summaryrefslogtreecommitdiff
path: root/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial
diff options
context:
space:
mode:
Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial')
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuGLSBackwardSearchAlgorithm.java13
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMeanMultipleAttributes.java7
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianAlgorithm.java13
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianMultipleAttributes.java11
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMoranScatterplotOutlier.java10
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuRandomWalkEC.java4
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuScatterplotOutlier.java13
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuZTestOutlier.java14
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/SLOM.java20
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/SOF.java15
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/TrimmedMeanApproach.java14
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/ExtendedNeighborhood.java11
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/ExternalNeighborhood.java4
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/PrecomputedKNearestNeighborNeighborhood.java4
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/weighted/LinearWeightedExtendedNeighborhood.java7
-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/weighted/UnweightedNeighborhoodAdapter.java4
16 files changed, 106 insertions, 58 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuGLSBackwardSearchAlgorithm.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuGLSBackwardSearchAlgorithm.java
index b4070e0c..7f3bac29 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuGLSBackwardSearchAlgorithm.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuGLSBackwardSearchAlgorithm.java
@@ -34,6 +34,7 @@ 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.ArrayModifiableDBIDs;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
+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.ModifiableDBIDs;
import de.lmu.ifi.dbs.elki.database.query.DistanceResultPair;
@@ -129,7 +130,7 @@ public class CTLuGLSBackwardSearchAlgorithm<V extends NumberVector<?, ?>, D exte
ModifiableDBIDs idview = DBIDUtil.newHashSet(relationx.getDBIDs());
ProxyView<V> proxy = new ProxyView<V>(relationx.getDatabase(), idview, relationx);
- double phialpha = NormalDistribution.standardNormalProbit(1.0 - alpha / 2);
+ double phialpha = NormalDistribution.standardNormalQuantile(1.0 - alpha / 2);
// Detect outliers while significant.
while(true) {
Pair<DBID, Double> candidate = singleIteration(proxy, relationy);
@@ -144,8 +145,8 @@ public class CTLuGLSBackwardSearchAlgorithm<V extends NumberVector<?, ?>, D exte
}
// Remaining objects are inliers
- for(DBID id : idview) {
- scores.putDouble(id, 0.0);
+ for (DBIDIter iter = idview.iter(); iter.valid(); iter.advance()) {
+ scores.putDouble(iter.getDBID(), 0.0);
}
}
@@ -204,7 +205,7 @@ public class CTLuGLSBackwardSearchAlgorithm<V extends NumberVector<?, ?>, D exte
KNNResult<D> neighbors = knnQuery.getKNNForDBID(id, k + 1);
ModifiableDBIDs neighborhood = DBIDUtil.newArray(neighbors.size());
for(DistanceResultPair<D> dpair : neighbors) {
- if(id.equals(dpair.getDBID())) {
+ if(id.sameDBID(dpair.getDBID())) {
continue;
}
neighborhood.add(dpair.getDBID());
@@ -213,8 +214,8 @@ public class CTLuGLSBackwardSearchAlgorithm<V extends NumberVector<?, ?>, D exte
F.set(i, i, 1.0);
final int nweight = -1 / neighborhood.size();
// We need to find the index positions of the neighbors, unfortunately.
- for(DBID nid : neighborhood) {
- int pos = ids.binarySearch(nid);
+ for (DBIDIter iter = neighborhood.iter(); iter.valid(); iter.advance()) {
+ int pos = ids.binarySearch(iter.getDBID());
assert (pos >= 0);
F.set(pos, i, nweight);
}
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMeanMultipleAttributes.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMeanMultipleAttributes.java
index 68e58ffa..a0c09057 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMeanMultipleAttributes.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMeanMultipleAttributes.java
@@ -32,6 +32,7 @@ import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDataStore;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
@@ -99,7 +100,8 @@ public class CTLuMeanMultipleAttributes<N, O extends NumberVector<?, ?>> extends
CovarianceMatrix covmaker = new CovarianceMatrix(DatabaseUtil.dimensionality(attributes));
WritableDataStore<Vector> deltas = DataStoreUtil.makeStorage(attributes.getDBIDs(), DataStoreFactory.HINT_TEMP, Vector.class);
- for(DBID id : attributes.iterDBIDs()) {
+ for(DBIDIter iditer = attributes.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
final O obj = attributes.get(id);
final DBIDs neighbors = npred.getNeighborDBIDs(id);
// TODO: remove object itself from neighbors?
@@ -117,7 +119,8 @@ public class CTLuMeanMultipleAttributes<N, O extends NumberVector<?, ?>> extends
DoubleMinMax minmax = new DoubleMinMax();
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC);
- for(DBID id : attributes.iterDBIDs()) {
+ for(DBIDIter iditer = attributes.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
Vector temp = deltas.get(id).minus(mean);
final double score = temp.transposeTimesTimes(cmati, temp);
minmax.put(score);
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianAlgorithm.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianAlgorithm.java
index 9b4534fe..20ab9a00 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianAlgorithm.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianAlgorithm.java
@@ -31,6 +31,7 @@ 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.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
@@ -94,18 +95,19 @@ public class CTLuMedianAlgorithm<N> extends AbstractNeighborhoodOutlier<N> {
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
MeanVariance mv = new MeanVariance();
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
DBIDs neighbors = npred.getNeighborDBIDs(id);
final double median;
{
double[] fi = new double[neighbors.size()];
// calculate and store Median of neighborhood
int c = 0;
- for(DBID n : neighbors) {
- if(id.equals(n)) {
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
+ if(id.sameDBID(iter)) {
continue;
}
- fi[c] = relation.get(n).doubleValue(1);
+ fi[c] = relation.get(iter).doubleValue(1);
c++;
}
@@ -125,7 +127,8 @@ public class CTLuMedianAlgorithm<N> extends AbstractNeighborhoodOutlier<N> {
final double mean = mv.getMean();
final double stddev = mv.getNaiveStddev();
DoubleMinMax minmax = new DoubleMinMax();
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
double score = Math.abs((scores.doubleValue(id) - mean) / stddev);
minmax.put(score);
scores.putDouble(id, score);
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianMultipleAttributes.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianMultipleAttributes.java
index cbf61c38..c8bcba74 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianMultipleAttributes.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMedianMultipleAttributes.java
@@ -32,6 +32,7 @@ import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDataStore;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
@@ -108,7 +109,8 @@ public class CTLuMedianMultipleAttributes<N, O extends NumberVector<?, ?>> exten
CovarianceMatrix covmaker = new CovarianceMatrix(dim);
WritableDataStore<Vector> deltas = DataStoreUtil.makeStorage(attributes.getDBIDs(), DataStoreFactory.HINT_TEMP, Vector.class);
- for(DBID id : attributes.iterDBIDs()) {
+ for(DBIDIter iditer = attributes.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
final O obj = attributes.get(id);
final DBIDs neighbors = npred.getNeighborDBIDs(id);
// Compute the median vector
@@ -117,9 +119,9 @@ public class CTLuMedianMultipleAttributes<N, O extends NumberVector<?, ?>> exten
double[][] data = new double[dim][neighbors.size()];
int i = 0;
// Load data
- for(DBID n : neighbors) {
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
// TODO: skip object itself within neighbors?
- O nobj = attributes.get(n);
+ O nobj = attributes.get(iter);
for(int d = 0; d < dim; d++) {
data[d][i] = nobj.doubleValue(d + 1);
}
@@ -143,7 +145,8 @@ public class CTLuMedianMultipleAttributes<N, O extends NumberVector<?, ?>> exten
DoubleMinMax minmax = new DoubleMinMax();
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(attributes.getDBIDs(), DataStoreFactory.HINT_STATIC);
- for(DBID id : attributes.iterDBIDs()) {
+ for(DBIDIter iditer = attributes.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
Vector temp = deltas.get(id).minus(mean);
final double score = temp.transposeTimesTimes(cmati, temp);
minmax.put(score);
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMoranScatterplotOutlier.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMoranScatterplotOutlier.java
index 9f19757d..7b88ae66 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMoranScatterplotOutlier.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuMoranScatterplotOutlier.java
@@ -33,6 +33,7 @@ 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.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.logging.Logging;
@@ -98,7 +99,8 @@ public class CTLuMoranScatterplotOutlier<N> extends AbstractNeighborhoodOutlier<
// Compute the global mean and variance
MeanVariance globalmv = new MeanVariance();
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
globalmv.put(relation.get(id).doubleValue(1));
}
@@ -107,12 +109,14 @@ public class CTLuMoranScatterplotOutlier<N> extends AbstractNeighborhoodOutlier<
// calculate normalized attribute values
// calculate neighborhood average of normalized attribute values.
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
// Compute global z score
final double globalZ = (relation.get(id).doubleValue(1) - globalmv.getMean()) / globalmv.getNaiveStddev();
// Compute local average z score
Mean localm = new Mean();
- for(DBID n : npred.getNeighborDBIDs(id)) {
+ for(DBIDIter iter = npred.getNeighborDBIDs(id).iter(); iter.valid(); iter.advance()) {
+ DBID n = iter.getDBID();
if(id.equals(n)) {
continue;
}
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 a6425d43..852c4be4 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
@@ -34,6 +34,7 @@ import de.lmu.ifi.dbs.elki.database.datastore.WritableDataStore;
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.DBID;
+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.ModifiableDBIDs;
@@ -208,7 +209,8 @@ public class CTLuRandomWalkEC<N, D extends NumberDistance<D, ?>> extends Abstrac
DBID id = ids.get(i);
double gmean = 1.0;
int cnt = 0;
- for(DBID n : neighbors.get(id)) {
+ for(DBIDIter iter = neighbors.get(id).iter(); iter.valid(); iter.advance()) {
+ DBID n = iter.getDBID();
if(id.equals(n)) {
continue;
}
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuScatterplotOutlier.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuScatterplotOutlier.java
index 8e4ab32c..4f11cb38 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuScatterplotOutlier.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuScatterplotOutlier.java
@@ -32,6 +32,7 @@ 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.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
@@ -102,12 +103,14 @@ public class CTLuScatterplotOutlier<N> extends AbstractNeighborhoodOutlier<N> {
// Calculate average of neighborhood for each object and perform a linear
// regression using the covariance matrix
CovarianceMatrix covm = new CovarianceMatrix(2);
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
final double local = relation.get(id).doubleValue(1);
// Compute mean of neighbors
Mean mean = new Mean();
DBIDs neighbors = npred.getNeighborDBIDs(id);
- for(DBID n : neighbors) {
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
+ DBID n = iter.getDBID();
if(id.equals(n)) {
continue;
}
@@ -139,7 +142,8 @@ public class CTLuScatterplotOutlier<N> extends AbstractNeighborhoodOutlier<N> {
// calculate mean and variance for error
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
MeanVariance mv = new MeanVariance();
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
// Compute the error from the linear regression
double y_i = relation.get(id).doubleValue(1);
double e = means.doubleValue(id) - (slope * y_i + inter);
@@ -152,7 +156,8 @@ public class CTLuScatterplotOutlier<N> extends AbstractNeighborhoodOutlier<N> {
{
final double mean = mv.getMean();
final double variance = mv.getNaiveStddev();
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
double score = Math.abs((scores.doubleValue(id) - mean) / variance);
minmax.put(score);
scores.putDouble(id, score);
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuZTestOutlier.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuZTestOutlier.java
index 573e1526..05729481 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuZTestOutlier.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/CTLuZTestOutlier.java
@@ -33,6 +33,7 @@ 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.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
@@ -102,17 +103,17 @@ public class CTLuZTestOutlier<N> extends AbstractNeighborhoodOutlier<N> {
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
MeanVariance zmv = new MeanVariance();
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
DBIDs neighbors = npred.getNeighborDBIDs(id);
// Compute Mean of neighborhood
Mean localmean = new Mean();
- for(DBID n : neighbors) {
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
+ DBID n = iter.getDBID();
if(id.equals(n)) {
continue;
}
- else {
- localmean.put(relation.get(n).doubleValue(1));
- }
+ localmean.put(relation.get(n).doubleValue(1));
}
final double localdiff;
if(localmean.getCount() > 0) {
@@ -127,7 +128,8 @@ public class CTLuZTestOutlier<N> extends AbstractNeighborhoodOutlier<N> {
// Normalize scores using mean and variance
DoubleMinMax minmax = new DoubleMinMax();
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
double score = Math.abs(scores.doubleValue(id) - zmv.getMean()) / zmv.getSampleStddev();
minmax.put(score);
scores.putDouble(id, score);
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/SLOM.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/SLOM.java
index e69d46d4..8ae23229 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/SLOM.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/SLOM.java
@@ -31,6 +31,7 @@ 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.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
@@ -53,7 +54,7 @@ import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
* Reference:<br>
* Sanjay Chawla and Pei Sun<br>
* SLOM: a new measure for local spatial outliers<br>
- * in Knowledge and Information Systems 2005
+ * in Knowledge and Information Systems 9(4), 412-429, 2006
* </p>
*
* This implementation works around some corner cases in SLOM, in particular
@@ -68,7 +69,7 @@ import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
*/
@Title("SLOM: a new measure for local spatial outliers")
@Description("Spatial local outlier measure (SLOM), which captures the local behaviour of datum in their spatial neighbourhood")
-@Reference(authors = "Sanjay Chawla and Pei Sun", title = "SLOM: a new measure for local spatial outliers", booktitle = "Knowledge and Information Systems 2005", url = "http://rp-www.cs.usyd.edu.au/~chawlarg/papers/KAIS_online.pdf")
+@Reference(authors = "Sanjay Chawla and Pei Sun", title = "SLOM: a new measure for local spatial outliers", booktitle = "Knowledge and Information Systems 9(4), 412-429, 2006", url = "http://dx.doi.org/10.1007/s10115-005-0200-2")
public class SLOM<N, O, D extends NumberDistance<D, ?>> extends AbstractDistanceBasedSpatialOutlier<N, O, D> {
/**
* The logger for this class.
@@ -98,13 +99,15 @@ public class SLOM<N, O, D extends NumberDistance<D, ?>> extends AbstractDistance
WritableDoubleDataStore modifiedDistance = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP);
// calculate D-Tilde
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
double sum = 0;
double maxDist = 0;
int cnt = 0;
final DBIDs neighbors = npred.getNeighborDBIDs(id);
- for(DBID neighbor : neighbors) {
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
+ DBID neighbor = iter.getDBID();
if(id.equals(neighbor)) {
continue;
}
@@ -127,12 +130,14 @@ public class SLOM<N, O, D extends NumberDistance<D, ?>> extends AbstractDistance
DoubleMinMax slomminmax = new DoubleMinMax();
WritableDoubleDataStore sloms = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
double sum = 0;
int cnt = 0;
final DBIDs neighbors = npred.getNeighborDBIDs(id);
- for(DBID neighbor : neighbors) {
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
+ DBID neighbor = iter.getDBID();
if(neighbor.equals(id)) {
continue;
}
@@ -146,7 +151,8 @@ public class SLOM<N, O, D extends NumberDistance<D, ?>> extends AbstractDistance
double avg = sum / cnt;
double beta = 0;
- for(DBID neighbor : neighbors) {
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
+ DBID neighbor = iter.getDBID();
final double dist = modifiedDistance.doubleValue(neighbor);
if(dist > avgPlus) {
beta += 1;
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/SOF.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/SOF.java
index abc3c481..e9987bf0 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/SOF.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/SOF.java
@@ -30,6 +30,7 @@ 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.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
@@ -108,11 +109,12 @@ public class SOF<N, O, D extends NumberDistance<D, ?>> extends AbstractDistanceB
DoubleMinMax lofminmax = new DoubleMinMax();
// Compute densities
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
DBIDs neighbors = npred.getNeighborDBIDs(id);
double avg = 0;
- for(DBID n : neighbors) {
- avg += distFunc.distance(id, n).doubleValue();
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
+ avg += distFunc.distance(id, iter.getDBID()).doubleValue();
}
double lrd = 1 / (avg / neighbors.size());
if (Double.isNaN(lrd)) {
@@ -122,11 +124,12 @@ public class SOF<N, O, D extends NumberDistance<D, ?>> extends AbstractDistanceB
}
// Compute density quotients
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
DBIDs neighbors = npred.getNeighborDBIDs(id);
double avg = 0;
- for(DBID n : neighbors) {
- avg += lrds.doubleValue(n);
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
+ avg += lrds.doubleValue(iter.getDBID());
}
final double lrd = (avg / neighbors.size()) / lrds.doubleValue(id);
if (!Double.isNaN(lrd)) {
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/TrimmedMeanApproach.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/TrimmedMeanApproach.java
index 75700bca..41022414 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/TrimmedMeanApproach.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/TrimmedMeanApproach.java
@@ -34,6 +34,7 @@ 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.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
@@ -116,13 +117,14 @@ public class TrimmedMeanApproach<N> extends AbstractNeighborhoodOutlier<N> {
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
FiniteProgress progress = logger.isVerbose() ? new FiniteProgress("Computing trimmed means", relation.size(), logger) : null;
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
DBIDs neighbors = npred.getNeighborDBIDs(id);
int num = 0;
double[] values = new double[neighbors.size()];
// calculate trimmedMean
- for(DBID n : neighbors) {
- values[num] = relation.get(n).doubleValue(1);
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
+ values[num] = relation.get(iter).doubleValue(1);
num++;
}
@@ -161,7 +163,8 @@ public class TrimmedMeanApproach<N> extends AbstractNeighborhoodOutlier<N> {
double[] ei = new double[relation.size()];
{
int i = 0;
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
ei[i] = errors.doubleValue(id);
i++;
}
@@ -180,7 +183,8 @@ public class TrimmedMeanApproach<N> extends AbstractNeighborhoodOutlier<N> {
}
// calculate score
DoubleMinMax minmax = new DoubleMinMax();
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
double score = Math.abs(errors.doubleValue(id)) * 0.6745 / median_dev_from_median;
scores.putDouble(id, score);
minmax.put(score);
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/ExtendedNeighborhood.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/ExtendedNeighborhood.java
index 9ee92d35..7a2fda52 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/ExtendedNeighborhood.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/ExtendedNeighborhood.java
@@ -29,6 +29,7 @@ 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;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
+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.HashSetModifiableDBIDs;
@@ -132,15 +133,17 @@ public class ExtendedNeighborhood extends AbstractPrecomputedNeighborhood {
// Expand multiple steps
FiniteProgress progress = logger.isVerbose() ? new FiniteProgress("Expanding neighborhoods", database.size(), logger) : null;
- for(final DBID id : database.iterDBIDs()) {
+ for(DBIDIter iter = database.iterDBIDs(); iter.valid(); iter.advance()) {
+ DBID id = iter.getDBID();
HashSetModifiableDBIDs res = DBIDUtil.newHashSet(id);
DBIDs todo = id;
for(int i = 0; i < steps; i++) {
ModifiableDBIDs ntodo = DBIDUtil.newHashSet();
- for(final DBID oid : todo) {
- DBIDs add = innerinst.getNeighborDBIDs(oid);
+ for(DBIDIter iter2 = todo.iter(); iter2.valid(); iter2.advance()) {
+ DBIDs add = innerinst.getNeighborDBIDs(iter2.getDBID());
if(add != null) {
- for(DBID nid : add) {
+ for(DBIDIter iter3 = add.iter(); iter.valid(); iter.advance()) {
+ DBID nid = iter3.getDBID();
if(res.contains(nid)) {
continue;
}
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/ExternalNeighborhood.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/ExternalNeighborhood.java
index f2586e2e..74e5bbcf 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/ExternalNeighborhood.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/ExternalNeighborhood.java
@@ -42,6 +42,7 @@ import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDataStore;
import de.lmu.ifi.dbs.elki.database.ids.ArrayModifiableDBIDs;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
+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.relation.Relation;
@@ -149,7 +150,8 @@ public class ExternalNeighborhood extends AbstractPrecomputedNeighborhood {
{
Relation<LabelList> olq = database.getDatabase().getRelation(TypeUtil.LABELLIST);
Relation<ExternalID> eidq = database.getDatabase().getRelation(TypeUtil.EXTERNALID);
- for(DBID id : database.iterDBIDs()) {
+ for(DBIDIter iditer = database.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
if(eidq != null) {
ExternalID eid = eidq.get(id);
if(eid != null) {
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/PrecomputedKNearestNeighborNeighborhood.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/PrecomputedKNearestNeighborNeighborhood.java
index f5ea7e15..9dd2dee1 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/PrecomputedKNearestNeighborNeighborhood.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/PrecomputedKNearestNeighborNeighborhood.java
@@ -30,6 +30,7 @@ import de.lmu.ifi.dbs.elki.database.datastore.DataStoreUtil;
import de.lmu.ifi.dbs.elki.database.datastore.WritableDataStore;
import de.lmu.ifi.dbs.elki.database.ids.ArrayModifiableDBIDs;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
+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.query.DistanceResultPair;
@@ -119,7 +120,8 @@ public class PrecomputedKNearestNeighborNeighborhood<D extends Distance<D>> exte
// TODO: use bulk?
WritableDataStore<DBIDs> s = DataStoreUtil.makeStorage(relation.getDBIDs(), DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_STATIC, DBIDs.class);
- for(DBID id : relation.iterDBIDs()) {
+ for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+ DBID id = iditer.getDBID();
KNNResult<D> neighbors = knnQuery.getKNNForDBID(id, k);
ArrayModifiableDBIDs neighbours = DBIDUtil.newArray(neighbors.size());
for(DistanceResultPair<D> dpair : neighbors) {
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/weighted/LinearWeightedExtendedNeighborhood.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/weighted/LinearWeightedExtendedNeighborhood.java
index 52fc2c46..d170571f 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/weighted/LinearWeightedExtendedNeighborhood.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/weighted/LinearWeightedExtendedNeighborhood.java
@@ -30,6 +30,7 @@ import java.util.List;
import de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
+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.ModifiableDBIDs;
@@ -99,8 +100,10 @@ public class LinearWeightedExtendedNeighborhood implements WeightedNeighborSetPr
final double weight = computeWeight(i);
// Collect newly discovered IDs
ModifiableDBIDs add = DBIDUtil.newHashSet();
- for(DBID id : cur) {
- for(DBID nid : inner.getNeighborDBIDs(id)) {
+ for(DBIDIter iter = cur.iter(); iter.valid(); iter.advance()) {
+ DBID id = iter.getDBID();
+ for(DBIDIter iter2 = inner.getNeighborDBIDs(id).iter(); iter2.valid(); iter2.advance()) {
+ DBID nid = iter2.getDBID();
// Seen before?
if(seen.contains(nid)) {
continue;
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/weighted/UnweightedNeighborhoodAdapter.java b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/weighted/UnweightedNeighborhoodAdapter.java
index 4378aa2e..ce0666df 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/weighted/UnweightedNeighborhoodAdapter.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/neighborhood/weighted/UnweightedNeighborhoodAdapter.java
@@ -29,6 +29,7 @@ import java.util.Collection;
import de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.neighborhood.NeighborSetPredicate;
import de.lmu.ifi.dbs.elki.data.type.TypeInformation;
import de.lmu.ifi.dbs.elki.database.ids.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
@@ -63,7 +64,8 @@ public class UnweightedNeighborhoodAdapter implements WeightedNeighborSetPredica
public Collection<DoubleObjPair<DBID>> getWeightedNeighbors(DBID reference) {
DBIDs neighbors = inner.getNeighborDBIDs(reference);
ArrayList<DoubleObjPair<DBID>> adapted = new ArrayList<DoubleObjPair<DBID>>(neighbors.size());
- for(DBID id : neighbors) {
+ for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
+ DBID id = iter.getDBID();
adapted.add(new DoubleObjPair<DBID>(1.0, id));
}
return adapted;
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-01 15:16:11 +0000