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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanFunctions/MeanLinear.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: 3.4 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 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 = "MeanLinear", Description = "Linear mean function for Gaussian processes.")]
34  public sealed class MeanLinear : ParameterizedNamedItem, IMeanFunction {
35    public IValueParameter<DoubleArray> WeightsParameter {
36      get { return (IValueParameter<DoubleArray>)Parameters["Weights"]; }
37    }
38
39    [StorableConstructor]
40    private MeanLinear(bool deserializing) : base(deserializing) { }
41    private MeanLinear(MeanLinear original, Cloner cloner)
42      : base(original, cloner) {
43    }
44    public MeanLinear()
45      : base() {
46      Parameters.Add(new OptionalValueParameter<DoubleArray>("Weights", "The weights parameter for the linear mean function."));
47    }
48
49    public override IDeepCloneable Clone(Cloner cloner) {
50      return new MeanLinear(this, cloner);
51    }
52
53    public int GetNumberOfParameters(int numberOfVariables) {
54      return WeightsParameter.Value != null ? 0 : numberOfVariables;
55    }
56
57    public void SetParameter(double[] p) {
58      double[] weights;
59      GetParameter(p, out weights);
60      WeightsParameter.Value = new DoubleArray(weights);
61    }
62
63    public void GetParameter(double[] p, out double[] weights) {
64      if (WeightsParameter.Value == null) {
65        weights = p;
66      } else {
67        if (p.Length != 0) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for the linear mean function.", "p");
68        weights = WeightsParameter.Value.ToArray();
69      }
70    }
71
72    public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, IEnumerable<int> columnIndices) {
73      double[] weights;
74      int[] columns = columnIndices.ToArray();
75      GetParameter(p, out weights);
76      var mf = new ParameterizedMeanFunction();
77      mf.Mean = (x, i) => {
78        // sanity check
79        if (weights.Length != columns.Length) throw new ArgumentException("The number of rparameters must match the number of variables for the linear mean function.");
80        return Util.ScalarProd(weights, Util.GetRow(x, i, columns));
81      };
82      mf.Gradient = (x, i, k) => {
83        if (k > columns.Length) throw new ArgumentException();
84        return x[i, columns[k]];
85      };
86      return mf;
87    }
88  }
89}
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