#region License Information
/* HeuristicLab
* Copyright (C) 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;
using HeuristicLab.Common;
using HeuristicLab.Random;
namespace HeuristicLab.Problems.Instances.DataAnalysis
{
public class ScalingProblem3 : ArtificialRegressionDataDescriptor
{
public override string Name { get { return "JKK-3 F3(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10) = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9 + X3*X6*X10)"; } }
public override string Description
{
get
{
return "Function: F3(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10) = X1*X2 + X3*X4 + X5*X6 + X1*X7*X9 + X3*X6*X10)" + Environment.NewLine
+ "Data: 250 points X1, X2, X3, X4, X5, X6, X7, X8, X9, X10 = Rand(10, 20)" + Environment.NewLine
+ "Function Set: +, -, *, /, square, e^x, e^-x, x^eps, x + eps, x * eps";
}
}
protected override string TargetVariable { get { return "Y"; } }
protected override string[] VariableNames { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10", "Y" }; } }
protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3", "X4", "X5", "X6", "X7", "X8", "X9", "X10" }; } }
protected override int TrainingPartitionStart { get { return 0; } }
protected override int TrainingPartitionEnd { get { return 250; } }
protected override int TestPartitionStart { get { return 250; } }
protected override int TestPartitionEnd { get { return 1000; } }
public int Seed { get; private set; }
public ScalingProblem3() : this((int)DateTime.Now.Ticks) { }
public ScalingProblem3(int seed) : base()
{
Seed = seed;
}
protected override List> GenerateValues()
{
var rand = new MersenneTwister((uint)Seed);
var data = Enumerable.Range(0, AllowedInputVariables.Count()).Select(_ => Enumerable.Range(0, TestPartitionEnd).Select(__ => rand.NextDouble() * 10 + 10).ToList()).ToList();
double x1, x2, x3, x4, x5, x6, x7, x8, x9, x10;
List results = new List();
for (int i = 0; i < data[0].Count; i++)
{
x1 = data[0][i];
x2 = data[1][i];
x3 = data[2][i];
x4 = data[3][i];
x5 = data[4][i];
x6 = data[5][i];
x7 = data[6][i];
x8 = data[7][i];
x9 = data[8][i];
x10 = data[9][i];
results.Add(x1 * x2 + x3 * x4 + x5 * x6 + x1 * x7 * x9 + x3 * x6 * x10);
}
data.Add(results);
return data;
}
}
}