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source: branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/RegressionBenchmarks/Keijzer/KeijzerFunctionFifteen.cs @ 7307

Last change on this file since 7307 was 7307, checked in by sforsten, 12 years ago

#1669:
-adjusted some benchmark problems from Keijzer

File size: 3.0 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 KeijzerFunctionFifteen : RegressionToyBenchmark {
28
29    public KeijzerFunctionFifteen() {
30      Name = "Keijzer 15 f(x) = 8 / (2 + x^2 + y^2)";
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) = 8 / (2 + x^2 + y^2)" + Environment.NewLine
34        + "range(train): 20 Train cases 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)" + Environment.NewLine + Environment.NewLine
37        + "Note: Test partition has been adjusted to only 100 random uniformly distributed test cases in the intercal [-3, 3] (not ca. 360000 as described) "
38        + ", but 5000 cases are created";
39      targetVariable = "F";
40      inputVariables = new List<string>() { "X", "Y" };
41      trainingPartition = new IntRange(0, 20);
42      testPartition = new IntRange(2500, 2601);
43    }
44
45    protected override List<double> GenerateTarget(List<List<double>> data) {
46      double x, y;
47      List<double> results = new List<double>();
48      for (int i = 0; i < data[0].Count; i++) {
49        x = data[0][i];
50        y = data[1][i];
51        results.Add(8 / (2 + Math.Pow(x, 2) + Math.Pow(y, 2)));
52      }
53      return results;
54    }
55
56    protected override List<List<double>> GenerateInput() {
57      List<List<double>> dataList = new List<List<double>>();
58      DoubleRange range = new DoubleRange(-3, 3);
59
60      List<double> oneVariableTestData = RegressionBenchmark.GenerateSteps(range, 0.01);
61      List<List<double>> testData = new List<List<double>>() { oneVariableTestData, oneVariableTestData };
62      testData = RegressionBenchmark.GenerateAllCombinationsOfValuesInLists(testData);
63
64      for (int i = 0; i < InputVariable.Count; i++) {
65        dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(2500, range));
66        dataList[i].AddRange(testData[i]);
67      }
68
69      return dataList;
70    }
71  }
72}
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