[8565] | 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 |
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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.Persistence.Default.CompositeSerializers.Storable;
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| 28 |
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| 29 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 30 | [StorableClass]
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| 31 | [Item(Name = "CovarianceRQArd",
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| 32 | Description = "Rational quadratic covariance function with automatic relevance determination for Gaussian processes.")]
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| 33 | public class CovarianceRQArd : Item, ICovarianceFunction {
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| 34 | [Storable]
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| 35 | private double sf2;
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| 36 | public double Scale { get { return sf2; } }
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| 37 | [Storable]
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| 38 | private double[] inverseLength;
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| 39 | public double[] InverseLength {
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| 40 | get {
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| 41 | if (inverseLength == null) return null;
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| 42 | double[] res = new double[inverseLength.Length];
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| 43 | Array.Copy(inverseLength, res, res.Length);
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| 44 | return res;
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| 45 | }
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| 46 | }
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| 47 | [Storable]
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| 48 | private double alpha;
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| 49 | public double Shape { get { return alpha; } }
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| 50 |
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| 51 | [StorableConstructor]
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| 52 | protected CovarianceRQArd(bool deserializing)
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| 53 | : base(deserializing) {
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| 54 | }
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| 55 |
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| 56 | protected CovarianceRQArd(CovarianceRQArd original, Cloner cloner)
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| 57 | : base(original, cloner) {
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| 58 | this.sf2 = original.sf2;
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| 59 | this.inverseLength = original.InverseLength; // array is cloned in the getter
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| 60 | this.alpha = original.alpha;
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| 61 | }
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| 62 |
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| 63 | public CovarianceRQArd()
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| 64 | : base() {
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| 65 | }
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| 66 |
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| 67 | public override IDeepCloneable Clone(Cloner cloner) {
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| 68 | return new CovarianceRQArd(this, cloner);
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| 69 | }
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| 70 |
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| 71 | public int GetNumberOfParameters(int numberOfVariables) {
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| 72 | return numberOfVariables + 2;
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| 73 | }
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| 74 |
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| 75 | public void SetParameter(double[] hyp) {
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| 76 | this.inverseLength = hyp.Take(hyp.Length - 2).Select(e => 1.0 / Math.Exp(e)).ToArray();
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| 77 | this.sf2 = Math.Exp(2 * hyp[hyp.Length - 2]);
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| 78 | this.alpha = Math.Exp(hyp[hyp.Length - 1]);
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| 79 | }
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| 80 |
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| 81 |
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| 82 | public double GetCovariance(double[,] x, int i, int j) {
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| 83 | double d = i == j
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| 84 | ? 0.0
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| 85 | : Util.SqrDist(x, i, j, inverseLength);
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| 86 | return sf2 * Math.Pow(1 + 0.5 * d / alpha, -alpha);
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| 87 | }
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| 88 |
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| 89 | public IEnumerable<double> GetGradient(double[,] x, int i, int j) {
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| 90 | double d = i == j
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| 91 | ? 0.0
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| 92 | : Util.SqrDist(x, i, j, inverseLength);
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| 93 | double b = 1 + 0.5 * d / alpha;
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| 94 | for (int k = 0; k < inverseLength.Length; k++) {
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| 95 | yield return sf2 * Math.Pow(b, -alpha - 1) * Util.SqrDist(x[i, k] * inverseLength[k], x[j, k] * inverseLength[k]);
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| 96 | }
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| 97 | yield return 2 * sf2 * Math.Pow(b, -alpha);
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| 98 | yield return sf2 * Math.Pow(b, -alpha) * (0.5 * d / b - alpha * Math.Log(b));
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| 99 | }
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| 100 |
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| 101 | public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j) {
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| 102 | double d = Util.SqrDist(x, i, xt, j, inverseLength);
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| 103 | return sf2 * Math.Pow(1 + 0.5 * d / alpha, -alpha);
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| 104 | }
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| 105 | }
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| 106 | }
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