Deprecated API

Contents

• Deprecated Classes
• Deprecated Methods
• Deprecated Constructors

Deprecated Classes

Class and Description
de.lmu.ifi.dbs.elki.datasource.parser.DoubleVectorLabelParser Use NumberVectorLabelParser instead, which defaults to DoubleVector.
de.lmu.ifi.dbs.elki.datasource.parser.FloatVectorLabelParser Use NumberVectorLabelParser instead, and use vector type FloatVector.
de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.IntervalConstraint Use two constraints instead.
de.lmu.ifi.dbs.elki.datasource.parser.SparseFloatVectorLabelParser Use SparseNumberVectorLabelParser instead!

Deprecated Methods

Method and Description
de.lmu.ifi.dbs.elki.database.ids.distance.ModifiableDoubleDistanceDBIDList.add(DoubleDistance, DBIDRef) Pass a double value instead.
de.lmu.ifi.dbs.elki.database.ids.distance.DoubleDistanceDBIDPairList.add(DoubleDistance, DBIDRef) Pass a double value instead.
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDList.add(DoubleDistance, DBIDRef)
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDPairList.add(DoubleDistance, DBIDRef)
de.lmu.ifi.dbs.elki.math.spacefillingcurves.ZCurveTransformer.asBigInteger(NumberVector)
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mktrees.AbstractMkTree.batchNN(N, DBIDs, int) Change to use by-object NN lookups instead.
de.lmu.ifi.dbs.elki.logging.progress.StepProgress.beginStep(int, String)
de.lmu.ifi.dbs.elki.data.SparseNumberVector.byteValue(int) As the vectors are sparse, try to iterate over the sparse dimensions only, see SparseNumberVector.iterByteValue(int).
de.lmu.ifi.dbs.elki.utilities.DatabaseUtil.dimensionality(Relation) Use RelationUtil.dimensionality(Relation) instead!
de.lmu.ifi.dbs.elki.data.SparseByteVector.doubleValue(int)
de.lmu.ifi.dbs.elki.data.SparseFloatVector.doubleValue(int)
de.lmu.ifi.dbs.elki.data.SparseNumberVector.doubleValue(int) As the vectors are sparse, try to iterate over the sparse dimensions only, see SparseNumberVector.iterDoubleValue(int).
de.lmu.ifi.dbs.elki.data.SparseDoubleVector.doubleValue(int)
de.lmu.ifi.dbs.elki.data.SparseShortVector.doubleValue(int)
de.lmu.ifi.dbs.elki.data.SparseIntegerVector.doubleValue(int)
de.lmu.ifi.dbs.elki.database.ids.DBID.equals(Object)
de.lmu.ifi.dbs.elki.database.ids.DBIDRef.equals(Object)
de.lmu.ifi.dbs.elki.database.ids.integer.IntegerDBID.equals(Object)
de.lmu.ifi.dbs.elki.evaluation.roc.ROC.SimpleAdapter.equals(Object)
de.lmu.ifi.dbs.elki.evaluation.roc.ROC.DistanceResultAdapter.equals(Object)
de.lmu.ifi.dbs.elki.evaluation.roc.ROC.OutlierScoreAdapter.equals(Object)
de.lmu.ifi.dbs.elki.data.SparseNumberVector.floatValue(int) As the vectors are sparse, try to iterate over the sparse dimensions only, see SparseNumberVector.iterFloatValue(int).
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.ByteSerializer.fromByteBuffer(ByteBuffer)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.ShortSerializer.fromByteBuffer(ByteBuffer)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.IntegerSerializer.fromByteBuffer(ByteBuffer)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.LongSerializer.fromByteBuffer(ByteBuffer)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.FloatSerializer.fromByteBuffer(ByteBuffer)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.DoubleSerializer.fromByteBuffer(ByteBuffer)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.VarintSerializer.fromByteBuffer(ByteBuffer)
de.lmu.ifi.dbs.elki.database.datastore.DoubleDataStore.get(DBIDRef) Use DoubleDataStore.doubleValue(de.lmu.ifi.dbs.elki.database.ids.DBIDRef) instead, to avoid boxing/unboxing cost.
de.lmu.ifi.dbs.elki.database.datastore.IntegerDataStore.get(DBIDRef) Use IntegerDataStore.intValue(de.lmu.ifi.dbs.elki.database.ids.DBIDRef) instead, to avoid boxing/unboxing cost.
de.lmu.ifi.dbs.elki.database.datastore.DoubleDistanceDataStore.get(DBIDRef) Use DoubleDistanceDataStore.doubleValue(de.lmu.ifi.dbs.elki.database.ids.DBIDRef) instead, to avoid boxing/unboxing cost.
de.lmu.ifi.dbs.elki.database.datastore.DBIDDataStore.get(DBIDRef) Use DBIDDataStore.assignVar(de.lmu.ifi.dbs.elki.database.ids.DBIDRef, de.lmu.ifi.dbs.elki.database.ids.DBIDVar) and a DBIDVar instead, to avoid boxing/unboxing cost.
de.lmu.ifi.dbs.elki.database.datastore.memory.MapIntegerDBIDDBIDStore.get(DBIDRef)
de.lmu.ifi.dbs.elki.database.datastore.memory.ArrayDoubleDistanceStore.get(DBIDRef)
de.lmu.ifi.dbs.elki.database.datastore.memory.MapIntegerDBIDIntegerStore.get(DBIDRef)
de.lmu.ifi.dbs.elki.database.datastore.memory.MapIntegerDBIDDoubleDistanceStore.get(DBIDRef)
de.lmu.ifi.dbs.elki.database.datastore.memory.ArrayIntegerStore.get(DBIDRef)
de.lmu.ifi.dbs.elki.database.datastore.memory.ArrayDBIDStore.get(DBIDRef)
de.lmu.ifi.dbs.elki.database.datastore.memory.ArrayDoubleStore.get(DBIDRef)
de.lmu.ifi.dbs.elki.database.datastore.memory.MapIntegerDBIDDoubleStore.get(DBIDRef)
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.DoubleArrayAdapter.get(double[], int)
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.FloatArrayAdapter.get(float[], int)
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.FlatMatrixAdapter.get(Matrix, int)
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.NumberVectorAdapter.get(NumberVector, int)
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.TDoubleListAdapter.get(TDoubleList, int)
de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.VectorAdapter.get(Vector, int)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.ByteSerializer.getByteSize(Byte)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.DoubleSerializer.getByteSize(Double)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.FloatSerializer.getByteSize(Float)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.IntegerSerializer.getByteSize(Integer)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.VarintSerializer.getByteSize(Integer)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.LongSerializer.getByteSize(Long)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.ShortSerializer.getByteSize(Short)
de.lmu.ifi.dbs.elki.database.ids.distance.DoubleDistanceDBIDListIter.getDistance() Use DoubleDistanceDBIDListIter.doubleDistance() to avoid creating unnecessary objects.
de.lmu.ifi.dbs.elki.database.ids.distance.DoubleDistanceDBIDPairList.Itr.getDistance()
de.lmu.ifi.dbs.elki.database.ids.distance.DoubleDistanceDBIDPair.getDistance() Would produce a DoubleDistance object. Use DoubleDistanceDBIDPair.doubleDistance() instead!
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNListHeap.Itr.getDistance() use DoubleDistanceDBIDPairKNNListHeap.Itr.doubleDistance()!
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNList.Itr.getDistance() use DoubleDistanceDBIDPairKNNList.Itr.doubleDistance()!
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceKNNSubList.Itr.getDistance()
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDList.Itr.getDistance()
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDPairList.Itr.getDistance()
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDPairKNNListHeap.Itr.getDistance() use DoubleDistanceIntegerDBIDPairKNNListHeap.Itr.doubleDistance()!
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDPair.getDistance()
de.lmu.ifi.dbs.elki.index.tree.AbstractNode.getEntries() Using this method means an extra copy - usually at the cost of performance.
de.lmu.ifi.dbs.elki.index.tree.IndexTree.getFile()
de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDPair.getFirst() Avoid autoboxing. Use DoubleDBIDPair.doubleValue()!
de.lmu.ifi.dbs.elki.database.ids.integer.IntegerDoubleDBIDPair.getFirst()
de.lmu.ifi.dbs.elki.utilities.datastructures.heap.DoublePriorityObject.getFirst()
de.lmu.ifi.dbs.elki.utilities.datastructures.heap.IntegerPriorityObject.getFirst()
de.lmu.ifi.dbs.elki.utilities.pairs.IntDoublePair.getFirst() use pair.first to avoid boxing!
de.lmu.ifi.dbs.elki.utilities.pairs.DoubleIntPair.getFirst() use pair.first to avoid boxing!
de.lmu.ifi.dbs.elki.utilities.pairs.DoubleObjPair.getFirst() use pair.first to avoid boxing!
de.lmu.ifi.dbs.elki.utilities.pairs.DoubleDoublePair.getFirst() use pair.first to avoid boxing!
de.lmu.ifi.dbs.elki.utilities.pairs.IntIntPair.getFirst() use pair.first to avoid boxing!
de.lmu.ifi.dbs.elki.database.ids.distance.DoubleDistanceKNNList.getKNNDistance() use doubleKNNDistance()!
de.lmu.ifi.dbs.elki.database.ids.distance.DoubleDistanceKNNHeap.getKNNDistance() if you know your distances are double-valued, you should be using the primitive type.
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNListHeap.getKNNDistance()
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNList.getKNNDistance()
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNHeap.getKNNDistance() if you know your distances are double-valued, you should be using the primitive type.
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceKNNSubList.getKNNDistance()
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDKNNList.getKNNDistance() Since you know this is a double distance heap, use DoubleDistanceIntegerDBIDKNNList.doubleKNNDistance()
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDKNNHeap.getKNNDistance()
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDPairKNNListHeap.getKNNDistance()
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering.AbstractBiclustering.getRowDBID(int) Expensive!
de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDPair.getSecond() Avoid autoboxing! Use DBIDRef interface!
de.lmu.ifi.dbs.elki.database.ids.integer.IntegerDoubleDBIDPair.getSecond()
de.lmu.ifi.dbs.elki.utilities.pairs.IntDoublePair.getSecond() use pair.first to avoid boxing!
de.lmu.ifi.dbs.elki.utilities.pairs.DoubleIntPair.getSecond() use pair.first to avoid boxing!
de.lmu.ifi.dbs.elki.utilities.pairs.DoubleDoublePair.getSecond() use pair.first to avoid boxing!
de.lmu.ifi.dbs.elki.utilities.pairs.IntIntPair.getSecond() use pair.first to avoid boxing!
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace.DimensionSelectingDistanceFunction.getSelectedDimensions()
de.lmu.ifi.dbs.elki.visualization.svg.SVGPlot.getStyle() Contents will be overwritten by CSS class manager!
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.KernelMatrix.getSubColumn(DBIDRef, DBIDs)
de.lmu.ifi.dbs.elki.data.ByteVector.getValue(int)
de.lmu.ifi.dbs.elki.data.SparseByteVector.getValue(int)
de.lmu.ifi.dbs.elki.data.SparseFloatVector.getValue(int)
de.lmu.ifi.dbs.elki.data.FloatVector.getValue(int)
de.lmu.ifi.dbs.elki.data.DoubleVector.getValue(int)
de.lmu.ifi.dbs.elki.data.OneDimensionalDoubleVector.getValue(int)
de.lmu.ifi.dbs.elki.data.SparseDoubleVector.getValue(int)
de.lmu.ifi.dbs.elki.data.SparseShortVector.getValue(int)
de.lmu.ifi.dbs.elki.data.SparseIntegerVector.getValue(int)
de.lmu.ifi.dbs.elki.data.IntegerVector.getValue(int)
de.lmu.ifi.dbs.elki.data.NumberVector.getValue(int)
de.lmu.ifi.dbs.elki.data.ShortVector.getValue(int)
de.lmu.ifi.dbs.elki.data.BitVector.getValue(int)
de.lmu.ifi.dbs.elki.index.tree.spatial.SpatialPointLeafEntry.getValue(int)
de.lmu.ifi.dbs.elki.math.linearalgebra.Vector.getValue(int)
de.lmu.ifi.dbs.elki.database.ids.DBIDRef.hashCode() Do not use this hash code. Some implementations will not offer stable hash codes!
de.lmu.ifi.dbs.elki.evaluation.roc.ROC.SimpleAdapter.hashCode()
de.lmu.ifi.dbs.elki.evaluation.roc.ROC.DistanceResultAdapter.hashCode()
de.lmu.ifi.dbs.elki.evaluation.roc.ROC.OutlierScoreAdapter.hashCode()
de.lmu.ifi.dbs.elki.persistent.PersistentPageFile.increaseReadAccess()
de.lmu.ifi.dbs.elki.persistent.PersistentPageFile.increaseWriteAccess()
de.lmu.ifi.dbs.elki.database.ids.distance.DoubleDistanceKNNHeap.insert(Double, DBIDRef)
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNListHeap.insert(Double, DBIDRef)
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNHeap.insert(Double, DBIDRef)
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDKNNHeap.insert(Double, DBIDRef)
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDPairKNNListHeap.insert(Double, DBIDRef)
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDSortedKNNList.insert(Double, DBIDRef)
de.lmu.ifi.dbs.elki.database.ids.distance.DoubleDistanceKNNHeap.insert(DoubleDistance, DBIDRef) if you know your distances are double-valued, you should be using the primitive type.
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNListHeap.insert(DoubleDistance, DBIDRef)
de.lmu.ifi.dbs.elki.database.ids.generic.DoubleDistanceDBIDPairKNNHeap.insert(DoubleDistance, DBIDRef) if you know your distances are double-valued, you should be using the primitive type.
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDKNNHeap.insert(DoubleDistance, DBIDRef)
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDPairKNNListHeap.insert(DoubleDistance, DBIDRef)
de.lmu.ifi.dbs.elki.database.ids.integer.DoubleDistanceIntegerDBIDSortedKNNList.insert(DoubleDistance, DBIDRef)
de.lmu.ifi.dbs.elki.data.SparseNumberVector.intValue(int) As the vectors are sparse, try to iterate over the sparse dimensions only, see SparseNumberVector.iterIntValue(int).
de.lmu.ifi.dbs.elki.data.SparseIntegerVector.intValue(int)
de.lmu.ifi.dbs.elki.data.SparseByteVector.longValue(int)
de.lmu.ifi.dbs.elki.data.SparseFloatVector.longValue(int)
de.lmu.ifi.dbs.elki.data.SparseNumberVector.longValue(int) As the vectors are sparse, try to iterate over the sparse dimensions only, see SparseNumberVector.iterLongValue(int).
de.lmu.ifi.dbs.elki.data.SparseDoubleVector.longValue(int)
de.lmu.ifi.dbs.elki.data.SparseShortVector.longValue(int)
de.lmu.ifi.dbs.elki.data.SparseIntegerVector.longValue(int)
de.lmu.ifi.dbs.elki.utilities.Util.mixHashCodes(int)
de.lmu.ifi.dbs.elki.database.datastore.WritableDBIDDataStore.put(DBIDRef, DBID) Use WritableDBIDDataStore.putDBID(de.lmu.ifi.dbs.elki.database.ids.DBIDRef, de.lmu.ifi.dbs.elki.database.ids.DBIDRef) instead, to avoid boxing/unboxing cost.
de.lmu.ifi.dbs.elki.database.datastore.memory.MapIntegerDBIDDBIDStore.put(DBIDRef, DBID)
de.lmu.ifi.dbs.elki.database.datastore.memory.ArrayDBIDStore.put(DBIDRef, DBID)
de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDataStore.put(DBIDRef, Double) Use WritableDoubleDataStore.putDouble(de.lmu.ifi.dbs.elki.database.ids.DBIDRef, double) instead, to avoid boxing/unboxing cost.
de.lmu.ifi.dbs.elki.database.datastore.memory.ArrayDoubleStore.put(DBIDRef, Double)
de.lmu.ifi.dbs.elki.database.datastore.memory.MapIntegerDBIDDoubleStore.put(DBIDRef, Double)
de.lmu.ifi.dbs.elki.database.datastore.WritableDoubleDistanceDataStore.put(DBIDRef, DoubleDistance) Use WritableDoubleDistanceDataStore.putDouble(de.lmu.ifi.dbs.elki.database.ids.DBIDRef, double) instead, to avoid boxing/unboxing cost.
de.lmu.ifi.dbs.elki.database.datastore.memory.ArrayDoubleDistanceStore.put(DBIDRef, DoubleDistance)
de.lmu.ifi.dbs.elki.database.datastore.memory.MapIntegerDBIDDoubleDistanceStore.put(DBIDRef, DoubleDistance)
de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore.put(DBIDRef, Integer) Use WritableIntegerDataStore.putInt(de.lmu.ifi.dbs.elki.database.ids.DBIDRef, int) instead, to avoid boxing/unboxing cost.
de.lmu.ifi.dbs.elki.database.datastore.memory.MapIntegerDBIDIntegerStore.put(DBIDRef, Integer)
de.lmu.ifi.dbs.elki.database.datastore.memory.ArrayIntegerStore.put(DBIDRef, Integer)
de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.DoubleArrayStaticHistogram.putData(double, double[]) use the explicit DoubleArrayStaticHistogram.increment(double, double[]) instead.
de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.IntArrayStaticHistogram.putData(double, int[]) use the explicit IntArrayStaticHistogram.increment(double, int[]) instead.
de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.LongArrayStaticHistogram.putData(double, long[]) use the explicit LongArrayStaticHistogram.increment(double, long[]) instead.
de.lmu.ifi.dbs.elki.math.statistics.distribution.ExponentiallyModifiedGaussianDistribution.quantile(double) Not yet implemented!
de.lmu.ifi.dbs.elki.math.statistics.distribution.WaldDistribution.quantile(double) NOT YET IMPLEMENTED.
de.lmu.ifi.dbs.elki.math.statistics.distribution.WaldDistribution.quantile(double, double, double) NOT YET IMPLEMENTED.
de.lmu.ifi.dbs.elki.math.statistics.distribution.ExponentiallyModifiedGaussianDistribution.quantile(double, double, double, double) Not yet implemented!
de.lmu.ifi.dbs.elki.logging.progress.StepProgress.setCompleted()
de.lmu.ifi.dbs.elki.logging.progress.IndefiniteProgress.setCompleted()
de.lmu.ifi.dbs.elki.visualization.batikutil.JSVGSynchronizedCanvas.setDocument(Document) Document cannot be synchronized - use JSVGSynchronizedCanvas.setPlot(de.lmu.ifi.dbs.elki.visualization.svg.SVGPlot) and a SVGPlot object!
de.lmu.ifi.dbs.elki.visualization.batikutil.JSVGSynchronizedCanvas.setSVGDocument(SVGDocument) Document cannot be synchronized - use JSVGSynchronizedCanvas.setPlot(de.lmu.ifi.dbs.elki.visualization.svg.SVGPlot) and a SVGPlot object!
de.lmu.ifi.dbs.elki.data.SparseNumberVector.shortValue(int) As the vectors are sparse, try to iterate over the sparse dimensions only, see SparseNumberVector.iterShortValue(int).
de.lmu.ifi.dbs.elki.data.SparseShortVector.shortValue(int)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.ByteSerializer.toByteBuffer(ByteBuffer, Byte)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.DoubleSerializer.toByteBuffer(ByteBuffer, Double)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.FloatSerializer.toByteBuffer(ByteBuffer, Float)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.IntegerSerializer.toByteBuffer(ByteBuffer, Integer)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.VarintSerializer.toByteBuffer(ByteBuffer, Integer)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.LongSerializer.toByteBuffer(ByteBuffer, Long)
de.lmu.ifi.dbs.elki.persistent.ByteArrayUtil.ShortSerializer.toByteBuffer(ByteBuffer, Short)

Deprecated Constructors

Constructor and Description
de.lmu.ifi.dbs.elki.distance.distancefunction.ArcCosineDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.data.Bit(boolean) Use Bit.valueOf(java.lang.String) to save memory.
de.lmu.ifi.dbs.elki.data.Bit(int) Use Bit.valueOf(java.lang.String) to save memory.
de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction() Use BrayCurtisDistanceFunction.STATIC_CONTINUOUS instance instead.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.CentroidLinkageMethod() use the static instance CentroidLinkageMethod.STATIC instead.
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.ChiSquaredDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.distance.distancefunction.ClarkDistanceFunction() Use ClarkDistanceFunction.STATIC instance instead.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.CompleteLinkageMethod() use the static instance CompleteLinkageMethod.STATIC instead.
de.lmu.ifi.dbs.elki.distance.distancefunction.CosineDistanceFunction() Use static instance
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.EuclideanDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress(String, int)
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.GroupAverageLinkageMethod() use the static instance GroupAverageLinkageMethod.STATIC instead.
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram.HistogramIntersectionDistanceFunction() Use static instance
de.lmu.ifi.dbs.elki.distance.distancefunction.histogram.HistogramMatchDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.logging.progress.IndefiniteProgress(String)
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JeffreyDivergenceDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JensenShannonDivergenceDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.distance.distancefunction.histogram.KolmogorovSmirnovDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.distance.distancefunction.Kulczynski1DistanceFunction() Use Kulczynski1DistanceFunction.STATIC instance instead.
de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski1SimilarityFunction() Use Kulczynski1SimilarityFunction.STATIC instance instead.
de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski2SimilarityFunction() Use Kulczynski2SimilarityFunction.STATIC_CONTINUOUS instance instead.
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceAsymmetricDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.distance.distancefunction.strings.LevenshteinDistanceFunction()
de.lmu.ifi.dbs.elki.distance.similarityfunction.kernel.LinearKernelFunction()
de.lmu.ifi.dbs.elki.distance.distancefunction.LorentzianDistanceFunction() Use LorentzianDistanceFunction.STATIC instance instead.
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.ManhattanDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.MaximumDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.MedianLinkageMethod() use the static instance MedianLinkageMethod.STATIC instead.
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.MinimumDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.datasource.bundle.MultipleObjectsBundle(BundleMeta, List>)
de.lmu.ifi.dbs.elki.distance.distancefunction.strings.NormalizedLevenshteinDistanceFunction()
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.PearsonCorrelationDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SingleLinkageMethod() use the static instance SingleLinkageMethod.STATIC instead.
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SparseEuclideanDistanceFunction() Use static instance instead.
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SparseManhattanDistanceFunction() Use static instance instead.
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SparseMaximumDistanceFunction() Use static instance
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.SqrtJensenShannonDivergenceDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.distance.distancefunction.minkowski.SquaredEuclideanDistanceFunction() Use static instance!
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation.SquaredPearsonCorrelationDistanceFunction() use static instance!
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.WardLinkageMethod() use the static instance WardLinkageMethod.STATIC instead.
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.WeightedAverageLinkageMethod() use the static instance WeightedAverageLinkageMethod.STATIC instead.