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source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman81.cs @ 17966

Last change on this file since 17966 was 17966, checked in by mkommend, 3 years ago

#3075: Changed Feynman problem instances to new normal distributed RNG.

File size: 3.2 KB
Line 
1using System;
2using System.Collections.Generic;
3using System.Linq;
4using HeuristicLab.Common;
5using HeuristicLab.Random;
6
7namespace HeuristicLab.Problems.Instances.DataAnalysis {
8  public class Feynman81 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public Feynman81() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public Feynman81(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public Feynman81(int seed, int trainingSamples, int testSamples, double? noiseRatio) {
22      Seed                 = seed;
23      this.trainingSamples = trainingSamples;
24      this.testSamples     = testSamples;
25      this.noiseRatio      = noiseRatio;
26    }
27
28    public override string Name {
29      get {
30        return string.Format("II.35.21 n_rho*mom*tanh(mom*B/(kb*T)) | {0}",
31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
32      }
33    }
34
35    protected override string TargetVariable { get { return noiseRatio == null ? "M" : "M_noise"; } }
36
37    protected override string[] VariableNames {
38      get { return new[] {"n_rho", "mom", "B", "kb", "T", noiseRatio == null ? "M" : "M_noise"}; }
39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"n_rho", "mom", "B", "kb", "T"}; } }
42
43    public int Seed { get; private set; }
44
45    protected override int TrainingPartitionStart { get { return 0; } }
46    protected override int TrainingPartitionEnd { get { return trainingSamples; } }
47    protected override int TestPartitionStart { get { return trainingSamples; } }
48    protected override int TestPartitionEnd { get { return trainingSamples + testSamples; } }
49
50    protected override List<List<double>> GenerateValues() {
51      var rand = new MersenneTwister((uint) Seed);
52
53      var data  = new List<List<double>>();
54      var n_rho = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
55      var mom   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
56      var B     = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
57      var kb    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
58      var T     = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
59
60      var M = new List<double>();
61
62      data.Add(n_rho);
63      data.Add(mom);
64      data.Add(B);
65      data.Add(kb);
66      data.Add(T);
67      data.Add(M);
68
69      for (var i = 0; i < n_rho.Count; i++) {
70        var res = n_rho[i] * mom[i] * Math.Tanh(mom[i] * B[i] / (kb[i] * T[i]));
71        M.Add(res);
72      }
73
74      if (noiseRatio != null) {
75        var M_noise     = new List<double>();
76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * M.StandardDeviationPop();
77        M_noise.AddRange(M.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78        data.Remove(M);
79        data.Add(M_noise);
80      }
81
82      return data;
83    }
84  }
85}
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