Uses of Class
org.tribuo.classification.Label

Packages that use Label

Package	Description
org.tribuo.classification	Provides classes and infrastructure for multiclass classification problems.
org.tribuo.classification.baseline	Provides simple baseline multiclass classifiers.
org.tribuo.classification.ensemble	Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
org.tribuo.classification.evaluation	Evaluation classes for multi-class classification.
org.tribuo.classification.example	Provides a multiclass data generator used for testing implementations, along with several synthetic data generators for 2d binary classification problems to be used in demos or tutorials.
org.tribuo.classification.sequence	Provides infrastructure for SequenceModels which emit Labels at each step of the sequence.
org.tribuo.classification.sequence.example	Provides a classification sequence data generator for smoke testing implementations.
org.tribuo.classification.sequence.viterbi	Provides an implementation of Viterbi for generating structured outputs, which can sit on top of any Label based classification model.

Uses of Label in org.tribuo.classification

Classes in org.tribuo.classification with type parameters of type Label

Modifier and Type	Interface	Description
interface	ClassificationOptions<TRAINER extends Trainer<Label>>	An Options that can produce a classification Trainer based on the provided arguments.

Fields in org.tribuo.classification declared as Label

Modifier and Type	Field	Description
static final Label	LabelFactory.UNKNOWN_LABEL	The singleton unknown label, used for unlablled examples.

Fields in org.tribuo.classification with type parameters of type Label

Modifier and Type	Field	Description
protected Map<String,Label>	LabelInfo.labels	The label domain.

Methods in org.tribuo.classification that return Label

Modifier and Type	Method	Description
Label	Label.copy()
<V> Label	LabelFactory.generateOutput(V label)	Generates the Label string by calling toString on the input.
Label	ImmutableLabelInfo.getOutput(int id)
Label	LabelFactory.getUnknownOutput()

Methods in org.tribuo.classification that return types with arguments of type Label

Modifier and Type	Method	Description
ImmutableOutputInfo<Label>	LabelFactory.constructInfoForExternalModel(Map<Label,Integer> mapping)
ImmutableOutputInfo<Label>	LabelInfo.generateImmutableOutputInfo()
MutableOutputInfo<Label>	LabelFactory.generateInfo()	Generates an empty MutableLabelInfo.
MutableOutputInfo<Label>	LabelInfo.generateMutableOutputInfo()
Set<Label>	ImmutableLabelInfo.getDomain()	Returns the set of possible Labels that this LabelInfo has seen.
Set<Label>	LabelInfo.getDomain()	Returns the set of possible Labels that this LabelInfo has seen.
Evaluator<Label,LabelEvaluation>	LabelFactory.getEvaluator()
Iterator<com.oracle.labs.mlrg.olcut.util.Pair<Integer,Label>>	ImmutableLabelInfo.iterator()
static Model<Label>	TrainTestHelper.run(com.oracle.labs.mlrg.olcut.config.ConfigurationManager cm, org.tribuo.data.DataOptions dataOptions, Trainer<Label> trainer)	This method trains a model on the specified training data, and evaluates it on the specified test data.

Methods in org.tribuo.classification with parameters of type Label

Modifier and Type	Method	Description
boolean	Label.fullEquals(Label o)
int	ImmutableLabelInfo.getID(Label output)
long	LabelInfo.getLabelCount(Label label)	Gets the count of the supplied label, or 0 if the label is unknown.
void	MutableLabelInfo.observe(Label output)

Method parameters in org.tribuo.classification with type arguments of type Label

Modifier and Type	Method	Description
ImmutableOutputInfo<Label>	LabelFactory.constructInfoForExternalModel(Map<Label,Integer> mapping)
boolean	ImmutableLabelInfo.domainAndIDEquals(ImmutableOutputInfo<Label> other)
static Model<Label>	TrainTestHelper.run(com.oracle.labs.mlrg.olcut.config.ConfigurationManager cm, org.tribuo.data.DataOptions dataOptions, Trainer<Label> trainer)	This method trains a model on the specified training data, and evaluates it on the specified test data.
void	WeightedLabels.setLabelWeights(Map<Label,Float> map)	Sets the label weights used by this trainer.

Uses of Label in org.tribuo.classification.baseline

Methods in org.tribuo.classification.baseline that return types with arguments of type Label

Modifier and Type	Method	Description
Optional<Excuse<Label>>	DummyClassifierModel.getExcuse(Example<Label> example)
Prediction<Label>	DummyClassifierModel.predict(Example<Label> example)
Model<Label>	DummyClassifierTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)
Model<Label>	DummyClassifierTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount)

Method parameters in org.tribuo.classification.baseline with type arguments of type Label

Modifier and Type	Method	Description
Optional<Excuse<Label>>	DummyClassifierModel.getExcuse(Example<Label> example)
Prediction<Label>	DummyClassifierModel.predict(Example<Label> example)
Model<Label>	DummyClassifierTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)
Model<Label>	DummyClassifierTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance, int invocationCount)

Uses of Label in org.tribuo.classification.ensemble

Fields in org.tribuo.classification.ensemble with type parameters of type Label

Modifier and Type	Field	Description
protected Trainer<Label>	AdaBoostTrainer.innerTrainer

Methods in org.tribuo.classification.ensemble that return types with arguments of type Label

Modifier and Type	Method	Description
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Model<Label>	AdaBoostTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	If the trainer implements WeightedExamples then do boosting by weighting, otherwise do boosting by sampling.
Model<Label>	AdaBoostTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)
Trainer<Label>	ClassificationEnsembleOptions.wrapTrainer(Trainer<Label> trainer)	Wraps the supplied trainer using the ensemble trainer described by these options.

Method parameters in org.tribuo.classification.ensemble with type arguments of type Label

Modifier and Type	Method	Description
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Prediction<Label>	FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions)
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Prediction<Label>	VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo, List<Prediction<Label>> predictions, float[] weights)
Model<Label>	AdaBoostTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	If the trainer implements WeightedExamples then do boosting by weighting, otherwise do boosting by sampling.
Model<Label>	AdaBoostTrainer.train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount)
Trainer<Label>	ClassificationEnsembleOptions.wrapTrainer(Trainer<Label> trainer)	Wraps the supplied trainer using the ensemble trainer described by these options.

Constructor parameters in org.tribuo.classification.ensemble with type arguments of type Label

Modifier	Constructor	Description
	AdaBoostTrainer(Trainer<Label> trainer, int numMembers)	Constructs an adaboost trainer using the supplied weak learner trainer and the specified number of boosting rounds.
	AdaBoostTrainer(Trainer<Label> trainer, int numMembers, long seed)	Constructs an adaboost trainer using the supplied weak learner trainer, the specified number of boosting rounds and the supplied seed.

Uses of Label in org.tribuo.classification.evaluation

Methods in org.tribuo.classification.evaluation that return types with arguments of type Label

Modifier and Type	Method	Description
ConfusionMatrix<Label>	LabelMetric.Context.getCM()	Gets the confusion matrix.
ImmutableOutputInfo<Label>	LabelConfusionMatrix.getDomain()
ToDoubleBiFunction<MetricTarget<Label>,LabelMetric.Context>	LabelMetrics.getImpl()	Returns the implementing function for this metric.
List<Label>	LabelConfusionMatrix.getLabelOrder()	Gets the current label order.
MetricTarget<Label>	LabelMetric.getTarget()
Set<Label>	LabelConfusionMatrix.observed()

Methods in org.tribuo.classification.evaluation with parameters of type Label

Modifier and Type	Method	Description
double	LabelEvaluation.accuracy(Label label)	The per label accuracy of the evaluation.
double	LabelEvaluation.AUCROC(Label label)	Area under the ROC curve.
static double	LabelMetrics.AUCROC(Label label, List<Prediction<Label>> predictions)	Area under the ROC curve.
double	LabelEvaluation.averagedPrecision(Label label)	Summarises a Precision-Recall Curve by taking the weighted mean of the precisions at a given threshold, where the weight is the recall achieved at that threshold.
static double	LabelMetrics.averagedPrecision(Label label, List<Prediction<Label>> predictions)
double	LabelConfusionMatrix.confusion(Label predicted, Label trueClass)
double	LabelConfusionMatrix.fn(Label cls)
double	LabelConfusionMatrix.fp(Label cls)
LabelEvaluationUtil.PRCurve	LabelEvaluation.precisionRecallCurve(Label label)	Calculates the Precision Recall curve for a single label.
static LabelEvaluationUtil.PRCurve	LabelMetrics.precisionRecallCurve(Label label, List<Prediction<Label>> predictions)
double	LabelConfusionMatrix.support(Label label)
double	LabelConfusionMatrix.tn(Label cls)
double	LabelConfusionMatrix.tp(Label cls)

Method parameters in org.tribuo.classification.evaluation with type arguments of type Label

Modifier and Type	Method	Description
static double	LabelMetrics.AUCROC(Label label, List<Prediction<Label>> predictions)	Area under the ROC curve.
static double	LabelMetrics.AUCROC(MetricTarget<Label> tgt, List<Prediction<Label>> predictions)	Area under the ROC curve.
static double	LabelMetrics.AUCROC(MetricTarget<Label> tgt, List<Prediction<Label>> predictions)	Area under the ROC curve.
static double	LabelMetrics.averagedPrecision(Label label, List<Prediction<Label>> predictions)
static double	LabelMetrics.averagedPrecision(MetricTarget<Label> tgt, List<Prediction<Label>> predictions)
static double	LabelMetrics.averagedPrecision(MetricTarget<Label> tgt, List<Prediction<Label>> predictions)
protected LabelMetric.Context	LabelEvaluator.createContext(Model<Label> model, List<Prediction<Label>> predictions)
protected LabelMetric.Context	LabelEvaluator.createContext(Model<Label> model, List<Prediction<Label>> predictions)
LabelMetric.Context	LabelMetric.createContext(Model<Label> model, List<Prediction<Label>> predictions)
LabelMetric.Context	LabelMetric.createContext(Model<Label> model, List<Prediction<Label>> predictions)
protected LabelEvaluation	LabelEvaluator.createEvaluation(LabelMetric.Context ctx, Map<MetricID<Label>,Double> results, EvaluationProvenance provenance)
protected Set<LabelMetric>	LabelEvaluator.createMetrics(Model<Label> model)
LabelMetric	LabelMetrics.forTarget(MetricTarget<Label> tgt)	Gets the LabelMetric wrapped around the supplied MetricTarget.
static LabelEvaluationUtil.PRCurve	LabelMetrics.precisionRecallCurve(Label label, List<Prediction<Label>> predictions)
void	LabelConfusionMatrix.setLabelOrder(List<Label> newLabelOrder)	Sets the label order used in LabelConfusionMatrix.toString().

Constructor parameters in org.tribuo.classification.evaluation with type arguments of type Label

Modifier	Constructor	Description
	Context(Model<Label> model, List<Prediction<Label>> predictions)	Constructs a context and compute the confusion matrix using the specified model and predictions.
	Context(Model<Label> model, List<Prediction<Label>> predictions)	Constructs a context and compute the confusion matrix using the specified model and predictions.
	Context(SequenceModel<Label> model, List<Prediction<Label>> predictions)	Constructs a context and compute the confusion matrix using the specified model and predictions.
	Context(SequenceModel<Label> model, List<Prediction<Label>> predictions)	Constructs a context and compute the confusion matrix using the specified model and predictions.
	LabelConfusionMatrix(ImmutableOutputInfo<Label> domain, List<Prediction<Label>> predictions)	Creates a confusion matrix from the supplied predictions and label info.
	LabelConfusionMatrix(ImmutableOutputInfo<Label> domain, List<Prediction<Label>> predictions)	Creates a confusion matrix from the supplied predictions and label info.
	LabelConfusionMatrix(Model<Label> model, List<Prediction<Label>> predictions)	Creates a confusion matrix from the supplied predictions, using the label info from the supplied model.
	LabelConfusionMatrix(Model<Label> model, List<Prediction<Label>> predictions)	Creates a confusion matrix from the supplied predictions, using the label info from the supplied model.
	LabelMetric(MetricTarget<Label> tgt, String name, ToDoubleBiFunction<MetricTarget<Label>,LabelMetric.Context> impl)	Construct a new LabelMetric for the supplied metric target, using the supplied function.
	LabelMetric(MetricTarget<Label> tgt, String name, ToDoubleBiFunction<MetricTarget<Label>,LabelMetric.Context> impl)	Construct a new LabelMetric for the supplied metric target, using the supplied function.

Uses of Label in org.tribuo.classification.example

Fields in org.tribuo.classification.example declared as Label

Modifier and Type	Field	Description
static final Label	DemoLabelDataSource.FIRST_CLASS	The first class.
static final Label	DemoLabelDataSource.SECOND_CLASS	The second class.

Fields in org.tribuo.classification.example with type parameters of type Label

Modifier and Type	Field	Description
protected List<Example<Label>>	DemoLabelDataSource.examples

Methods in org.tribuo.classification.example that return types with arguments of type Label

Modifier and Type	Method	Description
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest()	Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest()	Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest(double negate)	Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.binarySparseTrainTest(double negate)	Generates a pair of datasets with sparse features and unknown features in the test data.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.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 classes, {Foo,Bar,Baz,Quux}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.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 classes, {Foo,Bar,Baz,Quux}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.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 classes, {Foo,Bar,Baz,Quux}.
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.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 classes, {Foo,Bar,Baz,Quux}.
static Example<Label>	LabelledDataGenerator.emptyExample()	Generates an example with no features.
protected List<Example<Label>>	CheckerboardDataSource.generate()
protected List<Example<Label>>	ConcentricCirclesDataSource.generate()
protected abstract List<Example<Label>>	DemoLabelDataSource.generate()	Generates the examples using the configured fields.
protected List<Example<Label>>	GaussianLabelDataSource.generate()
protected List<Example<Label>>	InterlockingCrescentsDataSource.generate()
protected List<Example<Label>>	NoisyInterlockingCrescentsDataSource.generate()
static Example<Label>	LabelledDataGenerator.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<Label>>	DemoLabelDataSource.iterator()
static com.oracle.labs.mlrg.olcut.util.Pair<Dataset<Label>,Dataset<Label>>	LabelledDataGenerator.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<Label>,Dataset<Label>>	LabelledDataGenerator.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<Label>,Dataset<Label>>	LabelledDataGenerator.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<Label>,Dataset<Label>>	LabelledDataGenerator.sparseTrainTest(double negate)	Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.

Uses of Label in org.tribuo.classification.sequence

Fields in org.tribuo.classification.sequence with type parameters of type Label

Modifier and Type	Field	Description
SequenceTrainer<Label>	SeqTrainTest.SeqTrainTestOptions.trainer	Name of the trainer in the configuration file.

Methods in org.tribuo.classification.sequence that return types with arguments of type Label

Modifier and Type	Method	Description
Map<MetricID<Label>,Double>	LabelSequenceEvaluation.asMap()
ConfusionMatrix<Label>	LabelSequenceEvaluation.getConfusionMatrix()	Gets the confusion matrix backing this evaluation.
List<Prediction<Label>>	LabelSequenceEvaluation.getPredictions()	Gets the flattened predictions.

Methods in org.tribuo.classification.sequence with parameters of type Label

Modifier and Type	Method	Description
double	LabelSequenceEvaluation.accuracy(Label label)	Gets the accuracy for this label.
double	LabelSequenceEvaluation.confusion(Label predictedLabel, Label trueLabel)	Note: confusion is not stored in the underlying map, so it won't show up in aggregation.
double	LabelSequenceEvaluation.f1(Label label)	The F1 for this label.
double	LabelSequenceEvaluation.fn(Label label)	The false negative count for this label.
double	LabelSequenceEvaluation.fp(Label label)	The false positive count for this label.
double	LabelSequenceEvaluation.precision(Label label)	The precision for this label.
double	LabelSequenceEvaluation.recall(Label label)	The recall for this label.
double	LabelSequenceEvaluation.tn(Label label)	The true negative count for this label.
double	LabelSequenceEvaluation.tp(Label label)	Gets the true positive count for that label.

Method parameters in org.tribuo.classification.sequence with type arguments of type Label

Modifier and Type	Method	Description
protected LabelMetric.Context	LabelSequenceEvaluator.createContext(SequenceModel<Label> model, List<List<Prediction<Label>>> predictions)
protected LabelMetric.Context	LabelSequenceEvaluator.createContext(SequenceModel<Label> model, List<List<Prediction<Label>>> predictions)
protected LabelSequenceEvaluation	LabelSequenceEvaluator.createEvaluation(LabelMetric.Context ctx, Map<MetricID<Label>,Double> results, EvaluationProvenance provenance)
protected Set<LabelMetric>	LabelSequenceEvaluator.createMetrics(SequenceModel<Label> model)
static <SUB extends ConfidencePredictingSequenceModel.Subsequence> List<Double>	ConfidencePredictingSequenceModel.multiplyWeights(List<Prediction<Label>> predictions, List<SUB> subsequences)	A scoring method which multiplies together the per prediction scores.
abstract <SUB extends ConfidencePredictingSequenceModel.Subsequence> List<Double>	ConfidencePredictingSequenceModel.scoreSubsequences(SequenceExample<Label> example, List<Prediction<Label>> predictions, List<SUB> subsequences)	The scoring function for the subsequences.
abstract <SUB extends ConfidencePredictingSequenceModel.Subsequence> List<Double>	ConfidencePredictingSequenceModel.scoreSubsequences(SequenceExample<Label> example, List<Prediction<Label>> predictions, List<SUB> subsequences)	The scoring function for the subsequences.

Constructor parameters in org.tribuo.classification.sequence with type arguments of type Label

Modifier	Constructor	Description
protected	ConfidencePredictingSequenceModel(String name, ModelProvenance description, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Label> labelIDMap)	Constructs a ConfidencePredictingSequenceModel with the supplied parameters.
protected	LabelSequenceEvaluation(Map<MetricID<Label>,Double> results, LabelMetric.Context ctx, EvaluationProvenance provenance)	Constructs a LabelSequenceEvaluation using the supplied parameters.

Uses of Label in org.tribuo.classification.sequence.example

Methods in org.tribuo.classification.sequence.example that return types with arguments of type Label

Modifier and Type	Method	Description
static SequenceExample<Label>	SequenceDataGenerator.generateEmptyExample()	This generates a sequence example with no examples.
static SequenceExample<Label>	SequenceDataGenerator.generateGorillaA()	Generates a sequence example with a mixture of features and three labels "O", "Status" and "Monkey".
static SequenceExample<Label>	SequenceDataGenerator.generateGorillaB()	Generates a sequence example with a mixture of features and three labels "O", "Status" and "Monkey".
static MutableSequenceDataset<Label>	SequenceDataGenerator.generateGorillaDataset(int numCopies)	Generates a simple dataset consisting of numCopies repeats of two sequences.
static SequenceExample<Label>	SequenceDataGenerator.generateInvalidExample()	This generates a sequence example with features that are unused by the training data.
static SequenceExample<Label>	SequenceDataGenerator.generateOtherInvalidExample()	This generates a sequence example where the first example has no features.

Uses of Label in org.tribuo.classification.sequence.viterbi

Methods in org.tribuo.classification.sequence.viterbi that return types with arguments of type Label

Modifier and Type	Method	Description
protected List<Label>	ViterbiModel.getTopLabels(Map<String,Label> distribution)
protected static List<Label>	ViterbiModel.getTopLabels(Map<String,Label> distribution, int stackSize)
List<List<Prediction<Label>>>	ViterbiModel.predict(SequenceDataset<Label> examples)
List<Prediction<Label>>	ViterbiModel.predict(SequenceExample<Label> examples)
SequenceModel<Label>	ViterbiTrainer.train(SequenceDataset<Label> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	The viterbi train method is unique because it delegates to a regular Model train method, but before it does, it adds features derived from preceding labels.

Method parameters in org.tribuo.classification.sequence.viterbi with type arguments of type Label

Modifier and Type	Method	Description
List<Feature>	DefaultFeatureExtractor.extractFeatures(List<Label> previousOutcomes, double value)
List<Feature>	LabelFeatureExtractor.extractFeatures(List<Label> previousOutcomes, double value)	Generates features based on the previously produced labels.
List<Feature>	NoopFeatureExtractor.extractFeatures(List<Label> previousOutcomes, double value)
ViterbiTrainer	ViterbiTrainerOptions.getSequenceTrainer(Trainer<Label> innerTrainer)	Creates a viterbi trainer wrapping the supplied label trainer.
protected List<Label>	ViterbiModel.getTopLabels(Map<String,Label> distribution)
protected static List<Label>	ViterbiModel.getTopLabels(Map<String,Label> distribution, int stackSize)
List<List<Prediction<Label>>>	ViterbiModel.predict(SequenceDataset<Label> examples)
List<Prediction<Label>>	ViterbiModel.predict(SequenceExample<Label> examples)
SequenceModel<Label>	ViterbiTrainer.train(SequenceDataset<Label> dataset, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)	The viterbi train method is unique because it delegates to a regular Model train method, but before it does, it adds features derived from preceding labels.

Constructor parameters in org.tribuo.classification.sequence.viterbi with type arguments of type Label

Modifier	Constructor	Description
	ViterbiTrainer(Trainer<Label> trainer, LabelFeatureExtractor labelFeatureExtractor, int stackSize, ViterbiModel.ScoreAggregation scoreAggregation)	Constructs a ViterbiTrainer wrapping the specified trainer.
	ViterbiTrainer(Trainer<Label> trainer, LabelFeatureExtractor labelFeatureExtractor, ViterbiModel.ScoreAggregation scoreAggregation)	Constructs a ViterbiTrainer wrapping the specified trainer, with an unbounded stack size.