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-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/benchmark/ValidateApproximativeKNNIndex.java81
1 files changed, 32 insertions, 49 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/benchmark/ValidateApproximativeKNNIndex.java b/src/de/lmu/ifi/dbs/elki/algorithm/benchmark/ValidateApproximativeKNNIndex.java
index 8b83b5d4..deaea154 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/benchmark/ValidateApproximativeKNNIndex.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/benchmark/ValidateApproximativeKNNIndex.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.algorithm.benchmark;
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
- Copyright (C) 2013
+ Copyright (C) 2014
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
@@ -34,7 +34,7 @@ import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDRange;
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.distance.KNNList;
+import de.lmu.ifi.dbs.elki.database.ids.KNNList;
import de.lmu.ifi.dbs.elki.database.query.DatabaseQuery;
import de.lmu.ifi.dbs.elki.database.query.LinearScanQuery;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
@@ -43,14 +43,12 @@ import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.datasource.DatabaseConnection;
import de.lmu.ifi.dbs.elki.datasource.bundle.MultipleObjectsBundle;
import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.NumberDistance;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.math.MeanVariance;
+import de.lmu.ifi.dbs.elki.math.random.RandomFactory;
import de.lmu.ifi.dbs.elki.result.Result;
import de.lmu.ifi.dbs.elki.utilities.DatabaseUtil;
-import de.lmu.ifi.dbs.elki.utilities.RandomFactory;
import de.lmu.ifi.dbs.elki.utilities.exceptions.AbortException;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
@@ -72,7 +70,7 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.RandomParameter;
*
* @apiviz.uses KNNQuery
*/
-public class ValidateApproximativeKNNIndex<O, D extends Distance<D>> extends AbstractDistanceBasedAlgorithm<O, D, Result> {
+public class ValidateApproximativeKNNIndex<O> extends AbstractDistanceBasedAlgorithm<O, Result> {
/**
* The logger for this class.
*/
@@ -119,7 +117,7 @@ public class ValidateApproximativeKNNIndex<O, D extends Distance<D>> extends Abs
* @param forcelinear Force the use of linear scanning.
* @param pattern
*/
- public ValidateApproximativeKNNIndex(DistanceFunction<? super O, D> distanceFunction, int k, DatabaseConnection queries, double sampling, boolean forcelinear, RandomFactory random, Pattern pattern) {
+ public ValidateApproximativeKNNIndex(DistanceFunction<? super O> distanceFunction, int k, DatabaseConnection queries, double sampling, boolean forcelinear, RandomFactory random, Pattern pattern) {
super(distanceFunction);
this.k = k;
this.queries = queries;
@@ -138,14 +136,14 @@ public class ValidateApproximativeKNNIndex<O, D extends Distance<D>> extends Abs
*/
public Result run(Database database, Relation<O> relation) {
// Get a distance and kNN query instance.
- DistanceQuery<O, D> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
+ DistanceQuery<O> distQuery = database.getDistanceQuery(relation, getDistanceFunction());
// Approximate query:
- KNNQuery<O, D> knnQuery = database.getKNNQuery(distQuery, k, DatabaseQuery.HINT_OPTIMIZED_ONLY);
+ KNNQuery<O> knnQuery = database.getKNNQuery(distQuery, k, DatabaseQuery.HINT_OPTIMIZED_ONLY);
if(knnQuery == null || knnQuery instanceof LinearScanQuery) {
throw new AbortException("Expected an accelerated query, but got a linear scan -- index is not used.");
}
// Exact query:
- KNNQuery<O, D> truekNNQuery;
+ KNNQuery<O> truekNNQuery;
if(forcelinear) {
truekNNQuery = QueryUtil.getLinearScanKNNQuery(distQuery);
}
@@ -180,26 +178,23 @@ public class ValidateApproximativeKNNIndex<O, D extends Distance<D>> extends Abs
for(DBIDIter iditer = sample.iter(); iditer.valid(); iditer.advance()) {
if(pattern == null || pattern.matcher(lrel.get(iditer)).find()) {
// Query index:
- KNNList<D> knns = knnQuery.getKNNForDBID(iditer, k);
+ KNNList knns = knnQuery.getKNNForDBID(iditer, k);
// Query reference:
- KNNList<D> trueknns = truekNNQuery.getKNNForDBID(iditer, k);
+ KNNList trueknns = truekNNQuery.getKNNForDBID(iditer, k);
// Put adjusted knn size:
mv.put(knns.size() * k / (double) trueknns.size());
// Put recall:
- mvrec.put(DBIDUtil.intersectionSize(knns, trueknns) / trueknns.size());
+ mvrec.put(DBIDUtil.intersectionSize(knns, trueknns) / (double) trueknns.size());
if(knns.size() >= k) {
- D kdist = knns.getKNNDistance();
- if(kdist instanceof NumberDistance) {
- final double dist = ((NumberDistance<?, ?>) kdist).doubleValue();
- final double tdist = ((NumberDistance<?, ?>) trueknns.getKNNDistance()).doubleValue();
- if(tdist > 0.0) {
- mvdist.put(dist);
- mvdaerr.put(dist - tdist);
- mvdrerr.put(dist / tdist);
- }
+ double kdist = knns.getKNNDistance();
+ final double tdist = trueknns.getKNNDistance();
+ if(tdist > 0.0) {
+ mvdist.put(kdist);
+ mvdaerr.put(kdist - tdist);
+ mvdrerr.put(kdist / tdist);
}
}
else {
@@ -207,13 +202,9 @@ public class ValidateApproximativeKNNIndex<O, D extends Distance<D>> extends Abs
misses++;
}
}
- if(prog != null) {
- prog.incrementProcessed(LOG);
- }
- }
- if(prog != null) {
- prog.ensureCompleted(LOG);
+ LOG.incrementProcessed(prog);
}
+ LOG.ensureCompleted(prog);
if(LOG.isStatistics()) {
LOG.statistics("Mean number of results: " + mv.getMean() + " +- " + mv.getNaiveStddev());
LOG.statistics("Recall of true results: " + mvrec.getMean() + " +- " + mvrec.getNaiveStddev());
@@ -268,39 +259,32 @@ public class ValidateApproximativeKNNIndex<O, D extends Distance<D>> extends Abs
O o = (O) bundle.data(off, col);
// Query index:
- KNNList<D> knns = knnQuery.getKNNForObject(o, k);
+ KNNList knns = knnQuery.getKNNForObject(o, k);
// Query reference:
- KNNList<D> trueknns = truekNNQuery.getKNNForObject(o, k);
+ KNNList trueknns = truekNNQuery.getKNNForObject(o, k);
// Put adjusted knn size:
mv.put(knns.size() * k / (double) trueknns.size());
// Put recall:
- mvrec.put(DBIDUtil.intersectionSize(knns, trueknns) / trueknns.size());
+ mvrec.put(DBIDUtil.intersectionSize(knns, trueknns) / (double) trueknns.size());
if(knns.size() >= k) {
- D kdist = knns.getKNNDistance();
- if(kdist instanceof NumberDistance) {
- final double dist = ((NumberDistance<?, ?>) kdist).doubleValue();
- final double tdist = ((NumberDistance<?, ?>) trueknns.getKNNDistance()).doubleValue();
- if(tdist > 0.0) {
- mvdist.put(dist);
- mvdaerr.put(dist - tdist);
- mvdrerr.put(dist / tdist);
- }
+ double kdist = knns.getKNNDistance();
+ final double tdist = trueknns.getKNNDistance();
+ if(tdist > 0.0) {
+ mvdist.put(kdist);
+ mvdaerr.put(kdist - tdist);
+ mvdrerr.put(kdist / tdist);
}
}
else {
// Less than k objects.
misses++;
}
- if(prog != null) {
- prog.incrementProcessed(LOG);
- }
- }
- if(prog != null) {
- prog.ensureCompleted(LOG);
+ LOG.incrementProcessed(prog);
}
+ LOG.ensureCompleted(prog);
if(LOG.isStatistics()) {
LOG.statistics("Mean number of results: " + mv.getMean() + " +- " + mv.getNaiveStddev());
LOG.statistics("Recall of true results: " + mvrec.getMean() + " +- " + mvrec.getNaiveStddev());
@@ -334,9 +318,8 @@ public class ValidateApproximativeKNNIndex<O, D extends Distance<D>> extends Abs
* @author Erich Schubert
*
* @param <O> Object type
- * @param <D> Distance type
*/
- public static class Parameterizer<O, D extends Distance<D>> extends AbstractDistanceBasedAlgorithm.Parameterizer<O, D> {
+ public static class Parameterizer<O> extends AbstractDistanceBasedAlgorithm.Parameterizer<O> {
/**
* Parameter for the number of neighbors.
*/
@@ -432,7 +415,7 @@ public class ValidateApproximativeKNNIndex<O, D extends Distance<D>> extends Abs
}
@Override
- protected ValidateApproximativeKNNIndex<O, D> makeInstance() {
+ protected ValidateApproximativeKNNIndex<O> makeInstance() {
return new ValidateApproximativeKNNIndex<>(distanceFunction, k, queries, sampling, forcelinear, random, pattern);
}
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-01 18:13:37 +0000