1 | using System;
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2 | using System.Linq;
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3 | using System.Text;
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4 | using System.Threading;
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5 | using HeuristicLab.Algorithms.DataAnalysis;
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6 | using HeuristicLab.Common;
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7 | using HeuristicLab.Core;
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8 | using HeuristicLab.Data;
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9 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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10 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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11 | using HeuristicLab.Problems.DataAnalysis;
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12 |
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13 | namespace HeuristicLab.Problems.GaussianProcessTuning {
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14 | [StorableClass]
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15 | [Item("Interpreter", "An interpreter for Gaussian process configurations represented as trees.")]
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16 | public class Interpreter : Item {
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17 | [StorableConstructor]
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18 | protected Interpreter(bool deserializing) : base(deserializing) { }
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19 | protected Interpreter(Interpreter original, Cloner cloner)
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20 | : base(original, cloner) {
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21 | }
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22 | public Interpreter()
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23 | : base() { }
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24 | public override IDeepCloneable Clone(Cloner cloner) {
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25 | return new Interpreter(this, cloner);
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26 | }
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27 |
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28 | public void EvaluateGaussianProcessConfiguration(ISymbolicExpressionTree tree, IRegressionProblemData problemData, out double negLogLikelihood, out IGaussianProcessSolution solution) {
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29 | var meanFunction = GetMeanFunction(tree);
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30 | var covFunction = GetCovFunction(tree);
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31 |
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32 | var gprAlg = new GaussianProcessRegression();
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33 | gprAlg.Problem.ProblemDataParameter.Value = problemData;
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34 | gprAlg.CovarianceFunction = covFunction;
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35 | gprAlg.MeanFunction = meanFunction;
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36 | gprAlg.GaussianProcessModelCreatorParameter.Value =
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37 | gprAlg.GaussianProcessModelCreatorParameter.ValidValues.First(
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38 | v => v is GaussianProcessRegressionModelCreator);
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39 | gprAlg.MinimizationIterations = 50;
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40 |
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41 | var signal = new AutoResetEvent(false);
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42 | double result = double.MaxValue;
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43 | IGaussianProcessSolution regSolution = null;
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44 | gprAlg.Stopped += (sender, args) => {
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45 | result = ((DoubleValue)gprAlg.Results["NegativeLogLikelihood"].Value).Value;
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46 | if (gprAlg.Results.ContainsKey("Solution"))
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47 | regSolution = (IGaussianProcessSolution)gprAlg.Results["Solution"].Value;
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48 | signal.Set();
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49 | };
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50 | Exception ex = null;
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51 | gprAlg.ExceptionOccurred += (sender, args) => {
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52 | result = double.MaxValue;
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53 | regSolution = null;
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54 | ex = args.Value;
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55 | signal.Set();
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56 | };
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57 |
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58 | gprAlg.Prepare();
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59 | gprAlg.Start();
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60 |
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61 | signal.WaitOne();
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62 | if (ex != null) throw ex;
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63 |
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64 | gprAlg.Prepare();
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65 | gprAlg.Problem = null;
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66 | solution = regSolution;
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67 | negLogLikelihood = result;
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68 | }
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69 |
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70 | private IMeanFunction GetMeanFunction(ISymbolicExpressionTree tree) {
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71 | return GetMeanFunction(tree.Root.GetSubtree(0).GetSubtree(0).GetSubtree(0));
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72 | }
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73 |
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74 | private ICovarianceFunction GetCovFunction(ISymbolicExpressionTree tree) {
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75 | return GetCovFunction(tree.Root.GetSubtree(0).GetSubtree(0).GetSubtree(1));
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76 | }
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77 |
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78 | private ICovarianceFunction GetCovFunction(ISymbolicExpressionTreeNode node) {
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79 | if (node.Symbol is CovConst) {
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80 | return new CovarianceConst();
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81 | } else if (node.Symbol is CovScale) {
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82 | var cov = new CovarianceScale();
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83 | cov.CovarianceFunctionParameter.Value = GetCovFunction(node.GetSubtree(0));
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84 | return cov;
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85 | } else if (node.Symbol is CovMask) {
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86 | var maskNode = node as CovMaskTreeNode;
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87 | var covSymbol = node.Symbol as CovMask;
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88 | var cov = new CovarianceMask();
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89 | cov.SelectedDimensionsParameter.Value = new IntArray((from i in Enumerable.Range(0, covSymbol.Dimension)
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90 | where maskNode.Mask[i]
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91 | select i).ToArray());
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92 | cov.CovarianceFunctionParameter.Value = GetCovFunction(node.GetSubtree(0));
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93 | return cov;
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94 | } else if (node.Symbol is CovLin) {
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95 | return new CovarianceLinear();
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96 | } else if (node.Symbol is CovLinArd) {
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97 | return new CovarianceLinearArd();
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98 | } else if (node.Symbol is CovMatern) {
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99 | var covSymbol = node.Symbol as CovMatern;
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100 | var cov = new CovarianceMaternIso();
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101 | cov.DParameter.Value = cov.DParameter.ValidValues.Single(x => x.Value == covSymbol.D);
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102 | return cov;
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103 | } else if (node.Symbol is CovSeArd) {
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104 | return new CovarianceSquaredExponentialArd();
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105 | } else if (node.Symbol is CovSeIso) {
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106 | return new CovarianceSquaredExponentialIso();
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107 | } else if (node.Symbol is CovRQIso) {
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108 | return new CovarianceRationalQuadraticIso();
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109 | } else if (node.Symbol is CovRQArd) {
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110 | return new CovarianceRationalQuadraticArd();
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111 | } else if (node.Symbol is CovNn) {
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112 | return new CovarianceNeuralNetwork();
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113 | } else if (node.Symbol is CovPeriodic) {
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114 | return new CovariancePeriodic();
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115 | } else if (node.Symbol is CovNoise) {
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116 | return new CovarianceNoise();
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117 | } else if (node.Symbol is CovSum) {
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118 | var covSum = new Algorithms.DataAnalysis.CovarianceSum();
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119 | covSum.Terms.Add(GetCovFunction(node.GetSubtree(0)));
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120 | foreach (var subTree in node.Subtrees.Skip(1)) {
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121 | covSum.Terms.Add(GetCovFunction(subTree));
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122 | }
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123 | return covSum;
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124 | } else if (node.Symbol is CovProd) {
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125 | var covProd = new Algorithms.DataAnalysis.CovarianceProduct();
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126 | covProd.Factors.Add(GetCovFunction(node.GetSubtree(0)));
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127 | foreach (var subTree in node.Subtrees.Skip(1)) {
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128 | covProd.Factors.Add(GetCovFunction(subTree));
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129 | }
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130 | return covProd;
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131 | } else {
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132 | throw new ArgumentException("unknown symbol " + node.Symbol);
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133 | }
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134 | }
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135 |
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136 |
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137 | private IMeanFunction GetMeanFunction(ISymbolicExpressionTreeNode node) {
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138 | if (node.Symbol is MeanConst) {
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139 | return new Algorithms.DataAnalysis.MeanConst();
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140 | } else if (node.Symbol is MeanLinear) {
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141 | return new Algorithms.DataAnalysis.MeanLinear();
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142 | } else if (node.Symbol is MeanProd) {
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143 | var meanProd = new Algorithms.DataAnalysis.MeanProduct();
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144 | meanProd.Factors.Add(GetMeanFunction(node.GetSubtree(0)));
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145 | foreach (var subTree in node.Subtrees.Skip(1)) {
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146 | meanProd.Factors.Add(GetMeanFunction(subTree));
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147 | }
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148 | return meanProd;
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149 | } else if (node.Symbol is MeanSum) {
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150 | var meanSum = new Algorithms.DataAnalysis.MeanSum();
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151 | meanSum.Terms.Add(GetMeanFunction(node.GetSubtree(0)));
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152 | foreach (var subTree in node.Subtrees.Skip(1)) {
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153 | meanSum.Terms.Add(GetMeanFunction(subTree));
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154 | }
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155 | return meanSum;
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156 | } else if (node.Symbol is MeanZero) {
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157 | return new Algorithms.DataAnalysis.MeanZero();
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158 | } else {
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159 | throw new ArgumentException("Unknown mean function" + node.Symbol);
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160 | }
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161 | }
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162 |
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163 | //private bool[] CalculateMask(ISymbolicExpressionTreeNode node) {
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164 | // var maskNode = node as MeanMaskTreeNode;
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165 | // if (maskNode != null) {
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166 | // bool[] newMask = CombineMasksProd(maskNode.Mask, CalculateMask(node.GetSubtree(0)));
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167 | // return newMask;
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168 | // } else if (node.Symbol is MeanProd) {
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169 | // bool[] newMask = CalculateMask(node.GetSubtree(0));
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170 | // foreach (var subTree in node.Subtrees.Skip(1)) {
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171 | // newMask = CombineMasksProd(newMask, CalculateMask(subTree));
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172 | // }
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173 | // return newMask;
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174 | // } else if (node.Symbol is MeanSum) {
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175 | // bool[] newMask = CalculateMask(node.GetSubtree(0));
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176 | // foreach (var subTree in node.Subtrees.Skip(1)) {
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177 | // newMask = CombineMasksSum(newMask, CalculateMask(subTree));
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178 | // }
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179 | // return newMask;
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180 | // } else if (node.SubtreeCount == 1) {
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181 | // return CalculateMask(node.GetSubtree(0));
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182 | // } else if (node is SymbolicExpressionTreeTerminalNode) {
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183 | // return null;
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184 | // } else {
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185 | // throw new NotImplementedException();
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186 | // }
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187 | //}
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188 |
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189 | //private bool[] CombineMasksProd(bool[] m, bool[] n) {
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190 | // if (m == null) return n;
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191 | // if (n == null) return m;
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192 | // if (m.Length != n.Length) throw new ArgumentException();
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193 | // bool[] res = new bool[m.Length];
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194 | // for (int i = 0; i < res.Length; i++)
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195 | // res[i] = m[i] | n[i];
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196 | // return res;
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197 | //}
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198 |
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199 |
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200 | //private bool[] CombineMasksSum(bool[] m, bool[] n) {
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201 | // if (m == null) return n;
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202 | // if (n == null) return m;
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203 | // if (m.Length != n.Length) throw new ArgumentException();
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204 | // bool[] res = new bool[m.Length];
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205 | // for (int i = 0; i < res.Length; i++)
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206 | // res[i] = m[i] & n[i];
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207 | // return res;
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208 | //}
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209 |
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210 | }
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211 | }
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