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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Clustering/ClusteringProblemData.cs @ 6675

Last change on this file since 6675 was 5809, checked in by mkommend, 14 years ago

#1418: Reintegrated branch into trunk.

File size: 4.1 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;
23using System.Collections.Generic;
24using System.IO;
25using System.Linq;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Data;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31
32namespace HeuristicLab.Problems.DataAnalysis {
33  [StorableClass]
34  [Item("ClusteringProblemData", "Represents an item containing all data defining a clustering problem.")]
35  public sealed class ClusteringProblemData : DataAnalysisProblemData, IClusteringProblemData {
36
37    #region default data
38    private static double[,] kozaF1 = new double[,] {
39          {2.017885919, -1.449165046},
40          {1.30060506,  -1.344523885},
41          {1.147134798, -1.317989331},
42          {0.877182504, -1.266142284},
43          {0.852562452, -1.261020794},
44          {0.431095788, -1.158793317},
45          {0.112586002, -1.050908405},
46          {0.04594507,  -1.021989402},
47          {0.042572879, -1.020438113},
48          {-0.074027291,  -0.959859562},
49          {-0.109178553,  -0.938094706},
50          {-0.259721109,  -0.803635355},
51          {-0.272991057,  -0.387519561},
52          {-0.161978191,  -0.193611001},
53          {-0.102489983,  -0.114215349},
54          {-0.01469968, -0.014918985},
55          {-0.008863365,  -0.008942626},
56          {0.026751057, 0.026054094},
57          {0.166922436, 0.14309643},
58          {0.176953808, 0.1504144},
59          {0.190233418, 0.159916534},
60          {0.199800708, 0.166635331},
61          {0.261502822, 0.207600348},
62          {0.30182879,  0.232370249},
63          {0.83763905,  0.468046718}
64    };
65    private static Dataset defaultDataset;
66    private static IEnumerable<string> defaultAllowedInputVariables;
67
68    static ClusteringProblemData() {
69      defaultDataset = new Dataset(new string[] { "y", "x" }, kozaF1);
70      defaultDataset.Name = "Fourth-order Polynomial Function Benchmark Dataset";
71      defaultDataset.Description = "f(x) = x^4 + x^3 + x^2 + x^1";
72      defaultAllowedInputVariables = new List<string>() { "x", "y" };
73    }
74    #endregion
75
76    [StorableConstructor]
77    private ClusteringProblemData(bool deserializing) : base(deserializing) { }
78    [StorableHook(HookType.AfterDeserialization)]
79    private void AfterDeserialization() {
80    }
81
82
83    private ClusteringProblemData(ClusteringProblemData original, Cloner cloner)
84      : base(original, cloner) {
85    }
86    public override IDeepCloneable Clone(Cloner cloner) { return new ClusteringProblemData(this, cloner); }
87
88    public ClusteringProblemData()
89      : this(defaultDataset, defaultAllowedInputVariables) {
90    }
91
92    public ClusteringProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables)
93      : base(dataset, allowedInputVariables) {
94    }
95
96
97    #region Import from file
98    public static ClusteringProblemData ImportFromFile(string fileName) {
99      TableFileParser csvFileParser = new TableFileParser();
100      csvFileParser.Parse(fileName);
101
102      Dataset dataset = new Dataset(csvFileParser.VariableNames, csvFileParser.Values);
103      dataset.Name = Path.GetFileName(fileName);
104
105      ClusteringProblemData problemData = new ClusteringProblemData(dataset, dataset.VariableNames);
106      problemData.Name = "Data imported from " + Path.GetFileName(fileName);
107      return problemData;
108    }
109    #endregion
110  }
111}
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