Uses of Class
org.tribuo.clustering.ClusterID
Packages that use ClusterID
Package
Description
Provides classes and infrastructure for working with clustering problems.
Evaluation classes for clustering.
Provides clustering data generators used for demos and testing implementations.
Provides an implementation of HDBSCAN*.
Provides a multithreaded implementation of K-Means, with a
configurable distance function.
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Uses of ClusterID in org.tribuo.clustering
Classes in org.tribuo.clustering that implement interfaces with type arguments of type ClusterIDModifier and TypeClassDescriptionclass
A clustering id.final class
A factory for making ClusterID related classes.class
The base class for a ClusterID OutputInfo.class
AnImmutableOutputInfo
object for ClusterIDs.class
A mutableClusteringInfo
.Fields in org.tribuo.clustering declared as ClusterIDModifier and TypeFieldDescriptionstatic 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 ClusterIDModifier and TypeMethodDescriptionClusterID.copy()
static ClusterID
ClusterID.deserializeFromProto
(int version, String className, com.google.protobuf.Any message) Deserialization factory.<V> ClusterID
ClusteringFactory.generateOutput
(V label) Generates a ClusterID by calling toString on the input, then calling Integer.parseInt.ImmutableClusteringInfo.getOutput
(int id) ClusteringFactory.getUnknownOutput()
Methods in org.tribuo.clustering that return types with arguments of type ClusterIDModifier and TypeMethodDescriptionClusteringFactory.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.ClusteringInfo.generateImmutableOutputInfo()
ClusteringFactory.generateInfo()
ClusteringInfo.generateMutableOutputInfo()
ClusteringInfo.getDomain()
ImmutableClusteringInfo.getDomain()
ClusteringFactory.getEvaluator()
ClusteringFactory.getTypeWitness()
ImmutableClusteringInfo.iterator()
Methods in org.tribuo.clustering with parameters of type ClusterIDMethod parameters in org.tribuo.clustering with type arguments of type ClusterIDModifier and TypeMethodDescriptionClusteringFactory.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
Subclasses with type arguments of type ClusterID in org.tribuo.clustering.evaluationModifier and TypeClassDescriptionclass
Subinterfaces with type arguments of type ClusterID in org.tribuo.clustering.evaluationClasses in org.tribuo.clustering.evaluation that implement interfaces with type arguments of type ClusterIDModifier and TypeClassDescriptionclass
A metric for evaluating clustering problems.Methods in org.tribuo.clustering.evaluation that return types with arguments of type ClusterIDModifier and TypeMethodDescriptionBiFunction
<MetricTarget<ClusterID>, org.tribuo.clustering.evaluation.ClusteringMetric.Context, Double> ClusteringMetrics.getImpl()
Gets the implementing function for this metric.ClusteringMetric.getTarget()
Method parameters in org.tribuo.clustering.evaluation with type arguments of type ClusterIDModifier and TypeMethodDescriptionprotected 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) 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 ClusterIDModifierConstructorDescriptionClusteringMetric
(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
Classes in org.tribuo.clustering.example that implement interfaces with type arguments of type ClusterIDModifier and TypeClassDescriptionfinal class
Generates a clustering dataset drawn from a mixture of 5 Gaussians.Methods in org.tribuo.clustering.example that return types with arguments of type ClusterIDModifier and TypeMethodDescriptionClusteringDataGenerator.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}.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}.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}.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}.ClusteringDataGenerator.emptyExample()
Generates an example with no features.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.GaussianClusterDataSource.getOutputFactory()
ClusteringDataGenerator.invalidSparseExample()
Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.GaussianClusterDataSource.iterator()
ClusteringDataGenerator.sparseTrainTest()
Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.ClusteringDataGenerator.sparseTrainTest()
Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.ClusteringDataGenerator.sparseTrainTest
(double negate) Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.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
Subclasses with type arguments of type ClusterID in org.tribuo.clustering.hdbscanModifier and TypeClassDescriptionfinal class
A trained HDBSCAN* model which provides the cluster assignment labels and outlier scores for every data point.Classes in org.tribuo.clustering.hdbscan that implement interfaces with type arguments of type ClusterIDModifier and TypeClassDescriptionfinal class
An HDBSCAN* trainer which generates a hierarchical, density-based clustering representation of the supplied data.Methods in org.tribuo.clustering.hdbscan that return types with arguments of type ClusterIDModifier and TypeMethodDescriptionMethod parameters in org.tribuo.clustering.hdbscan with type arguments of type ClusterID -
Uses of ClusterID in org.tribuo.clustering.kmeans
Subclasses with type arguments of type ClusterID in org.tribuo.clustering.kmeansModifier and TypeClassDescriptionclass
A K-Means model with a selectable distance function.Classes in org.tribuo.clustering.kmeans that implement interfaces with type arguments of type ClusterIDModifier and TypeClassDescriptionclass
A K-Means trainer, which generates a K-means clustering of the supplied data.Methods in org.tribuo.clustering.kmeans that return types with arguments of type ClusterIDModifier and TypeMethodDescriptionMethod parameters in org.tribuo.clustering.kmeans with type arguments of type ClusterIDModifier and TypeMethodDescriptionKMeansTrainer.train
(Dataset<ClusterID> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) KMeansTrainer.train
(Dataset<ClusterID> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)