[7860] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[11594] | 3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[7860] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Random;
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| 26 |
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| 27 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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| 28 | public class FriedmanTwo : ArtificialRegressionDataDescriptor {
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| 29 |
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| 30 | public override string Name { get { return "Friedman - II"; } }
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| 31 | public override string Description {
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| 32 | get {
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| 33 | return "Paper: Multivariate Adaptive Regression Splines" + Environment.NewLine
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| 34 | + "Authors: Jerome H. Friedman";
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| 35 | }
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| 36 | }
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| 37 | protected override string TargetVariable { get { return "Y"; } }
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[8825] | 38 | protected override string[] VariableNames { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "Y" }; } }
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[8672] | 39 | protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" }; } }
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[7860] | 40 | protected override int TrainingPartitionStart { get { return 0; } }
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| 41 | protected override int TrainingPartitionEnd { get { return 5000; } }
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| 42 | protected override int TestPartitionStart { get { return 5000; } }
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| 43 | protected override int TestPartitionEnd { get { return 10000; } }
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| 44 |
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| 45 | protected static FastRandom rand = new FastRandom();
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| 46 |
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| 47 | protected override List<List<double>> GenerateValues() {
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| 48 | List<List<double>> data = new List<List<double>>();
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| 49 | for (int i = 0; i < AllowedInputVariables.Count(); i++) {
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| 50 | data.Add(ValueGenerator.GenerateUniformDistributedValues(10000, 0, 1).ToList());
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| 51 | }
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| 52 |
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| 53 | double x1, x2, x3, x4, x5;
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| 54 | double f;
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| 55 | List<double> results = new List<double>();
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| 56 | for (int i = 0; i < data[0].Count; i++) {
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| 57 | x1 = data[0][i];
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| 58 | x2 = data[1][i];
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| 59 | x3 = data[2][i];
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| 60 | x4 = data[3][i];
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| 61 | x5 = data[4][i];
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| 62 |
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| 63 | f = 10 * Math.Sin(Math.PI * x1 * x2) + 20 * Math.Pow(x3 - 0.5, 2) + 10 * x4 + 5 * x5;
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| 64 |
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| 65 | results.Add(f + NormalDistributedRandom.NextDouble(rand, 0, 1));
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| 66 | }
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| 67 | data.Add(results);
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| 68 |
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| 69 | return data;
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| 70 | }
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| 71 | }
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| 72 | }
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