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source: branches/2988_ModelsOfModels2/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Symbols/ModelTreeNode.cs @ 17002

Last change on this file since 17002 was 17002, checked in by msemenki, 5 years ago

#2988:
Class HelpFuction get new static functions that are used in different Map’s classes and possible in other classes.
Branch was adapted to Hive.
New version of class structure for Maps:

  1. 3 new variants of maps (RankMap, SuccessMap and ZeroMap) are added.
  2. BaseMap class was simplified, some class members were deleted and other were transported to child class, because some of them are not used in all kinds of maps.
  3. Functions between base class and child class were divided in other way.
  4. Mutation operators were adapted to work with new class structure. Now mutation make less work for ModelNodes than previously.
  5. ModelNode and Model symbols were simplified. They should not take into account a map type.
  6. Models frequency analyzers were adapted for new variants of maps.
  7. EMMAlgorithm class was adapted to new maps
File size: 3.6 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.Random;
27using System.Linq;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
30  [StorableType("44B25E73-6C4D-404C-93E5-42B7D2807CF7")]
31  public sealed class TreeModelTreeNode : SymbolicExpressionTreeTerminalNode {
32    public new TreeModel Symbol {
33      get { return (TreeModel)base.Symbol; }
34    }
35    [Storable]
36    ISymbolicExpressionTree tree;
37    public ISymbolicExpressionTree Tree {
38      get { return tree; }
39      set { tree = value; }
40    }
41    [Storable]
42    int treeNumber = -10;
43    public int TreeNumber {
44      get { return treeNumber; }
45      set { treeNumber = value; }
46    }
47
48    [StorableConstructor]
49    private TreeModelTreeNode(StorableConstructorFlag _) : base(_) { }
50
51    private TreeModelTreeNode(TreeModelTreeNode original, Cloner cloner)
52      : base(original, cloner) {
53      tree = (ISymbolicExpressionTree)original.tree.Clone(cloner);
54      TreeNumber = original.TreeNumber;
55    }
56
57    private TreeModelTreeNode() : base() { }
58    public TreeModelTreeNode(TreeModel treeModelSymbol) : base(treeModelSymbol) { }
59
60    public override bool HasLocalParameters {
61      get { return true; }
62    }
63    public override void ShakeLocalParameters(IRandom random, double shakingFactor) {
64      base.ShakeLocalParameters(random, shakingFactor);
65      var parametricNodes = Tree.IterateNodesPrefix().Where(x => x.HasLocalParameters).ToList();
66      if (parametricNodes.Any()) {
67        var selectedPoint = parametricNodes.SampleRandom(random);
68        if (selectedPoint is VariableTreeNode variableTreeNode) {
69          var symbol = variableTreeNode.Symbol;
70          variableTreeNode.Weight = NormalDistributedRandom.NextDouble(random, symbol.WeightManipulatorMu, symbol.WeightManipulatorSigma);
71        } else {
72          selectedPoint.ShakeLocalParameters(random, shakingFactor);
73        }
74
75      }
76    }
77    public void SetLocalParameters(IRandom random, double shakingFactor) {
78      foreach (var node in Tree.IterateNodesPrefix().Where(x => x.HasLocalParameters)) {
79        if (node is VariableTreeNode variableTreeNode) {
80          var symbol = variableTreeNode.Symbol;
81          variableTreeNode.Weight = NormalDistributedRandom.NextDouble(random, symbol.WeightManipulatorMu, symbol.WeightManipulatorSigma);
82        } else {
83          node.ResetLocalParameters(random);
84        }
85      }
86    }
87    public override IDeepCloneable Clone(Cloner cloner) {
88      return new TreeModelTreeNode(this, cloner);
89    }
90
91    public override string ToString() {
92
93      string s = "model" + " " + TreeNumber.ToString() + "\n " + new InfixExpressionFormatter().Format(tree);
94      return s;
95    }
96  }
97}
98
99
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