1 | using System;
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2 | using System.Collections;
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3 | using System.Collections.Generic;
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4 | using HeuristicLab.Core;
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5 | using HeuristicLab.Problems.VehicleRouting.Interfaces;
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6 |
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7 | namespace HeuristicLab.Problems.VehicleRouting.Encodings.Potvin {
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8 |
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9 | public class ClusterAlgorithm<C,CO> where C : Cluster<CO> where CO : ClusterElement {
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10 |
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11 | public static List<C> KMeans(IRandom random, List<CO> clusterElements, int k, double changeThreshold) {
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12 | HashSet<int> initMeans = new HashSet<int>();
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13 | int nextMean = -1;
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14 |
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15 | List<C> clusters = CreateCList();
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16 |
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17 | // (1) initialize each cluster with a random element as mean
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18 | for (int i = 0; i < k && i < clusterElements.Count; i++) {
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19 | C cluster = (C)Activator.CreateInstance(typeof(C));
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20 | cluster.Id = i;
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21 |
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22 | do {
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23 | nextMean = random.Next(0, clusterElements.Count);
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24 | } while (initMeans.Contains(nextMean));
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25 | initMeans.Add(nextMean);
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26 | cluster.SetMean(clusterElements[nextMean]);
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27 | clusters.Add(cluster);
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28 | }
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29 |
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30 | // (2) repeat clustering until change rate is below threshold
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31 | double changeRate = 0.0;
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32 |
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33 | do {
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34 | int changes = KMeansRun(clusters, clusterElements);
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35 | changeRate = (double)changes / clusterElements.Count;
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36 | } while (changeRate > changeThreshold);
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37 |
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38 |
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39 | // remove empty clusters
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40 | clusters.RemoveAll(c => c.Elements.Count.Equals(0));
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41 |
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42 | return clusters;
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43 | }
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44 |
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45 | private static int KMeansRun(List<C> clusters, List<CO> clusterElements) {
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46 | int changes = 0;
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47 |
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48 | // clear clusters from previous assigned elements
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49 | foreach (var c in clusters) {
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50 | c.Elements.Clear();
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51 | }
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52 |
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53 | // assign elements to currently most suitable clusters
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54 | foreach (var e in clusterElements) {
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55 | int optClusterIdx = 0;
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56 | double optImpact = clusters[optClusterIdx].CalculateImpact(e);
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57 | for (int i = 1; i < clusters.Count; i++) {
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58 | double impact = clusters[i].CalculateImpact(e);
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59 | if (impact < optImpact) {
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60 | optImpact = impact;
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61 | optClusterIdx = i;
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62 | }
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63 | }
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64 | if (clusters[optClusterIdx].AddObject(e)) {
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65 | changes++;
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66 | }
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67 | }
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68 |
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69 | // update mean and variance
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70 | foreach (var c in clusters) {
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71 | c.CalculateMean();
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72 | c.CalculateVariance();
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73 | }
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74 |
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75 | return changes;
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76 | }
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77 |
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78 | private static List<C> CreateCList() {
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79 | var listType = typeof(List<>);
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80 | var constructedListType = listType.MakeGenericType(typeof(C));
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81 | return (List<C>)Activator.CreateInstance(constructedListType);
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82 | }
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83 | }
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84 |
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85 | #region Cluster
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86 | public interface ICluster<T> where T : ClusterElement {
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87 | void SetMean(T o);
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88 | bool AddObject(T o);
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89 | void CalculateMean();
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90 | void CalculateVariance();
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91 | double CalculateImpact(T e);
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92 | double CalculateDistance(T e1, T e2);
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93 |
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94 | }
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95 | public abstract class Cluster<T> : ICluster<T> where T : ClusterElement {
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96 | public int Id;
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97 |
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98 | public List<T> Elements { get; private set; }
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99 |
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100 | public T Mean { get; set; }
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101 |
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102 | public double Variance { get; set; }
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103 |
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104 | protected Cluster() {
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105 | Elements = new List<T>();
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106 | }
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107 |
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108 | protected Cluster(int id) {
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109 | Id = id;
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110 | Elements = new List<T>();
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111 |
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112 | }
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113 |
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114 | public bool AddObject(T e) {
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115 | Elements.Add(e);
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116 |
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117 | bool clusterChanged = e.ClusterId != Id;
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118 | e.ClusterId = Id;
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119 | return clusterChanged;
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120 | }
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121 |
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122 | public void SetMean(T e) {
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123 | Mean = e;
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124 | Mean.ClusterId = Id;
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125 | }
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126 |
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127 | public abstract void CalculateMean();
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128 |
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129 | public abstract double CalculateDistance(T e1, T e2);
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130 |
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131 | public virtual void CalculateVariance() {
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132 | if (Mean == null)
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133 | CalculateMean();
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134 |
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135 | Variance = 0.0;
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136 | foreach (T e in Elements) {
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137 | Variance += Math.Pow(CalculateDistance(Mean, e), 2);
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138 | }
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139 | Variance /= Elements.Count;
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140 | }
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141 |
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142 | public virtual double CalculateImpact(T e) {
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143 | if (Mean == null)
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144 | CalculateMean();
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145 |
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146 | double newVariance = (Variance * Elements.Count + Math.Pow(CalculateDistance(Mean, e), 2)) / (Elements.Count + 1);
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147 | return newVariance - Variance;
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148 | }
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149 |
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150 | }
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151 | public class SpatialDistanceCluster : Cluster<SpatialDistanceClusterElement> {
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152 | public SpatialDistanceCluster() : base() {
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153 | }
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154 |
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155 | public SpatialDistanceCluster(int id) : base(id) {
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156 | }
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157 |
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158 | public override void CalculateMean() {
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159 | int dimensions = (Mean != null) ? Mean.Coordinates.Length : (Elements.Count > 0) ? Elements[0].Coordinates.Length : 0;
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160 | SpatialDistanceClusterElement mean = new SpatialDistanceClusterElement(-1, dimensions, Id);
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161 | foreach (SpatialDistanceClusterElement e in Elements) {
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162 | for (int i = 0; i < Mean.Coordinates.Length; i++) {
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163 | mean.Coordinates[i] += (e.Coordinates[i] / Elements.Count);
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164 | }
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165 | }
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166 | Mean = mean;
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167 | }
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168 |
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169 | public override double CalculateDistance(SpatialDistanceClusterElement e1, SpatialDistanceClusterElement e2) {
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170 | if (!e1.Coordinates.Length.Equals(e2.Coordinates.Length)) {
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171 | throw new ArgumentException("Distance could not be calculated since number of dimensions is unequal.");
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172 | }
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173 |
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174 | double distance = 0.0;
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175 | for (int i = 0; i < e1.Coordinates.Length; i++) {
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176 | distance += Math.Pow(e1.Coordinates[i] - e2.Coordinates[i], 2);
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177 | }
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178 |
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179 | return Math.Sqrt(distance);
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180 | }
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181 | }
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182 |
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183 | public class TemporalDistanceCluster : Cluster<TemporalDistanceClusterElement> {
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184 | public TemporalDistanceCluster() : base() {
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185 | }
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186 |
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187 | public TemporalDistanceCluster(int id) : base(id) {
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188 | }
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189 |
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190 | public override void CalculateMean() {
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191 | TemporalDistanceClusterElement mean = new TemporalDistanceClusterElement(-1, Id);
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192 | foreach (TemporalDistanceClusterElement e in Elements) {
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193 | mean.ReadyTime += e.ReadyTime/Elements.Count;
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194 | mean.DueTime += e.DueTime/Elements.Count;
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195 | }
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196 | Mean = mean;
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197 | }
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198 |
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199 | public override double CalculateDistance(TemporalDistanceClusterElement e1, TemporalDistanceClusterElement e2) {
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200 | double distance = 0.0;
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201 |
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202 | distance += Math.Pow(e1.ReadyTime - e2.ReadyTime, 2);
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203 | distance += Math.Pow(e1.DueTime - e2.DueTime, 2);
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204 |
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205 | return Math.Sqrt(distance);
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206 | }
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207 | }
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208 |
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209 | #endregion Cluster
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210 |
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211 | #region ClusterElement
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212 | public abstract class ClusterElement {
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213 | public int Id { get; set; }
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214 | public int ClusterId { get; set; }
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215 |
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216 | protected ClusterElement() {
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217 | ClusterId = -1;
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218 | }
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219 |
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220 | public ClusterElement(int id, int clusterId = -1) {
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221 | Id = id;
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222 | ClusterId = clusterId;
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223 | }
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224 | }
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225 | public class SpatialDistanceClusterElement : ClusterElement {
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226 |
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227 | public double[] Coordinates { get; set; }
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228 |
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229 | public SpatialDistanceClusterElement() : base() {
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230 | }
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231 |
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232 | public SpatialDistanceClusterElement(int id, int clusterId = -1) : base(id, clusterId) {
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233 | }
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234 |
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235 | public SpatialDistanceClusterElement(int id, double[] coordinates, int clusterId = -1) : base(id, clusterId) {
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236 | Coordinates = coordinates;
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237 | }
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238 |
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239 | public SpatialDistanceClusterElement(int id, int dimensions, int clusterId = -1) : base(id, clusterId) {
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240 | Coordinates = new double[dimensions];
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241 | }
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242 |
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243 | }
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244 |
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245 | public class TemporalDistanceClusterElement : ClusterElement {
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246 | public double ReadyTime { get; set; }
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247 | public double DueTime { get; set; }
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248 |
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249 | public TemporalDistanceClusterElement() : base() {
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250 | }
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251 |
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252 | public TemporalDistanceClusterElement(int id, int clusterId = -1) : base(id, clusterId) {
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253 | }
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254 |
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255 | public TemporalDistanceClusterElement(int id, double readyTime, double dueTime, int clusterId = -1) : base(id, clusterId) {
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256 | ReadyTime = readyTime;
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257 | DueTime = dueTime;
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258 | }
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259 | }
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260 |
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261 | #endregion ClusterElement
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262 | }
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