How to use

getDistanceFunction

method

in

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

@Override
public TypeInformation[] getInputTypeRestriction() {
 // The input relation must match our distance function:
 return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}

@Override
public TypeInformation[] getInputTypeRestriction() {
 // The input relation must match our distance function:
 return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}

@Override
public TypeInformation[] getInputTypeRestriction() {
 // The input relation must match our distance function:
 return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}

DistanceQuery<O> dq = db.getDistanceQuery(relation, getDistanceFunction());
ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
final int size = ids.size();

 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);

 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);

builder.add(ix, linkage.restore(mindist, getDistanceFunction().isSquared()), iy);

builder.add(ix, linkage.restore(mindist, getDistanceFunction().isSquared()), iy);