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 clustering data generators used for demos and testing implementations.
org.tribuo.clustering.hdbscan	Provides an implementation of HDBSCAN*.
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	Description
static final ClusterID	ClusteringFactory.UNASSIGNED_CLUSTER_ID	The sentinel unassigned cluster id, used when there is no ground truth clustering.

Methods in org.tribuo.clustering that return ClusterID

Modifier and Type	Method	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	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>	ClusteringInfo.getDomain()
Set<ClusterID>	ImmutableClusteringInfo.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	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	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.
boolean	ImmutableClusteringInfo.domainAndIDEquals(ImmutableOutputInfo<ClusterID> other)

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	Description
BiFunction<MetricTarget<ClusterID>,org.tribuo.clustering.evaluation.ClusteringMetric.Context,Double>	ClusteringMetrics.getImpl()	Gets the implementing function for this metric.
MetricTarget<ClusterID>	ClusteringMetric.getTarget()

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

Modifier and Type	Method	Description
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)
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 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)	Constructs the metric for the specified metric target.

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

Modifier	Constructor	Description
	ClusteringMetric(MetricTarget<ClusterID> target, String name, BiFunction<MetricTarget<ClusterID>,org.tribuo.clustering.evaluation.ClusteringMetric.Context,Double> impl)	Constructs a clustering metric using the supplied parameters.
	ClusteringMetric(MetricTarget<ClusterID> target, String name, BiFunction<MetricTarget<ClusterID>,org.tribuo.clustering.evaluation.ClusteringMetric.Context,Double> impl)	Constructs a clustering metric using the supplied parameters.

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	Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.denseTrainTest()	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()	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 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 MutableDataset<ClusterID>	GaussianClusterDataSource.generateDataset(int numSamples, double[] mixingDistribution, double[] firstMean, double[] firstVariance, double[] secondMean, double[] secondVariance, double[] thirdMean, double[] thirdVariance, double[] fourthMean, double[] fourthVariance, double[] fifthMean, double[] fifthVariance, long seed)	Generates a clustering dataset drawn from a mixture of 5 Gaussians.
OutputFactory<ClusterID>	GaussianClusterDataSource.getOutputFactory()
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.
Iterator<Example<ClusterID>>	GaussianClusterDataSource.iterator()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<ClusterID>,Dataset<ClusterID>>	ClusteringDataGenerator.sparseTrainTest()	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()	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.
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.hdbscan

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

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

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

Modifier and Type	Method	Description
Optional<Excuse<ClusterID>>	HdbscanModel.getExcuse(Example<ClusterID> example)
Prediction<ClusterID>	HdbscanModel.predict(Example<ClusterID> example)
HdbscanModel	HdbscanTrainer.train(Dataset<ClusterID> dataset)
HdbscanModel	HdbscanTrainer.train(Dataset<ClusterID> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

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	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	Description
Optional<Excuse<ClusterID>>	KMeansModel.getExcuse(Example<ClusterID> example)
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)
KMeansModel	KMeansTrainer.train(Dataset<ClusterID> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)