source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman84.cs @ 18106

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

namespace HeuristicLab.Problems.Instances.DataAnalysis {
  public class Feynman84 : FeynmanDescriptor {
    private readonly int testSamples;
    private readonly int trainingSamples;

    public Feynman84() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }

    public Feynman84(int seed) {
      Seed            = seed;
      trainingSamples = 10000;
      testSamples     = 10000;
      noiseRatio      = null;
    }

    public Feynman84(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.38.3 Y*A*x/d | {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[] { "Y", "A", "d", "x", "F" } : new[] { "Y", "A", "d", "x", "F", "F_noise" }; }
    }

    protected override string[] AllowedInputVariables { get { return new[] {"Y", "A", "d", "x"}; } }

    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 Y    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
      var A    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
      var d    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
      var x    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();

      var F = new List<double>();

      data.Add(Y);
      data.Add(A);
      data.Add(d);
      data.Add(x);
      data.Add(F);

      for (var i = 0; i < Y.Count; i++) {
        var res = Y[i] * A[i] * x[i] / d[i];
        F.Add(res);
      }

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

      return data;
    }
  }
}