[7849] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 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 KeijzerFunctionFifteen : ArtificialRegressionDataDescriptor {
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[7849] | 29 |
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[8238] | 30 | public override string Name { get { return "Keijzer 15 f(x, y) = x³ / 5 + y³ / 2 - y - 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, y) = x³ / 5 + y³ / 2 - y - x" + Environment.NewLine
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[7849] | 36 | + "range(train): 20 Training cases x,y = rnd(-3, 3)" + Environment.NewLine
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| 37 | + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
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[9007] | 38 | + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
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[7849] | 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", "Y", "F" }; } }
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[7849] | 43 | protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } }
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| 44 | protected override int TrainingPartitionStart { get { return 0; } }
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| 45 | protected override int TrainingPartitionEnd { get { return 20; } }
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[9007] | 46 | protected override int TestPartitionStart { get { return 20; } }
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| 47 | protected override int TestPartitionEnd { get { return 20 + (601 * 601); } }
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[7849] | 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 | List<double> oneVariableTestData = SequenceGenerator.GenerateSteps(-3, 3, 0.01m).Select(v => (double)v).ToList();
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[9007] | 52 | List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
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| 53 |
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| 54 | var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList();
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| 55 |
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[7849] | 56 | for (int i = 0; i < AllowedInputVariables.Count(); i++) {
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[9007] | 57 | data.Add(ValueGenerator.GenerateUniformDistributedValues(20, -3, 3).ToList());
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| 58 | data[i].AddRange(combinations[i]);
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[7849] | 59 | }
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| 60 |
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| 61 | double x, y;
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| 62 | List<double> results = new List<double>();
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| 63 | for (int i = 0; i < data[0].Count; i++) {
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| 64 | x = data[0][i];
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| 65 | y = data[1][i];
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[9007] | 66 | results.Add(x * x * x / 5.0 + y * y * y / 2.0 - y - x);
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[7849] | 67 | }
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| 68 | data.Add(results);
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| 69 |
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| 70 | return data;
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| 71 | }
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| 72 | }
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| 73 | }
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