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source: branches/RBFRegression/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/MeanFunctions/MeanLinear.cs @ 14385

Last change on this file since 14385 was 14185, checked in by swagner, 8 years ago

#2526: Updated year of copyrights in license headers

File size: 3.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Algorithms.DataAnalysis {
31  [StorableClass]
32  [Item(Name = "MeanLinear", Description = "Linear mean function for Gaussian processes.")]
33  public sealed class MeanLinear : ParameterizedNamedItem, IMeanFunction {
34    public IValueParameter<DoubleArray> WeightsParameter {
35      get { return (IValueParameter<DoubleArray>)Parameters["Weights"]; }
36    }
37
38    [StorableConstructor]
39    private MeanLinear(bool deserializing) : base(deserializing) { }
40    private MeanLinear(MeanLinear original, Cloner cloner)
41      : base(original, cloner) {
42    }
43    public MeanLinear()
44      : base() {
45      Parameters.Add(new OptionalValueParameter<DoubleArray>("Weights", "The weights parameter for the linear mean function."));
46    }
47
48    public override IDeepCloneable Clone(Cloner cloner) {
49      return new MeanLinear(this, cloner);
50    }
51
52    public int GetNumberOfParameters(int numberOfVariables) {
53      return WeightsParameter.Value != null ? 0 : numberOfVariables;
54    }
55
56    public void SetParameter(double[] p) {
57      double[] weights;
58      GetParameter(p, out weights);
59      WeightsParameter.Value = new DoubleArray(weights);
60    }
61
62    public void GetParameter(double[] p, out double[] weights) {
63      if (WeightsParameter.Value == null) {
64        weights = p;
65      } else {
66        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");
67        weights = WeightsParameter.Value.ToArray();
68      }
69    }
70
71    public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, int[] columnIndices) {
72      double[] weights;
73      int[] columns = columnIndices;
74      GetParameter(p, out weights);
75      var mf = new ParameterizedMeanFunction();
76      mf.Mean = (x, i) => {
77        // sanity check
78        if (weights.Length != columns.Length) throw new ArgumentException("The number of rparameters must match the number of variables for the linear mean function.");
79        return Util.ScalarProd(weights, Util.GetRow(x, i, columns).ToArray());
80      };
81      mf.Gradient = (x, i, k) => {
82        if (k > columns.Length) throw new ArgumentException();
83        return x[i, columns[k]];
84      };
85      return mf;
86    }
87  }
88}
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