1 | using System;
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2 | using System.Collections.Generic;
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3 | using System.ComponentModel;
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4 | using System.Data;
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5 | using System.Drawing;
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6 | using System.Linq;
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7 | using System.Text;
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8 | using System.Threading.Tasks;
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9 | using System.Windows.Forms;
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10 | using HeuristicLab.Algorithms.DataAnalysis;
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11 | using HeuristicLab.Core;
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12 | using HeuristicLab.Problems.DataAnalysis;
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13 | using HeuristicLab.Problems.Instances.DataAnalysis;
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14 | using HeuristicLab.Random;
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15 |
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16 | namespace GaussianProcessDemo {
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17 | public partial class Form1 : Form {
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18 | private IRandom random;
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19 | private ICovarianceFunction covFunction;
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20 | private List<List<double>> data;
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21 | private double[] alpha;
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22 |
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23 |
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24 | public Form1() {
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25 | InitializeComponent();
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26 | this.random = new MersenneTwister();
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27 |
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28 | var sum = new CovarianceSum();
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29 | sum.Terms.Add(new CovariancePeriodic());
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30 | sum.Terms.Add(new CovarianceNoise());
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31 | this.covFunction = sum;
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32 | UpdateSliders();
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33 |
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34 | InitData();
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35 | UpdateChart();
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36 | }
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37 |
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38 | private void UpdateSliders() {
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39 | flowLayoutPanel1.Controls.Clear();
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40 | for (int i = 0; i < covFunction.GetNumberOfParameters(1); i++) {
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41 | var sliderControl = new TrackBar();
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42 | sliderControl.Minimum = -50;
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43 | sliderControl.Maximum = 50;
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44 | sliderControl.Value = 0;
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45 | sliderControl.ValueChanged += (sender, args) => UpdateChart();
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46 | flowLayoutPanel1.Controls.Add(sliderControl);
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47 | }
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48 | }
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49 |
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50 | private void InitData() {
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51 | int n = 20;
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52 | data = new List<List<double>>();
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53 | data.Add(ValueGenerator.GenerateSteps(0, 1, 1.0 / n).ToList());
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54 |
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55 | // sample from GP
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56 | var normalRand = new NormalDistributedRandom(random, 0, 1);
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57 | alpha = (from i in Enumerable.Range(0, n + 1)
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58 | select normalRand.NextDouble()).ToArray();
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59 | }
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60 |
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61 | private void UpdateChart() {
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62 | var hyp = GetSliderValues();
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63 | var cov = covFunction.GetParameterizedCovarianceFunction(hyp, null);
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64 | var y = Util.SampleGaussianProcess(random, cov, data, alpha);
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65 |
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66 | chart1.Series[0].Points.Clear();
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67 | foreach (var p in y.Zip(data[0], (t, x) => new { t, x })) {
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68 | chart1.Series[0].Points.AddXY(p.x, p.t);
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69 | }
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70 |
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71 | var allData = new List<List<double>>();
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72 | allData.Add(y);
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73 | allData.Add(data[0]);
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74 | var variableNames = new string[] { "y", "x" };
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75 | var ds = new Dataset(variableNames, allData);
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76 | var rows = Enumerable.Range(0, data[0].Count);
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77 | var correctModel = new GaussianProcessModel(ds, variableNames.First(), variableNames.Skip(1), rows, hyp, new MeanZero(),
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78 | (ICovarianceFunction)covFunction.Clone());
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79 | var yPred = correctModel.GetEstimatedValues(ds, rows);
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80 | chart1.Series[1].Points.Clear();
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81 | foreach (var p in yPred.Zip(data[0], (t, x) => new { t, x })) {
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82 | chart1.Series[1].Points.AddXY(p.x, p.t);
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83 | }
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84 | }
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85 |
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86 | private double[] GetSliderValues() {
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87 | var hyp = new List<double>();
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88 | foreach (var slider in flowLayoutPanel1.Controls.Cast<TrackBar>()) {
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89 | Console.Write(slider.Value / 10.0 + " ");
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90 | hyp.Add(slider.Value / 10.0);
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91 | }
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92 | Console.WriteLine();
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93 |
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94 | return hyp.ToArray();
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95 | }
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96 | }
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97 | }
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