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 System.Linq;
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25 | using HeuristicLab.Data;
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26 |
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27 | namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
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28 | public class KeijzerFunctionSeven : RegressionToyBenchmark {
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29 |
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30 | public KeijzerFunctionSeven() {
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31 | Name = "Keijzer 7 f(x) = Sum(1 / i) From 1 to X";
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32 | Description = "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
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33 | + "Authors: Maarten Keijzer" + Environment.NewLine
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34 | + "Function: f(x) = (30 * x * y) / ((x - 10) * y^2)" + Environment.NewLine
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35 | + "range(train): x = [1:1:50]" + Environment.NewLine
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36 | + "range(test): x = [1:1:120]" + Environment.NewLine
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37 | + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
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38 | targetVariable = "Y";
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39 | inputVariables = new List<string>() { "X" };
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40 | trainingPartition = new IntRange(0, 50);
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41 | testPartition = new IntRange(50, 170);
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42 | }
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43 |
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44 | protected override List<double> GenerateTarget(List<List<double>> data) {
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45 | double x;
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46 | List<double> results = new List<double>();
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47 | for (int i = 0; i < data[0].Count; i++) {
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48 | x = data[0][i];
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49 | results.Add(Enumerable.Range(1, (int)x).Sum(j => 1.0 / j));
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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 | dataList.Add(RegressionBenchmark.GenerateSteps(new DoubleRange(1, 50), 1));
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57 | dataList[0].AddRange(RegressionBenchmark.GenerateSteps(new DoubleRange(1, 120), 1));
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58 |
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59 | return dataList;
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60 | }
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61 | }
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62 | }
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