1 | package weka.clusterers.forMetisMQI; |
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2 | |
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3 | import java.util.HashSet; |
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4 | import java.util.Set; |
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5 | import java.util.Stack; |
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6 | |
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7 | import weka.clusterers.forMetisMQI.graph.Bisection; |
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8 | import weka.clusterers.forMetisMQI.graph.Node; |
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9 | import weka.clusterers.forMetisMQI.graph.UndirectedGraph; |
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10 | import weka.clusterers.forMetisMQI.util.CoarserGraphElement; |
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11 | import weka.clusterers.forMetisMQI.util.Util; |
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12 | |
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13 | public class GraphAlgorithms { |
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14 | |
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15 | |
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16 | /** |
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17 | * Given an undirected graph, performs the Kernighan-Li algorithm to find a bisection and |
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18 | * then returns it. |
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19 | * @param g |
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20 | * @return |
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21 | */ |
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22 | static public Bisection KL(UndirectedGraph g) { |
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23 | Bisection partition = new Bisection(g); |
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24 | Bisection result = partition; |
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25 | int bestEdgeCut = Integer.MAX_VALUE; |
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26 | Node u = partition.getCandidate(); |
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27 | while (u != null) { |
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28 | partition.swap(u); |
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29 | if (partition.edgeCut() <= bestEdgeCut) { |
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30 | bestEdgeCut = partition.edgeCut(); |
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31 | result = partition.copy(); |
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32 | } |
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33 | u = partition.getCandidate(); |
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34 | } |
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35 | return result; |
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36 | } |
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37 | |
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38 | static public Bisection metis(UndirectedGraph g, int sizeFinerGraph) { |
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39 | Coarse.setFinerSize(sizeFinerGraph); |
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40 | Stack<CoarserGraphElement> stack = Coarse.coarse(g); |
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41 | Bisection partition = null; |
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42 | if (stack.size() > 0) { |
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43 | partition = KL(stack.peek().getContracted()); |
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44 | partition = Uncoarse.uncoarse(stack, partition); |
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45 | } |
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46 | return partition; |
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47 | } |
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48 | |
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49 | /** |
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50 | * Given an UndirectedGraph, runs metis+mqi for <code>numberOfCluster</code> times and |
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51 | * returns a set of clusters. With the third parameter you can control the maximum size of the finer |
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52 | * graph during the coarsening phase. |
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53 | * @param g |
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54 | * @param numberOfCluster |
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55 | * @param sizeFinerGraph |
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56 | */ |
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57 | static public Set<Set<Node>> metisMqi(UndirectedGraph g, int numberOfCluster, int sizeFinerGraph) { |
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58 | Set<Set<Node>> clusters = new HashSet<Set<Node>>(); |
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59 | for (int i = 0; i < numberOfCluster; i++) { |
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60 | Bisection partition = metis(g,sizeFinerGraph); |
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61 | Set<Node> cluster = MQI.mqi(partition); |
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62 | clusters.add(cluster); |
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63 | } |
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64 | return clusters; |
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65 | } |
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66 | |
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67 | } |
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