1 | #region License Information
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2 |
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3 | /* HeuristicLab
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4 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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5 | *
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6 | * This file is part of HeuristicLab.
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7 | *
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8 | * HeuristicLab is free software: you can redistribute it and/or modify
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9 | * it under the terms of the GNU General Public License as published by
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10 | * the Free Software Foundation, either version 3 of the License, or
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11 | * (at your option) any later version.
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12 | *
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13 | * HeuristicLab is distributed in the hope that it will be useful,
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14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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16 | * GNU General Public License for more details.
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17 | *
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18 | * You should have received a copy of the GNU General Public License
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19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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20 | */
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21 |
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22 | #endregion
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23 |
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24 | using System;
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25 | using System.Collections.Generic;
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26 | using System.Linq;
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27 | using HEAL.Attic;
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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.Parameters;
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33 |
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34 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
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35 | [Item("NMSE Evaluator with shape constraints (multi-objective)",
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36 | "Calculates the NMSE and constraint violations for a symbolic regression model.")]
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37 | [StorableType("8E9D76B7-ED9C-43E7-9898-01FBD3633880")]
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38 | public class NMSEMultiObjectiveConstraintsEvaluator : SymbolicRegressionMultiObjectiveEvaluator, IMultiObjectiveConstraintsEvaluator {
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39 | private const string NumConstraintsParameterName = "NumConstraints";
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40 | private const string BoundsEstimatorParameterName = "BoundsEstimator";
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41 |
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42 | public IFixedValueParameter<IntValue> NumConstraintsParameter =>
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43 | (IFixedValueParameter<IntValue>)Parameters[NumConstraintsParameterName];
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44 |
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45 | public IValueParameter<IBoundsEstimator> BoundsEstimatorParameter =>
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46 | (IValueParameter<IBoundsEstimator>)Parameters[BoundsEstimatorParameterName];
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47 |
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48 | public int NumConstraints {
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49 | get => NumConstraintsParameter.Value.Value;
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50 | set {
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51 | NumConstraintsParameter.Value.Value = value;
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52 | }
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53 | }
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54 |
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55 | public IBoundsEstimator BoundsEstimator {
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56 | get => BoundsEstimatorParameter.Value;
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57 | set => BoundsEstimatorParameter.Value = value;
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58 | }
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59 |
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60 | public override IEnumerable<bool> Maximization => new bool[1 + NumConstraints]; // minimize all objectives
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61 |
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62 | #region Constructors
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63 |
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64 | public NMSEMultiObjectiveConstraintsEvaluator() {
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65 | Parameters.Add(new FixedValueParameter<IntValue>(NumConstraintsParameterName, new IntValue(0)));
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66 | Parameters.Add(new ValueParameter<IBoundsEstimator>(BoundsEstimatorParameterName, new IntervalArithBoundsEstimator()));
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67 | }
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68 |
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69 | [StorableConstructor]
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70 | protected NMSEMultiObjectiveConstraintsEvaluator(StorableConstructorFlag _) : base(_) { }
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71 |
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72 | protected NMSEMultiObjectiveConstraintsEvaluator(NMSEMultiObjectiveConstraintsEvaluator original, Cloner cloner) : base(original, cloner) { }
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73 |
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74 | #endregion
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75 |
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76 | [StorableHook(HookType.AfterDeserialization)]
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77 | private void AfterDeserialization() { }
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78 |
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79 | public override IDeepCloneable Clone(Cloner cloner) {
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80 | return new NMSEMultiObjectiveConstraintsEvaluator(this, cloner);
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81 | }
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82 |
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83 |
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84 | public override IOperation InstrumentedApply() {
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85 | var rows = GenerateRowsToEvaluate();
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86 | var tree = SymbolicExpressionTreeParameter.ActualValue;
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87 | var problemData = ProblemDataParameter.ActualValue;
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88 | var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
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89 | var estimationLimits = EstimationLimitsParameter.ActualValue;
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90 | var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
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91 |
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92 | if (UseConstantOptimization) {
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93 | SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, rows,
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94 | false,
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95 | ConstantOptimizationIterations,
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96 | ConstantOptimizationUpdateVariableWeights,
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97 | estimationLimits.Lower,
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98 | estimationLimits.Upper);
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99 | } else {
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100 | if (applyLinearScaling) {
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101 | var rootNode = new ProgramRootSymbol().CreateTreeNode();
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102 | var startNode = new StartSymbol().CreateTreeNode();
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103 | var offset = tree.Root.GetSubtree(0) //Start
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104 | .GetSubtree(0); //Offset
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105 | var scaling = offset.GetSubtree(0);
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106 |
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107 | //Check if tree contains offset and scaling nodes
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108 | if (!(offset.Symbol is Addition) || !(scaling.Symbol is Multiplication))
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109 | throw new ArgumentException($"{ItemName} can only be used with LinearScalingGrammar.");
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110 |
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111 |
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112 | var t = (ISymbolicExpressionTreeNode)scaling.GetSubtree(0).Clone();
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113 | rootNode.AddSubtree(startNode);
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114 | startNode.AddSubtree(t);
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115 | var newTree = new SymbolicExpressionTree(rootNode);
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116 |
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117 | //calculate alpha and beta for scaling
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118 | var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(newTree, problemData.Dataset, rows);
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119 |
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120 | var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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121 | OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out var alpha, out var beta,
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122 | out var errorState);
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123 | if (errorState == OnlineCalculatorError.None) {
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124 | //Set alpha and beta to the scaling nodes from ia grammar
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125 | var offsetParameter = offset.GetSubtree(1) as ConstantTreeNode;
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126 | offsetParameter.Value = alpha;
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127 | var scalingParameter = scaling.GetSubtree(1) as ConstantTreeNode;
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128 | scalingParameter.Value = beta;
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129 | }
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130 | } // else alpha and beta are evolved
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131 | }
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132 |
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133 | var qualities = Calculate(interpreter, tree, estimationLimits.Lower, estimationLimits.Upper, problemData,
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134 | rows, BoundsEstimator, DecimalPlaces);
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135 | QualitiesParameter.ActualValue = new DoubleArray(qualities);
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136 | return base.InstrumentedApply();
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137 | }
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138 |
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139 | public override double[] Evaluate(
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140 | IExecutionContext context, ISymbolicExpressionTree tree,
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141 | IRegressionProblemData problemData,
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142 | IEnumerable<int> rows) {
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143 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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144 | EstimationLimitsParameter.ExecutionContext = context;
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145 | ApplyLinearScalingParameter.ExecutionContext = context;
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146 |
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147 | var quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree,
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148 | EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
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149 | problemData, rows, BoundsEstimator, DecimalPlaces);
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150 |
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151 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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152 | EstimationLimitsParameter.ExecutionContext = null;
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153 | ApplyLinearScalingParameter.ExecutionContext = null;
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154 |
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155 | return quality;
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156 | }
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157 |
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158 |
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159 | public static double[] Calculate(
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160 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
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161 | ISymbolicExpressionTree solution, double lowerEstimationLimit,
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162 | double upperEstimationLimit,
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163 | IRegressionProblemData problemData, IEnumerable<int> rows, IBoundsEstimator estimator, int decimalPlaces) {
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164 | OnlineCalculatorError errorState;
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165 | var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
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166 | var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
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167 | var constraints = Enumerable.Empty<ShapeConstraint>();
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168 | if (problemData is ShapeConstrainedRegressionProblemData scProbData) {
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169 | constraints = scProbData.ShapeConstraints.EnabledConstraints;
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170 | }
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171 | var intervalCollection = problemData.VariableRanges;
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172 |
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173 | double nmse;
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174 |
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175 | var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
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176 | nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState);
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177 |
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178 | if (errorState != OnlineCalculatorError.None) nmse = 1.0;
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179 |
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180 | if (decimalPlaces >= 0)
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181 | nmse = Math.Round(nmse, decimalPlaces);
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182 |
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183 | if (nmse > 1)
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184 | nmse = 1.0;
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185 |
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186 | var objectives = new List<double> { nmse };
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187 | var violations = IntervalUtil.GetConstraintViolations(constraints, estimator, intervalCollection, solution);
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188 | foreach (var violation in violations) {
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189 | if (double.IsNaN(violation) || double.IsInfinity(violation)) {
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190 | objectives.Add(double.MaxValue);
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191 | } else {
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192 | objectives.Add(Math.Round(violation, decimalPlaces));
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193 | }
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194 | }
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195 |
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196 | return objectives.ToArray();
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197 | }
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198 | }
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199 | } |
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