How to use

getDistanceFunction

method

in

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

@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();
boolean square = WardLinkageMethod.class.isInstance(linkage) && !(SquaredEuclideanDistanceFunction.class.isInstance(getDistanceFunction()));
initializeDistanceMatrix(scratch, dq, ix, iy, square);

DistanceQuery<O> dq = db.getDistanceQuery(relation, getDistanceFunction());

DistanceQuery<O> dq = db.getDistanceQuery(relation, getDistanceFunction());

/**
 * Execute the cluster merge.
 *
 * @param end Active set size
 * @param mat Matrix paradigm
 * @param builder Hierarchy builder
 * @param mindist Distance that was used for merging
 * @param x First matrix position
 * @param y Second matrix position
 */
protected void merge(int end, MatrixParadigm mat, PointerHierarchyRepresentationBuilder builder, double mindist, int x, int y) {
 // Avoid allocating memory, by reusing existing iterators:
 final DBIDArrayIter ix = mat.ix.seek(x), iy = mat.iy.seek(y);
 if(LOG.isDebuggingFine()) {
  LOG.debugFine("Merging: " + DBIDUtil.toString(ix) + " -> " + DBIDUtil.toString(iy) + " " + mindist);
 }
 // Perform merge in data structure: x -> y
 assert (y < x);
 // Since y < x, prefer keeping y, dropping x.
 builder.add(ix, linkage.restore(mindist, getDistanceFunction().isSquared()), iy);
 // Update cluster size for y:
 final int sizex = builder.getSize(ix), sizey = builder.getSize(iy);
 builder.setSize(iy, sizex + sizey);
 updateMatrix(end, mat, builder, mindist, x, y, sizex, sizey);
}

/**
 * Execute the cluster merge.
 *
 * @param end Active set size
 * @param mat Matrix paradigm
 * @param builder Hierarchy builder
 * @param mindist Distance that was used for merging
 * @param x First matrix position
 * @param y Second matrix position
 */
protected void merge(int end, MatrixParadigm mat, PointerHierarchyRepresentationBuilder builder, double mindist, int x, int y) {
 // Avoid allocating memory, by reusing existing iterators:
 final DBIDArrayIter ix = mat.ix.seek(x), iy = mat.iy.seek(y);
 if(LOG.isDebuggingFine()) {
  LOG.debugFine("Merging: " + DBIDUtil.toString(ix) + " -> " + DBIDUtil.toString(iy) + " " + mindist);
 }
 // Perform merge in data structure: x -> y
 assert (y < x);
 // Since y < x, prefer keeping y, dropping x.
 builder.add(ix, linkage.restore(mindist, getDistanceFunction().isSquared()), iy);
 // Update cluster size for y:
 final int sizex = builder.getSize(ix), sizey = builder.getSize(iy);
 builder.setSize(iy, sizex + sizey);
 updateMatrix(end, mat, builder, mindist, x, y, sizex, sizey);
}