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source: branches/2839_HiveProjectManagement/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceRationalQuadraticArd.cs @ 16057

Last change on this file since 16057 was 16057, checked in by jkarder, 6 years ago

#2839:

File size: 6.8 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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 = "CovarianceRationalQuadraticArd",
34    Description = "Rational quadratic covariance function with automatic relevance determination for Gaussian processes.")]
35  public sealed class CovarianceRationalQuadraticArd : ParameterizedNamedItem, ICovarianceFunction {
36    public IValueParameter<DoubleValue> ScaleParameter {
37      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
38    }
39
40    public IValueParameter<DoubleArray> InverseLengthParameter {
41      get { return (IValueParameter<DoubleArray>)Parameters["InverseLength"]; }
42    }
43
44    public IValueParameter<DoubleValue> ShapeParameter {
45      get { return (IValueParameter<DoubleValue>)Parameters["Shape"]; }
46    }
47    private bool HasFixedScaleParameter {
48      get { return ScaleParameter.Value != null; }
49    }
50    private bool HasFixedInverseLengthParameter {
51      get { return InverseLengthParameter.Value != null; }
52    }
53    private bool HasFixedShapeParameter {
54      get { return ShapeParameter.Value != null; }
55    }
56
57    [StorableConstructor]
58    private CovarianceRationalQuadraticArd(bool deserializing)
59      : base(deserializing) {
60    }
61
62    private CovarianceRationalQuadraticArd(CovarianceRationalQuadraticArd original, Cloner cloner)
63      : base(original, cloner) {
64    }
65
66    public CovarianceRationalQuadraticArd()
67      : base() {
68      Name = ItemName;
69      Description = ItemDescription;
70
71      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the rational quadratic covariance function with ARD."));
72      Parameters.Add(new OptionalValueParameter<DoubleArray>("InverseLength", "The inverse length parameter for automatic relevance determination."));
73      Parameters.Add(new OptionalValueParameter<DoubleValue>("Shape", "The shape parameter (alpha) of the rational quadratic covariance function with ARD."));
74    }
75
76    public override IDeepCloneable Clone(Cloner cloner) {
77      return new CovarianceRationalQuadraticArd(this, cloner);
78    }
79
80    public int GetNumberOfParameters(int numberOfVariables) {
81      return
82        (HasFixedScaleParameter ? 0 : 1) +
83        (HasFixedShapeParameter ? 0 : 1) +
84        (HasFixedInverseLengthParameter ? 0 : numberOfVariables);
85    }
86
87    public void SetParameter(double[] p) {
88      double scale, shape;
89      double[] inverseLength;
90      GetParameterValues(p, out scale, out shape, out inverseLength);
91      ScaleParameter.Value = new DoubleValue(scale);
92      ShapeParameter.Value = new DoubleValue(shape);
93      InverseLengthParameter.Value = new DoubleArray(inverseLength);
94    }
95
96    private void GetParameterValues(double[] p, out double scale, out double shape, out double[] inverseLength) {
97      int c = 0;
98      // gather parameter values
99      if (HasFixedInverseLengthParameter) {
100        inverseLength = InverseLengthParameter.Value.ToArray();
101      } else {
102        int length = p.Length;
103        if (!HasFixedScaleParameter) length--;
104        if (!HasFixedShapeParameter) length--;
105        inverseLength = p.Select(e => 1.0 / Math.Exp(e)).Take(length).ToArray();
106        c += inverseLength.Length;
107      }
108      if (HasFixedScaleParameter) {
109        scale = ScaleParameter.Value.Value;
110      } else {
111        scale = Math.Exp(2 * p[c]);
112        c++;
113      }
114      if (HasFixedShapeParameter) {
115        shape = ShapeParameter.Value.Value;
116      } else {
117        shape = Math.Exp(p[c]);
118        c++;
119      }
120      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceRationalQuadraticArd", "p");
121    }
122
123    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
124      double scale, shape;
125      double[] inverseLength;
126      GetParameterValues(p, out scale, out shape, out inverseLength);
127      var fixedInverseLength = HasFixedInverseLengthParameter;
128      var fixedScale = HasFixedScaleParameter;
129      var fixedShape = HasFixedShapeParameter;
130      // create functions
131      var cov = new ParameterizedCovarianceFunction();
132      cov.Covariance = (x, i, j) => {
133        double d = i == j
134                    ? 0.0
135                    : Util.SqrDist(x, i, j, inverseLength, columnIndices);
136        return scale * Math.Pow(1 + 0.5 * d / shape, -shape);
137      };
138      cov.CrossCovariance = (x, xt, i, j) => {
139        double d = Util.SqrDist(x, i, xt, j, inverseLength, columnIndices);
140        return scale * Math.Pow(1 + 0.5 * d / shape, -shape);
141      };
142      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, shape, inverseLength, fixedInverseLength, fixedScale, fixedShape);
143      return cov;
144    }
145
146    private static IList<double> GetGradient(double[,] x, int i, int j, int[] columnIndices, double scale, double shape, double[] inverseLength,
147      bool fixedInverseLength, bool fixedScale, bool fixedShape) {
148      double d = i == j
149                   ? 0.0
150                   : Util.SqrDist(x, i, j, inverseLength, columnIndices);
151      double b = 1 + 0.5 * d / shape;
152      int k = 0;
153      var g = new List<double>(columnIndices.Length + 2);
154      if (!fixedInverseLength) {
155        foreach (var columnIndex in columnIndices) {
156          g.Add(
157            scale * Math.Pow(b, -shape - 1) *
158            Util.SqrDist(x[i, columnIndex] * inverseLength[k], x[j, columnIndex] * inverseLength[k]));
159          k++;
160        }
161      }
162      if (!fixedScale) g.Add(2 * scale * Math.Pow(b, -shape));
163      if (!fixedShape) g.Add(scale * Math.Pow(b, -shape) * (0.5 * d / b - shape * Math.Log(b)));
164      return g;
165    }
166  }
167}
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