1 | using System;
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2 | using System.Collections.Generic;
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3 | using System.Linq;
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4 | using HeuristicLab.Random;
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5 |
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6 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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7 | public class Feynman56 : FeynmanDescriptor {
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8 | private readonly int testSamples;
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9 | private readonly int trainingSamples;
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10 |
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11 | public Feynman56() : this((int) DateTime.Now.Ticks, 10000, 10000) { }
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12 |
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13 | public Feynman56(int seed) {
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14 | Seed = seed;
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15 | trainingSamples = 10000;
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16 | testSamples = 10000;
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17 | }
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18 |
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19 | public Feynman56(int seed, int trainingSamples, int testSamples) {
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20 | Seed = seed;
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21 | this.trainingSamples = trainingSamples;
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22 | this.testSamples = testSamples;
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23 | }
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24 |
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25 | public override string Name {
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26 | get {
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27 | return string.Format("Feynman II.6.15a p_d/(4*pi*epsilon)*3*z/r**5*sqrt(x**2+y**2) | {0} samples",
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28 | trainingSamples);
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29 | }
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30 | }
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31 |
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32 | protected override string TargetVariable { get { return "Ef"; } }
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33 | protected override string[] VariableNames { get { return new[] {"epsilon", "p_d", "r", "x", "y", "z", "Ef"}; } }
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34 | protected override string[] AllowedInputVariables { get { return new[] {"epsilon", "p_d", "r", "x", "y", "z"}; } }
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35 |
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36 | public int Seed { get; private set; }
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37 |
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38 | protected override int TrainingPartitionStart { get { return 0; } }
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39 | protected override int TrainingPartitionEnd { get { return trainingSamples; } }
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40 | protected override int TestPartitionStart { get { return trainingSamples; } }
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41 | protected override int TestPartitionEnd { get { return trainingSamples + testSamples; } }
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42 |
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43 | protected override List<List<double>> GenerateValues() {
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44 | var rand = new MersenneTwister((uint) Seed);
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45 |
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46 | var data = new List<List<double>>();
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47 | var epsilon = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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48 | var p_d = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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49 | var r = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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50 | var x = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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51 | var y = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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52 | var z = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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53 |
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54 | var Ef = new List<double>();
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55 |
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56 | data.Add(epsilon);
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57 | data.Add(p_d);
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58 | data.Add(r);
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59 | data.Add(x);
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60 | data.Add(y);
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61 | data.Add(z);
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62 | data.Add(Ef);
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63 |
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64 | for (var i = 0; i < epsilon.Count; i++) {
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65 | var res = p_d[i] / (4 * Math.PI * epsilon[i]) * 3 * z[i] / Math.Pow(r[i], 5) *
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66 | Math.Sqrt(Math.Pow(x[i], 2) + Math.Pow(y[i], 2));
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67 | Ef.Add(res);
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68 | }
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69 |
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70 | return data;
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71 | }
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72 | }
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73 | } |
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