1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System.Linq;
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23 | using HeuristicLab.Common;
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24 | using HeuristicLab.Core;
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25 | using HeuristicLab.Data;
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26 | using HeuristicLab.Operators;
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27 | using HeuristicLab.Optimization;
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28 | using HeuristicLab.Optimization.Operators.LCS;
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29 | using HeuristicLab.Parameters;
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30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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31 |
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32 | namespace HeuristicLab.Encodings.DecisionList {
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33 | [Item("BestTrainingDecisionListAnalyzer", "Description missing")]
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34 | [StorableClass]
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35 | public class BestTrainingDecisionListAnalyzer : SingleSuccessorOperator, IAnalyzer {
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36 | private const string BestTrainingSolutionName = "Best Training Solution";
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37 | private const string TrainingBestSolutionQualityParameterName = "Best training solution quality";
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38 |
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39 | public bool EnabledByDefault {
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40 | get { return true; }
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41 | }
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42 |
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43 | #region Parameter Properties
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44 | public ValueLookupParameter<ResultCollection> ResultsParameter {
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45 | get { return (ValueLookupParameter<ResultCollection>)Parameters["Results"]; }
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46 | }
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47 | public ScopeTreeLookupParameter<IGAssistIndividual> IndividualParameter {
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48 | get { return (ScopeTreeLookupParameter<IGAssistIndividual>)Parameters["Individual"]; }
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49 | }
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50 | public LookupParameter<IDecisionListClassificationProblemData> ProblemDataParameter {
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51 | get { return (LookupParameter<IDecisionListClassificationProblemData>)Parameters["ProblemData"]; }
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52 | }
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53 | public IScopeTreeLookupParameter<DoubleValue> QualityParameter {
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54 | get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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55 | }
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56 | public ILookupParameter<BoolValue> MaximizationParameter {
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57 | get { return (ILookupParameter<BoolValue>)Parameters["Maximization"]; }
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58 | }
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59 | #endregion
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60 |
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61 | public ResultCollection Results { get { return ResultsParameter.ActualValue; } }
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62 |
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63 | [StorableConstructor]
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64 | protected BestTrainingDecisionListAnalyzer(bool deserializing) : base(deserializing) { }
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65 | protected BestTrainingDecisionListAnalyzer(BestTrainingDecisionListAnalyzer original, Cloner cloner)
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66 | : base(original, cloner) {
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67 | }
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68 | public BestTrainingDecisionListAnalyzer()
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69 | : base() {
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70 | Parameters.Add(new ScopeTreeLookupParameter<IGAssistIndividual>("Individual", ""));
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71 | Parameters.Add(new ValueLookupParameter<ResultCollection>("Results", "The result collection where the solution should be stored."));
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72 | Parameters.Add(new LookupParameter<IDecisionListClassificationProblemData>("ProblemData", ""));
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73 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The qualities of the trees that should be analyzed."));
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74 | Parameters.Add(new LookupParameter<BoolValue>("Maximization", "The direction of optimization."));
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75 | }
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76 | public override IDeepCloneable Clone(Cloner cloner) {
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77 | return new BestTrainingDecisionListAnalyzer(this, cloner);
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78 | }
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79 |
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80 | public override IOperation Apply() {
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81 | ItemArray<IGAssistIndividual> individuals = IndividualParameter.ActualValue;
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82 | ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
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83 | bool maximization = MaximizationParameter.ActualValue.Value;
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84 | IDecisionListClassificationProblemData problemData = ProblemDataParameter.ActualValue;
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85 |
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86 | DoubleValue value = maximization ? qualities.MaxItems(x => x.Value).First() : qualities.MinItems(x => x.Value).First();
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87 |
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88 | int index = qualities.IndexOf(value);
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89 |
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90 | if (!Results.ContainsKey(BestTrainingSolutionName)) {
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91 | Results.Add(new Result(TrainingBestSolutionQualityParameterName, new DoubleValue(value.Value)));
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92 | Results.Add(new Result(BestTrainingSolutionName, new DecisionListSolution((DecisionList)individuals[index], problemData)));
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93 | } else {
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94 | double oldBestQuality = ((DoubleValue)Results[TrainingBestSolutionQualityParameterName].Value).Value;
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95 | if (maximization && oldBestQuality < value.Value || !maximization && oldBestQuality > value.Value) {
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96 | Results[BestTrainingSolutionName].Value = new DecisionListSolution((DecisionList)individuals[index], problemData);
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97 | Results[TrainingBestSolutionQualityParameterName].Value = value;
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98 | }
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99 | }
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100 |
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101 | return base.Apply();
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102 | }
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103 | }
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104 | }
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