diff options
author | Erich Schubert <erich@debian.org> | 2013-10-29 20:02:37 +0100 |
---|---|---|
committer | Andrej Shadura <andrewsh@debian.org> | 2019-03-09 22:30:37 +0000 |
commit | ec7f409f6e795bbcc6f3c005687954e9475c600c (patch) | |
tree | fbf36c0ab791c556198b487ca40ae56ae5ab1ee5 /src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/COPAC.java | |
parent | 974d4cf6d54cadc06258039f2cd0515cc34aeac6 (diff) | |
parent | 8300861dc4c62c5567a4e654976072f854217544 (diff) |
Import Debian changes 0.6.0~beta2-1
elki (0.6.0~beta2-1) unstable; urgency=low
* New upstream beta release.
* 3DPC extension is not yet included.
Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/COPAC.java')
-rw-r--r-- | src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/COPAC.java | 22 |
1 files changed, 11 insertions, 11 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/COPAC.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/COPAC.java index ac50559e..9a4b8512 100644 --- a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/COPAC.java +++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/COPAC.java @@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.algorithm.clustering.correlation; This file is part of ELKI: Environment for Developing KDD-Applications Supported by Index-Structures - Copyright (C) 2012 + Copyright (C) 2013 Ludwig-Maximilians-Universität München Lehr- und Forschungseinheit für Datenbanksysteme ELKI Development Team @@ -185,7 +185,7 @@ public class COPAC<V extends NumberVector<?>, D extends Distance<D>> extends Abs LocalProjectionIndex<V, ?> preprocin = partitionDistanceQuery.getIndex(); // partitioning - Map<Integer, ModifiableDBIDs> partitionMap = new HashMap<Integer, ModifiableDBIDs>(); + Map<Integer, ModifiableDBIDs> partitionMap = new HashMap<>(); FiniteProgress partitionProgress = LOG.isVerbose() ? new FiniteProgress("Partitioning", relation.size(), LOG) : null; int processed = 1; @@ -214,7 +214,7 @@ public class COPAC<V extends NumberVector<?>, D extends Distance<D>> extends Abs // convert for partition algorithm. // TODO: do this with DynamicDBIDs instead - Map<Integer, DBIDs> pmap = new HashMap<Integer, DBIDs>(); + Map<Integer, DBIDs> pmap = new HashMap<>(); for(Entry<Integer, ModifiableDBIDs> ent : partitionMap.entrySet()) { pmap.put(ent.getKey(), ent.getValue()); } @@ -230,14 +230,14 @@ public class COPAC<V extends NumberVector<?>, D extends Distance<D>> extends Abs * @param query The preprocessor based query function */ private Clustering<Model> runPartitionAlgorithm(Relation<V> relation, Map<Integer, DBIDs> partitionMap, DistanceQuery<V, D> query) { - Clustering<Model> result = new Clustering<Model>("COPAC clustering", "copac-clustering"); + Clustering<Model> result = new Clustering<>("COPAC clustering", "copac-clustering"); // TODO: use an extra finite progress for the partitions? for(Entry<Integer, DBIDs> pair : partitionMap.entrySet()) { // noise partition if(pair.getKey() == RelationUtil.dimensionality(relation)) { // Make a Noise cluster - result.addCluster(new Cluster<Model>(pair.getValue(), true, ClusterModel.CLUSTER)); + result.addToplevelCluster(new Cluster<Model>(pair.getValue(), true, ClusterModel.CLUSTER)); } else { DBIDs partids = pair.getValue(); @@ -251,10 +251,10 @@ public class COPAC<V extends NumberVector<?>, D extends Distance<D>> extends Abs // Re-Wrap resulting Clusters as DimensionModel clusters. for(Cluster<Model> clus : p.getAllClusters()) { if(clus.isNoise()) { - result.addCluster(new Cluster<Model>(clus.getIDs(), true, ClusterModel.CLUSTER)); + result.addToplevelCluster(new Cluster<Model>(clus.getIDs(), true, ClusterModel.CLUSTER)); } else { - result.addCluster(new Cluster<Model>(clus.getIDs(), new DimensionModel(pair.getKey()))); + result.addToplevelCluster(new Cluster<Model>(clus.getIDs(), new DimensionModel(pair.getKey()))); } } } @@ -316,12 +316,12 @@ public class COPAC<V extends NumberVector<?>, D extends Distance<D>> extends Abs @Override protected void makeOptions(Parameterization config) { super.makeOptions(config); - ClassParameter<Factory<V, ?>> indexP = new ClassParameter<LocalProjectionIndex.Factory<V, ?>>(PREPROCESSOR_ID, LocalProjectionIndex.Factory.class); + ClassParameter<Factory<V, ?>> indexP = new ClassParameter<>(PREPROCESSOR_ID, LocalProjectionIndex.Factory.class); if(config.grab(indexP)) { indexI = indexP.instantiateClass(config); } - ObjectParameter<FilteredLocalPCABasedDistanceFunction<V, ?, D>> pdistP = new ObjectParameter<FilteredLocalPCABasedDistanceFunction<V, ?, D>>(PARTITION_DISTANCE_ID, FilteredLocalPCABasedDistanceFunction.class, LocallyWeightedDistanceFunction.class); + ObjectParameter<FilteredLocalPCABasedDistanceFunction<V, ?, D>> pdistP = new ObjectParameter<>(PARTITION_DISTANCE_ID, FilteredLocalPCABasedDistanceFunction.class, LocallyWeightedDistanceFunction.class); if(config.grab(pdistP)) { ListParameterization predefinedDist = new ListParameterization(); predefinedDist.addParameter(IndexBasedDistanceFunction.INDEX_ID, indexI); @@ -332,7 +332,7 @@ public class COPAC<V extends NumberVector<?>, D extends Distance<D>> extends Abs } // Parameterize algorithm: - ClassParameter<ClusteringAlgorithm<Clustering<Model>>> algP = new ClassParameter<ClusteringAlgorithm<Clustering<Model>>>(PARTITION_ALGORITHM_ID, ClusteringAlgorithm.class); + ClassParameter<ClusteringAlgorithm<Clustering<Model>>> algP = new ClassParameter<>(PARTITION_ALGORITHM_ID, ClusteringAlgorithm.class); if(config.grab(algP)) { ListParameterization predefined = new ListParameterization(); predefined.addParameter(AbstractDistanceBasedAlgorithm.DISTANCE_FUNCTION_ID, pdistI); @@ -348,7 +348,7 @@ public class COPAC<V extends NumberVector<?>, D extends Distance<D>> extends Abs @Override protected COPAC<V, D> makeInstance() { - return new COPAC<V, D>(pdistI, algC, algO); + return new COPAC<>(pdistI, algC, algO); } } }
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