#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 .
*/
#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 {
[Storable]
public List Probabilities { get; private set; }
[Storable]
public List> 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();
SucsessStatistics = new List>();
}
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();
Map.Clear();
Map.Add(new List());
MapSizeCheck(ModelSet.Count);
ApplySucsessMapCreationAlgorithm(random, CalculateDistances(), Map, Probabilities, SucsessStatistics);
}
public static void ApplySucsessMapCreationAlgorithm(IRandom random, double[,] distances, List> map, List probabilities, List> 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 population) {
SucsessStatisticCollection(population);
HelpFunctions.ProbabilitiesUpDate(SucsessStatistics, Probabilities);
}
private void SucsessStatisticCollection(Dictionary population) {
if (SucsessStatistics.Count != 0)
SucsessStatistics.Clear();
for (int t = 0; t < Probabilities.Count; t++) {
SucsessStatistics.Add(new List());
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()) {
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
}
}