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source: branches/3075_aifeynman_instances/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman61.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: 3.3 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 Feynman61 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public Feynman61() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public Feynman61(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public Feynman61(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.11.3 q*Ef/(m*(omega_0**2-omega**2)) | {0} samples | {1}",
31          trainingSamples, 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 ? "x" : "x_noise"; } }
36
37    protected override string[] VariableNames {
38      get { return new[] {"q", "Ef", "m", "omega_0", "omega", noiseRatio == null ? "x" : "x_noise"}; }
39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"q", "Ef", "m", "omega_0", "omega"}; } }
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 q       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
55      var Ef      = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
56      var m       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
57      var omega_0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 3, 5).ToList();
58      var omega   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 2).ToList();
59
60      var x = new List<double>();
61
62      data.Add(q);
63      data.Add(Ef);
64      data.Add(m);
65      data.Add(omega_0);
66      data.Add(omega);
67      data.Add(x);
68
69      for (var i = 0; i < q.Count; i++) {
70        var res = q[i] * Ef[i] / (m[i] * (Math.Pow(omega_0[i], 2) - Math.Pow(omega[i], 2)));
71        x.Add(res);
72      }
73
74      if (noiseRatio != null) {
75        var x_noise     = new List<double>();
76        var sigma_noise = (double) noiseRatio * x.StandardDeviationPop();
77        x_noise.AddRange(x.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
78        data.Remove(x);
79        data.Add(x_noise);
80      }
81
82      return data;
83    }
84  }
85}
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