Free cookie consent management tool by TermsFeed Policy Generator

source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman48.cs @ 18032

Last change on this file since 18032 was 18032, checked in by chaider, 3 years ago

#3075 noise generation method to ValueGenerator; use same method for generating noise in friedman and feynman instances

File size: 3.0 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 Feynman48 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public Feynman48() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public Feynman48(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public Feynman48(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("I.44.4 n*kb*T*ln(V2/V1) | {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 ? "E_n" : "E_n_noise"; } }
36
37    protected override string[] VariableNames {
38      get { return noiseRatio == null ? new[] { "n", "kb", "T", "V1", "V2", "E_n" } : new[] { "n", "kb", "T", "V1", "V2", "E_n", "E_n_noise" }; }
39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"n", "kb", "T", "V1", "V2"}; } }
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    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
55      var kb   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
56      var T    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
57      var V1   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
58      var V2   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
59
60      var E_n = new List<double>();
61
62      data.Add(n);
63      data.Add(kb);
64      data.Add(T);
65      data.Add(V1);
66      data.Add(V2);
67      data.Add(E_n);
68
69      for (var i = 0; i < n.Count; i++) {
70        var res = n[i] * kb[i] * T[i] * Math.Log(V2[i] / V1[i]);
71        E_n.Add(res);
72      }
73
74      var targetNoise = ValueGenerator.GenerateNoise(E_n, rand, noiseRatio);
75      if (targetNoise != null) data.Add(targetNoise);
76
77      return data;
78    }
79  }
80}
Note: See TracBrowser for help on using the repository browser.