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source: branches/DataAnalysis Refactoring/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveProblem.cs @ 5623

Last change on this file since 5623 was 5623, checked in by mkommend, 13 years ago

#1418: Added possibility to import data from csv files.

File size: 3.1 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.Linq;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27
28namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
29  [Item("Symbolic Regression Problem (multi objective)", "Represents a multi objective symbolic regression problem.")]
30  [StorableClass]
31  [Creatable("Problems")]
32  public class SymbolicRegressionMultiObjectiveProblem : SymbolicDataAnalysisMultiObjectiveProblem<IRegressionProblemData, ISymbolicRegressionMultiObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator> {
33    private const double PunishmentFactor = 10;
34
35    [StorableConstructor]
36    protected SymbolicRegressionMultiObjectiveProblem(bool deserializing) : base(deserializing) { }
37    protected SymbolicRegressionMultiObjectiveProblem(SymbolicRegressionMultiObjectiveProblem original, Cloner cloner) : base(original, cloner) { }
38    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionMultiObjectiveProblem(this, cloner); }
39
40    public SymbolicRegressionMultiObjectiveProblem()
41      : base(new RegressionProblemData(), new SymbolicRegressionMultiObjectivePearsonRSquaredTreeSizeEvaluator(), new SymbolicDataAnalysisExpressionTreeCreator()) {
42      Maximization = new BoolArray(new bool[] { true, false });
43      MaximumSymbolicExpressionTreeDepth.Value = 8;
44      MaximumSymbolicExpressionTreeLength.Value = 25;
45    }
46
47    protected override void UpdateEstimationLimits() {
48      if (ProblemData.TrainingPartitionStart.Value < ProblemData.TrainingPartitionEnd.Value) {
49        var targetValues = ProblemData.Dataset.GetVariableValues(ProblemData.TargetVariable, ProblemData.TrainingPartitionStart.Value, ProblemData.TrainingPartitionEnd.Value);
50        var mean = targetValues.Average();
51        var range = targetValues.Max() - targetValues.Min();
52        UpperEstimationLimit.Value = mean + PunishmentFactor * range;
53        LowerEstimationLimit.Value = mean - PunishmentFactor * range;
54      }
55    }
56
57    public override void ImportProblemDataFromFile(string fileName) {
58      RegressionProblemData problemData = RegressionProblemData.ImportFromFile(fileName);
59      ProblemData = problemData;
60    }
61  }
62}
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