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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionScaledNormalizedMseEvaluator.cs @ 4194

Last change on this file since 4194 was 4194, checked in by gkronber, 13 years ago

Created a feature/exploration branch for new data analysis features #1142

File size: 4.1 KB
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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("SymbolicVectorRegressionScaledNormalizedMseEvaluator", "Represents an operator that calculates the sum of the normalized mean squared error over all components.")]
35  [StorableClass]
36  public class SymbolicVectorRegressionScaledNormalizedMseEvaluator : SingleObjectiveSymbolicVectorRegressionEvaluator {
37    private const string AlphaParameterName = "Alpha";
38    private const string BetaParameterName = "Beta";
39
40    #region parameter properties
41    public ILookupParameter<DoubleArray> AlphaParameter {
42      get { return (ILookupParameter<DoubleArray>)Parameters[AlphaParameterName]; }
43    }
44    public ILookupParameter<DoubleArray> BetaParameter {
45      get { return (ILookupParameter<DoubleArray>)Parameters[BetaParameterName]; }
46    }
47
48    #endregion
49
50    public SymbolicVectorRegressionScaledNormalizedMseEvaluator()
51      : base() {
52      Parameters.Add(new LookupParameter<DoubleArray>(AlphaParameterName, "The alpha parameter for linear scaling."));
53      Parameters.Add(new LookupParameter<DoubleArray>(BetaParameterName, "The beta parameter for linear scaling."));
54    }
55
56    public override double Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
57      List<string> targetVariablesList = targetVariables.ToList();
58      double nmseSum = 0.0;
59
60      DoubleArray alpha = new DoubleArray(targetVariablesList.Count);
61      DoubleArray beta = new DoubleArray(targetVariablesList.Count);
62
63      // use only the i-th vector component
64      List<SymbolicExpressionTreeNode> componentBranches = new List<SymbolicExpressionTreeNode>(tree.Root.SubTrees[0].SubTrees);
65      while (tree.Root.SubTrees[0].SubTrees.Count > 0) tree.Root.SubTrees[0].RemoveSubTree(0);
66      for (int i = 0; i < targetVariablesList.Count; i++) {
67        tree.Root.SubTrees[0].AddSubTree(componentBranches[i]);
68        double compAlpha;
69        double compBeta;
70        double nmse = SymbolicRegressionScaledNormalizedMeanSquaredErrorEvaluator.Calculate(interpreter, tree,
71          lowerEstimationBound[i], upperEstimationBound[i],
72          problemData.Dataset, targetVariablesList[i], rows, out compBeta, out compAlpha);
73        alpha[i] = compAlpha;
74        beta[i] = compBeta;
75        nmseSum += nmse;
76        tree.Root.SubTrees[0].RemoveSubTree(0);
77      }
78      // restore tree
79      foreach (var treeNode in componentBranches) {
80        tree.Root.SubTrees[0].AddSubTree(treeNode);
81      }
82      AlphaParameter.ActualValue = alpha;
83      BetaParameter.ActualValue = beta;
84      return nmseSum;
85    }
86  }
87}
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