How to use

complete

method

in

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.PointerHierarchyRepresentationBuilder

/**
 * Run the algorithm
 * 
 * @param db Database to run on
 * @param relation Data relation
 * @return Clustering result
 */
public PointerHierarchyRepresentationResult run(Database db, Relation<O> relation) {
 if(SingleLinkage.class.isInstance(linkage)) {
  LOG.verbose("Notice: SLINK is a much faster algorithm for single-linkage clustering!");
 }
 DistanceQuery<O> dq = db.getDistanceQuery(relation, getDistanceFunction());
 final DBIDs ids = relation.getDBIDs();
 MatrixParadigm mat = new MatrixParadigm(ids);
 // Compute the initial (lower triangular) distance matrix.
 initializeDistanceMatrix(mat, dq, linkage);
 // Initialize space for result:
 PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
 nnChainCore(mat, builder);
 return builder.complete();
}

/**
 * Run the algorithm
 * 
 * @param db Database to run on
 * @param relation Data relation
 * @return Clustering result
 */
public PointerHierarchyRepresentationResult run(Database db, Relation<O> relation) {
 if(SingleLinkage.class.isInstance(linkage)) {
  LOG.verbose("Notice: SLINK is a much faster algorithm for single-linkage clustering!");
 }
 DistanceQuery<O> dq = db.getDistanceQuery(relation, getDistanceFunction());
 final DBIDs ids = relation.getDBIDs();
 MatrixParadigm mat = new MatrixParadigm(ids);
 // Compute the initial (lower triangular) distance matrix.
 initializeDistanceMatrix(mat, dq, linkage);
 // Initialize space for result:
 PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
 nnChainCore(mat, builder);
 return builder.complete();
}

return builder.complete();

return builder.complete();

return builder.complete();

return builder.complete();

/**
 * Run the algorithm
 * 
 * @param db Database to run on
 * @param relation Data relation
 * @return Clustering result
 */
public PointerPrototypeHierarchyRepresentationResult run(Database db, Relation<O> relation) {
 DistanceQuery<O> dq = DatabaseUtil.precomputedDistanceQuery(db, relation, getDistanceFunction(), LOG);
 final DBIDs ids = relation.getDBIDs();
 // Initialize space for result:
 PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
 Int2ObjectOpenHashMap<ModifiableDBIDs> clusters = new Int2ObjectOpenHashMap<>(ids.size());
 MatrixParadigm mat = new MatrixParadigm(ids);
 ArrayModifiableDBIDs prots = DBIDUtil.newArray(MatrixParadigm.triangleSize(ids.size()));
 MiniMax.initializeMatrices(mat, prots, dq);
 nnChainCore(mat, prots.iter(), dq, builder, clusters);
 return (PointerPrototypeHierarchyRepresentationResult) builder.complete();
}

return builder.complete();

return builder.complete();

/**
 * Run the algorithm
 * 
 * @param db Database to run on
 * @param relation Data relation
 * @return Clustering result
 */
public PointerPrototypeHierarchyRepresentationResult run(Database db, Relation<O> relation) {
 DistanceQuery<O> dq = DatabaseUtil.precomputedDistanceQuery(db, relation, getDistanceFunction(), LOG);
 final DBIDs ids = relation.getDBIDs();
 // Initialize space for result:
 PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
 Int2ObjectOpenHashMap<ModifiableDBIDs> clusters = new Int2ObjectOpenHashMap<>(ids.size());
 MatrixParadigm mat = new MatrixParadigm(ids);
 ArrayModifiableDBIDs prots = DBIDUtil.newArray(MatrixParadigm.triangleSize(ids.size()));
 MiniMax.initializeMatrices(mat, prots, dq);
 nnChainCore(mat, prots.iter(), dq, builder, clusters);
 return (PointerPrototypeHierarchyRepresentationResult) builder.complete();
}

/**
 * Run the algorithm on a database.
 * 
 * @param db Database
 * @param relation Relation to process.
 * @return Hierarchical result
 */
public PointerPrototypeHierarchyRepresentationResult run(Database db, Relation<O> relation) {
 DistanceQuery<O> dq = DatabaseUtil.precomputedDistanceQuery(db, relation, getDistanceFunction(), LOG);
 final DBIDs ids = relation.getDBIDs();
 final int size = ids.size();
 // Initialize space for result:
 PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
 Int2ObjectOpenHashMap<ModifiableDBIDs> clusters = new Int2ObjectOpenHashMap<>(size);
 // Allocate working space:
 MatrixParadigm mat = new MatrixParadigm(ids);
 ArrayModifiableDBIDs prots = DBIDUtil.newArray(MatrixParadigm.triangleSize(size));
 initializeMatrices(mat, prots, dq);
 DBIDArrayMIter protiter = prots.iter();
 FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("MiniMax clustering", size - 1, LOG) : null;
 DBIDArrayIter ix = mat.ix;
 for(int i = 1, end = size; i < size; i++) {
  end = AGNES.shrinkActiveSet(ix, builder, end, //
    findMerge(end, mat, protiter, builder, clusters, dq));
  LOG.incrementProcessed(progress);
 }
 LOG.ensureCompleted(progress);
 return (PointerPrototypeHierarchyRepresentationResult) builder.complete();
}

/**
 * Run the algorithm on a database.
 * 
 * @param db Database
 * @param relation Relation to process.
 * @return Hierarchical result
 */
public PointerPrototypeHierarchyRepresentationResult run(Database db, Relation<O> relation) {
 DistanceQuery<O> dq = DatabaseUtil.precomputedDistanceQuery(db, relation, getDistanceFunction(), LOG);
 final DBIDs ids = relation.getDBIDs();
 final int size = ids.size();
 // Initialize space for result:
 PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
 Int2ObjectOpenHashMap<ModifiableDBIDs> clusters = new Int2ObjectOpenHashMap<>(size);
 // Allocate working space:
 MatrixParadigm mat = new MatrixParadigm(ids);
 ArrayModifiableDBIDs prots = DBIDUtil.newArray(MatrixParadigm.triangleSize(size));
 initializeMatrices(mat, prots, dq);
 DBIDArrayMIter protiter = prots.iter();
 FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("MiniMax clustering", size - 1, LOG) : null;
 DBIDArrayIter ix = mat.ix;
 for(int i = 1, end = size; i < size; i++) {
  end = AGNES.shrinkActiveSet(ix, builder, end, //
    findMerge(end, mat, protiter, builder, clusters, dq));
  LOG.incrementProcessed(progress);
 }
 LOG.ensureCompleted(progress);
 return (PointerPrototypeHierarchyRepresentationResult) builder.complete();
}

return (PointerPrototypeHierarchyRepresentationResult) builder.complete();

return (PointerPrototypeHierarchyRepresentationResult) builder.complete();