Uses of Class
org.tribuo.clustering.ClusterID

Packages that use ClusterID

Package	Description
org.tribuo.clustering	Provides classes and infrastructure for working with clustering problems.
org.tribuo.clustering.evaluation	Evaluation classes for clustering.
org.tribuo.clustering.example	Provides a clustering data generator used for testing implementations.
org.tribuo.clustering.kmeans	Provides a multithreaded implementation of K-Means, with a configurable distance function.

Uses of ClusterID in org.tribuo.clustering

Fields in org.tribuo.clustering declared as ClusterID

Modifier and Type	Field and Description
static ClusterID	ClusteringFactory.UNASSIGNED_CLUSTER_ID

Methods in org.tribuo.clustering that return ClusterID

Modifier and Type	Method and Description
ClusterID	ClusterID.copy()
<V> ClusterID	ClusteringFactory.generateOutput(V label) Generates a ClusterID by calling toString on the input, then calling Integer.parseInt.
ClusterID	ImmutableClusteringInfo.getOutput(int id)
ClusterID	ClusteringFactory.getUnknownOutput()

Methods in org.tribuo.clustering that return types with arguments of type ClusterID

Modifier and Type	Method and Description
ImmutableOutputInfo<ClusterID>	ClusteringFactory.constructInfoForExternalModel(Map<ClusterID,Integer> mapping) Unlike the other info types, clustering directly uses the integer IDs as the stored value, so this mapping discards the cluster IDs and just uses the supplied integers.
ImmutableOutputInfo<ClusterID>	ClusteringInfo.generateImmutableOutputInfo()
MutableOutputInfo<ClusterID>	ClusteringFactory.generateInfo()
MutableOutputInfo<ClusterID>	ClusteringInfo.generateMutableOutputInfo()
Set<ClusterID>	ImmutableClusteringInfo.getDomain()
Set<ClusterID>	ClusteringInfo.getDomain()
Evaluator<ClusterID,ClusteringEvaluation>	ClusteringFactory.getEvaluator()
Iterator<com.oracle.labs.mlrg.olcut.util.Pair<Integer,ClusterID>>	ImmutableClusteringInfo.iterator()

Methods in org.tribuo.clustering with parameters of type ClusterID

Modifier and Type	Method and Description
boolean	ClusterID.fullEquals(ClusterID o)
int	ImmutableClusteringInfo.getID(ClusterID output)
void	MutableClusteringInfo.observe(ClusterID output)

Method parameters in org.tribuo.clustering with type arguments of type ClusterID

Modifier and Type	Method and Description
ImmutableOutputInfo<ClusterID>	ClusteringFactory.constructInfoForExternalModel(Map<ClusterID,Integer> mapping) Unlike the other info types, clustering directly uses the integer IDs as the stored value, so this mapping discards the cluster IDs and just uses the supplied integers.

Uses of ClusterID in org.tribuo.clustering.evaluation

Methods in org.tribuo.clustering.evaluation that return types with arguments of type ClusterID

Modifier and Type	Method and Description
BiFunction<MetricTarget<ClusterID>,org.tribuo.clustering.evaluation.ClusteringMetric.Context,Double>	ClusteringMetrics.getImpl()
MetricTarget<ClusterID>	ClusteringMetric.getTarget()

Method parameters in org.tribuo.clustering.evaluation with type arguments of type ClusterID

Modifier and Type	Method and Description
org.tribuo.clustering.evaluation.ClusteringMetric.Context	ClusteringMetric.createContext(Model<ClusterID> model, List<Prediction<ClusterID>> predictions)
org.tribuo.clustering.evaluation.ClusteringMetric.Context	ClusteringMetric.createContext(Model<ClusterID> model, List<Prediction<ClusterID>> predictions)
protected org.tribuo.clustering.evaluation.ClusteringMetric.Context	ClusteringEvaluator.createContext(Model<ClusterID> model, List<Prediction<ClusterID>> predictions)
protected org.tribuo.clustering.evaluation.ClusteringMetric.Context	ClusteringEvaluator.createContext(Model<ClusterID> model, List<Prediction<ClusterID>> predictions)
protected ClusteringEvaluation	ClusteringEvaluator.createEvaluation(org.tribuo.clustering.evaluation.ClusteringMetric.Context context, Map<MetricID<ClusterID>,Double> results, EvaluationProvenance provenance)
protected Set<ClusteringMetric>	ClusteringEvaluator.createMetrics(Model<ClusterID> model)
ClusteringMetric	ClusteringMetrics.forTarget(MetricTarget<ClusterID> tgt)

Constructor parameters in org.tribuo.clustering.evaluation with type arguments of type ClusterID

Constructor and Description
ClusteringMetric(MetricTarget<ClusterID> target, String name, BiFunction<MetricTarget<ClusterID>,org.tribuo.clustering.evaluation.ClusteringMetric.Context,Double> impl)
ClusteringMetric(MetricTarget<ClusterID> target, String name, BiFunction<MetricTarget<ClusterID>,org.tribuo.clustering.evaluation.ClusteringMetric.Context,Double> impl)

Uses of ClusterID in org.tribuo.clustering.example

Methods in org.tribuo.clustering.example that return types with arguments of type ClusterID

Modifier and Type	Method and Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.denseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.denseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.denseTrainTest(double negate) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.denseTrainTest(double negate) Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
static Example<ClusterID>	ClusteringDataGenerator.emptyExample() Generates an example with no features.
static Dataset<ClusterID>	ClusteringDataGenerator.gaussianClusters(long size, long seed) Generates a dataset drawn from a mixture of 5 2d gaussians.
static Example<ClusterID>	ClusteringDataGenerator.invalidSparseExample() Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.sparseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.sparseTrainTest()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.sparseTrainTest(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.sparseTrainTest(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.

Uses of ClusterID in org.tribuo.clustering.kmeans

Methods in org.tribuo.clustering.kmeans that return types with arguments of type ClusterID

Modifier and Type	Method and Description
Optional<Excuse<ClusterID>>	KMeansModel.getExcuse(Example<ClusterID> example)
Prediction<ClusterID>	KMeansModel.predict(Example<ClusterID> example)

Method parameters in org.tribuo.clustering.kmeans with type arguments of type ClusterID

Modifier and Type	Method and Description
Optional<Excuse<ClusterID>>	KMeansModel.getExcuse(Example<ClusterID> example)
protected static DenseVector[]	KMeansTrainer.initialiseCentroids(int centroids, Dataset<ClusterID> examples, ImmutableFeatureMap featureMap, SplittableRandom rng) Initialisation method called at the start of each train call.
Prediction<ClusterID>	KMeansModel.predict(Example<ClusterID> example)
KMeansModel	KMeansTrainer.train(Dataset<ClusterID> dataset)
KMeansModel	KMeansTrainer.train(Dataset<ClusterID> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)