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source: branches/OaaS/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblem.cs @ 15655

Last change on this file since 15655 was 8458, checked in by mkommend, 12 years ago

#1081: Derived time series classes from regression classes to avoid code duplication.

File size: 1.7 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 HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
25
26namespace HeuristicLab.Problems.DataAnalysis {
27  [StorableClass]
28  [Item("Time-series Prognosis Problem", "A general time-series prognosis problem.")]
29  [Creatable("Problems")]
30  public class TimeSeriesPrognosisProblem : DataAnalysisProblem<ITimeSeriesPrognosisProblemData>, ITimeSeriesPrognosisProblem {
31    [StorableConstructor]
32    protected TimeSeriesPrognosisProblem(bool deserializing) : base(deserializing) { }
33    protected TimeSeriesPrognosisProblem(TimeSeriesPrognosisProblem original, Cloner cloner) : base(original, cloner) { }
34    public override IDeepCloneable Clone(Cloner cloner) { return new TimeSeriesPrognosisProblem(this, cloner); }
35
36    public TimeSeriesPrognosisProblem() : base(new TimeSeriesPrognosisProblemData()) { }
37  }
38}
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