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 FeynmanBonus2 : 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 FeynmanBonus2() : this((int) DateTime.Now.Ticks, 10000, 10000) { }
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12 |
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13 | public FeynmanBonus2(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 FeynmanBonus2(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(
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28 | "Feynman 3.55 Goldstein m*k_G/L**2*(1+sqrt(1+2*E_n*L**2/(m*k_G**2))*cos(theta1-theta2)) | {0} samples",
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29 | trainingSamples);
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30 | }
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31 | }
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32 |
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33 | protected override string TargetVariable { get { return "k"; } }
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34 |
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35 | protected override string[] VariableNames {
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36 | get { return new[] {"m", "k_G", "L", "E_n", "theta1", "theta2", "k"}; }
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37 | }
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38 |
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39 | protected override string[] AllowedInputVariables {
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40 | get { return new[] {"m", "k_G", "L", "E_n", "theta1", "theta2"}; }
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41 | }
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42 |
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43 | public int Seed { get; private set; }
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44 |
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45 | protected override int TrainingPartitionStart { get { return 0; } }
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46 | protected override int TrainingPartitionEnd { get { return trainingSamples; } }
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47 | protected override int TestPartitionStart { get { return trainingSamples; } }
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48 | protected override int TestPartitionEnd { get { return trainingSamples + testSamples; } }
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49 |
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50 | protected override List<List<double>> GenerateValues() {
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51 | var rand = new MersenneTwister((uint) Seed);
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52 |
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53 | var data = new List<List<double>>();
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54 | var m = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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55 | var k_G = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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56 | var L = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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57 | var E_n = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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58 | var theta1 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 6).ToList();
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59 | var theta2 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 6).ToList();
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60 |
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61 | var k = new List<double>();
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62 |
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63 | data.Add(m);
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64 | data.Add(k_G);
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65 | data.Add(L);
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66 | data.Add(E_n);
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67 | data.Add(theta1);
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68 | data.Add(theta2);
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69 | data.Add(k);
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70 |
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71 | for (var i = 0; i < m.Count; i++) {
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72 | var res = m[i] * k_G[i] / Math.Pow(L[i], 2) *
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73 | (1 + Math.Sqrt(1 + 2 * E_n[i] * Math.Pow(L[i], 2) / (m[i] * Math.Pow(k_G[i], 2))) *
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74 | Math.Cos(theta1[i] - theta2[i]));
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75 | k.Add(res);
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76 | }
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77 |
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78 | return data;
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79 | }
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80 | }
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81 | } |
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