Free cookie consent management tool by TermsFeed Policy Generator

source: branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/Maps/EMMRankMap.cs @ 17134

Last change on this file since 17134 was 17134, checked in by msemenki, 3 years ago

#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.
File size: 5.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using HEAL.Attic;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Problems.DataAnalysis.Symbolic;
27using System;
28using System.Collections.Generic;
29using System.Linq;
30
31namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
32  [Item("RankMap", "A map of models of models of models")]
33  [StorableType("1D4DD90E-553A-46DB-B0CD-6A899AA0B6D0")]
34  public class EMMRankMap : EMMMapBase<ISymbolicExpressionTree> { // it do not work absolutely
35    [Storable]
36    public List<List<double>> Probabilities { get; set; }
37    #region constructors
38    [StorableConstructor]
39    protected EMMRankMap(StorableConstructorFlag _) : base(_) { }
40    public override IDeepCloneable Clone(Cloner cloner) {
41      return new EMMRankMap(this, cloner);
42    }
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    }
52    #endregion
53    #region MapCreation
54    override public void CreateMap(IRandom random) {
55      MapSizeCheck(ModelSet.Count);
56      ApplyRankMapCreationAlgorithm(ModelSetPreparation.CalculateDistances(ModelSet), Map, Probabilities);
57    }
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) {
93      int mapSize = distances.GetLength(0);
94      double tempSum = 0;
95      for (int i = 0; i < mapSize; i++) {
96        tempSum += i;
97      }
98      List<List<double>> currentList = new List<List<double>>();
99      for (int t = 0; t < mapSize; t++) {
100        for (int i = 0; i < mapSize; i++) {
101          if (distances[i, t].IsAlmost(0))
102            continue;
103          currentList.Add(new List<double>());
104          currentList[i].Add(i);
105          currentList[i].Add(distances[i, t]);
106        }
107        currentList.Sort((a, b) => a[1].CompareTo(b[1])); ///workable sorting
108        for (int i = 0; i < currentList.Count; i++) {
109          currentList[i].Add(currentList.Count - i);
110        }
111        probabilities.Add(new List<double>());
112        for (int i = 0; i < mapSize; i++) {
113          if (distances[i, t].IsAlmost(0))
114            continue;
115          map[t].Add(Convert.ToInt32(currentList[i][0]));
116          probabilities[t].Add(currentList[i][2] / tempSum);
117        }
118        currentList.Clear();
119      }
120    }
121    #endregion
122    #region Map Apply Functions
123    public override ISymbolicExpressionTree NewModelForMutation(IRandom random, out int treeNumber, int parentTreeNumber) {
124      treeNumber = Map[parentTreeNumber][HelpFunctions.OneElementFromListProportionalSelection(random, Probabilities[parentTreeNumber])];
125      return (ISymbolicExpressionTree)ModelSet[treeNumber].Clone();
126    }
127    override public ISymbolicExpressionTree NewModelForInizializtionNotTree(IRandom random, out int treeNumber) {
128      return NewModelForInizializtion(random, out treeNumber);
129    }
130    #endregion
131  }
132}
Note: See TracBrowser for help on using the repository browser.