source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleProblemData.cs @ 7043

Last change on this file since 7043 was 7043, checked in by mkommend, 10 years ago

#1612: Created mean model for RegressionSolutionErrorCharacteristicsCurveView.

File size: 4.5 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.Parameters;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis {
30  [StorableClass]
31  [Item("ClassificationEnsembleProblemData", "Represents an item containing all data defining a classification problem.")]
32  public class ClassificationEnsembleProblemData : ClassificationProblemData {
33
34    public override bool IsTrainingSample(int index) {
35      return index >= 0 && index < Dataset.Rows &&
36             TrainingPartition.Start <= index && index < TrainingPartition.End;
37    }
38
39    public override bool IsTestSample(int index) {
40      return index >= 0 && index < Dataset.Rows &&
41             TestPartition.Start <= index && index < TestPartition.End;
42    }
43
44    private static readonly ClassificationEnsembleProblemData emptyProblemData;
45    public static new ClassificationEnsembleProblemData EmptyProblemData {
46      get { return emptyProblemData; }
47    }
48
49    static ClassificationEnsembleProblemData() {
50      var problemData = new ClassificationEnsembleProblemData();
51      problemData.Parameters.Clear();
52      problemData.Name = "Empty Classification ProblemData";
53      problemData.Description = "This ProblemData acts as place holder before the correct problem data is loaded.";
54      problemData.isEmpty = true;
55
56      problemData.Parameters.Add(new FixedValueParameter<Dataset>(DatasetParameterName, "", new Dataset()));
57      problemData.Parameters.Add(new FixedValueParameter<ReadOnlyCheckedItemList<StringValue>>(InputVariablesParameterName, ""));
58      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TrainingPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
59      problemData.Parameters.Add(new FixedValueParameter<IntRange>(TestPartitionParameterName, "", (IntRange)new IntRange(0, 0).AsReadOnly()));
60      problemData.Parameters.Add(new ConstrainedValueParameter<StringValue>(TargetVariableParameterName, new ItemSet<StringValue>()));
61      problemData.Parameters.Add(new FixedValueParameter<StringMatrix>(ClassNamesParameterName, "", new StringMatrix(0, 0).AsReadOnly()));
62      problemData.Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, "", (DoubleMatrix)new DoubleMatrix(0, 0).AsReadOnly()));
63      emptyProblemData = problemData;
64    }
65
66    [StorableConstructor]
67    protected ClassificationEnsembleProblemData(bool deserializing) : base(deserializing) { }
68    protected ClassificationEnsembleProblemData(ClassificationEnsembleProblemData original, Cloner cloner) : base(original, cloner) { }
69    public override IDeepCloneable Clone(Cloner cloner) {
70      if (this == emptyProblemData) return emptyProblemData;
71      return new ClassificationEnsembleProblemData(this, cloner);
72    }
73
74    public ClassificationEnsembleProblemData() : base() { }
75    public ClassificationEnsembleProblemData(IClassificationProblemData classificationProblemData)
76      : base(classificationProblemData.Dataset, classificationProblemData.AllowedInputVariables, classificationProblemData.TargetVariable) {
77      this.TrainingPartition.Start = classificationProblemData.TrainingPartition.Start;
78      this.TrainingPartition.End = classificationProblemData.TrainingPartition.End;
79      this.TestPartition.Start = classificationProblemData.TestPartition.Start;
80      this.TestPartition.End = classificationProblemData.TestPartition.End;
81    }
82
83    public ClassificationEnsembleProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable)
84      : base(dataset, allowedInputVariables, targetVariable) {
85    }
86  }
87}
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