[8473] | 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.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 = "CovarianceRQiso",
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| 31 | Description = "Isotropic rational quadratic covariance function for Gaussian processes.")]
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| 32 | public class CovarianceRQiso : Item, ICovarianceFunction {
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| 33 | [Storable]
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| 34 | private double[,] x;
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| 35 | [Storable]
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| 36 | private double[,] xt;
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| 37 | [Storable]
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| 38 | private double sf2;
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| 39 | public double Scale { get { return sf2; } }
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| 40 | [Storable]
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| 41 | private double l;
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| 42 | public double Length { get { return l; } }
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| 43 | [Storable]
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| 44 | private double alpha;
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| 45 | public double Shape { get { return alpha; } }
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| 46 | [Storable]
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| 47 | private bool symmetric;
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| 48 | private double[,] d2;
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| 49 |
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| 50 | [StorableConstructor]
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| 51 | protected CovarianceRQiso(bool deserializing)
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| 52 | : base(deserializing) {
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| 53 | }
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| 54 |
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| 55 | protected CovarianceRQiso(CovarianceRQiso original, Cloner cloner)
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| 56 | : base(original, cloner) {
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| 57 | if (original.x != null) {
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| 58 | this.x = new double[original.x.GetLength(0), original.x.GetLength(1)];
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| 59 | Array.Copy(original.x, this.x, x.Length);
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| 60 |
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| 61 | this.xt = new double[original.xt.GetLength(0), original.xt.GetLength(1)];
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| 62 | Array.Copy(original.xt, this.xt, xt.Length);
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| 63 |
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| 64 | this.d2 = new double[original.d2.GetLength(0), original.d2.GetLength(1)];
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| 65 | Array.Copy(original.d2, this.d2, d2.Length);
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| 66 | this.sf2 = original.sf2;
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| 67 | }
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| 68 | this.sf2 = original.sf2;
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| 69 | this.l = original.l;
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| 70 | this.alpha = original.alpha;
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| 71 | this.symmetric = original.symmetric;
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| 72 | }
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| 73 |
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| 74 | public CovarianceRQiso()
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| 75 | : base() {
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| 76 | }
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| 77 |
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| 78 | public override IDeepCloneable Clone(Cloner cloner) {
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| 79 | return new CovarianceRQiso(this, cloner);
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| 80 | }
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| 81 |
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| 82 | public int GetNumberOfParameters(int numberOfVariables) {
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| 83 | return 3;
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| 84 | }
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| 85 |
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| 86 | public void SetParameter(double[] hyp) {
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| 87 | this.l = Math.Exp(hyp[0]);
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| 88 | this.sf2 = Math.Exp(2 * hyp[1]);
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| 89 | this.alpha = Math.Exp(hyp[2]);
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| 90 | d2 = null;
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| 91 | }
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| 92 | public void SetData(double[,] x) {
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| 93 | SetData(x, x);
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| 94 | this.symmetric = true;
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| 95 | }
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| 96 |
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| 97 |
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| 98 | public void SetData(double[,] x, double[,] xt) {
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| 99 | this.symmetric = false;
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| 100 | this.x = x;
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| 101 | this.xt = xt;
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| 102 | d2 = null;
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| 103 | }
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| 104 |
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| 105 | public double GetCovariance(int i, int j) {
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| 106 | if (d2 == null) CalculateSquaredDistances();
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| 107 | return sf2 * Math.Pow(1 + 0.5 * d2[i, j] / alpha, -alpha);
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| 108 | }
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| 109 |
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| 110 | public double GetGradient(int i, int j, int k) {
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| 111 | switch (k) {
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| 112 | case 0: return sf2 * Math.Pow(1 + 0.5 * d2[i, j] / alpha, -alpha - 1) * d2[i, j];
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| 113 | case 1: return 2 * sf2 * Math.Pow((1 + 0.5 * d2[i, j] / alpha), (-alpha));
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| 114 | case 2: {
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| 115 | double g = (1 + 0.5 * d2[i, j] / alpha);
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| 116 | g = sf2 * Math.Pow(g, -alpha) * (0.5 * d2[i, j] / g - alpha * Math.Log(g));
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| 117 | return g;
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| 118 | }
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| 119 | default: throw new ArgumentException("CovarianceRQiso has three hyperparameters", "k");
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| 120 | }
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| 121 | }
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| 122 |
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| 123 | private void CalculateSquaredDistances() {
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| 124 | if (x.GetLength(1) != xt.GetLength(1)) throw new InvalidOperationException();
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| 125 | int rows = x.GetLength(0);
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| 126 | int cols = xt.GetLength(0);
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| 127 | d2 = new double[rows, cols];
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| 128 | double lInv = 1.0 / l;
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| 129 | if (symmetric) {
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| 130 | for (int i = 0; i < rows; i++) {
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| 131 | for (int j = i; j < rows; j++) {
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| 132 | d2[i, j] = Util.SqrDist(Util.GetRow(x, i).Select(e => e * lInv), Util.GetRow(xt, j).Select(e => e * lInv));
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| 133 | d2[j, i] = d2[i, j];
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| 134 | }
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| 135 | }
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| 136 | } else {
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| 137 | for (int i = 0; i < rows; i++) {
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| 138 | for (int j = 0; j < cols; j++) {
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| 139 | d2[i, j] = Util.SqrDist(Util.GetRow(x, i).Select(e => e * lInv), Util.GetRow(xt, j).Select(e => e * lInv));
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| 140 | }
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| 141 | }
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| 142 | }
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| 143 | }
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| 144 | }
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| 145 | }
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