source: branches/3043-Regression-Instances-For-Scaling/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/ScalingProblems/ScalingProblem1.cs @ 17399

#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 ScalingProblem1 : ArtificialRegressionDataDescriptor
  {

    public override string Name { get { return "JKK-1 F1(X1,X2) = log(X1 + X2)"; } }
    public override string Description
    {
      get
      {
        return "Function: F1(X1,X2) = log(X1 + Y1)" + Environment.NewLine
        + "Data: 100 points X1, X2 = Rand(1, 2)" + 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", "Y" }; } }
    protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
    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 500; } }

    public int Seed { get; private set; }

    public ScalingProblem1() : this((int)DateTime.Now.Ticks) { }

    public ScalingProblem1(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();

      double x1, x2;
      List<double> results = new List<double>();

      double res;

      for (int i = 0; i < data[0].Count; i++)
      {
        x1 = data[0][i];
        x2 = data[1][i];
        results.Add(Math.Log(x1 + x2));
      }
      data.Add(results);

      return data;
    }
  }
}