Examples with SimplePrototypeModel de.lmu.ifi.dbs.elki.data.model.SimplePrototypeModel used on opensource projects

Example 1 with SimplePrototypeModel

use of de.lmu.ifi.dbs.elki.data.model.SimplePrototypeModel in project elki by elki-project.

the class CanopyPreClustering method run.

/**
 * Run the algorithm
 *
 * @param database Database
 * @param relation Relation to process
 */
public Clustering<PrototypeModel<O>> run(Database database, Relation<O> relation) {
    if (!(t1 >= t2)) {
        throw new AbortException("T1 must be at least as large as T2.");
    }
    DistanceQuery<O> dq = database.getDistanceQuery(relation, getDistanceFunction());
    ModifiableDBIDs ids = DBIDUtil.newHashSet(relation.getDBIDs());
    ArrayList<Cluster<PrototypeModel<O>>> clusters = new ArrayList<>();
    final int size = relation.size();
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Canopy clustering", size, LOG) : null;
    DBIDVar first = DBIDUtil.newVar();
    while (!ids.isEmpty()) {
        // Remove first element:
        ids.pop(first);
        // Start a new cluster:
        ModifiableDBIDs cids = DBIDUtil.newArray();
        cids.add(first);
        // Compare to remaining objects:
        for (DBIDMIter iter = ids.iter(); iter.valid(); iter.advance()) {
            double dist = dq.distance(first, iter);
            // Inclusion threshold:
            if (dist > t1) {
                continue;
            }
            cids.add(iter);
            // Removal threshold:
            if (dist <= t2) {
                iter.remove();
            }
        }
        // TODO: remember the central object using a CanopyModel?
        // Construct cluster:
        clusters.add(new Cluster<>(cids, new SimplePrototypeModel<>(relation.get(first))));
        if (prog != null) {
            prog.setProcessed(size - ids.size(), LOG);
        }
    }
    LOG.ensureCompleted(prog);
    return new Clustering<>("Canopy clustering", "canopy-clustering", clusters);
}

Example 2 with SimplePrototypeModel

use of de.lmu.ifi.dbs.elki.data.model.SimplePrototypeModel in project elki by elki-project.

the class Leader method run.

/**
 * Run the leader clustering algorithm.
 *
 * @param relation Data set
 * @return Clustering result
 */
public Clustering<PrototypeModel<O>> run(Relation<O> relation) {
    RangeQuery<O> rq = relation.getRangeQuery(getDistanceFunction(), threshold);
    ModifiableDBIDs seen = DBIDUtil.newHashSet(relation.size());
    Clustering<PrototypeModel<O>> clustering = new Clustering<>("Prototype clustering", "prototype-clustering");
    int queries = 0;
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Leader clustering", relation.size(), LOG) : null;
    for (DBIDIter it = relation.iterDBIDs(); it.valid() && seen.size() < relation.size(); it.advance()) {
        if (seen.contains(it)) {
            continue;
        }
        DoubleDBIDList res = rq.getRangeForDBID(it, threshold);
        ++queries;
        ModifiableDBIDs ids = DBIDUtil.newArray(res.size());
        for (DBIDIter cand = res.iter(); cand.valid(); cand.advance()) {
            if (seen.add(cand)) {
                LOG.incrementProcessed(prog);
                ids.add(cand);
            }
        }
        assert (ids.size() > 0 && ids.contains(it));
        PrototypeModel<O> mod = new SimplePrototypeModel<>(relation.get(it));
        clustering.addToplevelCluster(new Cluster<>(ids, mod));
    }
    LOG.statistics(new LongStatistic(this.getClass().getName() + ".queries", queries));
    LOG.ensureCompleted(prog);
    return clustering;
}