1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 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.Globalization;
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25 | using System.Linq;
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26 | using System.Text;
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27 | using System.Threading;
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28 | using HeuristicLab.Common;
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29 | using HeuristicLab.Core;
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30 | using HeuristicLab.Data;
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31 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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32 | using HeuristicLab.Operators;
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33 | using HeuristicLab.Optimization;
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34 | using HeuristicLab.Parameters;
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35 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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36 | using HeuristicLab.Problems.DataAnalysis;
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37 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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38 |
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39 | using CloneMapType = HeuristicLab.Core.ItemDictionary<HeuristicLab.Core.IItem, HeuristicLab.Core.IItem>;
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40 | using TraceMapType = HeuristicLab.Core.ItemDictionary<HeuristicLab.Core.IItem, HeuristicLab.Core.IItemList<HeuristicLab.Core.IItem>>;
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41 |
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42 | namespace HeuristicLab.EvolutionaryTracking {
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43 | /// <summary>
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44 | /// An operator that tracks the genealogy of every individual
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45 | /// </summary>
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46 | [Item("SymbolicExpressionTreeGenealogyAnalyzer", "An operator that tracks tree lengths of Symbolic Expression Trees")]
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47 | [StorableClass]
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48 | public sealed class SymbolicExpressionTreeGenealogyAnalyzer : SingleSuccessorOperator, IAnalyzer {
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49 | private const string UpdateIntervalParameterName = "UpdateInterval";
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50 | private const string UpdateCounterParameterName = "UpdateCounter";
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51 | private const string ResultsParameterName = "Results";
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52 | private const string ElitesParameterName = "Elites";
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53 | private const string GenerationsParameterName = "Generations";
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54 | private const string MaximumGenerationsParameterName = "MaximumGenerations";
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55 | private const string MaximumSelectionPressureParameterName = "MaximumSelectionPressure";
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56 | private const string SelectionPressureParameterName = "SelectionPressure";
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57 | private const string GlobalTraceMapParameterName = "GlobalTraceMap";
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58 | private const string GlobalCloneMapParameterName = "GlobalCloneMap";
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59 | private const string PopulationGraphResultParameterName = "PopulationGraph";
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60 | private const string PopulationGraphResultParameterDescription = "Individual lineages";
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61 | private const string SymbolicExpressionInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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62 | private const string SymbolicRegressionProblemDataParameterName = "ProblemData";
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63 | private const string SymbolicDataAnalysisProblemEvaluatorParameterName = "Evaluator";
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64 |
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65 | #region Parameter properties
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66 | public ValueParameter<IntValue> UpdateIntervalParameter {
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67 | get { return (ValueParameter<IntValue>)Parameters[UpdateIntervalParameterName]; }
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68 | }
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69 | public ValueParameter<IntValue> UpdateCounterParameter {
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70 | get { return (ValueParameter<IntValue>)Parameters[UpdateCounterParameterName]; }
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71 | }
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72 | public LookupParameter<ResultCollection> ResultsParameter {
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73 | get { return (LookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
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74 | }
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75 | public LookupParameter<IntValue> ElitesParameter {
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76 | get { return (LookupParameter<IntValue>)Parameters[ElitesParameterName]; }
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77 | }
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78 | public LookupParameter<IntValue> GenerationsParameter {
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79 | get { return (LookupParameter<IntValue>)Parameters[GenerationsParameterName]; }
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80 | }
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81 | public LookupParameter<IntValue> MaximumGenerationsParameter {
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82 | get { return (LookupParameter<IntValue>)Parameters[MaximumGenerationsParameterName]; }
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83 | }
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84 | public LookupParameter<DoubleValue> SelectionPressureParameter {
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85 | get { return (LookupParameter<DoubleValue>)Parameters[SelectionPressureParameterName]; }
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86 | }
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87 | public LookupParameter<DoubleValue> MaximumSelectionPressureParameter {
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88 | get { return (LookupParameter<DoubleValue>)Parameters[MaximumSelectionPressureParameterName]; }
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89 | }
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90 | // problem data, interpreter and evaluator
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91 | public LookupParameter<SymbolicDataAnalysisExpressionTreeInterpreter> SymbolicExpressionInterpreterParameter {
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92 | get { return (LookupParameter<SymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicExpressionInterpreterParameterName]; }
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93 | }
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94 | public LookupParameter<RegressionProblemData> SymbolicRegressionProblemDataParameter {
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95 | get { return (LookupParameter<RegressionProblemData>)Parameters[SymbolicRegressionProblemDataParameterName]; }
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96 | }
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97 | public LookupParameter<IEvaluator> SymbolicDataAnalysisProblemEvaluatorParameter {
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98 | get { return (LookupParameter<IEvaluator>)Parameters[SymbolicDataAnalysisProblemEvaluatorParameterName]; }
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99 | }
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100 | // genealogy global parameters
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101 | public LookupParameter<TraceMapType> GlobalTraceMapParameter {
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102 | get { return (LookupParameter<TraceMapType>)Parameters[GlobalTraceMapParameterName]; }
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103 | }
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104 | public LookupParameter<CloneMapType> GlobalCloneMapParameter {
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105 | get { return (LookupParameter<CloneMapType>)Parameters[GlobalCloneMapParameterName]; }
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106 | }
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107 | #endregion
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108 |
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109 | #region Properties
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110 | public bool EnabledByDefault {
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111 | get { return true; }
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112 | }
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113 | public IntValue UpdateInterval {
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114 | get { return UpdateIntervalParameter.Value; }
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115 | }
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116 | public IntValue UpdateCounter {
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117 | get { return UpdateCounterParameter.Value; }
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118 | }
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119 | public ResultCollection Results {
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120 | get { return ResultsParameter.ActualValue; }
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121 | }
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122 | public IntValue Elites {
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123 | get { return ElitesParameter.ActualValue; }
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124 | }
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125 | public IntValue Generations {
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126 | get { return GenerationsParameter.ActualValue; }
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127 | }
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128 | public IntValue MaximumGenerations {
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129 | get { return MaximumGenerationsParameter.ActualValue; }
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130 | }
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131 | public DoubleValue SelectionPressure {
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132 | get { return SelectionPressureParameter.ActualValue; }
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133 | }
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134 | public DoubleValue MaximumSelectionPressure {
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135 | get { return MaximumSelectionPressureParameter.ActualValue; }
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136 | }
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137 | public CloneMapType GlobalCloneMap {
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138 | get { return GlobalCloneMapParameter.ActualValue; }
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139 | }
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140 | public TraceMapType GlobalTraceMap {
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141 | get { return GlobalTraceMapParameter.ActualValue; }
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142 | }
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143 | public SymbolicDataAnalysisExpressionTreeInterpreter SymbolicExpressionInterpreter {
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144 | get { return SymbolicExpressionInterpreterParameter.ActualValue; }
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145 | }
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146 | public RegressionProblemData SymbolicRegressionProblemData {
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147 | get { return SymbolicRegressionProblemDataParameter.ActualValue; }
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148 | }
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149 | public IEvaluator SymbolicDataAnalysisEvaluator {
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150 | get { return SymbolicDataAnalysisProblemEvaluatorParameter.ActualValue; }
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151 | }
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152 | #endregion
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153 |
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154 | [StorableConstructor]
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155 | private SymbolicExpressionTreeGenealogyAnalyzer(bool deserializing)
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156 | : base() {
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157 | }
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158 | private SymbolicExpressionTreeGenealogyAnalyzer(SymbolicExpressionTreeGenealogyAnalyzer original, Cloner cloner)
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159 | : base(original, cloner) {
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160 | }
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161 | public override IDeepCloneable Clone(Cloner cloner) {
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162 | return new SymbolicExpressionTreeGenealogyAnalyzer(this, cloner);
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163 | }
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164 | public SymbolicExpressionTreeGenealogyAnalyzer()
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165 | : base() {
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166 | Parameters.Add(new LookupParameter<IntValue>(ElitesParameterName, "The number of elites."));
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167 | Parameters.Add(new LookupParameter<IntValue>(GenerationsParameterName, "The number of generations so far."));
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168 | Parameters.Add(new LookupParameter<IntValue>(MaximumGenerationsParameterName, "The maximum number of generations"));
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169 | Parameters.Add(new LookupParameter<DoubleValue>(SelectionPressureParameterName, "The current selection (ony for OSGA)"));
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170 | Parameters.Add(new LookupParameter<DoubleValue>(MaximumSelectionPressureParameterName, "Maximum allowed value of selection pressure."));
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171 | Parameters.Add(new ValueParameter<IntValue>(UpdateIntervalParameterName, "The interval in which the tree length analysis should be applied.", new IntValue(1)));
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172 | Parameters.Add(new ValueParameter<IntValue>(UpdateCounterParameterName, "The value which counts how many times the operator was called since the last update", new IntValue(0)));
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173 | Parameters.Add(new LookupParameter<TraceMapType>(GlobalTraceMapParameterName, "A global cache containing the whole genealogy."));
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174 | Parameters.Add(new LookupParameter<CloneMapType>(GlobalCloneMapParameterName, "A global map keeping track of trees and their clones (made during selection)."));
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175 | Parameters.Add(new ValueLookupParameter<ResultCollection>(ResultsParameterName, "The results collection where the analysis values should be stored."));
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176 | Parameters.Add(new LookupParameter<SymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicExpressionInterpreterParameterName, "Interpreter for symbolic expression trees"));
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177 | Parameters.Add(new LookupParameter<RegressionProblemData>(SymbolicRegressionProblemDataParameterName, "The symbolic data analysis problem."));
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178 | Parameters.Add(new LookupParameter<IEvaluator>(SymbolicDataAnalysisProblemEvaluatorParameterName, "The fitness evaluator"));
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179 |
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180 | UpdateCounterParameter.Hidden = true;
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181 | UpdateIntervalParameter.Hidden = true;
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182 | }
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183 |
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184 | [StorableHook(HookType.AfterDeserialization)]
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185 | private void AfterDeserialization() {
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186 | // check if all the parameters are present and accounted for
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187 | if (!Parameters.ContainsKey(UpdateIntervalParameterName)) {
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188 | Parameters.Add(new ValueParameter<IntValue>(UpdateIntervalParameterName, "The interval in which the tree length analysis should be applied.", new IntValue(1)));
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189 | }
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190 | if (Parameters.ContainsKey(UpdateCounterParameterName)) return;
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191 | Parameters.Add(new ValueParameter<IntValue>(UpdateCounterParameterName, "The value which counts how many times the operator was called since the last update", new IntValue(0)));
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192 | UpdateCounterParameter.Hidden = true;
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193 | }
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194 |
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195 | #region IStatefulItem members
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196 | public override void InitializeState() {
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197 | base.InitializeState();
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198 | UpdateCounter.Value = 0;
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199 | }
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200 |
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201 | public override void ClearState() {
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202 | base.ClearState();
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203 | UpdateCounter.Value = 0;
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204 | }
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205 | #endregion
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206 |
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207 | public override IOperation Apply() {
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208 | UpdateCounter.Value++;
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209 | // the analyzer runs periodically, every 'updateInterval' times
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210 | if (UpdateCounter.Value == UpdateInterval.Value) {
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211 | UpdateCounter.Value = 0; // reset counter
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212 |
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213 | var gScope = ExecutionContext.Scope;
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214 | while (gScope.Parent != null) gScope = gScope.Parent;
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215 | GenealogyGraph graph;
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216 | if (!Results.ContainsKey(PopulationGraphResultParameterName)) {
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217 | graph = new GenealogyGraph();
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218 | Results.Add(new Result(PopulationGraphResultParameterName, PopulationGraphResultParameterDescription, graph));
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219 | } else {
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220 | graph = (GenealogyGraph)Results[PopulationGraphResultParameterName].Value;
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221 | }
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222 | // get tree quality values
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223 | var qualities = (from s in gScope.SubScopes
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224 | let individual = s.Variables.First().Value
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225 | let quality = (DoubleValue)s.Variables["Quality"].Value
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226 | orderby quality.Value descending
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227 | select new Tuple<IItem, double>(individual, quality.Value)).ToDictionary(t => t.Item1, t => t.Item2);
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228 |
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229 | // add all individuals to the evolutionary graph
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230 | int generation = Generations.Value;
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231 | int count = GlobalTraceMap.Count;
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232 | string label;
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233 |
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234 | if (generation == 0) {
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235 | // at generation 0 no trace map is present (since no reproduction has taken place yet),
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236 | // so we only add the initial trees as nodes in the genealogy graph
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237 | for (int i = 0; i != qualities.Count; ++i) {
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238 | var tree = qualities.ElementAt(i).Key;
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239 | label = (i + 1).ToString(CultureInfo.InvariantCulture);
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240 | graph.AddNode(tree, qualities[tree], label, generation);
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241 | }
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242 | return base.Apply();
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243 | }
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244 |
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245 | // mark and add elites
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246 | // elites do not appear in the trace map (because they are never the product of a genetic operation)
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247 | var elites = qualities.OrderByDescending(x => x.Value).Take(Elites.Value).Select(x => x.Key).ToList();
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248 | for (int i = 0; i != Elites.Value; ++i) {
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249 | label = (generation * count + i + 1).ToString(CultureInfo.InvariantCulture);
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250 | var elite = elites[i];
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251 | if (!graph.HasNode(elite))
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252 | graph.AddNode(elite, qualities[elite], label, Generations.Value, true);
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253 | else
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254 | graph.GetNode(elite).Label += "\\n" + label;
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255 |
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256 | graph.GetNode(elite).Color = new Color { R = 0, G = 100, B = 150 };
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257 | }
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258 |
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259 | for (int i = 0; i != count; ++i) {
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260 | var trace = GlobalTraceMap.ElementAt(i);
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261 | var child = (ISymbolicExpressionTree)trace.Key;
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262 |
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263 | if (!graph.HasNode(child)) {
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264 | // due to the structure of the trace map, qualities[child] will never throw an exception, so we use it directly
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265 | label = (generation * count + i + 1 + Elites.Value).ToString(CultureInfo.InvariantCulture);
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266 | graph.AddNode(child, qualities[child], label, generation);
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267 | }
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268 | var parents = trace.Value;
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269 | foreach (var parent in parents) {
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270 | if (!graph.HasNode(parent)) {
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271 | // if the node is a clone introduced pre-mutation, then its quality value has to be evaluated
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272 | if (!qualities.ContainsKey(parent))
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273 | qualities[parent] = Evaluate((ISymbolicExpressionTree)parent);
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274 | label = ((generation - 1) * count + i + 1).ToString(CultureInfo.InvariantCulture);
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275 | graph.AddNode(parent, qualities[parent], label, generation - 1);
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276 | }
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277 | graph.AddArc(parent, child);
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278 | }
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279 | }
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280 | GlobalTraceMap.Clear(); // no need to check for null here (see line 212)
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281 |
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282 | // if we've reached the end of the run
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283 | bool maxGenerationsReached = (Generations.Value == MaximumGenerations.Value);
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284 | bool isOsga = (SelectionPressure != null && MaximumSelectionPressure != null);
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285 | bool maxSelectionPressureReached = isOsga && (SelectionPressure.Value >= MaximumSelectionPressure.Value);
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286 |
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287 | #region end of the run
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288 | if (maxGenerationsReached || maxSelectionPressureReached) {
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289 | var path = Environment.GetFolderPath(Environment.SpecialFolder.UserProfile);
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290 |
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291 | // write whole graph to a dot file
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292 | WriteDot(path + @"\lineage.dot", graph);
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293 |
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294 | // get genealogy of the best solution
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295 | var bestSolution = (ISymbolicExpressionTree)qualities.First().Key;
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296 | var genealogy = graph.GetNode(bestSolution).Genealogy();
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297 | WriteDot(path + @"\bestlineage.dot", genealogy);
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298 |
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299 | // write the direct root lineage of the best solution (is it useful?)
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300 |
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301 | // calculate impact values of nodes in the best solution, attempt to trace those with high impact to their origins
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302 | //var impactValuesCalculator = new RegressionSolutionImpactValuesCalculator();
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303 | //var impactValues = impactValuesCalculator.CalculateImpactValues(bestSolution, SymbolicExpressionInterpreter, SymbolicRegressionProblemData);
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304 | ////var impactValues = CalculateImpactValues(bestSolution);
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305 | //foreach (var pair in impactValues.Where(pair => !(pair.Key is ConstantTreeNode || pair.Key is VariableTreeNode) && pair.Value > 0.9)) {
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306 | // var node = pair.Key;
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307 |
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308 | // foreach (var ancestor in genealogy.Keys) {
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309 | // graph.GetNode(ancestor).Color = ContainsSubtree(ancestor as ISymbolicExpressionTree, node) ? new Color { R = 0, G = 0, B = 150 } : new Color { R = 255, G = 255, B = 255 };
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310 | // }
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311 | //}
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312 | //WriteDot(path + @"\impactancestry.dot", genealogy);
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313 |
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314 | // trim the graph
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315 | // exclude the individuals of the last generation
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316 | var individuals = graph.Keys.Except(qualities.Select(x => x.Key)).ToList();
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317 | bool done = false;
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318 | while (!done) {
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319 | done = true;
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320 | foreach (var ind in individuals) {
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321 | // if node has no outgoing connections (absence of offspring), remove it from the graph
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322 | var node = graph.GetNode(ind);
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323 | if (node == null) continue;
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324 | if (node.OutEdges == null) {
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325 | done = false; // we still have "dead" nodes
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326 | graph.RemoveNode(ind);
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327 | }
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328 | }
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329 | }
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330 | WriteDot(path + @"\trimmedlineage.dot", graph);
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331 | }
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332 | #endregion
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333 | }
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334 | return base.Apply();
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335 | }
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336 |
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337 | private double Evaluate(ISymbolicExpressionTree tree) {
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338 | // we perform evaluation by adding a temporary subscope with the tree in it, and calling evaluator.Apply()
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339 | var subScope = new Scope();
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340 | // inject expected variables into the subscope
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341 | subScope.Variables.Add(new Core.Variable("SymbolicExpressionTree", tree));
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342 | ExecutionContext.Scope.SubScopes.Add(subScope);
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343 | var context = new Core.ExecutionContext(ExecutionContext, SymbolicDataAnalysisEvaluator, subScope);
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344 | SymbolicDataAnalysisEvaluator.Execute(context, new CancellationToken());
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345 | // get the quality
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346 | double quality = ((DoubleValue)subScope.Variables["Quality"].Value).Value;
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347 | // remove the subscope
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348 | ExecutionContext.Scope.SubScopes.Remove(subScope);
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349 | return quality;
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350 | }
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351 |
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352 | #region Export to dot file
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353 | private static void WriteDot(string path, GenealogyGraph graph) {
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354 | using (var file = new System.IO.StreamWriter(path)) {
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355 | string nl = Environment.NewLine;
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356 | file.WriteLine("digraph \"lineage " + graph.AverageDegree.ToString(CultureInfo.InvariantCulture) + "\" {" + nl +
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357 | "ratio=auto;" + nl +
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358 | "mincross=2.0");
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359 | file.WriteLine("\tnode [shape=circle,label=\"\",style=filled]");
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360 |
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361 | foreach (var node in graph.Values) {
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362 | string fillColor = String.Format("#{0:x2}{1:x2}{2:x2}", node.Color.R, node.Color.G, node.Color.B);
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363 | if (node.IsElite)
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364 | fillColor = String.Format("#{0:x2}{1:x2}{2:x2}", node.Color.R, node.Color.G, 150);
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365 | file.WriteLine("\t\"" + node.Id + "\" [fillcolor=\"" + fillColor + "\",label=\"" + node.Label + "\"];");
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366 | if (node.InEdges == null)
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367 | continue;
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368 | foreach (var edge in node.InEdges) {
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369 | var edgeStyle = node.InEdges.Count == 1 ? "dashed" : String.Empty;
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370 | file.WriteLine("\t\"" + edge.Target.Id + "\" -> \"" + node.Id + "\" [arrowsize=.5, color=\"" + fillColor + "\", style=\"" + edgeStyle + "\"];");
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371 | }
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372 | }
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373 | foreach (var g in graph.Values.GroupBy(x => x.Generation)) {
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374 | var sb = new StringBuilder();
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375 | sb.Append("\t{rank=same;");
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376 | foreach (var node in g)
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377 | sb.Append("\"" + node.Id + "\" ");
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378 | sb.Append("}\n");
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379 | file.Write(sb.ToString());
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380 | }
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381 | file.WriteLine("}");
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382 | }
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383 | }
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384 | #endregion
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385 |
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386 | #region Impact values (code for calculating to be moved in separate class)
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387 | private Dictionary<ISymbolicExpressionTreeNode, double> CalculateImpactValues(ISymbolicExpressionTree tree) {
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388 | var interpreter = SymbolicExpressionInterpreter;
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389 | var problemData = (IRegressionProblemData)SymbolicDataAnalysisEvaluator.Parameters["ProblemData"].ActualValue;
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390 | var dataset = problemData.Dataset;
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391 | var rows = problemData.TrainingIndizes;
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392 | string targetVariable = problemData.TargetVariable;
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393 | var impactValues = new Dictionary<ISymbolicExpressionTreeNode, double>();
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394 | var nodes = tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPostfix().ToList();
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395 | var originalOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows);
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396 | var targetValues = dataset.GetDoubleValues(targetVariable, rows);
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397 | OnlineCalculatorError errorState;
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398 | double originalR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, originalOutput, out errorState);
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399 | if (errorState != OnlineCalculatorError.None) originalR2 = 0.0;
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400 |
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401 | var constantNode = ((ConstantTreeNode)new Constant().CreateTreeNode());
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402 | var root = new ProgramRootSymbol().CreateTreeNode(); // root node
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403 | var start = new StartSymbol().CreateTreeNode(); // start node
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404 | root.AddSubtree(start);
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405 | var tempTree = new SymbolicExpressionTree(root);
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406 |
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407 | foreach (ISymbolicExpressionTreeNode node in nodes) {
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408 | var parent = node.Parent;
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409 | constantNode.Value = CalculateReplacementValue(tempTree, node, tree);
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410 | ISymbolicExpressionTreeNode replacementNode = constantNode;
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411 | SwitchNode(parent, node, replacementNode);
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412 | var newOutput = interpreter.GetSymbolicExpressionTreeValues(tree, dataset, rows);
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413 | double newR2 = OnlinePearsonsRSquaredCalculator.Calculate(targetValues, newOutput, out errorState);
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414 | if (errorState != OnlineCalculatorError.None) newR2 = 0.0;
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415 |
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416 | // impact = 0 if no change
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417 | // impact < 0 if new solution is better
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418 | // impact > 0 if new solution is worse
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419 | impactValues[node] = originalR2 - newR2;
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420 | SwitchNode(parent, replacementNode, node);
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421 | }
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422 | return impactValues;
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423 | }
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424 |
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425 | private double CalculateReplacementValue(ISymbolicExpressionTree tempTree, ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree) {
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426 | // remove old ADFs
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427 | while (tempTree.Root.SubtreeCount > 1) tempTree.Root.RemoveSubtree(1);
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428 | // clone ADFs of source tree
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429 | for (int i = 1; i < sourceTree.Root.SubtreeCount; i++) {
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430 | tempTree.Root.AddSubtree((ISymbolicExpressionTreeNode)sourceTree.Root.GetSubtree(i).Clone());
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431 | }
|
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432 | var start = tempTree.Root.GetSubtree(0);
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433 | while (start.SubtreeCount > 0) start.RemoveSubtree(0);
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434 | start.AddSubtree((ISymbolicExpressionTreeNode)node.Clone());
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435 | var interpreter = SymbolicExpressionInterpreter;
|
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436 | var rows = SymbolicRegressionProblemData.TrainingIndizes;
|
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437 | return interpreter.GetSymbolicExpressionTreeValues(tempTree, SymbolicRegressionProblemData.Dataset, rows).Median();
|
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438 | }
|
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439 |
|
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440 | private static void SwitchNode(ISymbolicExpressionTreeNode root, ISymbolicExpressionTreeNode oldBranch, ISymbolicExpressionTreeNode newBranch) {
|
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441 | for (int i = 0; i < root.SubtreeCount; i++) {
|
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442 | if (root.GetSubtree(i) == oldBranch) {
|
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443 | root.RemoveSubtree(i);
|
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444 | root.InsertSubtree(i, newBranch);
|
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445 | return;
|
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446 | }
|
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447 | }
|
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448 | }
|
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449 | #endregion
|
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450 |
|
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451 | #region Allele tracking
|
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452 | private bool ContainsSubtree(ISymbolicExpressionTree tree, ISymbolicExpressionTreeNode node) {
|
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453 | return tree.IterateNodesPostfix().Where(n => n.Symbol == node.Symbol && n.GetLength() == node.GetLength())
|
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454 | .Where(n => n.Subtrees.Any() && node.Subtrees.Any())
|
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455 | .Any(n => n.Subtrees.First().Symbol == node.Subtrees.First().Symbol);
|
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456 | }
|
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457 | #endregion
|
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458 |
|
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459 | #region Extra / not really needed
|
---|
460 | private IEnumerable<ISymbolicExpressionTreeNode> IterateNodes(ISymbolicExpressionTree tree) {
|
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461 | return IterateNodes(tree.Root);
|
---|
462 | }
|
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463 |
|
---|
464 | private static IEnumerable<ISymbolicExpressionTreeNode> IterateNodes(ISymbolicExpressionTreeNode root) {
|
---|
465 | var list = new List<ISymbolicExpressionTreeNode> { root };
|
---|
466 | int offset = 0, count = 1;
|
---|
467 | while (offset != count) {
|
---|
468 | var c = count;
|
---|
469 | for (int i = offset; i != count; ++i) {
|
---|
470 | yield return list[i];
|
---|
471 | if (!list[i].Subtrees.Any()) continue;
|
---|
472 | list.AddRange(list[i].Subtrees);
|
---|
473 | }
|
---|
474 | offset = c;
|
---|
475 | count = list.Count;
|
---|
476 | }
|
---|
477 | }
|
---|
478 | #endregion
|
---|
479 | }
|
---|
480 | }
|
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