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Timestamp:
07/11/19 16:30:22 (5 years ago)
Author:
msemenki
Message:

#2988:

  1. The file system was changed, folders was added and part of files was transferred in these folders.
  2. HelpFunctions class was divided on 2 parts: HelpFuctions for common purposes static functions and SelfConfiguration that include functions for self-configuration mechanism realization (is used in EMMSucsessMap).
  3. Parts of self-configuration mechanism was transferred from EMMSucsessMap.cs to SelfConfiguration.cs. Now EMMSucsessMap used SelfConfiguration like one of data member. Other parts of project was adopted for this changing.
  4. FileComunication class was added. It include the majority of functions for printing to files or reading from files. Here were realized possibility to write and read to hl files.
  5. ModelTreeNode.cs has additional possibility - to write sub-model in string (then it is possible to write it in file).
  6. InfixExpressionFormatter.cs can work with TreeModelNode.
  7. Possibility for different map types to be readable from files was extended and cheeked.
  8. Such parameters like - ClusterNumbers, ClusterNumbersShow, NegbourNumber, NegbourType (that is used only in several maps) was transferred from EMMAlgorithm to Map Parameters. Now EMMBaseMap class inherited from ParameterizedNamedItem (not from Item). And EMMIslandMap and EMMNetworkMap contains their parameters (constructors was modified). CreationMap calls functions were simplified.
  9. Functions for different distance metric calculation was added. Now, it is possible to calculate different types of distances between models (with different random values of constants).
  10. DistanceParametr was added. Now maps can be created according different types of distance calculations.
  11. The class EMMClustering has new name KMeansClusterizationAlgorithm. On KMeansClusterizationAlgorithm bug with bloating of centroids list was fixed. Algorithm was adopted for working with different type of distance metric and get maximum number of iterations.
  12. Possibilities for constants optimization in sub-models an whole tree was added. EMMAlgorithm get new function for evaluation of individuals (and some additional technical stuff for that). Function for trees with model in usual tree transformation and back was added.
  13. EMMAlgorithm was divided on 2 parts:
  • EMMAlgorithm, that contain evolutionary algorithm working with sub-models, and use ready to use maps;
  • ModelSetPreparation, that contain distance calculation, model set simplification and map creation.
Location:
branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/Maps
Files:
1 added
1 moved

Legend:

Unmodified
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  • branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/Maps/EMMRankMap.cs

    r17133 r17134  
    2424using HeuristicLab.Core;
    2525using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
     26using HeuristicLab.Problems.DataAnalysis.Symbolic;
    2627using System;
    2728using System.Collections.Generic;
     29using System.Linq;
    2830
    2931namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
    3032  [Item("RankMap", "A map of models of models of models")]
    3133  [StorableType("1D4DD90E-553A-46DB-B0CD-6A899AA0B6D0")]
    32   public class EMMRankMap : EMMMapBase<ISymbolicExpressionTree> {
     34  public class EMMRankMap : EMMMapBase<ISymbolicExpressionTree> { // it do not work absolutely
    3335    [Storable]
    3436    public List<List<double>> Probabilities { get; set; }
    35     #region conctructors
     37    #region constructors
    3638    [StorableConstructor]
    3739    protected EMMRankMap(StorableConstructorFlag _) : base(_) { }
     
    3941      return new EMMRankMap(this, cloner);
    4042    }
    41     public EMMRankMap() : base() { ModelSet = new List<ISymbolicExpressionTree>(); }
    42     public EMMRankMap(EMMRankMap original, Cloner cloner) : base(original, cloner) { }
     43    public EMMRankMap() : base() {
     44      ModelSet = new List<ISymbolicExpressionTree>();
     45      Probabilities = new List<List<double>>();
     46    }
     47    public EMMRankMap(EMMRankMap original, Cloner cloner) : base(original, cloner) {
     48      if (original.Probabilities != null) {
     49        Probabilities = original.Probabilities.Select(x => x.ToList()).ToList();
     50      }
     51    }
    4352    #endregion
    4453    #region MapCreation
    45     override public void CreateMap(IRandom random, int k) {
    46 
    47       Probabilities = new List<List<double>>();
     54    override public void CreateMap(IRandom random) {
    4855      MapSizeCheck(ModelSet.Count);
    49       ApplyRankMapCreationAlgorithm(random, CalculateDistances(), Map, Probabilities);
     56      ApplyRankMapCreationAlgorithm(ModelSetPreparation.CalculateDistances(ModelSet), Map, Probabilities);
    5057    }
    51     public static void ApplyRankMapCreationAlgorithm(IRandom random, double[,] distances, List<List<int>> map, List<List<double>> probabilities) {
     58    override public void CreateMap(IRandom random, ISymbolicDataAnalysisSingleObjectiveProblem problem) {
     59      MapSizeCheck(ModelSet.Count);
     60      ApplyRankMapCreationAlgorithm(ModelSetPreparation.DistanceMatrixCalculation(ModelSet, DistanceParametr, problem), Map, Probabilities);
     61    }
     62    override public void MapRead(IEnumerable<ISymbolicExpressionTree> trees) {
     63      base.MapRead(trees);
     64      MapFullment(trees.Count());
     65      string fileName = ("Map" + DistanceParametr + ".txt");
     66      Probabilities = FileComuncations.DoubleMatrixFromFileRead(fileName);
     67    }
     68    protected void MapFullment(int mapSize) {
     69      if (Map != null) {
     70        Map.Clear();
     71      }
     72      for (int t = 0; t < mapSize; t++) {
     73        for (int i = 0; i < mapSize; i++) {
     74          if (i == t)
     75            continue;
     76          Map[t].Add(i);
     77        }
     78      }
     79    }
     80    override public string[] MapToStoreInFile() { // Function that prepare Map to printing in .txt File: create a set of strings for future reading by computer
     81      string[] s;
     82      s = new string[Map.Count];
     83      for (int i = 0; i < Map.Count; i++) {
     84        s[i] = "";
     85        for (int j = 0; j < (Map.Count - 1); j++) {
     86          s[i] += Probabilities[i][j].ToString();
     87          if (j != (Map.Count - 2)) { s[i] += " "; }
     88        }
     89      }
     90      return s;
     91    }
     92    public static void ApplyRankMapCreationAlgorithm(double[,] distances, List<List<int>> map, List<List<double>> probabilities) {
    5293      int mapSize = distances.GetLength(0);
    5394      double tempSum = 0;
     
    64105          currentList[i].Add(distances[i, t]);
    65106        }
    66         currentList.Sort((a, b) => a[1].CompareTo(b[1])); ///загадкой является то нафига оно мне вообще понадобилось in DistanceMap.. но это работающая сортировка.
     107        currentList.Sort((a, b) => a[1].CompareTo(b[1])); ///workable sorting
    67108        for (int i = 0; i < currentList.Count; i++) {
    68109          currentList[i].Add(currentList.Count - i);
     
    79120    }
    80121    #endregion
    81     #region MapApplayFunctions
     122    #region Map Apply Functions
    82123    public override ISymbolicExpressionTree NewModelForMutation(IRandom random, out int treeNumber, int parentTreeNumber) {
    83124      treeNumber = Map[parentTreeNumber][HelpFunctions.OneElementFromListProportionalSelection(random, Probabilities[parentTreeNumber])];
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