source: branches/CEDMA-Refactoring-Ticket419/HeuristicLab.CEDMA.Core/Problem.cs @ 992

Last change on this file since 992 was 992, checked in by gkronber, 14 years ago

Added basic design for ProblemView #419 (Refactor CEDMA plugins)

File size: 3.0 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2008 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;
23using System.Collections.Generic;
24using System.Linq;
25using System.Text;
26using HeuristicLab.Core;
27using System.Xml;
28using HeuristicLab.CEDMA.DB.Interfaces;
29using HeuristicLab.Operators;
30
31namespace HeuristicLab.CEDMA.Core {
32  /// <summary>
33  /// Problem describes the data mining task.
34  /// Contains the actual data and meta-data:
35  ///  * which variables should be modelled
36  ///  * regression, time-series or classification problem
37  /// </summary>
38  public class Problem : ItemBase {
39    private string name;
40    public string Name {
41      get { return name; }
42    }
43    private HeuristicLab.DataAnalysis.Dataset dataset;
44    public HeuristicLab.DataAnalysis.Dataset DataSet {
45      get { return dataset; }
46    }
47
48    private int trainingSamplesStart;
49    public int TrainingSamplesStart {
50      get { return trainingSamplesStart; }
51    }
52
53    private int trainingSamplesEnd;
54    public int TrainingSamplesEnd {
55      get { return trainingSamplesEnd; }
56    }
57
58    private int validationSamplesStart;
59
60    public int ValidationSamplesStart {
61      get { return validationSamplesStart; }
62      set { validationSamplesStart = value; }
63    }
64    private int validationSamplesEnd;
65
66    public int ValidationSamplesEnd {
67      get { return validationSamplesEnd; }
68      set { validationSamplesEnd = value; }
69    }
70
71    private int testSamplesStart;
72
73    public int TestSamplesStart {
74      get { return testSamplesStart; }
75      set { testSamplesStart = value; }
76    }
77    private int testSamplesEnd;
78
79    public int TestSamplesEnd {
80      get { return testSamplesEnd; }
81      set { testSamplesEnd = value; }
82    }
83
84    private List<int> allowedInputVariables;
85    private List<int> allowedTargetVariables;
86
87    private List<int> minTimeOffsets;
88    private List<int> maxTimeOffsets;
89
90    private bool autoRegressive;
91
92    public bool AutoRegressive {
93      get { return autoRegressive; }
94      set { autoRegressive = value; }
95    }
96
97    private bool timeSeries;
98
99    public bool TimeSeries {
100      get { return timeSeries; }
101      set { timeSeries = value; }
102    }
103
104    public Problem()
105      : base() {
106    }
107  }
108}
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