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-rw-r--r--src/tutorial/clustering/NaiveAgglomerativeHierarchicalClustering3.java82
1 files changed, 41 insertions, 41 deletions
diff --git a/src/tutorial/clustering/NaiveAgglomerativeHierarchicalClustering3.java b/src/tutorial/clustering/NaiveAgglomerativeHierarchicalClustering3.java
index d3312115..0b1590bd 100644
--- a/src/tutorial/clustering/NaiveAgglomerativeHierarchicalClustering3.java
+++ b/src/tutorial/clustering/NaiveAgglomerativeHierarchicalClustering3.java
@@ -4,7 +4,7 @@ package tutorial.clustering;
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
- Copyright (C) 2013
+ Copyright (C) 2014
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
@@ -47,7 +47,6 @@ import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.NumberDistance;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.result.Result;
@@ -81,7 +80,7 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
* @param <O> Object type
*/
@Reference(title = "A Review of Classification", authors = "R. M. Cormack", booktitle = "Journal of the Royal Statistical Society. Series A, Vol. 134, No. 3", url = "http://www.jstor.org/stable/2344237")
-public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm<O, D, Result> {
+public class NaiveAgglomerativeHierarchicalClustering3<O> extends AbstractDistanceBasedAlgorithm<O, Result> {
/**
* Class logger
*/
@@ -94,6 +93,8 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
* R. M. Cormack, A Review of Classification
*
* @author Erich Schubert
+ *
+ * @apiviz.exclude
*/
public static enum Linkage {//
SINGLE {
@@ -168,7 +169,7 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
* @param numclusters Number of clusters
* @param linkage Linkage strategy
*/
- public NaiveAgglomerativeHierarchicalClustering3(DistanceFunction<? super O, D> distanceFunction, int numclusters, Linkage linkage) {
+ public NaiveAgglomerativeHierarchicalClustering3(DistanceFunction<? super O> distanceFunction, int numclusters, Linkage linkage) {
super(distanceFunction);
this.numclusters = numclusters;
this.linkage = linkage;
@@ -182,14 +183,14 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
* @return Clustering hierarchy
*/
public Result run(Database db, Relation<O> relation) {
- DistanceQuery<O, D> dq = db.getDistanceQuery(relation, getDistanceFunction());
+ DistanceQuery<O> dq = db.getDistanceQuery(relation, getDistanceFunction());
ArrayDBIDs ids = DBIDUtil.ensureArray(relation.getDBIDs());
final int size = ids.size();
- if (size > 0x10000) {
+ 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)) {
+ if(Linkage.SINGLE.equals(linkage)) {
LOG.verbose("Notice: SLINK is a much faster algorithm for single-linkage clustering!");
}
@@ -199,12 +200,12 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
// 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()) {
+ 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).doubleValue();
+ for(int y = 0; y < x; y++, iy.advance()) {
+ scratch[pos] = dq.distance(ix, iy);
// Ward uses variances -- i.e. squared values
- if (square) {
+ if(square) {
scratch[pos] *= scratch[pos];
}
pos++;
@@ -223,20 +224,20 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
// Repeat until everything merged, except the desired number of clusters:
final int stop = size - numclusters;
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Agglomerative clustering", stop, LOG) : null;
- for (int i = 0; i < stop; i++) {
+ for(int i = 0; i < stop; i++) {
double min = Double.POSITIVE_INFINITY;
int minx = -1, miny = -1;
- for (int x = 0; x < size; x++) {
- if (height[x] < Double.POSITIVE_INFINITY) {
+ for(int x = 0; x < size; x++) {
+ if(height[x] < Double.POSITIVE_INFINITY) {
continue;
}
final int xbase = triangleSize(x);
- for (int y = 0; y < x; y++) {
- if (height[y] < Double.POSITIVE_INFINITY) {
+ for(int y = 0; y < x; y++) {
+ if(height[y] < Double.POSITIVE_INFINITY) {
continue;
}
final int idx = xbase + y;
- if (scratch[idx] < min) {
+ if(scratch[idx] < min) {
min = scratch[idx];
minx = x;
miny = y;
@@ -255,15 +256,17 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
ModifiableDBIDs cx = clusters.get(minx);
ModifiableDBIDs cy = clusters.get(miny);
int sizex = 1, sizey = 1; // cluster sizes, for averaging
- if (cy == null) {
+ if(cy == null) {
cy = DBIDUtil.newHashSet();
cy.add(iy);
- } else {
+ }
+ else {
sizey = cy.size();
}
- if (cx == null) {
+ if(cx == null) {
cy.add(ix);
- } else {
+ }
+ else {
sizex = cx.size();
cy.addDBIDs(cx);
clusters.remove(minx);
@@ -276,8 +279,8 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
// hashmap lookup.
final int xbase = triangleSize(minx), ybase = triangleSize(miny);
// Write to (y, j), with j < y
- for (int j = 0; j < miny; j++) {
- if (height[j] < Double.POSITIVE_INFINITY) {
+ for(int j = 0; j < miny; j++) {
+ if(height[j] < Double.POSITIVE_INFINITY) {
continue;
}
final DBIDs idsj = clusters.get(j);
@@ -285,8 +288,8 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
scratch[ybase + j] = linkage.combine(sizex, scratch[xbase + j], sizey, scratch[ybase + j], sizej, min);
}
// Write to (j, y), with y < j < x
- for (int j = miny + 1; j < minx; j++) {
- if (height[j] < Double.POSITIVE_INFINITY) {
+ for(int j = miny + 1; j < minx; j++) {
+ if(height[j] < Double.POSITIVE_INFINITY) {
continue;
}
final int jbase = triangleSize(j);
@@ -295,8 +298,8 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
scratch[jbase + miny] = linkage.combine(sizex, scratch[xbase + j], sizey, scratch[jbase + miny], sizej, min);
}
// Write to (j, y), with y < x < j
- for (int j = minx + 1; j < size; j++) {
- if (height[j] < Double.POSITIVE_INFINITY) {
+ for(int j = minx + 1; j < size; j++) {
+ if(height[j] < Double.POSITIVE_INFINITY) {
continue;
}
final DBIDs idsj = clusters.get(j);
@@ -304,20 +307,16 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
final int jbase = triangleSize(j);
scratch[jbase + miny] = linkage.combine(sizex, scratch[jbase + minx], sizey, scratch[jbase + miny], sizej, min);
}
- if (prog != null) {
- prog.incrementProcessed(LOG);
- }
- }
- if (prog != null) {
- prog.ensureCompleted(LOG);
+ LOG.incrementProcessed(prog);
}
+ LOG.ensureCompleted(prog);
// Build the clustering result
final Clustering<Model> dendrogram = new Clustering<>("Hierarchical-Clustering", "hierarchical-clustering");
- for (int x = 0; x < size; x++) {
- if (height[x] < Double.POSITIVE_INFINITY) {
+ for(int x = 0; x < size; x++) {
+ if(height[x] < Double.POSITIVE_INFINITY) {
DBIDs cids = clusters.get(x);
- if (cids == null) {
+ if(cids == null) {
ix.seek(x);
cids = DBIDUtil.deref(ix);
}
@@ -355,10 +354,11 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
*
* @author Erich Schubert
*
+ * @apiviz.exclude
+ *
* @param <O> Object type
- * @param <D> Distance type
*/
- public static class Parameterizer<O, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm.Parameterizer<O, D> {
+ public static class Parameterizer<O> extends AbstractDistanceBasedAlgorithm.Parameterizer<O> {
/**
* Option ID for linkage parameter.
*/
@@ -379,19 +379,19 @@ public class NaiveAgglomerativeHierarchicalClustering3<O, D extends NumberDistan
super.makeOptions(config);
IntParameter numclustersP = new IntParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.MINCLUSTERS_ID);
numclustersP.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
- if (config.grab(numclustersP)) {
+ if(config.grab(numclustersP)) {
numclusters = numclustersP.intValue();
}
EnumParameter<Linkage> linkageP = new EnumParameter<>(LINKAGE_ID, Linkage.class);
linkageP.setDefaultValue(Linkage.WARD);
- if (config.grab(linkageP)) {
+ if(config.grab(linkageP)) {
linkage = linkageP.getValue();
}
}
@Override
- protected NaiveAgglomerativeHierarchicalClustering3<O, D> makeInstance() {
+ protected NaiveAgglomerativeHierarchicalClustering3<O> makeInstance() {
return new NaiveAgglomerativeHierarchicalClustering3<>(distanceFunction, numclusters, linkage);
}
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-17 00:42:49 +0000