#region License Information
/* HeuristicLab
* Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Linq;
namespace HeuristicLab.Problems.Instances.DataAnalysis {
public class KeijzerFunctionTen : ArtificialRegressionDataDescriptor {
public override string Name { get { return "Keijzer 10 f(x, y) = x ^ y"; } }
public override string Description {
get {
return "Paper: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling" + Environment.NewLine
+ "Authors: Maarten Keijzer" + Environment.NewLine
+ "Function: f(x, y) = x ^ y" + Environment.NewLine
+ "range(train): 100 Train cases x,y = rnd(0, 1)" + Environment.NewLine
+ "range(test): x,y = [0:0.01:1]" + Environment.NewLine
+ "Function Set: x + y, x * y, 1/x, -x, sqrt(x)";
}
}
protected override string TargetVariable { get { return "F"; } }
protected override string[] VariableNames { get { return new string[] { "X", "Y", "F" }; } }
protected override string[] AllowedInputVariables { get { return new string[] { "X", "Y" }; } }
protected override int TrainingPartitionStart { get { return 0; } }
protected override int TrainingPartitionEnd { get { return 100; } }
protected override int TestPartitionStart { get { return 100; } }
protected override int TestPartitionEnd { get { return 100 + (101 * 101); } }
protected override List> GenerateValues() {
List> data = new List>();
List oneVariableTestData = ValueGenerator.GenerateSteps(0, 1, 0.01).ToList();
List> testData = new List>() { oneVariableTestData, oneVariableTestData };
var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList>();
for (int i = 0; i < AllowedInputVariables.Count(); i++) {
data.Add(ValueGenerator.GenerateUniformDistributedValues(100, 0, 1).ToList());
data[i].AddRange(combinations[i]);
}
double x, y;
List results = new List();
for (int i = 0; i < data[0].Count; i++) {
x = data[0][i];
y = data[1][i];
results.Add(Math.Pow(x, y));
}
data.Add(results);
return data;
}
}
}