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source: branches/HeuristicLab.DatasetRefactor/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanFunctions/MeanLinear.cs @ 12031

Last change on this file since 12031 was 12031, checked in by bburlacu, 9 years ago

#2276: Merged trunk changes.

File size: 3.4 KB
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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 = "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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