Free cookie consent management tool by TermsFeed Policy Generator

source: stable/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Friedman/FriedmanRandomFunctionInstanceProvider.cs @ 17709

Last change on this file since 17709 was 17181, checked in by swagner, 5 years ago

#2875: Merged r17180 from trunk to stable

File size: 2.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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    public int Seed { get; private set; }
43
44    public FriedmanRandomFunctionInstanceProvider() : base() {
45    }
46
47    public FriedmanRandomFunctionInstanceProvider(int seed) : base() {
48      Seed = seed;
49    }
50
51    public override IEnumerable<IDataDescriptor> GetDataDescriptors() {
52      var numVariables = new int[] { 10, 25, 50, 100 };
53      var noiseRatios = new double[] { 0.01, 0.05, 0.1 };
54      var rand = new MersenneTwister((uint)Seed); // use fixed seed for deterministic problem generation
55      return (from size in numVariables
56              from noiseRatio in noiseRatios
57              select new FriedmanRandomFunction(size, noiseRatio, new MersenneTwister((uint)rand.Next())))
58              .Cast<IDataDescriptor>()
59              .ToList();
60    }
61
62    public override IRegressionProblemData LoadData(IDataDescriptor descriptor) {
63      var frfDescriptor = descriptor as FriedmanRandomFunction;
64      if (frfDescriptor == null) throw new ArgumentException("FriedmanRandomFunctionInstanceProvider expects an FriedmanRandomFunction data descriptor.");
65      // base call generates a regression problem data
66      var problemData = base.LoadData(frfDescriptor);
67      return problemData;
68    }
69  }
70}
Note: See TracBrowser for help on using the repository browser.