using System; using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Random; namespace HeuristicLab.Problems.Instances.DataAnalysis { public class Feynman9 : FeynmanDescriptor { private readonly int testSamples; private readonly int trainingSamples; public Feynman9() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { } public Feynman9(int seed) { Seed = seed; trainingSamples = 10000; testSamples = 10000; noiseRatio = null; } public Feynman9(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.12.2 q1*q2/(4*pi*epsilon*r**2) | {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 new[] {"q1", "q2", "epsilon", "r", noiseRatio == null ? "F" : "F_noise"}; } } protected override string[] AllowedInputVariables { get { return new[] {"q1", "q2", "epsilon", "r"}; } } 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 q1 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList(); var q2 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList(); var epsilon = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList(); var r = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList(); var F = new List(); data.Add(q1); data.Add(q2); data.Add(epsilon); data.Add(r); data.Add(F); for (var i = 0; i < q1.Count; i++) { var res = q1[i] * q2[i] / (4 * Math.PI * epsilon[i] * Math.Pow(r[i], 2)); F.Add(res); } if (noiseRatio != null) { var F_noise = new List(); var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * F.StandardDeviationPop(); F_noise.AddRange(F.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise))); data.Remove(F); data.Add(F_noise); } return data; } } }