[8439] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8439] | 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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[8982] | 21 |
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| 22 | using System.Collections.Generic;
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[8439] | 23 | using System.Linq;
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| 24 | using HeuristicLab.Common;
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| 25 | using HeuristicLab.Core;
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| 26 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 27 |
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| 28 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 29 | [StorableClass]
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| 30 | [Item(Name = "MeanSum", Description = "Sum of mean functions for Gaussian processes.")]
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[8612] | 31 | public sealed class MeanSum : Item, IMeanFunction {
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[8439] | 32 | [Storable]
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| 33 | private ItemList<IMeanFunction> terms;
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| 34 |
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| 35 | [Storable]
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| 36 | private int numberOfVariables;
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| 37 | public ItemList<IMeanFunction> Terms {
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| 38 | get { return terms; }
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| 39 | }
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| 40 |
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| 41 | [StorableConstructor]
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[8612] | 42 | private MeanSum(bool deserializing) : base(deserializing) { }
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| 43 | private MeanSum(MeanSum original, Cloner cloner)
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[8439] | 44 | : base(original, cloner) {
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| 45 | this.terms = cloner.Clone(original.terms);
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| 46 | this.numberOfVariables = original.numberOfVariables;
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| 47 | }
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| 48 | public MeanSum() {
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| 49 | this.terms = new ItemList<IMeanFunction>();
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| 50 | }
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| 51 |
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[8612] | 52 | public override IDeepCloneable Clone(Cloner cloner) {
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| 53 | return new MeanSum(this, cloner);
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| 54 | }
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| 55 |
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| 56 | public int GetNumberOfParameters(int numberOfVariables) {
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| 57 | this.numberOfVariables = numberOfVariables;
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| 58 | return terms.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
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| 59 | }
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| 60 |
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[8982] | 61 | public void SetParameter(double[] p) {
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[8439] | 62 | int offset = 0;
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| 63 | foreach (var t in terms) {
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| 64 | var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
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[8982] | 65 | t.SetParameter(p.Skip(offset).Take(numberOfParameters).ToArray());
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[8439] | 66 | offset += numberOfParameters;
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| 67 | }
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| 68 | }
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| 69 |
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[8982] | 70 | public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, IEnumerable<int> columnIndices) {
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| 71 | var termMf = new List<ParameterizedMeanFunction>();
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| 72 | int totalNumberOfParameters = GetNumberOfParameters(numberOfVariables);
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| 73 | int[] termIndexMap = new int[totalNumberOfParameters]; // maps k-th parameter to the correct mean-term
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| 74 | int[] hyperParameterIndexMap = new int[totalNumberOfParameters]; // maps k-th parameter to the l-th parameter of the correct mean-term
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| 75 | int c = 0;
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| 76 | // get the parameterized mean function for each term
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| 77 | for (int termIndex = 0; termIndex < terms.Count; termIndex++) {
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| 78 | var numberOfParameters = terms[termIndex].GetNumberOfParameters(numberOfVariables);
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| 79 | termMf.Add(terms[termIndex].GetParameterizedMeanFunction(p.Take(numberOfParameters).ToArray(), columnIndices));
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| 80 | p = p.Skip(numberOfParameters).ToArray();
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| 81 |
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| 82 | for (int hyperParameterIndex = 0; hyperParameterIndex < numberOfParameters; hyperParameterIndex++) {
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| 83 | termIndexMap[c] = termIndex;
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| 84 | hyperParameterIndexMap[c] = hyperParameterIndex;
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| 85 | c++;
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| 86 | }
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[8439] | 87 | }
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| 88 |
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[8982] | 89 | var mf = new ParameterizedMeanFunction();
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| 90 | mf.Mean = (x, i) => termMf.Select(t => t.Mean(x, i)).Sum();
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| 91 | mf.Gradient = (x, i, k) => {
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| 92 | return termMf[termIndexMap[k]].Gradient(x, i, hyperParameterIndexMap[k]);
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| 93 | };
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| 94 | return mf;
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[8439] | 95 | }
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| 96 | }
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| 97 | }
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