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source: stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSquaredExponentialIso.cs @ 14648

Last change on this file since 14648 was 14186, checked in by swagner, 8 years ago

#2526: Updated year of copyrights in license headers

File size: 5.4 KB
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[8401]1#region License Information
2/* HeuristicLab
[14186]3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[8401]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
22using System;
[8484]23using System.Collections.Generic;
[8323]24using HeuristicLab.Common;
25using HeuristicLab.Core;
[8612]26using HeuristicLab.Data;
[8982]27using HeuristicLab.Parameters;
[8323]28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
[8371]30namespace HeuristicLab.Algorithms.DataAnalysis {
[8323]31  [StorableClass]
[8615]32  [Item(Name = "CovarianceSquaredExponentialIso",
[8323]33    Description = "Isotropic squared exponential covariance function for Gaussian processes.")]
[8615]34  public sealed class CovarianceSquaredExponentialIso : ParameterizedNamedItem, ICovarianceFunction {
[8982]35    public IValueParameter<DoubleValue> ScaleParameter {
36      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
37    }
[8612]38
[8982]39    public IValueParameter<DoubleValue> InverseLengthParameter {
40      get { return (IValueParameter<DoubleValue>)Parameters["InverseLength"]; }
41    }
[8323]42
[10530]43    private bool HasFixedInverseLengthParameter {
44      get { return InverseLengthParameter.Value != null; }
45    }
46    private bool HasFixedScaleParameter {
47      get { return ScaleParameter.Value != null; }
48    }
49
[8323]50    [StorableConstructor]
[8615]51    private CovarianceSquaredExponentialIso(bool deserializing)
[8323]52      : base(deserializing) {
53    }
54
[8615]55    private CovarianceSquaredExponentialIso(CovarianceSquaredExponentialIso original, Cloner cloner)
[8323]56      : base(original, cloner) {
57    }
58
[8615]59    public CovarianceSquaredExponentialIso()
[8323]60      : base() {
[8612]61      Name = ItemName;
62      Description = ItemDescription;
63
[8982]64      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the isometric squared exponential covariance function."));
65      Parameters.Add(new OptionalValueParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric squared exponential covariance function."));
[8323]66    }
67
68    public override IDeepCloneable Clone(Cloner cloner) {
[8615]69      return new CovarianceSquaredExponentialIso(this, cloner);
[8323]70    }
71
[8982]72    public int GetNumberOfParameters(int numberOfVariables) {
73      return
[10530]74        (HasFixedScaleParameter ? 0 : 1) +
75        (HasFixedInverseLengthParameter ? 0 : 1);
[8612]76    }
77
[8982]78    public void SetParameter(double[] p) {
79      double scale, inverseLength;
80      GetParameterValues(p, out scale, out inverseLength);
81      ScaleParameter.Value = new DoubleValue(scale);
82      InverseLengthParameter.Value = new DoubleValue(inverseLength);
[8612]83    }
84
[8323]85
[8982]86    private void GetParameterValues(double[] p, out double scale, out double inverseLength) {
87      // gather parameter values
88      int c = 0;
[10530]89      if (HasFixedInverseLengthParameter) {
[8982]90        inverseLength = InverseLengthParameter.Value.Value;
91      } else {
92        inverseLength = 1.0 / Math.Exp(p[c]);
93        c++;
[8612]94      }
[8982]95
[10530]96      if (HasFixedScaleParameter) {
[8982]97        scale = ScaleParameter.Value.Value;
98      } else {
99        scale = Math.Exp(2 * p[c]);
100        c++;
[8612]101      }
[8982]102      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceSquaredExponentialIso", "p");
[8416]103    }
[8323]104
[13981]105    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
[8982]106      double inverseLength, scale;
107      GetParameterValues(p, out scale, out inverseLength);
[10530]108      var fixedInverseLength = HasFixedInverseLengthParameter;
109      var fixedScale = HasFixedScaleParameter;
[8982]110      // create functions
111      var cov = new ParameterizedCovarianceFunction();
112      cov.Covariance = (x, i, j) => {
113        double d = i == j
114                ? 0.0
[13981]115                : Util.SqrDist(x, i, j, columnIndices, inverseLength);
[8982]116        return scale * Math.Exp(-d / 2.0);
117      };
118      cov.CrossCovariance = (x, xt, i, j) => {
[13981]119        double d = Util.SqrDist(x, i, xt, j, columnIndices, inverseLength);
[8982]120        return scale * Math.Exp(-d / 2.0);
121      };
[10530]122      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, scale, inverseLength, columnIndices,
123        fixedInverseLength, fixedScale);
[8982]124      return cov;
[8323]125    }
126
[9108]127    // order of returned gradients must match the order in GetParameterValues!
[13981]128    private static IList<double> GetGradient(double[,] x, int i, int j, double sf2, double inverseLength, int[] columnIndices,
[10530]129      bool fixedInverseLength, bool fixedScale) {
[8484]130      double d = i == j
131                   ? 0.0
[13981]132                   : Util.SqrDist(x, i, j, columnIndices, inverseLength);
[8484]133      double g = Math.Exp(-d / 2.0);
[13981]134      var gr = new List<double>(2);
135      if (!fixedInverseLength) gr.Add(sf2 * g * d);
136      if (!fixedScale) gr.Add(2.0 * sf2 * g);
137      return gr;
[8323]138    }
139  }
140}
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