[8416] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
[15584] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
[8416] | 4 | *
|
---|
| 5 | * This file is part of HeuristicLab.
|
---|
| 6 | *
|
---|
| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
|
---|
| 8 | * it under the terms of the GNU General Public License as published by
|
---|
| 9 | * the Free Software Foundation, either version 3 of the License, or
|
---|
| 10 | * (at your option) any later version.
|
---|
| 11 | *
|
---|
| 12 | * HeuristicLab is distributed in the hope that it will be useful,
|
---|
| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
|
---|
| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
---|
| 15 | * GNU General Public License for more details.
|
---|
| 16 | *
|
---|
| 17 | * You should have received a copy of the GNU General Public License
|
---|
| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
|
---|
| 19 | */
|
---|
| 20 | #endregion
|
---|
| 21 |
|
---|
[8463] | 22 | using System;
|
---|
| 23 | using System.Collections.Generic;
|
---|
[8323] | 24 | using System.Linq;
|
---|
[8366] | 25 | using HeuristicLab.Common;
|
---|
| 26 | using HeuristicLab.Core;
|
---|
| 27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
[8323] | 28 |
|
---|
[8416] | 29 | namespace HeuristicLab.Algorithms.DataAnalysis {
|
---|
[8366] | 30 | [StorableClass]
|
---|
| 31 | [Item(Name = "CovarianceSum",
|
---|
| 32 | Description = "Sum covariance function for Gaussian processes.")]
|
---|
[8612] | 33 | public sealed class CovarianceSum : Item, ICovarianceFunction {
|
---|
[8366] | 34 | [Storable]
|
---|
| 35 | private ItemList<ICovarianceFunction> terms;
|
---|
[8323] | 36 |
|
---|
[8366] | 37 | [Storable]
|
---|
| 38 | private int numberOfVariables;
|
---|
| 39 | public ItemList<ICovarianceFunction> Terms {
|
---|
| 40 | get { return terms; }
|
---|
[8323] | 41 | }
|
---|
| 42 |
|
---|
[8366] | 43 | [StorableConstructor]
|
---|
[8612] | 44 | private CovarianceSum(bool deserializing)
|
---|
[8366] | 45 | : base(deserializing) {
|
---|
[8323] | 46 | }
|
---|
| 47 |
|
---|
[8612] | 48 | private CovarianceSum(CovarianceSum original, Cloner cloner)
|
---|
[8366] | 49 | : base(original, cloner) {
|
---|
[8416] | 50 | this.terms = cloner.Clone(original.terms);
|
---|
| 51 | this.numberOfVariables = original.numberOfVariables;
|
---|
[8323] | 52 | }
|
---|
| 53 |
|
---|
[8366] | 54 | public CovarianceSum()
|
---|
| 55 | : base() {
|
---|
[8416] | 56 | this.terms = new ItemList<ICovarianceFunction>();
|
---|
[8323] | 57 | }
|
---|
| 58 |
|
---|
[8366] | 59 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 60 | return new CovarianceSum(this, cloner);
|
---|
| 61 | }
|
---|
| 62 |
|
---|
| 63 | public int GetNumberOfParameters(int numberOfVariables) {
|
---|
| 64 | this.numberOfVariables = numberOfVariables;
|
---|
| 65 | return terms.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
|
---|
| 66 | }
|
---|
| 67 |
|
---|
[8982] | 68 | public void SetParameter(double[] p) {
|
---|
[8366] | 69 | int offset = 0;
|
---|
| 70 | foreach (var t in terms) {
|
---|
[8416] | 71 | var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
|
---|
[8982] | 72 | t.SetParameter(p.Skip(offset).Take(numberOfParameters).ToArray());
|
---|
[8416] | 73 | offset += numberOfParameters;
|
---|
[8323] | 74 | }
|
---|
| 75 | }
|
---|
[8484] | 76 |
|
---|
[13981] | 77 | public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
|
---|
[8982] | 78 | if (terms.Count == 0) throw new ArgumentException("at least one term is necessary for the product covariance function.");
|
---|
| 79 | var functions = new List<ParameterizedCovarianceFunction>();
|
---|
| 80 | foreach (var t in terms) {
|
---|
| 81 | var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
|
---|
| 82 | functions.Add(t.GetParameterizedCovarianceFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
|
---|
| 83 | p = p.Skip(numberOfParameters).ToArray();
|
---|
| 84 | }
|
---|
[8323] | 85 |
|
---|
[8982] | 86 | var sum = new ParameterizedCovarianceFunction();
|
---|
| 87 | sum.Covariance = (x, i, j) => functions.Select(e => e.Covariance(x, i, j)).Sum();
|
---|
| 88 | sum.CrossCovariance = (x, xt, i, j) => functions.Select(e => e.CrossCovariance(x, xt, i, j)).Sum();
|
---|
[13981] | 89 | sum.CovarianceGradient = (x, i, j) => {
|
---|
| 90 | var g = new List<double>();
|
---|
| 91 | foreach (var e in functions)
|
---|
| 92 | g.AddRange(e.CovarianceGradient(x, i, j));
|
---|
| 93 | return g;
|
---|
| 94 | };
|
---|
[8982] | 95 | return sum;
|
---|
[8366] | 96 | }
|
---|
[8323] | 97 | }
|
---|
| 98 | }
|
---|