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source: branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMOperators/EMMMutators.cs @ 17134

Last change on this file since 17134 was 17134, checked in by msemenki, 5 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
RevLine 
[16722]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.Parameters;
27using HeuristicLab.Problems.DataAnalysis.Symbolic;
28using System.Collections.Generic;
29using System.Linq;
30
31namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
32  [Item("EMMChangeNodeTypeManipulation", "Selects a random tree node and changes the symbol.")]
[16734]33  [StorableType("990D3946-7F06-48B4-B8DB-F8E308D6304D")]
[16722]34  public sealed class EMMMutators : SymbolicExpressionTreeManipulator {
35    private const int MAX_TRIES = 100;
36    private const string MapParameterName = "Map";
37
[16899]38    public ILookupParameter<EMMMapBase<ISymbolicExpressionTree>> MapParameter {
39      get { return (ILookupParameter<EMMMapBase<ISymbolicExpressionTree>>)Parameters[MapParameterName]; }
[16722]40    }
41
[16899]42    public List<ISymbolicExpressionTree> ModelSet => MapParameter.ActualValue.ModelSet;
43    public List<List<int>> Map => MapParameter.ActualValue.Map;
[16722]44
45    [StorableConstructor]
46    private EMMMutators(StorableConstructorFlag _) : base(_) { }
47    private EMMMutators(EMMMutators original, Cloner cloner) : base(original, cloner) { }
48    public EMMMutators() : base() {
[16899]49      Parameters.Add(new LookupParameter<EMMMapBase<ISymbolicExpressionTree>>(MapParameterName));
[16722]50    }
51
52    public override IDeepCloneable Clone(Cloner cloner) {
53      return new EMMMutators(this, cloner);
54    }
55
56    protected override void Manipulate(IRandom random, ISymbolicExpressionTree symbolicExpressionTree) {
[17002]57      EMMMutatorsPart(random, symbolicExpressionTree, ModelSet, Map);
[16722]58    }
[17002]59    public void EMMMutatorsPart(IRandom random, ISymbolicExpressionTree symbolicExpressionTree, List<ISymbolicExpressionTree> modelSet, List<List<int>> map) {
[16722]60      List<ISymbol> allowedSymbols = new List<ISymbol>();
61      ISymbolicExpressionTreeNode parent;
62      int childIndex;
63      ISymbolicExpressionTreeNode child;
64      // repeat until a fitting parent and child are found (MAX_TRIES times)
65      int tries = 0;
66      do {
67
68#pragma warning disable 612, 618
69        parent = symbolicExpressionTree.Root.IterateNodesPrefix().Skip(1).Where(n => n.SubtreeCount > 0).SelectRandom(random);
70#pragma warning restore 612, 618
71
72        childIndex = random.Next(parent.SubtreeCount);
73
74        child = parent.GetSubtree(childIndex);
75        int existingSubtreeCount = child.SubtreeCount;
76        allowedSymbols.Clear();
77        foreach (var symbol in parent.Grammar.GetAllowedChildSymbols(parent.Symbol, childIndex)) {
78          // check basic properties that the new symbol must have
79          if (symbol.Name != child.Symbol.Name &&
80            symbol.InitialFrequency > 0 &&
81            existingSubtreeCount <= parent.Grammar.GetMinimumSubtreeCount(symbol) &&
82            existingSubtreeCount >= parent.Grammar.GetMaximumSubtreeCount(symbol)) {
83            // check that all existing subtrees are also allowed for the new symbol
84            bool allExistingSubtreesAllowed = true;
85            for (int existingSubtreeIndex = 0; existingSubtreeIndex < existingSubtreeCount && allExistingSubtreesAllowed; existingSubtreeIndex++) {
86              var existingSubtree = child.GetSubtree(existingSubtreeIndex);
87              allExistingSubtreesAllowed &= parent.Grammar.IsAllowedChildSymbol(symbol, existingSubtree.Symbol, existingSubtreeIndex);
88            }
89            if (allExistingSubtreesAllowed) {
90              allowedSymbols.Add(symbol);
91            }
92          }
93        }
94        tries++;
95      } while (tries < MAX_TRIES && allowedSymbols.Count == 0);
96
97      if (tries < MAX_TRIES) {
98        var weights = allowedSymbols.Select(s => s.InitialFrequency).ToList();
99#pragma warning disable 612, 618
100        var newSymbol = allowedSymbols.SelectRandom(weights, random);
101#pragma warning restore 612, 618
102
103        // replace the old node with the new node
104
105        var newNode = newSymbol.CreateTreeNode();
[16760]106
107        if (newNode is TreeModelTreeNode treeNode) {
[17002]108          int p = random.Next(modelSet.Count);
109          if (child is TreeModelTreeNode chNode) { p = chNode.TreeNumber; }
110          treeNode.TreeNumber = p;
111          MapParameter.ActualValue.NodeForMutationChange(random, treeNode);
[16760]112        } else if (newNode.HasLocalParameters)
113          newNode.ResetLocalParameters(random);
[16722]114        foreach (var subtree in child.Subtrees)
115          newNode.AddSubtree(subtree);
116        parent.RemoveSubtree(childIndex);
117        parent.InsertSubtree(childIndex, newNode);
118      }
119    }
120  }
121}
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