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package tutorial.clustering;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import gnu.trove.map.TIntObjectMap;
import gnu.trove.map.hash.TIntObjectHashMap;
import java.util.Arrays;
import de.lmu.ifi.dbs.elki.algorithm.AbstractDistanceBasedAlgorithm;
import de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SLINK;
import de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction.ExtractFlatClusteringFromHierarchy;
import de.lmu.ifi.dbs.elki.data.Cluster;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.ids.ArrayDBIDs;
import de.lmu.ifi.dbs.elki.database.ids.ArrayModifiableDBIDs;
import de.lmu.ifi.dbs.elki.database.ids.DBIDArrayIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs;
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.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.result.Result;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.CommonConstraints;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
/**
* This tutorial will step you through implementing a well known clustering
* algorithm, agglomerative hierarchical clustering, in multiple steps.
*
* This is the first step, where we implement it with single linkage only, and
* extract a fixed number of clusters. The follow up variants will be made more
* flexible.
*
* This is the naive O(n^3) algorithm. See {@link SLINK} for a much faster
* algorithm (however, only for single-linkage).
*
* @author Erich Schubert
*
* @param <O> Object type
*/
public class NaiveAgglomerativeHierarchicalClustering1<O> extends AbstractDistanceBasedAlgorithm<O, Result> {
/**
* Class logger
*/
private static final Logging LOG = Logging.getLogger(NaiveAgglomerativeHierarchicalClustering1.class);
/**
* Threshold, how many clusters to extract.
*/
int numclusters;
/**
* Constructor.
*
* @param distanceFunction Distance function to use
* @param numclusters Number of clusters
*/
public NaiveAgglomerativeHierarchicalClustering1(DistanceFunction<? super O> distanceFunction, int numclusters) {
super(distanceFunction);
this.numclusters = numclusters;
}
/**
* Run the algorithm
*
* @param db Database
* @param relation Relation
* @return Clustering hierarchy
*/
public Result run(Database db, Relation<O> relation) {
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!");
// Compute the initial distance matrix.
double[][] matrix = new double[size][size];
DBIDArrayIter ix = ids.iter(), iy = ids.iter();
for(int x = 0; ix.valid(); x++, ix.advance()) {
iy.seek(0);
for(int y = 0; y < x; y++, iy.advance()) {
final double dist = dq.distance(ix, iy);
matrix[x][y] = dist;
matrix[y][x] = dist;
}
}
// Initialize space for result:
double[] height = new double[size];
Arrays.fill(height, Double.POSITIVE_INFINITY);
// Parent node, to track merges
// have every object point to itself initially
ArrayModifiableDBIDs parent = DBIDUtil.newArray(ids);
// Active clusters, when not trivial.
TIntObjectMap<ModifiableDBIDs> clusters = new TIntObjectHashMap<>();
// 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++) {
double min = Double.POSITIVE_INFINITY;
int minx = -1, miny = -1;
for(int x = 0; x < size; x++) {
if(height[x] < Double.POSITIVE_INFINITY) {
continue;
}
for(int y = 0; y < x; y++) {
if(height[y] < Double.POSITIVE_INFINITY) {
continue;
}
if(matrix[x][y] < min) {
min = matrix[x][y];
minx = x;
miny = y;
}
}
}
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.
height[minx] = min;
parent.set(minx, iy);
// Merge into cluster
ModifiableDBIDs cx = clusters.get(minx);
ModifiableDBIDs cy = clusters.get(miny);
if(cy == null) {
cy = DBIDUtil.newHashSet();
cy.add(iy);
}
if(cx == null) {
cy.add(ix);
}
else {
cy.addDBIDs(cx);
clusters.remove(minx);
}
clusters.put(miny, cy);
// Update distance matrix for y:
for(int j = 0; j < size; j++) {
matrix[j][miny] = Math.min(matrix[j][minx], matrix[j][miny]);
matrix[miny][j] = Math.min(matrix[minx][j], matrix[miny][j]);
}
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) {
DBIDs cids = clusters.get(x);
if(cids == null) {
ix.seek(x);
cids = DBIDUtil.deref(ix);
}
Cluster<Model> cluster = new Cluster<>("Cluster", cids);
dendrogram.addToplevelCluster(cluster);
}
}
return dendrogram;
}
@Override
public TypeInformation[] getInputTypeRestriction() {
// The input relation must match our distance function:
return TypeUtil.array(getDistanceFunction().getInputTypeRestriction());
}
@Override
protected Logging getLogger() {
return LOG;
}
/**
* Parameterization class
*
* @author Erich Schubert
*
* @apiviz.exclude
*
* @param <O> Object type
*/
public static class Parameterizer<O> extends AbstractDistanceBasedAlgorithm.Parameterizer<O> {
/**
* Desired number of clusters.
*/
int numclusters = 0;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
IntParameter numclustersP = new IntParameter(ExtractFlatClusteringFromHierarchy.Parameterizer.MINCLUSTERS_ID);
numclustersP.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
if(config.grab(numclustersP)) {
numclusters = numclustersP.intValue();
}
}
@Override
protected NaiveAgglomerativeHierarchicalClustering1<O> makeInstance() {
return new NaiveAgglomerativeHierarchicalClustering1<>(distanceFunction, numclusters);
}
}
}
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