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source: branches/3106_AnalyticContinuedFractionsRegression/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus7.cs @ 17970

Last change on this file since 17970 was 17970, checked in by gkronber, 3 years ago

#3106 merged r17856:17969 from trunk to branch

File size: 3.3 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 FeynmanBonus7 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public FeynmanBonus7() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public FeynmanBonus7(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public FeynmanBonus7(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("Goldstein 3.16: sqrt(2/m*(E_n-U-L**2/(2*m*r**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 ? "v" : "v_noise"; } }
36
37    protected override string[] VariableNames {
38      get { return new[] {"m", "E_n", "U", "L", "r", noiseRatio == null ? "v" : "v_noise"}; }
39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"m", "E_n", "U", "L", "r"}; } }
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 m    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
55      var E_n  = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 8, 12).ToList();
56      var U    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
57      var L    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
58      var r    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
59
60      var v = new List<double>();
61
62      data.Add(m);
63      data.Add(E_n);
64      data.Add(U);
65      data.Add(L);
66      data.Add(r);
67      data.Add(v);
68
69      for (var i = 0; i < m.Count; i++) {
70        var res = Math.Sqrt(2 / m[i] * (E_n[i] - U[i] - Math.Pow(L[i], 2) / (2 * m[i] * Math.Pow(r[i], 2))));
71        v.Add(res);
72      }
73
74      if (noiseRatio != null) {
75        var v_noise     = new List<double>();
76        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * v.StandardDeviationPop();
77        v_noise.AddRange(v.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
78        data.Remove(v);
79        data.Add(v_noise);
80      }
81
82      return data;
83    }
84  }
85}
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