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source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman33.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: 3.7 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 Feynman33 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public Feynman33() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public Feynman33(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public Feynman33(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          "I.32.17 (1/2*epsilon*c*Ef**2)*(8*pi*r**2/3)*(omega**4/(omega**2-omega_0**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 ? "Pwr" : "Pwr_noise"; } }
37
38    protected override string[] VariableNames {
39      get { return new[] {"epsilon", "c", "Ef", "r", "omega", "omega_0", noiseRatio == null ? "Pwr" : "Pwr_noise"}; }
40    }
41
42    protected override string[] AllowedInputVariables {
43      get { return new[] {"epsilon", "c", "Ef", "r", "omega", "omega_0"}; }
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 epsilon = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
58      var c       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
59      var Ef      = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
60      var r       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
61      var omega   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
62      var omega_0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 3, 5).ToList();
63
64      var Pwr = new List<double>();
65
66      data.Add(epsilon);
67      data.Add(c);
68      data.Add(Ef);
69      data.Add(r);
70      data.Add(omega);
71      data.Add(omega_0);
72      data.Add(Pwr);
73
74      for (var i = 0; i < epsilon.Count; i++) {
75        var res = 1.0 / 2 * epsilon[i] * c[i] * Math.Pow(Ef[i], 2) * (8 * Math.PI * Math.Pow(r[i], 2) / 3) *
76                  (Math.Pow(omega[i], 4) / Math.Pow(Math.Pow(omega[i], 2) - Math.Pow(omega_0[i], 2), 2));
77        Pwr.Add(res);
78      }
79
80      if (noiseRatio != null) {
81        var Pwr_noise   = new List<double>();
82        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * Pwr.StandardDeviationPop();
83        Pwr_noise.AddRange(Pwr.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
84        data.Remove(Pwr);
85        data.Add(Pwr_noise);
86      }
87
88      return data;
89    }
90  }
91}
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