[11219] | 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.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 28 | using HeuristicLab.Optimization.Operators;
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| 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 30 |
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[11221] | 31 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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[11219] | 32 | [StorableClass]
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[11239] | 33 | [Item("BottomUpTreeSimilarityCalculator", "A similarity calculator which uses the tree bottom-up distance as a similarity metric.")]
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| 34 | public class BottomUpTreeSimilarityCalculator : SingleObjectiveSolutionSimilarityCalculator {
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| 35 | private readonly HashSet<string> commutativeSymbols;
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[11219] | 36 |
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[11239] | 37 | public BottomUpTreeSimilarityCalculator() {
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| 38 | commutativeSymbols = new HashSet<string>();
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| 39 | }
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[11219] | 40 |
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[11239] | 41 | public BottomUpTreeSimilarityCalculator(IEnumerable<string> commutativeSymbols) {
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| 42 | this.commutativeSymbols = new HashSet<string>(commutativeSymbols);
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| 43 | }
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| 44 |
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[11219] | 45 | public override IDeepCloneable Clone(Cloner cloner) {
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[11239] | 46 | return new BottomUpTreeSimilarityCalculator(this, cloner);
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[11219] | 47 | }
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| 48 |
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[11239] | 49 | protected BottomUpTreeSimilarityCalculator(BottomUpTreeSimilarityCalculator original, Cloner cloner)
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[11219] | 50 | : base(original, cloner) {
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| 51 | }
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| 52 |
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| 53 | public override double CalculateSolutionSimilarity(IScope leftSolution, IScope rightSolution) {
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| 54 | var t1 = leftSolution.Variables[SolutionVariableName].Value as ISymbolicExpressionTree;
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| 55 | var t2 = rightSolution.Variables[SolutionVariableName].Value as ISymbolicExpressionTree;
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| 56 |
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| 57 | if (t1 == null || t2 == null)
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| 58 | throw new ArgumentException("Cannot calculate similarity when one of the arguments is null.");
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| 59 |
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[11224] | 60 | var similarity = CalculateSolutionSimilarity(t1, t2);
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| 61 | if (similarity > 1.0)
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| 62 | throw new Exception("Similarity value cannot be greater than 1");
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| 63 |
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| 64 | return similarity;
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[11219] | 65 | }
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| 66 |
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[11239] | 67 | public bool AddCommutativeSymbol(string symbolName) {
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| 68 | return commutativeSymbols.Add(symbolName);
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| 69 | }
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| 70 |
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| 71 | public bool RemoveCommutativeSymbol(string symbolName) {
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| 72 | return commutativeSymbols.Remove(symbolName);
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| 73 | }
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| 74 |
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[11219] | 75 | public double CalculateSolutionSimilarity(ISymbolicExpressionTree t1, ISymbolicExpressionTree t2) {
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| 76 | if (t1 == t2)
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| 77 | return 1;
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| 78 |
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[11221] | 79 | var map = ComputeBottomUpMapping(t1.Root, t2.Root);
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[11219] | 80 | return 2.0 * map.Count / (t1.Length + t2.Length);
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| 81 | }
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| 82 |
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[11221] | 83 | public Dictionary<ISymbolicExpressionTreeNode, ISymbolicExpressionTreeNode> ComputeBottomUpMapping(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2) {
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| 84 | var compactedGraph = Compact(n1, n2);
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[11219] | 85 |
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| 86 | var forwardMap = new Dictionary<ISymbolicExpressionTreeNode, ISymbolicExpressionTreeNode>(); // nodes of t1 => nodes of t2
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| 87 | var reverseMap = new Dictionary<ISymbolicExpressionTreeNode, ISymbolicExpressionTreeNode>(); // nodes of t2 => nodes of t1
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| 88 |
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[11225] | 89 | // visit nodes in order of decreasing height to ensure correct mapping
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[11229] | 90 | foreach (var v in n1.IterateNodesPrefix().OrderByDescending(x => compactedGraph[x].Depth)) {
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[11225] | 91 | if (forwardMap.ContainsKey(v))
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| 92 | continue;
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[11219] | 93 | var kv = compactedGraph[v];
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| 94 | ISymbolicExpressionTreeNode w = null;
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[11225] | 95 | foreach (var t in n2.IterateNodesPrefix()) {
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| 96 | if (reverseMap.ContainsKey(t) || compactedGraph[t] != kv)
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| 97 | continue;
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[11219] | 98 | w = t;
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| 99 | break;
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| 100 | }
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| 101 | if (w == null) continue;
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| 102 |
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| 103 | // 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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| 104 | // 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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| 105 | var eV = IterateBreadthOrdered(v).GetEnumerator();
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| 106 | var eW = IterateBreadthOrdered(w).GetEnumerator();
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| 107 |
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| 108 | while (eV.MoveNext() && eW.MoveNext()) {
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| 109 | var s = eV.Current;
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| 110 | var t = eW.Current;
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[11225] | 111 |
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| 112 | if (reverseMap.ContainsKey(t)) {
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| 113 | throw new Exception("A mapping to this node already exists.");
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| 114 | }
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| 115 |
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[11219] | 116 | forwardMap[s] = t;
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| 117 | reverseMap[t] = s;
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| 118 | }
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| 119 | }
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| 120 |
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| 121 | return forwardMap;
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| 122 | }
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| 123 |
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| 124 | /// <summary>
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| 125 | /// Creates a compact representation of the two trees as a directed acyclic graph
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| 126 | /// </summary>
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[11229] | 127 | /// <param name="n1">The root of the first tree</param>
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| 128 | /// <param name="n2">The root of the second tree</param>
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[11219] | 129 | /// <returns>The compacted DAG representing the two trees</returns>
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[11229] | 130 | private Dictionary<ISymbolicExpressionTreeNode, GraphNode> Compact(ISymbolicExpressionTreeNode n1, ISymbolicExpressionTreeNode n2) {
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| 131 | var nodeMap = new Dictionary<ISymbolicExpressionTreeNode, GraphNode>(); // K
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| 132 | var labelMap = new Dictionary<string, GraphNode>(); // L
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[11219] | 133 | var childrenCount = new Dictionary<ISymbolicExpressionTreeNode, int>(); // Children
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| 134 |
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[11221] | 135 | var nodes = n1.IterateNodesPostfix().Concat(n2.IterateNodesPostfix()); // the disjoint union F
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[11229] | 136 | var list = new List<GraphNode>();
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[11219] | 137 | var queue = new Queue<ISymbolicExpressionTreeNode>();
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| 138 |
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| 139 | foreach (var n in nodes) {
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| 140 | if (n.SubtreeCount == 0) {
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| 141 | var label = n.ToString();
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[11229] | 142 | if (!labelMap.ContainsKey(label)) {
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| 143 | var z = new GraphNode { SymbolicExpressionTreeNode = n, Label = label };
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| 144 | labelMap[z.Label] = z;
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| 145 | list.Add(z);
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[11225] | 146 | }
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[11229] | 147 | nodeMap[n] = labelMap[label];
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[11219] | 148 | queue.Enqueue(n);
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| 149 | } else {
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| 150 | childrenCount[n] = n.SubtreeCount;
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| 151 | }
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| 152 | }
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| 153 | while (queue.Any()) {
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[11229] | 154 | var n = queue.Dequeue();
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[11225] | 155 |
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[11229] | 156 | if (n.SubtreeCount > 0) {
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| 157 | var label = n.Symbol.Name;
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[11219] | 158 | bool found = false;
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[11229] | 159 | var depth = n.GetDepth();
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[11219] | 160 |
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| 161 | bool sort = commutativeSymbols.Contains(label);
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[11229] | 162 | var nNodes = n.Subtrees.Select(x => nodeMap[x]).ToList();
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| 163 | if (sort) nNodes.Sort((a, b) => String.Compare(a.Label, b.Label, StringComparison.Ordinal));
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[11219] | 164 |
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[11229] | 165 | for (int i = list.Count - 1; i >= 0; --i) {
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| 166 | var w = list[i];
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| 167 |
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| 168 | if (!(n.SubtreeCount == w.ChildrenCount && label == w.Label && depth == w.Depth))
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[11219] | 169 | continue;
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| 170 |
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| 171 | // sort V and W when the symbol is commutative because we are dealing with unordered trees
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[11229] | 172 | var m = w.SymbolicExpressionTreeNode;
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| 173 | var mNodes = m.Subtrees.Select(x => nodeMap[x]).ToList();
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| 174 | if (sort) mNodes.Sort((a, b) => String.Compare(a.Label, b.Label, StringComparison.Ordinal));
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[11219] | 175 |
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[11229] | 176 | if (nNodes.SequenceEqual(mNodes)) {
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| 177 | nodeMap[n] = w;
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[11219] | 178 | found = true;
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| 179 | break;
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| 180 | }
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[11229] | 181 | }
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[11219] | 182 |
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| 183 | if (!found) {
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[11229] | 184 | var w = new GraphNode { SymbolicExpressionTreeNode = n, Label = label, Depth = depth, ChildrenCount = n.SubtreeCount };
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| 185 | list.Add(w);
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| 186 | nodeMap[n] = w;
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| 187 | }
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| 188 | }
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[11219] | 189 |
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[11229] | 190 | var p = n.Parent;
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[11219] | 191 | if (p == null)
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| 192 | continue;
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| 193 |
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| 194 | childrenCount[p]--;
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| 195 |
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| 196 | if (childrenCount[p] == 0)
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| 197 | queue.Enqueue(p);
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[11220] | 198 | }
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[11219] | 199 |
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[11229] | 200 | return nodeMap;
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[11219] | 201 | }
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| 202 |
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[11239] | 203 | /// <summary>
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| 204 | /// This method iterates the nodes of a subtree in breadth order while also sorting the subtrees of commutative symbols based on their label.
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| 205 | /// This is necessary in order for the mapping to be realized correctly.
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| 206 | /// </summary>
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| 207 | /// <param name="node">The root of the subtree</param>
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| 208 | /// <returns>A list of nodes in breadth order (with children of commutative symbols sorted by label)</returns>
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[11219] | 209 | private IEnumerable<ISymbolicExpressionTreeNode> IterateBreadthOrdered(ISymbolicExpressionTreeNode node) {
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| 210 | var list = new List<ISymbolicExpressionTreeNode> { node };
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| 211 | int i = 0;
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| 212 | while (i < list.Count) {
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| 213 | var n = list[i];
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| 214 | if (n.SubtreeCount > 0) {
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[11229] | 215 | var subtrees = commutativeSymbols.Contains(node.Symbol.Name) ? n.Subtrees.OrderBy(x => x.ToString(), StringComparer.Ordinal) : n.Subtrees;
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[11219] | 216 | list.AddRange(subtrees);
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| 217 | }
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| 218 | i++;
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| 219 | }
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| 220 | return list;
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| 221 | }
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| 222 |
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[11229] | 223 | private class GraphNode {
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| 224 | public ISymbolicExpressionTreeNode SymbolicExpressionTreeNode;
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| 225 | public string Label;
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| 226 | public int Depth;
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| 227 | public int ChildrenCount;
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[11219] | 228 | }
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| 229 | }
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| 230 | }
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