--- /dev/null
+/*
+ * Copyright (c) 2004, the JUNG Project and the Regents of the University
+ * of California
+ * All rights reserved.
+ *
+ * This software is open-source under the BSD license; see either
+ * "license.txt" or
+ * http://jung.sourceforge.net/license.txt for a description.
+ *
+ * Created on Aug 12, 2004
+ */
+package edu.uci.ics.jung.algorithms.cluster;
+
+import edu.uci.ics.jung.algorithms.scoring.VoltageScorer;
+import edu.uci.ics.jung.algorithms.util.DiscreteDistribution;
+import edu.uci.ics.jung.algorithms.util.KMeansClusterer;
+import edu.uci.ics.jung.algorithms.util.KMeansClusterer.NotEnoughClustersException;
+import edu.uci.ics.jung.graph.Graph;
+
+import java.util.ArrayList;
+import java.util.Collection;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.Iterator;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+import java.util.Set;
+
+/**
+ * <p>Clusters vertices of a <code>Graph</code> based on their ranks as
+ * calculated by <code>VoltageScorer</code>. This algorithm is based on,
+ * but not identical with, the method described in the paper below.
+ * The primary difference is that Wu and Huberman assume a priori that the clusters
+ * are of approximately the same size, and therefore use a more complex
+ * method than k-means (which is used here) for determining cluster
+ * membership based on co-occurrence data.</p>
+ *
+ * <p>The algorithm proceeds as follows:
+ * <ul>
+ * <li/>first, generate a set of candidate clusters as follows:
+ * <ul>
+ * <li/>pick (widely separated) vertex pair, run VoltageScorer
+ * <li/>group the vertices in two clusters according to their voltages
+ * <li/>store resulting candidate clusters
+ * </ul>
+ * <li/>second, generate k-1 clusters as follows:
+ * <ul>
+ * <li/>pick a vertex v as a cluster 'seed'
+ * <br>(Wu/Huberman: most frequent vertex in candidate clusters)
+ * <li/>calculate co-occurrence over all candidate clusters of v with each other
+ * vertex
+ * <li/>separate co-occurrence counts into high/low;
+ * high vertices constitute a cluster
+ * <li/>remove v's vertices from candidate clusters; continue
+ * </ul>
+ * <li/>finally, remaining unassigned vertices are assigned to the kth ("garbage")
+ * cluster.
+ * </ul></p>
+ *
+ * <p><b>NOTE</b>: Depending on how the co-occurrence data splits the data into
+ * clusters, the number of clusters returned by this algorithm may be less than the
+ * number of clusters requested. The number of clusters will never be more than
+ * the number requested, however.</p>
+ *
+ * @author Joshua O'Madadhain
+ * @see "'Finding communities in linear time: a physics approach', Fang Wu and Bernardo Huberman, http://www.hpl.hp.com/research/idl/papers/linear/"
+ * @see VoltageScorer
+ * @see KMeansClusterer
+ */
+public class VoltageClusterer<V,E>
+{
+ protected int num_candidates;
+ protected KMeansClusterer<V> kmc;
+ protected Random rand;
+ protected Graph<V,E> g;
+
+ /**
+ * Creates an instance of a VoltageCluster with the specified parameters.
+ * These are mostly parameters that are passed directly to VoltageScorer
+ * and KMeansClusterer.
+ *
+ * @param num_candidates the number of candidate clusters to create
+ */
+ public VoltageClusterer(Graph<V,E> g, int num_candidates)
+ {
+ if (num_candidates < 1)
+ throw new IllegalArgumentException("must generate >=1 candidates");
+
+ this.num_candidates = num_candidates;
+ this.kmc = new KMeansClusterer<V>();
+ rand = new Random();
+ this.g = g;
+ }
+
+ protected void setRandomSeed(int random_seed)
+ {
+ rand = new Random(random_seed);
+ }
+
+ /**
+ * Returns a community (cluster) centered around <code>v</code>.
+ * @param v the vertex whose community we wish to discover
+ */
+ public Collection<Set<V>> getCommunity(V v)
+ {
+ return cluster_internal(v, 2);
+ }
+
+ /**
+ * Clusters the vertices of <code>g</code> into
+ * <code>num_clusters</code> clusters, based on their connectivity.
+ * @param num_clusters the number of clusters to identify
+ */
+ public Collection<Set<V>> cluster(int num_clusters)
+ {
+ return cluster_internal(null, num_clusters);
+ }
+
+ /**
+ * Does the work of <code>getCommunity</code> and <code>cluster</code>.
+ * @param origin the vertex around which clustering is to be done
+ * @param num_clusters the (maximum) number of clusters to find
+ */
+ protected Collection<Set<V>> cluster_internal(V origin, int num_clusters)
+ {
+ // generate candidate clusters
+ // repeat the following 'samples' times:
+ // * pick (widely separated) vertex pair, run VoltageScorer
+ // * use k-means to identify 2 communities in ranked graph
+ // * store resulting candidate communities
+ ArrayList<V> v_array = new ArrayList<V>(g.getVertices());
+
+ LinkedList<Set<V>> candidates = new LinkedList<Set<V>>();
+
+ for (int j = 0; j < num_candidates; j++)
+ {
+ V source;
+ if (origin == null)
+ source = v_array.get((int)(rand.nextDouble() * v_array.size()));
+ else
+ source = origin;
+ V target = null;
+ do
+ {
+ target = v_array.get((int)(rand.nextDouble() * v_array.size()));
+ }
+ while (source == target);
+ VoltageScorer<V,E> vs = new VoltageScorer<V,E>(g, source, target);
+ vs.evaluate();
+
+ Map<V, double[]> voltage_ranks = new HashMap<V, double[]>();
+ for (V v : g.getVertices())
+ voltage_ranks.put(v, new double[] {vs.getVertexScore(v)});
+
+// addOneCandidateCluster(candidates, voltage_ranks);
+ addTwoCandidateClusters(candidates, voltage_ranks);
+ }
+
+ // repeat the following k-1 times:
+ // * pick a vertex v as a cluster seed
+ // (Wu/Huberman: most frequent vertex in candidates)
+ // * calculate co-occurrence (in candidate clusters)
+ // of this vertex with all others
+ // * use k-means to separate co-occurrence counts into high/low;
+ // high vertices are a cluster
+ // * remove v's vertices from candidate clusters
+
+ Collection<Set<V>> clusters = new LinkedList<Set<V>>();
+ Set<V> remaining = new HashSet<V>(g.getVertices());
+
+ List<V> seed_candidates = getSeedCandidates(candidates);
+ int seed_index = 0;
+
+ for (int j = 0; j < (num_clusters - 1); j++)
+ {
+ if (remaining.isEmpty())
+ break;
+
+ V seed;
+ if (seed_index == 0 && origin != null)
+ seed = origin;
+ else
+ {
+ do { seed = seed_candidates.get(seed_index++); }
+ while (!remaining.contains(seed));
+ }
+
+ Map<V, double[]> occur_counts = getObjectCounts(candidates, seed);
+ if (occur_counts.size() < 2)
+ break;
+
+ // now that we have the counts, cluster them...
+ try
+ {
+ Collection<Map<V, double[]>> high_low = kmc.cluster(occur_counts, 2);
+ // ...get the cluster with the highest-valued centroid...
+ Iterator<Map<V, double[]>> h_iter = high_low.iterator();
+ Map<V, double[]> cluster1 = h_iter.next();
+ Map<V, double[]> cluster2 = h_iter.next();
+ double[] centroid1 = DiscreteDistribution.mean(cluster1.values());
+ double[] centroid2 = DiscreteDistribution.mean(cluster2.values());
+ Set<V> new_cluster;
+ if (centroid1[0] >= centroid2[0])
+ new_cluster = cluster1.keySet();
+ else
+ new_cluster = cluster2.keySet();
+
+ // ...remove the elements of new_cluster from each candidate...
+ for (Set<V> cluster : candidates)
+ cluster.removeAll(new_cluster);
+ clusters.add(new_cluster);
+ remaining.removeAll(new_cluster);
+ }
+ catch (NotEnoughClustersException nece)
+ {
+ // all remaining vertices are in the same cluster
+ break;
+ }
+ }
+
+ // identify remaining vertices (if any) as a 'garbage' cluster
+ if (!remaining.isEmpty())
+ clusters.add(remaining);
+
+ return clusters;
+ }
+
+ /**
+ * Do k-means with three intervals and pick the
+ * smaller two clusters (presumed to be on the ends); this is closer to the Wu-Huberman method.
+ * @param candidates
+ * @param voltage_ranks
+ */
+ protected void addTwoCandidateClusters(LinkedList<Set<V>> candidates,
+ Map<V, double[]> voltage_ranks)
+ {
+ try
+ {
+ List<Map<V, double[]>> clusters = new ArrayList<Map<V, double[]>>(kmc.cluster(voltage_ranks, 3));
+ boolean b01 = clusters.get(0).size() > clusters.get(1).size();
+ boolean b02 = clusters.get(0).size() > clusters.get(2).size();
+ boolean b12 = clusters.get(1).size() > clusters.get(2).size();
+ if (b01 && b02)
+ {
+ candidates.add(clusters.get(1).keySet());
+ candidates.add(clusters.get(2).keySet());
+ }
+ else if (!b01 && b12)
+ {
+ candidates.add(clusters.get(0).keySet());
+ candidates.add(clusters.get(2).keySet());
+ }
+ else if (!b02 && !b12)
+ {
+ candidates.add(clusters.get(0).keySet());
+ candidates.add(clusters.get(1).keySet());
+ }
+ }
+ catch (NotEnoughClustersException e)
+ {
+ // no valid candidates, continue
+ }
+ }
+
+ /**
+ * alternative to addTwoCandidateClusters(): cluster vertices by voltages into 2 clusters.
+ * We only consider the smaller of the two clusters returned
+ * by k-means to be a 'true' cluster candidate; the other is a garbage cluster.
+ * @param candidates
+ * @param voltage_ranks
+ */
+ protected void addOneCandidateCluster(LinkedList<Set<V>> candidates,
+ Map<V, double[]> voltage_ranks)
+ {
+ try
+ {
+ List<Map<V, double[]>> clusters;
+ clusters = new ArrayList<Map<V, double[]>>(kmc.cluster(voltage_ranks, 2));
+ if (clusters.get(0).size() < clusters.get(1).size())
+ candidates.add(clusters.get(0).keySet());
+ else
+ candidates.add(clusters.get(1).keySet());
+ }
+ catch (NotEnoughClustersException e)
+ {
+ // no valid candidates, continue
+ }
+ }
+
+ /**
+ * Returns an array of cluster seeds, ranked in decreasing order
+ * of number of appearances in the specified collection of candidate
+ * clusters.
+ * @param candidates
+ */
+ protected List<V> getSeedCandidates(Collection<Set<V>> candidates)
+ {
+ final Map<V, double[]> occur_counts = getObjectCounts(candidates, null);
+
+ ArrayList<V> occurrences = new ArrayList<V>(occur_counts.keySet());
+ Collections.sort(occurrences, new MapValueArrayComparator(occur_counts));
+
+ System.out.println("occurrences: ");
+ for (int i = 0; i < occurrences.size(); i++)
+ System.out.println(occur_counts.get(occurrences.get(i))[0]);
+
+ return occurrences;
+ }
+
+ protected Map<V, double[]> getObjectCounts(Collection<Set<V>> candidates, V seed)
+ {
+ Map<V, double[]> occur_counts = new HashMap<V, double[]>();
+ for (V v : g.getVertices())
+ occur_counts.put(v, new double[]{0});
+
+ for (Set<V> candidate : candidates)
+ {
+ if (seed == null)
+ System.out.println(candidate.size());
+ if (seed == null || candidate.contains(seed))
+ {
+ for (V element : candidate)
+ {
+ double[] count = occur_counts.get(element);
+ count[0]++;
+ }
+ }
+ }
+
+ if (seed == null)
+ {
+ System.out.println("occur_counts size: " + occur_counts.size());
+ for (V v : occur_counts.keySet())
+ System.out.println(occur_counts.get(v)[0]);
+ }
+
+ return occur_counts;
+ }
+
+ protected class MapValueArrayComparator implements Comparator<V>
+ {
+ private Map<V, double[]> map;
+
+ protected MapValueArrayComparator(Map<V, double[]> map)
+ {
+ this.map = map;
+ }
+
+ public int compare(V o1, V o2)
+ {
+ double[] count0 = map.get(o1);
+ double[] count1 = map.get(o2);
+ if (count0[0] < count1[0])
+ return 1;
+ else if (count0[0] > count1[0])
+ return -1;
+ return 0;
+ }
+
+ }
+
+}
Code Review / controller.git / blobdiff