[7849] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16140] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[7849] | 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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[12292] | 25 | using HeuristicLab.Common;
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[7849] | 26 |
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| 27 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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[8238] | 28 | public class KeijzerFunctionSix : ArtificialRegressionDataDescriptor {
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[7849] | 29 |
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[8238] | 30 | public override string Name { get { return "Keijzer 6 f(x) = Sum(1 / i) From 1 to X"; } }
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[7849] | 31 | public override string Description {
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| 32 | get {
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| 33 | return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
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| 34 | + "Authors: Maarten Keijzer" + Environment.NewLine
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[8238] | 35 | + "Function: f(x) = Sum(1 / i) From 1 to X" + Environment.NewLine
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[7849] | 36 | + "range(train): x = [1:1:50]" + Environment.NewLine
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| 37 | + "range(test): x = [1:1:120]" + Environment.NewLine
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| 38 | + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
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| 39 | }
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| 40 | }
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| 41 | protected override string TargetVariable { get { return "F"; } }
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[8825] | 42 | protected override string[] VariableNames { get { return new string[] { "X", "F" }; } }
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[7849] | 43 | protected override string[] AllowedInputVariables { get { return new string[] { "X" }; } }
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| 44 | protected override int TrainingPartitionStart { get { return 0; } }
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| 45 | protected override int TrainingPartitionEnd { get { return 50; } }
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| 46 | protected override int TestPartitionStart { get { return 50; } }
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| 47 | protected override int TestPartitionEnd { get { return 170; } }
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| 48 |
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| 49 | protected override List<List<double>> GenerateValues() {
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| 50 | List<List<double>> data = new List<List<double>>();
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[12292] | 51 | data.Add(SequenceGenerator.GenerateSteps(1m, 50, 1).Select(v => (double)v).ToList());
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| 52 | data[0].AddRange(SequenceGenerator.GenerateSteps(1m, 120, 1).Select(v => (double)v));
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[7849] | 53 |
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| 54 | double x;
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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 | x = data[0][i];
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| 58 | results.Add(Enumerable.Range(1, (int)x).Sum(j => 1.0 / j));
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| 59 | }
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| 60 | data.Add(results);
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| 61 |
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| 62 | return data;
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| 63 | }
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| 64 | }
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| 65 | }
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