source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceConst.cs @ 13721

Last change on this file since 13721 was 13721, checked in by mkommend, 5 years ago

#2591: Changed all GP covariance and mean functions to use int[] for column indices instead of IEnumerable<int>. Changed GP utils, GPModel and StudentTProcessModell as well to use fewer iterators and adapted unit tests to new interface.

File size: 3.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  [StorableClass]
33  [Item(Name = "CovarianceConst",
34    Description = "Constant covariance function for Gaussian processes.")]
35  public sealed class CovarianceConst : ParameterizedNamedItem, ICovarianceFunction {
36    public IValueParameter<DoubleValue> ScaleParameter {
37      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
38    }
39    private bool HasFixedScaleParameter {
40      get { return ScaleParameter.Value != null; }
41    }
42    [StorableConstructor]
43    private CovarianceConst(bool deserializing)
44      : base(deserializing) {
45    }
46
47    private CovarianceConst(CovarianceConst original, Cloner cloner)
48      : base(original, cloner) {
49    }
50
51    public CovarianceConst()
52      : base() {
53      Name = ItemName;
54      Description = ItemDescription;
55
56      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale of the constant covariance function."));
57    }
58
59    public override IDeepCloneable Clone(Cloner cloner) {
60      return new CovarianceConst(this, cloner);
61    }
62
63    public int GetNumberOfParameters(int numberOfVariables) {
64      return HasFixedScaleParameter ? 0 : 1;
65    }
66
67    public void SetParameter(double[] p) {
68      double scale;
69      GetParameterValues(p, out scale);
70      ScaleParameter.Value = new DoubleValue(scale);
71    }
72
73    private void GetParameterValues(double[] p, out double scale) {
74      int c = 0;
75      // gather parameter values
76      if (HasFixedScaleParameter) {
77        scale = ScaleParameter.Value.Value;
78      } else {
79        scale = Math.Exp(2 * p[c]);
80        c++;
81      }
82      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceConst", "p");
83    }
84
85    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
86      double scale;
87      GetParameterValues(p, out scale);
88      // create functions
89      var cov = new ParameterizedCovarianceFunction();
90      cov.Covariance = (x, i, j) => scale;
91      cov.CrossCovariance = (x, xt, i, j) => scale;
92      if (HasFixedScaleParameter) {
93        cov.CovarianceGradient = (x, i, j) => Enumerable.Empty<double>();
94      } else {
95        cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, scale, columnIndices);
96      }
97      return cov;
98    }
99
100    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double scale, int[] columnIndices) {
101      yield return 2.0 * scale;
102    }
103  }
104}
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