source: branches/3043-Regression-Instances-For-Scaling/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/ScalingProblems/ScalingProblem5.cs @ 18060

#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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Random;

namespace HeuristicLab.Problems.Instances.DataAnalysis
{
  public class ScalingProblem5 : ArtificialRegressionDataDescriptor
  {

    public override string Name { get { return "JKK-5 F5(X1,X2,X3) = sqrt(X1/log(X2)) + X1/X3"; } }
    public override string Description
    {
      get
      {
        return "Function: F5(X1,X2,X3) = sqrt(X1/log(X2)) + X1/X3" + Environment.NewLine
        + "Data: 250 points X1, X3 = Rand(1, 2), X2 = Rand(2,100)" + 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", "Y" }; } }
    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2", "X3" }; } }
    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 ScalingProblem5() : this((int)DateTime.Now.Ticks) { }

    public ScalingProblem5(int seed) : base()
    {
      Seed = seed;
    }
    protected override List<List<double>> GenerateValues()
    {
      var rand = new MersenneTwister((uint)Seed);

      var data = Enumerable.Range(0, AllowedInputVariables.Count()).Select(_ => Enumerable.Range(0, TestPartitionEnd).Select(__ => rand.NextDouble() + 1).ToList()).ToList();
      data[1] = Enumerable.Range(0, TestPartitionEnd).Select(_ => rand.NextDouble() * 99 + 1).ToList();

      double x1, x2, x3;
      List<double> results = new List<double>();
      for (int i = 0; i < data[0].Count; i++)
      {
        x1 = data[0][i]; 
        x2 = data[1][i];
        x3 = data[2][i];
        results.Add(Math.Sqrt(x1 / (Math.Log(x2))) + (x1 / x3));
      }
      data.Add(results);

      return data;
    }
  }
}