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source: branches/3026_IntegrationIntoSymSpace/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/FeynmanBonus18.cs @ 18027

Last change on this file since 18027 was 18027, checked in by dpiringe, 3 years ago

#3026

  • merged trunk into branch
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 FeynmanBonus18 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public FeynmanBonus18() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public FeynmanBonus18(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public FeynmanBonus18(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("Weinberg 15.2.1: 3/(8*pi*G)*(c**2*k_f/r**2+H_G**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 ? "rho_0" : "rho_0_noise"; } }
36
37    protected override string[] VariableNames {
38      get { return noiseRatio == null ? new[] { "G", "k_f", "r", "H_G", "c", "rho_0" } : new[] { "G", "k_f", "r", "H_G", "c", "rho_0", "rho_0_noise" }; }
39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"G", "k_f", "r", "H_G", "c"}; } }
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 G    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
55      var k_f  = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
56      var r    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
57      var H_G  = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
58      var c    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
59
60      var rho_0 = new List<double>();
61
62      data.Add(G);
63      data.Add(k_f);
64      data.Add(r);
65      data.Add(H_G);
66      data.Add(c);
67      data.Add(rho_0);
68
69      for (var i = 0; i < G.Count; i++) {
70        var res = 3.0 / (8 * Math.PI * G[i]) * (Math.Pow(c[i], 2) * k_f[i] / Math.Pow(r[i], 2) + Math.Pow(H_G[i], 2));
71        rho_0.Add(res);
72      }
73
74      var targetNoise = GetNoisyTarget(rho_0, rand);
75      if (targetNoise != null) data.Add(targetNoise);
76
77      return data;
78    }
79  }
80}
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