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-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/OnlineLOF.java192
1 files changed, 98 insertions, 94 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/OnlineLOF.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/OnlineLOF.java
index c01c914f..6033ae3e 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/OnlineLOF.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/lof/OnlineLOF.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.algorithm.outlier.lof;
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
@@ -27,15 +27,16 @@ import java.util.List;
import de.lmu.ifi.dbs.elki.database.Database;
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.datastore.WritableDoubleDataStore;
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.DBIDIter;
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.ModifiableDBIDs;
-import de.lmu.ifi.dbs.elki.database.ids.distance.DistanceDBIDList;
-import de.lmu.ifi.dbs.elki.database.ids.distance.KNNList;
import de.lmu.ifi.dbs.elki.database.query.DatabaseQuery;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery;
@@ -43,7 +44,6 @@ import de.lmu.ifi.dbs.elki.database.query.knn.PreprocessorKNNQuery;
import de.lmu.ifi.dbs.elki.database.query.rknn.RKNNQuery;
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.distancevalue.NumberDistance;
import de.lmu.ifi.dbs.elki.index.preprocessed.knn.AbstractMaterializeKNNPreprocessor;
import de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNChangeEvent;
import de.lmu.ifi.dbs.elki.index.preprocessed.knn.KNNListener;
@@ -66,7 +66,7 @@ import de.lmu.ifi.dbs.elki.utilities.pairs.Pair;
* @apiviz.has FlexibleLOF.LOFResult oneway - - updates
*/
// TODO: related to publication?
-public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O, D> {
+public class OnlineLOF<O> extends FlexibleLOF<O> {
/**
* The logger for this class.
*/
@@ -80,7 +80,7 @@ public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O,
* @param neighborhoodDistanceFunction the neighborhood distance function
* @param reachabilityDistanceFunction the reachability distance function
*/
- public OnlineLOF(int krefer, int kreach, DistanceFunction<? super O, D> neighborhoodDistanceFunction, DistanceFunction<? super O, D> reachabilityDistanceFunction) {
+ public OnlineLOF(int krefer, int kreach, DistanceFunction<? super O> neighborhoodDistanceFunction, DistanceFunction<? super O> reachabilityDistanceFunction) {
super(krefer, kreach, neighborhoodDistanceFunction, reachabilityDistanceFunction);
}
@@ -93,20 +93,20 @@ public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O,
public OutlierResult run(Database database, Relation<O> relation) {
StepProgress stepprog = LOG.isVerbose() ? new StepProgress("OnlineLOF", 3) : null;
- Pair<Pair<KNNQuery<O, D>, KNNQuery<O, D>>, Pair<RKNNQuery<O, D>, RKNNQuery<O, D>>> queries = getKNNAndRkNNQueries(database, relation, stepprog);
- KNNQuery<O, D> kNNRefer = queries.getFirst().getFirst();
- KNNQuery<O, D> kNNReach = queries.getFirst().getSecond();
- RKNNQuery<O, D> rkNNRefer = queries.getSecond().getFirst();
- RKNNQuery<O, D> rkNNReach = queries.getSecond().getSecond();
+ Pair<Pair<KNNQuery<O>, KNNQuery<O>>, Pair<RKNNQuery<O>, RKNNQuery<O>>> queries = getKNNAndRkNNQueries(database, relation, stepprog);
+ KNNQuery<O> kNNRefer = queries.getFirst().getFirst();
+ KNNQuery<O> kNNReach = queries.getFirst().getSecond();
+ RKNNQuery<O> rkNNRefer = queries.getSecond().getFirst();
+ RKNNQuery<O> rkNNReach = queries.getSecond().getSecond();
- LOFResult<O, D> lofResult = super.doRunInTime(relation.getDBIDs(), kNNRefer, kNNReach, stepprog);
+ LOFResult<O> lofResult = super.doRunInTime(relation.getDBIDs(), kNNRefer, kNNReach, stepprog);
lofResult.setRkNNRefer(rkNNRefer);
lofResult.setRkNNReach(rkNNReach);
// add listener
KNNListener l = new LOFKNNListener(lofResult);
- ((MaterializeKNNPreprocessor<O, D>) ((PreprocessorKNNQuery<O, D, ? extends KNNList<D>>) lofResult.getKNNRefer()).getPreprocessor()).addKNNListener(l);
- ((MaterializeKNNPreprocessor<O, D>) ((PreprocessorKNNQuery<O, D, ? extends KNNList<D>>) lofResult.getKNNReach()).getPreprocessor()).addKNNListener(l);
+ ((MaterializeKNNPreprocessor<O>) ((PreprocessorKNNQuery<O>) lofResult.getKNNRefer()).getPreprocessor()).addKNNListener(l);
+ ((MaterializeKNNPreprocessor<O>) ((PreprocessorKNNQuery<O>) lofResult.getKNNReach()).getPreprocessor()).addKNNListener(l);
return lofResult.getResult();
}
@@ -118,47 +118,48 @@ public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O,
* @param stepprog Progress logger
* @return the kNN and rkNN queries
*/
- private Pair<Pair<KNNQuery<O, D>, KNNQuery<O, D>>, Pair<RKNNQuery<O, D>, RKNNQuery<O, D>>> getKNNAndRkNNQueries(Database database, Relation<O> relation, StepProgress stepprog) {
+ private Pair<Pair<KNNQuery<O>, KNNQuery<O>>, Pair<RKNNQuery<O>, RKNNQuery<O>>> getKNNAndRkNNQueries(Database database, Relation<O> relation, StepProgress stepprog) {
// Use "HEAVY" flag, since this is an online algorithm
- KNNQuery<O, D> kNNRefer = QueryUtil.getKNNQuery(relation, referenceDistanceFunction, krefer, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
- RKNNQuery<O, D> rkNNRefer = QueryUtil.getRKNNQuery(relation, referenceDistanceFunction, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
+ KNNQuery<O> kNNRefer = QueryUtil.getKNNQuery(relation, referenceDistanceFunction, krefer, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
+ RKNNQuery<O> rkNNRefer = QueryUtil.getRKNNQuery(relation, referenceDistanceFunction, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
// No optimized kNN query or RkNN query - use a preprocessor!
- if (kNNRefer == null || rkNNRefer == null) {
- if (stepprog != null) {
+ if(kNNRefer == null || rkNNRefer == null) {
+ if(stepprog != null) {
stepprog.beginStep(1, "Materializing neighborhood w.r.t. reference neighborhood distance function.", LOG);
}
- MaterializeKNNAndRKNNPreprocessor<O, D> preproc = new MaterializeKNNAndRKNNPreprocessor<>(relation, referenceDistanceFunction, krefer);
- DistanceQuery<O, D> ndq = database.getDistanceQuery(relation, referenceDistanceFunction);
+ MaterializeKNNAndRKNNPreprocessor<O> preproc = new MaterializeKNNAndRKNNPreprocessor<>(relation, referenceDistanceFunction, krefer);
+ DistanceQuery<O> ndq = database.getDistanceQuery(relation, referenceDistanceFunction);
kNNRefer = preproc.getKNNQuery(ndq, krefer, DatabaseQuery.HINT_HEAVY_USE);
rkNNRefer = preproc.getRKNNQuery(ndq, krefer, DatabaseQuery.HINT_HEAVY_USE);
// add as index
- relation.getDatabase().addIndex(preproc);
- } else {
- if (stepprog != null) {
+ database.addIndex(preproc);
+ }
+ else {
+ if(stepprog != null) {
stepprog.beginStep(1, "Optimized neighborhood w.r.t. reference neighborhood distance function provided by database.", LOG);
}
}
- KNNQuery<O, D> kNNReach = QueryUtil.getKNNQuery(relation, reachabilityDistanceFunction, kreach, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
- RKNNQuery<O, D> rkNNReach = QueryUtil.getRKNNQuery(relation, reachabilityDistanceFunction, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
- if (kNNReach == null || rkNNReach == null) {
- if (stepprog != null) {
+ KNNQuery<O> kNNReach = QueryUtil.getKNNQuery(relation, reachabilityDistanceFunction, kreach, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
+ RKNNQuery<O> rkNNReach = QueryUtil.getRKNNQuery(relation, reachabilityDistanceFunction, DatabaseQuery.HINT_HEAVY_USE, DatabaseQuery.HINT_OPTIMIZED_ONLY, DatabaseQuery.HINT_NO_CACHE);
+ if(kNNReach == null || rkNNReach == null) {
+ if(stepprog != null) {
stepprog.beginStep(2, "Materializing neighborhood w.r.t. reachability distance function.", LOG);
}
ListParameterization config = new ListParameterization();
config.addParameter(AbstractMaterializeKNNPreprocessor.Factory.DISTANCE_FUNCTION_ID, reachabilityDistanceFunction);
config.addParameter(AbstractMaterializeKNNPreprocessor.Factory.K_ID, kreach);
- MaterializeKNNAndRKNNPreprocessor<O, D> preproc = new MaterializeKNNAndRKNNPreprocessor<>(relation, reachabilityDistanceFunction, kreach);
- DistanceQuery<O, D> rdq = database.getDistanceQuery(relation, reachabilityDistanceFunction);
+ MaterializeKNNAndRKNNPreprocessor<O> preproc = new MaterializeKNNAndRKNNPreprocessor<>(relation, reachabilityDistanceFunction, kreach);
+ DistanceQuery<O> rdq = database.getDistanceQuery(relation, reachabilityDistanceFunction);
kNNReach = preproc.getKNNQuery(rdq, kreach, DatabaseQuery.HINT_HEAVY_USE);
rkNNReach = preproc.getRKNNQuery(rdq, kreach, DatabaseQuery.HINT_HEAVY_USE);
// add as index
relation.getDatabase().addIndex(preproc);
}
- Pair<KNNQuery<O, D>, KNNQuery<O, D>> kNNPair = new Pair<>(kNNRefer, kNNReach);
- Pair<RKNNQuery<O, D>, RKNNQuery<O, D>> rkNNPair = new Pair<>(rkNNRefer, rkNNReach);
+ Pair<KNNQuery<O>, KNNQuery<O>> kNNPair = new Pair<>(kNNRefer, kNNReach);
+ Pair<RKNNQuery<O>, RKNNQuery<O>> rkNNPair = new Pair<>(rkNNRefer, rkNNReach);
return new Pair<>(kNNPair, rkNNPair);
}
@@ -182,36 +183,40 @@ public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O,
/**
* Holds the result of a former run of the LOF algorithm.
*/
- private LOFResult<O, D> lofResult;
+ private LOFResult<O> lofResult;
/**
* Constructs a listener for the LOF algorithm.
*
* @param lofResult the result of a former run of the LOF algorithm
*/
- public LOFKNNListener(LOFResult<O, D> lofResult) {
+ public LOFKNNListener(LOFResult<O> lofResult) {
this.lofResult = lofResult;
}
@Override
public void kNNsChanged(KNNChangeEvent e) {
- AbstractMaterializeKNNPreprocessor<O, D, ?> p1 = ((PreprocessorKNNQuery<O, D, ?>) lofResult.getKNNRefer()).getPreprocessor();
- AbstractMaterializeKNNPreprocessor<O, D, ?> p2 = ((PreprocessorKNNQuery<O, D, ?>) lofResult.getKNNReach()).getPreprocessor();
+ AbstractMaterializeKNNPreprocessor<O> p1 = ((PreprocessorKNNQuery<O>) lofResult.getKNNRefer()).getPreprocessor();
+ AbstractMaterializeKNNPreprocessor<O> p2 = ((PreprocessorKNNQuery<O>) lofResult.getKNNReach()).getPreprocessor();
- if (firstEventReceived == null) {
- if (e.getSource().equals(p1) && e.getSource().equals(p2)) {
+ if(firstEventReceived == null) {
+ if(e.getSource().equals(p1) && e.getSource().equals(p2)) {
kNNsChanged(e, e);
- } else {
+ }
+ else {
firstEventReceived = e;
}
- } else {
- if (e.getSource().equals(p1) && firstEventReceived.getSource().equals(p2)) {
+ }
+ else {
+ if(e.getSource().equals(p1) && firstEventReceived.getSource().equals(p2)) {
kNNsChanged(e, firstEventReceived);
firstEventReceived = null;
- } else if (e.getSource().equals(p2) && firstEventReceived.getSource().equals(p1)) {
+ }
+ else if(e.getSource().equals(p2) && firstEventReceived.getSource().equals(p1)) {
kNNsChanged(firstEventReceived, e);
firstEventReceived = null;
- } else {
+ }
+ else {
throw new UnsupportedOperationException("Event sources do not fit!");
}
}
@@ -225,18 +230,20 @@ public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O,
* @param e2 the change event of the second preprocessor
*/
private void kNNsChanged(KNNChangeEvent e1, KNNChangeEvent e2) {
- if (!e1.getType().equals(e2.getType())) {
+ if(!e1.getType().equals(e2.getType())) {
throw new UnsupportedOperationException("Event types do not fit: " + e1.getType() + " != " + e2.getType());
}
- if (!e1.getObjects().equals(e2.getObjects())) {
+ if(!e1.getObjects().equals(e2.getObjects())) {
throw new UnsupportedOperationException("Objects do not fit: " + e1.getObjects() + " != " + e2.getObjects());
}
- if (e1.getType().equals(KNNChangeEvent.Type.DELETE)) {
+ if(e1.getType().equals(KNNChangeEvent.Type.DELETE)) {
kNNsRemoved(e1.getObjects(), e1.getUpdates(), e2.getUpdates(), lofResult);
- } else if (e1.getType().equals(KNNChangeEvent.Type.INSERT)) {
+ }
+ else if(e1.getType().equals(KNNChangeEvent.Type.INSERT)) {
kNNsInserted(e1.getObjects(), e1.getUpdates(), e2.getUpdates(), lofResult);
- } else {
+ }
+ else {
throw new UnsupportedOperationException("Unsupported event type: " + e1.getType());
}
}
@@ -251,44 +258,43 @@ public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O,
* reachability distance function
* @param lofResult the result of the former LOF run
*/
- private void kNNsInserted(DBIDs insertions, DBIDs updates1, DBIDs updates2, LOFResult<O, D> lofResult) {
+ private void kNNsInserted(DBIDs insertions, DBIDs updates1, DBIDs updates2, LOFResult<O> lofResult) {
StepProgress stepprog = LOG.isVerbose() ? new StepProgress(3) : null;
// recompute lrds
- if (stepprog != null) {
+ if(stepprog != null) {
stepprog.beginStep(1, "Recompute LRDs.", LOG);
}
ArrayDBIDs lrd_ids = DBIDUtil.ensureArray(DBIDUtil.union(insertions, updates2));
- List<? extends DistanceDBIDList<D>> reachDistRKNNs = lofResult.getRkNNReach().getRKNNForBulkDBIDs(lrd_ids, kreach);
+ List<? extends DoubleDBIDList> reachDistRKNNs = lofResult.getRkNNReach().getRKNNForBulkDBIDs(lrd_ids, kreach);
ArrayDBIDs affected_lrd_id_candidates = mergeIDs(reachDistRKNNs, lrd_ids);
ArrayModifiableDBIDs affected_lrd_ids = DBIDUtil.newArray(affected_lrd_id_candidates.size());
- WritableDoubleDataStore new_lrds = computeLRDs(affected_lrd_id_candidates, lofResult.getKNNReach());
- for (DBIDIter iter = affected_lrd_id_candidates.iter(); iter.valid(); iter.advance()) {
+ WritableDoubleDataStore new_lrds = DataStoreUtil.makeDoubleStorage(affected_lrd_id_candidates, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
+ computeLRDs(lofResult.getKNNReach(), affected_lrd_id_candidates, new_lrds);
+ for(DBIDIter iter = affected_lrd_id_candidates.iter(); iter.valid(); iter.advance()) {
double new_lrd = new_lrds.doubleValue(iter);
double old_lrd = lofResult.getLrds().doubleValue(iter);
- if (Double.isNaN(old_lrd) || old_lrd != new_lrd) {
+ if(Double.isNaN(old_lrd) || old_lrd != new_lrd) {
lofResult.getLrds().putDouble(iter, new_lrd);
affected_lrd_ids.add(iter);
}
}
// recompute lofs
- if (stepprog != null) {
+ if(stepprog != null) {
stepprog.beginStep(2, "Recompute LOFS.", LOG);
}
- List<? extends DistanceDBIDList<D>> primDistRKNNs = lofResult.getRkNNRefer().getRKNNForBulkDBIDs(affected_lrd_ids, krefer);
+ List<? extends DoubleDBIDList> primDistRKNNs = lofResult.getRkNNRefer().getRKNNForBulkDBIDs(affected_lrd_ids, krefer);
ArrayDBIDs affected_lof_ids = mergeIDs(primDistRKNNs, affected_lrd_ids, insertions, updates1);
recomputeLOFs(affected_lof_ids, lofResult);
// fire result changed
- if (stepprog != null) {
+ if(stepprog != null) {
stepprog.beginStep(3, "Inform listeners.", LOG);
}
lofResult.getResult().getHierarchy().resultChanged(lofResult.getResult());
- if (stepprog != null) {
- stepprog.setCompleted(LOG);
- }
+ LOG.setCompleted(stepprog);
}
/**
@@ -301,53 +307,52 @@ public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O,
* reachability distance function
* @param lofResult the result of the former LOF run
*/
- private void kNNsRemoved(DBIDs deletions, DBIDs updates1, DBIDs updates2, LOFResult<O, D> lofResult) {
+ private void kNNsRemoved(DBIDs deletions, DBIDs updates1, DBIDs updates2, LOFResult<O> lofResult) {
StepProgress stepprog = LOG.isVerbose() ? new StepProgress(4) : null;
// delete lrds and lofs
- if (stepprog != null) {
+ if(stepprog != null) {
stepprog.beginStep(1, "Delete old LRDs and LOFs.", LOG);
}
- for (DBIDIter iter = deletions.iter(); iter.valid(); iter.advance()) {
+ for(DBIDIter iter = deletions.iter(); iter.valid(); iter.advance()) {
lofResult.getLrds().delete(iter);
lofResult.getLofs().delete(iter);
}
// recompute lrds
- if (stepprog != null) {
+ if(stepprog != null) {
stepprog.beginStep(2, "Recompute LRDs.", LOG);
}
ArrayDBIDs lrd_ids = DBIDUtil.ensureArray(updates2);
- List<? extends DistanceDBIDList<D>> reachDistRKNNs = lofResult.getRkNNReach().getRKNNForBulkDBIDs(lrd_ids, kreach);
+ List<? extends DoubleDBIDList> reachDistRKNNs = lofResult.getRkNNReach().getRKNNForBulkDBIDs(lrd_ids, kreach);
ArrayDBIDs affected_lrd_id_candidates = mergeIDs(reachDistRKNNs, lrd_ids);
ArrayModifiableDBIDs affected_lrd_ids = DBIDUtil.newArray(affected_lrd_id_candidates.size());
- WritableDoubleDataStore new_lrds = computeLRDs(affected_lrd_id_candidates, lofResult.getKNNReach());
- for (DBIDIter iter = affected_lrd_id_candidates.iter(); iter.valid(); iter.advance()) {
+ WritableDoubleDataStore new_lrds = DataStoreUtil.makeDoubleStorage(affected_lrd_id_candidates, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
+ computeLRDs(lofResult.getKNNReach(), affected_lrd_id_candidates, new_lrds);
+ for(DBIDIter iter = affected_lrd_id_candidates.iter(); iter.valid(); iter.advance()) {
double new_lrd = new_lrds.doubleValue(iter);
double old_lrd = lofResult.getLrds().doubleValue(iter);
- if (old_lrd != new_lrd) {
+ if(old_lrd != new_lrd) {
lofResult.getLrds().putDouble(iter, new_lrd);
affected_lrd_ids.add(iter);
}
}
// recompute lofs
- if (stepprog != null) {
+ if(stepprog != null) {
stepprog.beginStep(3, "Recompute LOFS.", LOG);
}
- List<? extends DistanceDBIDList<D>> primDistRKNNs = lofResult.getRkNNRefer().getRKNNForBulkDBIDs(affected_lrd_ids, krefer);
+ List<? extends DoubleDBIDList> primDistRKNNs = lofResult.getRkNNRefer().getRKNNForBulkDBIDs(affected_lrd_ids, krefer);
ArrayDBIDs affected_lof_ids = mergeIDs(primDistRKNNs, affected_lrd_ids, updates1);
recomputeLOFs(affected_lof_ids, lofResult);
// fire result changed
- if (stepprog != null) {
+ if(stepprog != null) {
stepprog.beginStep(4, "Inform listeners.", LOG);
}
lofResult.getResult().getHierarchy().resultChanged(lofResult.getResult());
- if (stepprog != null) {
- stepprog.setCompleted(LOG);
- }
+ LOG.setCompleted(stepprog);
}
/**
@@ -358,12 +363,12 @@ public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O,
* @return a set containing the ids of the query result and the specified
* ids
*/
- private ArrayModifiableDBIDs mergeIDs(List<? extends DistanceDBIDList<D>> queryResults, DBIDs... ids) {
+ private ArrayModifiableDBIDs mergeIDs(List<? extends DoubleDBIDList> queryResults, DBIDs... ids) {
ModifiableDBIDs result = DBIDUtil.newHashSet();
- for (DBIDs dbids : ids) {
+ for(DBIDs dbids : ids) {
result.addDBIDs(dbids);
}
- for (DistanceDBIDList<D> queryResult : queryResults) {
+ for(DoubleDBIDList queryResult : queryResults) {
result.addDBIDs(queryResult);
}
return DBIDUtil.newArray(result);
@@ -375,24 +380,23 @@ public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O,
* @param ids the ids of the lofs to be recomputed
* @param lofResult the result of the former LOF run
*/
- private void recomputeLOFs(DBIDs ids, LOFResult<O, D> lofResult) {
- Pair<WritableDoubleDataStore, DoubleMinMax> lofsAndMax = computeLOFs(ids, lofResult.getLrds(), lofResult.getKNNRefer());
- WritableDoubleDataStore new_lofs = lofsAndMax.getFirst();
- for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+ private void recomputeLOFs(DBIDs ids, LOFResult<O> lofResult) {
+ WritableDoubleDataStore new_lofs = DataStoreUtil.makeDoubleStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
+ DoubleMinMax new_lofminmax = new DoubleMinMax();
+ computeLOFs(lofResult.getKNNRefer(), ids, lofResult.getLrds(), new_lofs, new_lofminmax);
+ for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
lofResult.getLofs().putDouble(iter, new_lofs.doubleValue(iter));
}
- // track the maximum value for normalization.
- DoubleMinMax new_lofminmax = lofsAndMax.getSecond();
-
// Actualize meta info
- if (new_lofminmax.isValid() && lofResult.getResult().getOutlierMeta().getActualMaximum() < new_lofminmax.getMax()) {
- BasicOutlierScoreMeta scoreMeta = (BasicOutlierScoreMeta) lofResult.getResult().getOutlierMeta();
- scoreMeta.setActualMaximum(new_lofminmax.getMax());
- }
-
- if (new_lofminmax.isValid() && lofResult.getResult().getOutlierMeta().getActualMinimum() > new_lofminmax.getMin()) {
- BasicOutlierScoreMeta scoreMeta = (BasicOutlierScoreMeta) lofResult.getResult().getOutlierMeta();
- scoreMeta.setActualMinimum(new_lofminmax.getMin());
+ if(new_lofminmax.isValid()) {
+ if(lofResult.getResult().getOutlierMeta().getActualMaximum() < new_lofminmax.getMax()) {
+ BasicOutlierScoreMeta scoreMeta = (BasicOutlierScoreMeta) lofResult.getResult().getOutlierMeta();
+ scoreMeta.setActualMaximum(new_lofminmax.getMax());
+ }
+ if(lofResult.getResult().getOutlierMeta().getActualMinimum() > new_lofminmax.getMin()) {
+ BasicOutlierScoreMeta scoreMeta = (BasicOutlierScoreMeta) lofResult.getResult().getOutlierMeta();
+ scoreMeta.setActualMinimum(new_lofminmax.getMin());
+ }
}
}
}
@@ -409,9 +413,9 @@ public class OnlineLOF<O, D extends NumberDistance<D, ?>> extends FlexibleLOF<O,
*
* @apiviz.exclude
*/
- public static class Parameterizer<O, D extends NumberDistance<D, ?>> extends FlexibleLOF.Parameterizer<O, D> {
+ public static class Parameterizer<O> extends FlexibleLOF.Parameterizer<O> {
@Override
- protected OnlineLOF<O, D> makeInstance() {
+ protected OnlineLOF<O> makeInstance() {
return new OnlineLOF<>(kreach, krefer, distanceFunction, reachabilityDistanceFunction);
}
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-21 06:15:13 +0000