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source: branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/RegressionBenchmarks/Vladislavleva/SalutowiczFunctionOneDimensional.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: 2.7 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 SalutowiczFunctionOneDimensional : RegressionToyBenchmark {
28
29    public SalutowiczFunctionOneDimensional() {
30      Name = "Vladislavleva Salutowicz";
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: F2(X) = e^-X * X^3 * cos(X) * sin(X) * (cos(X)sin(X)^2 - 1)" + Environment.NewLine
34        + "Training Data: 100 points X = (0.05:0.1:10)" + Environment.NewLine
35        + "Test Data: 221 points X = (-0.5:0.05:10.5)" + 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>() { "X" };
39      trainingPartition = new IntRange(0, 100);
40      testPartition = new IntRange(100, 321);
41    }
42
43    protected override List<double> GenerateTarget(List<List<double>> data) {
44      double x;
45      List<double> results = new List<double>();
46      for (int i = 0; i < data[0].Count; i++) {
47        x = data[0][i];
48        results.Add(Math.Exp(-x) * Math.Pow(x, 3) * Math.Cos(x) * Math.Sin(x) * (Math.Cos(x) * Math.Pow(Math.Sin(x), 2) - 1));
49      }
50      return results;
51    }
52
53    protected override List<List<double>> GenerateInput() {
54      List<List<double>> dataList = new List<List<double>>();
55      dataList.Add(RegressionBenchmark.GenerateSteps(new DoubleRange(0.05, 10), 0.1));
56      dataList[0].AddRange(RegressionBenchmark.GenerateSteps(new DoubleRange(-0.5, 10.5), 0.05));
57
58      return dataList;
59    }
60  }
61}
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