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source: branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/RegressionBenchmarks/Korns/KornFunctionSix.cs @ 7025

Last change on this file since 7025 was 7025, checked in by sforsten, 12 years ago

#1669: benchmark problems of Nguyen, Korns and Keijzer from http://groups.csail.mit.edu/EVO-DesignOpt/GPBenchmarks/ have been added. The benchmark problems from http://www.vanillamodeling.com/ have been adapted to the ones from Vladislavleva.

Not all benchmarks are working correctly so far, but they will be tested soon.

File size: 2.9 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using HeuristicLab.Data;
25
26namespace HeuristicLab.Problems.DataAnalysis.Benchmarks {
27  public class KornsFunctionSix : RegressionBenchmark {
28
29    public KornsFunctionSix() {
30      Name = "Korn 6 y = 1.3 + (0.13 * sqrt(X0))";
31      Description = "Paper: Accuracy in Symbolic Regression" + Environment.NewLine
32        + "Authors: Michael F. Korns" + Environment.NewLine
33        + "Function: y = 1.3 + (0.13 * sqrt(X0))" + Environment.NewLine
34        + "Real Numbers: 3.45, -.982, 100.389, and all other real constants" + Environment.NewLine
35        + "Row Features: x1, x2, x9, and all other features" + Environment.NewLine
36        + "Binary Operators: +, -, *, /" + Environment.NewLine
37        + "Unary Operators: sqrt, square, cube, cos, sin, tan, tanh, log, exp" + Environment.NewLine
38        + "\"Our testing regimen uses only statistical best practices out-of-sample testing techniques. "
39        + "We test each of the test cases on matrices of 10000 rows by 1 to 5 columns with no noise. "
40        + "For each test a training matrix is filled with random numbers between -50 and +50. The test case "
41        + "target expressions are limited to one basis function whose maximum depth is three grammar nodes.\"";
42      targetVariable = "Y";
43      inputVariables = new List<string>() { "X0" };
44      trainingPartition = new IntRange(0, 5000);
45      testPartition = new IntRange(5001, 10000);
46    }
47
48    protected override List<double> CalculateFunction(List<List<double>> data) {
49      double x0;
50      List<double> results = new List<double>();
51      for (int i = 0; i < data[0].Count; i++) {
52        x0 = data[0][i];
53        results.Add(1.3 + (0.13 * Math.Sqrt(x0)));
54      }
55      return results;
56    }
57
58    protected override List<List<double>> GenerateInput(List<List<double>> dataList) {
59      DoubleRange range = new DoubleRange(-50, 50);
60      for (int i = 0; i < inputVariables.Count; i++) {
61        dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(testPartition.End, range));
62      }
63
64      return dataList;
65    }
66  }
67}
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