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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/MultiObjective/SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs @ 5514

Last change on this file since 5514 was 5514, checked in by mkommend, 13 years ago

#1418: Fixed compilation errors in Problems.DataAnalysis.*.

File size: 3.4 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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 HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification.MultiObjective {
30  [Item("Mean squared error & Tree size Evaluator", "Calculates the mean squared error and the tree size of a symbolic classification solution.")]
31  [StorableClass]
32  public class SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator : SymbolicClassificationMultiObjectiveEvaluator {
33    [StorableConstructor]
34    protected SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator(bool deserializing) : base(deserializing) { }
35    protected SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator(SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator original, Cloner cloner)
36      : base(original, cloner) {
37    }
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator(this, cloner);
40    }
41
42    public SymbolicClassificationMultiObjectiveMeanSquaredErrorTreeSizeEvaluator() : base() { }
43
44    public override IEnumerable<bool> Maximization { get { return new bool[2] { false, false }; } }
45
46    public override IOperation Apply() {
47      IEnumerable<int> rows = GenerateRowsToEvaluate();
48      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, SymbolicExpressionTreeParameter.ActualValue, LowerEstimationLimit.Value, UpperEstimationLimit.Value, ProblemData, rows);
49      QualitiesParameter.ActualValue = new DoubleArray(qualities);
50      return base.Apply();
51    }
52
53    public static double[] Calculate(ISymbolicDataAnalysisTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable<int> rows) {
54      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
55      IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
56      IEnumerable<double> boundedEstimationValues = BoundEstimatedValues(estimatedValues, lowerEstimationLimit, upperEstimationLimit);
57      double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues);
58      return new double[2] { mse, solution.Size };
59    }
60  }
61}
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