/*
 * 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;
        }

    }

}