using System; using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Random; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class Feynman47 : FeynmanDescriptor { private readonly int testSamples; private readonly int trainingSamples; public Feynman47() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { } public Feynman47(int seed) { Seed = seed; trainingSamples = 10000; testSamples = 10000; noiseRatio = null; } public Feynman47(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("I.43.43 1/(gamma-1)*kb*v/A | {0}", noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio)); } } protected override string TargetVariable { get { return noiseRatio == null ? "kappa" : "kappa_noise"; } } protected override string[] VariableNames { get { return new[] {"gamma", "kb", "A", "v", noiseRatio == null ? "kappa" : "kappa_noise"}; } } protected override string[] AllowedInputVariables { get { return new[] {"gamma", "kb", "A", "v"}; } } 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> GenerateValues() { var rand = new MersenneTwister((uint) Seed); var data = new List>(); var gamma = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 2, 5).ToList(); var kb = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList(); var A = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList(); var v = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList(); var kappa = new List(); data.Add(gamma); data.Add(kb); data.Add(A); data.Add(v); data.Add(kappa); for (var i = 0; i < gamma.Count; i++) { var res = 1.0 / (gamma[i] - 1) * kb[i] * v[i] / A[i]; kappa.Add(res); } if (noiseRatio != null) { var kappa_noise = new List(); var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * kappa.StandardDeviationPop(); kappa_noise.AddRange(kappa.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise))); data.Remove(kappa); data.Add(kappa_noise); } return data; } } }