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source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman24.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: 3.0 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 Feynman24 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public Feynman24() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public Feynman24(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public Feynman24(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.24.6 1/4*m*(omega**2 + omega_0**2)*x**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 ? "E_n" : "E_n_noise"; } }
36
37    protected override string[] VariableNames {
38      get { return noiseRatio == null ? new[] { "m", "omega", "omega_0", "x", "E_n" } : new[] { "m", "omega", "omega_0", "x", "E_n", "E_n_noise" }; }
39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"m", "omega", "omega_0", "x"}; } }
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 m       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
55      var omega   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
56      var omega_0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
57      var x       = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
58
59      var E_n = new List<double>();
60
61      data.Add(m);
62      data.Add(omega);
63      data.Add(omega_0);
64      data.Add(x);
65      data.Add(E_n);
66
67      for (var i = 0; i < m.Count; i++) {
68        var res = 1.0 / 4 * m[i] * (Math.Pow(omega[i], 2) + Math.Pow(omega_0[i], 2)) * Math.Pow(x[i], 2);
69        E_n.Add(res);
70      }
71
72      var targetNoise = GetNoisyTarget(E_n, rand);
73      if (targetNoise != null) data.Add(targetNoise);
74
75      return data;
76    }
77  }
78}
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