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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionScaledMseEvaluator.cs @ 4112

Last change on this file since 4112 was 4112, checked in by gkronber, 13 years ago

Fixed some bugs in multi-variate regression classes. #1089

File size: 4.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Interfaces;
30using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;
31using HeuristicLab.Problems.DataAnalysis.Symbolic;
32
33namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
34  [Item("SymbolicVectorRegressionScaledMseEvaluator", "Represents an operator that calculates the scaled mean squared error for all components independently.")]
35  [StorableClass]
36  public class SymbolicVectorRegressionScaledMseEvaluator : SymbolicVectorRegressionEvaluator, IMultiObjectiveSymbolicVectorRegressionEvaluator {
37    private const string QualitiesParameterName = "ScaledMeanSquaredErrors";
38    private const string AlphaParameterName = "Alpha";
39    private const string BetaParameterName = "Beta";
40
41    #region parameter properties
42    public ILookupParameter<DoubleArray> QualitiesParameter {
43      get { return (ILookupParameter<DoubleArray>)Parameters[QualitiesParameterName]; }
44    }
45    public ILookupParameter<DoubleArray> AlphaParameter {
46      get { return (ILookupParameter<DoubleArray>)Parameters[AlphaParameterName]; }
47    }
48    public ILookupParameter<DoubleArray> BetaParameter {
49      get { return (ILookupParameter<DoubleArray>)Parameters[BetaParameterName]; }
50    }
51
52    #endregion
53
54    public SymbolicVectorRegressionScaledMseEvaluator()
55      : base() {
56      Parameters.Add(new LookupParameter<DoubleArray>(QualitiesParameterName, "The mean squared errors for each component of the symbolic vector regression solution encoded as a symbolic expression tree."));
57      Parameters.Add(new LookupParameter<DoubleArray>(AlphaParameterName, "The alpha parameter for linear scaling."));
58      Parameters.Add(new LookupParameter<DoubleArray>(BetaParameterName, "The beta parameter for linear scaling."));
59    }
60
61    public override void Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
62      List<string> targetVariablesList = targetVariables.ToList();
63      DoubleArray qualities = new DoubleArray(targetVariables.Count());
64      DoubleArray alpha = new DoubleArray(qualities.Length);
65      DoubleArray beta = new DoubleArray(qualities.Length);
66      // use only the i-th vector component
67      List<SymbolicExpressionTreeNode> componentBranches = new List<SymbolicExpressionTreeNode>(tree.Root.SubTrees[0].SubTrees);
68      while (tree.Root.SubTrees[0].SubTrees.Count > 0) tree.Root.SubTrees[0].RemoveSubTree(0);
69
70      for (int i = 0; i < targetVariables.Count(); i++) {
71        tree.Root.SubTrees[0].AddSubTree(componentBranches[i]);
72
73        double compAlpha;
74        double compBeta;
75        double mse = SymbolicRegressionScaledMeanSquaredErrorEvaluator.Calculate(interpreter, tree,
76          lowerEstimationBound[i], upperEstimationBound[i],
77          problemData.Dataset, targetVariablesList[i], rows, out compBeta, out compAlpha);
78
79        qualities[i] = mse;
80        alpha[i] = compAlpha;
81        beta[i] = compBeta;
82        tree.Root.SubTrees[0].RemoveSubTree(0);
83      }
84      // restore tree
85      foreach (var treeNode in componentBranches) {
86        tree.Root.SubTrees[0].AddSubTree(treeNode);
87      }
88
89      QualitiesParameter.ActualValue = qualities;
90      AlphaParameter.ActualValue = alpha;
91      BetaParameter.ActualValue = beta;
92    }
93  }
94}
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