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source: trunk/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman13.cs @ 18242

Last change on this file since 18242 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: 2.9 KB
RevLine 
[17647]1using System;
2using System.Collections.Generic;
3using System.Linq;
4using HeuristicLab.Common;
5using HeuristicLab.Random;
6
7namespace HeuristicLab.Problems.Instances.DataAnalysis {
[17677]8  public class Feynman13 : FeynmanDescriptor {
[17647]9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
[17677]12    public Feynman13() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
[17647]13
[17677]14    public Feynman13(int seed) {
[17647]15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
[17677]21    public Feynman13(int seed, int trainingSamples, int testSamples, double? noiseRatio) {
[17647]22      Seed                 = seed;
23      this.trainingSamples = trainingSamples;
24      this.testSamples     = testSamples;
25      this.noiseRatio      = noiseRatio;
26    }
27
28    public override string Name {
29      get {
[17805]30        return string.Format("I.13.4 1/2*m*(v**2+u**2+w**2) | {0}",
[17678]31          noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
[17647]32      }
33    }
34
35    protected override string TargetVariable { get { return noiseRatio == null ? "K" : "K_noise"; } }
36
37    protected override string[] VariableNames {
[17973]38      get { return noiseRatio == null ? new[] { "m", "v", "u", "w", "K" } : new[] { "m", "v", "u", "w", "K", "K_noise" }; }
[17647]39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"m", "v", "u", "w"}; } }
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, 5).ToList();
55      var v    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
56      var u    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
57      var w    = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
58
59      var K = new List<double>();
60
61      data.Add(m);
62      data.Add(v);
63      data.Add(u);
64      data.Add(w);
65      data.Add(K);
66
67      for (var i = 0; i < m.Count; i++) {
68        var res = 1.0 / 2 * m[i] * (Math.Pow(v[i], 2) + Math.Pow(u[i], 2) + Math.Pow(w[i], 2));
69        K.Add(res);
70      }
71
[18032]72      var targetNoise = ValueGenerator.GenerateNoise(K, rand, noiseRatio);
[17973]73      if (targetNoise != null) data.Add(targetNoise);
[17647]74
75      return data;
76    }
77  }
78}
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