1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2010 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 HeuristicLab.Analysis;
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24 | using HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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28 | using HeuristicLab.Operators;
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29 | using HeuristicLab.Optimization;
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30 | using HeuristicLab.Optimization.Operators;
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31 | using HeuristicLab.Parameters;
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32 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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33 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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34 | using System.Collections.Generic;
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35 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
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36 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Symbols;
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37 |
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38 | namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
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39 | /// <summary>
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40 | /// An operator that analyzes the population diversity using fine grained structural tree similarity estimation.
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41 | /// </summary>
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42 | [Item("FineGrainedStructuralPopulationDiversityAnalyzer", "An operator that analyzes the population diversity using fine grained structural tree similarity estimation.")]
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43 | [StorableClass]
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44 | public sealed class FineGrainedStructuralPopulationDiversityAnalyzer : SymbolicRegressionPopulationDiversityAnalyzer {
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45 |
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46 | // properties: min level delts, max level delta, etc.
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47 |
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48 | private const string FunctionTreeGrammarParameterName = "FunctionTreeGrammar";
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49 |
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50 | public IValueLookupParameter<GlobalSymbolicExpressionGrammar> FunctionTreeGrammarParameter {
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51 | get { return (IValueLookupParameter<GlobalSymbolicExpressionGrammar>)Parameters[FunctionTreeGrammarParameterName]; }
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52 | }
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53 | public GlobalSymbolicExpressionGrammar FunctionTreeGrammar {
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54 | get { return FunctionTreeGrammarParameter.ActualValue; }
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55 | }
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56 |
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57 |
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58 | [StorableConstructor]
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59 | private FineGrainedStructuralPopulationDiversityAnalyzer(bool deserializing) : base(deserializing) { }
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60 | private FineGrainedStructuralPopulationDiversityAnalyzer(FineGrainedStructuralPopulationDiversityAnalyzer original, Cloner cloner) : base(original, cloner) { }
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61 | public FineGrainedStructuralPopulationDiversityAnalyzer() : base() {
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62 | Parameters.Add(new ValueLookupParameter<GlobalSymbolicExpressionGrammar>(FunctionTreeGrammarParameterName, "The grammar that is used for symbolic regression models."));
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63 | }
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64 |
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65 | public override IDeepCloneable Clone(Cloner cloner) {
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66 | return new FineGrainedStructuralPopulationDiversityAnalyzer(this, cloner);
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67 | }
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68 |
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69 | protected override double[,] CalculateSimilarities(SymbolicExpressionTree[] solutions) {
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70 | Constant constant = null;
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71 | foreach (Symbol symbol in FunctionTreeGrammar.Symbols) {
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72 | if (symbol is Constant) {
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73 | if (constant != null) throw new InvalidProgramException("There are more and one constnat definitions in the given function tree grammar.");
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74 | constant = (Constant)symbol;
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75 | }
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76 | }
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77 | int n = solutions.Length;
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78 | List<string> variableNames = new List<string>();
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79 | foreach (StringValue variableName in ProblemData.InputVariables) {
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80 | variableNames.Add(variableName.Value);
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81 | }
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82 | variableNames.Add(ProblemData.TargetVariable.Value);
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83 | IList<GeneticInformationItem>[] geneticInformationItemsLists = new List<GeneticInformationItem>[n];
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84 | for (int i = 0; i < n; i++) {
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85 | // geneticInformationItemsList[i] = GeneticInformationItem.getGeneticInformationItems(solutions[i].Root, variableNames, MinimumLEvelDelta, MaximumLevelDelta);
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86 | geneticInformationItemsLists[i] = GeneticInformationItem.getGeneticInformationItems(solutions[i].Root, variableNames, 0, int.MaxValue);
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87 | }
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88 | double[,] result = new double[n, n];
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89 | for (int i = 0; i < n; i++) {
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90 | for (int j = 0; j < n; j++) {
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91 | if (i == j)
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92 | result[i, j] = 1;
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93 | else {
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94 | IList<GeneticInformationItem> solution1GeneticItems = GeneticInformationItem.CopyList(geneticInformationItemsLists[i]);
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95 | IList<GeneticInformationItem> solution2GeneticItems = GeneticInformationItem.CopyList(geneticInformationItemsLists[j]);
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96 | double similarity = 0;
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97 | for (int k = 0; k < solution1GeneticItems.Count; k++) {
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98 | double bestPendantSimilarity;
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99 | // GeneticInformationItem bestPendant = GeneticInformationItem.FindBestPendant(solution1GeneticItems[k],
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100 | // ConstantMinimum, ConstantMaximum, VariableWeightSigma, MaximumTreeHeight, MaximumTimeOffset,
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101 | // LevelDifferenceCoefficient, AncestorIndexCoefficient, ConstantValueCoefficient, VariableWeightSigma, TimeOffsetCoefficient,
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102 | // VariableWeightCoefficient, AdditiveSimilarityCalculation, out bestPendantSimilarity);
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103 | GeneticInformationItem bestPendant = GeneticInformationItem.FindBestPendant(solution1GeneticItems[k], solution2GeneticItems,
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104 | -10, 10, 1, 100, 10,
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105 | 1, 1, 1, 1, 1, 1, true, out bestPendantSimilarity);
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106 | if (bestPendant != null) {
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107 | similarity += bestPendantSimilarity;
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108 | // if (PreventMultipleComparisonContribution)
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109 | if (true)
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110 | solution2GeneticItems.Remove(bestPendant);
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111 | }
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112 | }
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113 | result[i, j] = similarity / solution1GeneticItems.Count;
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114 | }
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115 | }
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116 | }
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117 | return result;
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118 | }
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119 |
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120 |
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121 | #region private class GeneticInformationItem
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122 |
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123 | private class GeneticInformationItem {
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124 |
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125 | private Type myAncestorDefinition;
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126 | public Type AncestorDefinition {
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127 | get { return myAncestorDefinition; }
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128 | }
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129 |
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130 | private int myAncestorIndex;
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131 | public int AncestorIndex {
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132 | get { return myAncestorIndex; }
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133 | }
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134 |
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135 | private Type myDescendantDefinition;
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136 | public Type DescendantDefinition {
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137 | get { return myDescendantDefinition; }
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138 | }
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139 |
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140 | private int myAncestorLevel;
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141 | public int AncestorLevel {
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142 | get { return myAncestorLevel; }
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143 | }
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144 |
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145 | private double myDescendantCoefficient;
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146 | public double DescendantCoefficient {
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147 | get { return myDescendantCoefficient; }
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148 | }
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149 |
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150 | private double myDescendantVariableIndex;
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151 | public double DescendantVariableIndex {
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152 | get { return myDescendantVariableIndex; }
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153 | }
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154 |
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155 | private int myDescendantTimeOffset;
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156 | public int DescendantTimeOffset {
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157 | get { return myDescendantTimeOffset; }
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158 | }
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159 |
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160 | private SymbolicExpressionTreeNode myDescendantTreeNode;
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161 | public SymbolicExpressionTreeNode DescendantTreeNode {
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162 | get { return myDescendantTreeNode; }
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163 | }
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164 |
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165 | private int myDescendantLevel;
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166 | public int DescendantLevel {
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167 | get { return myDescendantLevel; }
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168 | }
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169 |
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170 | public int LevelDelta {
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171 | get { return (myDescendantLevel - myAncestorLevel); }
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172 | }
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173 |
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174 | public static IList<GeneticInformationItem> CopyList(IList<GeneticInformationItem> GeneticInformationItemsList) {
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175 | List<GeneticInformationItem> list = new List<GeneticInformationItem>(GeneticInformationItemsList.Count);
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176 | list.AddRange(GeneticInformationItemsList);
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177 | return list;
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178 | }
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179 |
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180 | public static GeneticInformationItem FindBestPendant(GeneticInformationItem Item, IList<GeneticInformationItem> ComparisonItems,
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181 | double ConstantMinimum, double ConstantMaximum, double VariableWeightSigma,
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182 | int MaximumTreeHeight, int MaximumTimeOffset,
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183 | double LevelDifferenceCoefficient, double AncestorIndexCoefficient,
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184 | double ConstantValueCoefficient, double VariableWeightCoefficient, double TimeOffsetCoefficient, double VariableIndexCoefficient,
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185 | bool AdditiveSimilarityCalculation,
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186 | out double BestPendantSimilarity) {
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187 | int maxSimilarityIndex = -1;
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188 | double similarity, maxSimilarity = -double.MaxValue;
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189 | for (int i = 0; i < ComparisonItems.Count; i++) {
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190 | similarity = Similarity(Item, ComparisonItems[i], ConstantMinimum, ConstantMaximum, VariableWeightSigma, MaximumTreeHeight, MaximumTimeOffset,
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191 | LevelDifferenceCoefficient, AncestorIndexCoefficient, ConstantValueCoefficient, VariableWeightSigma, TimeOffsetCoefficient,
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192 | VariableWeightCoefficient, AdditiveSimilarityCalculation);
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193 | if (!double.IsNaN(similarity) && similarity > maxSimilarity) {
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194 | maxSimilarity = similarity;
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195 | maxSimilarityIndex = i;
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196 | }
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197 | }
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198 | BestPendantSimilarity = maxSimilarity;
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199 | if (maxSimilarityIndex >= 0)
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200 | return ComparisonItems[maxSimilarityIndex];
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201 | else
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202 | return null;
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203 | }
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204 |
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205 | public static double Similarity(GeneticInformationItem Item1, GeneticInformationItem Item2,
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206 | double ConstantMinimum, double ConstantMaximum, double VariableWeightSigma,
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207 | int MaximumTreeHeight, int MaximumTimeOffset,
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208 | double LevelDifferenceCoefficient, double AncestorIndexCoefficient,
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209 | double ConstantValueCoefficient, double VariableWeightCoefficient, double TimeOffsetCoefficient, double VariableIndexCoefficient,
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210 | bool AdditiveSimilarityCalculation) {
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211 |
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212 | if (Item1.AncestorDefinition != Item2.AncestorDefinition || Item1.DescendantDefinition != Item2.DescendantDefinition)
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213 | return double.NaN;
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214 |
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215 | // the two items for sure have the same behavior definitions
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216 |
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217 | #region initialize punishments
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218 |
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219 | double punishmentContributionSum = 0;
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220 | double punishmentCoefficientsProduct = 1;
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221 |
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222 | double ancestorIndexDifferencePunishment = 0;
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223 | double levelDifferencePunishment = 0;
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224 |
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225 | double descendantConstantValueDifferencePunishment = 0;
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226 | double descendantVariableWeightDifferencePunishment = 0;
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227 | double descendantTimeOffsetDifferencePunishment = 0;
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228 | double descendantVariableIndexDifferencePunishment = 0;
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229 |
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230 | #endregion
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231 |
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232 | if (LevelDifferenceCoefficient > 0) {
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233 | levelDifferencePunishment = Item1.LevelDelta - Item2.LevelDelta;
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234 | if (levelDifferencePunishment < 0)
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235 | levelDifferencePunishment = -levelDifferencePunishment;
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236 | levelDifferencePunishment /= MaximumTreeHeight;
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237 | if (levelDifferencePunishment > 1)
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238 | levelDifferencePunishment = 1;
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239 | levelDifferencePunishment *= LevelDifferenceCoefficient;
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240 | punishmentContributionSum += LevelDifferenceCoefficient;
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241 | punishmentCoefficientsProduct *= (1 - LevelDifferenceCoefficient);
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242 | }
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243 | if (AncestorIndexCoefficient > 0) {
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244 | if (Item1.AncestorIndex != Item2.AncestorIndex)
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245 | ancestorIndexDifferencePunishment = 1;
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246 | else
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247 | ancestorIndexDifferencePunishment = 0;
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248 | ancestorIndexDifferencePunishment *= AncestorIndexCoefficient;
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249 | punishmentContributionSum += AncestorIndexCoefficient;
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250 | punishmentCoefficientsProduct *= (1 - AncestorIndexCoefficient);
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251 | }
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252 |
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253 | if (Item1.DescendantTreeNode is ConstantTreeNode) {
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254 | if (ConstantValueCoefficient > 0) {
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255 | double constantValueCoefficientDifference = Math.Abs(Item1.DescendantCoefficient - Item2.DescendantCoefficient);
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256 | // assume uniform distribution within given minimum and maximum
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257 | descendantConstantValueDifferencePunishment = (constantValueCoefficientDifference / (ConstantMaximum - ConstantMinimum));
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258 | if (descendantConstantValueDifferencePunishment > 1)
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259 | descendantConstantValueDifferencePunishment = 1;
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260 | descendantConstantValueDifferencePunishment *= ConstantValueCoefficient;
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261 | punishmentContributionSum += ConstantValueCoefficient;
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262 | punishmentCoefficientsProduct *= (1 - ConstantValueCoefficient);
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263 | }
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264 | }
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265 | if(Item1.DescendantTreeNode is VariableTreeNode) {
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266 | if (VariableWeightCoefficient > 0) {
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267 | double variableWeightDifference = Math.Abs(Item1.DescendantCoefficient - Item2.DescendantCoefficient);
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268 | // assume normal distribution within given sigma
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269 | descendantVariableWeightDifferencePunishment = variableWeightDifference / (VariableWeightSigma * 4);
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270 | if (descendantVariableWeightDifferencePunishment > 1)
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271 | descendantVariableWeightDifferencePunishment = 1;
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272 | descendantVariableWeightDifferencePunishment *= VariableWeightCoefficient;
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273 | punishmentContributionSum += VariableWeightCoefficient;
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274 | punishmentCoefficientsProduct *= (1 - VariableWeightCoefficient);
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275 | }
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276 | if (TimeOffsetCoefficient > 0) {
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277 | double timeOffsetDifference = Math.Abs(Item1.DescendantTimeOffset - Item2.DescendantTimeOffset);
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278 | if (MaximumTimeOffset > 0)
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279 | descendantTimeOffsetDifferencePunishment = timeOffsetDifference / MaximumTimeOffset;
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280 | descendantTimeOffsetDifferencePunishment *= TimeOffsetCoefficient;
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281 | punishmentContributionSum += TimeOffsetCoefficient;
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282 | punishmentCoefficientsProduct *= (1 - TimeOffsetCoefficient);
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283 | }
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284 | if (VariableIndexCoefficient > 0) {
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285 | if (Item1.DescendantVariableIndex != Item2.DescendantVariableIndex)
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286 | descendantVariableIndexDifferencePunishment = 1;
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287 | else
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288 | descendantVariableIndexDifferencePunishment = 0;
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289 | descendantVariableIndexDifferencePunishment *= VariableIndexCoefficient;
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290 | punishmentContributionSum += VariableIndexCoefficient;
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291 | punishmentCoefficientsProduct *= (1 - VariableIndexCoefficient);
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292 | }
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293 | }
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294 |
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295 | double result;
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296 |
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297 | if (AdditiveSimilarityCalculation) {
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298 | double punishmentsSum = ancestorIndexDifferencePunishment + levelDifferencePunishment +
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299 | descendantConstantValueDifferencePunishment + descendantVariableWeightDifferencePunishment +
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300 | descendantTimeOffsetDifferencePunishment + descendantVariableIndexDifferencePunishment;
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301 | result = (1 - punishmentsSum / punishmentContributionSum);
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302 | } else {
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303 | result =
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304 | (1 - ancestorIndexDifferencePunishment) *
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305 | (1 - levelDifferencePunishment) *
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306 | (1 - descendantConstantValueDifferencePunishment) *
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307 | (1 - descendantVariableWeightDifferencePunishment) *
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308 | (1 - descendantTimeOffsetDifferencePunishment) *
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309 | (1 - descendantVariableIndexDifferencePunishment);
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310 | // worst possible result is (1-punishmentCoefficientsProduct), so scale linearly to [0;1]:
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311 | result = (result - punishmentCoefficientsProduct) / (1 - punishmentCoefficientsProduct);
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312 | }
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313 |
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314 | if (result < 0 || result > 1)
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315 | throw new InvalidOperationException("ERROR in GeneticInformationItem.Similarity: An invalid similarity value (" + result.ToString() + ") has been calculated.");
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316 |
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317 | return result;
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318 |
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319 | }
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320 |
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321 | public static IList<GeneticInformationItem> getGeneticInformationItems(SymbolicExpressionTreeNode node, List<string> variableNames,
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322 | int MinimumLevelDelta, int MaximumLevelDelta) {
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323 | // first we have to collect all items, then we filter; it is not possible to filter while collecting because the items are
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324 | // collected recursively and used for collecting the parents' items.
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325 | if (MinimumLevelDelta > MaximumLevelDelta)
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326 | return new List<GeneticInformationItem>();
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327 | IList<GeneticInformationItem> list = getGeneticInformationItems(node, variableNames, 0);
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328 | List<GeneticInformationItem> resultList = new List<GeneticInformationItem>();
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329 | foreach (GeneticInformationItem item in list)
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330 | if (item.LevelDelta >= MinimumLevelDelta && item.LevelDelta <= MaximumLevelDelta)
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331 | resultList.Add(item);
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332 | return resultList;
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333 | }
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334 |
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335 | private static IList<GeneticInformationItem> getGeneticInformationItems(SymbolicExpressionTreeNode node, List<string> variableNames, int level) {
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336 | // Idea: collect all descendants' lists and then add new items using the retrieved ones.
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337 | // This should save lots of time and reduce complexity of the items retrieval process.
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338 | // Program roots are not considered, neither are start symbol nodes
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339 | if (node.Symbol is ProgramRootSymbol)
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340 | return getGeneticInformationItems(node.SubTrees[0], variableNames, level + 1);
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341 | List<GeneticInformationItem> list = new List<GeneticInformationItem>();
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342 | // add item for this node:
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343 | if (!(node.Symbol is StartSymbol)) {
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344 | list.Add(new GeneticInformationItem(node, variableNames, level));
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345 | }
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346 | // add items for the descendants, but prevent multiple references to descendant nodes:
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347 | List<SymbolicExpressionTreeNode> descendantNodes = new List<SymbolicExpressionTreeNode>();
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348 | for (int i = 0; i < node.SubTrees.Count; i++) {
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349 | IList<GeneticInformationItem> descendantItems = getGeneticInformationItems(node.SubTrees[i], variableNames, level + 1);
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350 | list.AddRange(descendantItems);
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351 | if (!(node.Symbol is StartSymbol))
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352 | foreach (GeneticInformationItem item in descendantItems) {
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353 | if (!descendantNodes.Contains(item.DescendantTreeNode)) {
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354 | list.Add(new GeneticInformationItem(node, item, i, level));
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355 | descendantNodes.Add(item.DescendantTreeNode);
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356 | }
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357 | }
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358 | }
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359 | return list;
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360 | }
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361 |
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362 | private GeneticInformationItem (SymbolicExpressionTreeNode node, List<string> variableNames, int level) {
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363 | myAncestorIndex = -1;
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364 | VariableTreeNode variableTreeNode = node as VariableTreeNode;
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365 | LaggedVariableTreeNode laggedVariableTreeNode = node as LaggedVariableTreeNode;
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366 | ConstantTreeNode constantTreeNode = node as ConstantTreeNode;
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367 | myAncestorDefinition = node.Symbol.GetType();
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368 | myDescendantDefinition = myAncestorDefinition;
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369 | if (variableTreeNode != null)
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370 | myDescendantCoefficient = variableTreeNode.Weight;
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371 | else if (constantTreeNode != null)
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372 | myDescendantCoefficient = constantTreeNode.Value;
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373 | else
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374 | myDescendantCoefficient = double.NaN;
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375 | if (laggedVariableTreeNode != null)
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376 | myDescendantTimeOffset = laggedVariableTreeNode.Lag;
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377 | else
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378 | myDescendantTimeOffset = 0;
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379 | if (variableTreeNode != null)
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380 | myDescendantVariableIndex = variableNames.IndexOf(variableTreeNode.VariableName);
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381 | else
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382 | myDescendantVariableIndex = -1;
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383 | myAncestorLevel = level;
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384 | myDescendantLevel = level;
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385 | myDescendantTreeNode = node;
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386 | }
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387 |
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388 | private GeneticInformationItem(SymbolicExpressionTreeNode parentNode, GeneticInformationItem descendantGeneticInformationItem,
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389 | int ancestorIndex, int parentNodeLevel) {
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390 | myAncestorIndex = ancestorIndex;
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391 | myAncestorLevel = parentNodeLevel;
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392 | myAncestorDefinition = parentNode.Symbol.GetType();
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393 | myDescendantCoefficient = descendantGeneticInformationItem.DescendantCoefficient;
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394 | myDescendantDefinition = descendantGeneticInformationItem.DescendantDefinition;
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395 | myDescendantTimeOffset = descendantGeneticInformationItem.DescendantTimeOffset;
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396 | myDescendantVariableIndex = descendantGeneticInformationItem.DescendantVariableIndex;
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397 | myDescendantLevel = descendantGeneticInformationItem.DescendantLevel;
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398 | myDescendantTreeNode = descendantGeneticInformationItem.DescendantTreeNode;
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399 | }
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400 |
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401 | public override string ToString() {
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402 | return "ancestor: " + AncestorDefinition.Name.ToString() + ", [" + AncestorIndex + "]; "
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403 | + "descendant: " + DescendantDefinition.Name.ToString() + " (varIndex " + DescendantVariableIndex + ", "
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404 | + DescendantCoefficient + ", t-" + DescendantTimeOffset + ");"
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405 | + " level delta = " + DescendantLevel + "-" + AncestorLevel + " = " + LevelDelta;
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406 | }
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407 |
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408 | }
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409 |
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410 | #endregion
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411 |
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412 | }
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413 | }
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