1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2011 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.Drawing.Printing;
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25 | using System.Linq;
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26 | using HeuristicLab.Common;
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27 | using HeuristicLab.Core;
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28 | using HeuristicLab.Data;
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29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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31 |
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32 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
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33 | [Item("Mean squared error Evaluator", "Calculates the mean squared error of a symbolic time-series prognosis solution.")]
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34 | [StorableClass]
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35 | public class SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator : SymbolicTimeSeriesPrognosisSingleObjectiveEvaluator {
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36 | [StorableConstructor]
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37 | protected SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
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38 | protected SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator original, Cloner cloner)
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39 | : base(original, cloner) {
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40 | }
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41 | public override IDeepCloneable Clone(Cloner cloner) {
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42 | return new SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(this, cloner);
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43 | }
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44 |
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45 | public SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator() : base() { }
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46 |
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47 | public override bool Maximization { get { return false; } }
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48 |
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49 | public override IOperation Apply() {
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50 | var solution = SymbolicExpressionTreeParameter.ActualValue;
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51 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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52 |
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53 | double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
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54 | solution,
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55 | EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
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56 | ProblemDataParameter.ActualValue,
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57 | rows, HorizonParameter.ActualValue.Value);
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58 | QualityParameter.ActualValue = new DoubleValue(quality);
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59 |
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60 | return base.Apply();
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61 | }
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62 |
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63 | public static double Calculate(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows, int horizon) {
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64 | double[] alpha;
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65 | double[] beta;
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66 | DetermineScalingFactors(solution, problemData, interpreter, rows, out alpha, out beta);
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67 | var scaledSolution = Scale(solution, alpha, beta);
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68 | string[] targetVariables = problemData.TargetVariables.ToArray();
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69 | var meanSquaredErrorCalculators = Enumerable.Range(0, problemData.TargetVariables.Count())
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70 | .Select(i => new OnlineMeanSquaredErrorCalculator()).ToArray();
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71 |
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72 | var allContinuationsEnumerator = interpreter.GetSymbolicExpressionTreeValues(scaledSolution, problemData.Dataset,
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73 | targetVariables,
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74 | rows, horizon).GetEnumerator();
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75 | allContinuationsEnumerator.MoveNext();
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76 | // foreach row
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77 | foreach (var row in rows) {
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78 | // foreach horizon
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79 | for (int h = 0; h < horizon; h++) {
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80 | // foreach component
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81 | for (int i = 0; i < meanSquaredErrorCalculators.Length; i++) {
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82 | double e = Math.Min(upperEstimationLimit, Math.Max(lowerEstimationLimit, allContinuationsEnumerator.Current));
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83 | meanSquaredErrorCalculators[i].Add(problemData.Dataset.GetDoubleValue(targetVariables[i], row + h), e);
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84 | if (meanSquaredErrorCalculators[i].ErrorState == OnlineCalculatorError.InvalidValueAdded)
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85 | return double.MaxValue;
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86 | allContinuationsEnumerator.MoveNext();
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87 | }
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88 | }
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89 | }
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90 | var meanCalculator = new OnlineMeanAndVarianceCalculator();
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91 | foreach (var calc in meanSquaredErrorCalculators) {
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92 | if (calc.ErrorState != OnlineCalculatorError.None) return double.MaxValue;
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93 | meanCalculator.Add(calc.MeanSquaredError);
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94 | }
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95 | //int i = 0;
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96 | //foreach (var targetVariable in problemData.TargetVariables) {
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97 | // var predictedContinuations = allPredictedContinuations.Select(v => v.ElementAt(i));
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98 | // for (int h = 0; h < horizon; h++) {
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99 | // OnlineCalculatorError errorState;
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100 | // meanCalculator.Add(OnlineMeanSquaredErrorCalculator.Calculate(predictedContinuations
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101 | // .Select(x => x.ElementAt(h))
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102 | // .LimitToRange(lowerEstimationLimit,
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103 | // upperEstimationLimit),
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104 | // actualContinuations.Select(x => x.ElementAt(h)),
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105 | // out errorState));
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106 | // if (errorState != OnlineCalculatorError.None) return double.NaN;
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107 | // }
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108 | //}
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109 | return meanCalculator.MeanErrorState == OnlineCalculatorError.None ? meanCalculator.Mean : double.MaxValue;
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110 | }
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111 |
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112 | private static ISymbolicExpressionTree Scale(ISymbolicExpressionTree solution, double[] alpha, double[] beta) {
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113 | var clone = (ISymbolicExpressionTree)solution.Clone();
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114 | int n = alpha.Length;
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115 | for (int i = 0; i < n; i++) {
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116 | var parent = clone.Root.GetSubtree(0);
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117 | var rpb = clone.Root.GetSubtree(0).GetSubtree(i);
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118 | var scaledRpb = MakeSum(
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119 | MakeProduct(rpb,
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120 | MakeConstant(beta[i], clone.Root.Grammar), clone.Root.Grammar),
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121 | MakeConstant(alpha[i], clone.Root.Grammar), clone.Root.Grammar);
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122 | parent.RemoveSubtree(i);
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123 | parent.InsertSubtree(i, scaledRpb);
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124 | }
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125 | return clone;
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126 | }
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127 |
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128 | private static ISymbolicExpressionTreeNode MakeSum(ISymbolicExpressionTreeNode a, ISymbolicExpressionTreeNode b, ISymbolicExpressionTreeGrammar grammar) {
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129 | var sum = grammar.Symbols.Where(s => s is Addition).First().CreateTreeNode();
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130 | sum.AddSubtree(a);
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131 | sum.AddSubtree(b);
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132 | return sum;
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133 | }
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134 |
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135 | private static ISymbolicExpressionTreeNode MakeProduct(ISymbolicExpressionTreeNode a, ISymbolicExpressionTreeNode b, ISymbolicExpressionTreeGrammar grammar) {
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136 | var prod = grammar.Symbols.Where(s => s is Multiplication).First().CreateTreeNode();
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137 | prod.AddSubtree(a);
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138 | prod.AddSubtree(b);
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139 | return prod;
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140 | }
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141 |
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142 | private static ISymbolicExpressionTreeNode MakeConstant(double c, ISymbolicExpressionTreeGrammar grammar) {
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143 | var node = (ConstantTreeNode)grammar.Symbols.Where(s => s is Constant).First().CreateTreeNode();
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144 | node.Value = c;
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145 | return node;
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146 | }
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147 |
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148 | private static void DetermineScalingFactors(ISymbolicExpressionTree solution, ITimeSeriesPrognosisProblemData problemData, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, IEnumerable<int> rows, out double[] alpha, out double[] beta) {
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149 | string[] targetVariables = problemData.TargetVariables.ToArray();
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150 | int nComponents = targetVariables.Length;
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151 | alpha = new double[nComponents];
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152 | beta = new double[nComponents];
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153 | var oneStepPredictionsEnumerator = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, targetVariables, rows).GetEnumerator();
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154 | var scalingParameterCalculators =
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155 | Enumerable.Repeat(0, nComponents).Select(x => new OnlineLinearScalingParameterCalculator()).ToArray();
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156 | var targetValues = problemData.Dataset.GetVectorEnumerable(targetVariables, rows);
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157 | var targetValueEnumerator = targetValues.GetEnumerator();
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158 |
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159 | var more = oneStepPredictionsEnumerator.MoveNext() & targetValueEnumerator.MoveNext();
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160 | while (more) {
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161 | for (int i = 0; i < nComponents; i++) {
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162 | scalingParameterCalculators[i].Add(oneStepPredictionsEnumerator.Current, targetValueEnumerator.Current);
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163 | more = oneStepPredictionsEnumerator.MoveNext() & targetValueEnumerator.MoveNext();
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164 | }
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165 | }
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166 |
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167 | for (int i = 0; i < nComponents; i++) {
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168 | if (scalingParameterCalculators[i].ErrorState == OnlineCalculatorError.None) {
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169 | alpha[i] = scalingParameterCalculators[i].Alpha;
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170 | beta[i] = scalingParameterCalculators[i].Beta;
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171 | } else {
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172 | alpha[i] = 0.0;
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173 | beta[i] = 1.0;
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174 | }
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175 | }
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176 | }
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177 |
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178 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
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179 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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180 | EstimationLimitsParameter.ExecutionContext = context;
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181 | HorizonParameter.ExecutionContext = context;
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182 |
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183 | double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, HorizonParameter.ActualValue.Value);
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184 |
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185 | HorizonParameter.ExecutionContext = null;
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186 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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187 | EstimationLimitsParameter.ExecutionContext = null;
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188 |
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189 | return mse;
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190 | }
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191 | }
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192 | }
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