1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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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 HeuristicLab.Data;
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25 |
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26 | namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
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27 | public class RippleFunction : RegressionToyBenchmark {
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28 |
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29 | public RippleFunction() {
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30 | Name = "Vladislavleva Ripple";
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31 | Description = "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
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32 | + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
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33 | + "Function: F7(X1, X2) = (X1 - 3)(X2 - 3) + 2 * sin((X1 - 4)(X2 - 4))" + Environment.NewLine
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34 | + "Training Data: 300 points X1, X2 = Rand(0.05, 6.05)" + Environment.NewLine
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35 | + "Test Data: 1000 points X1, X2 = Rand(-0.25, 6.35)" + Environment.NewLine
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36 | + "Function Set: +, -, *, /, sqaure, x^real, x + real, x + real, e^x, e^-x, sin(x), cos(x)";
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37 | targetVariable = "Y";
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38 | inputVariables = new List<string>() { "X1", "X2" };
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39 | trainingPartition = new IntRange(0, 300);
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40 | testPartition = new IntRange(301, 1300);
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41 | }
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42 |
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43 | protected override List<double> GenerateTarget(List<List<double>> data) {
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44 | double x1, x2;
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45 | List<double> results = new List<double>();
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46 | for (int i = 0; i < data[0].Count; i++) {
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47 | x1 = data[0][i];
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48 | x2 = data[1][i];
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49 | results.Add((x1 - 3) * (x2 - 3) + 2 * Math.Sin((x1 - 4) * (x2 - 4)));
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50 | }
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51 | return results;
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52 | }
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53 |
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54 | protected override List<List<double>> GenerateInput() {
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55 | List<List<double>> dataList = new List<List<double>>();
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56 | DoubleRange range = new DoubleRange(0.05, 6.05);
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57 | for (int i = 0; i < InputVariable.Count; i++) {
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58 | dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(TrainingPartition.End, range));
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59 | }
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60 |
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61 | range = new DoubleRange(-0.25, 6.35);
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62 | for (int i = 0; i < InputVariable.Count; i++) {
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63 | dataList[i].AddRange(RegressionBenchmark.GenerateUniformDistributedValues(TrainingPartition.End, range));
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64 | }
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65 |
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66 | return dataList;
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67 | }
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68 | }
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69 | }
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