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source: trunk/sources/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Various/SpatialCoevolution.cs @ 12292

Last change on this file since 12292 was 12292, checked in by pfleck, 9 years ago

#2301 Removed the GenerateSteps from the ValueGenerator and put it into the new SequenceGenerator.
Adapted DataAnalysis-Instances and scripts (samples and unit tests).

File size: 3.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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 System.Linq;
25using HeuristicLab.Common;
26
27namespace HeuristicLab.Problems.Instances.DataAnalysis {
28  public class SpatialCoevolution : ArtificialRegressionDataDescriptor {
29
30    public override string Name { get { return "Spatial co-evolution F(x,y) = 1/(1 + x^(-4)) + 1/(1 + y^(-4))"; } }
31    public override string Description {
32      get {
33        return "Paper: Evolutionary consequences of coevolving targets" + Environment.NewLine
34        + "Authors: Ludo Pagie and Paulien Hogeweg" + Environment.NewLine
35        + "Function: F(x,y) = 1/(1 + x^(-4)) + 1/(1 + y^(-4))" + Environment.NewLine
36        + "Non-terminals: +, -, *, % (protected division), sin, cos, exp, ln(|x|) (protected log)" + Environment.NewLine
37        + "Terminals: only variables (no random constants)" + Environment.NewLine
38        + "The fitness of a solution is defined as the mean of the absolute differences between "
39        + "the target function and the solution over all problems on the basis of which it is evaluated. "
40        + "A solution is considered completely ’correct’ if, for all 676 problems in the ’complete’ "
41        + "problem set used in the static evaluation scheme, the absolute difference between "
42        + "solution and target function is less than 0.01 (this is a so-called hit).";
43      }
44    }
45    protected override string TargetVariable { get { return "F"; } }
46    protected override string[] VariableNames { get { return new string[] { "X", "Y", "F" }; } }
47    protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } }
48    protected override int TrainingPartitionStart { get { return 0; } }
49    protected override int TrainingPartitionEnd { get { return 676; } }
50    protected override int TestPartitionStart { get { return 676; } }
51    protected override int TestPartitionEnd { get { return 1676; } }
52
53    protected override List<List<double>> GenerateValues() {
54      List<List<double>> data = new List<List<double>>();
55
56      List<double> evenlySpacedSequence = SequenceGenerator.GenerateSteps(-5, 5, 0.4m).Select(v => (double)v).ToList();
57      List<List<double>> trainingData = new List<List<double>>() { evenlySpacedSequence, evenlySpacedSequence };
58      var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(trainingData).ToList();
59
60      for (int i = 0; i < AllowedInputVariables.Count(); i++) {
61        data.Add(combinations[i].ToList());
62        data[i].AddRange(ValueGenerator.GenerateUniformDistributedValues(1000, -5, 5).ToList());
63      }
64
65      double x, y;
66      List<double> results = new List<double>();
67      for (int i = 0; i < data[0].Count; i++) {
68        x = data[0][i];
69        y = data[1][i];
70        results.Add(1 / (1 + Math.Pow(x, -4)) + 1 / (1 + Math.Pow(y, -4)));
71      }
72      data.Add(results);
73
74      return data;
75    }
76  }
77}
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