[7849] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[9456] | 3 | * Copyright (C) 2002-2013 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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| 25 |
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| 26 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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| 27 | public class KornsFunctionFiveteen : ArtificialRegressionDataDescriptor {
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| 28 |
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[8225] | 29 | public override string Name { get { return "Korns 15 y = 12.0 - (6.0 * ((tan(X0) / exp(X1)) * (ln(X2) - tan(X3))))"; } }
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[7849] | 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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[8225] | 34 | + "Function: y = 12.0 - (6.0 * ((tan(X0) / exp(X1)) * (ln(X2) - tan(X3))))" + 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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[7849] | 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.\"" + Environment.NewLine + Environment.NewLine
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[8900] | 42 | + "Note: Because of the logarithm and the exponential function as denominator only non-negatic values are created for their input variables!";
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[7849] | 43 | }
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| 44 | }
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| 45 | protected override string TargetVariable { get { return "Y"; } }
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[8825] | 46 | protected override string[] VariableNames { get { return new string[] { "X0", "X1", "X2", "X3", "X4", "Y" }; } }
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[7849] | 47 | protected override string[] AllowedInputVariables { get { return new string[] { "X0", "X1", "X2", "X3", "X4" }; } }
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| 48 | protected override int TrainingPartitionStart { get { return 0; } }
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[8225] | 49 | protected override int TrainingPartitionEnd { get { return 10000; } }
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| 50 | protected override int TestPartitionStart { get { return 10000; } }
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| 51 | protected override int TestPartitionEnd { get { return 20000; } }
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[7849] | 52 |
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| 53 | protected override List<List<double>> GenerateValues() {
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| 54 | List<List<double>> data = new List<List<double>>();
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[8245] | 55 | data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
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[9007] | 56 | data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
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[8245] | 57 | data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, 0, 50).ToList()); // note: range is only [0,50] to prevent NaN values (deviates from gp benchmark paper)
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| 58 | data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
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| 59 | data.Add(ValueGenerator.GenerateUniformDistributedValues(TestPartitionEnd, -50, 50).ToList());
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[7849] | 60 |
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| 61 | double x0, x1, x2, x3;
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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 | x0 = data[0][i];
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| 65 | x1 = data[1][i];
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| 66 | x2 = data[2][i];
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| 67 | x3 = data[3][i];
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| 68 | results.Add(12.0 - (6.0 * ((Math.Tan(x0) / Math.Exp(x1)) * (Math.Log(x2) - Math.Tan(x3)))));
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| 69 | }
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| 70 | data.Add(results);
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| 71 |
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| 72 | return data;
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| 73 | }
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| 74 | }
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| 75 | }
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