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source: branches/3107_LearningALPS/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman91.cs

Last change on this file was 17805, checked in by gkronber, 4 years ago

#3075 Use the same noise levels and calculation as in our experiments for the IEEE TeC paper. Reordered instances by name first and noise level second. Removed number of samples from the name.

File size: 3.1 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 Feynman91 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public Feynman91() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public Feynman91(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public Feynman91(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("III.10.19 mom*sqrt(Bx**2+By**2+Bz**2) | {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 ? "E_n" : "E_n_noise"; } }
36
37    protected override string[] VariableNames {
38      get { return new[] {"mom", "Bx", "By", "Bz", noiseRatio == null ? "E_n" : "E_n_noise"}; }
39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"mom", "Bx", "By", "Bz"}; } }
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 mom  = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
55      var Bx   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
56      var By   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
57      var Bz   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
58
59      var E_n = new List<double>();
60
61      data.Add(mom);
62      data.Add(Bx);
63      data.Add(By);
64      data.Add(Bz);
65      data.Add(E_n);
66
67      for (var i = 0; i < mom.Count; i++) {
68        var res = mom[i] * Math.Sqrt(Math.Pow(Bx[i], 2) + Math.Pow(By[i], 2) + Math.Pow(Bz[i], 2));
69        E_n.Add(res);
70      }
71
72      if (noiseRatio != null) {
73        var E_n_noise   = new List<double>();
74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
75        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
76        data.Remove(E_n);
77        data.Add(E_n_noise);
78      }
79
80      return data;
81    }
82  }
83}
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