package de.lmu.ifi.dbs.elki.math.linearalgebra.pca;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2011
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 java.util.Collection;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.query.DistanceResultPair;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.distancevalue.NumberDistance;
import de.lmu.ifi.dbs.elki.math.linearalgebra.EigenvalueDecomposition;
import de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix;
import de.lmu.ifi.dbs.elki.math.linearalgebra.SortedEigenPairs;
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;
/**
* Class to run PCA on given data.
*
* The various methods will start PCA at different places (e.g. with database
* IDs, database query results, a precomputed covariance matrix or eigenvalue
* decomposition).
*
* The runner can be parameterized by setting a covariance matrix builder (e.g.
* to a weighted covariance matrix builder)
*
* @author Erich Schubert
*
* @apiviz.landmark
* @apiviz.uses PCAResult oneway - - «create»
* @apiviz.composedOf CovarianceMatrixBuilder
*
* @param <V> Vector type
*/
public class PCARunner<V extends NumberVector<? extends V, ?>> implements Parameterizable {
/**
* Parameter to specify the class to compute the covariance matrix, must be a
* subclass of {@link CovarianceMatrixBuilder}.
* <p>
* Default value: {@link CovarianceMatrixBuilder}
* </p>
* <p>
* Key: {@code -pca.covariance}
* </p>
*/
public static final OptionID PCA_COVARIANCE_MATRIX = OptionID.getOrCreateOptionID("pca.covariance", "Class used to compute the covariance matrix.");
/**
* The covariance computation class.
*/
protected CovarianceMatrixBuilder<V> covarianceMatrixBuilder;
/**
* Constructor.
*
* @param covarianceMatrixBuilder Class for computing the covariance matrix
*/
public PCARunner(CovarianceMatrixBuilder<V> covarianceMatrixBuilder) {
super();
this.covarianceMatrixBuilder = covarianceMatrixBuilder;
}
/**
* Run PCA on the complete database
*
* @param database the database used
* @return PCA result
*/
public PCAResult processDatabase(Relation<? extends V> database) {
return processCovarMatrix(covarianceMatrixBuilder.processDatabase(database));
}
/**
* Run PCA on a collection of database IDs
*
* @param ids a collection of ids
* @param database the database used
* @return PCA result
*/
public PCAResult processIds(DBIDs ids, Relation<? extends V> database) {
return processCovarMatrix(covarianceMatrixBuilder.processIds(ids, database));
}
/**
* Run PCA on a QueryResult Collection
*
* @param results a collection of QueryResults
* @param database the database used
* @return PCA result
*/
public <D extends NumberDistance<?, ?>> PCAResult processQueryResult(Collection<DistanceResultPair<D>> results, Relation<? extends V> database) {
return processCovarMatrix(covarianceMatrixBuilder.processQueryResults(results, database));
}
/**
* Process an existing covariance Matrix
*
* @param covarMatrix the matrix used for performing pca
* @return PCA result
*/
public PCAResult processCovarMatrix(Matrix covarMatrix) {
// TODO: add support for a different implementation to do EVD?
EigenvalueDecomposition evd = covarMatrix.eig();
return processEVD(evd);
}
/**
* Process an existing eigenvalue decomposition
*
* @param evd eigenvalue decomposition to use
* @return PCA result
*/
public PCAResult processEVD(EigenvalueDecomposition evd) {
SortedEigenPairs eigenPairs = new SortedEigenPairs(evd, false);
return new PCAResult(eigenPairs);
}
/**
* Get covariance matrix builder
*
* @return covariance matrix builder in use
*/
public CovarianceMatrixBuilder<V> getCovarianceMatrixBuilder() {
return covarianceMatrixBuilder;
}
/**
* Set covariance matrix builder.
*
* @param covarianceBuilder New covariance matrix builder.
*/
public void setCovarianceMatrixBuilder(CovarianceMatrixBuilder<V> covarianceBuilder) {
this.covarianceMatrixBuilder = covarianceBuilder;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer<V extends NumberVector<? extends V, ?>> extends AbstractParameterizer {
/**
* The covariance computation class.
*/
protected CovarianceMatrixBuilder<V> covarianceMatrixBuilder;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
ObjectParameter<CovarianceMatrixBuilder<V>> covarianceP = new ObjectParameter<CovarianceMatrixBuilder<V>>(PCA_COVARIANCE_MATRIX, CovarianceMatrixBuilder.class, StandardCovarianceMatrixBuilder.class);
if(config.grab(covarianceP)) {
covarianceMatrixBuilder = covarianceP.instantiateClass(config);
}
}
@Override
protected PCARunner<V> makeInstance() {
return new PCARunner<V>(covarianceMatrixBuilder);
}
}
}