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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionNormalizedMseEvaluator.cs @ 5275

Last change on this file since 5275 was 5275, checked in by gkronber, 13 years ago

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142

File size: 3.9 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;
32using HeuristicLab.Common;
33
34namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
35  [Item("SymbolicVectorRegressionNormalizedMseEvaluator", "Represents an operator that calculates the sum of the normalized mean squared error over all components.")]
36  [StorableClass]
37  public class SymbolicVectorRegressionNormalizedMseEvaluator : SingleObjectiveSymbolicVectorRegressionEvaluator {
38
39
40    [StorableConstructor]
41    protected SymbolicVectorRegressionNormalizedMseEvaluator(bool deserializing) : base(deserializing) { }
42    protected SymbolicVectorRegressionNormalizedMseEvaluator(SymbolicVectorRegressionNormalizedMseEvaluator original, Cloner cloner)
43      : base(original, cloner) {
44    }
45    public SymbolicVectorRegressionNormalizedMseEvaluator()
46      : base() {
47    }
48    public override IDeepCloneable Clone(Cloner cloner) {
49      return new SymbolicVectorRegressionNormalizedMseEvaluator(this, cloner);
50    }
51
52    public override double Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
53      return Calculate(tree, interpreter, problemData, targetVariables, rows, lowerEstimationBound, upperEstimationBound);
54    }
55
56    public static double Calculate(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      // use only the i-th vector component
60      List<SymbolicExpressionTreeNode> componentBranches = new List<SymbolicExpressionTreeNode>(tree.Root.SubTrees[0].SubTrees);
61      while (tree.Root.SubTrees[0].SubTrees.Count > 0) tree.Root.SubTrees[0].RemoveSubTree(0);
62
63      for (int i = 0; i < targetVariablesList.Count; i++) {
64        tree.Root.SubTrees[0].AddSubTree(componentBranches[i]);
65        double nmse = SymbolicRegressionNormalizedMeanSquaredErrorEvaluator.Calculate(interpreter, tree,
66          lowerEstimationBound[i], upperEstimationBound[i],
67          problemData.Dataset, targetVariablesList[i], rows);
68        tree.Root.SubTrees[0].RemoveSubTree(0);
69        nmseSum += nmse;
70      }
71      // restore tree
72      foreach (var treeNode in componentBranches) {
73        tree.Root.SubTrees[0].AddSubTree(treeNode);
74      }
75      return nmseSum;
76    }
77  }
78}
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