#region License Information /* HeuristicLab * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Collections.Generic; using System.Linq; using HeuristicLab.Problems.DataAnalysis; using HeuristicLab.Random; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class FriedmanRandomFunctionInstanceProvider : ArtificialRegressionInstanceProvider { public override string Name { get { return "Friedman Random Functions"; } } public override string Description { get { return "A set of regression benchmark instances as described by Friedman in the Greedy Function Approximation paper"; } } public override Uri WebLink { get { return new Uri("http://dev.heuristiclab.com"); } } public override string ReferencePublication { get { return "Friedman, Jerome H. 'Greedy function approximation: a gradient boosting machine.' Annals of statistics (2001): 1189-1232."; } } public override IEnumerable GetDataDescriptors() { var numVariables = new int[] { 10, 25, 50, 100 }; var noiseRatios = new double[] { 0.01, 0.05, 0.1 }; var rand = new System.Random(1234); // use fixed seed for deterministic problem generation return (from size in numVariables from noiseRatio in noiseRatios select new FriedmanRandomFunction(size, noiseRatio, new MersenneTwister((uint)rand.Next()))) .Cast() .ToList(); } public override IRegressionProblemData LoadData(IDataDescriptor descriptor) { var varNetwork = descriptor as FriedmanRandomFunction; if (varNetwork == null) throw new ArgumentException("FriedmanRandomFunctionInstanceProvider expects an FriedmanRandomFunction data descriptor."); // base call generates a regression problem data var regProblemData = base.LoadData(varNetwork); var problemData = new RegressionProblemData( regProblemData.Dataset, regProblemData.AllowedInputVariables, regProblemData.TargetVariable); // copy values from regProblemData to feature selection problem data problemData.Name = regProblemData.Name; problemData.Description = regProblemData.Description; problemData.TrainingPartition.Start = regProblemData.TrainingPartition.Start; problemData.TrainingPartition.End = regProblemData.TrainingPartition.End; problemData.TestPartition.Start = regProblemData.TestPartition.Start; problemData.TestPartition.End = regProblemData.TestPartition.End; return problemData; } } }