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source: branches/3026_IntegrationIntoSymSpace/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus6.cs @ 18027

Last change on this file since 18027 was 18027, checked in by dpiringe, 3 years ago

#3026

  • merged trunk into branch
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 FeynmanBonus6 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public FeynmanBonus6() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public FeynmanBonus6(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public FeynmanBonus6(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          "N-slit diffraction: I_0*(sin(alpha/2)*sin(n*delta/2)/(alpha/2*sin(delta/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 ? "I" : "I_noise"; } }
37
38    protected override string[] VariableNames {
39      get { return noiseRatio == null ? new[] { "I_0", "alpha", "delta", "n", "I" } : new[] { "I_0", "alpha", "delta", "n", "I", "I_noise" }; }
40    }
41
42    protected override string[] AllowedInputVariables { get { return new[] {"I_0", "alpha", "delta", "n"}; } }
43
44    public int Seed { get; private set; }
45
46    protected override int TrainingPartitionStart { get { return 0; } }
47    protected override int TrainingPartitionEnd { get { return trainingSamples; } }
48    protected override int TestPartitionStart { get { return trainingSamples; } }
49    protected override int TestPartitionEnd { get { return trainingSamples + testSamples; } }
50
51    protected override List<List<double>> GenerateValues() {
52      var rand = new MersenneTwister((uint) Seed);
53
54      var data  = new List<List<double>>();
55      var I_0   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
56      var alpha = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
57      var delta = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
58      var n     = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
59
60      var I = new List<double>();
61
62      data.Add(I_0);
63      data.Add(alpha);
64      data.Add(delta);
65      data.Add(n);
66      data.Add(I);
67
68      for (var i = 0; i < I_0.Count; i++) {
69        var res = I_0[i] * Math.Pow(
70                    Math.Sin(alpha[i] / 2) * Math.Sin(n[i] * delta[i] / 2) / (alpha[i] / 2 * Math.Sin(delta[i] / 2)),
71                    2);
72        I.Add(res);
73      }
74
75      var targetNoise = GetNoisyTarget(I, rand);
76      if (targetNoise != null) data.Add(targetNoise);
77
78      return data;
79    }
80  }
81}
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