[8439] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
| 3 | * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
| 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 | using System.Linq;
|
---|
| 22 | using HeuristicLab.Common;
|
---|
| 23 | using HeuristicLab.Core;
|
---|
| 24 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
| 25 |
|
---|
| 26 | namespace HeuristicLab.Algorithms.DataAnalysis {
|
---|
| 27 | [StorableClass]
|
---|
| 28 | [Item(Name = "MeanProd", Description = "Product of mean functions for Gaussian processes.")]
|
---|
| 29 | public class MeanProd : Item, IMeanFunction {
|
---|
| 30 | [Storable]
|
---|
| 31 | private ItemList<IMeanFunction> factors;
|
---|
| 32 |
|
---|
| 33 | [Storable]
|
---|
| 34 | private int numberOfVariables;
|
---|
| 35 |
|
---|
| 36 | public ItemList<IMeanFunction> Factors {
|
---|
| 37 | get { return factors; }
|
---|
| 38 | }
|
---|
| 39 |
|
---|
| 40 | public int GetNumberOfParameters(int numberOfVariables) {
|
---|
| 41 | this.numberOfVariables = numberOfVariables;
|
---|
| 42 | return factors.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
|
---|
| 43 | }
|
---|
| 44 |
|
---|
| 45 | [StorableConstructor]
|
---|
| 46 | protected MeanProd(bool deserializing)
|
---|
| 47 | : base(deserializing) {
|
---|
| 48 | }
|
---|
| 49 |
|
---|
| 50 | protected MeanProd(MeanProd original, Cloner cloner)
|
---|
| 51 | : base(original, cloner) {
|
---|
| 52 | this.factors = cloner.Clone(original.factors);
|
---|
| 53 | this.numberOfVariables = original.numberOfVariables;
|
---|
| 54 | }
|
---|
| 55 |
|
---|
| 56 | public MeanProd() {
|
---|
| 57 | this.factors = new ItemList<IMeanFunction>();
|
---|
| 58 | }
|
---|
| 59 |
|
---|
| 60 | public void SetParameter(double[] hyp) {
|
---|
| 61 | int offset = 0;
|
---|
| 62 | foreach (var t in factors) {
|
---|
| 63 | var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
|
---|
| 64 | t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
|
---|
| 65 | offset += numberOfParameters;
|
---|
| 66 | }
|
---|
| 67 | }
|
---|
| 68 |
|
---|
| 69 | public void SetData(double[,] x) {
|
---|
| 70 | foreach (var t in factors) t.SetData(x);
|
---|
| 71 | }
|
---|
| 72 |
|
---|
| 73 | public double[] GetMean(double[,] x) {
|
---|
| 74 | var res = factors.First().GetMean(x);
|
---|
| 75 | foreach (var t in factors.Skip(1)) {
|
---|
| 76 | var a = t.GetMean(x);
|
---|
| 77 | for (int i = 0; i < res.Length; i++) res[i] *= a[i];
|
---|
| 78 | }
|
---|
| 79 | return res;
|
---|
| 80 | }
|
---|
| 81 |
|
---|
| 82 | public double[] GetGradients(int k, double[,] x) {
|
---|
| 83 | double[] res = Enumerable.Repeat(1.0, x.GetLength(0)).ToArray();
|
---|
[8463] | 84 | // find index of factor for the given k
|
---|
| 85 | int j = 0;
|
---|
| 86 | while (k >= factors[j].GetNumberOfParameters(numberOfVariables)) {
|
---|
| 87 | k -= factors[j].GetNumberOfParameters(numberOfVariables);
|
---|
| 88 | j++;
|
---|
| 89 | }
|
---|
| 90 | for (int i = 0; i < factors.Count; i++) {
|
---|
| 91 | var f = factors[i];
|
---|
| 92 | if (i == j) {
|
---|
[8439] | 93 | // multiply gradient
|
---|
| 94 | var g = f.GetGradients(k, x);
|
---|
[8463] | 95 | for (int ii = 0; ii < res.Length; ii++) res[ii] *= g[ii];
|
---|
[8439] | 96 | } else {
|
---|
| 97 | // multiply mean
|
---|
| 98 | var m = f.GetMean(x);
|
---|
[8463] | 99 | for (int ii = 0; ii < res.Length; ii++) res[ii] *= m[ii];
|
---|
[8439] | 100 | }
|
---|
| 101 | }
|
---|
| 102 | return res;
|
---|
| 103 | }
|
---|
| 104 |
|
---|
| 105 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 106 | return new MeanProd(this, cloner);
|
---|
| 107 | }
|
---|
| 108 | }
|
---|
| 109 | }
|
---|