- Add the Codota plugin to your IDE and get smart completions
private void myMethod () {SimpleDateFormat s =
String pattern;new SimpleDateFormat(pattern)
- Smart code suggestions by Codota
}
/** * 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(); }
PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids);
PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids);
PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
/** * 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(); }
PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared());
/** * 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(); }
PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared()); Int2ObjectOpenHashMap<ModifiableDBIDs> clusters = new Int2ObjectOpenHashMap<>();
PointerHierarchyRepresentationBuilder builder = new PointerHierarchyRepresentationBuilder(ids, dq.getDistanceFunction().isSquared()); Int2ObjectOpenHashMap<ModifiableDBIDs> clusters = new Int2ObjectOpenHashMap<>();