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source: trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessRegressionSolutionCreator.cs @ 8401

Last change on this file since 8401 was 8401, checked in by gkronber, 12 years ago

#1423 moved LM-BFGS implementation from data-analysis into the gradient descent algorithm plugin.

File size: 4.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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.Operators;
26using HeuristicLab.Optimization;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Problems.DataAnalysis;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  [StorableClass]
33  [Item(Name = "GaussianProcessRegressionSolutionCreator",
34    Description = "Creates a Gaussian process solution from a trained model.")]
35  public sealed class GaussianProcessRegressionSolutionCreator : SingleSuccessorOperator {
36    private const string ProblemDataParameterName = "ProblemData";
37    private const string ModelParameterName = "GaussianProcessRegressionModel";
38    private const string SolutionParameterName = "Solution";
39    private const string ResultsParameterName = "Results";
40    private const string TrainingRSquaredResultName = "Training R²";
41    private const string TestRSquaredResultName = "Test R²";
42
43    #region Parameter Properties
44    public ILookupParameter<IRegressionProblemData> ProblemDataParameter {
45      get { return (ILookupParameter<IRegressionProblemData>)Parameters[ProblemDataParameterName]; }
46    }
47    public ILookupParameter<IGaussianProcessSolution> SolutionParameter {
48      get { return (ILookupParameter<IGaussianProcessSolution>)Parameters[SolutionParameterName]; }
49    }
50    public ILookupParameter<IGaussianProcessModel> ModelParameter {
51      get { return (ILookupParameter<IGaussianProcessModel>)Parameters[ModelParameterName]; }
52    }
53    public ILookupParameter<ResultCollection> ResultsParameter {
54      get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
55    }
56    #endregion
57
58    [StorableConstructor]
59    private GaussianProcessRegressionSolutionCreator(bool deserializing) : base(deserializing) { }
60    private GaussianProcessRegressionSolutionCreator(GaussianProcessRegressionSolutionCreator original, Cloner cloner) : base(original, cloner) { }
61    public GaussianProcessRegressionSolutionCreator()
62      : base() {
63      // in
64      Parameters.Add(new LookupParameter<IRegressionProblemData>(ProblemDataParameterName, "The regression problem data for the Gaussian process solution."));
65      Parameters.Add(new LookupParameter<IGaussianProcessModel>(ModelParameterName, "The Gaussian process regression model to use for the solution."));
66      // in & out
67      Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection of the algorithm."));
68      // out
69      Parameters.Add(new LookupParameter<IGaussianProcessSolution>(SolutionParameterName, "The produced Gaussian process solution."));
70    }
71
72    public override IDeepCloneable Clone(Cloner cloner) {
73      return new GaussianProcessRegressionSolutionCreator(this, cloner);
74    }
75
76    public override IOperation Apply() {
77      var m = ModelParameter.ActualValue;
78      var data = ProblemDataParameter.ActualValue;
79      var s = new GaussianProcessRegressionSolution(m, data);
80
81
82      SolutionParameter.ActualValue = s;
83      var results = ResultsParameter.ActualValue;
84      if (!results.ContainsKey(SolutionParameterName)) {
85        results.Add(new Result(ResultsParameterName, "The Gaussian process regression solution", s));
86        results.Add(new Result(TrainingRSquaredResultName, "The Pearson's R² of the Gaussian process solution on the training partition.", new DoubleValue(s.TrainingRSquared)));
87        results.Add(new Result(TestRSquaredResultName, "The Pearson's R² of the Gaussian process solution on the test partition.", new DoubleValue(s.TestRSquared)));
88      } else {
89        results[ResultsParameterName].Value = s;
90        results[TrainingRSquaredResultName].Value = new DoubleValue(s.TrainingRSquared);
91        results[TestRSquaredResultName].Value = new DoubleValue(s.TestRSquared);
92      }
93      return base.Apply();
94    }
95  }
96}
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