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source: branches/3087_Ceres_Integration/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus14.cs @ 18006

Last change on this file since 18006 was 18006, checked in by gkronber, 3 years ago

#3087: merged r17784:18004 from trunk to branch to prepare for trunk reintegration (fixed a conflict in CrossValidation.cs)

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 FeynmanBonus14 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public FeynmanBonus14() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public FeynmanBonus14(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public FeynmanBonus14(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          "Jackson 2.11: q/(4*pi*epsilon*y**2)*(4*pi*epsilon*Volt*d-q*d*y**3/(y**2-d**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 ? "F" : "F_noise"; } }
37
38    protected override string[] VariableNames {
39      get { return noiseRatio == null ? new[] { "q", "y", "Volt", "d", "epsilon", "F" } : new[] { "q", "y", "Volt", "d", "epsilon", "F", "F_noise"}; }
40    }
41
42    protected override string[] AllowedInputVariables { get { return new[] {"q", "y", "Volt", "d", "epsilon"}; } }
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 q       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
56      var y       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
57      var Volt    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
58      var d       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 4, 6).ToList();
59      var epsilon = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
60
61      var F = new List<double>();
62
63      data.Add(q);
64      data.Add(y);
65      data.Add(Volt);
66      data.Add(d);
67      data.Add(epsilon);
68      data.Add(F);
69
70      for (var i = 0; i < q.Count; i++) {
71        var res = q[i] / (4 * Math.PI * epsilon[i] * Math.Pow(y[i], 2)) * (
72                    4 * Math.PI * epsilon[i] * Volt[i] * d[i] - q[i] * d[i] * Math.Pow(y[i], 3) /
73                    Math.Pow(Math.Pow(y[i], 2) - Math.Pow(d[i], 2), 2));
74        F.Add(res);
75      }
76
77      var targetNoise = GetNoisyTarget(F, rand);
78      if (targetNoise != null) data.Add(targetNoise);
79
80      return data;
81    }
82  }
83}
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