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source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman62.cs @ 17973

Last change on this file since 17973 was 17973, checked in by chaider, 3 years ago

#3075

  • Added target without noise to noisy instances
  • Moved noise calculation to descriptor
File size: 3.2 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 Feynman62 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public Feynman62() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public Feynman62(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public Feynman62(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("II.11.17 n_0*(1 + p_d*Ef*cos(theta)/(kb*T)) | {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 ? "n" : "n_noise"; } }
36
37    protected override string[] VariableNames {
38      get { return noiseRatio == null ? new[] { "n_0", "kb", "T", "theta", "p_d", "Ef", "n" } : new[] { "n_0", "kb", "T", "theta", "p_d", "Ef", "n", "n_noise" }; }
39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"n_0", "kb", "T", "theta", "p_d", "Ef"}; } }
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 n_0   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
55      var kb    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
56      var T     = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
57      var theta = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
58      var p_d   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
59      var Ef    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
60
61      var n = new List<double>();
62
63      data.Add(n_0);
64      data.Add(kb);
65      data.Add(T);
66      data.Add(theta);
67      data.Add(p_d);
68      data.Add(Ef);
69      data.Add(n);
70
71      for (var i = 0; i < n_0.Count; i++) {
72        var res = n_0[i] * (1 + p_d[i] * Ef[i] * Math.Cos(theta[i]) / (kb[i] * T[i]));
73        n.Add(res);
74      }
75
76      var targetNoise = GetNoisyTarget(n, rand);
77      if (targetNoise != null) data.Add(targetNoise);
78
79      return data;
80    }
81  }
82}
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