source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Friedman/FriedmanRandomFunctionInstanceProvider.cs @ 14110

Last change on this file since 14110 was 14110, checked in by mkommend, 6 years ago

#2618: Removed obsolete code in friedman random functions and variable networks instance providers.

File size: 2.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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 HeuristicLab.Problems.DataAnalysis;
26using HeuristicLab.Random;
27
28namespace HeuristicLab.Problems.Instances.DataAnalysis {
29  public class FriedmanRandomFunctionInstanceProvider : ArtificialRegressionInstanceProvider {
30    public override string Name {
31      get { return "Friedman Random Functions"; }
32    }
33    public override string Description {
34      get { return "A set of regression benchmark instances as described by Friedman in the Greedy Function Approximation paper"; }
35    }
36    public override Uri WebLink {
37      get { return new Uri("http://dev.heuristiclab.com"); }
38    }
39    public override string ReferencePublication {
40      get { return "Friedman, Jerome H. 'Greedy function approximation: a gradient boosting machine.' Annals of statistics (2001): 1189-1232."; }
41    }
42
43    public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
44      var numVariables = new int[] { 10, 25, 50, 100 };
45      var noiseRatios = new double[] { 0.01, 0.05, 0.1 };
46      var rand = new System.Random(1234); // use fixed seed for deterministic problem generation
47      return (from size in numVariables
48              from noiseRatio in noiseRatios
49              select new FriedmanRandomFunction(size, noiseRatio, new MersenneTwister((uint)rand.Next())))
50              .Cast<IDataDescriptor>()
51              .ToList();
52    }
53
54    public override IRegressionProblemData LoadData(IDataDescriptor descriptor) {
55      var frfDescriptor = descriptor as FriedmanRandomFunction;
56      if (frfDescriptor == null) throw new ArgumentException("FriedmanRandomFunctionInstanceProvider expects an FriedmanRandomFunction data descriptor.");
57      // base call generates a regression problem data
58      var problemData = base.LoadData(frfDescriptor);
59      return problemData;
60    }
61  }
62}
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