[17643] | 1 | using System;
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| 2 | using System.Collections.Generic;
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| 3 | using System.Linq;
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[17649] | 4 | using HeuristicLab.Common;
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[17643] | 5 | using HeuristicLab.Random;
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| 6 |
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| 7 | namespace HeuristicLab.Problems.Instances.DataAnalysis {
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[17677] | 8 | public class FeynmanBonus12 : FeynmanDescriptor {
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[17643] | 9 | private readonly int testSamples;
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| 10 | private readonly int trainingSamples;
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| 11 |
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[17677] | 12 | public FeynmanBonus12() : this((int) DateTime.Now.Ticks, 10000, 10000, null) { }
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[17643] | 13 |
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[17677] | 14 | public FeynmanBonus12(int seed) {
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[17643] | 15 | Seed = seed;
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| 16 | trainingSamples = 10000;
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| 17 | testSamples = 10000;
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[17649] | 18 | noiseRatio = null;
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[17643] | 19 | }
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| 20 |
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[17677] | 21 | public FeynmanBonus12(int seed, int trainingSamples, int testSamples, double? noiseRatio) {
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[17643] | 22 | Seed = seed;
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| 23 | this.trainingSamples = trainingSamples;
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| 24 | this.testSamples = testSamples;
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[17649] | 25 | this.noiseRatio = noiseRatio;
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[17643] | 26 | }
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| 27 |
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| 28 | public override string Name {
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| 29 | get {
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[17649] | 30 | return string.Format(
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[17805] | 31 | "Goldstein 8.56: sqrt((p-q*A_vec)**2*c**2+m**2*c**4)+q*Volt | {0}",
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| 32 | noiseRatio == null ? "no noise" : string.Format(System.Globalization.CultureInfo.InvariantCulture, "noise={0:g}",noiseRatio));
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[17643] | 33 | }
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| 34 | }
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| 35 |
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[17649] | 36 | protected override string TargetVariable { get { return noiseRatio == null ? "E_n" : "E_n_noise"; } }
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| 37 |
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| 38 | protected override string[] VariableNames {
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| 39 | get { return new[] {"m", "c", "p", "q", "A_vec", "Volt", noiseRatio == null ? "E_n" : "E_n_noise"}; }
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| 40 | }
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| 41 |
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[17643] | 42 | protected override string[] AllowedInputVariables { get { return new[] {"m", "c", "p", "q", "A_vec", "Volt"}; } }
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| 43 |
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| 44 | public int Seed { get; private set; }
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| 45 |
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| 46 | protected override int TrainingPartitionStart { get { return 0; } }
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| 47 | protected override int TrainingPartitionEnd { get { return trainingSamples; } }
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| 48 | protected override int TestPartitionStart { get { return trainingSamples; } }
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| 49 | protected override int TestPartitionEnd { get { return trainingSamples + testSamples; } }
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| 50 |
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| 51 | protected override List<List<double>> GenerateValues() {
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| 52 | var rand = new MersenneTwister((uint) Seed);
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| 53 |
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| 54 | var data = new List<List<double>>();
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| 55 | var m = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 56 | var c = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 57 | var p = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 58 | var q = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 59 | var A_vec = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 60 | var Volt = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1, 5).ToList();
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| 61 |
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| 62 | var E_n = new List<double>();
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| 63 |
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| 64 | data.Add(m);
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| 65 | data.Add(c);
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| 66 | data.Add(p);
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| 67 | data.Add(q);
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| 68 | data.Add(A_vec);
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| 69 | data.Add(Volt);
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| 70 | data.Add(E_n);
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| 71 |
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| 72 | for (var i = 0; i < m.Count; i++) {
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[17649] | 73 | var res = Math.Sqrt(Math.Pow(p[i] - q[i] * A_vec[i], 2) * Math.Pow(c[i], 2) +
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[17643] | 74 | Math.Pow(m[i], 2) * Math.Pow(c[i], 4)) + q[i] * Volt[i];
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| 75 | E_n.Add(res);
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| 76 | }
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| 77 |
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[17649] | 78 | if (noiseRatio != null) {
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| 79 | var E_n_noise = new List<double>();
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[17805] | 80 | var sigma_noise = (double) Math.Sqrt(noiseRatio.Value) * E_n.StandardDeviationPop();
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[17649] | 81 | E_n_noise.AddRange(E_n.Select(md => md + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));
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| 82 | data.Remove(E_n);
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| 83 | data.Add(E_n_noise);
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| 84 | }
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| 85 |
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[17643] | 86 | return data;
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| 87 | }
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| 88 | }
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| 89 | } |
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