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source: branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/RegressionBenchmarks/Keijzer/KeijzerFunctionTwelve.cs @ 7031

Last change on this file since 7031 was 7031, checked in by sforsten, 13 years ago

#1669: A few mistakes have been corrected. All benchmark problems now generate problem data without throwing an error.

File size: 2.7 KB
Line 
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 KeijzerFunctionTwelve : RegressionBenchmark {
28
29    public KeijzerFunctionTwelve() {
30      Name = "Keijzer 12 f(x) = xy + sin((x - 1)(y - 1))";
31      Description = "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
32        + "Authors: Maarten Keijzer" + Environment.NewLine
33        + "Function: f(x, y) = xy + sin((x - 1)(y - 1))" + Environment.NewLine
34        + "range(train): 20 Testcases x,y = rnd(-3, 3)" + Environment.NewLine
35        + "range(test): x,y = [-3:0.01:3]" + Environment.NewLine
36        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
37      targetVariable = "F";
38      inputVariables = new List<string>() { "X", "Y" };
39      trainingPartition = new IntRange(0, 20);
40      testPartition = new IntRange(21, 621);
41    }
42
43    protected override List<double> CalculateFunction(List<List<double>> data) {
44      double x, y;
45      List<double> results = new List<double>();
46      for (int i = 0; i < data[0].Count; i++) {
47        x = data[0][i];
48        y = data[1][i];
49        results.Add(x * y + Math.Sin((x - 1) * (y - 1)));
50      }
51      return results;
52    }
53
54    protected override List<List<double>> GenerateInput(List<List<double>> dataList) {
55      DoubleRange range = new DoubleRange(-3, 3);
56
57      List<double> oneVariableTestData = RegressionBenchmark.GenerateSteps(range, 0.01);
58      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
59      testData = RegressionBenchmark.AllCombinationsOf(testData);
60
61      for (int i = 0; i < InputVariable.Count; i++) {
62        dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(20, range));
63        dataList[i].AddRange(testData[i]);
64      }
65
66      return dataList;
67    }
68  }
69}
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