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-rw-r--r--src/de/lmu/ifi/dbs/elki/algorithm/outlier/spatial/TrimmedMeanApproach.java66
1 files changed, 32 insertions, 34 deletions
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 41022414..9aa21b66 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
@@ -33,11 +33,11 @@ import de.lmu.ifi.dbs.elki.database.Database;
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;
+import de.lmu.ifi.dbs.elki.database.relation.RelationUtil;
import de.lmu.ifi.dbs.elki.logging.Logging;
import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
@@ -45,14 +45,13 @@ import de.lmu.ifi.dbs.elki.math.Mean;
import de.lmu.ifi.dbs.elki.result.outlier.BasicOutlierScoreMeta;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
import de.lmu.ifi.dbs.elki.result.outlier.OutlierScoreMeta;
-import de.lmu.ifi.dbs.elki.utilities.DatabaseUtil;
import de.lmu.ifi.dbs.elki.utilities.datastructures.QuickSelect;
import de.lmu.ifi.dbs.elki.utilities.documentation.Description;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.documentation.Title;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.IntervalConstraint;
-import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.IntervalConstraint.IntervalBoundary;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GreaterConstraint;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.LessConstraint;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
@@ -83,15 +82,15 @@ public class TrimmedMeanApproach<N> extends AbstractNeighborhoodOutlier<N> {
/**
* The logger for this class.
*/
- private static final Logging logger = Logging.getLogger(TrimmedMeanApproach.class);
+ private static final Logging LOG = Logging.getLogger(TrimmedMeanApproach.class);
/**
- * the parameter p
+ * the parameter p.
*/
private double p;
/**
- * Constructor
+ * Constructor.
*
* @param p Parameter p
* @param npredf Neighborhood factory.
@@ -102,29 +101,28 @@ public class TrimmedMeanApproach<N> extends AbstractNeighborhoodOutlier<N> {
}
/**
- * Run the algorithm
+ * Run the algorithm.
*
* @param database Database
* @param nrel Neighborhood relation
* @param relation Data Relation (1 dimensional!)
* @return Outlier detection result
*/
- public OutlierResult run(Database database, Relation<N> nrel, Relation<? extends NumberVector<?, ?>> relation) {
- assert (DatabaseUtil.dimensionality(relation) == 1) : "TrimmedMean can only process one-dimensional data sets.";
+ public OutlierResult run(Database database, Relation<N> nrel, Relation<? extends NumberVector<?>> relation) {
+ assert (RelationUtil.dimensionality(relation) == 1) : "TrimmedMean can only process one-dimensional data sets.";
final NeighborSetPredicate npred = getNeighborSetPredicateFactory().instantiate(nrel);
WritableDoubleDataStore errors = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP);
WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_STATIC);
- FiniteProgress progress = logger.isVerbose() ? new FiniteProgress("Computing trimmed means", relation.size(), logger) : null;
+ FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("Computing trimmed means", relation.size(), LOG) : null;
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
- DBID id = iditer.getDBID();
- DBIDs neighbors = npred.getNeighborDBIDs(id);
+ DBIDs neighbors = npred.getNeighborDBIDs(iditer);
int num = 0;
double[] values = new double[neighbors.size()];
// calculate trimmedMean
for(DBIDIter iter = neighbors.iter(); iter.valid(); iter.advance()) {
- values[num] = relation.get(iter).doubleValue(1);
+ values[num] = relation.get(iter).doubleValue(0);
num++;
}
@@ -141,21 +139,21 @@ public class TrimmedMeanApproach<N> extends AbstractNeighborhoodOutlier<N> {
tm = mean.getMean();
}
else {
- tm = relation.get(id).doubleValue(1);
+ tm = relation.get(iditer).doubleValue(0);
}
// Error: deviation from trimmed mean
- errors.putDouble(id, relation.get(id).doubleValue(1) - tm);
+ errors.putDouble(iditer, relation.get(iditer).doubleValue(0) - tm);
if(progress != null) {
- progress.incrementProcessed(logger);
+ progress.incrementProcessed(LOG);
}
}
if(progress != null) {
- progress.ensureCompleted(logger);
+ progress.ensureCompleted(LOG);
}
- if(logger.isVerbose()) {
- logger.verbose("Computing median error.");
+ if(LOG.isVerbose()) {
+ LOG.verbose("Computing median error.");
}
double median_dev_from_median;
{
@@ -164,8 +162,7 @@ public class TrimmedMeanApproach<N> extends AbstractNeighborhoodOutlier<N> {
{
int i = 0;
for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
- DBID id = iditer.getDBID();
- ei[i] = errors.doubleValue(id);
+ ei[i] = errors.doubleValue(iditer);
i++;
}
}
@@ -178,15 +175,14 @@ public class TrimmedMeanApproach<N> extends AbstractNeighborhoodOutlier<N> {
median_dev_from_median = QuickSelect.median(ei);
}
- if(logger.isVerbose()) {
- logger.verbose("Normalizing scores.");
+ if(LOG.isVerbose()) {
+ LOG.verbose("Normalizing scores.");
}
// calculate score
DoubleMinMax minmax = new DoubleMinMax();
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);
+ double score = Math.abs(errors.doubleValue(iditer)) * 0.6745 / median_dev_from_median;
+ scores.putDouble(iditer, score);
minmax.put(score);
}
//
@@ -199,17 +195,17 @@ public class TrimmedMeanApproach<N> extends AbstractNeighborhoodOutlier<N> {
@Override
protected Logging getLogger() {
- return logger;
+ return LOG;
}
@Override
public TypeInformation[] getInputTypeRestriction() {
// Get one dimensional attribute for analysis.
- return TypeUtil.array(getNeighborSetPredicateFactory().getInputTypeRestriction(), VectorFieldTypeInformation.get(NumberVector.class, 1));
+ return TypeUtil.array(getNeighborSetPredicateFactory().getInputTypeRestriction(), new VectorFieldTypeInformation<NumberVector<?>>(NumberVector.class, 1));
}
/**
- * Parameterizer
+ * Parameterizer.
*
* @author Ahmed Hettab
*
@@ -219,19 +215,21 @@ public class TrimmedMeanApproach<N> extends AbstractNeighborhoodOutlier<N> {
*/
public static class Parameterizer<N> extends AbstractNeighborhoodOutlier.Parameterizer<N> {
/**
- * Parameter for the percentile value p
+ * Parameter for the percentile value p.
*/
- public static final OptionID P_ID = OptionID.getOrCreateOptionID("tma.p", "the percentile parameter");
+ public static final OptionID P_ID = new OptionID("tma.p", "the percentile parameter");
/**
- * Percentile parameter p
+ * Percentile parameter p.
*/
protected double p = 0.2;
@Override
protected void makeOptions(Parameterization config) {
super.makeOptions(config);
- DoubleParameter pP = new DoubleParameter(P_ID, new IntervalConstraint(0.0, IntervalBoundary.OPEN, 0.5, IntervalBoundary.OPEN));
+ DoubleParameter pP = new DoubleParameter(P_ID);
+ pP.addConstraint(new GreaterConstraint(0.0));
+ pP.addConstraint(new LessConstraint(0.5));
if(config.grab(pP)) {
p = pP.getValue();
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-05-17 00:13:43 +0000