1 | using System;
|
---|
2 | using System.Collections.Generic;
|
---|
3 | using System.Linq;
|
---|
4 | using HeuristicLab.Common;
|
---|
5 | using HeuristicLab.Random;
|
---|
6 |
|
---|
7 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
|
---|
8 | public class FeynmanBonus15 : FeynmanDescriptor {
|
---|
9 | private readonly int testSamples;
|
---|
10 | private readonly int trainingSamples;
|
---|
11 |
|
---|
12 | public FeynmanBonus15() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
|
---|
13 |
|
---|
14 | public FeynmanBonus15(int seed) {
|
---|
15 | Seed = seed;
|
---|
16 | trainingSamples = 10000;
|
---|
17 | testSamples = 10000;
|
---|
18 | noiseRatio = null;
|
---|
19 | }
|
---|
20 |
|
---|
21 | public FeynmanBonus15(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(
|
---|
31 | "Jackson 3.45: q/sqrt(r**2+d**2-2*r*d*cos(alpha)) | {0}",
|
---|
32 | noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
|
---|
33 | }
|
---|
34 | }
|
---|
35 |
|
---|
36 | protected override string TargetVariable { get { return noiseRatio == null ? "Volt" : "Volt_noise"; } }
|
---|
37 |
|
---|
38 | protected override string[] VariableNames {
|
---|
39 | get { return noiseRatio == null ? new[] { "q", "r", "d", "alpha", "Volt" } : new[] { "q", "r", "d", "alpha", "Volt", "Volt_noise" }; }
|
---|
40 | }
|
---|
41 |
|
---|
42 | protected override string[] AllowedInputVariables { get { return new[] {"q", "r", "d", "alpha"}; } }
|
---|
43 |
|
---|
44 | public int Seed { get; private set; }
|
---|
45 |
|
---|
46 | protected override int TrainingPartitionStart { get { return 0; } }
|
---|
47 | protected override int TrainingPartitionEnd { get { return trainingSamples; } }
|
---|
48 | protected override int TestPartitionStart { get { return trainingSamples; } }
|
---|
49 | protected override int TestPartitionEnd { get { return trainingSamples + testSamples; } }
|
---|
50 |
|
---|
51 | protected override List<List<double>> GenerateValues() {
|
---|
52 | var rand = new MersenneTwister((uint) Seed);
|
---|
53 |
|
---|
54 | var data = new List<List<double>>();
|
---|
55 | var q = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
|
---|
56 | var r = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
|
---|
57 | var d = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 4, 6).ToList();
|
---|
58 | var alpha = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 6).ToList();
|
---|
59 |
|
---|
60 | var Volt = new List<double>();
|
---|
61 |
|
---|
62 | data.Add(q);
|
---|
63 | data.Add(r);
|
---|
64 | data.Add(d);
|
---|
65 | data.Add(alpha);
|
---|
66 | data.Add(Volt);
|
---|
67 |
|
---|
68 | for (var i = 0; i < q.Count; i++) {
|
---|
69 | var res = q[i] /
|
---|
70 | Math.Sqrt(Math.Pow(r[i], 2) + Math.Pow(d[i], 2) - 2 * r[i] * d[i] * Math.Cos(alpha[i]));
|
---|
71 | Volt.Add(res);
|
---|
72 | }
|
---|
73 |
|
---|
74 | var targetNoise = ValueGenerator.GenerateNoise(Volt, rand, noiseRatio);
|
---|
75 | if (targetNoise != null) data.Add(targetNoise);
|
---|
76 |
|
---|
77 | return data;
|
---|
78 | }
|
---|
79 | }
|
---|
80 | } |
---|