1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Diagnostics;
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25 | using System.Linq;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Core;
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28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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29 | using HeuristicLab.Optimization.Operators;
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30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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31 |
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32 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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33 | [StorableClass]
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34 | [Item("BottomUpSimilarityCalculator", "A similarity calculator which uses the tree bottom-up distance as a similarity metric.")]
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35 | public class BottomUpSimilarityCalculator : SingleObjectiveSolutionSimilarityCalculator, ISymbolicDataAnalysisExpressionSimilarityCalculator {
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36 | private readonly HashSet<string> commutativeSymbols = new HashSet<string> { "Addition", "Multiplication", "Average", "And", "Or", "Xor" };
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37 | public bool MatchVariableWeights { get; set; }
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38 | public bool MatchConstantValues { get; set; }
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39 |
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40 | public BottomUpSimilarityCalculator() { }
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41 |
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42 | protected BottomUpSimilarityCalculator(BottomUpSimilarityCalculator original, Cloner cloner)
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43 | : base(original, cloner) {
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44 | }
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45 |
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46 | public override IDeepCloneable Clone(Cloner cloner) {
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47 | return new BottomUpSimilarityCalculator(this, cloner);
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48 | }
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49 |
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50 | public double CalculateSimilarity(ISymbolicExpressionTree t1, ISymbolicExpressionTree t2) {
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51 | if (t1 == t2)
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52 | return 1;
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53 |
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54 | var map = ComputeBottomUpMapping(t1.Root, t2.Root);
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55 | return 2.0 * map.Count / (t1.Length + t2.Length);
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56 | }
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57 |
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58 | public override double CalculateSolutionSimilarity(IScope leftSolution, IScope rightSolution) {
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59 | var t1 = leftSolution.Variables[SolutionVariableName].Value as ISymbolicExpressionTree;
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60 | var t2 = rightSolution.Variables[SolutionVariableName].Value as ISymbolicExpressionTree;
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61 |
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62 | if (t1 == null || t2 == null)
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63 | throw new ArgumentException("Cannot calculate similarity when one of the arguments is null.");
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64 |
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65 | var similarity = CalculateSimilarity(t1, t2);
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66 | if (similarity > 1.0)
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67 | throw new Exception("Similarity value cannot be greater than 1");
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68 |
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69 | return similarity;
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70 | }
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71 |
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72 | public Dictionary<ISymbolicExpressionTreeNode, ISymbolicExpressionTreeNode> ComputeBottomUpMapping(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2) {
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73 | var compactedGraph = Compact(n1, n2);
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74 |
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75 | var forwardMap = new Dictionary<ISymbolicExpressionTreeNode, ISymbolicExpressionTreeNode>(); // nodes of t1 => nodes of t2
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76 | var reverseMap = new Dictionary<ISymbolicExpressionTreeNode, ISymbolicExpressionTreeNode>(); // nodes of t2 => nodes of t1
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77 |
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78 | // visit nodes in order of decreasing height to ensure correct mapping
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79 | foreach (var v in n1.IterateNodesPrefix().OrderByDescending(x => compactedGraph[x].Depth)) {
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80 | if (forwardMap.ContainsKey(v))
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81 | continue;
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82 | var kv = compactedGraph[v];
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83 | ISymbolicExpressionTreeNode w = null;
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84 | foreach (var t in n2.IterateNodesPrefix()) {
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85 | if (reverseMap.ContainsKey(t) || compactedGraph[t] != kv)
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86 | continue;
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87 | w = t;
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88 | break;
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89 | }
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90 | if (w == null) continue;
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91 |
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92 | // at this point we know that v and w are isomorphic, however, the mapping cannot be done directly (as in the paper) because the trees are unordered (subtree order might differ)
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93 | // the solution is to sort subtrees by label using IterateBreadthOrdered (this will work because the subtrees are isomorphic!) and simultaneously iterate over the two subtrees
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94 | var eV = IterateBreadthOrdered(v).GetEnumerator();
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95 | var eW = IterateBreadthOrdered(w).GetEnumerator();
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96 |
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97 | while (eV.MoveNext() && eW.MoveNext()) {
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98 | var s = eV.Current;
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99 | var t = eW.Current;
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100 |
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101 | Debug.Assert(!reverseMap.ContainsKey(t));
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102 |
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103 | forwardMap[s] = t;
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104 | reverseMap[t] = s;
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105 | }
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106 | }
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107 |
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108 | return forwardMap;
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109 | }
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110 |
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111 | /// <summary>
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112 | /// Creates a compact representation of the two trees as a directed acyclic graph
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113 | /// </summary>
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114 | /// <param name="n1">The root of the first tree</param>
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115 | /// <param name="n2">The root of the second tree</param>
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116 | /// <returns>The compacted DAG representing the two trees</returns>
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117 | private Dictionary<ISymbolicExpressionTreeNode, GraphNode> Compact(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2) {
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118 | var nodeMap = new Dictionary<ISymbolicExpressionTreeNode, GraphNode>(); // K
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119 | var labelMap = new Dictionary<string, GraphNode>(); // L
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120 | var childrenCount = new Dictionary<ISymbolicExpressionTreeNode, int>(); // Children
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121 |
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122 | var nodes = n1.IterateNodesPostfix().Concat(n2.IterateNodesPostfix()); // the disjoint union F
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123 | var list = new List<GraphNode>();
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124 | var queue = new Queue<ISymbolicExpressionTreeNode>();
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125 |
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126 | foreach (var n in nodes) {
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127 | if (n.SubtreeCount == 0) {
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128 | var label = Label(n);
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129 | if (!labelMap.ContainsKey(label)) {
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130 | var z = new GraphNode { SymbolicExpressionTreeNode = n, Label = label };
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131 | labelMap[z.Label] = z;
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132 | list.Add(z);
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133 | }
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134 | nodeMap[n] = labelMap[label];
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135 | queue.Enqueue(n);
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136 | } else {
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137 | childrenCount[n] = n.SubtreeCount;
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138 | }
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139 | }
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140 | while (queue.Any()) {
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141 | var n = queue.Dequeue();
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142 |
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143 | if (n.SubtreeCount > 0) {
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144 | var label = n.Symbol.Name;
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145 | bool found = false;
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146 | var depth = n.GetDepth();
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147 |
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148 | bool sort = commutativeSymbols.Contains(label);
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149 | var nNodes = n.Subtrees.Select(x => nodeMap[x]).ToList();
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150 | if (sort) nNodes.Sort((a, b) => String.Compare(a.Label, b.Label, StringComparison.Ordinal));
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151 |
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152 | for (int i = list.Count - 1; i >= 0; --i) {
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153 | var w = list[i];
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154 |
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155 | if (!(n.SubtreeCount == w.ChildrenCount && label == w.Label && depth == w.Depth))
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156 | continue;
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157 |
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158 | // sort V and W when the symbol is commutative because we are dealing with unordered trees
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159 | var m = w.SymbolicExpressionTreeNode;
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160 | var mNodes = m.Subtrees.Select(x => nodeMap[x]).ToList();
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161 | if (sort) mNodes.Sort((a, b) => String.Compare(a.Label, b.Label, StringComparison.Ordinal));
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162 |
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163 | if (nNodes.SequenceEqual(mNodes)) {
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164 | nodeMap[n] = w;
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165 | found = true;
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166 | break;
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167 | }
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168 | }
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169 |
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170 | if (!found) {
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171 | var w = new GraphNode { SymbolicExpressionTreeNode = n, Label = label, Depth = depth, ChildrenCount = n.SubtreeCount };
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172 | list.Add(w);
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173 | nodeMap[n] = w;
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174 | }
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175 | }
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176 |
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177 | if (n == n1 || n == n2)
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178 | continue;
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179 |
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180 | var p = n.Parent;
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181 | if (p == null)
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182 | continue;
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183 |
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184 | childrenCount[p]--;
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185 |
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186 | if (childrenCount[p] == 0)
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187 | queue.Enqueue(p);
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188 | }
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189 |
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190 | return nodeMap;
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191 | }
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192 |
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193 | private IEnumerable<ISymbolicExpressionTreeNode> IterateBreadthOrdered(ISymbolicExpressionTreeNode node) {
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194 | var list = new List<ISymbolicExpressionTreeNode> { node };
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195 | int i = 0;
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196 | while (i < list.Count) {
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197 | var n = list[i];
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198 | if (n.SubtreeCount > 0) {
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199 | var subtrees = commutativeSymbols.Contains(node.Symbol.Name) ? n.Subtrees.OrderBy(x => x.ToString(), StringComparer.Ordinal) : n.Subtrees;
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200 | list.AddRange(subtrees);
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201 | }
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202 | i++;
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203 | }
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204 | return list;
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205 | }
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206 |
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207 | private string Label(ISymbolicExpressionTreeNode node) {
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208 | var constant = node as ConstantTreeNode;
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209 | if (constant != null && !MatchConstantValues)
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210 | return constant.Symbol.Name;
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211 | var variable = node as VariableTreeNode;
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212 | if (variable != null && !MatchVariableWeights) {
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213 | return variable.VariableName;
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214 | }
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215 | return node.ToString();
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216 | }
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217 |
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218 | private class GraphNode {
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219 | public ISymbolicExpressionTreeNode SymbolicExpressionTreeNode;
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220 | public string Label;
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221 | public int Depth;
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222 | public int ChildrenCount;
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223 | }
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224 | }
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225 | }
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