source: branches/3075_aifeynman_instances/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman33.cs @ 17639

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

namespace HeuristicLab.Problems.Instances.DataAnalysis {
  public class Feynman33 : FeynmanDescriptor {
    public override string Name {
      get { return "Feynman I.32.17 (1/2*epsilon*c*Ef**2)*(8*pi*r**2/3)*(omega**4/(omega**2-omega_0**2)**2)"; }
    }

    protected override string TargetVariable { get { return "Pwr"; } }

    protected override string[] VariableNames {
      get { return new[] {"epsilon", "c", "Ef", "r", "omega", "omega_0", "Pwr"}; }
    }

    protected override string[] AllowedInputVariables {
      get { return new[] {"epsilon", "c", "Ef", "r", "omega", "omega_0"}; }
    }

    public int Seed { get; private set; }

    public Feynman33() : this((int) DateTime.Now.Ticks) { }

    public Feynman33(int seed) {
      Seed = seed;
    }

    protected override List<List<double>> GenerateValues() {
      var rand = new MersenneTwister((uint) Seed);

      var data    = new List<List<double>>();
      var epsilon = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
      var c       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
      var Ef      = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
      var r       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
      var omega   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
      var omega_0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 3, 5).ToList();

      var Pwr = new List<double>();

      data.Add(epsilon);
      data.Add(c);
      data.Add(Ef);
      data.Add(r);
      data.Add(omega);
      data.Add(omega_0);
      data.Add(Pwr);

      for (var i = 0; i < epsilon.Count; i++) {
        var res = 1.0 / 2 * epsilon[i] * c[i] * Math.Pow(Ef[i], 2) * (8 * Math.PI + Math.Pow(r[i], 2) / 3) * (Math.Pow(omega[i], 4) / Math.Pow((Math.Pow(omega[i], 2) - Math.Pow(omega_0[i], 2)),2));
        Pwr.Add(res);
      }

      return data;
    }
  }
}