[8401] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[11170] | 3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8401] | 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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[8484] | 23 | using System.Collections.Generic;
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[8931] | 24 | using System.Linq;
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[8366] | 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 28 |
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[8371] | 29 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[8366] | 30 | [StorableClass]
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[8417] | 31 | [Item(Name = "CovarianceLinear", Description = "Linear covariance function for Gaussian processes.")]
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[8612] | 32 | public sealed class CovarianceLinear : Item, ICovarianceFunction {
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[8366] | 33 | [StorableConstructor]
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[8612] | 34 | private CovarianceLinear(bool deserializing) : base(deserializing) { }
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| 35 | private CovarianceLinear(CovarianceLinear original, Cloner cloner)
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[8366] | 36 | : base(original, cloner) {
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| 37 | }
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| 38 | public CovarianceLinear()
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| 39 | : base() {
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| 40 | }
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| 41 |
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| 42 | public override IDeepCloneable Clone(Cloner cloner) {
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| 43 | return new CovarianceLinear(this, cloner);
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| 44 | }
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| 45 |
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[8612] | 46 | public int GetNumberOfParameters(int numberOfVariables) {
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| 47 | return 0;
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| 48 | }
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| 49 |
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[8982] | 50 | public void SetParameter(double[] p) {
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| 51 | if (p.Length > 0) throw new ArgumentException("No parameters are allowed for the linear covariance function.");
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[8416] | 52 | }
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| 53 |
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[8982] | 54 | public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
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| 55 | if (p.Length > 0) throw new ArgumentException("No parameters are allowed for the linear covariance function.");
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| 56 | // create functions
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| 57 | var cov = new ParameterizedCovarianceFunction();
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| 58 | cov.Covariance = (x, i, j) => Util.ScalarProd(x, i, j, 1, columnIndices);
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| 59 | cov.CrossCovariance = (x, xt, i, j) => Util.ScalarProd(x, i, xt, j, 1.0 , columnIndices);
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| 60 | cov.CovarianceGradient = (x, i, j) => Enumerable.Empty<double>();
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| 61 | return cov;
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[8366] | 62 | }
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| 63 | }
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| 64 | }
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