source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman82.cs @ 18106

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

namespace HeuristicLab.Problems.Instances.DataAnalysis {
  public class Feynman82 : FeynmanDescriptor {
    private readonly int testSamples;
    private readonly int trainingSamples;

    public Feynman82() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }

    public Feynman82(int seed) {
      Seed            = seed;
      trainingSamples = 10000;
      testSamples     = 10000;
      noiseRatio      = null;
    }

    public Feynman82(int seed, int trainingSamples, int testSamples, double? noiseRatio) {
      Seed                 = seed;
      this.trainingSamples = trainingSamples;
      this.testSamples     = testSamples;
      this.noiseRatio      = noiseRatio;
    }

    public override string Name {
      get {
        return string.Format(
          "II.36.38 mom*B/(kb*T)+(mom*alpha*M)/(epsilon*c**2*kb*T) | {0}",
          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
      }
    }

    protected override string TargetVariable { get { return noiseRatio == null ? "f" : "f_noise"; } }

    protected override string[] VariableNames {
      get { return noiseRatio == null ? new[] { "mom", "B", "kb", "T", "alpha", "epsilon", "c", "M", "f" } : new[] { "mom", "B", "kb", "T", "alpha", "epsilon", "c", "M", "f", "f_noise" }; }
    }

    protected override string[] AllowedInputVariables {
      get { return new[] {"mom", "B", "kb", "T", "alpha", "epsilon", "c", "M"}; }
    }

    public int Seed { get; private set; }

    protected override int TrainingPartitionStart { get { return 0; } }
    protected override int TrainingPartitionEnd { get { return trainingSamples; } }
    protected override int TestPartitionStart { get { return trainingSamples; } }
    protected override int TestPartitionEnd { get { return trainingSamples + testSamples; } }

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

      var data    = new List<List<double>>();
      var mom     = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
      var B       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
      var kb      = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
      var T       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
      var alpha   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
      var epsilon = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
      var c       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
      var M       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();

      var f = new List<double>();

      data.Add(mom);
      data.Add(B);
      data.Add(kb);
      data.Add(T);
      data.Add(alpha);
      data.Add(epsilon);
      data.Add(c);
      data.Add(M);
      data.Add(f);

      for (var i = 0; i < mom.Count; i++) {
        var res = mom[i] * B[i] / (kb[i] * T[i]) +
                  mom[i] * alpha[i] * M[i] / (epsilon[i] * Math.Pow(c[i], 2) * kb[i] * T[i]);
        f.Add(res);
      }

      var targetNoise = ValueGenerator.GenerateNoise(f, rand, noiseRatio);
      if (targetNoise != null) data.Add(targetNoise);

      return data;
    }
  }
}