1 | using System;
|
---|
2 | using System.Collections.Generic;
|
---|
3 | using System.Linq;
|
---|
4 | using HeuristicLab.Random;
|
---|
5 |
|
---|
6 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
|
---|
7 | public class Feynman90 : FeynmanDescriptor {
|
---|
8 | public override string Name {
|
---|
9 | get { return "Feynman III.9.52 (p_d*Ef*t/(h/(2*pi)))*sin((omega-omega_0)*t/2)**2/((omega-omega_0)*t/2)**2"; }
|
---|
10 | }
|
---|
11 |
|
---|
12 | protected override string TargetVariable { get { return "prob"; } }
|
---|
13 |
|
---|
14 | protected override string[] VariableNames {
|
---|
15 | get { return new[] {"p_d", "Ef", "t", "h", "omega", "omega_0", "prob"}; }
|
---|
16 | }
|
---|
17 |
|
---|
18 | protected override string[] AllowedInputVariables {
|
---|
19 | get { return new[] {"p_d", "Ef", "t", "h", "omega", "omega_0"}; }
|
---|
20 | }
|
---|
21 |
|
---|
22 | public int Seed { get; private set; }
|
---|
23 |
|
---|
24 | public Feynman90() : this((int) DateTime.Now.Ticks) { }
|
---|
25 |
|
---|
26 | public Feynman90(int seed) {
|
---|
27 | Seed = seed;
|
---|
28 | }
|
---|
29 |
|
---|
30 | protected override List<List<double>> GenerateValues() {
|
---|
31 | var rand = new MersenneTwister((uint) Seed);
|
---|
32 |
|
---|
33 | var data = new List<List<double>>();
|
---|
34 | var p_d = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
|
---|
35 | var Ef = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
|
---|
36 | var t = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
|
---|
37 | var h = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
|
---|
38 | var omega = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
|
---|
39 | var omega_0 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
|
---|
40 |
|
---|
41 | var prob = new List<double>();
|
---|
42 |
|
---|
43 | data.Add(p_d);
|
---|
44 | data.Add(Ef);
|
---|
45 | data.Add(t);
|
---|
46 | data.Add(h);
|
---|
47 | data.Add(omega);
|
---|
48 | data.Add(omega_0);
|
---|
49 | data.Add(prob);
|
---|
50 |
|
---|
51 | for (var i = 0; i < p_d.Count; i++) {
|
---|
52 | var res = p_d[i] * Ef[i] * t[i] / (h[i] / (2 * Math.PI)) * Math.Pow(Math.Sin((omega[i] - omega_0[i]) * t[i] / 2),2) / Math.Pow(((omega[i] - omega_0[i]) * t[i] / 2), 2);
|
---|
53 | prob.Add(res);
|
---|
54 | }
|
---|
55 |
|
---|
56 | return data;
|
---|
57 | }
|
---|
58 | }
|
---|
59 | } |
---|