diff options
Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuRandomWalkEC.java')
| -rw-r--r-- | src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuRandomWalkEC.java | 36 |
1 files changed, 16 insertions, 20 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuRandomWalkEC.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuRandomWalkEC.java index 85524b4e..e11785af 100644 --- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuRandomWalkEC.java +++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuRandomWalkEC.java @@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.algorithm.outlier.spatial; This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures - Copyright (C) 2012
+ Copyright (C) 2014
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team @@ -28,7 +28,6 @@ import de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm; import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.data.type.TypeUtil;
-import de.lmu.ifi.dbs.elki.data.type.VectorFieldTypeInformation;
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.WritableDataStore;
@@ -37,13 +36,13 @@ import de.lmu.ifi.dbs.elki.database.ids.ArrayDBIDs; 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.KNNHeap;
import de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs;
-import de.lmu.ifi.dbs.elki.database.ids.distance.KNNHeap;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
-import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
+import de.lmu.ifi.dbs.elki.database.relation.DoubleRelation;
+import de.lmu.ifi.dbs.elki.database.relation.MaterializedDoubleRelation;
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.logging.Logging;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
import de.lmu.ifi.dbs.elki.math.MathUtil;
@@ -78,12 +77,11 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter; * @author Ahmed Hettab
*
* @param <N> Spatial Vector type
- * @param <D> Distance to use
*/
@Title("Random Walk on Exhaustive Combination")
@Description("Spatial Outlier Detection using Random Walk on Exhaustive Combination")
@Reference(authors = "X. Liu and C.-T. Lu and F. Chen", title = "Spatial outlier detection: random walk based approaches", booktitle = "Proc. 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010", url = "http://dx.doi.org/10.1145/1869790.1869841")
-public class CTLuRandomWalkEC<N, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm<N, D, OutlierResult> implements OutlierAlgorithm {
+public class CTLuRandomWalkEC<N> extends AbstractDistanceBasedAlgorithm<N, OutlierResult> implements OutlierAlgorithm {
/**
* Logger.
*/
@@ -112,7 +110,7 @@ public class CTLuRandomWalkEC<N, D extends NumberDistance<D, ?>> extends Abstrac * @param c C parameter
* @param k Number of neighbors
*/
- public CTLuRandomWalkEC(DistanceFunction<N, D> distanceFunction, double alpha, double c, int k) {
+ public CTLuRandomWalkEC(DistanceFunction<N> distanceFunction, double alpha, double c, int k) {
super(distanceFunction);
this.alpha = alpha;
this.c = c;
@@ -126,8 +124,8 @@ public class CTLuRandomWalkEC<N, D extends NumberDistance<D, ?>> extends Abstrac * @param relation Attribute value relation
* @return Outlier result
*/
- public OutlierResult run(Relation<N> spatial, Relation<? extends NumberVector<?>> relation) {
- DistanceQuery<N, D> distFunc = getDistanceFunction().instantiate(spatial);
+ public OutlierResult run(Relation<N> spatial, Relation<? extends NumberVector> relation) {
+ DistanceQuery<N> distFunc = getDistanceFunction().instantiate(spatial);
WritableDataStore<Vector> similarityVectors = DataStoreUtil.makeStorage(spatial.getDBIDs(), DataStoreFactory.HINT_TEMP, Vector.class);
WritableDataStore<DBIDs> neighbors = DataStoreUtil.makeStorage(spatial.getDBIDs(), DataStoreFactory.HINT_TEMP, DBIDs.class);
@@ -136,7 +134,7 @@ public class CTLuRandomWalkEC<N, D extends NumberDistance<D, ?>> extends Abstrac // construct the relation Matrix of the ec-graph
Matrix E = new Matrix(ids.size(), ids.size());
- KNNHeap<D> heap = DBIDUtil.newHeap(distFunc.getDistanceFactory(), k);
+ KNNHeap heap = DBIDUtil.newHeap(k);
{
int i = 0;
for(DBIDIter id = ids.iter(); id.valid(); id.advance(), i++) {
@@ -148,10 +146,9 @@ public class CTLuRandomWalkEC<N, D extends NumberDistance<D, ?>> extends Abstrac continue;
}
final double e;
- final D distance = distFunc.distance(id, n);
+ final double distance = distFunc.distance(id, n);
heap.insert(distance, n);
- double dist = distance.doubleValue();
- if(dist == 0) {
+ if(distance == 0) {
LOG.warning("Zero distances are not supported - skipping: " + DBIDUtil.toString(id) + " " + DBIDUtil.toString(n));
e = 0;
}
@@ -160,7 +157,7 @@ public class CTLuRandomWalkEC<N, D extends NumberDistance<D, ?>> extends Abstrac double exp = Math.exp(Math.pow(diff, alpha));
// Implementation note: not inverting exp worked a lot better.
// Therefore we diverge from the article here.
- e = exp / dist;
+ e = exp / distance;
}
E.set(j, i, e);
}
@@ -225,14 +222,14 @@ public class CTLuRandomWalkEC<N, D extends NumberDistance<D, ?>> extends Abstrac scores.putDouble(id, score);
}
- Relation<Double> scoreResult = new MaterializedRelation<>("randomwalkec", "RandomWalkEC", TypeUtil.DOUBLE, scores, relation.getDBIDs());
+ DoubleRelation scoreResult = new MaterializedDoubleRelation("randomwalkec", "RandomWalkEC", scores, relation.getDBIDs());
OutlierScoreMeta scoreMeta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0.0, Double.POSITIVE_INFINITY, 0.0);
return new OutlierResult(scoreMeta, scoreResult);
}
@Override
public TypeInformation[] getInputTypeRestriction() {
- return TypeUtil.array(getDistanceFunction().getInputTypeRestriction(), new VectorFieldTypeInformation<NumberVector<?>>(NumberVector.class, 1));
+ return TypeUtil.array(getDistanceFunction().getInputTypeRestriction(), TypeUtil.NUMBER_VECTOR_FIELD_1D);
}
@Override
@@ -248,9 +245,8 @@ public class CTLuRandomWalkEC<N, D extends NumberDistance<D, ?>> extends Abstrac * @apiviz.exclude
*
* @param <N> Vector type
- * @param <D> Distance type
*/
- public static class Parameterizer<N, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedAlgorithm.Parameterizer<N, D> {
+ public static class Parameterizer<N> extends AbstractDistanceBasedAlgorithm.Parameterizer<N> {
/**
* Parameter to specify the number of neighbors.
*/
@@ -327,7 +323,7 @@ public class CTLuRandomWalkEC<N, D extends NumberDistance<D, ?>> extends Abstrac }
@Override
- protected CTLuRandomWalkEC<N, D> makeInstance() {
+ protected CTLuRandomWalkEC<N> makeInstance() {
return new CTLuRandomWalkEC<>(distanceFunction, alpha, c, k);
}
}
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