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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression/3.3/Symbolic/Evaluators/SymbolicVectorRegressionScaledMseEvaluator.cs @ 6274

Last change on this file since 6274 was 5275, checked in by gkronber, 14 years ago

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

File size: 4.5 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;
32using HeuristicLab.Common;
33
34namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic.Evaluators {
35  [Item("SymbolicVectorRegressionScaledMseEvaluator", "Represents an operator that calculates the scaled mean squared error for all components independently.")]
36  [StorableClass]
37  public class SymbolicVectorRegressionScaledMseEvaluator : MultiObjectiveSymbolicVectorRegressionEvaluator {
38    private const string AlphaParameterName = "Alpha";
39    private const string BetaParameterName = "Beta";
40
41    #region parameter properties
42    public ILookupParameter<DoubleArray> AlphaParameter {
43      get { return (ILookupParameter<DoubleArray>)Parameters[AlphaParameterName]; }
44    }
45    public ILookupParameter<DoubleArray> BetaParameter {
46      get { return (ILookupParameter<DoubleArray>)Parameters[BetaParameterName]; }
47    }
48
49    #endregion
50
51    [StorableConstructor]
52    protected SymbolicVectorRegressionScaledMseEvaluator(bool deserializing) : base(deserializing) { }
53    protected SymbolicVectorRegressionScaledMseEvaluator(SymbolicVectorRegressionScaledMseEvaluator original, Cloner cloner)
54      : base(original, cloner) {
55    }
56    public SymbolicVectorRegressionScaledMseEvaluator()
57      : base() {
58      Parameters.Add(new LookupParameter<DoubleArray>(AlphaParameterName, "The alpha parameter for linear scaling."));
59      Parameters.Add(new LookupParameter<DoubleArray>(BetaParameterName, "The beta parameter for linear scaling."));
60    }
61    public override IDeepCloneable Clone(Cloner cloner) {
62      return new SymbolicVectorRegressionScaledMseEvaluator(this, cloner);
63    }
64    public override double[] Evaluate(SymbolicExpressionTree tree, ISymbolicExpressionTreeInterpreter interpreter, MultiVariateDataAnalysisProblemData problemData, IEnumerable<string> targetVariables, IEnumerable<int> rows, DoubleArray lowerEstimationBound, DoubleArray upperEstimationBound) {
65      List<string> targetVariablesList = targetVariables.ToList();
66      double[] qualities = new double[targetVariables.Count()];
67      DoubleArray alpha = new DoubleArray(qualities.Length);
68      DoubleArray beta = new DoubleArray(qualities.Length);
69      // use only the i-th vector component
70      List<SymbolicExpressionTreeNode> componentBranches = new List<SymbolicExpressionTreeNode>(tree.Root.SubTrees[0].SubTrees);
71      while (tree.Root.SubTrees[0].SubTrees.Count > 0) tree.Root.SubTrees[0].RemoveSubTree(0);
72
73      for (int i = 0; i < targetVariables.Count(); i++) {
74        tree.Root.SubTrees[0].AddSubTree(componentBranches[i]);
75
76        double compAlpha;
77        double compBeta;
78        double mse = SymbolicRegressionScaledMeanSquaredErrorEvaluator.Calculate(interpreter, tree,
79          lowerEstimationBound[i], upperEstimationBound[i],
80          problemData.Dataset, targetVariablesList[i], rows, out compBeta, out compAlpha);
81
82        qualities[i] = mse;
83        alpha[i] = compAlpha;
84        beta[i] = compBeta;
85        tree.Root.SubTrees[0].RemoveSubTree(0);
86      }
87      // restore tree
88      foreach (var treeNode in componentBranches) {
89        tree.Root.SubTrees[0].AddSubTree(treeNode);
90      }
91
92      AlphaParameter.ActualValue = alpha;
93      BetaParameter.ActualValue = beta;
94      return qualities;
95    }
96  }
97}
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