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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/ThresholdCalculator.cs @ 13042

Last change on this file since 13042 was 12012, checked in by ascheibe, 10 years ago

#2212 merged r12008, r12009, r12010 back into trunk

File size: 1.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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.Persistence.Default.CompositeSerializers.Storable;
26
27namespace HeuristicLab.Problems.DataAnalysis {
28  /// <summary>
29  /// Base class for threshold calculators for discriminant function classification models.
30  /// </summary>
31  [StorableClass]
32  public abstract class ThresholdCalculator : NamedItem, IDiscriminantFunctionThresholdCalculator {
33
34    [StorableConstructor]
35    protected ThresholdCalculator(bool deserializing) : base(deserializing) { }
36    protected ThresholdCalculator(ThresholdCalculator original, Cloner cloner)
37      : base(original, cloner) {
38    }
39    public ThresholdCalculator()
40      : base() {
41      this.name = ItemName;
42      this.description = ItemDescription;
43    }
44
45    public abstract void Calculate(IClassificationProblemData problemData, IEnumerable<double> estimatedValues, IEnumerable<double> targetClassValues, out double[] classValues, out double[] thresholds);
46  }
47}
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