[8678] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[14186] | 3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8678] | 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.Linq;
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| 23 | using HeuristicLab.Common;
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| 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Parameters;
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| 27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 28 |
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| 29 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 30 | [StorableClass]
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| 31 | [Item(Name = "CovarianceMask",
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| 32 | Description = "Masking covariance function for dimension selection can be used to apply a covariance function only on certain input dimensions.")]
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| 33 | public sealed class CovarianceMask : ParameterizedNamedItem, ICovarianceFunction {
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| 34 | public IValueParameter<IntArray> SelectedDimensionsParameter {
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[8982] | 35 | get { return (IValueParameter<IntArray>)Parameters["SelectedDimensions"]; }
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[8678] | 36 | }
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| 37 | public IValueParameter<ICovarianceFunction> CovarianceFunctionParameter {
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[8982] | 38 | get { return (IValueParameter<ICovarianceFunction>)Parameters["CovarianceFunction"]; }
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[8678] | 39 | }
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| 40 |
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| 41 | [StorableConstructor]
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| 42 | private CovarianceMask(bool deserializing)
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| 43 | : base(deserializing) {
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| 44 | }
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| 45 |
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| 46 | private CovarianceMask(CovarianceMask original, Cloner cloner)
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| 47 | : base(original, cloner) {
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| 48 | }
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| 49 |
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| 50 | public CovarianceMask()
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| 51 | : base() {
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| 52 | Name = ItemName;
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| 53 | Description = ItemDescription;
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| 54 |
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[8982] | 55 | Parameters.Add(new OptionalValueParameter<IntArray>("SelectedDimensions", "The dimensions on which the specified covariance function should be applied to."));
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| 56 | Parameters.Add(new ValueParameter<ICovarianceFunction>("CovarianceFunction", "The covariance function that should be scaled.", new CovarianceSquaredExponentialIso()));
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[8678] | 57 | }
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| 58 |
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| 59 | public override IDeepCloneable Clone(Cloner cloner) {
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| 60 | return new CovarianceMask(this, cloner);
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| 61 | }
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| 62 |
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| 63 | public int GetNumberOfParameters(int numberOfVariables) {
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[8982] | 64 | if (SelectedDimensionsParameter.Value == null) return CovarianceFunctionParameter.Value.GetNumberOfParameters(numberOfVariables);
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| 65 | else return CovarianceFunctionParameter.Value.GetNumberOfParameters(SelectedDimensionsParameter.Value.Length);
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[8678] | 66 | }
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| 67 |
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[8982] | 68 | public void SetParameter(double[] p) {
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| 69 | CovarianceFunctionParameter.Value.SetParameter(p);
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[8678] | 70 | }
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| 71 |
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[13981] | 72 | public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
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[8982] | 73 | var cov = CovarianceFunctionParameter.Value;
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| 74 | var selectedDimensions = SelectedDimensionsParameter.Value;
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[8933] | 75 |
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[13981] | 76 | return cov.GetParameterizedCovarianceFunction(p, selectedDimensions.Intersect(columnIndices).ToArray());
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[8678] | 77 | }
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| 78 | }
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| 79 | }
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