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 FeynmanBonus4 : 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 FeynmanBonus4() : this((int) DateTime.Now.Ticks, 10000, 10000) { }
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12 |
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13 | public FeynmanBonus4(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 FeynmanBonus4(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 3.16 Goldstein sqrt(2/m*(E_n-U-L**2/(2*m*r**2))) | {0} samples", trainingSamples);
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28 | }
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29 | }
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30 |
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31 | protected override string TargetVariable { get { return "v"; } }
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32 | protected override string[] VariableNames { get { return new[] {"m", "E_n", "U", "L", "r", "v"}; } }
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33 | protected override string[] AllowedInputVariables { get { return new[] {"m", "E_n", "U", "L", "r"}; } }
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34 |
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35 | public int Seed { get; private set; }
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36 |
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37 | protected override int TrainingPartitionStart { get { return 0; } }
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38 | protected override int TrainingPartitionEnd { get { return trainingSamples; } }
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39 | protected override int TestPartitionStart { get { return trainingSamples; } }
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40 | protected override int TestPartitionEnd { get { return trainingSamples + testSamples; } }
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41 |
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42 | protected override List<List<double>> GenerateValues() {
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43 | var rand = new MersenneTwister((uint) Seed);
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44 |
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45 | var data = new List<List<double>>();
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46 | var m = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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47 | var E_n = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 8, 12).ToList();
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48 | var U = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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49 | var L = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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50 | var r = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 3).ToList();
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51 |
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52 | var v = new List<double>();
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53 |
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54 | data.Add(m);
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55 | data.Add(E_n);
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56 | data.Add(U);
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57 | data.Add(L);
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58 | data.Add(r);
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59 | data.Add(v);
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60 |
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61 | for (var i = 0; i < m.Count; i++) {
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62 | var res = Math.Sqrt(2 / m[i] * (E_n[i] - U[i] - Math.Pow(L[i], 2) / (2 * m[i] * Math.Pow(r[i], 2))));
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63 | v.Add(res);
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64 | }
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65 |
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66 | return data;
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67 | }
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68 | }
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69 | } |
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