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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanLinear.cs @ 8612

Last change on this file since 8612 was 8612, checked in by gkronber, 12 years ago

#1902 implemented all mean and covariance functions with parameters as ParameterizedNamedItems

File size: 3.8 KB
Line 
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.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Algorithms.DataAnalysis {
30  [StorableClass]
31  [Item(Name = "MeanLinear", Description = "Linear mean function for Gaussian processes.")]
32  public sealed class MeanLinear : ParameterizedNamedItem, IMeanFunction {
33    [Storable]
34    private double[] weights;
35    [Storable]
36    private readonly HyperParameter<DoubleArray> weightsParameter;
37    public IValueParameter<DoubleArray> WeightsParameter { get { return weightsParameter; } }
38
39    [StorableConstructor]
40    private MeanLinear(bool deserializing) : base(deserializing) { }
41    private MeanLinear(MeanLinear original, Cloner cloner)
42      : base(original, cloner) {
43      if (original.weights != null) {
44        this.weights = new double[original.weights.Length];
45        Array.Copy(original.weights, weights, original.weights.Length);
46      }
47      weightsParameter = cloner.Clone(original.weightsParameter);
48      RegisterEvents();
49    }
50    public MeanLinear()
51      : base() {
52      this.weightsParameter = new HyperParameter<DoubleArray>("Weights", "The weights parameter for the linear mean function.");
53      Parameters.Add(weightsParameter);
54      RegisterEvents();
55    }
56
57    public override IDeepCloneable Clone(Cloner cloner) {
58      return new MeanLinear(this, cloner);
59    }
60
61    [StorableHook(HookType.AfterDeserialization)]
62    private void AfterDeserialization() {
63      RegisterEvents();
64    }
65
66    private void RegisterEvents() {
67      Util.AttachArrayChangeHandler<DoubleArray, double>(weightsParameter, () => {
68        weights = weightsParameter.Value.ToArray();
69      });
70    }
71
72    public int GetNumberOfParameters(int numberOfVariables) {
73      return weightsParameter.Fixed ? 0 : numberOfVariables;
74    }
75
76    public void SetParameter(double[] hyp) {
77      if (!weightsParameter.Fixed) {
78        weightsParameter.SetValue(new DoubleArray(hyp));
79      } else if (hyp.Length != 0) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for the linear mean function.", "hyp");
80    }
81
82    public double[] GetMean(double[,] x) {
83      // sanity check
84      if (weights.Length != x.GetLength(1)) throw new ArgumentException("The number of hyperparameters must match the number of variables for the linear mean function.");
85      int cols = x.GetLength(1);
86      int n = x.GetLength(0);
87      return (from i in Enumerable.Range(0, n)
88              let rowVector = Enumerable.Range(0, cols).Select(j => x[i, j])
89              select Util.ScalarProd(weights, rowVector))
90        .ToArray();
91    }
92
93    public double[] GetGradients(int k, double[,] x) {
94      int cols = x.GetLength(1);
95      int n = x.GetLength(0);
96      if (k > cols) throw new ArgumentException();
97      return (Enumerable.Range(0, n).Select(r => x[r, k])).ToArray();
98    }
99  }
100}
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