1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 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.Parameters;
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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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35 | public class CovarianceMaternIso : CovarianceFunction {
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36 | public IValueParameter<DoubleValue> ScaleParameter {
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37 | get { return scaleParameter; }
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38 | }
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39 | public IValueParameter<DoubleValue> InverseLengthParameter {
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40 | get { return inverseLengthParameter; }
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41 | }
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42 | public IConstrainedValueParameter<IntValue> DParameter {
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43 | get { return dParameter; }
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44 | }
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45 |
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46 | [Storable]
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47 | private readonly HyperParameter<DoubleValue> inverseLengthParameter;
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48 | [Storable]
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49 | private readonly HyperParameter<DoubleValue> scaleParameter;
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50 | [Storable]
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51 | private readonly ConstrainedValueParameter<IntValue> dParameter;
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52 |
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53 | [Storable]
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54 | private double inverseLength;
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55 | [Storable]
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56 | private double sf2;
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57 | [Storable]
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58 | private int d;
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59 |
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60 | [StorableConstructor]
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61 | protected CovarianceMaternIso(bool deserializing)
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62 | : base(deserializing) {
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63 | }
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64 |
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65 | protected CovarianceMaternIso(CovarianceMaternIso original, Cloner cloner)
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66 | : base(original, cloner) {
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67 | this.scaleParameter = cloner.Clone(original.scaleParameter);
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68 | this.sf2 = original.sf2;
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69 | this.inverseLengthParameter = cloner.Clone(original.inverseLengthParameter);
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70 | this.inverseLength = original.inverseLength;
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71 | this.dParameter = cloner.Clone(original.dParameter);
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72 | this.d = original.d;
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73 | RegisterEvents();
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74 | }
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75 |
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76 | public CovarianceMaternIso()
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77 | : base() {
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78 | inverseLengthParameter = new HyperParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric Matern covariance function.");
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79 | scaleParameter = new HyperParameter<DoubleValue>("Scale", "The scale parameter of the isometric Matern covariance function.");
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80 | var validDValues = new ItemSet<IntValue>();
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81 | validDValues.Add((IntValue)new IntValue(1).AsReadOnly());
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82 | validDValues.Add((IntValue)new IntValue(3).AsReadOnly());
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83 | validDValues.Add((IntValue)new IntValue(5).AsReadOnly());
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84 | dParameter = 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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85 | d = dParameter.Value.Value;
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86 |
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87 | Parameters.Add(inverseLengthParameter);
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88 | Parameters.Add(scaleParameter);
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89 | Parameters.Add(dParameter);
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90 |
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91 | RegisterEvents();
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92 | }
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93 |
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94 | [StorableHook(HookType.AfterDeserialization)]
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95 | private void AfterDeserialization() {
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96 | RegisterEvents();
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97 | }
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98 |
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99 | public override IDeepCloneable Clone(Cloner cloner) {
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100 | return new CovarianceMaternIso(this, cloner);
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101 | }
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102 |
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103 | // caching
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104 | private void RegisterEvents() {
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105 | AttachValueChangeHandler<DoubleValue, double>(inverseLengthParameter, () => { inverseLength = inverseLengthParameter.Value.Value; });
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106 | AttachValueChangeHandler<DoubleValue, double>(scaleParameter, () => { sf2 = scaleParameter.Value.Value; });
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107 | AttachValueChangeHandler<IntValue, int>(dParameter, () => { d = dParameter.Value.Value; });
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108 | }
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109 |
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110 | public override int GetNumberOfParameters(int numberOfVariables) {
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111 | return
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112 | (inverseLengthParameter.Fixed ? 0 : 1) +
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113 | (scaleParameter.Fixed ? 0 : 1);
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114 | }
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115 |
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116 | public override void SetParameter(double[] hyp) {
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117 | int i = 0;
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118 | if (!inverseLengthParameter.Fixed) {
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119 | inverseLengthParameter.SetValue(new DoubleValue(1.0 / Math.Exp(hyp[i])));
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120 | i++;
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121 | }
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122 | if (!scaleParameter.Fixed) {
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123 | scaleParameter.SetValue(new DoubleValue(Math.Exp(2 * hyp[i])));
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124 | i++;
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125 | }
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126 | if (hyp.Length != i) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceMaternIso", "hyp");
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127 | }
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128 |
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129 |
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130 | private double m(double t) {
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131 | double f;
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132 | switch (d) {
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133 | case 1: { f = 1; break; }
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134 | case 3: { f = 1 + t; break; }
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135 | case 5: { f = 1 + t * (1 + t / 3.0); break; }
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136 | default: throw new InvalidOperationException();
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137 | }
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138 | return f * Math.Exp(-t);
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139 | }
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140 |
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141 | private double dm(double t) {
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142 | double df;
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143 | switch (d) {
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144 | case 1: { df = 1; break; }
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145 | case 3: { df = t; break; }
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146 | case 5: { df = t * (1 + t) / 3.0; break; }
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147 | default: throw new InvalidOperationException();
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148 | }
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149 | return df * t * Math.Exp(-t);
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150 | }
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151 |
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152 | public override double GetCovariance(double[,] x, int i, int j) {
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153 | double dist = i == j
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154 | ? 0.0
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155 | : Math.Sqrt(Util.SqrDist(x, i, j, Math.Sqrt(d) * inverseLength));
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156 | return sf2 * m(dist);
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157 | }
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158 |
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159 | public override IEnumerable<double> GetGradient(double[,] x, int i, int j) {
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160 | double dist = i == j
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161 | ? 0.0
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162 | : Math.Sqrt(Util.SqrDist(x, i, j, Math.Sqrt(d) * inverseLength));
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163 |
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164 | yield return sf2 * dm(dist);
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165 | yield return 2 * sf2 * m(dist);
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166 | }
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167 |
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168 | public override double GetCrossCovariance(double[,] x, double[,] xt, int i, int j) {
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169 | double dist = Math.Sqrt(Util.SqrDist(x, i, xt, j, Math.Sqrt(d) * inverseLength));
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170 | return sf2 * m(dist);
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171 | }
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172 | }
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173 | }
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