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source: branches/LearningClassifierSystems/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceConst.cs @ 13042

Last change on this file since 13042 was 8982, checked in by gkronber, 12 years ago

#1902: removed class HyperParameter and changed implementations of covariance and mean functions to remove the parameter value caching and event handlers for parameter caching. Instead it is now possible to create the actual covariance and mean functions as Func from templates and specified parameter values. The instances of mean and covariance functions configured in the GUI are actually templates where the structure and fixed parameters can be specified.

File size: 3.4 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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 HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Algorithms.DataAnalysis {
31  [StorableClass]
32  [Item(Name = "CovarianceConst",
33    Description = "Constant covariance function for Gaussian processes.")]
34  public sealed class CovarianceConst : ParameterizedNamedItem, ICovarianceFunction {
35    public IValueParameter<DoubleValue> ScaleParameter {
36      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
37    }
38
39    [StorableConstructor]
40    private CovarianceConst(bool deserializing)
41      : base(deserializing) {
42    }
43
44    private CovarianceConst(CovarianceConst original, Cloner cloner)
45      : base(original, cloner) {
46    }
47
48    public CovarianceConst()
49      : base() {
50      Name = ItemName;
51      Description = ItemDescription;
52
53      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale of the constant covariance function."));
54    }
55
56    public override IDeepCloneable Clone(Cloner cloner) {
57      return new CovarianceConst(this, cloner);
58    }
59
60    public int GetNumberOfParameters(int numberOfVariables) {
61      return ScaleParameter.Value != null ? 0 : 1;
62    }
63
64    public void SetParameter(double[] p) {
65      double scale;
66      GetParameterValues(p, out scale);
67      ScaleParameter.Value = new DoubleValue(scale);
68    }
69
70    private void GetParameterValues(double[] p, out double scale) {
71      int c = 0;
72      // gather parameter values
73      if (ScaleParameter.Value != null) {
74        scale = ScaleParameter.Value.Value;
75      } else {
76        scale = Math.Exp(2 * p[c]);
77        c++;
78      }
79      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceConst", "p");
80    }
81
82    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
83      double scale;
84      GetParameterValues(p, out scale);
85      // create functions
86      var cov = new ParameterizedCovarianceFunction();
87      cov.Covariance = (x, i, j) => scale;
88      cov.CrossCovariance = (x, xt, i, j) => scale;
89      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, scale, columnIndices);
90      return cov;
91    }
92
93    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double scale, IEnumerable<int> columnIndices) {
94      yield return 2.0 * scale;
95    }
96  }
97}
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