[8562] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[15584] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8562] | 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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[8582] | 24 | using System.Linq;
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[8562] | 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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[8582] | 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Parameters;
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[8562] | 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 30 |
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| 31 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 32 | [StorableClass]
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| 33 | [Item(Name = "CovarianceMaternIso",
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| 34 | Description = "Matern covariance function for Gaussian processes.")]
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[8612] | 35 | public sealed class CovarianceMaternIso : ParameterizedNamedItem, ICovarianceFunction {
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[8582] | 36 | public IValueParameter<DoubleValue> InverseLengthParameter {
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[8982] | 37 | get { return (IValueParameter<DoubleValue>)Parameters["InverseLength"]; }
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[8582] | 38 | }
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| 39 |
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[8612] | 40 | public IValueParameter<DoubleValue> ScaleParameter {
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[8982] | 41 | get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
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[8612] | 42 | }
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[8582] | 43 |
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[8612] | 44 | public IConstrainedValueParameter<IntValue> DParameter {
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[8982] | 45 | get { return (IConstrainedValueParameter<IntValue>)Parameters["D"]; }
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[8612] | 46 | }
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[10530] | 47 | private bool HasFixedScaleParameter {
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| 48 | get { return ScaleParameter.Value != null; }
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| 49 | }
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| 50 | private bool HasFixedInverseLengthParameter {
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| 51 | get { return InverseLengthParameter.Value != null; }
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| 52 | }
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[8562] | 53 |
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| 54 | [StorableConstructor]
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[8612] | 55 | private CovarianceMaternIso(bool deserializing)
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[8562] | 56 | : base(deserializing) {
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| 57 | }
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| 58 |
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[8612] | 59 | private CovarianceMaternIso(CovarianceMaternIso original, Cloner cloner)
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[8562] | 60 | : base(original, cloner) {
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| 61 | }
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| 62 |
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| 63 | public CovarianceMaternIso()
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| 64 | : base() {
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[8612] | 65 | Name = ItemName;
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| 66 | Description = ItemDescription;
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| 67 |
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[8982] | 68 | Parameters.Add(new OptionalValueParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric Matern covariance function."));
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| 69 | Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the isometric Matern covariance function."));
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[8582] | 70 | var validDValues = new ItemSet<IntValue>();
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| 71 | validDValues.Add((IntValue)new IntValue(1).AsReadOnly());
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| 72 | validDValues.Add((IntValue)new IntValue(3).AsReadOnly());
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| 73 | validDValues.Add((IntValue)new IntValue(5).AsReadOnly());
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[8982] | 74 | Parameters.Add(new ConstrainedValueParameter<IntValue>("D", "The d parameter (allowed values: 1, 3, or 5) of the isometric Matern covariance function.", validDValues, validDValues.First()));
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[8562] | 75 | }
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| 76 |
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| 77 | public override IDeepCloneable Clone(Cloner cloner) {
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| 78 | return new CovarianceMaternIso(this, cloner);
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| 79 | }
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| 80 |
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[8612] | 81 | public int GetNumberOfParameters(int numberOfVariables) {
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[8582] | 82 | return
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[10530] | 83 | (HasFixedInverseLengthParameter ? 0 : 1) +
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| 84 | (HasFixedScaleParameter ? 0 : 1);
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[8562] | 85 | }
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| 86 |
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[8982] | 87 | public void SetParameter(double[] p) {
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| 88 | double inverseLength, scale;
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| 89 | GetParameterValues(p, out scale, out inverseLength);
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| 90 | InverseLengthParameter.Value = new DoubleValue(inverseLength);
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| 91 | ScaleParameter.Value = new DoubleValue(scale);
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| 92 | }
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| 93 |
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| 94 | private void GetParameterValues(double[] p, out double scale, out double inverseLength) {
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| 95 | // gather parameter values
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| 96 | int c = 0;
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[10530] | 97 | if (HasFixedInverseLengthParameter) {
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[8982] | 98 | inverseLength = InverseLengthParameter.Value.Value;
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| 99 | } else {
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| 100 | inverseLength = 1.0 / Math.Exp(p[c]);
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| 101 | c++;
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[8582] | 102 | }
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[8982] | 103 |
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[10530] | 104 | if (HasFixedScaleParameter) {
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[8982] | 105 | scale = ScaleParameter.Value.Value;
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| 106 | } else {
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| 107 | scale = Math.Exp(2 * p[c]);
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| 108 | c++;
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[8582] | 109 | }
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[8982] | 110 | if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceMaternIso", "p");
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[8582] | 111 | }
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[8562] | 112 |
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[13981] | 113 | public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
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[8982] | 114 | double inverseLength, scale;
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| 115 | int d = DParameter.Value.Value;
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| 116 | GetParameterValues(p, out scale, out inverseLength);
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[10530] | 117 | var fixedInverseLength = HasFixedInverseLengthParameter;
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| 118 | var fixedScale = HasFixedScaleParameter;
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[8982] | 119 | // create functions
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| 120 | var cov = new ParameterizedCovarianceFunction();
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| 121 | cov.Covariance = (x, i, j) => {
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| 122 | double dist = i == j
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| 123 | ? 0.0
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[13981] | 124 | : Math.Sqrt(Util.SqrDist(x, i, j, columnIndices, Math.Sqrt(d) * inverseLength));
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[8982] | 125 | return scale * m(d, dist);
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| 126 | };
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| 127 | cov.CrossCovariance = (x, xt, i, j) => {
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[13981] | 128 | double dist = Math.Sqrt(Util.SqrDist(x, i, xt, j, columnIndices, Math.Sqrt(d) * inverseLength));
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[8982] | 129 | return scale * m(d, dist);
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| 130 | };
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[10530] | 131 | cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, d, scale, inverseLength, columnIndices, fixedInverseLength, fixedScale);
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[8982] | 132 | return cov;
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| 133 | }
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[8582] | 134 |
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[8982] | 135 | private static double m(int d, double t) {
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[8562] | 136 | double f;
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| 137 | switch (d) {
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| 138 | case 1: { f = 1; break; }
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| 139 | case 3: { f = 1 + t; break; }
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| 140 | case 5: { f = 1 + t * (1 + t / 3.0); break; }
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| 141 | default: throw new InvalidOperationException();
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| 142 | }
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| 143 | return f * Math.Exp(-t);
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| 144 | }
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| 145 |
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[8982] | 146 | private static double dm(int d, double t) {
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[8562] | 147 | double df;
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| 148 | switch (d) {
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| 149 | case 1: { df = 1; break; }
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| 150 | case 3: { df = t; break; }
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| 151 | case 5: { df = t * (1 + t) / 3.0; break; }
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| 152 | default: throw new InvalidOperationException();
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| 153 | }
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| 154 | return df * t * Math.Exp(-t);
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| 155 | }
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| 156 |
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[13981] | 157 | private static IList<double> GetGradient(double[,] x, int i, int j, int d, double scale, double inverseLength, int[] columnIndices,
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[10530] | 158 | bool fixedInverseLength, bool fixedScale) {
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[8562] | 159 | double dist = i == j
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| 160 | ? 0.0
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[13981] | 161 | : Math.Sqrt(Util.SqrDist(x, i, j, columnIndices, Math.Sqrt(d) * inverseLength));
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[8562] | 162 |
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[13981] | 163 | var g = new List<double>(2);
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| 164 | if (!fixedInverseLength) g.Add(scale * dm(d, dist));
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| 165 | if (!fixedScale) g.Add(2 * scale * m(d, dist));
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| 166 | return g;
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[8562] | 167 | }
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| 168 | }
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| 169 | }
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