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source: branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/RegressionBenchmarks/Vladislavleva/RippleFunction.cs @ 7127

Last change on this file since 7127 was 7127, checked in by sforsten, 12 years ago

#1669:
-Spatial co-evolution benchmark has been added
-Benchmarks of Trent McConaghy have been added
-2 Classification benchmarks have been added (Mammography and Iris dataset)
-Training and test set include now all samples from the dataset
-Load button and combo box are now disabled when the algorithm is running

File size: 2.9 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 RippleFunction : RegressionToyBenchmark {
28
29    public RippleFunction() {
30      Name = "Vladislavleva Ripple";
31      Description = "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
32        + "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
33        + "Function: F7(X1, X2) = (X1 - 3)(X2 - 3) + 2 * sin((X1 - 4)(X2 - 4))" + Environment.NewLine
34        + "Training Data: 300 points X1, X2 = Rand(0.05, 6.05)" + Environment.NewLine
35        + "Test Data: 1000 points X1, X2 = Rand(-0.25, 6.35)" + Environment.NewLine
36        + "Function Set: +, -, *, /, sqaure, x^real, x + real, x + real, e^x, e^-x, sin(x), cos(x)";
37      targetVariable = "Y";
38      inputVariables = new List<string>() { "X1", "X2" };
39      trainingPartition = new IntRange(0, 300);
40      testPartition = new IntRange(300, 1300);
41    }
42
43    protected override List<double> GenerateTarget(List<List<double>> data) {
44      double x1, x2;
45      List<double> results = new List<double>();
46      for (int i = 0; i < data[0].Count; i++) {
47        x1 = data[0][i];
48        x2 = data[1][i];
49        results.Add((x1 - 3) * (x2 - 3) + 2 * Math.Sin((x1 - 4) * (x2 - 4)));
50      }
51      return results;
52    }
53
54    protected override List<List<double>> GenerateInput() {
55      List<List<double>> dataList = new List<List<double>>();
56      DoubleRange range = new DoubleRange(0.05, 6.05);
57      for (int i = 0; i < InputVariable.Count; i++) {
58        dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(TrainingPartition.End, range));
59      }
60
61      range = new DoubleRange(-0.25, 6.35);
62      for (int i = 0; i < InputVariable.Count; i++) {
63        dataList[i].AddRange(RegressionBenchmark.GenerateUniformDistributedValues(TrainingPartition.End, range));
64      }
65
66      return dataList;
67    }
68  }
69}
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