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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceLinearArd.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: 3.9 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 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 = "CovarianceLinearArd",
34    Description = "Linear covariance function with automatic relevance determination for Gaussian processes.")]
35  public sealed class CovarianceLinearArd : ParameterizedNamedItem, ICovarianceFunction {
36    public IValueParameter<DoubleArray> InverseLengthParameter {
37      get { return (IValueParameter<DoubleArray>)Parameters["InverseLength"]; }
38    }
39
40    [StorableConstructor]
41    private CovarianceLinearArd(bool deserializing) : base(deserializing) { }
42    private CovarianceLinearArd(CovarianceLinearArd original, Cloner cloner)
43      : base(original, cloner) {
44    }
45    public CovarianceLinearArd()
46      : base() {
47      Name = ItemName;
48      Description = ItemDescription;
49
50      Parameters.Add(new OptionalValueParameter<DoubleArray>("InverseLength",
51                                                             "The inverse length parameter for ARD."));
52    }
53
54    public override IDeepCloneable Clone(Cloner cloner) {
55      return new CovarianceLinearArd(this, cloner);
56    }
57
58    public int GetNumberOfParameters(int numberOfVariables) {
59      if (InverseLengthParameter.Value == null)
60        return numberOfVariables;
61      else
62        return 0;
63    }
64
65    public void SetParameter(double[] p) {
66      double[] inverseLength;
67      GetParameterValues(p, out inverseLength);
68      InverseLengthParameter.Value = new DoubleArray(inverseLength);
69    }
70
71    private void GetParameterValues(double[] p, out double[] inverseLength) {
72      // gather parameter values
73      if (InverseLengthParameter.Value != null) {
74        inverseLength = InverseLengthParameter.Value.ToArray();
75      } else {
76        inverseLength = p.Select(e => 1.0 / Math.Exp(e)).ToArray();
77      }
78    }
79
80    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, IEnumerable<int> columnIndices) {
81      double[] inverseLength;
82      GetParameterValues(p, out inverseLength);
83      // create functions
84      var cov = new ParameterizedCovarianceFunction();
85      cov.Covariance = (x, i, j) => Util.ScalarProd(x, i, j, inverseLength, columnIndices);
86      cov.CrossCovariance = (x, xt, i, j) => Util.ScalarProd(x, i, xt, j, inverseLength, columnIndices);
87      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, inverseLength, columnIndices);
88      return cov;
89    }
90
91    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, double[] inverseLength, IEnumerable<int> columnIndices) {
92      if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
93
94      int k = 0;
95      foreach (int columnIndex in columnIndices) {
96        yield return -2.0 * x[i, columnIndex] * x[j, columnIndex] * inverseLength[k] * inverseLength[k];
97        k++;
98      }
99    }
100  }
101}
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