[7025] | 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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[7044] | 27 | public class KeijzerFunctionFifteen : RegressionToyBenchmark {
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[7025] | 28 |
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| 29 | public KeijzerFunctionFifteen() {
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| 30 | Name = "Keijzer 15 f(x) = 8 / (2 + x^2 + y^2)";
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| 31 | Description = "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
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| 32 | + "Authors: Maarten Keijzer" + Environment.NewLine
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| 33 | + "Function: f(x, y) = 8 / (2 + x^2 + y^2)" + Environment.NewLine
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| 34 | + "range(train): 20 Testcases x,y = rnd(-3, 3)" + Environment.NewLine
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| 35 | + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
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| 36 | + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
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| 37 | targetVariable = "F";
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[7031] | 38 | inputVariables = new List<string>() { "X", "Y" };
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[7025] | 39 | trainingPartition = new IntRange(0, 20);
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| 40 | testPartition = new IntRange(21, 621);
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| 41 | }
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| 42 |
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[7044] | 43 | protected override List<double> GenerateTarget(List<List<double>> data) {
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[7025] | 44 | double x, y;
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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 | x = data[0][i];
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| 48 | y = data[1][i];
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| 49 | results.Add(8 / (2 + Math.Pow(x, 2) + Math.Pow(y, 2)));
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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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[7044] | 54 | protected override List<List<double>> GenerateInput() {
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| 55 | List<List<double>> dataList = new List<List<double>>();
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[7025] | 56 | DoubleRange range = new DoubleRange(-3, 3);
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| 57 |
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| 58 | List<double> oneVariableTestData = RegressionBenchmark.GenerateSteps(range, 0.01);
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| 59 | List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
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| 60 | testData = RegressionBenchmark.AllCombinationsOf(testData);
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| 61 |
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| 62 | for (int i = 0; i < InputVariable.Count; i++) {
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| 63 | dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(20, range));
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| 64 | dataList[i].AddRange(testData[i]);
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| 65 | }
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| 66 |
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| 67 | return dataList;
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| 68 | }
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| 69 | }
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| 70 | }
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