1 | #region License Information
|
---|
2 | /* HeuristicLab
|
---|
3 | * Copyright (C) 2002-2013 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 |
|
---|
22 | using System;
|
---|
23 | using System.Collections.Generic;
|
---|
24 | using System.Linq;
|
---|
25 | using HeuristicLab.Common;
|
---|
26 | using HeuristicLab.Core;
|
---|
27 | using HeuristicLab.Data;
|
---|
28 | using HeuristicLab.Parameters;
|
---|
29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
30 |
|
---|
31 | namespace HeuristicLab.Algorithms.DataAnalysis {
|
---|
32 | [StorableClass]
|
---|
33 | [Item(Name = "MeanConst", Description = "Constant mean function for Gaussian processes.")]
|
---|
34 | public sealed class MeanConst : ParameterizedNamedItem, IMeanFunction {
|
---|
35 | public IValueParameter<DoubleValue> ValueParameter {
|
---|
36 | get { return (IValueParameter<DoubleValue>)Parameters["Value"]; }
|
---|
37 | }
|
---|
38 |
|
---|
39 | [StorableConstructor]
|
---|
40 | private MeanConst(bool deserializing) : base(deserializing) { }
|
---|
41 | private MeanConst(MeanConst original, Cloner cloner)
|
---|
42 | : base(original, cloner) {
|
---|
43 | }
|
---|
44 | public MeanConst()
|
---|
45 | : base() {
|
---|
46 | this.name = ItemName;
|
---|
47 | this.description = ItemDescription;
|
---|
48 |
|
---|
49 | Parameters.Add(new OptionalValueParameter<DoubleValue>("Value", "The constant value for the constant mean function."));
|
---|
50 | }
|
---|
51 |
|
---|
52 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
53 | return new MeanConst(this, cloner);
|
---|
54 | }
|
---|
55 |
|
---|
56 | public int GetNumberOfParameters(int numberOfVariables) {
|
---|
57 | return ValueParameter.Value != null ? 0 : 1;
|
---|
58 | }
|
---|
59 |
|
---|
60 | public void SetParameter(double[] p) {
|
---|
61 | double c;
|
---|
62 | GetParameters(p, out c);
|
---|
63 | ValueParameter.Value = new DoubleValue(c);
|
---|
64 | }
|
---|
65 |
|
---|
66 | private void GetParameters(double[] p, out double c) {
|
---|
67 | if (ValueParameter.Value == null) {
|
---|
68 | c = p[0];
|
---|
69 | } else {
|
---|
70 | if (p.Length > 0)
|
---|
71 | throw new ArgumentException(
|
---|
72 | "The length of the parameter vector does not match the number of free parameters for the constant mean function.",
|
---|
73 | "p");
|
---|
74 | c = ValueParameter.Value.Value;
|
---|
75 | }
|
---|
76 | }
|
---|
77 |
|
---|
78 | public ParameterizedMeanFunction GetParameterizedMeanFunction(double[] p, IEnumerable<int> columnIndices) {
|
---|
79 | double c;
|
---|
80 | GetParameters(p, out c);
|
---|
81 | var mf = new ParameterizedMeanFunction();
|
---|
82 | mf.Mean = (x, i) => c;
|
---|
83 | mf.Gradient = (x, i, k) => {
|
---|
84 | if (k > 0) throw new ArgumentException();
|
---|
85 | return 1.0;
|
---|
86 | };
|
---|
87 | return mf;
|
---|
88 | }
|
---|
89 | }
|
---|
90 | }
|
---|