[8620] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17181] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8620] | 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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[17097] | 29 | using HEAL.Attic;
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[8620] | 30 |
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| 31 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[17097] | 32 | [StorableType("4871900E-8B7A-4D74-969A-773D63198733")]
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[8620] | 33 | [Item(Name = "CovarianceScale",
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| 34 | Description = "Scale covariance function for Gaussian processes.")]
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| 35 | public sealed class CovarianceScale : ParameterizedNamedItem, ICovarianceFunction {
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| 36 | public IValueParameter<DoubleValue> ScaleParameter {
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[8982] | 37 | get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
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[8620] | 38 | }
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[10530] | 39 | private bool HasFixedScaleParameter {
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| 40 | get { return ScaleParameter.Value != null; }
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| 41 | }
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[8620] | 42 |
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| 43 | public IValueParameter<ICovarianceFunction> CovarianceFunctionParameter {
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[8982] | 44 | get { return (IValueParameter<ICovarianceFunction>)Parameters["CovarianceFunction"]; }
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[8620] | 45 | }
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| 46 |
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| 47 | [StorableConstructor]
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[17097] | 48 | private CovarianceScale(StorableConstructorFlag _) : base(_) {
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[8620] | 49 | }
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| 50 |
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| 51 | private CovarianceScale(CovarianceScale original, Cloner cloner)
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| 52 | : base(original, cloner) {
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| 53 | }
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| 54 |
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| 55 | public CovarianceScale()
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| 56 | : base() {
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| 57 | Name = ItemName;
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| 58 | Description = ItemDescription;
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| 59 |
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[8982] | 60 | Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter."));
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| 61 | Parameters.Add(new ValueParameter<ICovarianceFunction>("CovarianceFunction", "The covariance function that should be scaled.", new CovarianceSquaredExponentialIso()));
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[8620] | 62 | }
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| 63 |
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| 64 | public override IDeepCloneable Clone(Cloner cloner) {
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| 65 | return new CovarianceScale(this, cloner);
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| 66 | }
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| 67 |
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[8982] | 68 | public int GetNumberOfParameters(int numberOfVariables) {
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[10530] | 69 | return (HasFixedScaleParameter ? 0 : 1) + CovarianceFunctionParameter.Value.GetNumberOfParameters(numberOfVariables);
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[8620] | 70 | }
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| 71 |
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[8982] | 72 | public void SetParameter(double[] p) {
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| 73 | double scale;
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| 74 | GetParameterValues(p, out scale);
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| 75 | ScaleParameter.Value = new DoubleValue(scale);
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| 76 | CovarianceFunctionParameter.Value.SetParameter(p.Skip(1).ToArray());
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[8620] | 77 | }
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| 78 |
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[8982] | 79 | private void GetParameterValues(double[] p, out double scale) {
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| 80 | // gather parameter values
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[10530] | 81 | if (HasFixedScaleParameter) {
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[8982] | 82 | scale = ScaleParameter.Value.Value;
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| 83 | } else {
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| 84 | scale = Math.Exp(2 * p[0]);
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[8620] | 85 | }
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| 86 | }
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| 87 |
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[13981] | 88 | public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
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[8982] | 89 | double scale;
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| 90 | GetParameterValues(p, out scale);
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[10530] | 91 | var fixedScale = HasFixedScaleParameter;
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[8982] | 92 | var subCov = CovarianceFunctionParameter.Value.GetParameterizedCovarianceFunction(p.Skip(1).ToArray(), columnIndices);
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| 93 | // create functions
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| 94 | var cov = new ParameterizedCovarianceFunction();
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| 95 | cov.Covariance = (x, i, j) => scale * subCov.Covariance(x, i, j);
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| 96 | cov.CrossCovariance = (x, xt, i, j) => scale * subCov.CrossCovariance(x, xt, i, j);
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[10530] | 97 | cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, subCov, fixedScale);
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[8982] | 98 | return cov;
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[8620] | 99 | }
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| 100 |
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[13981] | 101 | private static IList<double> GetGradient(double[,] x, int i, int j, int[] columnIndices, double scale, ParameterizedCovarianceFunction cov,
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[10530] | 102 | bool fixedScale) {
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[13981] | 103 | var gr = new List<double>((!fixedScale ? 1 : 0) + cov.CovarianceGradient(x, i, j).Count);
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[10530] | 104 | if (!fixedScale) {
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[13981] | 105 | gr.Add(2 * scale * cov.Covariance(x, i, j));
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[10530] | 106 | }
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[8982] | 107 | foreach (var g in cov.CovarianceGradient(x, i, j))
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[13981] | 108 | gr.Add(scale * g);
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| 109 | return gr;
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[8620] | 110 | }
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| 111 | }
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| 112 | }
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