source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSquaredExponentialArd.cs @ 13721

Last change on this file since 13721 was 13721, checked in by mkommend, 5 years ago

#2591: Changed all GP covariance and mean functions to use int[] for column indices instead of IEnumerable<int>. Changed GP utils, GPModel and StudentTProcessModell as well to use fewer iterators and adapted unit tests to new interface.

File size: 5.8 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 = "CovarianceSquaredExponentialArd", Description = "Squared exponential covariance function with automatic relevance determination for Gaussian processes.")]
34  public sealed class CovarianceSquaredExponentialArd : ParameterizedNamedItem, ICovarianceFunction {
35    public IValueParameter<DoubleValue> ScaleParameter {
36      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
37    }
38
39    public IValueParameter<DoubleArray> InverseLengthParameter {
40      get { return (IValueParameter<DoubleArray>)Parameters["InverseLength"]; }
41    }
42    private bool HasFixedInverseLengthParameter {
43      get { return InverseLengthParameter.Value != null; }
44    }
45    private bool HasFixedScaleParameter {
46      get { return ScaleParameter.Value != null; }
47    }
48
49    [StorableConstructor]
50    private CovarianceSquaredExponentialArd(bool deserializing) : base(deserializing) { }
51    private CovarianceSquaredExponentialArd(CovarianceSquaredExponentialArd original, Cloner cloner)
52      : base(original, cloner) {
53    }
54    public CovarianceSquaredExponentialArd()
55      : base() {
56      Name = ItemName;
57      Description = ItemDescription;
58
59      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the squared exponential covariance function with ARD."));
60      Parameters.Add(new OptionalValueParameter<DoubleArray>("InverseLength", "The inverse length parameter for automatic relevance determination."));
61    }
62
63    public override IDeepCloneable Clone(Cloner cloner) {
64      return new CovarianceSquaredExponentialArd(this, cloner);
65    }
66
67    public int GetNumberOfParameters(int numberOfVariables) {
68      return
69        (HasFixedScaleParameter ? 0 : 1) +
70        (HasFixedInverseLengthParameter ? 0 : numberOfVariables);
71    }
72
73    public void SetParameter(double[] p) {
74      double scale;
75      double[] inverseLength;
76      GetParameterValues(p, out scale, out inverseLength);
77      ScaleParameter.Value = new DoubleValue(scale);
78      InverseLengthParameter.Value = new DoubleArray(inverseLength);
79    }
80
81    private void GetParameterValues(double[] p, out double scale, out double[] inverseLength) {
82      int c = 0;
83      // gather parameter values
84      if (HasFixedInverseLengthParameter) {
85        inverseLength = InverseLengthParameter.Value.ToArray();
86      } else {
87        int length = p.Length;
88        if (!HasFixedScaleParameter) length--;
89        inverseLength = p.Select(e => 1.0 / Math.Exp(e)).Take(length).ToArray();
90        c += inverseLength.Length;
91      }
92      if (HasFixedScaleParameter) {
93        scale = ScaleParameter.Value.Value;
94      } else {
95        scale = Math.Exp(2 * p[c]);
96        c++;
97      }
98      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceSquaredExponentialArd", "p");
99    }
100
101    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
102      double scale;
103      double[] inverseLength;
104      GetParameterValues(p, out scale, out inverseLength);
105      var fixedInverseLength = HasFixedInverseLengthParameter;
106      var fixedScale = HasFixedScaleParameter;
107      // create functions
108      var cov = new ParameterizedCovarianceFunction();
109      cov.Covariance = (x, i, j) => {
110        double d = i == j
111                 ? 0.0
112                 : Util.SqrDist(x, i, j, inverseLength, columnIndices);
113        return scale * Math.Exp(-d / 2.0);
114      };
115      cov.CrossCovariance = (x, xt, i, j) => {
116        double d = Util.SqrDist(x, i, xt, j, inverseLength, columnIndices);
117        return scale * Math.Exp(-d / 2.0);
118      };
119      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, inverseLength, fixedInverseLength, fixedScale);
120      return cov;
121    }
122
123    // order of returned gradients must match the order in GetParameterValues!
124    private static IEnumerable<double> GetGradient(double[,] x, int i, int j, int[] columnIndices, double scale, double[] inverseLength,
125      bool fixedInverseLength, bool fixedScale) {
126      double d = i == j
127                   ? 0.0
128                   : Util.SqrDist(x, i, j, inverseLength, columnIndices);
129
130      int k = 0;
131      if (!fixedInverseLength) {
132        for (int c = 0; c < columnIndices.Length; c++) {
133          var columnIndex = columnIndices[c];
134          double sqrDist = Util.SqrDist(x[i, columnIndex] * inverseLength[k], x[j, columnIndex] * inverseLength[k]);
135          yield return scale * Math.Exp(-d / 2.0) * sqrDist;
136          k++;
137        }
138      }
139      if (!fixedScale) yield return 2.0 * scale * Math.Exp(-d / 2.0);
140    }
141  }
142}
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