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package de.lmu.ifi.dbs.elki.index.preprocessed.localpca;
/*
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.NumberVector;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
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.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
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.distanceresultlist.DistanceDBIDResult;
import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
import de.lmu.ifi.dbs.elki.index.preprocessed.AbstractPreprocessorIndex;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredResult;
import de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner;
import de.lmu.ifi.dbs.elki.utilities.ClassGenericsUtil;
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.exceptions.ExceptionMessages;
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.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
/**
* Abstract base class for a local PCA based index.
*
* @author Elke Achtert
* @author Erich Schubert
*
* @apiviz.has PCAFilteredRunner
*
* @param <NV> Vector type
*/
// TODO: loosen DoubleDistance restriction.
@Title("Local PCA Preprocessor")
@Description("Materializes the local PCA and the locally weighted matrix of objects of a database.")
public abstract class AbstractFilteredPCAIndex<NV extends NumberVector<?>> extends AbstractPreprocessorIndex<NV, PCAFilteredResult> implements FilteredLocalPCAIndex<NV> {
/**
* PCA utility object.
*/
protected final PCAFilteredRunner<NV> pca;
/**
* Constructor.
*
* @param relation Relation to use
* @param pca PCA runner to use
*/
public AbstractFilteredPCAIndex(Relation<NV> relation, PCAFilteredRunner<NV> pca) {
super(relation);
this.pca = pca;
}
/**
* Preprocessing step.
*/
protected void preprocess() {
if(relation == null || relation.size() <= 0) {
throw new IllegalArgumentException(ExceptionMessages.DATABASE_EMPTY);
}
// Note: this is required for ERiC to work properly, otherwise the data is
// recomputed for the partitions!
if(storage != null) {
return;
}
storage = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, PCAFilteredResult.class);
long start = System.currentTimeMillis();
FiniteProgress progress = getLogger().isVerbose() ? new FiniteProgress("Performing local PCA", relation.size(), getLogger()) : null;
// TODO: use a bulk operation?
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
DistanceDBIDResult<DoubleDistance> objects = objectsForPCA(iditer);
PCAFilteredResult pcares = pca.processQueryResult(objects, relation);
storage.put(iditer, pcares);
if(progress != null) {
progress.incrementProcessed(getLogger());
}
}
if(progress != null) {
progress.ensureCompleted(getLogger());
}
long end = System.currentTimeMillis();
if(getLogger().isVerbose()) {
long elapsedTime = end - start;
getLogger().verbose(this.getClass().getName() + " runtime: " + elapsedTime + " milliseconds.");
}
}
@Override
public PCAFilteredResult getLocalProjection(DBIDRef objid) {
if(storage == null) {
preprocess();
}
return storage.get(objid);
}
/**
* Returns the objects to be considered within the PCA for the specified query
* object.
*
* @param id the id of the query object for which a PCA should be performed
* @return the list of the objects (i.e. the ids and the distances to the
* query object) to be considered within the PCA
*/
protected abstract DistanceDBIDResult<DoubleDistance> objectsForPCA(DBIDRef id);
/**
* Factory class.
*
* @author Erich Schubert
*
* @apiviz.stereotype factory
* @apiviz.uses AbstractFilteredPCAIndex oneway - - «create»
*/
public abstract static class Factory<NV extends NumberVector<?>, I extends AbstractFilteredPCAIndex<NV>> implements FilteredLocalPCAIndex.Factory<NV, I>, Parameterizable {
/**
* Parameter to specify the distance function used for running PCA.
*
* Key: {@code -localpca.distancefunction}
*/
public static final OptionID PCA_DISTANCE_ID = new OptionID("localpca.distancefunction", "The distance function used to select objects for running PCA.");
/**
* Holds the instance of the distance function specified by
* {@link #PCA_DISTANCE_ID}.
*/
protected DistanceFunction<NV, DoubleDistance> pcaDistanceFunction;
/**
* PCA utility object.
*/
protected PCAFilteredRunner<NV> pca;
/**
* Constructor.
*
* @param pcaDistanceFunction distance Function
* @param pca PCA runner
*/
public Factory(DistanceFunction<NV, DoubleDistance> pcaDistanceFunction, PCAFilteredRunner<NV> pca) {
super();
this.pcaDistanceFunction = pcaDistanceFunction;
this.pca = pca;
}
@Override
public abstract I instantiate(Relation<NV> relation);
@Override
public TypeInformation getInputTypeRestriction() {
return pcaDistanceFunction.getInputTypeRestriction();
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public abstract static class Parameterizer<NV extends NumberVector<?>, I extends AbstractFilteredPCAIndex<NV>> extends AbstractParameterizer {
/**
* Holds the instance of the distance function specified by
* {@link #PCA_DISTANCE_ID}.
*/
protected DistanceFunction<NV, DoubleDistance> pcaDistanceFunction;
/**
* PCA utility object.
*/
protected PCAFilteredRunner<NV> pca;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
final ObjectParameter<DistanceFunction<NV, DoubleDistance>> pcaDistanceFunctionP = new ObjectParameter<DistanceFunction<NV, DoubleDistance>>(PCA_DISTANCE_ID, DistanceFunction.class, EuclideanDistanceFunction.class);
if(config.grab(pcaDistanceFunctionP)) {
pcaDistanceFunction = pcaDistanceFunctionP.instantiateClass(config);
}
Class<PCAFilteredRunner<NV>> cls = ClassGenericsUtil.uglyCastIntoSubclass(PCAFilteredRunner.class);
pca = config.tryInstantiate(cls);
}
}
}
}
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