source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceRationalQuadraticIso.cs @ 13784

Last change on this file since 13784 was 13784, checked in by pfleck, 5 years ago

#2591 Made the creation of a GaussianProcessModel faster by avoiding additional iterators during calculation of the hyperparameter gradients.
The gradients of the hyperparameters are now calculated in one sweep and returned as IList, instead of returning an iterator (with yield return).
This avoids a large amount of Move-calls of the iterator, especially for covariance functions with a lot of hyperparameters.
Besides, the signature of the CovarianceGradientFunctionDelegate is changed, to return an IList instead of an IEnumerable to avoid unnececary ToList or ToArray calls.

File size: 6.2 KB
Line 
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 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 = "CovarianceRationalQuadraticIso",
33    Description = "Isotropic rational quadratic covariance function for Gaussian processes.")]
34  public sealed class CovarianceRationalQuadraticIso : ParameterizedNamedItem, ICovarianceFunction {
35    public IValueParameter<DoubleValue> ScaleParameter {
36      get { return (IValueParameter<DoubleValue>)Parameters["Scale"]; }
37    }
38
39    public IValueParameter<DoubleValue> InverseLengthParameter {
40      get { return (IValueParameter<DoubleValue>)Parameters["InverseLength"]; }
41    }
42
43    public IValueParameter<DoubleValue> ShapeParameter {
44      get { return (IValueParameter<DoubleValue>)Parameters["Shape"]; }
45    }
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
58    [StorableConstructor]
59    private CovarianceRationalQuadraticIso(bool deserializing)
60      : base(deserializing) {
61    }
62
63    private CovarianceRationalQuadraticIso(CovarianceRationalQuadraticIso original, Cloner cloner)
64      : base(original, cloner) {
65    }
66
67    public CovarianceRationalQuadraticIso()
68      : base() {
69      Name = ItemName;
70      Description = ItemDescription;
71
72      Parameters.Add(new OptionalValueParameter<DoubleValue>("Scale", "The scale parameter of the isometric rational quadratic covariance function."));
73      Parameters.Add(new OptionalValueParameter<DoubleValue>("InverseLength", "The inverse length parameter of the isometric rational quadratic covariance function."));
74      Parameters.Add(new OptionalValueParameter<DoubleValue>("Shape", "The shape parameter (alpha) of the isometric rational quadratic covariance function."));
75    }
76
77    public override IDeepCloneable Clone(Cloner cloner) {
78      return new CovarianceRationalQuadraticIso(this, cloner);
79    }
80
81    public int GetNumberOfParameters(int numberOfVariables) {
82      return (HasFixedScaleParameter ? 0 : 1) +
83        (HasFixedShapeParameter ? 0 : 1) +
84        (HasFixedInverseLengthParameter ? 0 : 1);
85    }
86
87    public void SetParameter(double[] p) {
88      double scale, shape, inverseLength;
89      GetParameterValues(p, out scale, out shape, out inverseLength);
90      ScaleParameter.Value = new DoubleValue(scale);
91      ShapeParameter.Value = new DoubleValue(shape);
92      InverseLengthParameter.Value = new DoubleValue(inverseLength);
93    }
94
95    private void GetParameterValues(double[] p, out double scale, out double shape, out double inverseLength) {
96      int c = 0;
97      // gather parameter values
98      if (HasFixedInverseLengthParameter) {
99        inverseLength = InverseLengthParameter.Value.Value;
100      } else {
101        inverseLength = 1.0 / Math.Exp(p[c]);
102        c++;
103      }
104      if (HasFixedScaleParameter) {
105        scale = ScaleParameter.Value.Value;
106      } else {
107        scale = Math.Exp(2 * p[c]);
108        c++;
109      }
110      if (HasFixedShapeParameter) {
111        shape = ShapeParameter.Value.Value;
112      } else {
113        shape = Math.Exp(p[c]);
114        c++;
115      }
116      if (p.Length != c) throw new ArgumentException("The length of the parameter vector does not match the number of free parameters for CovarianceRationalQuadraticIso", "p");
117    }
118
119    public ParameterizedCovarianceFunction GetParameterizedCovarianceFunction(double[] p, int[] columnIndices) {
120      double scale, shape, inverseLength;
121      GetParameterValues(p, out scale, out shape, out inverseLength);
122      var fixedInverseLength = HasFixedInverseLengthParameter;
123      var fixedScale = HasFixedScaleParameter;
124      var fixedShape = HasFixedShapeParameter;
125      // create functions
126      var cov = new ParameterizedCovarianceFunction();
127      cov.Covariance = (x, i, j) => {
128        double d = i == j
129                    ? 0.0
130                    : Util.SqrDist(x, i, j, columnIndices, inverseLength);
131        return scale * Math.Pow(1 + 0.5 * d / shape, -shape);
132      };
133      cov.CrossCovariance = (x, xt, i, j) => {
134        double d = Util.SqrDist(x, i, xt, j, columnIndices, inverseLength);
135        return scale * Math.Pow(1 + 0.5 * d / shape, -shape);
136      };
137      cov.CovarianceGradient = (x, i, j) => GetGradient(x, i, j, columnIndices, scale, shape, inverseLength, fixedInverseLength, fixedScale, fixedShape);
138      return cov;
139    }
140
141    private static IList<double> GetGradient(double[,] x, int i, int j, int[] columnIndices, double scale, double shape, double inverseLength,
142      bool fixedInverseLength, bool fixedScale, bool fixedShape) {
143      double d = i == j
144                   ? 0.0
145                   : Util.SqrDist(x, i, j, columnIndices, inverseLength);
146
147      double b = 1 + 0.5 * d / shape;
148      var g = new List<double>(3);
149      if (!fixedInverseLength) g.Add(scale * Math.Pow(b, -shape - 1) * d);
150      if (!fixedScale) g.Add(2 * scale * Math.Pow(b, -shape));
151      if (!fixedShape) g.Add(scale * Math.Pow(b, -shape) * (0.5 * d / b - shape * Math.Log(b)));
152      return g;
153    }
154  }
155}
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