[7849] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[11170] | 3 | * Copyright (C) 2002-2014 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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| 25 |
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| 26 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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| 27 | public class KotanchekFunction : ArtificialRegressionDataDescriptor {
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| 28 |
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[8240] | 29 | public override string Name { get { return "Vladislavleva-1 F1(X1,X2) = exp(-(X1 - 1))² / (1.2 + (X2 -2.5)²"; } }
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[7849] | 30 | public override string Description {
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| 31 | get {
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| 32 | return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
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| 33 | + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
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[8240] | 34 | + "Function: F1(X1, X2) = exp(-(X1 - 1))² / (1.2 + (X2 -2.5)²" + Environment.NewLine
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[7849] | 35 | + "Training Data: 100 points X1, X2 = Rand(0.3, 4)" + Environment.NewLine
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[9013] | 36 | + "Test Data: 45*45 points (X1, X2) = (-0.2:0.1:4.2)" + Environment.NewLine
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[8241] | 37 | + "Function Set: +, -, *, /, square, e^x, e^-x, x^eps, x + eps, x * eps";
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[7849] | 38 | }
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| 39 | }
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| 40 | protected override string TargetVariable { get { return "Y"; } }
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[8825] | 41 | protected override string[] VariableNames { get { return new string[] { "X1", "X2", "Y" }; } }
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[7849] | 42 | protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
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| 43 | protected override int TrainingPartitionStart { get { return 0; } }
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| 44 | protected override int TrainingPartitionEnd { get { return 100; } }
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[8240] | 45 | protected override int TestPartitionStart { get { return 100; } }
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[9013] | 46 | protected override int TestPartitionEnd { get { return 100 + (45 * 45); } }
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[7849] | 47 |
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| 48 | protected override List<List<double>> GenerateValues() {
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| 49 | List<List<double>> data = new List<List<double>>();
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| 50 |
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[9013] | 51 | List<double> oneVariableTestData = ValueGenerator.GenerateSteps(-0.2, 4.2, 0.1).ToList();
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[7849] | 52 | List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
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| 53 | var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList<IEnumerable<double>>();
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| 54 | for (int i = 0; i < AllowedInputVariables.Count(); i++) {
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[8240] | 55 | data.Add(ValueGenerator.GenerateUniformDistributedValues(100, 0.3, 4).ToList());
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[7849] | 56 | data[i].AddRange(combinations[i]);
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| 57 | }
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| 58 |
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| 59 | double x1, x2;
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| 60 | List<double> results = new List<double>();
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| 61 | for (int i = 0; i < data[0].Count; i++) {
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| 62 | x1 = data[0][i];
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| 63 | x2 = data[1][i];
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| 64 | results.Add(Math.Exp(-Math.Pow(x1 - 1, 2)) / (1.2 + Math.Pow(x2 - 2.5, 2)));
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| 65 | }
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| 66 | data.Add(results);
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| 67 |
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| 68 | return data;
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| 69 | }
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| 70 | }
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| 71 | }
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