1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 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.Linq;
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24 | using HEAL.Attic;
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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.Encodings.RealVectorEncoding;
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29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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30 | using HeuristicLab.Optimization;
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31 | using HeuristicLab.Parameters;
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32 | using HeuristicLab.Problems.Instances;
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33 | using HeuristicLab.Problems.Instances.DataAnalysis;
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34 |
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35 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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36 | [StorableType("7464E84B-65CC-440A-91F0-9FA920D730F9")]
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37 | [Item(Name = "Structured Symbolic Regression Single Objective Problem (single-objective)", Description = "A problem with a structural definition and unfixed subfunctions.")]
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38 | [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 150)]
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39 | public class StructuredSymbolicRegressionSingleObjectiveProblem : SingleObjectiveBasicProblem<MultiEncoding>, IRegressionProblem, IProblemInstanceConsumer<IRegressionProblemData> {
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40 |
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41 | #region Constants
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42 | private const string ProblemDataParameterName = "ProblemData";
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43 | private const string StructureTemplateParameterName = "Structure Template";
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44 | private const string InterpreterParameterName = "Interpreter";
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45 | private const string EstimationLimitsParameterName = "EstimationLimits";
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46 | private const string BestTrainingSolutionParameterName = "Best Training Solution";
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47 | private const string ApplyLinearScalingParameterName = "Apply Linear Scaling";
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48 | private const string OptimizeParametersParameterName = "Optimize Parameters";
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49 |
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50 | private const string SymbolicExpressionTreeName = "SymbolicExpressionTree";
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51 | private const string NumericParametersEncoding = "Numeric Parameters";
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52 |
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53 | private const string StructureTemplateDescriptionText =
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54 | "Enter your expression as string in infix format into the empty input field.\n" +
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55 | "By checking the \"Apply Linear Scaling\" checkbox you can add the relevant scaling terms to your expression.\n" +
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56 | "After entering the expression click parse to build the tree.\n" +
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57 | "To edit the defined sub-functions, click on the corresponding-colored node in the tree view.\n" +
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58 | "Check the info box besides the input field for more information.";
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59 | #endregion
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60 |
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61 | #region Parameters
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62 | public IValueParameter<IRegressionProblemData> ProblemDataParameter => (IValueParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName];
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63 | public IFixedValueParameter<StructureTemplate> StructureTemplateParameter => (IFixedValueParameter<StructureTemplate>)Parameters[StructureTemplateParameterName];
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64 | public IValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> InterpreterParameter => (IValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[InterpreterParameterName];
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65 | public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter => (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName];
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66 | public IResultParameter<ISymbolicRegressionSolution> BestTrainingSolutionParameter => (IResultParameter<ISymbolicRegressionSolution>)Parameters[BestTrainingSolutionParameterName];
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67 |
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68 | public IFixedValueParameter<BoolValue> ApplyLinearScalingParameter => (IFixedValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName];
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69 | public IFixedValueParameter<BoolValue> OptimizeParametersParameter => (IFixedValueParameter<BoolValue>)Parameters[OptimizeParametersParameterName];
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70 | #endregion
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71 |
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72 | #region Properties
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73 |
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74 | public IRegressionProblemData ProblemData {
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75 | get => ProblemDataParameter.Value;
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76 | set {
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77 | ProblemDataParameter.Value = value;
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78 | ProblemDataChanged?.Invoke(this, EventArgs.Empty);
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79 | }
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80 | }
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81 |
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82 | public StructureTemplate StructureTemplate => StructureTemplateParameter.Value;
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83 |
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84 | public ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter => InterpreterParameter.Value;
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85 |
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86 | IParameter IDataAnalysisProblem.ProblemDataParameter => ProblemDataParameter;
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87 | IDataAnalysisProblemData IDataAnalysisProblem.ProblemData => ProblemData;
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88 |
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89 | public DoubleLimit EstimationLimits => EstimationLimitsParameter.Value;
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90 |
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91 | public bool ApplyLinearScaling {
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92 | get => ApplyLinearScalingParameter.Value.Value;
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93 | set => ApplyLinearScalingParameter.Value.Value = value;
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94 | }
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95 |
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96 | public bool OptimizeParameters {
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97 | get => OptimizeParametersParameter.Value.Value;
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98 | set => OptimizeParametersParameter.Value.Value = value;
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99 | }
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100 |
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101 | public override bool Maximization => false;
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102 | #endregion
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103 |
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104 | #region EventHandlers
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105 | public event EventHandler ProblemDataChanged;
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106 | #endregion
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107 |
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108 | #region Constructors & Cloning
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109 | public StructuredSymbolicRegressionSingleObjectiveProblem() {
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110 | var provider = new PhysicsInstanceProvider();
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111 | var descriptor = new SheetBendingProcess();
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112 | var problemData = provider.LoadData(descriptor);
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113 | var shapeConstraintProblemData = new ShapeConstrainedRegressionProblemData(problemData);
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114 |
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115 | var structureTemplate = new StructureTemplate();
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116 |
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117 | Parameters.Add(new ValueParameter<IRegressionProblemData>(
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118 | ProblemDataParameterName,
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119 | shapeConstraintProblemData));
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120 |
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121 | Parameters.Add(new FixedValueParameter<StructureTemplate>(
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122 | StructureTemplateParameterName,
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123 | StructureTemplateDescriptionText,
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124 | structureTemplate));
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125 |
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126 | Parameters.Add(new FixedValueParameter<BoolValue>(
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127 | ApplyLinearScalingParameterName, new BoolValue(true)
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128 | ));
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129 |
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130 | Parameters.Add(new FixedValueParameter<BoolValue>(
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131 | OptimizeParametersParameterName, new BoolValue(true)
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132 | ));
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133 |
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134 | Parameters.Add(new ValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(
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135 | InterpreterParameterName,
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136 | new SymbolicDataAnalysisExpressionTreeBatchInterpreter()) { Hidden = true });
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137 | Parameters.Add(new FixedValueParameter<DoubleLimit>(
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138 | EstimationLimitsParameterName,
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139 | new DoubleLimit(double.NegativeInfinity, double.PositiveInfinity)) { Hidden = true });
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140 | Parameters.Add(new ResultParameter<ISymbolicRegressionSolution>(BestTrainingSolutionParameterName, "") { Hidden = true });
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141 |
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142 | this.EvaluatorParameter.Hidden = true;
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143 |
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144 | Operators.Add(new SymbolicDataAnalysisVariableFrequencyAnalyzer());
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145 | Operators.Add(new MinAverageMaxSymbolicExpressionTreeLengthAnalyzer());
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146 | Operators.Add(new SymbolicExpressionSymbolFrequencyAnalyzer());
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147 |
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148 | RegisterEventHandlers();
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149 |
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150 | StructureTemplate.ApplyLinearScaling = ApplyLinearScaling;
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151 | StructureTemplate.Template =
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152 | "(" +
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153 | "(210000 / (210000 + h)) * ((sigma_y * t * t) / (wR * Rt * t)) + " +
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154 | "PlasticHardening(_) - Elasticity(_)" +
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155 | ")" +
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156 | " * C(_)";
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157 | }
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158 |
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159 | public StructuredSymbolicRegressionSingleObjectiveProblem(StructuredSymbolicRegressionSingleObjectiveProblem original, Cloner cloner) : base(original, cloner) {
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160 | RegisterEventHandlers();
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161 | }
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162 |
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163 | public override IDeepCloneable Clone(Cloner cloner) =>
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164 | new StructuredSymbolicRegressionSingleObjectiveProblem(this, cloner);
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165 |
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166 | [StorableConstructor]
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167 | protected StructuredSymbolicRegressionSingleObjectiveProblem(StorableConstructorFlag _) : base(_) { }
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168 |
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169 |
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170 | [StorableHook(HookType.AfterDeserialization)]
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171 | private void AfterDeserialization() {
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172 | if (!Parameters.ContainsKey(ApplyLinearScalingParameterName)) {
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173 | Parameters.Add(new FixedValueParameter<BoolValue>(ApplyLinearScalingParameterName, new BoolValue(StructureTemplate.ApplyLinearScaling)));
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174 | }
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175 |
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176 | if (!Parameters.ContainsKey(OptimizeParametersParameterName)) {
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177 | Parameters.Add(new FixedValueParameter<BoolValue>(OptimizeParametersParameterName, new BoolValue(false)));
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178 | }
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179 |
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180 | RegisterEventHandlers();
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181 | }
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182 |
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183 | #endregion
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184 |
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185 | private void RegisterEventHandlers() {
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186 | if (StructureTemplate != null) {
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187 | StructureTemplate.Changed += OnTemplateChanged;
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188 | }
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189 |
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190 | ProblemDataParameter.ValueChanged += ProblemDataParameterValueChanged;
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191 | ApplyLinearScalingParameter.Value.ValueChanged += (o, e) => StructureTemplate.ApplyLinearScaling = ApplyLinearScaling;
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192 | }
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193 |
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194 | private void ProblemDataParameterValueChanged(object sender, EventArgs e) {
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195 | StructureTemplate.Reset();
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196 | // InfoBox for Reset?
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197 | }
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198 |
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199 | private void OnTemplateChanged(object sender, EventArgs args) {
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200 | ApplyLinearScaling = StructureTemplate.ApplyLinearScaling;
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201 | SetupEncoding();
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202 | }
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203 |
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204 | private void SetupEncoding() {
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205 | foreach (var e in Encoding.Encodings.ToArray())
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206 | Encoding.Remove(e);
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207 |
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208 |
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209 | var templateNumberTreeNodes = StructureTemplate.Tree.IterateNodesPrefix().OfType<NumberTreeNode>();
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210 | if (templateNumberTreeNodes.Any()) {
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211 | var templateParameterValues = templateNumberTreeNodes.Select(n => n.Value).ToArray();
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212 | var encoding = new RealVectorEncoding(NumericParametersEncoding, templateParameterValues.Length);
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213 |
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214 | var creator = encoding.Operators.OfType<NormalDistributedRealVectorCreator>().First();
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215 | creator.MeanParameter.Value = new RealVector(templateParameterValues);
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216 | creator.SigmaParameter.Value = new DoubleArray(templateParameterValues.Length);
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217 | encoding.SolutionCreator = creator;
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218 |
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219 | Encoding.Add(encoding);
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220 | }
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221 |
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222 | foreach (var subFunction in StructureTemplate.SubFunctions) {
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223 | subFunction.SetupVariables(ProblemData.AllowedInputVariables);
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224 | // prevent the same encoding twice
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225 | if (Encoding.Encodings.Any(x => x.Name == subFunction.Name)) continue;
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226 |
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227 | var encoding = new SymbolicExpressionTreeEncoding(
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228 | subFunction.Name,
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229 | subFunction.Grammar,
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230 | subFunction.MaximumSymbolicExpressionTreeLength,
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231 | subFunction.MaximumSymbolicExpressionTreeDepth);
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232 | Encoding.Add(encoding);
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233 | }
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234 |
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235 | //set single point crossover for numeric parameters
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236 | var multiCrossover = (IParameterizedItem)Encoding.Operators.OfType<MultiEncodingCrossover>().First();
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237 | foreach (var param in multiCrossover.Parameters.OfType<ConstrainedValueParameter<ICrossover>>()) {
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238 | var singlePointCrossover = param.ValidValues.OfType<SinglePointCrossover>().FirstOrDefault();
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239 | param.Value = singlePointCrossover ?? param.ValidValues.First();
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240 | }
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241 |
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242 | //adapt crossover probability for subtree crossover
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243 | foreach (var param in multiCrossover.Parameters.OfType<ConstrainedValueParameter<ICrossover>>()) {
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244 | var subtreeCrossover = param.ValidValues.OfType<SubtreeCrossover>().FirstOrDefault();
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245 | if (subtreeCrossover != null) {
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246 | subtreeCrossover.CrossoverProbability = 1.0 / Encoding.Encodings.OfType<SymbolicExpressionTreeEncoding>().Count();
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247 | param.Value = subtreeCrossover;
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248 | }
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249 | }
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250 |
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251 | //set multi manipulator as default manipulator for all symbolic expression tree encoding parts
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252 | var manipulator = (IParameterizedItem)Encoding.Operators.OfType<MultiEncodingManipulator>().First();
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253 | foreach (var param in manipulator.Parameters.OfType<ConstrainedValueParameter<IManipulator>>()) {
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254 | var m = param.ValidValues.OfType<MultiSymbolicExpressionTreeManipulator>().FirstOrDefault();
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255 | param.Value = m ?? param.ValidValues.First();
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256 | }
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257 | }
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258 |
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259 | public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
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260 | base.Analyze(individuals, qualities, results, random);
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261 |
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262 | var best = GetBestIndividual(individuals, qualities).Item1;
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263 |
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264 | if (!results.ContainsKey(BestTrainingSolutionParameter.ActualName)) {
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265 | results.Add(new Result(BestTrainingSolutionParameter.ActualName, typeof(SymbolicRegressionSolution)));
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266 | }
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267 |
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268 | var tree = (ISymbolicExpressionTree)best[SymbolicExpressionTreeName];
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269 | var model = new SymbolicRegressionModel(ProblemData.TargetVariable, tree, Interpreter);
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270 | var solution = model.CreateRegressionSolution(ProblemData);
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271 |
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272 | results[BestTrainingSolutionParameter.ActualName].Value = solution;
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273 | }
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274 |
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275 |
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276 | public override double Evaluate(Individual individual, IRandom random) {
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277 | var templateTree = StructureTemplate.Tree;
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278 | if (templateTree == null)
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279 | throw new ArgumentException("No structure template defined!");
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280 |
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281 | var tree = BuildTreeFromIndividual(templateTree, individual, updateNumericParameters: StructureTemplate.ContainsNumericParameters);
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282 | individual[SymbolicExpressionTreeName] = tree;
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283 |
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284 | if (OptimizeParameters) {
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285 | ParameterOptimization.OptimizeTreeParameters(ProblemData, tree, interpreter: Interpreter);
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286 | } else if (ApplyLinearScaling) {
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287 | LinearScaling.AdjustLinearScalingParams(ProblemData, tree, Interpreter);
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288 | }
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289 |
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290 | UpdateIndividualFromTree(tree, individual, updateNumericParameters: StructureTemplate.ContainsNumericParameters);
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291 |
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292 | //calculate NMSE
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293 | var estimatedValues = Interpreter.GetSymbolicExpressionTreeValues(tree, ProblemData.Dataset, ProblemData.TrainingIndices);
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294 | var boundedEstimatedValues = estimatedValues.LimitToRange(EstimationLimits.Lower, EstimationLimits.Upper);
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295 | var targetValues = ProblemData.TargetVariableTrainingValues;
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296 | var nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out var errorState);
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297 | if (errorState != OnlineCalculatorError.None)
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298 | nmse = 1.0;
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299 |
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300 | //evaluate constraints
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301 | var constraints = Enumerable.Empty<ShapeConstraint>();
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302 | if (ProblemData is ShapeConstrainedRegressionProblemData scProbData)
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303 | constraints = scProbData.ShapeConstraints.EnabledConstraints;
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304 | if (constraints.Any()) {
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305 | var boundsEstimator = new IntervalArithBoundsEstimator();
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306 | var constraintViolations = IntervalUtil.GetConstraintViolations(constraints, boundsEstimator, ProblemData.VariableRanges, tree);
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307 |
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308 | // infinite/NaN constraints
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309 | if (constraintViolations.Any(x => double.IsNaN(x) || double.IsInfinity(x)))
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310 | nmse = 1.0;
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311 |
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312 | if (constraintViolations.Any(x => x > 0.0))
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313 | nmse = 1.0;
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314 | }
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315 |
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316 | return nmse;
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317 | }
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318 |
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319 | private static ISymbolicExpressionTree BuildTreeFromIndividual(ISymbolicExpressionTree template, Individual individual, bool updateNumericParameters) {
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320 | var resolvedTree = (ISymbolicExpressionTree)template.Clone();
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321 |
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322 | //set numeric parameter values
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323 | if (updateNumericParameters) {
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324 | var realVector = individual.RealVector(NumericParametersEncoding);
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325 | var numberTreeNodes = resolvedTree.IterateNodesPrefix().OfType<NumberTreeNode>().ToArray();
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326 |
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327 | if (realVector.Length != numberTreeNodes.Length)
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328 | throw new InvalidOperationException("The number of numeric parameters in the tree does not match the provided numerical values.");
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329 |
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330 | for (int i = 0; i < numberTreeNodes.Length; i++)
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331 | numberTreeNodes[i].Value = realVector[i];
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332 | }
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333 |
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334 | // build main tree
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335 | foreach (var subFunctionTreeNode in resolvedTree.IterateNodesPrefix().OfType<SubFunctionTreeNode>()) {
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336 | var subFunctionTree = individual.SymbolicExpressionTree(subFunctionTreeNode.Name);
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337 |
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338 | // extract function tree
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339 | var subTree = subFunctionTree.Root.GetSubtree(0) // StartSymbol
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340 | .GetSubtree(0); // First Symbol
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341 | subTree = (ISymbolicExpressionTreeNode)subTree.Clone();
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342 | subFunctionTreeNode.AddSubtree(subTree);
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343 | }
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344 | return resolvedTree;
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345 | }
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346 |
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347 | private static void UpdateIndividualFromTree(ISymbolicExpressionTree tree, Individual individual, bool updateNumericParameters) {
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348 | var clonedTree = (ISymbolicExpressionTree)tree.Clone();
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349 |
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350 | foreach (var subFunctionTreeNode in clonedTree.IterateNodesPrefix().OfType<SubFunctionTreeNode>()) {
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351 | var grammar = ((ISymbolicExpressionTree)individual[subFunctionTreeNode.Name]).Root.Grammar;
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352 | var functionTreeNode = subFunctionTreeNode.GetSubtree(0);
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353 | //remove function code to make numeric parameters extraction easier
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354 | subFunctionTreeNode.RemoveSubtree(0);
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355 |
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356 |
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357 | var rootNode = (SymbolicExpressionTreeTopLevelNode)new ProgramRootSymbol().CreateTreeNode();
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358 | rootNode.SetGrammar(grammar);
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359 |
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360 | var startNode = (SymbolicExpressionTreeTopLevelNode)new StartSymbol().CreateTreeNode();
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361 | startNode.SetGrammar(grammar);
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362 |
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363 | rootNode.AddSubtree(startNode);
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364 | startNode.AddSubtree(functionTreeNode);
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365 | var functionTree = new SymbolicExpressionTree(rootNode);
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366 | individual[subFunctionTreeNode.Name] = functionTree;
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367 | }
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368 |
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369 | //set numeric parameter values
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370 | if (updateNumericParameters) {
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371 | var realVector = individual.RealVector(NumericParametersEncoding);
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372 | var numberTreeNodes = clonedTree.IterateNodesPrefix().OfType<NumberTreeNode>().ToArray();
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373 |
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374 | if (realVector.Length != numberTreeNodes.Length)
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375 | throw new InvalidOperationException("The number of numeric parameters in the tree does not match the provided numerical values.");
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376 |
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377 | for (int i = 0; i < numberTreeNodes.Length; i++)
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378 | realVector[i] = numberTreeNodes[i].Value;
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379 | }
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380 | }
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381 |
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382 | public void Load(IRegressionProblemData data) {
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383 | ProblemData = data;
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384 | }
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385 | }
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386 | }
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