1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2015 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.Linq;
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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.Operators;
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30 | using HeuristicLab.Optimization;
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31 | using HeuristicLab.Parameters;
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32 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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33 | using HeuristicLab.Problems.DataAnalysis;
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34 |
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35 | namespace HeuristicLab.Algorithms.DataAnalysis {
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36 | [Item("NcaGradientCalculator", "Calculates the quality and gradient of a certain NCA matrix.")]
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37 | [StorableType("5ED9020C-0D3E-41C6-B6DC-48E56301015C")]
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38 | public class NcaGradientCalculator : SingleSuccessorOperator, ISingleObjectiveOperator {
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39 |
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40 | #region Parameter Properties
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41 | public ILookupParameter<IntValue> DimensionsParameter {
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42 | get { return (ILookupParameter<IntValue>)Parameters["Dimensions"]; }
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43 | }
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44 |
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45 | public ILookupParameter<IntValue> NeighborSamplesParameter {
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46 | get { return (ILookupParameter<IntValue>)Parameters["NeighborSamples"]; }
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47 | }
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48 |
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49 | public ILookupParameter<DoubleValue> RegularizationParameter {
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50 | get { return (ILookupParameter<DoubleValue>)Parameters["Regularization"]; }
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51 | }
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52 |
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53 | public ILookupParameter<RealVector> NcaMatrixParameter {
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54 | get { return (ILookupParameter<RealVector>)Parameters["NcaMatrix"]; }
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55 | }
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56 |
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57 | public ILookupParameter<RealVector> NcaMatrixGradientsParameter {
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58 | get { return (ILookupParameter<RealVector>)Parameters["NcaMatrixGradients"]; }
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59 | }
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60 |
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61 | public ILookupParameter<DoubleValue> QualityParameter {
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62 | get { return (ILookupParameter<DoubleValue>)Parameters["Quality"]; }
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63 | }
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64 |
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65 | public ILookupParameter<IClassificationProblemData> ProblemDataParameter {
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66 | get { return (ILookupParameter<IClassificationProblemData>)Parameters["ProblemData"]; }
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67 | }
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68 | #endregion
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69 |
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70 | [StorableConstructor]
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71 | protected NcaGradientCalculator(bool deserializing) : base(deserializing) { }
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72 | protected NcaGradientCalculator(NcaGradientCalculator original, Cloner cloner) : base(original, cloner) { }
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73 | public NcaGradientCalculator()
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74 | : base() {
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75 | Parameters.Add(new LookupParameter<IntValue>("Dimensions", "The dimensions to which the feature space should be reduced to."));
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76 | Parameters.Add(new LookupParameter<IntValue>("NeighborSamples", "The number of neighbors that should be taken into account at maximum."));
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77 | Parameters.Add(new LookupParameter<DoubleValue>("Regularization", "The regularization term that constrains the expansion of the projected space."));
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78 | Parameters.Add(new LookupParameter<RealVector>("NcaMatrix", "The optimized matrix."));
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79 | Parameters.Add(new LookupParameter<RealVector>("NcaMatrixGradients", "The gradients from the matrix that is being optimized."));
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80 | Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The quality of the current matrix."));
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81 | Parameters.Add(new LookupParameter<IClassificationProblemData>("ProblemData", "The classification problem data."));
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82 | }
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83 |
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84 | public override IDeepCloneable Clone(Cloner cloner) {
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85 | return new NcaGradientCalculator(this, cloner);
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86 | }
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87 |
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88 | public override IOperation Apply() {
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89 | var problemData = ProblemDataParameter.ActualValue;
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90 | var dimensions = DimensionsParameter.ActualValue.Value;
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91 | var neighborSamples = NeighborSamplesParameter.ActualValue.Value;
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92 | var regularization = RegularizationParameter.ActualValue.Value;
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93 |
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94 | var vector = NcaMatrixParameter.ActualValue;
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95 | var gradients = NcaMatrixGradientsParameter.ActualValue;
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96 | if (gradients == null) {
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97 | gradients = new RealVector(vector.Length);
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98 | NcaMatrixGradientsParameter.ActualValue = gradients;
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99 | }
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100 |
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101 | var data = AlglibUtil.PrepareInputMatrix(problemData.Dataset, problemData.AllowedInputVariables,
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102 | problemData.TrainingIndices);
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103 | var classes = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).ToArray();
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104 |
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105 | var quality = Gradient(vector, gradients, data, classes, dimensions, neighborSamples, regularization);
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106 | QualityParameter.ActualValue = new DoubleValue(quality);
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107 |
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108 | return base.Apply();
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109 | }
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110 |
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111 | private static double Gradient(RealVector A, RealVector grad, double[,] data, double[] classes, int dimensions, int neighborSamples, double regularization) {
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112 | var instances = data.GetLength(0);
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113 | var attributes = data.GetLength(1);
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114 |
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115 | var AMatrix = new Matrix(A, A.Length / dimensions, dimensions);
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116 |
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117 | alglib.sparsematrix probabilities;
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118 | alglib.sparsecreate(instances, instances, out probabilities);
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119 | var transformedDistances = new Dictionary<int, double>(instances);
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120 | for (int i = 0; i < instances; i++) {
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121 | var iVector = new Matrix(GetRow(data, i), data.GetLength(1));
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122 | for (int k = 0; k < instances; k++) {
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123 | if (k == i) {
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124 | transformedDistances.Remove(k);
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125 | continue;
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126 | }
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127 | var kVector = new Matrix(GetRow(data, k));
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128 | transformedDistances[k] = Math.Exp(-iVector.Multiply(AMatrix).Subtract(kVector.Multiply(AMatrix)).SumOfSquares());
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129 | }
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130 | var normalization = transformedDistances.Sum(x => x.Value);
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131 | if (normalization <= 0) continue;
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132 | foreach (var s in transformedDistances.Where(x => x.Value > 0).OrderByDescending(x => x.Value).Take(neighborSamples)) {
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133 | alglib.sparseset(probabilities, i, s.Key, s.Value / normalization);
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134 | }
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135 | }
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136 | alglib.sparseconverttocrs(probabilities); // needed to enumerate in order (top-down and left-right)
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137 |
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138 | int t0 = 0, t1 = 0, r, c;
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139 | double val;
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140 | var pi = new double[instances];
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141 | while (alglib.sparseenumerate(probabilities, ref t0, ref t1, out r, out c, out val)) {
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142 | if (classes[r].IsAlmost(classes[c])) {
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143 | pi[r] += val;
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144 | }
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145 | }
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146 |
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147 | var innerSum = new double[attributes, attributes];
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148 | while (alglib.sparseenumerate(probabilities, ref t0, ref t1, out r, out c, out val)) {
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149 | var vector = new Matrix(GetRow(data, r)).Subtract(new Matrix(GetRow(data, c)));
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150 | vector.OuterProduct(vector).Multiply(val * pi[r]).AddTo(innerSum);
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151 |
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152 | if (classes[r].IsAlmost(classes[c])) {
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153 | vector.OuterProduct(vector).Multiply(-val).AddTo(innerSum);
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154 | }
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155 | }
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156 |
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157 | var func = -pi.Sum() + regularization * AMatrix.SumOfSquares();
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158 |
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159 | r = 0;
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160 | var newGrad = AMatrix.Multiply(-2.0).Transpose().Multiply(new Matrix(innerSum)).Transpose();
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161 | foreach (var g in newGrad) {
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162 | grad[r] = g + regularization * 2 * A[r];
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163 | r++;
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164 | }
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165 |
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166 | return func;
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167 | }
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168 |
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169 | private static IEnumerable<double> GetRow(double[,] data, int row) {
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170 | for (int i = 0; i < data.GetLength(1); i++)
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171 | yield return data[row, i];
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172 | }
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173 | }
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174 | }
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