatisticCollection(population);
      HelpFunctions.ProbabilitiesUpDate(SucsessStatistics, Probabilities);
    }
    private void SucsessStatisticCollection(Dictionary<ISymbolicExpressionTree, double> population) {
      if (SucsessStatistics.Count != 0)
        SucsessStatistics.Clear();
      for (int t = 0; t < Probabilities.Count; t++) {
        SucsessStatistics.Add(new List<double>());
        SucsessStatistics[t].Add(0);
        SucsessStatistics[t].Add(0);
      }
      foreach (var solution in population) {
        TreeCheck(solution.Key, solution.Value);
      }
    }
    private void TreeCheck(ISymbolicExpressionTree tree, double treeQuality) {
      foreach (var treeNode in tree.IterateNodesPrefix().OfType<TreeModelTreeNode>()) {
        SucsessStatistics[treeNode.TreeNumber][0] += 1;
        SucsessStatistics[treeNode.TreeNumber][1] += treeQuality;
      }
    }
    #endregion
    #region MapApplayFunctions
    public override ISymbolicExpressionTree NewModelForMutation(IRandom random, out int treeNumber, int parentTreeNumber) {
      treeNumber = Map[HelpFunctions.OneElementFromListProportionalSelection(random, Probabilities)][0];
      return (ISymbolicExpressionTree)ModelSet[treeNumber].Clone();
    }
    override public ISymbolicExpressionTree NewModelForInizializtionNotTree(IRandom random, out int treeNumber) {
      return NewModelForInizializtion(random, out treeNumber);
    }
    #endregion
  }
}