[17647] | 1 | using System;
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| 2 | using System.Collections.Generic;
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| 3 | using System.Linq;
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| 4 | using HeuristicLab.Common;
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| 5 | using HeuristicLab.Random;
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| 6 |
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| 7 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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| 8 | public class Feynman43 : FeynmanDescriptor {
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| 9 | private readonly int testSamples;
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| 10 | private readonly int trainingSamples;
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| 11 |
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| 12 | public Feynman43() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
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| 13 |
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| 14 | public Feynman43(int seed) {
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| 15 | Seed = seed;
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| 16 | trainingSamples = 10000;
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| 17 | testSamples = 10000;
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| 18 | noiseRatio = null;
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| 19 | }
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| 20 |
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| 21 | public Feynman43(int seed, int trainingSamples, int testSamples, double? noiseRatio) {
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| 22 | Seed = seed;
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| 23 | this.trainingSamples = trainingSamples;
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| 24 | this.testSamples = testSamples;
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| 25 | this.noiseRatio = noiseRatio;
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| 26 | }
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| 27 |
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| 28 | public override string Name {
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| 29 | get {
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[17805] | 30 | return string.Format("I.40.1 n_0*exp(-m*g*x/(kb*T)) | {0}",
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[17678] | 31 | noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
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[17647] | 32 | }
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| 33 | }
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| 34 |
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| 35 | protected override string TargetVariable { get { return noiseRatio == null ? "n" : "n_noise"; } }
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| 36 |
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| 37 | protected override string[] VariableNames {
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| 38 | get { return new[] {"n_0", "m", "x", "T", "g", "kb", noiseRatio == null ? "n" : "n_noise"}; }
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| 39 | }
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| 40 |
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| 41 | protected override string[] AllowedInputVariables { get { return new[] {"n_0", "m", "x", "T", "g", "kb"}; } }
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| 42 |
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| 43 | public int Seed { get; private set; }
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| 44 |
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| 45 | protected override int TrainingPartitionStart { get { return 0; } }
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| 46 | protected override int TrainingPartitionEnd { get { return trainingSamples; } }
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| 47 | protected override int TestPartitionStart { get { return trainingSamples; } }
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| 48 | protected override int TestPartitionEnd { get { return trainingSamples + testSamples; } }
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| 49 |
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| 50 | protected override List<List<double>> GenerateValues() {
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| 51 | var rand = new MersenneTwister((uint) Seed);
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| 52 |
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| 53 | var data = new List<List<double>>();
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| 54 | var n_0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 55 | var m = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 56 | var x = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 57 | var T = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 58 | var g = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 59 | var kb = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 60 |
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| 61 | var n = new List<double>();
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| 62 |
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| 63 | data.Add(n_0);
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| 64 | data.Add(m);
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| 65 | data.Add(x);
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| 66 | data.Add(T);
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| 67 | data.Add(g);
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| 68 | data.Add(kb);
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| 69 | data.Add(n);
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| 70 |
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| 71 | for (var i = 0; i < n_0.Count; i++) {
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| 72 | var res = n_0[i] * Math.Exp(-m[i] * g[i] * x[i] / (kb[i] * T[i]));
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| 73 | n.Add(res);
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| 74 | }
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| 75 |
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| 76 | if (noiseRatio != null) {
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| 77 | var n_noise = new List<double>();
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[17805] | 78 | var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * n.StandardDeviationPop();
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[17647] | 79 | n_noise.AddRange(n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
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| 80 | data.Remove(n);
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| 81 | data.Add(n_noise);
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| 82 | }
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| 83 |
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| 84 | return data;
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| 85 | }
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| 86 | }
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| 87 | } |
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