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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceMask.cs @ 8982

Last change on this file since 8982 was 8982, checked in by gkronber, 11 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: 3.5 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 System.Linq;
25using System.Linq.Expressions;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Data;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31
32namespace HeuristicLab.Algorithms.DataAnalysis {
33  [StorableClass]
34  [Item(Name = "CovarianceMask",
35    Description = "Masking covariance function for dimension selection can be used to apply a covariance function only on certain input dimensions.")]
36  public sealed class CovarianceMask : ParameterizedNamedItem, ICovarianceFunction {
37    public IValueParameter<IntArray> SelectedDimensionsParameter {
38      get { return (IValueParameter<IntArray>)Parameters["SelectedDimensions"]; }
39    }
40    public IValueParameter<ICovarianceFunction> CovarianceFunctionParameter {
41      get { return (IValueParameter<ICovarianceFunction>)Parameters["CovarianceFunction"]; }
42    }
43
44    [StorableConstructor]
45    private CovarianceMask(bool deserializing)
46      : base(deserializing) {
47    }
48
49    private CovarianceMask(CovarianceMask original, Cloner cloner)
50      : base(original, cloner) {
51    }
52
53    public CovarianceMask()
54      : base() {
55      Name = ItemName;
56      Description = ItemDescription;
57
58      Parameters.Add(new OptionalValueParameter<IntArray>("SelectedDimensions", "The dimensions on which the specified covariance function should be applied to."));
59      Parameters.Add(new ValueParameter<ICovarianceFunction>("CovarianceFunction", "The covariance function that should be scaled.", new CovarianceSquaredExponentialIso()));
60    }
61
62    public override IDeepCloneable Clone(Cloner cloner) {
63      return new CovarianceMask(this, cloner);
64    }
65
66    public int GetNumberOfParameters(int numberOfVariables) {
67      if (SelectedDimensionsParameter.Value == null) return CovarianceFunctionParameter.Value.GetNumberOfParameters(numberOfVariables);
68      else return CovarianceFunctionParameter.Value.GetNumberOfParameters(SelectedDimensionsParameter.Value.Length);
69    }
70
71    public void SetParameter(double[] p) {
72      CovarianceFunctionParameter.Value.SetParameter(p);
73    }
74
75    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
76      if (columnIndices != null)
77        throw new InvalidOperationException("Stacking of masking covariance functions is not supported.");
78      var cov = CovarianceFunctionParameter.Value;
79      var selectedDimensions = SelectedDimensionsParameter.Value;
80
81      return cov.GetParameterizedCovarianceFunction(p, selectedDimensions);
82    }
83  }
84}
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