[8439] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 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 | using System.Linq;
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| 22 | using HeuristicLab.Common;
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| 23 | using HeuristicLab.Core;
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| 24 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 25 |
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| 26 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 27 | [StorableClass]
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| 28 | [Item(Name = "MeanProd", Description = "Product of mean functions for Gaussian processes.")]
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[8612] | 29 | public sealed class MeanProd : Item, IMeanFunction {
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[8439] | 30 | [Storable]
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| 31 | private ItemList<IMeanFunction> factors;
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| 32 |
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| 33 | [Storable]
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| 34 | private int numberOfVariables;
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| 35 |
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| 36 | public ItemList<IMeanFunction> Factors {
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| 37 | get { return factors; }
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| 38 | }
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| 39 |
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| 40 | [StorableConstructor]
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[8612] | 41 | private MeanProd(bool deserializing)
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[8439] | 42 | : base(deserializing) {
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| 43 | }
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| 44 |
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[8612] | 45 | private MeanProd(MeanProd original, Cloner cloner)
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[8439] | 46 | : base(original, cloner) {
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| 47 | this.factors = cloner.Clone(original.factors);
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| 48 | this.numberOfVariables = original.numberOfVariables;
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| 49 | }
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| 50 |
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| 51 | public MeanProd() {
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| 52 | this.factors = new ItemList<IMeanFunction>();
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| 53 | }
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[8612] | 54 | public override IDeepCloneable Clone(Cloner cloner) {
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| 55 | return new MeanProd(this, cloner);
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| 56 | }
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[8439] | 57 |
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[8612] | 58 | public int GetNumberOfParameters(int numberOfVariables) {
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| 59 | this.numberOfVariables = numberOfVariables;
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| 60 | return factors.Select(t => t.GetNumberOfParameters(numberOfVariables)).Sum();
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| 61 | }
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| 62 |
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[8439] | 63 | public void SetParameter(double[] hyp) {
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| 64 | int offset = 0;
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| 65 | foreach (var t in factors) {
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| 66 | var numberOfParameters = t.GetNumberOfParameters(numberOfVariables);
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| 67 | t.SetParameter(hyp.Skip(offset).Take(numberOfParameters).ToArray());
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| 68 | offset += numberOfParameters;
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| 69 | }
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| 70 | }
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| 71 |
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| 72 | public double[] GetMean(double[,] x) {
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| 73 | var res = factors.First().GetMean(x);
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| 74 | foreach (var t in factors.Skip(1)) {
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| 75 | var a = t.GetMean(x);
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| 76 | for (int i = 0; i < res.Length; i++) res[i] *= a[i];
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| 77 | }
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| 78 | return res;
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| 79 | }
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| 80 |
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| 81 | public double[] GetGradients(int k, double[,] x) {
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| 82 | double[] res = Enumerable.Repeat(1.0, x.GetLength(0)).ToArray();
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[8463] | 83 | // find index of factor for the given k
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| 84 | int j = 0;
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| 85 | while (k >= factors[j].GetNumberOfParameters(numberOfVariables)) {
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| 86 | k -= factors[j].GetNumberOfParameters(numberOfVariables);
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| 87 | j++;
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| 88 | }
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| 89 | for (int i = 0; i < factors.Count; i++) {
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| 90 | var f = factors[i];
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| 91 | if (i == j) {
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[8439] | 92 | // multiply gradient
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| 93 | var g = f.GetGradients(k, x);
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[8463] | 94 | for (int ii = 0; ii < res.Length; ii++) res[ii] *= g[ii];
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[8439] | 95 | } else {
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| 96 | // multiply mean
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| 97 | var m = f.GetMean(x);
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[8463] | 98 | for (int ii = 0; ii < res.Length; ii++) res[ii] *= m[ii];
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[8439] | 99 | }
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| 100 | }
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| 101 | return res;
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| 102 | }
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| 103 | }
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| 104 | }
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