source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceScale.cs @ 13784

Last change on this file since 13784 was 13784, checked in by pfleck, 5 years ago

#2591 Made the creation of a GaussianProcessModel faster by avoiding additional iterators during calculation of the hyperparameter gradients.
The gradients of the hyperparameters are now calculated in one sweep and returned as IList, instead of returning an iterator (with yield return).
This avoids a large amount of Move-calls of the iterator, especially for covariance functions with a lot of hyperparameters.
Besides, the signature of the CovarianceGradientFunctionDelegate is changed, to return an IList instead of an IEnumerable to avoid unnececary ToList or ToArray calls.

File size: 4.3 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 = "CovarianceScale",
34    Description = "Scale covariance function for Gaussian processes.")]
35  public sealed class CovarianceScale : 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
43    public IValueParameter<ICovarianceFunction> CovarianceFunctionParameter {
44      get { return (IValueParameter<ICovarianceFunction>)Parameters["CovarianceFunction"]; }
45    }
46
47    [StorableConstructor]
48    private CovarianceScale(bool deserializing)
49      : base(deserializing) {
50    }
51
52    private CovarianceScale(CovarianceScale original, Cloner cloner)
53      : base(original, cloner) {
54    }
55
56    public CovarianceScale()
57      : base() {
58      Name = ItemName;
59      Description = ItemDescription;
60
61      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter."));
62      Parameters.Add(new ValueParameter<ICovarianceFunction>("CovarianceFunction", "The covariance function that should be scaled.", new CovarianceSquaredExponentialIso()));
63    }
64
65    public override IDeepCloneable Clone(Cloner cloner) {
66      return new CovarianceScale(this, cloner);
67    }
68
69    public int GetNumberOfParameters(int numberOfVariables) {
70      return (HasFixedScaleParameter ? 0 : 1) + CovarianceFunctionParameter.Value.GetNumberOfParameters(numberOfVariables);
71    }
72
73    public void SetParameter(double[] p) {
74      double scale;
75      GetParameterValues(p, out scale);
76      ScaleParameter.Value = new DoubleValue(scale);
77      CovarianceFunctionParameter.Value.SetParameter(p.Skip(1).ToArray());
78    }
79
80    private void GetParameterValues(double[] p, out double scale) {
81      // gather parameter values
82      if (HasFixedScaleParameter) {
83        scale = ScaleParameter.Value.Value;
84      } else {
85        scale = Math.Exp(2 * p[0]);
86      }
87    }
88
89    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
90      double scale;
91      GetParameterValues(p, out scale);
92      var fixedScale = HasFixedScaleParameter;
93      var subCov = CovarianceFunctionParameter.Value.GetParameterizedCovarianceFunction(p.Skip(1).ToArray(), columnIndices);
94      // create functions
95      var cov = new ParameterizedCovarianceFunction();
96      cov.Covariance = (x, i, j) => scale * subCov.Covariance(x, i, j);
97      cov.CrossCovariance = (x, xt, i, j) => scale * subCov.CrossCovariance(x, xt, i, j);
98      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, subCov, fixedScale);
99      return cov;
100    }
101
102    private static IList<double> GetGradient(double[,] x, int i, int j, int[] columnIndices, double scale, ParameterizedCovarianceFunction cov,
103      bool fixedScale) {
104      var gr = new List<double>((!fixedScale ? 1 : 0) + cov.CovarianceGradient(x, i, j).Count);
105      if (!fixedScale) {
106        gr.Add(2 * scale * cov.Covariance(x, i, j));
107      }
108      foreach (var g in cov.CovarianceGradient(x, i, j))
109        gr.Add(scale * g);
110      return gr;
111    }
112  }
113}
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