/*
* This file is part of ELKI:
* Environment for Developing KDD-Applications Supported by Index-Structures
*
* Copyright (C) 2017
* 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/>.
*/
package de.lmu.ifi.dbs.elki.index.distancematrix;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.database.ids.DBIDArrayIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDRange;
import de.lmu.ifi.dbs.elki.database.ids.DBIDRef;
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.DoubleDBIDList;
import de.lmu.ifi.dbs.elki.database.ids.ModifiableDoubleDBIDList;
import de.lmu.ifi.dbs.elki.database.query.range.RangeQuery;
import de.lmu.ifi.dbs.elki.database.query.similarity.SimilarityQuery;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.distance.similarityfunction.SimilarityFunction;
import de.lmu.ifi.dbs.elki.index.AbstractIndex;
import de.lmu.ifi.dbs.elki.index.SimilarityIndex;
import de.lmu.ifi.dbs.elki.index.SimilarityRangeIndex;
import de.lmu.ifi.dbs.elki.index.IndexFactory;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.logging.statistics.LongStatistic;
import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
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.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.ObjectParameter;
/**
* Precomputed similarity matrix, for a small data set.
*
* This class uses a linear memory layout (not a ragged array), and assumes
* symmetry as well as strictness. This way, it only stores the upper triangle
* matrix with double precision. It has to store (n-1) * (n-2) similarity values
* in memory, requiring 8 * (n-1) * (n-2) bytes. Since Java has a size limit of
* arrays of 31 bits (signed integer), we can store at most 2^16 objects
* (precisely, 65536 objects) in a single array, which needs about 16 GB of RAM.
*
* @author Erich Schubert
* @since 0.7.0
*
* @apiviz.has PrecomputedSimilarityQuery
*
* @param <O> Object type
*/
public class PrecomputedSimilarityMatrix<O> extends AbstractIndex<O> implements SimilarityIndex<O>, SimilarityRangeIndex<O> {
/**
* Class logger.
*/
private static final Logging LOG = Logging.getLogger(PrecomputedSimilarityMatrix.class);
/**
* Nested similarity function.
*/
final protected SimilarityFunction<? super O> similarityFunction;
/**
* Nested similarity query.
*/
protected SimilarityQuery<O> similarityQuery;
/**
* Similarity matrix.
*/
private double[] matrix = null;
/**
* DBID range.
*/
private DBIDRange ids;
/**
* Size of DBID range.
*/
private int size;
/**
* Constructor.
*
* @param relation Data relation
* @param similarityFunction Similarity function
*/
public PrecomputedSimilarityMatrix(Relation<O> relation, SimilarityFunction<? super O> similarityFunction) {
super(relation);
this.similarityFunction = similarityFunction;
if(!similarityFunction.isSymmetric()) {
throw new AbortException("Similarity matrixes currently only support symmetric similarity functions (Patches welcome).");
}
}
@Override
public void initialize() {
DBIDs rids = relation.getDBIDs();
if(!(rids instanceof DBIDRange)) {
throw new AbortException("Similarity matrixes are currently only supported for DBID ranges (as used by static databases) for performance reasons (Patches welcome).");
}
ids = (DBIDRange) rids;
size = ids.size();
if(size > 65536) {
throw new AbortException("Similarity matrixes currently have a limit of 65536 objects (~16 GB). After this, the array size exceeds the Java integer range, and a different data structure needs to be used.");
}
similarityQuery = similarityFunction.instantiate(relation);
int msize = triangleSize(size);
matrix = new double[msize];
DBIDArrayIter ix = ids.iter(), iy = ids.iter();
FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Precomputing similarity matrix", msize, LOG) : null;
int pos = 0;
for(ix.seek(0); ix.valid(); ix.advance()) {
// y < x -- must match {@link #getOffset}!
for(iy.seek(0); iy.getOffset() < ix.getOffset(); iy.advance()) {
matrix[pos] = similarityQuery.similarity(ix, iy);
pos++;
}
if(prog != null) {
prog.setProcessed(prog.getProcessed() + ix.getOffset(), LOG);
}
}
LOG.ensureCompleted(prog);
}
/**
* Compute the size of a complete x by x triangle (minus diagonal)
*
* @param x Offset
* @return Size of complete triangle
*/
protected static int triangleSize(int x) {
return (x * (x - 1)) >>> 1;
}
/**
* Array offset computation.
*
* @param x X parameter
* @param y Y parameter
* @return Array offset
*/
private int getOffset(int x, int y) {
return (y < x) ? (triangleSize(x) + y) : (triangleSize(y) + x);
}
@Override
public void logStatistics() {
if(matrix != null) {
LOG.statistics(new LongStatistic(this.getClass().getName() + ".matrix-size", matrix.length));
}
}
@Override
public String getLongName() {
return "Precomputed Similarity Matrix";
}
@Override
public String getShortName() {
return "similarity-matrix";
}
@Override
public SimilarityQuery<O> getSimilarityQuery(SimilarityFunction<? super O> similarityFunction, Object... hints) {
if(this.similarityFunction.equals(similarityFunction)) {
return new PrecomputedSimilarityQuery();
}
return null;
}
@Override
public RangeQuery<O> getSimilarityRangeQuery(SimilarityQuery<O> simQuery, Object... hints) {
if(this.similarityFunction.equals(simQuery.getSimilarityFunction())) {
return new PrecomputedSimilarityRangeQuery();
}
return null;
}
/**
* Similarity query using the precomputed matrix.
*
* @author Erich Schubert
*/
private class PrecomputedSimilarityQuery implements SimilarityQuery<O> {
@Override
public double similarity(DBIDRef id1, DBIDRef id2) {
final int x = ids.getOffset(id1), y = ids.getOffset(id2);
return (x != y) ? matrix[getOffset(x, y)] : 0.;
}
@Override
public double similarity(O o1, DBIDRef id2) {
return similarityQuery.similarity(o1, id2);
}
@Override
public double similarity(DBIDRef id1, O o2) {
return similarityQuery.similarity(id1, o2);
}
@Override
public double similarity(O o1, O o2) {
return similarityQuery.similarity(o1, o2);
}
@Override
public SimilarityFunction<? super O> getSimilarityFunction() {
return similarityQuery.getSimilarityFunction();
}
@Override
public Relation<? extends O> getRelation() {
return relation;
}
}
/**
* Range query using the distance matrix.
*
* @author Erich Schubert
*/
private class PrecomputedSimilarityRangeQuery implements RangeQuery<O> {
@Override
public DoubleDBIDList getRangeForDBID(DBIDRef id, double range) {
ModifiableDoubleDBIDList ret = DBIDUtil.newDistanceDBIDList();
getRangeForDBID(id, range, ret);
ret.sort();
return ret;
}
@Override
public void getRangeForDBID(DBIDRef id, double range, ModifiableDoubleDBIDList result) {
result.add(0., id);
DBIDArrayIter it = ids.iter();
final int x = ids.getOffset(id);
// Case y < x: triangleSize(x) + y
int pos = triangleSize(x);
for(int y = 0; y < x; y++) {
final double sim = matrix[pos];
if(sim >= range) {
result.add(sim, it.seek(y));
}
pos++;
}
assert (pos == triangleSize(x + 1));
// Case y > x: triangleSize(y) + x
pos = triangleSize(x + 1) + x;
for(int y = x + 1; y < size; y++) {
final double sim = matrix[pos];
if(sim >= range) {
result.add(sim, it.seek(y));
}
pos += y;
}
}
@Override
public DoubleDBIDList getRangeForObject(O obj, double range) {
throw new AbortException("Preprocessor KNN query only supports ID queries.");
}
@Override
public void getRangeForObject(O obj, double range, ModifiableDoubleDBIDList result) {
throw new AbortException("Preprocessor KNN query only supports ID queries.");
}
}
/**
* Factory for the index.
*
* @author Erich Schubert
*
* @apiviz.has PrecomputedSimilarityMatrix
*
* @param <O> Object type
*/
public static class Factory<O> implements IndexFactory<O, PrecomputedSimilarityMatrix<O>> {
/**
* Nested similarity function.
*/
final protected SimilarityFunction<? super O> similarityFunction;
/**
* Constructor.
*
* @param similarityFunction Similarity function
*/
public Factory(SimilarityFunction<? super O> similarityFunction) {
super();
this.similarityFunction = similarityFunction;
}
@Override
public PrecomputedSimilarityMatrix<O> instantiate(Relation<O> relation) {
return new PrecomputedSimilarityMatrix<>(relation, similarityFunction);
}
@Override
public TypeInformation getInputTypeRestriction() {
return similarityFunction.getInputTypeRestriction();
}
/**
* Parameterizer.
*
* @author Erich Schubert
*
* @apiviz.exclude
*
* @param <O> Object type
*/
public static class Parameterizer<O> extends AbstractParameterizer {
/**
* Option parameter for the precomputed similarity matrix.
*/
public static final OptionID DISTANCE_ID = new OptionID("matrix.similarity", "Similarity function for the precomputed similarity matrix.");
/**
* Nested similarity function.
*/
protected SimilarityFunction<? super O> similarityFunction;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
ObjectParameter<SimilarityFunction<? super O>> similarityP = new ObjectParameter<>(DISTANCE_ID, SimilarityFunction.class);
if(config.grab(similarityP)) {
similarityFunction = similarityP.instantiateClass(config);
}
}
@Override
protected Factory<O> makeInstance() {
return new Factory<>(similarityFunction);
}
}
}
}