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source: branches/3040_VectorBasedGP/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus1.cs @ 18242

Last change on this file since 18242 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: 3.7 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 FeynmanBonus1 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public FeynmanBonus1() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public FeynmanBonus1(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public FeynmanBonus1(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(
31          "Rutherford scattering: (Z_1*Z_2*alpha*hbar*c/(4*E_n*sin(theta/2)**2))**2 | {0}",
32          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
33      }
34    }
35
36    protected override string TargetVariable { get { return noiseRatio == null ? "A" : "A_noise"; } }
37
38    protected override string[] VariableNames {
39      get { return new[] {"Z_1", "Z_2", "alpha", "hbar", "c", "E_n", "theta", noiseRatio == null ? "A" : "A_noise"}; }
40    }
41
42    protected override string[] AllowedInputVariables {
43      get { return new[] {"Z_1", "Z_2", "alpha", "hbar", "c", "E_n", "theta"}; }
44    }
45
46    public int Seed { get; private set; }
47
48    protected override int TrainingPartitionStart { get { return 0; } }
49    protected override int TrainingPartitionEnd { get { return trainingSamples; } }
50    protected override int TestPartitionStart { get { return trainingSamples; } }
51    protected override int TestPartitionEnd { get { return trainingSamples + testSamples; } }
52
53    protected override List<List<double>> GenerateValues() {
54      var rand = new MersenneTwister((uint) Seed);
55
56      var data  = new List<List<double>>();
57      var Z_1   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
58      var Z_2   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
59      var alpha = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
60      var hbar  = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
61      var c     = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
62      var E_n   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
63      var theta = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
64
65      var A = new List<double>();
66
67      data.Add(Z_1);
68      data.Add(Z_2);
69      data.Add(alpha);
70      data.Add(hbar);
71      data.Add(c);
72      data.Add(E_n);
73      data.Add(theta);
74      data.Add(A);
75
76      for (var i = 0; i < Z_1.Count; i++) {
77        var res = Math.Pow(
78          Z_1[i] * Z_2[i] * alpha[i] * hbar[i] * c[i] / (4 * E_n[i] * Math.Pow(Math.Sin(theta[i] / 2), 2)), 2);
79        A.Add(res);
80      }
81
82      if (noiseRatio != null) {
83        var A_noise     = new List<double>();
84        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * A.StandardDeviationPop();
85        A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
86        data.Remove(A);
87        data.Add(A_noise);
88      }
89
90      return data;
91    }
92  }
93}
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