@Override
public DBIDArrayIter iter() {
DBIDArrayIter it = inner.iter();
if (it instanceof DBIDMIter) {
return new UnmodifiableDBIDArrayIter(it);
}
return it;
}
public Result run(Database database, Relation<O> rel) {
DistanceQuery<O> dq = rel.getDistanceQuery(getDistanceFunction());
int size = rel.size();
long pairs = (size * (long) size) >> 1;
final long ssize = sampling <= 1 ? (long) Math.ceil(sampling * pairs) : (long) sampling;
if (ssize > Integer.MAX_VALUE) {
throw new AbortException("Sampling size too large.");
}
final int qsize = quantile <= 0 ? 1 : (int) Math.ceil(quantile * ssize);
DoubleMaxHeap heap = new DoubleMaxHeap(qsize);
ArrayDBIDs ids = DBIDUtil.ensureArray(rel.getDBIDs());
DBIDArrayIter i1 = ids.iter(), i2 = ids.iter();
Random r = rand.getSingleThreadedRandom();
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Sampling", (int) ssize, LOG) : null;
for (long i = 0; i < ssize; i++) {
int x = r.nextInt(size - 1) + 1, y = r.nextInt(x);
double dist = dq.distance(i1.seek(x), i2.seek(y));
// Skip NaN, and/or zeros.
if (dist != dist || (nozeros && dist < Double.MIN_NORMAL)) {
continue;
}
heap.add(dist, qsize);
LOG.incrementProcessed(prog);
}
LOG.statistics(new DoubleStatistic(PREFIX + ".quantile", quantile));
LOG.statistics(new LongStatistic(PREFIX + ".samplesize", ssize));
LOG.statistics(new DoubleStatistic(PREFIX + ".distance", heap.peek()));
LOG.ensureCompleted(prog);
Collection<String> header = Arrays.asList(new String[] {"Distance"});
Collection<Vector> data = Arrays.asList(new Vector[] {new Vector(heap.peek())});
return new CollectionResult<Vector>("Distances sample", "distance-sample", data, header);
}
/**
* Run the algorithm
*
* @param db Database
* @param relation Relation
* @return Clustering hierarchy
*/
public PointerHierarchyRepresentationResult run(Database db, Relation<O> relation) {
DistanceQuery<O> dq = db.getDistanceQuery(relation, getDistanceFunction());
ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
final int size = ids.size();
if (size > 0x10000) {
throw new AbortException(
"This implementation does not scale to data sets larger than "
+ 0x10000
+ " instances (~17 GB RAM), which results in an integer overflow.");
}
if (Linkage.SINGLE.equals(linkage)) {
LOG.verbose("Notice: SLINK is a much faster algorithm for single-linkage clustering!");
}
// Compute the initial (lower triangular) distance matrix.
double[] scratch = new double[triangleSize(size)];
DBIDArrayIter ix = ids.iter(), iy = ids.iter(), ij = ids.iter();
// Position counter - must agree with computeOffset!
int pos = 0;
boolean square =
Linkage.WARD.equals(linkage)
&& !(SquaredEuclideanDistanceFunction.class.isInstance(getDistanceFunction()));
for (int x = 0; ix.valid(); x++, ix.advance()) {
iy.seek(0);
for (int y = 0; y < x; y++, iy.advance()) {
scratch[pos] = dq.distance(ix, iy);
// Ward uses variances -- i.e. squared values
if (square) {
scratch[pos] *= scratch[pos];
}
pos++;
}
}
// Initialize space for result:
WritableDBIDDataStore parent =
DataStoreUtil.makeDBIDStorage(
ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC);
WritableDoubleDataStore height =
DataStoreUtil.makeDoubleStorage(
ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC);
WritableIntegerDataStore csize =
DataStoreUtil.makeIntegerStorage(
ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
for (DBIDIter it = ids.iter(); it.valid(); it.advance()) {
parent.put(it, it);
height.put(it, Double.POSITIVE_INFINITY);
csize.put(it, 1);
}
// Repeat until everything merged, except the desired number of clusters:
FiniteProgress prog =
LOG.isVerbose() ? new FiniteProgress("Agglomerative clustering", size - 1, LOG) : null;
for (int i = 1; i < size; i++) {
double min = Double.POSITIVE_INFINITY;
int minx = -1, miny = -1;
for (ix.seek(0); ix.valid(); ix.advance()) {
if (height.doubleValue(ix) < Double.POSITIVE_INFINITY) {
continue;
}
final int xbase = triangleSize(ix.getOffset());
for (iy.seek(0); iy.getOffset() < ix.getOffset(); iy.advance()) {
if (height.doubleValue(iy) < Double.POSITIVE_INFINITY) {
continue;
}
final int idx = xbase + iy.getOffset();
if (scratch[idx] <= min) {
min = scratch[idx];
minx = ix.getOffset();
miny = iy.getOffset();
}
}
}
assert (minx >= 0 && miny >= 0);
// Avoid allocating memory, by reusing existing iterators:
ix.seek(minx);
iy.seek(miny);
// Perform merge in data structure: x -> y
// Since y < x, prefer keeping y, dropping x.
int sizex = csize.intValue(ix), sizey = csize.intValue(iy);
height.put(ix, min);
parent.put(ix, iy);
csize.put(iy, sizex + sizey);
// Update distance matrix. Note: miny < minx
final int xbase = triangleSize(minx), ybase = triangleSize(miny);
// Write to (y, j), with j < y
for (ij.seek(0); ij.getOffset() < miny; ij.advance()) {
if (height.doubleValue(ij) < Double.POSITIVE_INFINITY) {
continue;
}
final int sizej = csize.intValue(ij);
scratch[ybase + ij.getOffset()] =
linkage.combine(
sizex,
scratch[xbase + ij.getOffset()],
sizey,
scratch[ybase + ij.getOffset()],
sizej,
min);
}
// Write to (j, y), with y < j < x
for (ij.seek(miny + 1); ij.getOffset() < minx; ij.advance()) {
if (height.doubleValue(ij) < Double.POSITIVE_INFINITY) {
continue;
}
final int jbase = triangleSize(ij.getOffset());
final int sizej = csize.intValue(ij);
scratch[jbase + miny] =
linkage.combine(
sizex, scratch[xbase + ij.getOffset()], sizey, scratch[jbase + miny], sizej, min);
}
// Write to (j, y), with y < x < j
for (ij.seek(minx + 1); ij.valid(); ij.advance()) {
if (height.doubleValue(ij) < Double.POSITIVE_INFINITY) {
continue;
}
final int jbase = triangleSize(ij.getOffset());
final int sizej = csize.intValue(ij);
scratch[jbase + miny] =
linkage.combine(sizex, scratch[jbase + minx], sizey, scratch[jbase + miny], sizej, min);
}
LOG.incrementProcessed(prog);
}
LOG.ensureCompleted(prog);
return new PointerHierarchyRepresentationResult(ids, parent, height);
}