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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessModelCreator.cs @ 10614

Last change on this file since 10614 was 9456, checked in by swagner, 12 years ago

Updated copyright year and added some missing license headers (#1889)

File size: 4.5 KB
Line 
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
22using HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Data;
25using HeuristicLab.Encodings.RealVectorEncoding;
26using HeuristicLab.Operators;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Algorithms.DataAnalysis {
31  [StorableClass]
32  // base class for GaussianProcessModelCreators (specific for classification and regression)
33  public abstract class GaussianProcessModelCreator : SingleSuccessorOperator {
34    private const string HyperparameterParameterName = "Hyperparameter";
35    private const string MeanFunctionParameterName = "MeanFunction";
36    private const string CovarianceFunctionParameterName = "CovarianceFunction";
37    private const string ModelParameterName = "Model";
38    private const string NegativeLogLikelihoodParameterName = "NegativeLogLikelihood";
39    private const string HyperparameterGradientsParameterName = "HyperparameterGradients";
40
41    #region Parameter Properties
42    // in
43    public ILookupParameter<RealVector> HyperparameterParameter {
44      get { return (ILookupParameter<RealVector>)Parameters[HyperparameterParameterName]; }
45    }
46    public ILookupParameter<IMeanFunction> MeanFunctionParameter {
47      get { return (ILookupParameter<IMeanFunction>)Parameters[MeanFunctionParameterName]; }
48    }
49    public ILookupParameter<ICovarianceFunction> CovarianceFunctionParameter {
50      get { return (ILookupParameter<ICovarianceFunction>)Parameters[CovarianceFunctionParameterName]; }
51    }
52    // out
53    public ILookupParameter<IGaussianProcessModel> ModelParameter {
54      get { return (ILookupParameter<IGaussianProcessModel>)Parameters[ModelParameterName]; }
55    }
56    public ILookupParameter<RealVector> HyperparameterGradientsParameter {
57      get { return (ILookupParameter<RealVector>)Parameters[HyperparameterGradientsParameterName]; }
58    }
59    public ILookupParameter<DoubleValue> NegativeLogLikelihoodParameter {
60      get { return (ILookupParameter<DoubleValue>)Parameters[NegativeLogLikelihoodParameterName]; }
61    }
62
63    #endregion
64
65    #region Properties
66    protected RealVector Hyperparameter { get { return HyperparameterParameter.ActualValue; } }
67    protected IMeanFunction MeanFunction { get { return MeanFunctionParameter.ActualValue; } }
68    protected ICovarianceFunction CovarianceFunction { get { return CovarianceFunctionParameter.ActualValue; } }
69    #endregion
70
71    [StorableConstructor]
72    protected GaussianProcessModelCreator(bool deserializing) : base(deserializing) { }
73    protected GaussianProcessModelCreator(GaussianProcessModelCreator original, Cloner cloner) : base(original, cloner) { }
74    protected GaussianProcessModelCreator()
75      : base() {
76      // in
77      Parameters.Add(new LookupParameter<RealVector>(HyperparameterParameterName, "The hyperparameters for the Gaussian process model."));
78      Parameters.Add(new LookupParameter<IMeanFunction>(MeanFunctionParameterName, "The mean function for the Gaussian process model."));
79      Parameters.Add(new LookupParameter<ICovarianceFunction>(CovarianceFunctionParameterName, "The covariance function for the Gaussian process model."));
80      // out
81      Parameters.Add(new LookupParameter<IGaussianProcessModel>(ModelParameterName, "The resulting Gaussian process model"));
82      Parameters.Add(new LookupParameter<RealVector>(HyperparameterGradientsParameterName, "The gradients of the hyperparameters for the produced Gaussian process model (necessary for hyperparameter optimization)"));
83      Parameters.Add(new LookupParameter<DoubleValue>(NegativeLogLikelihoodParameterName, "The negative log-likelihood of the produced Gaussian process model given the data."));
84    }
85  }
86}
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