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source: branches/RegressionBenchmarks/HeuristicLab.Problems.DataAnalysis.Benchmarks/3.4/RegressionBenchmarks/Keijzer/KeijzerFunctionSix.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 KeijzerFunctionSix : RegressionBenchmark {
28
29    public KeijzerFunctionSix() {
30      Name = "Keijzer 6 f(x) = (30 * x * z) / ((x - 10)  * 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) = (30 * x * z) / ((x - 10)  * y^2)" + Environment.NewLine
34        + "range(train): 1000 points x,z = rnd(-1, 1), y = rnd(1, 2)" + Environment.NewLine
35        + "range(test): 10000 points x,z = rnd(-1, 1), y = rnd(1, 2)" + Environment.NewLine
36        + "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
37      targetVariable = "F";
38      inputVariables = new List<string>() { "X", "Y", "Z" };
39      trainingPartition = new IntRange(0, 1000);
40      testPartition = new IntRange(1001, 11000);
41    }
42
43    protected override List<double> CalculateFunction(List<List<double>> data) {
44      double x, y, z;
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        z = data[2][i];
50        results.Add((30 * x * z) / ((x - 10) * Math.Pow(y, 2)));
51      }
52      return results;
53    }
54
55    protected override List<List<double>> GenerateInput(List<List<double>> dataList) {
56      DoubleRange rangeXZ = new DoubleRange(-1, 1);
57      DoubleRange rangeY = new DoubleRange(1, 2);
58      dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(testPartition.End, rangeXZ));
59      dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(testPartition.End, rangeY));
60      dataList.Add(RegressionBenchmark.GenerateUniformDistributedValues(testPartition.End, rangeXZ));
61
62      return dataList;
63    }
64  }
65}
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