1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2016 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 |
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26 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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27 | public class KornsFunctionTen : ArtificialRegressionDataDescriptor {
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28 |
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29 | public override string Name { get { return "Korns 10 y = 0.81 + (24.3 * (((2.0 * X1) + (3.0 * X2²)) / ((4.0 * X3³) + (5.0 * X4^4))))"; } }
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30 | public override string Description {
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31 | get {
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32 | return "Paper: Accuracy in Symbolic Regression" + Environment.NewLine
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33 | + "Authors: Michael F. Korns" + Environment.NewLine
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34 | + "Function: y = 0.81 + (24.3 * (((2.0 * X1) + (3.0 * X2²)) / ((4.0 * X3³) + (5.0 * X4^4))))" + Environment.NewLine
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35 | + "Binary Operators: +, -, *, % (protected division)" + Environment.NewLine
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36 | + "Unary Operators: sqrt, square, cube, cos, sin, tan, tanh, ln(|x|) (protected log), exp" + Environment.NewLine
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37 | + "Constants: random finit 64-bit IEEE double" + Environment.NewLine
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38 | + "\"Our testing regimen uses only statistical best practices out-of-sample testing techniques. "
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39 | + "We test each of the test cases on matrices of 10000 rows by 1 to 5 columns with no noise. "
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40 | + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
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41 | + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"";
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42 | }
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43 | }
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44 | protected override string TargetVariable { get { return "Y"; } }
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45 | protected override string[] VariableNames { get { return new string[] { "X0", "X1", "X2", "X3", "X4", "Y" }; } }
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46 | protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } }
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47 | protected override int TrainingPartitionStart { get { return 0; } }
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48 | protected override int TrainingPartitionEnd { get { return 10000; } }
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49 | protected override int TestPartitionStart { get { return 10000; } }
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50 | protected override int TestPartitionEnd { get { return 20000; } }
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51 |
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52 | protected override List<List<double>> GenerateValues() {
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53 | List<List<double>> data = new List<List<double>>();
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54 | for (int i = 0; i < AllowedInputVariables.Count(); i++) {
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55 | data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
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56 | }
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57 |
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58 | double x1, x2, x3, x4;
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59 | List<double> results = new List<double>();
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60 | for (int i = 0; i < data[0].Count; i++) {
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61 | x1 = data[1][i];
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62 | x2 = data[2][i];
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63 | x3 = data[3][i];
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64 | x4 = data[4][i];
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65 | results.Add(0.81 + (24.3 * (((2.0 * x1) + (3.0 * Math.Pow(x2, 2))) / ((4.0 * Math.Pow(x3, 3)) + (5.0 * Math.Pow(x4, 4))))));
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66 | }
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67 | data.Add(results);
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68 |
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69 | return data;
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70 | }
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71 | }
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72 | }
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