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source: branches/3075_aifeynman_instances/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman87.cs @ 17678

Last change on this file since 17678 was 17678, checked in by gkronber, 4 years ago

#3075: changed title for noise part

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 Feynman87 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public Feynman87() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public Feynman87(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public Feynman87(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          "III.4.33 h*omega/(exp(h*omega/(kb*T))-1) | {0} samples | {1}",
32          trainingSamples, 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 ? "E_n" : "E_n_noise"; } }
37
38    protected override string[] VariableNames {
39      get { return new[] {"h", "omega", "kb", "T", noiseRatio == null ? "E_n" : "E_n_noise"}; }
40    }
41
42    protected override string[] AllowedInputVariables { get { return new[] {"h", "omega", "kb", "T"}; } }
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 h     = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
56      var omega = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
57      var kb    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
58      var T     = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
59
60      var E_n = new List<double>();
61
62      data.Add(h);
63      data.Add(omega);
64      data.Add(kb);
65      data.Add(T);
66      data.Add(E_n);
67
68      for (var i = 0; i < h.Count; i++) {
69        var res = h[i] * omega[i] / (Math.Exp(h[i] * omega[i] / (kb[i] * T[i])) - 1);
70        E_n.Add(res);
71      }
72
73      if (noiseRatio != null) {
74        var E_n_noise   = new List<double>();
75        var sigma_noise = (double) noiseRatio * E_n.StandardDeviationPop();
76        E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
77        data.Remove(E_n);
78        data.Add(E_n_noise);
79      }
80
81      return data;
82    }
83  }
84}
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