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source: branches/3106_AnalyticContinuedFractionsRegression/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman4.cs @ 17970

Last change on this file since 17970 was 17970, checked in by gkronber, 3 years ago

#3106 merged r17856:17969 from trunk to 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 Feynman4 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public Feynman4() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public Feynman4(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public Feynman4(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.8.14 sqrt((x2-x1)**2+(y2-y1)**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 ? "d" : "d_noise"; } }
36
37    protected override string[] VariableNames {
38      get { return new[] {"x1", "x2", "y1", "y2", noiseRatio == null ? "d" : "d_noise"}; }
39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"x1", "x2", "y1", "y2"}; } }
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 x1   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
55      var x2   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
56      var y1   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
57      var y2   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
58
59      var d = new List<double>();
60
61      data.Add(x1);
62      data.Add(x2);
63      data.Add(y1);
64      data.Add(y2);
65      data.Add(d);
66
67      for (var i = 0; i < x1.Count; i++) {
68        var res = Math.Sqrt(Math.Pow(x2[i] - x1[i], 2) + Math.Pow(y2[i] - y1[i], 2));
69        d.Add(res);
70      }
71
72      if (noiseRatio != null) {
73        var d_noise     = new List<double>();
74        var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * d.StandardDeviationPop();
75        d_noise.AddRange(d.Select(md => md + NormalDistributedRandomPolar.NextDouble(rand, 0, sigma_noise)));
76        data.Remove(d);
77        data.Add(d_noise);
78      }
79
80      return data;
81    }
82  }
83}
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