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.Algorithms.DataAnalysis;
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26 | using HeuristicLab.Data;
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27 |
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28 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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29 | public class InstanceProvider : ArtificialRegressionInstanceProvider {
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30 | public override string Name {
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31 | get { return "GPR Benchmark Problems"; }
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32 | }
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33 | public override string Description {
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34 | get { return ""; }
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35 | }
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36 | public override Uri WebLink {
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37 | get { return new Uri("http://dev.heuristiclab.com/trac/hl/core/wiki/AdditionalMaterial"); }
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38 | }
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39 | public override string ReferencePublication {
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40 | get { return ""; }
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41 | }
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42 |
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43 | public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
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44 | List<IDataDescriptor> descriptorList = new List<IDataDescriptor>();
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45 | descriptorList.Add(new GaussianProcessSEIso());
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46 | descriptorList.Add(new GaussianProcessSEIso1());
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47 | descriptorList.Add(new GaussianProcessSEIso2());
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48 | descriptorList.Add(new GaussianProcessSEIso3());
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49 | descriptorList.Add(new GaussianProcessSEIso4());
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50 | descriptorList.Add(new GaussianProcessSEIso5());
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51 | descriptorList.Add(new GaussianProcessSEIso6());
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52 | descriptorList.Add(new GaussianProcessPolyTen());
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53 | descriptorList.Add(new GaussianProcessSEIsoDependentNoise());
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54 |
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55 | {
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56 | var cov = new CovarianceSum();
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57 | cov.Terms.Add(new CovarianceSquaredExponentialIso());
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58 | cov.Terms.Add(new CovarianceSquaredExponentialIso());
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59 | cov.Terms.Add(new CovarianceNoise());
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60 | var hyp = new double[] { -2.8, -0.1, 0.5, 0.3, -1.5 };
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61 | descriptorList.Add(new GaussianProcessRegressionInstance("SE+SE", cov, hyp));
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62 | }
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63 | {
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64 | var cov = new CovarianceSum();
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65 | cov.Terms.Add(new CovarianceRationalQuadraticIso());
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66 | cov.Terms.Add(new CovarianceRationalQuadraticIso());
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67 | cov.Terms.Add(new CovarianceNoise());
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68 | var hyp = new double[] { -3, 0, 0, -1.5, 0, 2.5, -1.5 };
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69 | descriptorList.Add(new GaussianProcessRegressionInstance("RQ+RQ", cov, hyp));
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70 | }
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71 | {
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72 | var cov = new CovarianceSum();
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73 | cov.Terms.Add(new CovariancePeriodic());
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74 | cov.Terms.Add(new CovariancePeriodic());
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75 | cov.Terms.Add(new CovarianceNoise());
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76 | var hyp = new double[] { 0, -1.8, -1.5, 0, -0.5, -1, -2.1 };
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77 | descriptorList.Add(new GaussianProcessRegressionInstance("Periodic+Periodic", cov, hyp));
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78 | }
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79 | {
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80 | var cov = new CovarianceSum();
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81 | cov.Terms.Add(new CovarianceMaternIso());
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82 | cov.Terms.Add(new CovarianceMaternIso());
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83 | cov.Terms.Add(new CovarianceNoise());
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84 | var hyp = new double[] { 0, 0, -1, 1, -4 };
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85 | descriptorList.Add(new GaussianProcessRegressionInstance("Matern1+Matern1", cov, hyp));
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86 | }
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87 | {
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88 | var cov = new CovarianceSum();
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89 | var m1 = new CovarianceMaternIso();
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90 | m1.DParameter.Value = m1.DParameter.ValidValues.First(v => v.Value == 3);
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91 | var m2 = new CovarianceMaternIso();
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92 | m2.DParameter.Value = m2.DParameter.ValidValues.First(v => v.Value == 3);
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93 | cov.Terms.Add(m1);
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94 | cov.Terms.Add(m2);
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95 | cov.Terms.Add(new CovarianceNoise());
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96 | var hyp = new double[] { -2.7, 0, -1, 1, -1.5 };
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97 | descriptorList.Add(new GaussianProcessRegressionInstance("Matern3+Matern3", cov, hyp));
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98 | }
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99 | {
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100 | var cov = new CovarianceSum();
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101 | cov.Terms.Add(new CovarianceRationalQuadraticIso());
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102 | cov.Terms.Add(new CovarianceSquaredExponentialIso());
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103 | cov.Terms.Add(new CovarianceNoise());
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104 | var hyp = new double[] { -1.5, -0.5, -3, -1, -1, -3 };
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105 | descriptorList.Add(new GaussianProcessRegressionInstance("RQ+SE", cov, hyp));
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106 | }
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107 | {
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108 | var cov = new CovarianceSum();
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109 | cov.Terms.Add(new CovarianceSquaredExponentialIso());
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110 | var prod = new CovarianceProduct();
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111 | prod.Factors.Add(new CovarianceLinear());
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112 | prod.Factors.Add(new CovarianceNoise());
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113 | cov.Terms.Add(prod);
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114 | cov.Terms.Add(new CovarianceNoise());
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115 | var hyp = new double[] { -3, 0, 0, -1.5 };
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116 | descriptorList.Add(new GaussianProcessRegressionInstance("SE+Linear*Noise", cov, hyp));
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117 | }
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118 | {
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119 | var cov = new CovarianceSum();
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120 | cov.Terms.Add(new CovarianceSquaredExponentialIso());
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121 | cov.Terms.Add(new CovariancePeriodic());
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122 | cov.Terms.Add(new CovarianceNoise());
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123 | var hyp = new double[] { -1, 0, 0, -1.5, 0, -2 };
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124 | descriptorList.Add(new GaussianProcessRegressionInstance("SE+Periodic", cov, hyp));
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125 | }
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126 |
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127 | {
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128 | var cov = new CovarianceSum();
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129 | cov.Terms.Add(new CovarianceSquaredExponentialIso());
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130 | cov.Terms.Add(new CovarianceNoise());
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131 | var hyp = new double[] { -2.5, 0, -7 };
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132 | descriptorList.Add(new GaussianProcessRegressionInstance1D("1D: SE", cov, hyp));
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133 | }
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134 | {
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135 | var cov = new CovarianceSum();
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136 | cov.Terms.Add(new CovarianceRationalQuadraticIso());
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137 | cov.Terms.Add(new CovarianceNoise());
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138 | var hyp = new double[] { -2.5, 0, -2, -7 };
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139 | descriptorList.Add(new GaussianProcessRegressionInstance1D("1D: RQ", cov, hyp));
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140 | }
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141 | {
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142 | var cov = new CovarianceSum();
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143 | cov.Terms.Add(new CovarianceMaternIso());
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144 | cov.Terms.Add(new CovarianceNoise());
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145 | var hyp = new double[] { -2, 0, -7 };
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146 | descriptorList.Add(new GaussianProcessRegressionInstance1D("1D: Matern1", cov, hyp));
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147 | }
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148 | {
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149 | var cov = new CovarianceSum();
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150 | cov.Terms.Add(new CovariancePeriodic());
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151 | cov.Terms.Add(new CovarianceNoise());
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152 | var hyp = new double[] { 0, -1.3, 0, -7 };
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153 | descriptorList.Add(new GaussianProcessRegressionInstance1D("1D: Periodic", cov, hyp));
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154 | }
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155 | {
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156 | var cov = new CovarianceSum();
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157 | cov.Terms.Add(new CovarianceSquaredExponentialIso());
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158 | cov.Terms.Add(new CovarianceNoise());
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159 | var hyp = new double[] { -2.5, 0, -7 };
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160 | descriptorList.Add(new GaussianProcessRegressionInstance1D("1D: SE", cov, hyp));
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161 | }
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162 | {
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163 | var cov = new CovarianceSum();
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164 | var m1 = new CovarianceMask();
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165 | m1.SelectedDimensionsParameter.Value = new IntArray(new int[] { 0 });
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166 | m1.CovarianceFunctionParameter.Value = new CovarianceSquaredExponentialIso();
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167 |
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168 | var m2 = new CovarianceMask();
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169 | m2.SelectedDimensionsParameter.Value = new IntArray(new int[] { 1 });
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170 | m2.CovarianceFunctionParameter.Value = new CovarianceRationalQuadraticIso();
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171 |
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172 | cov.Terms.Add(m1);
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173 | cov.Terms.Add(m2);
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174 | cov.Terms.Add(new CovarianceNoise());
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175 | var hyp = new double[] { -2.5, 0, -2.0, 0, -2, -7 };
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176 | descriptorList.Add(new GaussianProcessRegressionInstance2D("2D: SE+RQ", cov, hyp));
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177 | }
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178 | {
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179 | var cov = new CovarianceSum();
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180 | var m1 = new CovarianceMask();
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181 | m1.SelectedDimensionsParameter.Value = new IntArray(new int[] { 0 });
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182 | m1.CovarianceFunctionParameter.Value = new CovarianceSquaredExponentialIso();
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183 |
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184 | var m2 = new CovarianceMask();
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185 | m2.SelectedDimensionsParameter.Value = new IntArray(new int[] { 1 });
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186 | m2.CovarianceFunctionParameter.Value = new CovarianceMaternIso();
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187 |
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188 | cov.Terms.Add(m1);
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189 | cov.Terms.Add(m2);
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190 | cov.Terms.Add(new CovarianceNoise());
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191 | var hyp = new double[] { -2.5, 0, 2, 0, -7 };
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192 | descriptorList.Add(new GaussianProcessRegressionInstance2D("2D: SE+Matern1", cov, hyp));
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193 | }
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194 | {
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195 | var cov = new CovarianceSum();
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196 | var m1 = new CovarianceMask();
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197 | m1.SelectedDimensionsParameter.Value = new IntArray(new int[] { 0 });
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198 | m1.CovarianceFunctionParameter.Value = new CovarianceSquaredExponentialIso();
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199 |
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200 | var m2 = new CovarianceMask();
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201 | m2.SelectedDimensionsParameter.Value = new IntArray(new int[] { 1 });
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202 | m2.CovarianceFunctionParameter.Value = new CovariancePeriodic();
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203 |
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204 | cov.Terms.Add(m1);
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205 | cov.Terms.Add(m2);
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206 | cov.Terms.Add(new CovarianceNoise());
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207 | var hyp = new double[] { -2.5, 0, 0, -1.3, 0, -7 };
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208 | descriptorList.Add(new GaussianProcessRegressionInstance2D("2D: SE+Periodic", cov, hyp));
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209 | }
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210 |
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211 |
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212 |
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213 | return descriptorList;
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214 | }
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215 | }
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216 | }
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217 |
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