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source: branches/3075_aifeynman_instances/HeuristicLab.Problems.Instances.DataAnalysis/3.3/Regression/Feynman/Feynman7.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 Feynman7 : FeynmanDescriptor {
9    private readonly int testSamples;
10    private readonly int trainingSamples;
11
12    public Feynman7() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
13
14    public Feynman7(int seed) {
15      Seed            = seed;
16      trainingSamples = 10000;
17      testSamples     = 10000;
18      noiseRatio      = null;
19    }
20
21    public Feynman7(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.11.19 x1*y1+x2*y2+x3*y3 | {0} samples | {1}", trainingSamples,
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 ? "A" : "A_noise"; } }
36
37    protected override string[] VariableNames {
38      get { return new[] {"x1", "x2", "x3", "y1", "y2", "y3", noiseRatio == null ? "A" : "A_noise"}; }
39    }
40
41    protected override string[] AllowedInputVariables { get { return new[] {"x1", "x2", "x3", "y1", "y2", "y3"}; } }
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 x3   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
57      var y1   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
58      var y2   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
59      var y3   = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
60
61      var A = new List<double>();
62
63      data.Add(x1);
64      data.Add(x2);
65      data.Add(x3);
66      data.Add(y1);
67      data.Add(y2);
68      data.Add(y3);
69      data.Add(A);
70
71      for (var i = 0; i < x1.Count; i++) {
72        var res = x1[i] * y1[i] + x2[i] * y2[i] + x3[i] * y3[i];
73        A.Add(res);
74      }
75
76      if (noiseRatio != null) {
77        var A_noise     = new List<double>();
78        var sigma_noise = (double) noiseRatio * A.StandardDeviationPop();
79        A_noise.AddRange(A.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
80        data.Remove(A);
81        data.Add(A_noise);
82      }
83
84      return data;
85    }
86  }
87}
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