Changeset 15973 for branches/2522_RefactorPluginInfrastructure/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSpectralMixture.cs
- Timestamp:
- 06/28/18 11:13:37 (6 years ago)
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- branches/2522_RefactorPluginInfrastructure
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- 4 edited
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branches/2522_RefactorPluginInfrastructure
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old new 24 24 protoc.exe 25 25 obj 26 .vs
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branches/2522_RefactorPluginInfrastructure/HeuristicLab.Algorithms.DataAnalysis
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branches/2522_RefactorPluginInfrastructure/HeuristicLab.Algorithms.DataAnalysis/3.4
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/stable/HeuristicLab.Algorithms.DataAnalysis/3.4 merged eligible /trunk/HeuristicLab.Algorithms.DataAnalysis/3.4 merged eligible /branches/1721-RandomForestPersistence/HeuristicLab.Algorithms.DataAnalysis/3.4 10321-10322 /branches/Async/HeuristicLab.Algorithms.DataAnalysis/3.4 13329-15286 /branches/Benchmarking/sources/HeuristicLab.Algorithms.DataAnalysis/3.4 6917-7005 /branches/ClassificationModelComparison/HeuristicLab.Algorithms.DataAnalysis/3.4 9070-13099 /branches/CloningRefactoring/HeuristicLab.Algorithms.DataAnalysis/3.4 4656-4721 /branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.4 5471-5808 /branches/DataAnalysis SolutionEnsembles/HeuristicLab.Algorithms.DataAnalysis/3.4 5815-6180 /branches/DataAnalysis/HeuristicLab.Algorithms.DataAnalysis/3.4 4458-4459,4462,4464 /branches/DataPreprocessing/HeuristicLab.Algorithms.DataAnalysis/3.4 10085-11101 /branches/GP.Grammar.Editor/HeuristicLab.Algorithms.DataAnalysis/3.4 6284-6795 /branches/GP.Symbols (TimeLag, Diff, Integral)/HeuristicLab.Algorithms.DataAnalysis/3.4 5060 /branches/HeuristicLab.DatasetRefactor/sources/HeuristicLab.Algorithms.DataAnalysis/3.4 11570-12508 /branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Algorithms.DataAnalysis/3.4 11130-12721 /branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis/3.4 13819-14091 /branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.4 8116-8789 /branches/LogResidualEvaluator/HeuristicLab.Algorithms.DataAnalysis/3.4 10202-10483 /branches/NET40/sources/HeuristicLab.Algorithms.DataAnalysis/3.4 5138-5162 /branches/ParallelEngine/HeuristicLab.Algorithms.DataAnalysis/3.4 5175-5192 /branches/ProblemInstancesRegressionAndClassification/HeuristicLab.Algorithms.DataAnalysis/3.4 7773-7810 /branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.4 6350-6627 /branches/Restructure trunk solution/HeuristicLab.Algorithms.DataAnalysis/3.4 6828 /branches/SpectralKernelForGaussianProcesses/HeuristicLab.Algorithms.DataAnalysis/3.4 10204-10479 /branches/SuccessProgressAnalysis/HeuristicLab.Algorithms.DataAnalysis/3.4 5370-5682 /branches/Trunk/HeuristicLab.Algorithms.DataAnalysis/3.4 6829-6865 /branches/VNS/HeuristicLab.Algorithms.DataAnalysis/3.4 5594-5752 /branches/Weighted TSNE/3.4 15451-15531 /branches/histogram/HeuristicLab.Algorithms.DataAnalysis/3.4 5959-6341 /branches/symbreg-factors-2650/HeuristicLab.Algorithms.DataAnalysis/3.4 14232-14825 /trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4 13402-15674
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branches/2522_RefactorPluginInfrastructure/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSpectralMixture.cs
r12012 r15973 1 1 #region License Information 2 2 /* HeuristicLab 3 * Copyright (C) 2002-201 5Heuristic and Evolutionary Algorithms Laboratory (HEAL)3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL) 4 4 * 5 5 * This file is part of HeuristicLab. … … 23 23 using System.Collections.Generic; 24 24 using System.Linq; 25 using System.Linq.Expressions;26 25 using HeuristicLab.Common; 27 26 using HeuristicLab.Core; … … 131 130 } 132 131 133 public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int>columnIndices) {132 public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) { 134 133 double[] weight, frequency, lengthScale; 135 134 GetParameterValues(p, out weight, out frequency, out lengthScale); … … 152 151 } 153 152 154 private static double GetCovariance(double[,] x, double[,] xt, int i, int j, int maxQ, double[] weight, double[] frequency, double[] lengthScale, IEnumerable<int>columnIndices) {153 private static double GetCovariance(double[,] x, double[,] xt, int i, int j, int maxQ, double[] weight, double[] frequency, double[] lengthScale, int[] columnIndices) { 155 154 // tau = x - x' (only for selected variables) 156 155 double[] tau = … … 164 163 int idx = 0; // helper index for tau 165 164 // for each selected variable 166 foreach (var c in columnIndices) { 167 kc *= f1(tau[idx], lengthScale[q * numberOfVariables + c]) * f2(tau[idx], frequency[q * numberOfVariables + c]); 165 for (int c = 0; c < columnIndices.Length; c++) { 166 var col = columnIndices[c]; 167 kc *= f1(tau[idx], lengthScale[q * numberOfVariables + col]) * f2(tau[idx], frequency[q * numberOfVariables + col]); 168 168 idx++; 169 169 } … … 181 181 182 182 // order of returned gradients must match the order in GetParameterValues! 183 private static I Enumerable<double> GetGradient(double[,] x, int i, int j, int maxQ, double[] weight, double[] frequency, double[] lengthScale, IEnumerable<int>columnIndices,183 private static IList<double> GetGradient(double[,] x, int i, int j, int maxQ, double[] weight, double[] frequency, double[] lengthScale, int[] columnIndices, 184 184 bool fixedWeight, bool fixedFrequency, bool fixedLengthScale) { 185 185 double[] tau = Util.GetRow(x, i, columnIndices).Zip(Util.GetRow(x, j, columnIndices), (xi, xj) => xi - xj).ToArray(); 186 186 int numberOfVariables = lengthScale.Length / maxQ; 187 187 188 var g = new List<double>((!fixedWeight ? maxQ : 0) + (!fixedFrequency ? maxQ * columnIndices.Length : 0) + (!fixedLengthScale ? maxQ * columnIndices.Length : 0)); 188 189 if (!fixedWeight) { 189 190 // weight … … 193 194 int idx = 0; // helper index for tau 194 195 // for each selected variable 195 foreach (var c in columnIndices) { 196 k *= f1(tau[idx], lengthScale[q * numberOfVariables + c]) * f2(tau[idx], frequency[q * numberOfVariables + c]); 196 for (int c = 0; c < columnIndices.Length; c++) { 197 var col = columnIndices[c]; 198 k *= f1(tau[idx], lengthScale[q * numberOfVariables + col]) * f2(tau[idx], frequency[q * numberOfVariables + col]); 197 199 idx++; 198 200 } 199 yield return k;201 g.Add(k); 200 202 } 201 203 } … … 212 214 Math.Sin(2 * Math.PI * tau[idx] * frequency[q * numberOfVariables + c]); 213 215 idx++; 214 yield return weight[q] * k;216 g.Add(weight[q] * k); 215 217 } 216 218 } … … 228 230 f2(tau[idx], frequency[q * numberOfVariables + c]); 229 231 idx++; 230 yield return weight[q] * k;232 g.Add(weight[q] * k); 231 233 } 232 234 } 233 235 } 236 237 return g; 234 238 } 235 239 }
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