package de.lmu.ifi.dbs.elki.index.preprocessed.snn;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
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 de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.database.QueryUtil;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreFactory;
import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
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.DBID;
import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil;
import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery;
import de.lmu.ifi.dbs.elki.database.query.knn.KNNResult;
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.EuclideanDistanceFunction;
import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
import de.lmu.ifi.dbs.elki.index.preprocessed.AbstractPreprocessorIndex;
import de.lmu.ifi.dbs.elki.index.preprocessed.knn.MaterializeKNNPreprocessor;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GreaterEqualConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
/**
* A preprocessor for annotation of the ids of nearest neighbors to each
* database object.
* <p/>
* The k nearest neighbors are assigned based on an arbitrary distance function.
*
* This functionality is similar but not identical to
* {@link MaterializeKNNPreprocessor}: While it also computes the k nearest
* neighbors, it does not keep the actual distances, but organizes the NN set in
* a TreeSet for fast set operations.
*
* @author Arthur Zimek
* @author Erich Schubert
*
* @apiviz.has DistanceFunction
*
* @param <O> the type of database objects the preprocessor can be applied to
* @param <D> the type of distance the used distance function will return
*/
@Title("Shared nearest neighbor Preprocessor")
@Description("Computes the k nearest neighbors of objects of a certain database.")
public class SharedNearestNeighborPreprocessor<O, D extends Distance<D>> extends AbstractPreprocessorIndex<O, ArrayDBIDs> implements SharedNearestNeighborIndex<O> {
/**
* Get a logger for this class.
*/
private static final Logging logger = Logging.getLogger(SharedNearestNeighborPreprocessor.class);
/**
* Holds the number of nearest neighbors to be used.
*/
protected int numberOfNeighbors;
/**
* Hold the distance function to be used.
*/
protected DistanceFunction<O, D> distanceFunction;
/**
* Constructor.
*
* @param relation Database to use
* @param numberOfNeighbors Number of neighbors
* @param distanceFunction Distance function
*/
public SharedNearestNeighborPreprocessor(Relation<O> relation, int numberOfNeighbors, DistanceFunction<O, D> distanceFunction) {
super(relation);
this.numberOfNeighbors = numberOfNeighbors;
this.distanceFunction = distanceFunction;
}
/**
* Preprocessing step.
*/
protected void preprocess() {
if(getLogger().isVerbose()) {
getLogger().verbose("Assigning nearest neighbor lists to database objects");
}
storage = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, ArrayDBIDs.class);
KNNQuery<O, D> knnquery = QueryUtil.getKNNQuery(relation, distanceFunction, numberOfNeighbors);
FiniteProgress progress = getLogger().isVerbose() ? new FiniteProgress("assigning nearest neighbor lists", relation.size(), getLogger()) : null;
for(DBID id : relation.iterDBIDs()) {
ArrayModifiableDBIDs neighbors = DBIDUtil.newArray(numberOfNeighbors);
KNNResult<D> kNN = knnquery.getKNNForDBID(id, numberOfNeighbors);
for(int i = 0; i < kNN.size(); i++) {
final DBID nid = kNN.get(i).getDBID();
// if(!id.equals(nid)) {
neighbors.add(nid);
// }
// Size limitation to exactly numberOfNeighbors
if(neighbors.size() >= numberOfNeighbors) {
break;
}
}
neighbors.sort();
storage.put(id, neighbors);
if(progress != null) {
progress.incrementProcessed(getLogger());
}
}
if(progress != null) {
progress.ensureCompleted(getLogger());
}
}
@Override
public ArrayDBIDs getNearestNeighborSet(DBID objid) {
if(storage == null) {
preprocess();
}
return storage.get(objid);
}
@Override
protected Logging getLogger() {
return logger;
}
@Override
public String getLongName() {
return "SNN id index";
}
@Override
public String getShortName() {
return "SNN-index";
}
/**
* Get the number of neighbors
*
* @return NN size
*/
@Override
public int getNumberOfNeighbors() {
return numberOfNeighbors;
}
/**
* Factory class
*
* @author Erich Schubert
*
* @apiviz.stereotype factory
* @apiviz.uses SharedNearestNeighborPreprocessor oneway - - «create»
*/
public static class Factory<O, D extends Distance<D>> implements SharedNearestNeighborIndex.Factory<O, SharedNearestNeighborPreprocessor<O, D>>, Parameterizable {
/**
* Parameter to indicate the number of neighbors to be taken into account
* for the shared-nearest-neighbor similarity.
* <p/>
* <p>
* Default value: 1
* </p>
* <p>
* Key: {@code sharedNearestNeighbors}
* </p>
*/
public static final OptionID NUMBER_OF_NEIGHBORS_ID = OptionID.getOrCreateOptionID("sharedNearestNeighbors", "number of nearest neighbors to consider (at least 1)");
/**
* Parameter to indicate the distance function to be used to ascertain the
* nearest neighbors.
* <p/>
* <p>
* Default value: {@link EuclideanDistanceFunction}
* </p>
* <p>
* Key: {@code SNNDistanceFunction}
* </p>
*/
public static final OptionID DISTANCE_FUNCTION_ID = OptionID.getOrCreateOptionID("SNNDistanceFunction", "the distance function to asses the nearest neighbors");
/**
* Holds the number of nearest neighbors to be used.
*/
protected int numberOfNeighbors;
/**
* Hold the distance function to be used.
*/
protected DistanceFunction<O, D> distanceFunction;
/**
* Constructor.
*
* @param numberOfNeighbors Number of neighbors
* @param distanceFunction Distance function
*/
public Factory(int numberOfNeighbors, DistanceFunction<O, D> distanceFunction) {
super();
this.numberOfNeighbors = numberOfNeighbors;
this.distanceFunction = distanceFunction;
}
@Override
public SharedNearestNeighborPreprocessor<O, D> instantiate(Relation<O> relation) {
return new SharedNearestNeighborPreprocessor<O, D>(relation, numberOfNeighbors, distanceFunction);
}
/**
* Get the number of neighbors
*
* @return NN size
*/
@Override
public int getNumberOfNeighbors() {
return numberOfNeighbors;
}
@Override
public TypeInformation getInputTypeRestriction() {
return distanceFunction.getInputTypeRestriction();
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<O, D extends Distance<D>> extends AbstractParameterizer {
/**
* Holds the number of nearest neighbors to be used.
*/
protected int numberOfNeighbors;
/**
* Hold the distance function to be used.
*/
protected DistanceFunction<O, D> distanceFunction;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final IntParameter numberOfNeighborsP = new IntParameter(NUMBER_OF_NEIGHBORS_ID, new GreaterEqualConstraint(1), 1);
if(config.grab(numberOfNeighborsP)) {
numberOfNeighbors = numberOfNeighborsP.getValue();
}
final ObjectParameter<DistanceFunction<O, D>> distanceFunctionP = new ObjectParameter<DistanceFunction<O, D>>(DISTANCE_FUNCTION_ID, DistanceFunction.class, EuclideanDistanceFunction.class);
if(config.grab(distanceFunctionP)) {
distanceFunction = distanceFunctionP.instantiateClass(config);
}
}
@Override
protected Factory<O, D> makeInstance() {
return new Factory<O, D>(numberOfNeighbors, distanceFunction);
}
}
}
}