Index (Tribuo 4.3.1 API)

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

a - Variable in class org.tribuo.util.infotheory.impl.CachedTriple: The first element.
ABSOLUTE - Enum constant in enum class org.tribuo.regression.sgd.fm.TrainTest.LossEnum: Uses AbsoluteLoss.
ABSOLUTE - Enum constant in enum class org.tribuo.regression.sgd.TrainTest.LossEnum: Uses AbsoluteLoss.
AbsoluteLoss - Class in org.tribuo.regression.sgd.objectives: Absolute loss (i.e., l1).
AbsoluteLoss() - Constructor for class org.tribuo.regression.sgd.objectives.AbsoluteLoss: Constructs an absolute loss.
AbstractCARTTrainer<T extends Output<T>> - Class in org.tribuo.common.tree: Base class for Trainer's that use an approximation of the CART algorithm to build a decision tree.
AbstractCARTTrainer(int, float, float, float, boolean, long) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer: After calls to this superconstructor subclasses must call postConfig().
AbstractCARTTrainer.AbstractCARTTrainerProvenance - Class in org.tribuo.common.tree: Deprecated.
AbstractCARTTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer.AbstractCARTTrainerProvenance: Deprecated.

Deserialization constructor.
AbstractCARTTrainerProvenance(AbstractCARTTrainer<T>) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer.AbstractCARTTrainerProvenance: Deprecated.

Constructs a provenance for the host AbstractCARTTrainer.
AbstractEvaluator<T extends Output<T>,C extends MetricContext<T>,E extends Evaluation<T>,M extends EvaluationMetric<T,C>> - Class in org.tribuo.evaluation: Base class for evaluators.
AbstractEvaluator() - Constructor for class org.tribuo.evaluation.AbstractEvaluator
AbstractFMModel<T extends Output<T>> - Class in org.tribuo.common.sgd: A quadratic factorization machine model trained using SGD.
AbstractFMModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, FMParameters, boolean) - Constructor for class org.tribuo.common.sgd.AbstractFMModel: Constructs a factorization machine model trained via SGD.
AbstractFMTrainer<T extends Output<T>,U,V extends AbstractFMModel<T>> - Class in org.tribuo.common.sgd: A trainer for a quadratic factorization machine model which uses SGD.
AbstractFMTrainer() - Constructor for class org.tribuo.common.sgd.AbstractFMTrainer: For olcut.
AbstractFMTrainer(StochasticGradientOptimiser, int, int, int, long, int, double) - Constructor for class org.tribuo.common.sgd.AbstractFMTrainer: Constructs an SGD trainer for a factorization machine.
AbstractLinearSGDModel<T extends Output<T>> - Class in org.tribuo.common.sgd: A linear model trained using SGD.
AbstractLinearSGDModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, LinearParameters, boolean) - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDModel: Constructs a linear model trained via SGD.
AbstractLinearSGDTrainer<T extends Output<T>,U,V extends AbstractLinearSGDModel<T>> - Class in org.tribuo.common.sgd: A trainer for a linear model which uses SGD.
AbstractLinearSGDTrainer() - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDTrainer: For olcut.
AbstractLinearSGDTrainer(StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDTrainer: Constructs an SGD trainer for a linear model.
AbstractSequenceEvaluator<T extends Output<T>,C extends MetricContext<T>,E extends SequenceEvaluation<T>,M extends EvaluationMetric<T,C>> - Class in org.tribuo.sequence: Base class for sequence evaluators.
AbstractSequenceEvaluator() - Constructor for class org.tribuo.sequence.AbstractSequenceEvaluator
AbstractSGDModel<T extends Output<T>> - Class in org.tribuo.common.sgd: A model trained using SGD.
AbstractSGDModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, FeedForwardParameters, boolean, boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDModel: Constructs a linear model trained via SGD.
AbstractSGDModel.PredAndActive - Class in org.tribuo.common.sgd: A nominal tuple used to capture the prediction and the number of active features used by the model.
AbstractSGDTrainer<T extends Output<T>,U,V extends Model<T>,X extends FeedForwardParameters> - Class in org.tribuo.common.sgd: A trainer for a model which uses SGD.
AbstractSGDTrainer(boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDTrainer: Base constructor called by subclass no-args constructors used by OLCUT.
AbstractSGDTrainer(StochasticGradientOptimiser, int, int, int, long, boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDTrainer: Constructs an SGD trainer.
AbstractTrainingNode<T extends Output<T>> - Class in org.tribuo.common.tree: Base class for decision tree nodes used at training time.
AbstractTrainingNode(int, int, AbstractTrainingNode.LeafDeterminer) - Constructor for class org.tribuo.common.tree.AbstractTrainingNode: Builds an abstract training node.
AbstractTrainingNode.LeafDeterminer - Class in org.tribuo.common.tree: Contains parameters needed to determine whether a node is a leaf.
accuracy() - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: The overall accuracy of the evaluation.
accuracy() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The accuracy.
accuracy(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: The per label accuracy of the evaluation.
accuracy(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the accuracy for this label.
accuracy(EvaluationMetric.Average, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the accuracy using the specified average type and confusion matrix.
accuracy(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the accuracy given this confusion matrix.
accuracy(T, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates a per label accuracy given this confusion matrix.
ACCURACY - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics: The accuracy.
ADABOOST - Enum constant in enum class org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Creates an AdaBoostTrainer.
AdaBoostTrainer - Class in org.tribuo.classification.ensemble: Implements Adaboost.SAMME one of the more popular algorithms for multiclass boosting.
AdaBoostTrainer(Trainer<Label>, int) - Constructor for class org.tribuo.classification.ensemble.AdaBoostTrainer: Constructs an adaboost trainer using the supplied weak learner trainer and the specified number of boosting rounds.
AdaBoostTrainer(Trainer<Label>, int, long) - Constructor for class org.tribuo.classification.ensemble.AdaBoostTrainer: Constructs an adaboost trainer using the supplied weak learner trainer, the specified number of boosting rounds and the supplied seed.
AdaDelta - Class in org.tribuo.math.optimisers: An implementation of the AdaDelta gradient optimiser.
AdaDelta() - Constructor for class org.tribuo.math.optimisers.AdaDelta: Sets rho to 0.95 and epsilon to 1e-6.
AdaDelta(double) - Constructor for class org.tribuo.math.optimisers.AdaDelta: Keeps rho at 0.95, passes through epsilon.
AdaDelta(double, double) - Constructor for class org.tribuo.math.optimisers.AdaDelta: It's recommended to keep rho at 0.95.
ADADELTA - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser: The AdaDelta optimiser.
ADADELTA - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType: The AdaDelta optimiser.
AdaGrad - Class in org.tribuo.math.optimisers: An implementation of the AdaGrad gradient optimiser.
AdaGrad(double) - Constructor for class org.tribuo.math.optimisers.AdaGrad: Creates an AdaGrad optimiser using the specified initial learning rate.
AdaGrad(double, double) - Constructor for class org.tribuo.math.optimisers.AdaGrad: Creates an AdaGrad optimiser using the specified learning rate and epsilon.
AdaGrad(double, double, double) - Constructor for class org.tribuo.math.optimisers.AdaGrad: Creates an AdaGrad optimiser using the specified learning rate, epsilon and initial accumulator value.
ADAGRAD - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser: The AdaGrad optimiser.
ADAGRAD - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType: The AdaGrad optimiser.
ADAGRADDA - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser: The AdaGrad Dual Averaging optimiser.
AdaGradRDA - Class in org.tribuo.math.optimisers: An implementation of the AdaGrad gradient optimiser with regularized dual averaging.
AdaGradRDA(double, double) - Constructor for class org.tribuo.math.optimisers.AdaGradRDA: Creates an AdaGradRDA optimiser with the specified parameter values.
AdaGradRDA(double, double, double, double, int) - Constructor for class org.tribuo.math.optimisers.AdaGradRDA: Creates an AdaGradRDA optimiser with the specified parameter values.
ADAGRADRDA - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType: The AdaGrad Regularised Dual Averaging optimiser.
Adam - Class in org.tribuo.math.optimisers: An implementation of the Adam gradient optimiser.
Adam() - Constructor for class org.tribuo.math.optimisers.Adam: Sets initialLearningRate to 0.001, betaOne to 0.9, betaTwo to 0.999, epsilon to 1e-6.
Adam(double, double) - Constructor for class org.tribuo.math.optimisers.Adam: Sets betaOne to 0.9 and betaTwo to 0.999
Adam(double, double, double, double) - Constructor for class org.tribuo.math.optimisers.Adam: It's highly recommended not to modify these parameters, use one of the other constructors.
ADAM - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser: The Adam optimiser.
ADAM - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType: The Adam optimiser.
ADAMAX - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser: The Adamax optimiser.
add - Enum constant in enum class org.tribuo.transform.transformations.SimpleTransform.Operation: Adds the specified constant.
add() - Static method in interface org.tribuo.util.Merger: A merger which adds the elements.
add(double) - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that adds the operand to each value.
add(int) - Method in class org.tribuo.regression.rtree.impl.InvertedFeature: Adds an index where the feature value occurs.
add(int, double) - Method in class org.tribuo.math.la.DenseVector
add(int, double) - Method in interface org.tribuo.math.la.SGDVector: Adds value to the element at index.
add(int, double) - Method in class org.tribuo.math.la.SparseVector
add(int, int, double) - Method in class org.tribuo.math.la.DenseMatrix
add(int, int, double) - Method in class org.tribuo.math.la.DenseSparseMatrix
add(int, int, double) - Method in interface org.tribuo.math.la.Matrix: Adds the argument value to the value at the supplied index.
add(int, Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
add(String) - Method in class org.tribuo.impl.BinaryFeaturesExample: Adds a single feature with a value of 1.
add(String, double) - Method in class org.tribuo.impl.ArrayExample: Adds a single feature.
add(String, double) - Method in class org.tribuo.MutableFeatureMap: Adds an occurrence of a feature with a given name.
add(Example<T>) - Method in class org.tribuo.ImmutableDataset: Adds an Example to the dataset, which will remove features with unknown names.
add(Example<T>) - Method in class org.tribuo.MutableDataset: Adds an example to the dataset, which observes the output and each feature value.
add(Example<T>, Merger) - Method in class org.tribuo.ImmutableDataset: Adds a Example to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids (merging duplicate ids).
add(Feature) - Method in class org.tribuo.Example: Adds a feature.
add(Feature) - Method in class org.tribuo.impl.ArrayExample
add(Feature) - Method in class org.tribuo.impl.BinaryFeaturesExample: Adds a feature to this example.
add(Feature) - Method in class org.tribuo.impl.IndexedArrayExample
add(Feature) - Method in class org.tribuo.impl.ListExample
add(SGDVector) - Method in class org.tribuo.math.la.DenseVector: Adds other to this vector, producing a new DenseVector.
add(SGDVector) - Method in interface org.tribuo.math.la.SGDVector: Adds other to this vector, producing a new SGDVector.
add(SGDVector) - Method in class org.tribuo.math.la.SparseVector: Adds other to this vector, producing a new SGDVector.
add(SequenceExample<T>) - Method in class org.tribuo.sequence.ImmutableSequenceDataset: Adds a SequenceExample to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids.
add(SequenceExample<T>) - Method in class org.tribuo.sequence.MutableSequenceDataset: Adds a SequenceExample to this dataset.
add(SequenceExample<T>, Merger) - Method in class org.tribuo.sequence.ImmutableSequenceDataset: Adds a SequenceExample to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids.
add(Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
ADD - Enum constant in enum class org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation: Adds the scores.
ADD - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators: Element-wise addition with broadcasting.
addAcrossDim1(int[], double) - Method in class org.tribuo.math.la.DenseMatrix: Adds the specified value to the specified elements across dimension 1.
addAcrossDim2(int[], double) - Method in class org.tribuo.math.la.DenseMatrix: Adds the specified value to the specified elements across dimension 2.
addAll(int, Collection<? extends Row<T>>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
addAll(Collection<? extends Example<T>>) - Method in class org.tribuo.MutableDataset: Adds all the Examples in the supplied collection to this dataset.
addAll(Collection<? extends Feature>) - Method in class org.tribuo.Example: Adds a collection of features.
addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.ArrayExample
addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.BinaryFeaturesExample: Adds a collection of features to this example.
addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.IndexedArrayExample
addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.ListExample
addAll(Collection<? extends Row<T>>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
addAll(Collection<SequenceExample<T>>) - Method in class org.tribuo.sequence.MutableSequenceDataset: Adds all the SequenceExamples in the supplied collection to this dataset.
addBias - Variable in class org.tribuo.common.sgd.AbstractSGDModel
addBias - Variable in class org.tribuo.common.sgd.AbstractSGDTrainer
addChar() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer: Add a character to the buffer that we're building for a token.
addConsideration(CommandInterpreter, String) - Method in class org.tribuo.interop.modelcard.ModelCardCLI: Adds a consideration for the model documented by the ModelCard to its list of considerations.
addExample(Example<T>) - Method in class org.tribuo.sequence.SequenceExample: Adds an Example to this sequence.
addFactor(CommandInterpreter, String) - Method in class org.tribuo.interop.modelcard.ModelCardCLI: Adds a factor for the model documented by the ModelCard to its list of factors.
addFeatureProcessor(FeatureProcessor) - Method in class org.tribuo.data.columnar.RowProcessor.Builder: Add a single feature processor to the builder.
addFieldProcessor(FieldProcessor) - Method in class org.tribuo.data.columnar.RowProcessor.Builder: Add a single field processor to the builder.
addMetadataExtractor(FieldExtractor<?>) - Method in class org.tribuo.data.columnar.RowProcessor.Builder: Add a single metadata extractor to the builder.
addOutOfScopeUse(CommandInterpreter, String) - Method in class org.tribuo.interop.modelcard.ModelCardCLI: Adds an out-of-scope use of the model documented by the ModelCard to its list of out-of-scope uses.
addPreProcessingStep(CommandInterpreter, String) - Method in class org.tribuo.interop.modelcard.ModelCardCLI: Adds a pre-processing step for the model documented by the ModelCard to its list of pre-processing steps.
addRegexMappingProcessor(String, FieldProcessor) - Method in class org.tribuo.data.columnar.RowProcessor.Builder: Add a single regex FieldProcessor mapping to the builder.
addResource(CommandInterpreter, String) - Method in class org.tribuo.interop.modelcard.ModelCardCLI: Adds a resource for the model documented by the ModelCard to its list of resources.
ADJUSTED_MI - Enum constant in enum class org.tribuo.clustering.evaluation.ClusteringMetrics: The normalized mutual information adjusted for chance.
adjustedMI() - Method in interface org.tribuo.clustering.evaluation.ClusteringEvaluation: Measures the adjusted normalized mutual information between the predicted ids and the supplied ids.
adjustedMI(List<Integer>, List<Integer>) - Static method in enum class org.tribuo.clustering.evaluation.ClusteringMetrics: Calculates the adjusted normalized mutual information between two clusterings.
adjustedMI(ClusteringMetric.Context) - Static method in enum class org.tribuo.clustering.evaluation.ClusteringMetrics: Calculates the adjusted normalized mutual information between two clusterings.
advance() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.NonTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.SplitFunctionTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.wordpiece.WordpieceTokenizer
advance() - Method in interface org.tribuo.util.tokens.Tokenizer: Advances the tokenizer to the next token.
advance() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
aggregate(List<Feature>) - Method in interface org.tribuo.data.text.FeatureAggregator: Aggregates feature values with the same names.
aggregate(List<Feature>) - Method in class org.tribuo.data.text.impl.AverageAggregator
aggregate(List<Feature>) - Method in class org.tribuo.data.text.impl.SumAggregator
aggregate(List<Feature>) - Method in class org.tribuo.data.text.impl.UniqueAggregator
AggregateConfigurableDataSource<T extends Output<T>> - Class in org.tribuo.datasource: Aggregates multiple ConfigurableDataSources, uses AggregateDataSource.IterationOrder to control the iteration order.
AggregateConfigurableDataSource(List<ConfigurableDataSource<T>>) - Constructor for class org.tribuo.datasource.AggregateConfigurableDataSource: Creates an aggregate data source which will iterate the provided sources in the order of the list (i.e., using AggregateDataSource.IterationOrder.SEQUENTIAL.
AggregateConfigurableDataSource(List<ConfigurableDataSource<T>>, AggregateDataSource.IterationOrder) - Constructor for class org.tribuo.datasource.AggregateConfigurableDataSource: Creates an aggregate data source using the supplied sources and iteration order.
AggregateConfigurableDataSource.AggregateConfigurableDataSourceProvenance - Class in org.tribuo.datasource: Provenance for the AggregateConfigurableDataSource.
AggregateConfigurableDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.datasource.AggregateConfigurableDataSource.AggregateConfigurableDataSourceProvenance: Deserialization constructor.
AggregateDataSource<T extends Output<T>> - Class in org.tribuo.datasource: Aggregates multiple DataSources, uses AggregateDataSource.IterationOrder to control the iteration order.
AggregateDataSource(List<DataSource<T>>) - Constructor for class org.tribuo.datasource.AggregateDataSource: Creates an aggregate data source which will iterate the provided sources in the order of the list (i.e., using AggregateDataSource.IterationOrder.SEQUENTIAL.
AggregateDataSource(List<DataSource<T>>, AggregateDataSource.IterationOrder) - Constructor for class org.tribuo.datasource.AggregateDataSource: Creates an aggregate data source using the supplied sources and iteration order.
AggregateDataSource.AggregateDataSourceProvenance - Class in org.tribuo.datasource: Provenance for the AggregateDataSource.
AggregateDataSource.IterationOrder - Enum Class in org.tribuo.datasource: Specifies the iteration order of the inner sources.
AggregateDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance: Deserialization constructor.
algorithm - Variable in class org.tribuo.classification.experiments.AllTrainerOptions: Type of learner (or base learner).
algorithm - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions: Type of SVR.
algorithm - Variable in class org.tribuo.regression.slm.TrainTest.SLMOptions: Choose the training algorithm (stepwise forward selection or least angle regression).
ALL - Enum constant in enum class org.tribuo.json.StripProvenance.ProvenanceTypes: Selects all provenance stored in the model.
ALL_OUTPUTS - Static variable in class org.tribuo.Model: Used in getTopFeatures when the Model doesn't support per output feature lists.
AllClassificationOptions() - Constructor for class org.tribuo.classification.experiments.TrainTest.AllClassificationOptions
allProvenances() - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance
allProvenances() - Method in class org.tribuo.dataset.MinimumCardinalityDataset.MinimumCardinalityDatasetProvenance
allProvenances() - Method in class org.tribuo.dataset.SelectedFeatureDataset.SelectedFeatureDatasetProvenance
allProvenances() - Method in class org.tribuo.provenance.DatasetProvenance: Returns a list of all the provenances.
allProvenances() - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset.MinimumCardinalitySequenceDatasetProvenance
AllTrainerOptions - Class in org.tribuo.classification.experiments: Aggregates all the classification algorithms.
AllTrainerOptions() - Constructor for class org.tribuo.classification.experiments.AllTrainerOptions
AllTrainerOptions.AlgorithmType - Enum Class in org.tribuo.classification.experiments: Types of algorithms supported.
alpha - Variable in class org.tribuo.regression.slm.TrainTest.SLMOptions: Regularisation strength in the Elastic Net.
alpha - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions: L1 regularization term for weights (default 0).
alpha - Variable in class org.tribuo.regression.xgboost.XGBoostOptions: L1 regularization term for weights (default 0).
alphas - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults: The alpha values array from forward propagation.
annotateGraph(Graph, Session) - Static method in class org.tribuo.interop.tensorflow.TensorFlowUtil: Annotates a graph with an extra placeholder and assign operation for each VariableV2.
ANOMALOUS - Enum constant in enum class org.tribuo.anomaly.Event.EventType: An anomalous event, with id 1.
ANOMALOUS_EVENT - Static variable in class org.tribuo.anomaly.AnomalyFactory: The anomalous event.
ANOMALY_DETECTION - Enum constant in enum class org.tribuo.interop.oci.OCIUtil.OCIModelType: Anomaly detection, maps to Tribuo's Event.
anomalyCount - Variable in class org.tribuo.anomaly.AnomalyInfo: The number of anomalous events observed.
AnomalyDataGenerator - Class in org.tribuo.anomaly.example: Generates three example train and test datasets, used for unit testing.
AnomalyDataGenerator() - Constructor for class org.tribuo.anomaly.example.AnomalyDataGenerator
AnomalyEvaluation - Interface in org.tribuo.anomaly.evaluation: An Evaluation for anomaly detection Events.
AnomalyEvaluator - Class in org.tribuo.anomaly.evaluation: An Evaluator for anomaly detection Events.
AnomalyEvaluator() - Constructor for class org.tribuo.anomaly.evaluation.AnomalyEvaluator
AnomalyFactory - Class in org.tribuo.anomaly: A factory for generating events.
AnomalyFactory() - Constructor for class org.tribuo.anomaly.AnomalyFactory: Create an AnomalyFactory.
AnomalyFactory.AnomalyFactoryProvenance - Class in org.tribuo.anomaly: Provenance for AnomalyFactory.
AnomalyFactoryProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.anomaly.AnomalyFactory.AnomalyFactoryProvenance: Constructs an anomaly factory provenance from the marshalled form.
AnomalyInfo - Class in org.tribuo.anomaly: The base class for tracking anomalous events.
AnomalyInfo() - Constructor for class org.tribuo.anomaly.AnomalyInfo: Constructs a new empty anomaly info.
AnomalyInfo(long, long, int) - Constructor for class org.tribuo.anomaly.AnomalyInfo: Deserialization constructor.
AnomalyInfo(AnomalyInfo) - Constructor for class org.tribuo.anomaly.AnomalyInfo: Copies the supplied anomaly info.
AnomalyMetric - Class in org.tribuo.anomaly.evaluation: A metric for evaluating anomaly detection problems.
AnomalyMetric(MetricTarget<Event>, String, ToDoubleBiFunction<MetricTarget<Event>, AnomalyMetric.Context>) - Constructor for class org.tribuo.anomaly.evaluation.AnomalyMetric: Creates an anomaly detection metric, with a specific name, using the supplied evaluation function.
AnomalyMetrics - Enum Class in org.tribuo.anomaly.evaluation: Default metrics for evaluating anomaly detection.
apply(int, int, CharSequence) - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer.SplitCharactersSplitterFunction
apply(int, int, CharSequence) - Method in interface org.tribuo.util.tokens.impl.SplitFunctionTokenizer.SplitFunction: Applies the split function.
apply(E) - Method in interface org.tribuo.evaluation.EvaluationRenderer: Convert the evaluation to a string.
apply(ONNXOperator) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperator, List, String), using this ONNXRef as the argument to inputs.
apply(ONNXOperator, String) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperator, List, String), using this ONNXRef as the argument to inputs.
apply(ONNXOperator, List<String>, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperator, List, List, Map), using this ONNXRef as the argument to inputs.
apply(ONNXOperator, List<ONNXRef<?>>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperator, List, String, Map), using this ONNXRef as the first argument to inputs, with otherInputs append as subsequent arguments.
apply(ONNXOperator, List<ONNXRef<?>>, String) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperator, List, String, Map), using this ONNXRef as the argument to inputs, with otherInputs append as subsequent arguments.
apply(ONNXOperator, List<ONNXRef<?>>, List<String>, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperator, List, List, Map), using this ONNXRef as the first argument to inputs, with otherInputs append as subsequent arguments.
apply(ONNXOperator, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperator, List, String, Map), using this ONNXRef as the argument to inputs.
apply(ONNXOperator, ONNXRef<?>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperator, List, String, Map), passing this ONNXRef and other as a length 2 list to inputs.
apply(ONNXOperator, ONNXRef<?>, String) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperator, List, String), passing this ONNXRef and other as a length 2 list to inputs.
apply(ONNXOperator, ONNXRef<?>, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperator, List, String, Map), passing this ONNXRef and other as a length 2 list to inputs.
applyCase(String) - Method in enum class org.tribuo.data.text.impl.CasingPreprocessor.CasingOperation: Apply the appropriate casing operation.
applyOptimiser(Graph, Operand<T>, Map<String, Float>) - Method in enum class org.tribuo.interop.tensorflow.GradientOptimiser: Applies the optimiser to the graph and returns the optimiser step operation.
applyTransformerList(double, List<Transformer>) - Static method in class org.tribuo.transform.TransformerMap: Applies a List of Transformers to the supplied double value, returning the transformed value.
APPROX - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.TreeMethod: Approximate greedy algorithm, using a quantile sketch of the data and a gradient histogram.
approximateParentNode(SGDVector) - Method in class org.tribuo.math.neighbour.kdtree.KDTree: This makes a fast approximation of the provided point's parent node, if it were being inserted into the tree.
ARCH_STRING - Static variable in class org.tribuo.provenance.ModelProvenance: The name of the architecture name field.
archString - Variable in class org.tribuo.provenance.ModelProvenance: The system CPU architecture string.
argmax(List<R>, Function<R, Double>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Calculates the argmax of a metric across the supplied evaluations.
argmax(List<T>) - Static method in class org.tribuo.util.Util: Find the index of the maximum value in a list.
argmax(EvaluationMetric<T, C>, List<? extends Model<T>>, Dataset<T>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Calculates the argmax of a metric across the supplied models (i.e., the index of the model which performed the best).
argmax(EvaluationMetric<T, C>, Model<T>, List<? extends Dataset<T>>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Calculates the argmax of a metric across the supplied datasets.
argmin(List<T>) - Static method in class org.tribuo.util.Util: Find the index of the minimum value in a list.
array - Variable in class org.tribuo.common.tree.impl.IntArrayContainer: The array of ints.
array(String, double[]) - Method in class org.tribuo.util.onnx.ONNXContext: Creates a float tensor for this ONNXContext, populated according to parameters.
array(String, double[], boolean) - Method in class org.tribuo.util.onnx.ONNXContext: Creates a tensor for this ONNXContext, populated according to parameters.
array(String, float[]) - Method in class org.tribuo.util.onnx.ONNXContext: Creates a float tensor for this ONNXContext, populated according to parameters.
array(String, int[]) - Method in class org.tribuo.util.onnx.ONNXContext: Creates an int tensor for this ONNXContext, populated according to parameters.
array(String, long[]) - Method in class org.tribuo.util.onnx.ONNXContext: Creates a long tensor for this ONNXContext, populated according to parameters.
ARRAY_FEATURE_EXTRACTOR - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators: Array feature extractor, selects the indices specified by the second tensor from the last dimension of the first tensor.
arrayBuilder(ONNXContext, String, double[]) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a TensorProto containing the array.
arrayBuilder(ONNXContext, String, double[], boolean) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a TensorProto containing the array.
arrayBuilder(ONNXContext, String, float[]) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a TensorProto containing the array.
arrayBuilder(ONNXContext, String, int[]) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a TensorProto containing the array.
arrayBuilder(ONNXContext, String, long[]) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a TensorProto containing the array.
ArrayExample<T extends Output<T>> - Class in org.tribuo.impl: An Example backed by two arrays, one of String and one of double.
ArrayExample(Example<T>) - Constructor for class org.tribuo.impl.ArrayExample: Copy constructor.
ArrayExample(T) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output.
ArrayExample(T, float) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output and a weight.
ArrayExample(T, float, int) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output and a weight, with an initial size for the feature arrays.
ArrayExample(T, float, Map<String, Object>) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output, a weight and the metadata.
ArrayExample(T, String[], double[]) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output, an array of names and an array of values.
ArrayExample(T, List<? extends Feature>) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output and a list of features.
ArrayExample(T, Map<String, Object>) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output and the metadata.
ArrayExample(T, Example, float) - Constructor for class org.tribuo.impl.ArrayExample: Clones an example's features, but uses the supplied output and weight.
asMap() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
asMap() - Method in interface org.tribuo.evaluation.Evaluation: Get a map of all the metrics stored in this evaluation.
asMap() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
asMap() - Method in interface org.tribuo.sequence.SequenceEvaluation: Get a map of all the metrics stored in this evaluation.
ASSIGN_OP - Static variable in class org.tribuo.interop.tensorflow.TensorFlowUtil: The name of the assignment op.
ASSIGN_PLACEHOLDER - Static variable in class org.tribuo.interop.tensorflow.TensorFlowUtil: The name given to the assignment operation from the placeholders.
assignTo(Ret) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.assignTo(ONNXRef, ONNXRef), using this ONNXRef as the argument to input.
assignTo(RHS, LHS) - Method in class org.tribuo.util.onnx.ONNXContext: Creates an ONNXOperators.IDENTITY node connecting input to output, effectively permitting assignment of values.
ATTENTION_MASK - Static variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor: Input name for the attention mask.
attributes - Variable in enum class org.tribuo.util.onnx.ONNXOperators: The operator attributes.
auc(double[], double[]) - Static method in class org.tribuo.util.Util: Calculates the area under the curve, bounded below by the x axis.
AUCROC - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics: The area under the receiver-operator curve (ROC).
AUCROC(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Area under the ROC curve.
AUCROC(Label, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics: Area under the ROC curve.
AUCROC(MetricTarget<Label>, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics: Area under the ROC curve.
AUTO - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.TreeMethod: XGBoost chooses between XGBoostTrainer.TreeMethod.EXACT and XGBoostTrainer.TreeMethod.APPROX depending on dataset size.
AverageAggregator - Class in org.tribuo.data.text.impl: A feature aggregator that averages feature values across a feature list.
AverageAggregator() - Constructor for class org.tribuo.data.text.impl.AverageAggregator
averageAUCROC(boolean) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Area under the ROC curve averaged across labels.
AVERAGED_PRECISION - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics: The averaged precision.
averagedExplainedVariance() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: The average explained variance across all dimensions.
averagedPrecision(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil: 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.
averagedPrecision(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: 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.
averagedPrecision(Label, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics
averagedPrecision(MetricTarget<Label>, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics
averageMAE() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: The average Mean Absolute Error across all dimensions.
averageR2() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: The average R2 across all dimensions.
averageRMSE() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: The average RMSE across all dimensions.
AveragingCombiner - Class in org.tribuo.regression.ensemble: A combiner which performs a weighted or unweighted average of the predicted regressors independently across the output dimensions.
AveragingCombiner() - Constructor for class org.tribuo.regression.ensemble.AveragingCombiner: Constructs an averaging combiner.

B

b - Variable in class org.tribuo.util.infotheory.impl.CachedTriple: The second element.
backRef - Variable in class org.tribuo.util.onnx.ONNXRef: Protobuf reference.
BAGGING - Enum constant in enum class org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Creates a BaggingTrainer.
BaggingTrainer<T extends Output<T>> - Class in org.tribuo.ensemble: A Trainer that wraps another trainer and produces a bagged ensemble.
BaggingTrainer() - Constructor for class org.tribuo.ensemble.BaggingTrainer: For the configuration system.
BaggingTrainer(Trainer<T>, EnsembleCombiner<T>, int) - Constructor for class org.tribuo.ensemble.BaggingTrainer: Constructs a bagging trainer with the supplied parameters using Trainer.DEFAULT_SEED as the RNG seed.
BaggingTrainer(Trainer<T>, EnsembleCombiner<T>, int, long) - Constructor for class org.tribuo.ensemble.BaggingTrainer: Constructs a bagging trainer with the supplied parameters.
BALANCED_ERROR_RATE - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics: The balanced error rate, i.e., the mean of the per class recalls.
BALANCED_ERROR_RATE - Enum constant in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics: The balanced error rate, i.e., the mean of the per class recalls.
balancedErrorRate() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the balanced error rate, i.e., the mean of the per label recalls.
balancedErrorRate() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the balanced error rate.
balancedErrorRate() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
balancedErrorRate(ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the balanced error rate, i.e., the mean of the recalls.
bandwidth - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions: Model bandwidth in MBps.
bandwidth - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelDeploymentConfig: The bandwidth for the load balancer in MBps.
BasicPipeline - Class in org.tribuo.data.text.impl: An example implementation of TextPipeline.
BasicPipeline(Tokenizer, int) - Constructor for class org.tribuo.data.text.impl.BasicPipeline: Constructs a basic text pipeline which tokenizes the input and generates word n-gram features in the range 1 to ngram.
batchSize - Variable in class org.tribuo.interop.tensorflow.TensorFlowModel
batchSize - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions: Minibatch size.
begin - Variable in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence: The subsequence start index.
begin - Variable in class org.tribuo.classification.sgd.crf.Chunk: The starting point of this chunk.
beliefPropagation(ChainHelper.ChainCliqueValues) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper: Runs belief propagation on a linear chain CRF.
bert - Variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor.BERTFeatureExtractorOptions: BERTFeatureExtractor instance
BERTFeatureExtractor<T extends Output<T>> - Class in org.tribuo.interop.onnx.extractors: Builds examples and sequence examples using features from BERT.
BERTFeatureExtractor(OutputFactory<T>, Path, Path) - Constructor for class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor: Constructs a BERTFeatureExtractor.
BERTFeatureExtractor(OutputFactory<T>, Path, Path, BERTFeatureExtractor.OutputPooling, int, boolean) - Constructor for class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor: Constructs a BERTFeatureExtractor.
BERTFeatureExtractor.BERTFeatureExtractorOptions - Class in org.tribuo.interop.onnx.extractors: CLI options for running BERT.
BERTFeatureExtractor.OutputPooling - Enum Class in org.tribuo.interop.onnx.extractors: The type of output pooling to perform.
BERTFeatureExtractorOptions() - Constructor for class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor.BERTFeatureExtractorOptions
betas - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults: The beta values array from backward propagation.
BIAS_FEATURE - Static variable in class org.tribuo.Model: Used to denote the bias feature in a linear model.
binarise - Enum constant in enum class org.tribuo.transform.transformations.SimpleTransform.Operation: Binarises the output around 1.0.
binarise() - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that sets negative and zero values to zero and positive values to one.
BINARISED_CATEGORICAL - Enum constant in enum class org.tribuo.data.columnar.FieldProcessor.GeneratedFeatureType: Categoricals binarised into separate features.
BINARY_CLASSIFICATION - Enum constant in enum class org.tribuo.interop.oci.OCIUtil.OCIModelType: Binary classification.
binaryAUCROC(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil: Calculates the area under the receiver operator characteristic curve, i.e., the AUC of the ROC curve.
BinaryCrossEntropy - Class in org.tribuo.multilabel.sgd.objectives: A multilabel version of binary cross entropy loss which expects logits.
BinaryCrossEntropy() - Constructor for class org.tribuo.multilabel.sgd.objectives.BinaryCrossEntropy: Constructs a BinaryCrossEntropy objective.
BinaryFeaturesExample<T extends Output<T>> - Class in org.tribuo.impl: An Example backed by a single array of feature names.
BinaryFeaturesExample(Example<T>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Copy constructor.
BinaryFeaturesExample(T) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output.
BinaryFeaturesExample(T, float) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and a weight.
BinaryFeaturesExample(T, float, int) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and a weight, with an initial size for the feature arrays.
BinaryFeaturesExample(T, float, Map<String, Object>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output, a weight and the metadata.
BinaryFeaturesExample(T, String[]) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and an array of names.
BinaryFeaturesExample(T, List<? extends Feature>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and a list of features.
BinaryFeaturesExample(T, Map<String, Object>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and the metadata.
BinaryFeaturesExample(T, Example, float) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Clones an example's features, but uses the supplied output and weight.
BinaryResponseProcessor<T extends Output<T>> - Class in org.tribuo.data.columnar.processors.response: A ResponseProcessor that takes a single value of the field as the positive class and all other values as the negative class.
BinaryResponseProcessor(String, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
BinaryResponseProcessor(List<String>, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
BinaryResponseProcessor(List<String>, List<String>, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
BinaryResponseProcessor(List<String>, List<String>, OutputFactory<T>, boolean) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
BinaryResponseProcessor(List<String>, List<String>, OutputFactory<T>, String, String, boolean) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
binarySearch(List<? extends Comparable<? super T>>, T) - Static method in class org.tribuo.util.Util: A binary search function.
binarySearch(List<? extends Comparable<? super T>>, T, int, int) - Static method in class org.tribuo.util.Util: A binary search function.
binarySearch(List<? extends T>, int, ToIntFunction<T>) - Static method in class org.tribuo.util.Util: A binary search function.
binarySparseTrainTest() - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a pair of datasets with sparse features and unknown features in the test data.
binarySparseTrainTest(double) - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a pair of datasets with sparse features and unknown features in the test data.
BinningTransformation - Class in org.tribuo.transform.transformations: A Transformation which bins values.
BinningTransformation.BinningTransformationProvenance - Class in org.tribuo.transform.transformations: Provenance for BinningTransformation.
BinningTransformation.BinningTransformer - Class in org.tribuo.transform.transformations: The implementation of a Transformer which splits the input into n bins.
BinningTransformation.BinningType - Enum Class in org.tribuo.transform.transformations: The allowed binning types.
BinningTransformationProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.transform.transformations.BinningTransformation.BinningTransformationProvenance: Deserialization constructor.
BinningTransformer(BinningTransformation.BinningType, double[], double[]) - Constructor for class org.tribuo.transform.transformations.BinningTransformation.BinningTransformer: Constructs a binning transformer with the supplied bin widths and bin values.
BREAK_ITERATOR - Enum constant in enum class org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType: Creates a BreakIteratorTokenizer.
breakIteratorOptions - Variable in class org.tribuo.util.tokens.options.CoreTokenizerOptions: Options for the break iterator tokenizer.
BreakIteratorTokenizer - Class in org.tribuo.util.tokens.impl: A tokenizer wrapping a BreakIterator instance.
BreakIteratorTokenizer(Locale) - Constructor for class org.tribuo.util.tokens.impl.BreakIteratorTokenizer: Constructs a BreakIteratorTokenizer using the specified locale.
BreakIteratorTokenizerOptions - Class in org.tribuo.util.tokens.options: CLI options for a BreakIteratorTokenizer.
BreakIteratorTokenizerOptions() - Constructor for class org.tribuo.util.tokens.options.BreakIteratorTokenizerOptions
broadcastIntersectAndAddInPlace(SGDVector, boolean) - Method in class org.tribuo.math.la.DenseMatrix: Broadcasts the input vector and adds it to each row/column of the matrix.
BRUTE_FORCE - Enum constant in enum class org.tribuo.math.neighbour.NeighboursQueryFactoryType: A factory which emits nearest neighbour query objects using a brute-force search.
buff - Variable in class org.tribuo.util.tokens.universal.Range: The character buffer.
build() - Method in class org.tribuo.interop.modelcard.UsageDetailsBuilder: Builds an instance of UsageDetails using the recorded field values or their default values.
build(Object) - Method in class org.tribuo.util.onnx.ONNXAttribute: Builds the attribute proto using the supplied value.
build(ResponseProcessor<T>) - Method in class org.tribuo.data.columnar.RowProcessor.Builder: Construct the RowProcessor represented by this builder's state.
build(ONNXContext, String[], String) - Method in interface org.tribuo.util.onnx.ONNXOperator: Builds this node based on the supplied inputs and output.
build(ONNXContext, String[], String[]) - Method in interface org.tribuo.util.onnx.ONNXOperator: Builds this node based on the supplied inputs and outputs.
build(ONNXContext, String[], String[], Map<String, Object>) - Method in interface org.tribuo.util.onnx.ONNXOperator: Builds this node based on the supplied inputs and outputs.
build(ONNXContext, String[], String, Map<String, Object>) - Method in interface org.tribuo.util.onnx.ONNXOperator: Builds this node based on the supplied inputs and output.
build(ONNXContext, String, String) - Method in interface org.tribuo.util.onnx.ONNXOperator: Builds this node based on the supplied inputs and output.
build(ONNXContext, String, String[]) - Method in interface org.tribuo.util.onnx.ONNXOperator: Builds this node based on the supplied input and outputs.
build(ONNXContext, String, String[], Map<String, Object>) - Method in interface org.tribuo.util.onnx.ONNXOperator: Builds this node based on the supplied input and outputs.
build(ONNXContext, String, String, Map<String, Object>) - Method in interface org.tribuo.util.onnx.ONNXOperator: Builds this node based on the supplied inputs and output.
BUILD_TIMESTAMP - Static variable in class org.tribuo.Tribuo: The build timestamp.
Builder() - Constructor for class org.tribuo.data.columnar.RowProcessor.Builder: Builder for RowProcessor, see RowProcessor constructors for argument details.
buildGraph() - Method in class org.tribuo.util.onnx.ONNXContext: Builds the ONNX graph represented by this context.
buildLeNetGraph(String, int, int, int) - Static method in class org.tribuo.interop.tensorflow.example.CNNExamples: Builds a LeNet 5 style CNN (usually used for MNIST).
buildMLPGraph(String, int, int[], int) - Static method in class org.tribuo.interop.tensorflow.example.MLPExamples: Builds an MLP which expects the supplied number of inputs, has hiddenSizes.length hidden layers, before emitting numOutput outputs.
buildModel(ONNXContext, String, long, M) - Static method in interface org.tribuo.ONNXExportable: Creates an ONNX model protobuf for the supplied context.
buildRandomTree(int[], SplittableRandom) - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode: Builds a CART tree with randomly chosen split points.
buildRuntimeYaml(String, String) - Static method in class org.tribuo.interop.oci.OCIUtil: Builds the runtime.yaml String from the supplied arguments, throwing IllegalArgumentException if they are invalid.
buildTensorTypeNode(ONNXShape, OnnxMl.TensorProto.DataType) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a type proto for the specified shape and tensor type.
buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode: Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.common.tree.AbstractTrainingNode: Builds next level of a tree.
buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode: Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode: Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
BYTE - Enum constant in enum class org.tribuo.datasource.IDXDataSource.IDXType: A signed byte.

C

c - Variable in class org.tribuo.util.infotheory.impl.CachedTriple

The third element.

C_SVC - Enum constant in enum class org.tribuo.classification.libsvm.SVMClassificationType.SVMMode

Original SVM algorithm.

CachedPair<T1,T2> - Class in org.tribuo.util.infotheory.impl

A pair of things with a cached hashcode.

CachedPair(T1, T2) - Constructor for class org.tribuo.util.infotheory.impl.CachedPair

Constructs a CachedPair.

CachedTriple<T1,T2,T3> - Class in org.tribuo.util.infotheory.impl

A triple of things.

CachedTriple(T1, T2, T3) - Constructor for class org.tribuo.util.infotheory.impl.CachedTriple

Constructs a CachedTriple.

cacheProvenance() - Method in class org.tribuo.data.text.impl.SimpleStringDataSource

cacheProvenance() - Method in class org.tribuo.data.text.impl.SimpleTextDataSource

Computes the provenance.

calculateCountDist(List<T>) - Static method in class org.tribuo.util.infotheory.InformationTheory

Generate the counts for a single vector.

calculateEntropy(DoubleStream) - Static method in class org.tribuo.util.infotheory.InformationTheory

Calculates the discrete Shannon entropy of a stream, assuming each element of the stream is an element of the same probability distribution.

calculateEntropy(Stream<Double>) - Static method in class org.tribuo.util.infotheory.InformationTheory

Calculates the discrete Shannon entropy of a stream, assuming each element of the stream is an element of the same probability distribution.

calculateHashCode() - Method in class org.tribuo.util.infotheory.impl.CachedTriple

Overridden hashcode.

calculateWeightedCountDist(ArrayList<T>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory

Generate the counts for a single vector.

CALENDAR_QUARTER - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType

The calendar quarter of the year.

canonicalise(FeatureMap) - Method in class org.tribuo.sequence.SequenceExample

Reassigns feature name Strings in each Example inside this SequenceExample to point to those in the FeatureMap.

canonicalize(FeatureMap) - Method in class org.tribuo.Example

Reassigns feature name Strings in the Example to point to those in the FeatureMap.

canonicalize(FeatureMap) - Method in class org.tribuo.impl.ArrayExample

canonicalize(FeatureMap) - Method in class org.tribuo.impl.BinaryFeaturesExample

canonicalize(FeatureMap) - Method in class org.tribuo.impl.ListExample

CART - Enum constant in enum class org.tribuo.classification.dtree.CARTClassificationOptions.TreeType

Builds a CART model.

CART - Enum constant in enum class org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType

Creates a CARTClassificationTrainer.

CART_INDEPENDENT - Enum constant in enum class org.tribuo.regression.rtree.TrainTest.TreeType

Creates a CARTRegressionTrainer which treats each regression dimension independently.

CART_JOINT - Enum constant in enum class org.tribuo.regression.rtree.TrainTest.TreeType

Creates a CARTJointRegressionTrainer which jointly minimises the impurity across all output dimensions.

CARTClassificationOptions - Class in org.tribuo.classification.dtree

Options for building a classification tree trainer.

CARTClassificationOptions() - Constructor for class org.tribuo.classification.dtree.CARTClassificationOptions

CARTClassificationOptions.ImpurityType - Enum Class in org.tribuo.classification.dtree

The impurity algorithm.

CARTClassificationOptions.TreeType - Enum Class in org.tribuo.classification.dtree

Type of decision tree algorithm.

CARTClassificationTrainer - Class in org.tribuo.classification.dtree

A Trainer that uses an approximation of the CART algorithm to build a decision tree.

CARTClassificationTrainer() - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer

Creates a CART Trainer.

CARTClassificationTrainer(int) - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer

Creates a CART trainer.

CARTClassificationTrainer(int, float, boolean, long) - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer

Creates a CART Trainer.

CARTClassificationTrainer(int, float, float, float, boolean, LabelImpurity, long) - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer

Creates a CART Trainer.

CARTClassificationTrainer(int, float, float, float, LabelImpurity, long) - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer

Creates a CART Trainer.

CARTClassificationTrainer(int, float, long) - Constructor for class org.tribuo.classification.dtree.CARTClassificationTrainer

Creates a CART Trainer.

cartImpurity - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions

Impurity measure to use.

CARTJointRegressionTrainer - Class in org.tribuo.regression.rtree

A Trainer that uses an approximation of the CART algorithm to build a decision tree.

CARTJointRegressionTrainer() - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer

Creates a CART Trainer.

CARTJointRegressionTrainer(int) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer

Creates a CART Trainer.

CARTJointRegressionTrainer(int, boolean) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer

Creates a CART Trainer.

CARTJointRegressionTrainer(int, float, float, float, boolean, RegressorImpurity, boolean, long) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer

Creates a CART Trainer.

CARTJointRegressionTrainer(int, float, float, float, RegressorImpurity, boolean, long) - Constructor for class org.tribuo.regression.rtree.CARTJointRegressionTrainer

Creates a CART Trainer.

cartMaxDepth - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions

Maximum depth in the decision tree.

cartMinChildWeight - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions

Minimum child weight.

cartMinImpurityDecrease - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions

Minimum impurity decrease.

cartOptions - Variable in class org.tribuo.classification.dtree.TrainTest.TrainTestOptions

The CART trainer options.

cartOptions - Variable in class org.tribuo.classification.experiments.AllTrainerOptions

Options for CART trainers.

cartPrintTree - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions

Prints the decision tree.

cartRandomSplit - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions

Whether to choose split points for features at random.

CARTRegressionTrainer - Class in org.tribuo.regression.rtree

A Trainer that uses an approximation of the CART algorithm to build a decision tree.

CARTRegressionTrainer() - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer

Creates a CART trainer.

CARTRegressionTrainer(int) - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer

Creates a CART trainer.

CARTRegressionTrainer(int, float, float, float, boolean, RegressorImpurity, long) - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer

Creates a CART Trainer.

CARTRegressionTrainer(int, float, float, float, RegressorImpurity, long) - Constructor for class org.tribuo.regression.rtree.CARTRegressionTrainer

Creates a CART Trainer.

cartSeed - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions

RNG seed.

cartSplitFraction - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions

Fraction of features in split.

cartTreeAlgorithm - Variable in class org.tribuo.classification.dtree.CARTClassificationOptions

Tree algorithm to use (options are CART).

CasingPreprocessor - Class in org.tribuo.data.text.impl

A document preprocessor which uppercases or lowercases the input.

CasingPreprocessor(CasingPreprocessor.CasingOperation) - Constructor for class org.tribuo.data.text.impl.CasingPreprocessor

Construct a casing preprocessor.

CasingPreprocessor.CasingOperation - Enum Class in org.tribuo.data.text.impl

The possible casing operations.

cast(Class<?>) - Method in class org.tribuo.util.onnx.ONNXRef

Casts this ONNXRef to a different type using the ONNXOperators.CAST operation, and returning the output node of that op.

CAST - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators

Cast input to specified type.

castDataset(Dataset<?>, Class<T>) - Static method in class org.tribuo.Dataset

Casts the dataset to the specified output type, assuming it is valid.

castDataset(SequenceDataset<?>, Class<T>) - Static method in class org.tribuo.sequence.SequenceDataset

Casts the dataset to the specified output type, assuming it is valid.

castModel(Class) - Method in class org.tribuo.Model

Casts the model to the specified output type, assuming it is valid.

castModel(Class) - Method in class org.tribuo.sequence.SequenceModel

Casts the model to the specified output type, assuming it is valid.

CATEGORICAL - Enum constant in enum class org.tribuo.data.columnar.FieldProcessor.GeneratedFeatureType

Unordered categorical features with the values converted into doubles.

CategoricalIDInfo - Class in org.tribuo

Same as a CategoricalInfo, but with an additional int id field.

CategoricalIDInfo(CategoricalInfo, int) - Constructor for class org.tribuo.CategoricalIDInfo

Constructs a categorical id info copying the information from the supplied info, with the specified id.

CategoricalInfo - Class in org.tribuo

Stores information about Categorical features.

CategoricalInfo(String) - Constructor for class org.tribuo.CategoricalInfo

Constructs a new empty categorical info for the supplied feature name.

CategoricalInfo(CategoricalInfo) - Constructor for class org.tribuo.CategoricalInfo

Constructs a deep copy of the supplied categorical info.

CategoricalInfo(CategoricalInfo, String) - Constructor for class org.tribuo.CategoricalInfo

Constructs a deep copy of the supplied categorical info, with the new feature name.

CC_NEGATIVE - Static variable in class org.tribuo.multilabel.baseline.ClassifierChainTrainer

The string used in the feature name for negative labels.

CC_POSITIVE - Static variable in class org.tribuo.multilabel.baseline.ClassifierChainTrainer

The string used in the feature name for positive labels.

CC_PREFIX - Static variable in class org.tribuo.multilabel.baseline.ClassifierChainTrainer

The prefix for classifier chain added features.

CC_SEPARATOR - Static variable in class org.tribuo.multilabel.baseline.ClassifierChainTrainer

The joiner character for classifier chain added features.

CCEnsembleTrainer - Class in org.tribuo.multilabel.ensemble

A trainer for an ensemble of randomly ordered Classifier Chains.

CCEnsembleTrainer(Trainer<Label>, int, long) - Constructor for class org.tribuo.multilabel.ensemble.CCEnsembleTrainer

Constructs a classifier chain ensemble trainer.

cdf - Variable in class org.tribuo.CategoricalInfo

The CDF to sample from.

centroids - Variable in class org.tribuo.clustering.kmeans.KMeansOptions

Number of centroids in K-Means.

centroids - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions

Number of clusters to infer.

ChainHelper - Class in org.tribuo.classification.sgd.crf

A collection of helper methods for performing training and inference in a CRF.

ChainHelper.ChainBPResults - Class in org.tribuo.classification.sgd.crf

Belief Propagation results.

ChainHelper.ChainCliqueValues - Class in org.tribuo.classification.sgd.crf

Clique scores within a chain.

ChainHelper.ChainViterbiResults - Class in org.tribuo.classification.sgd.crf

Viterbi output from a linear chain.

charAt(int) - Method in class org.tribuo.util.tokens.universal.Range

CheckerboardDataSource - Class in org.tribuo.classification.example

Creates a data source using a 2d checkerboard of alternating classes.

CheckerboardDataSource(int, long, int, double, double) - Constructor for class org.tribuo.classification.example.CheckerboardDataSource

Creates a checkboard with the required number of squares per dimension, where each feature value lies between min and max.

checkIsBinary(Feature) - Static method in class org.tribuo.impl.BinaryFeaturesExample

Checks if the supplied feature is binary, if not throw an IllegalArgumentException.

CHECKPOINT - Enum constant in enum class org.tribuo.interop.tensorflow.TensorFlowTrainer.TFModelFormat

Saves the model state inside a TensorFlow checkpoint, emits a TensorFlowCheckpointModel.

checkpointPath - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions

Path to the checkpoint base directory.

choleskyFactorization() - Method in class org.tribuo.math.la.DenseMatrix

Computes the Cholesky factorization of a positive definite matrix.

Chunk - Class in org.tribuo.classification.sgd.crf

Chunk class used for chunk level confidence prediction in the CRFModel.

Chunk(int, int[]) - Constructor for class org.tribuo.classification.sgd.crf.Chunk

Constructs a chunk.

Classifiable<T extends Classifiable<T>> - Interface in org.tribuo.classification

A tag interface for multi-class and multi-label classification tasks.

CLASSIFICATION_TOKEN - Static variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor

Default classification token name.

ClassificationEnsembleOptions - Class in org.tribuo.classification.ensemble

Options for building a classification ensemble.

ClassificationEnsembleOptions() - Constructor for class org.tribuo.classification.ensemble.ClassificationEnsembleOptions

ClassificationEnsembleOptions.EnsembleType - Enum Class in org.tribuo.classification.ensemble

The type of ensemble.

ClassificationOptions<TRAINER extends Trainer<Label>> - Interface in org.tribuo.classification

An Options that can produce a classification Trainer based on the provided arguments.

ClassifierChainModel - Class in org.tribuo.multilabel.baseline

A Classifier Chain Model.

ClassifierChainTrainer - Class in org.tribuo.multilabel.baseline

A trainer for a Classifier Chain.

ClassifierChainTrainer(Trainer<Label>, long) - Constructor for class org.tribuo.multilabel.baseline.ClassifierChainTrainer

Builds a classifier chain trainer using the specified member trainer and seed.

ClassifierChainTrainer(Trainer<Label>, List<String>) - Constructor for class org.tribuo.multilabel.baseline.ClassifierChainTrainer

Builds a classifier chain trainer using the specified member trainer and seed.

ClassifierEvaluation<T extends Classifiable<T>> - Interface in org.tribuo.classification.evaluation

Defines methods that calculate classification performance, used for both multi-class and multi-label classification.

ClassifierTrainingNode - Class in org.tribuo.classification.dtree.impl

A decision tree node used at training time.

ClassifierTrainingNode(LabelImpurity, Dataset<Label>, AbstractTrainingNode.LeafDeterminer) - Constructor for class org.tribuo.classification.dtree.impl.ClassifierTrainingNode

Constructor which creates the inverted file.

className - Variable in class org.tribuo.interop.tensorflow.TensorFlowUtil.TensorTuple

The tensor class name.

className - Variable in class org.tribuo.provenance.ModelProvenance

The name of the host class.

clear() - Method in class org.tribuo.anomaly.MutableAnomalyInfo

clear() - Method in class org.tribuo.classification.MutableLabelInfo

clear() - Method in class org.tribuo.clustering.MutableClusteringInfo

clear() - Method in class org.tribuo.impl.ListExample

Clears the features from this example.

clear() - Method in class org.tribuo.multilabel.MutableMultiLabelInfo

clear() - Method in class org.tribuo.MutableDataset

Clears all the examples out of this dataset, and flushes the FeatureMap, OutputInfo, and transform provenances.

clear() - Method in class org.tribuo.MutableFeatureMap

Clears all the feature observations.

clear() - Method in interface org.tribuo.MutableOutputInfo

Clears the OutputInfo, removing all things it's observed.

clear() - Method in class org.tribuo.regression.MutableRegressionInfo

clear() - Method in class org.tribuo.sequence.MutableSequenceDataset

Clears all the examples out of this dataset, and flushes the FeatureMap, OutputInfo, and transform provenances.

clear() - Method in class org.tribuo.util.infotheory.impl.RowList

Unsupported.

clearUsageDetails(CommandInterpreter) - Method in class org.tribuo.interop.modelcard.ModelCardCLI

Creates a new instance of UsageDetailsBuilder to allow a new UsageDetails to be written.

clone() - Method in class org.tribuo.Feature

clone() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer

clone() - Method in class org.tribuo.util.tokens.impl.NonTokenizer

clone() - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer

clone() - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer

clone() - Method in class org.tribuo.util.tokens.impl.SplitFunctionTokenizer

clone() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer

clone() - Method in class org.tribuo.util.tokens.impl.WhitespaceTokenizer

clone() - Method in class org.tribuo.util.tokens.impl.wordpiece.WordpieceBasicTokenizer

clone() - Method in class org.tribuo.util.tokens.impl.wordpiece.WordpieceTokenizer

clone() - Method in interface org.tribuo.util.tokens.Tokenizer

Clones a tokenizer with it's configuration.

clone() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer

close() - Method in class org.tribuo.data.csv.CSVIterator

close() - Method in class org.tribuo.data.sql.SQLDataSource

close() - Method in class org.tribuo.interop.oci.OCIModel

close() - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor

close() - Method in class org.tribuo.interop.onnx.ONNXExternalModel

close() - Method in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceModel

Close the session and graph if they exist.

close() - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel

close() - Method in class org.tribuo.interop.tensorflow.TensorFlowModel

close() - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel

close() - Method in class org.tribuo.interop.tensorflow.TensorMap

close() - Method in class org.tribuo.json.JsonFileIterator

close(CommandInterpreter) - Method in class org.tribuo.interop.modelcard.ModelCardCLI

Closes the command shell

closed - Variable in class org.tribuo.interop.tensorflow.TensorFlowModel

closeTensorCollection(Collection<Tensor>) - Static method in class org.tribuo.interop.tensorflow.TensorFlowUtil

Closes a collection of Tensors.

CLS - Enum constant in enum class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor.OutputPooling

Returns the CLS embedding.

CLS_AND_MEAN - Enum constant in enum class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor.OutputPooling

Takes the average of the token embeddings and the CLS token.

CLS_OUTPUT - Static variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor

Output name for the classification token output.

clusterCounts - Variable in class org.tribuo.clustering.ClusteringInfo

The cluster counts for each id.

ClusterID - Class in org.tribuo.clustering

A clustering id.

ClusterID(int) - Constructor for class org.tribuo.clustering.ClusterID

Creates a ClusterID with the sentinel score of Double.NaN.

ClusterID(int, double) - Constructor for class org.tribuo.clustering.ClusterID

Creates a ClusterID with the specified id number and score.

CLUSTERING - Enum constant in enum class org.tribuo.interop.oci.OCIUtil.OCIModelType

Clustering, maps to Tribuo's ClusterID.

ClusteringDataGenerator - Class in org.tribuo.clustering.example

Generates three example train and test datasets, used for unit testing.

ClusteringDataGenerator() - Constructor for class org.tribuo.clustering.example.ClusteringDataGenerator

ClusteringEvaluation - Interface in org.tribuo.clustering.evaluation

An Evaluation for clustering tasks.

ClusteringEvaluator - Class in org.tribuo.clustering.evaluation

A Evaluator for clustering using ClusterIDs.

ClusteringEvaluator() - Constructor for class org.tribuo.clustering.evaluation.ClusteringEvaluator

ClusteringFactory - Class in org.tribuo.clustering

A factory for making ClusterID related classes.

ClusteringFactory() - Constructor for class org.tribuo.clustering.ClusteringFactory

ClusteringFactory is stateless and immutable, but we need to be able to construct them via the config system.

ClusteringFactory.ClusteringFactoryProvenance - Class in org.tribuo.clustering

Provenance for ClusteringFactory.

ClusteringFactoryProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.clustering.ClusteringFactory.ClusteringFactoryProvenance

Rebuilds a clustering factory provenance from the marshalled form.

ClusteringInfo - Class in org.tribuo.clustering

The base class for a ClusterID OutputInfo.

ClusteringMetric - Class in org.tribuo.clustering.evaluation

A metric for evaluating clustering problems.

ClusteringMetric(MetricTarget<ClusterID>, String, BiFunction<MetricTarget<ClusterID>, ClusteringMetric.Context, Double>) - Constructor for class org.tribuo.clustering.evaluation.ClusteringMetric

Constructs a clustering metric using the supplied parameters.

ClusteringMetrics - Enum Class in org.tribuo.clustering.evaluation

Default metrics for evaluating clusterings.

cmi(List<T1>, List<T2>, Set<List<T3>>) - Static method in class org.tribuo.util.infotheory.InformationTheory

Calculates the conditional mutual information between first and second conditioned on the set.

CMIM - Class in org.tribuo.classification.fs

Selects features according to the Conditional Mutual Information Maximisation algorithm.

CMIM(int, int, int) - Constructor for class org.tribuo.classification.fs.CMIM

Constructs a CMIM feature selector that ranks the top k features.

CNNExamples - Class in org.tribuo.interop.tensorflow.example

Static factory methods which produce Convolutional Neural Network architectures.

coeff - Variable in class org.tribuo.regression.libsvm.TrainTest.LibSVMOptions

Intercept in kernel function.

COLUMNAR - Enum constant in enum class org.tribuo.data.DataOptions.InputFormat

A CSV file parsed using a configured RowProcessor.

ColumnarDataSource<T extends Output<T>> - Class in org.tribuo.data.columnar

A ConfigurableDataSource base class which takes columnar data (e.g., csv or DB table rows) and generates Examples.

ColumnarDataSource() - Constructor for class org.tribuo.data.columnar.ColumnarDataSource

For OLCUT.

ColumnarDataSource(OutputFactory<T>, RowProcessor<T>, boolean) - Constructor for class org.tribuo.data.columnar.ColumnarDataSource

Constructs a columnar data source with the specified parameters.

ColumnarExplainer<T extends Output<T>> - Interface in org.tribuo.classification.explanations

An explainer for data using Tribuo's columnar data package.

ColumnarFeature - Class in org.tribuo.data.columnar

A Feature with extra bookkeeping for use inside the columnar package.

ColumnarFeature(String, double) - Constructor for class org.tribuo.data.columnar.ColumnarFeature

Constructs a ColumnarFeature from the field name.

ColumnarFeature(String, String, double) - Constructor for class org.tribuo.data.columnar.ColumnarFeature

Constructs a ColumnarFeature from the field name, column entry and value.

ColumnarFeature(String, String, String, double) - Constructor for class org.tribuo.data.columnar.ColumnarFeature

Constructs a ColumnarFeature which is the conjunction of features from two fields.

ColumnarIterator - Class in org.tribuo.data.columnar

An abstract class for iterators that read data in to a columnar format, usually from a file of some kind.

ColumnarIterator() - Constructor for class org.tribuo.data.columnar.ColumnarIterator

Constructs a ColumnarIterator wrapped around a buffering spliterator.

ColumnarIterator(int, int, long) - Constructor for class org.tribuo.data.columnar.ColumnarIterator

Constructs a ColumnarIterator wrapped around a buffering spliterator.

ColumnarIterator.Row - Class in org.tribuo.data.columnar

A representation of a row of untyped data from a columnar data source.

columnSum() - Method in class org.tribuo.math.la.DenseMatrix

Returns a dense vector containing each column sum.

columnSum(int) - Method in class org.tribuo.math.la.DenseMatrix

Calculates the sum of the specified column.

combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner

combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.ensemble.VotingCombiner

combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>, float[]) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner

combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>, float[]) - Method in class org.tribuo.classification.ensemble.VotingCombiner

combine(ImmutableOutputInfo<MultiLabel>, List<Prediction<MultiLabel>>) - Method in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner

combine(ImmutableOutputInfo<MultiLabel>, List<Prediction<MultiLabel>>, float[]) - Method in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner

combine(ImmutableOutputInfo<Regressor>, List<Prediction<Regressor>>) - Method in class org.tribuo.regression.ensemble.AveragingCombiner

combine(ImmutableOutputInfo<Regressor>, List<Prediction<Regressor>>, float[]) - Method in class org.tribuo.regression.ensemble.AveragingCombiner

combine(ImmutableOutputInfo<T>, List<Prediction<T>>) - Method in interface org.tribuo.ensemble.EnsembleCombiner

Combine the predictions.

combine(ImmutableOutputInfo<T>, List<Prediction<T>>, float[]) - Method in interface org.tribuo.ensemble.EnsembleCombiner

Combine the supplied predictions.

combiner - Variable in class org.tribuo.ensemble.BaggingTrainer

combiner - Variable in class org.tribuo.ensemble.WeightedEnsembleModel

The ensemble combination function.

COMMA - Enum constant in enum class org.tribuo.data.DataOptions.Delimiter

Comma separator.

compareTo(Feature) - Method in class org.tribuo.Feature

compareTo(MatrixIterator) - Method in interface org.tribuo.math.la.MatrixIterator

compareTo(VectorIterator) - Method in interface org.tribuo.math.la.VectorIterator

compareTo(InvertedFeature) - Method in class org.tribuo.regression.rtree.impl.InvertedFeature

compartmentID - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions

Compartment ID.

compartmentID - Variable in class org.tribuo.interop.oci.OCIUtil.OCIDSConfig

OCI compartment ID.

CompletelyConfigurableTrainTest - Class in org.tribuo.data

Build and run a predictor for a standard dataset.

CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions - Class in org.tribuo.data

Command line options.

compute(C) - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric

Compute the result of this metric from the input context.

compute(AnomalyMetric.Context) - Method in class org.tribuo.anomaly.evaluation.AnomalyMetric

compute(LabelMetric.Context) - Method in class org.tribuo.classification.evaluation.LabelMetric

compute(ClusteringMetric.Context) - Method in class org.tribuo.clustering.evaluation.ClusteringMetric

compute(MultiLabelMetric.Context) - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric

compute(RegressionMetric.Context) - Method in class org.tribuo.regression.evaluation.RegressionMetric

computeDepth(int, Node<T>) - Static method in class org.tribuo.common.tree.TreeModel

Computes the depth of the tree.

computeDistance(SGDVector, SGDVector) - Method in class org.tribuo.math.distance.CosineDistance

computeDistance(SGDVector, SGDVector) - Method in interface org.tribuo.math.distance.Distance

Computes the distance between the two vectors.

computeDistance(SGDVector, SGDVector) - Method in enum class org.tribuo.math.distance.DistanceType

Calculates the distance between two vectors.

computeDistance(SGDVector, SGDVector) - Method in class org.tribuo.math.distance.L1Distance

computeDistance(SGDVector, SGDVector) - Method in class org.tribuo.math.distance.L2Distance

computeResults(C, Set<? extends EvaluationMetric<T, C>>) - Method in class org.tribuo.evaluation.AbstractEvaluator

Computes each metric given the context.

computeResults(C, Set<? extends EvaluationMetric<T, C>>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator

Computes each metric given the context.

CONCAT - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators

Concatenates tensors.

ConcentricCirclesDataSource - Class in org.tribuo.classification.example

A data source for two concentric circles, one per class.

ConcentricCirclesDataSource(int, long, double, double) - Constructor for class org.tribuo.classification.example.ConcentricCirclesDataSource

Constructs a data source for two concentric circles, one per class.

condaName - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions

OCI DS conda environment name.

condaName - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelArtifactConfig

The Conda environment name.

condaPath - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions

OCI DS conda environment path in object storage.

condaPath - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelArtifactConfig

The Conda environment path in object storage.

conditionalEntropy(List<T1>, List<T2>) - Static method in class org.tribuo.util.infotheory.InformationTheory

Calculates the discrete Shannon conditional entropy of two arrays, using histogram probability estimators.

conditionalMI(List<T1>, List<T2>, List<T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory

Calculates the discrete Shannon conditional mutual information, using histogram probability estimators.

conditionalMI(List<T1>, List<T2>, List<T3>, List<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory

Calculates the discrete weighted conditional mutual information, using histogram probability estimators.

conditionalMI(TripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory

Calculates the discrete Shannon conditional mutual information, using histogram probability estimators.

conditionalMI(TripleDistribution<T1, T2, T3>, Map<?, Double>, WeightedInformationTheory.VariableSelector) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory

Calculates the discrete weighted conditional mutual information, using histogram probability estimators.

conditionalMI(WeightedTripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory

Calculates the discrete weighted conditional mutual information, using histogram probability estimators.

conditionalMIFlipped(TripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory

Calculates the discrete Shannon conditional mutual information, using histogram probability estimators.

ConfidencePredictingSequenceModel - Class in org.tribuo.classification.sequence

A Sequence model which can provide confidence predictions for subsequence predictions.

ConfidencePredictingSequenceModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>) - Constructor for class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel

Constructs a ConfidencePredictingSequenceModel with the supplied parameters.

ConfidencePredictingSequenceModel.Subsequence - Class in org.tribuo.classification.sequence

A range class used to define a subsequence of a SequenceExample.

ConfigurableDataSource<T extends Output<T>> - Interface in org.tribuo

It's a DataSource that's also Configurable.

ConfigurableTestOptions() - Constructor for class org.tribuo.classification.experiments.Test.ConfigurableTestOptions

ConfigurableTrainTest - Class in org.tribuo.classification.experiments

Build and run a classifier for a standard dataset.

ConfigurableTrainTest - Class in org.tribuo.data

Build and run a predictor for a standard dataset.

ConfigurableTrainTest() - Constructor for class org.tribuo.classification.experiments.ConfigurableTrainTest

ConfigurableTrainTest.ConfigurableTrainTestOptions - Class in org.tribuo.classification.experiments

Command line options.

ConfigurableTrainTest.ConfigurableTrainTestOptions - Class in org.tribuo.data

Command line options.

ConfigurableTrainTestOptions() - Constructor for class org.tribuo.classification.experiments.ConfigurableTrainTest.ConfigurableTrainTestOptions

ConfigurableTrainTestOptions() - Constructor for class org.tribuo.data.CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions

ConfigurableTrainTestOptions() - Constructor for class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions

configured - Variable in class org.tribuo.data.columnar.RowProcessor

Has this row processor been configured?

ConfiguredDataSourceProvenance - Interface in org.tribuo.provenance

A tag interface for configurable data source provenance.

confusion(Label, Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix

confusion(Label, Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation

Note: confusion is not stored in the underlying map, so it won't show up in aggregation.

confusion(MultiLabel, MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix

confusion(MultiLabel, MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl

confusion(T, T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation

Returns the number of times label truth was predicted as label predicted.

confusion(T, T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix

The number of times the supplied predicted label was returned for the supplied true class.

ConfusionMatrix<T extends Classifiable<T>> - Interface in org.tribuo.classification.evaluation

A confusion matrix for Classifiables.

ConfusionMetrics - Class in org.tribuo.classification.evaluation

Static functions for computing classification metrics based on a ConfusionMatrix.

confusionString() - Method in interface org.tribuo.anomaly.evaluation.AnomalyEvaluation

Returns a confusion matrix formatted String for display.

CONJUNCTION - Static variable in class org.tribuo.data.columnar.ColumnarFeature

The string used as the field name of conjunction features.

connString - Variable in class org.tribuo.data.sql.SQLToCSV.SQLToCSVOptions

Connection string to the SQL database

considerations(List<String>) - Method in class org.tribuo.interop.modelcard.UsageDetailsBuilder

Sets the considerations of the model for which an instance of UsageDetails will be built.

constant(String, float) - Method in class org.tribuo.util.onnx.ONNXContext

Creates a float scalar constant for this ONNXContext.

constant(String, long) - Method in class org.tribuo.util.onnx.ONNXContext

Creates a long scalar constant for this ONNXContext.

CONSTANT - Enum constant in enum class org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType

Returns the supplied label for all inputs.

CONSTANT - Enum constant in enum class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyType

Returns the specified constant value.

CONSTANT_OF_SHAPE - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators

Makes a constant of the size of the supplied shape containing the value.

CONSTANTSGD - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType

SGD with a constant learning rate.

CONSTRAINED_BP - Enum constant in enum class org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType

Constrained Belief Propagation from "Confidence Estimation for Information Extraction" Culotta and McCallum 2004.

constrainedBeliefPropagation(ChainHelper.ChainCliqueValues, int[]) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper

Runs constrained belief propagation on a linear chain CRF.

constructFromLists(List<T1>, List<T2>) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution

Generates the counts for two vectors.

constructFromLists(List<T1>, List<T2>, List<Double>) - Static method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution

Generates the counts for two vectors.

constructFromLists(List<T1>, List<T2>, List<T3>) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution

Constructs a TripleDistribution from three lists of the same length.

constructFromLists(List<T1>, List<T2>, List<T3>, List<Double>) - Static method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution

Constructs a WeightedTripleDistribution from three lists of the same length and a list of weights of the same length.

constructFromMap(Map<CachedPair<T1, T2>, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution

Constructs a distribution from a joint count.

constructFromMap(Map<CachedPair<T1, T2>, MutableLong>, int, int) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution

Constructs a distribution from a joint count.

constructFromMap(Map<CachedPair<T1, T2>, MutableLong>, Map<T1, MutableLong>, Map<T2, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.PairDistribution

Constructs a joint distribution from the counts.

constructFromMap(Map<CachedPair<T1, T2>, WeightCountTuple>) - Static method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution

Generates a WeightedPairDistribution by generating the marginal distributions for the first and second elements.

constructFromMap(Map<CachedTriple<T1, T2, T3>, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution

Constructs a TripleDistribution by marginalising the supplied joint distribution.

constructFromMap(Map<CachedTriple<T1, T2, T3>, MutableLong>, int, int, int, int, int, int) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution

Constructs a TripleDistribution by marginalising the supplied joint distribution.

constructFromMap(Map<CachedTriple<T1, T2, T3>, MutableLong>, Map<CachedPair<T1, T2>, MutableLong>, Map<CachedPair<T1, T3>, MutableLong>, Map<CachedPair<T2, T3>, MutableLong>, Map<T1, MutableLong>, Map<T2, MutableLong>, Map<T3, MutableLong>) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution

Constructs a TripleDistribution by marginalising the supplied joint distribution.

constructFromMap(Map<CachedTriple<T1, T2, T3>, WeightCountTuple>) - Static method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution

Constructs a WeightedTripleDistribution by marginalising the supplied joint distribution.

constructInfoForExternalModel(Map<Event, Integer>) - Method in class org.tribuo.anomaly.AnomalyFactory

constructInfoForExternalModel(Map<Label, Integer>) - Method in class org.tribuo.classification.LabelFactory

constructInfoForExternalModel(Map<ClusterID, Integer>) - Method in class org.tribuo.clustering.ClusteringFactory

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.

constructInfoForExternalModel(Map<MultiLabel, Integer>) - Method in class org.tribuo.multilabel.MultiLabelFactory

constructInfoForExternalModel(Map<Regressor, Integer>) - Method in class org.tribuo.regression.RegressionFactory

constructInfoForExternalModel(Map<T, Integer>) - Method in interface org.tribuo.OutputFactory

Creates an ImmutableOutputInfo from the supplied mapping.

contains(int) - Method in class org.tribuo.impl.IndexedArrayExample

Does this example contain a feature with id i.

contains(Object) - Method in class org.tribuo.util.infotheory.impl.RowList

contains(String) - Method in class org.tribuo.multilabel.MultiLabel

Does this MultiLabel contain this string?

contains(Label) - Method in class org.tribuo.multilabel.MultiLabel

Does this MultiLabel contain this Label?

containsAll(Collection<?>) - Method in class org.tribuo.util.infotheory.impl.RowList

containsMetadata(String) - Method in class org.tribuo.Example

Test if the metadata contains the supplied key.

context - Variable in class org.tribuo.util.onnx.ONNXRef

The ONNX context.

Context(Model<Label>, List<Prediction<Label>>) - Constructor for class org.tribuo.classification.evaluation.LabelMetric.Context

Constructs a context and compute the confusion matrix using the specified model and predictions.

Context(SequenceModel<Label>, List<Prediction<Label>>) - Constructor for class org.tribuo.classification.evaluation.LabelMetric.Context

Constructs a context and compute the confusion matrix using the specified model and predictions.

convert(byte) - Static method in enum class org.tribuo.datasource.IDXDataSource.IDXType

Converts the byte into the enum.

convert(List<? extends Example<?>>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.DenseFeatureConverter

convert(List<? extends Example<?>>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.FeatureConverter

Converts a batch of Examples into a single TensorMap suitable for supplying as an input to a graph.

convert(List<? extends Example<?>>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.ImageConverter

Transform implicitly pads unseen values with zero.

convert(List<? extends SGDVector>) - Method in class org.tribuo.interop.tensorflow.DenseFeatureConverter

convert(List<? extends SGDVector>) - Method in interface org.tribuo.interop.tensorflow.FeatureConverter

Converts a list of SGDVectors representing a batch of features into a TensorMap.

convert(List<? extends SGDVector>) - Method in class org.tribuo.interop.tensorflow.ImageConverter

convert(Example<?>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.DenseFeatureConverter

convert(Example<?>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.FeatureConverter

Converts an Example into a TensorMap suitable for supplying as an input to a graph.

convert(Example<?>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.ImageConverter

Transform implicitly pads unseen values with zero.

convert(SGDVector) - Method in class org.tribuo.interop.tensorflow.DenseFeatureConverter

convert(SGDVector) - Method in interface org.tribuo.interop.tensorflow.FeatureConverter

Converts a SGDVector representing the features into a TensorMap.

convert(SGDVector) - Method in class org.tribuo.interop.tensorflow.ImageConverter

convert(SequenceExample<Label>, ImmutableFeatureMap, ImmutableOutputInfo<Label>) - Static method in class org.tribuo.classification.sgd.crf.CRFModel

Deprecated.

As it's replaced with CRFModel.convertToVector(org.tribuo.sequence.SequenceExample<T>, org.tribuo.ImmutableFeatureMap) which is more flexible.

convert(SequenceExample<T>, ImmutableFeatureMap) - Static method in class org.tribuo.classification.sgd.crf.CRFModel

Deprecated.

As it's replaced with CRFModel.convertToVector(org.tribuo.sequence.SequenceExample<T>, org.tribuo.ImmutableFeatureMap) which is more flexible.

convertBatchOutput(ImmutableOutputInfo<Label>, List<float[][]>, int[], Example<Label>[]) - Method in class org.tribuo.classification.xgboost.XGBoostClassificationConverter

convertBatchOutput(ImmutableOutputInfo<Regressor>, List<float[][]>, int[], Example<Regressor>[]) - Method in class org.tribuo.regression.xgboost.XGBoostRegressionConverter

convertBatchOutput(ImmutableOutputInfo<T>, List<float[][]>, int[], Example<T>[]) - Method in interface org.tribuo.common.xgboost.XGBoostOutputConverter

Converts a list of float arrays from XGBoost Boosters into a Tribuo Prediction.

convertDataset(Dataset<T>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer

Converts a dataset into a DMatrix.

convertDataset(Dataset<T>, Function<T, Float>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer

Converts a dataset into a DMatrix.

convertExample(Example<T>, ImmutableFeatureMap) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer

Converts an example into a DMatrix.

convertExample(Example<T>, ImmutableFeatureMap, Function<T, Float>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer

Converts an example into a DMatrix.

convertExamples(Iterable<Example<T>>, ImmutableFeatureMap) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer

Converts an iterable of examples into a DMatrix.

convertExamples(Iterable<Example<T>>, ImmutableFeatureMap, Function<T, Float>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer

Converts an iterable of examples into a DMatrix.

convertFeatures(SparseVector) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel

convertFeatures(SparseVector) - Method in class org.tribuo.interop.ExternalModel

Converts from a SparseVector using the external model's indices into the ingestion format for the external model.

convertFeatures(SparseVector) - Method in class org.tribuo.interop.oci.OCIModel

convertFeatures(SparseVector) - Method in class org.tribuo.interop.onnx.ONNXExternalModel

convertFeatures(SparseVector) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel

convertFeatures(SparseVector) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel

convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel

convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.interop.ExternalModel

Converts from a list of SparseVector using the external model's indices into the ingestion format for the external model.

convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.interop.oci.OCIModel

convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.interop.onnx.ONNXExternalModel

convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel

convertFeaturesList(List<SparseVector>) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel

convertOutput(float[][], int[], List<Example<T>>) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel

convertOutput(float[][], int, Example<T>) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel

convertOutput(List<OnnxValue>, int[], List<Example<T>>) - Method in class org.tribuo.interop.onnx.ONNXExternalModel

Converts a tensor into a prediction.

convertOutput(List<OnnxValue>, int, Example<T>) - Method in class org.tribuo.interop.onnx.ONNXExternalModel

Converts a tensor into a prediction.

convertOutput(Tensor, int[], List<Example<T>>) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel

Converts a tensor into a prediction.

convertOutput(Tensor, int, Example<T>) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel

Converts a tensor into a prediction.

convertOutput(ImmutableOutputInfo<Label>, List<float[]>, int, Example<Label>) - Method in class org.tribuo.classification.xgboost.XGBoostClassificationConverter

convertOutput(ImmutableOutputInfo<Regressor>, List<float[]>, int, Example<Regressor>) - Method in class org.tribuo.regression.xgboost.XGBoostRegressionConverter

convertOutput(ImmutableOutputInfo<T>, List<float[]>, int, Example<T>) - Method in interface org.tribuo.common.xgboost.XGBoostOutputConverter

Converts a list of float arrays from XGBoost Boosters into a Tribuo Prediction.

convertOutput(TensorMap, int[], List<Example<T>>) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel

Converts a tensor into a prediction.

convertOutput(TensorMap, int, Example<T>) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel

Converts a tensor into a prediction.

convertOutput(DenseMatrix, int[], List<Example<Label>>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.oci.OCILabelConverter

convertOutput(DenseMatrix, int[], List<Example<MultiLabel>>, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.oci.OCIMultiLabelConverter

convertOutput(DenseMatrix, int[], List<Example<Regressor>>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.oci.OCIRegressorConverter

convertOutput(DenseMatrix, int[], List<Example<T>>) - Method in class org.tribuo.interop.oci.OCIModel

convertOutput(DenseMatrix, int[], List<Example<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.oci.OCIOutputConverter

Converts a dense matrix into a list of predictions of the appropriate type.

convertOutput(DenseMatrix, int, Example<T>) - Method in class org.tribuo.interop.oci.OCIModel

convertOutput(DenseVector, int, Example<Label>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.oci.OCILabelConverter

convertOutput(DenseVector, int, Example<MultiLabel>, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.oci.OCIMultiLabelConverter

convertOutput(DenseVector, int, Example<Regressor>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.oci.OCIRegressorConverter

convertOutput(DenseVector, int, Example<T>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.oci.OCIOutputConverter

Converts a dense vector into a single prediction of the appropriate type.

convertOutput(V, int[], List<Example<T>>) - Method in class org.tribuo.interop.ExternalModel

Converts the output of the external model into a list of Predictions.

convertOutput(V, int, Example<T>) - Method in class org.tribuo.interop.ExternalModel

Converts the output of the external model into a Prediction.

convertSingleExample(Example<T>, ImmutableFeatureMap, ArrayList<Float>, ArrayList<Integer>, ArrayList<Long>, long) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer

Writes out the features from an example into the three supplied ArrayLists.

convertSparseVector(SparseVector) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer

Used when predicting with an externally trained XGBoost model.

convertSparseVectors(List<SparseVector>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer

Used when predicting with an externally trained XGBoost model.

convertToBatchOutput(Tensor, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter

convertToBatchOutput(Tensor, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter

convertToBatchOutput(Tensor, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter

convertToBatchOutput(Tensor, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter

Converts a Tensor containing multiple outputs into a list of Outputs.

convertToBatchPrediction(Tensor, ImmutableOutputInfo<Label>, int[], List<Example<Label>>) - Method in class org.tribuo.interop.tensorflow.LabelConverter

convertToBatchPrediction(Tensor, ImmutableOutputInfo<MultiLabel>, int[], List<Example<MultiLabel>>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter

convertToBatchPrediction(Tensor, ImmutableOutputInfo<Regressor>, int[], List<Example<Regressor>>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter

convertToBatchPrediction(Tensor, ImmutableOutputInfo<T>, int[], List<Example<T>>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter

Converts a Tensor containing multiple outputs into a list of Predictions.

convertToCheckpointModel(String, String) - Method in class org.tribuo.interop.tensorflow.TensorFlowNativeModel

Creates a TensorFlowCheckpointModel version of this model.

convertToDense() - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix

convertToDense() - Method in interface org.tribuo.math.optimisers.util.ShrinkingTensor

Converts the tensor into a dense tensor.

convertToDense() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector

convertToDenseVector(ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.multilabel.MultiLabel

Converts this MultiLabel into a DenseVector using the indices from the output info.

convertToMap(ObjectNode) - Static method in class org.tribuo.json.JsonUtil

Converts a Json node into a Map from String to String for use in downstream processing by RowProcessor.

convertToNativeModel() - Method in class org.tribuo.interop.tensorflow.TensorFlowCheckpointModel

Creates a TensorFlowNativeModel version of this model.

convertToOutput(Tensor, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter

convertToOutput(Tensor, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter

convertToOutput(Tensor, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter

convertToOutput(Tensor, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter

Converts a Tensor into the specified output type.

convertToPrediction(Tensor, ImmutableOutputInfo<Label>, int, Example<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter

convertToPrediction(Tensor, ImmutableOutputInfo<MultiLabel>, int, Example<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter

convertToPrediction(Tensor, ImmutableOutputInfo<Regressor>, int, Example<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter

convertToPrediction(Tensor, ImmutableOutputInfo<T>, int, Example<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter

Converts a Tensor into a Prediction.

convertToSparseVector(ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.multilabel.MultiLabel

Converts this MultiLabel into a SparseVector using the indices from the output info.

convertToTensor(List<Example<Label>>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter

convertToTensor(List<Example<MultiLabel>>, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter

convertToTensor(List<Example<Regressor>>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter

convertToTensor(List<Example<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter

Converts a list of Example into a Tensor representing all the outputs in the list.

convertToTensor(Label, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter

convertToTensor(MultiLabel, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter

convertToTensor(Regressor, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter

convertToTensor(T, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter

Converts an Output into a Tensor representing it's output.

convertToVector(SequenceExample<Label>, ImmutableFeatureMap, ImmutableOutputInfo<Label>) - Static method in class org.tribuo.classification.sgd.crf.CRFModel

Converts a SequenceExample into an array of SGDVectors and labels suitable for CRF prediction.

convertToVector(SequenceExample<T>, ImmutableFeatureMap) - Static method in class org.tribuo.classification.sgd.crf.CRFModel

Converts a SequenceExample into an array of SGDVectors suitable for CRF prediction.

convertTree() - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode

Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.

convertTree() - Method in class org.tribuo.common.tree.AbstractTrainingNode

Converts a tree from a training representation to the final inference time representation.

convertTree() - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode

Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.

convertTree() - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode

Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.

copy() - Method in class org.tribuo.anomaly.AnomalyInfo

copy() - Method in class org.tribuo.anomaly.Event

copy() - Method in class org.tribuo.anomaly.ImmutableAnomalyInfo

copy() - Method in class org.tribuo.anomaly.MutableAnomalyInfo

copy() - Method in class org.tribuo.CategoricalIDInfo

copy() - Method in class org.tribuo.CategoricalInfo

copy() - Method in class org.tribuo.classification.ImmutableLabelInfo

copy() - Method in class org.tribuo.classification.Label

copy() - Method in class org.tribuo.classification.LabelInfo

copy() - Method in class org.tribuo.classification.MutableLabelInfo

copy() - Method in class org.tribuo.clustering.ClusterID

copy() - Method in class org.tribuo.clustering.ClusteringInfo

copy() - Method in class org.tribuo.clustering.hdbscan.HdbscanTrainer.ClusterExemplar

Copies this cluster exemplar.

copy() - Method in class org.tribuo.clustering.ImmutableClusteringInfo

copy() - Method in class org.tribuo.clustering.MutableClusteringInfo

copy() - Method in class org.tribuo.common.sgd.FMParameters

copy() - Method in class org.tribuo.common.tree.AbstractTrainingNode

copy() - Method in class org.tribuo.common.tree.impl.IntArrayContainer

Returns a copy of the elements in use.

copy() - Method in class org.tribuo.common.tree.LeafNode

copy() - Method in interface org.tribuo.common.tree.Node

Copies the node and it's children.

copy() - Method in class org.tribuo.common.tree.SplitNode

copy() - Method in class org.tribuo.data.columnar.RowProcessor

Deprecated.

In a future release this API will change, in the meantime this is the correct way to get a row processor with clean state.

When using regexMappingProcessors, RowProcessor is stateful in a way that can sometimes make it fail the second time it is used. Concretely:

     RowProcessor rp;
     Dataset ds1 = new MutableDataset(new CSVDataSource(csvfile1, rp));
     Dataset ds2 = new MutableDataset(new CSVDataSource(csvfile2, rp)); // this may fail due to state in rp

This method returns a RowProcessor with clean state and the same configuration as this row processor.

copy() - Method in class org.tribuo.Example

Returns a deep copy of this Example.

copy() - Method in class org.tribuo.impl.ArrayExample

copy() - Method in class org.tribuo.impl.BinaryFeaturesExample

copy() - Method in class org.tribuo.impl.IndexedArrayExample

copy() - Method in class org.tribuo.impl.ListExample

copy() - Method in interface org.tribuo.math.FeedForwardParameters

Returns a copy of the parameters.

copy() - Method in class org.tribuo.math.la.DenseMatrix

Copies the matrix.

copy() - Method in class org.tribuo.math.la.DenseSparseMatrix

copy() - Method in class org.tribuo.math.la.DenseVector

copy() - Method in interface org.tribuo.math.la.Matrix

Copies the matrix.

copy() - Method in interface org.tribuo.math.la.SGDVector

Returns a deep copy of this vector.

copy() - Method in class org.tribuo.math.la.SparseVector

copy() - Method in interface org.tribuo.math.la.Tensor

Returns a copy of this Tensor.

copy() - Method in class org.tribuo.math.LinearParameters

copy() - Method in class org.tribuo.math.optimisers.AdaDelta

copy() - Method in class org.tribuo.math.optimisers.AdaGrad

copy() - Method in class org.tribuo.math.optimisers.AdaGradRDA

copy() - Method in class org.tribuo.math.optimisers.Adam

copy() - Method in class org.tribuo.math.optimisers.ParameterAveraging

copy() - Method in class org.tribuo.math.optimisers.Pegasos

copy() - Method in class org.tribuo.math.optimisers.RMSProp

copy() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector

copy() - Method in interface org.tribuo.math.StochasticGradientOptimiser

Copies a gradient optimiser with it's configuration.

copy() - Method in class org.tribuo.Model

Copies a model, returning a deep copy of any mutable state, and a shallow copy otherwise.

copy() - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo

copy() - Method in class org.tribuo.multilabel.MultiLabel

copy() - Method in class org.tribuo.multilabel.MultiLabelInfo

copy() - Method in class org.tribuo.multilabel.MutableMultiLabelInfo

copy() - Method in interface org.tribuo.Output

Deep copy of the output up to its immutable state.

copy() - Method in interface org.tribuo.OutputInfo

Generates a copy of this OutputInfo, including it's mutability.

copy() - Method in class org.tribuo.RealIDInfo

copy() - Method in class org.tribuo.RealInfo

copy() - Method in class org.tribuo.regression.ImmutableRegressionInfo

copy() - Method in class org.tribuo.regression.MutableRegressionInfo

copy() - Method in class org.tribuo.regression.RegressionInfo

copy() - Method in class org.tribuo.regression.Regressor

copy() - Method in class org.tribuo.regression.Regressor.DimensionTuple

copy() - Method in class org.tribuo.sequence.SequenceExample

Returns a deep copy of this SequenceExample.

copy() - Method in class org.tribuo.SparseModel

copy() - Method in interface org.tribuo.VariableInfo

Returns a copy of this variable info.

copy(String) - Method in interface org.tribuo.data.columnar.FieldProcessor

Returns a copy of this FieldProcessor bound to the supplied newFieldName.

copy(String) - Method in class org.tribuo.data.columnar.processors.field.DateFieldProcessor

copy(String) - Method in class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor

copy(String) - Method in class org.tribuo.data.columnar.processors.field.IdentityProcessor

copy(String) - Method in class org.tribuo.data.columnar.processors.field.RegexFieldProcessor

copy(String) - Method in class org.tribuo.data.columnar.processors.field.TextFieldProcessor

Note: the copy shares the text pipeline with the original.

copy(String, EnsembleModelProvenance, List<Model<T>>) - Method in class org.tribuo.ensemble.EnsembleModel

Copies this ensemble model.

copy(String, EnsembleModelProvenance, List<Model<T>>) - Method in class org.tribuo.ensemble.WeightedEnsembleModel

copy(String, ModelProvenance) - Method in class org.tribuo.anomaly.liblinear.LibLinearAnomalyModel

copy(String, ModelProvenance) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyModel

copy(String, ModelProvenance) - Method in class org.tribuo.classification.baseline.DummyClassifierModel

copy(String, ModelProvenance) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationModel

copy(String, ModelProvenance) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationModel

copy(String, ModelProvenance) - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesModel

copy(String, ModelProvenance) - Method in class org.tribuo.classification.sgd.fm.FMClassificationModel

copy(String, ModelProvenance) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel

copy(String, ModelProvenance) - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel

copy(String, ModelProvenance) - Method in class org.tribuo.clustering.hdbscan.HdbscanModel

copy(String, ModelProvenance) - Method in class org.tribuo.clustering.kmeans.KMeansModel

copy(String, ModelProvenance) - Method in class org.tribuo.common.nearest.KNNModel

copy(String, ModelProvenance) - Method in class org.tribuo.common.tree.TreeModel

copy(String, ModelProvenance) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel

copy(String, ModelProvenance) - Method in class org.tribuo.common.xgboost.XGBoostModel

copy(String, ModelProvenance) - Method in class org.tribuo.ensemble.EnsembleModel

copy(String, ModelProvenance) - Method in class org.tribuo.interop.oci.OCIModel

copy(String, ModelProvenance) - Method in class org.tribuo.interop.onnx.ONNXExternalModel

copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowCheckpointModel

copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel

copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowNativeModel

copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel

copy(String, ModelProvenance) - Method in class org.tribuo.Model

Copies a model, replacing its provenance and name with the supplied values.

copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.baseline.ClassifierChainModel

copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.baseline.IndependentMultiLabelModel

copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.sgd.fm.FMMultiLabelModel

copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.sgd.linear.LinearSGDModel

copy(String, ModelProvenance) - Method in class org.tribuo.regression.baseline.DummyRegressionModel

copy(String, ModelProvenance) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionModel

copy(String, ModelProvenance) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionModel

copy(String, ModelProvenance) - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel

copy(String, ModelProvenance) - Method in class org.tribuo.regression.sgd.fm.FMRegressionModel

copy(String, ModelProvenance) - Method in class org.tribuo.regression.sgd.linear.LinearSGDModel

copy(String, ModelProvenance) - Method in class org.tribuo.regression.slm.SparseLinearModel

copy(String, ModelProvenance) - Method in class org.tribuo.transform.TransformedModel

copyDataset(Dataset<T>) - Static method in class org.tribuo.ImmutableDataset

Creates an immutable deep copy of the supplied dataset.

copyDataset(Dataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.ImmutableDataset

Creates an immutable deep copy of the supplied dataset, using a different feature and output map.

copyDataset(Dataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>, Merger) - Static method in class org.tribuo.ImmutableDataset

Creates an immutable deep copy of the supplied dataset.

copyDataset(SequenceDataset<T>) - Static method in class org.tribuo.sequence.ImmutableSequenceDataset

Creates an immutable deep copy of the supplied dataset.

copyDataset(SequenceDataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.sequence.ImmutableSequenceDataset

Creates an immutable deep copy of the supplied dataset, using a different feature and output map.

copyDataset(SequenceDataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>, Merger) - Static method in class org.tribuo.sequence.ImmutableSequenceDataset

Creates an immutable deep copy of the supplied dataset.

copyModel(Model) - Static method in class org.tribuo.common.liblinear.LibLinearModel

Copies the model by writing it out to a String and loading it back in.

copyModel(svm_model) - Static method in class org.tribuo.common.libsvm.LibSVMModel

Copies an svm_model, as it does not provide a copy method.

copyParameters(svm_parameter) - Static method in class org.tribuo.common.libsvm.SVMParameters

Deep copy of the svm_parameters including the arrays.

copyParams(Map<String, ?>) - Method in class org.tribuo.common.xgboost.XGBoostTrainer

Returns a copy of the supplied parameter map which has the appropriate type for passing to XGBoost.train.

copyResourceToTmp(String) - Static method in class org.tribuo.tests.Resources

Copies a classpath resource to a temporary file.

copyValues(int) - Method in class org.tribuo.impl.ArrayExample

Returns a copy of the feature values array at the specific size.

CoreTokenizerOptions - Class in org.tribuo.util.tokens.options

CLI Options for all the tokenizers in the core package.

CoreTokenizerOptions() - Constructor for class org.tribuo.util.tokens.options.CoreTokenizerOptions

CoreTokenizerOptions.CoreTokenizerType - Enum Class in org.tribuo.util.tokens.options

Tokenizer type.

coreTokenizerType - Variable in class org.tribuo.util.tokens.options.CoreTokenizerOptions

Type of tokenizer

CORRELATED - Enum constant in enum class org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType

Correlated data.

COSINE - Enum constant in enum class org.tribuo.clustering.hdbscan.HdbscanTrainer.Distance

Deprecated.

Cosine similarity as a distance measure.

COSINE - Enum constant in enum class org.tribuo.clustering.kmeans.KMeansTrainer.Distance

Deprecated.

Cosine similarity as a distance measure.

COSINE - Enum constant in enum class org.tribuo.common.nearest.KNNTrainer.Distance

Deprecated.

Cosine similarity used as a distance measure.

COSINE - Enum constant in enum class org.tribuo.math.distance.DistanceType

Cosine similarity used as a distance measure.

cosineDistance(SGDVector) - Method in interface org.tribuo.math.la.SGDVector

Calculates the cosine distance of two vectors.

CosineDistance - Class in org.tribuo.math.distance

Cosine similarity used as a distance measure.

CosineDistance() - Constructor for class org.tribuo.math.distance.CosineDistance

Constructs a Cosine distance function.

cosineSimilarity(SGDVector) - Method in interface org.tribuo.math.la.SGDVector

Calculates the cosine similarity of two vectors.

cost - Variable in class org.tribuo.common.liblinear.LibLinearTrainer

cost - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions

Cost penalty for SVM.

count - Variable in class org.tribuo.SkeletalVariableInfo

How often the feature occurs in the dataset.

count - Variable in class org.tribuo.util.infotheory.impl.PairDistribution

The number of samples this distribution has seen.

count - Variable in class org.tribuo.util.infotheory.impl.TripleDistribution

The number of samples in this distribution.

count - Variable in class org.tribuo.util.infotheory.impl.WeightCountTuple

The current count.

count - Variable in class org.tribuo.util.infotheory.impl.WeightedPairDistribution

The sample count.

count - Variable in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution

The sample count.

countMap - Variable in class org.tribuo.regression.RegressionInfo

The occurrence count per dimension.

countNodes(Node<T>) - Method in class org.tribuo.common.tree.TreeModel

Counts the number of nodes in the tree rooted at the supplied node, including that node.

CREATE_AND_DEPLOY - Enum constant in enum class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions.Mode

Create a Model artifact, upload it to OCI and create a Model Deployment.

createBootstrapView(Dataset<T>, int, long) - Static method in class org.tribuo.dataset.DatasetView

Generates a DatasetView bootstrapped from the supplied Dataset.

createBootstrapView(Dataset<T>, int, long, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.dataset.DatasetView

Generates a DatasetView bootstrapped from the supplied Dataset.

createConstantTrainer(double) - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer

Creates a trainer which create models which return a fixed value.

createConstantTrainer(String) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer

Creates a trainer which creates models which return a fixed label.

createContext(Model<Event>, List<Prediction<Event>>) - Method in class org.tribuo.anomaly.evaluation.AnomalyEvaluator

createContext(Model<Event>, List<Prediction<Event>>) - Method in class org.tribuo.anomaly.evaluation.AnomalyMetric

createContext(Model<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.evaluation.LabelEvaluator

createContext(Model<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.evaluation.LabelMetric

createContext(Model<ClusterID>, List<Prediction<ClusterID>>) - Method in class org.tribuo.clustering.evaluation.ClusteringEvaluator

createContext(Model<ClusterID>, List<Prediction<ClusterID>>) - Method in class org.tribuo.clustering.evaluation.ClusteringMetric

createContext(Model<MultiLabel>, List<Prediction<MultiLabel>>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluator

createContext(Model<MultiLabel>, List<Prediction<MultiLabel>>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric

createContext(Model<Regressor>, List<Prediction<Regressor>>) - Method in class org.tribuo.regression.evaluation.RegressionEvaluator

createContext(Model<Regressor>, List<Prediction<Regressor>>) - Method in class org.tribuo.regression.evaluation.RegressionMetric

createContext(Model<T>, List<Prediction<T>>) - Method in class org.tribuo.evaluation.AbstractEvaluator

Create the context needed for evaluation.

createContext(Model<T>, List<Prediction<T>>) - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric

Creates the context this metric uses to compute it's value.

createContext(Model<T>, Dataset<T>) - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric

Creates the metric context used to compute this metric's value, generating Predictions for each Example in the supplied dataset.

createContext(SequenceModel<Label>, List<List<Prediction<Label>>>) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator

createContext(SequenceModel<T>, List<List<Prediction<T>>>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator

Create the context needed for evaluation.

createDataCarrier() - Method in class org.tribuo.Model

Constructs the data carrier for serialization.

createDataCarrier() - Method in class org.tribuo.sequence.SequenceModel

Constructs the data carrier for serialization.

createDataCarrier(FeatureMap, OutputInfo<T>) - Method in class org.tribuo.Dataset

Constructs the data carrier for serialization.

createDataCarrier(FeatureMap, OutputInfo<T>) - Method in class org.tribuo.sequence.SequenceDataset

Constructs the data carrier for serialization.

createDataCarrier(FeatureMap, OutputInfo<T>, List<ObjectProvenance>) - Method in class org.tribuo.Dataset

Constructs the data carrier for serialization.

createDeepCopy(Dataset<T>) - Static method in class org.tribuo.MutableDataset

Creates a deep copy of the supplied Dataset which is mutable.

createDenseMatrix(double[][]) - Static method in class org.tribuo.math.la.DenseMatrix

Defensively copies the values before construction.

createDenseMatrix(SGDVector[]) - Static method in class org.tribuo.math.la.DenseMatrix

Constructs a new DenseMatrix copying the values from the supplied vectors.

createDenseVector(double[]) - Static method in class org.tribuo.math.la.DenseVector

Defensively copies the values before construction.

createDenseVector(Example<T>, ImmutableFeatureMap, boolean) - Static method in class org.tribuo.math.la.DenseVector

Builds a DenseVector from an Example.

createDiagonal(SGDVector) - Static method in class org.tribuo.math.la.DenseSparseMatrix

Creates a diagonal matrix using the supplied values.

createEnsembleFromExistingModels(String, List<Model<T>>, EnsembleCombiner<T>) - Static method in class org.tribuo.ensemble.WeightedEnsembleModel

Creates an ensemble from existing models.

createEnsembleFromExistingModels(String, List<Model<T>>, EnsembleCombiner<T>, float[]) - Static method in class org.tribuo.ensemble.WeightedEnsembleModel

Creates an ensemble from existing models.

createEvaluation(C, Map<MetricID<T>, Double>, EvaluationProvenance) - Method in class org.tribuo.evaluation.AbstractEvaluator

Create an evaluation for the given results

createEvaluation(C, Map<MetricID<T>, Double>, EvaluationProvenance) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator

Create an evaluation for the given results

createEvaluation(AnomalyMetric.Context, Map<MetricID<Event>, Double>, EvaluationProvenance) - Method in class org.tribuo.anomaly.evaluation.AnomalyEvaluator

createEvaluation(LabelMetric.Context, Map<MetricID<Label>, Double>, EvaluationProvenance) - Method in class org.tribuo.classification.evaluation.LabelEvaluator

createEvaluation(LabelMetric.Context, Map<MetricID<Label>, Double>, EvaluationProvenance) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator

createEvaluation(ClusteringMetric.Context, Map<MetricID<ClusterID>, Double>, EvaluationProvenance) - Method in class org.tribuo.clustering.evaluation.ClusteringEvaluator

createEvaluation(MultiLabelMetric.Context, Map<MetricID<MultiLabel>, Double>, EvaluationProvenance) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluator

createEvaluation(RegressionMetric.Context, Map<MetricID<Regressor>, Double>, EvaluationProvenance) - Method in class org.tribuo.regression.evaluation.RegressionEvaluator

createFeatureMap(Set<String>) - Static method in class org.tribuo.interop.ExternalModel

Creates an immutable feature map from a set of feature names.

createFeatures(Example<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionModel

Creates the feature vector.

createFeatures(Example<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel

Creates the feature vector.

createFeatures(Example<Regressor>) - Method in class org.tribuo.regression.slm.SparseLinearModel

Creates the feature vector.

createFromPairList(List<Pair<String, Boolean>>) - Static method in class org.tribuo.multilabel.MultiLabel

Creates a MultiLabel from a list of dimensions.

createFromPairList(List<Pair<String, Double>>) - Static method in class org.tribuo.regression.Regressor

Creates a Regressor from a list of dimension tuples.

createFromSparseVectors(SparseVector[]) - Static method in class org.tribuo.math.la.DenseSparseMatrix

Defensively copies the values.

createGaussianTrainer(long) - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer

Creates a trainer which create models which sample the output from a gaussian distribution fit to the training data.

createIdentity(int) - Static method in class org.tribuo.math.la.DenseSparseMatrix

Creates an identity matrix of the specified size.

createIDXData(IDXDataSource.IDXType, int[], double[]) - Static method in class org.tribuo.datasource.IDXDataSource.IDXData

Constructs an IDXData, validating the input and defensively copying it.

createLabel(Label) - Method in class org.tribuo.multilabel.MultiLabel

Creates a binary label from this multilabel.

createMeanTrainer() - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer

Creates a trainer which create models which return the mean of the training data.

createMedianTrainer() - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer

Creates a trainer which create models which return the median of the training data.

createMetrics(Model<Event>) - Method in class org.tribuo.anomaly.evaluation.AnomalyEvaluator

createMetrics(Model<Label>) - Method in class org.tribuo.classification.evaluation.LabelEvaluator

createMetrics(Model<ClusterID>) - Method in class org.tribuo.clustering.evaluation.ClusteringEvaluator

createMetrics(Model<MultiLabel>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluator

createMetrics(Model<Regressor>) - Method in class org.tribuo.regression.evaluation.RegressionEvaluator

createMetrics(Model<T>) - Method in class org.tribuo.evaluation.AbstractEvaluator

Creates the appropriate set of metrics for this model, by querying for it's OutputInfo.

createMetrics(SequenceModel<Label>) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator

createMetrics(SequenceModel<T>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator

Creates the appropriate set of metrics for this model, by querying for it's OutputInfo.

createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, FMParameters) - Method in class org.tribuo.classification.sgd.fm.FMClassificationTrainer

createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, LinearParameters) - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer

createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<MultiLabel>, FMParameters) - Method in class org.tribuo.multilabel.sgd.fm.FMMultiLabelTrainer

createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<MultiLabel>, LinearParameters) - Method in class org.tribuo.multilabel.sgd.linear.LinearSGDTrainer

createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, FMParameters) - Method in class org.tribuo.regression.sgd.fm.FMRegressionTrainer

createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, LinearParameters) - Method in class org.tribuo.regression.sgd.linear.LinearSGDTrainer

createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Booster>, XGBoostOutputConverter<T>) - Method in class org.tribuo.common.xgboost.XGBoostTrainer

Creates an XGBoost model from the booster list.

createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, X) - Method in class org.tribuo.common.sgd.AbstractSGDTrainer

Creates the appropriate model subclass for this subclass of AbstractSGDTrainer.

createModel(Path, ModelProvenance, OCIUtil.OCIModelType, DataScienceClient, ObjectMapper, OCIUtil.OCIModelArtifactConfig) - Static method in class org.tribuo.interop.oci.OCIUtil

Creates an OCI DS model and uploads the model artifact.

createModel(Map<String, T>, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer

Constructs the appropriate subclass of SkeletalIndependentRegressionModel for this trainer.

createModel(Map<String, T>, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer

Constructs the appropriate subclass of SkeletalIndependentRegressionModel for this trainer.

createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Event>, List<Model>) - Method in class org.tribuo.anomaly.liblinear.LibLinearAnomalyTrainer

createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Event>, List<svm_model>) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer

createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, List<Model>) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationTrainer

createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, List<svm_model>) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationTrainer

createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, List<Model>) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionTrainer

createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, List<svm_model>) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionTrainer

createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Model>) - Method in class org.tribuo.common.liblinear.LibLinearTrainer

Construct the appropriate subtype of LibLinearModel for the prediction task.

createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<svm_model>) - Method in class org.tribuo.common.libsvm.LibSVMTrainer

Construct the appropriate subtype of LibSVMModel for the prediction task.

createModel(U, DataScienceClient, ObjectMapper, OCIUtil.OCIModelArtifactConfig) - Static method in class org.tribuo.interop.oci.OCIUtil

Creates an OCI DS model and uploads the model artifact.

createModelArtifact(Path, OCIUtil.OCIModelArtifactConfig) - Static method in class org.tribuo.interop.oci.OCIUtil

Creates the OCI DS model artifact zip file.

createMostFrequentTrainer() - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer

Creates a trainer which creates models which return a fixed label, the one which was most frequent in the training data.

createNeighboursQuery(SGDVector[]) - Method in class org.tribuo.math.neighbour.bruteforce.NeighboursBruteForceFactory

Constructs a brute-force nearest neighbor query object using the supplied array of SGDVector.

createNeighboursQuery(SGDVector[]) - Method in class org.tribuo.math.neighbour.kdtree.KDTreeFactory

Constructs a k-d tree nearest neighbor query object using the supplied array of SGDVector.

createNeighboursQuery(SGDVector[]) - Method in interface org.tribuo.math.neighbour.NeighboursQueryFactory

Constructs a nearest neighbour query object using the supplied array of SGDVector.

createObjectMapper() - Static method in class org.tribuo.interop.oci.OCIUtil

Creates an ObjectMapper capable of parsing the OCI DS json.

createOCIModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, Path, String, String, OCIOutputConverter<T>) - Static method in class org.tribuo.interop.oci.OCIModel

Creates an OCIModel by wrapping an OCI DS Model Deployment endpoint.

createOCIModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, Path, String, OCIOutputConverter<T>) - Static method in class org.tribuo.interop.oci.OCIModel

Creates an OCIModel by wrapping an OCI DS Model Deployment endpoint.

createOnlineEvaluator(Model<T>, DataProvenance) - Method in interface org.tribuo.evaluation.Evaluator

Creates an online evaluator that maintains a list of all the predictions it has seen and can evaluate them upon request.

createOnnxModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, ExampleTransformer, OutputTransformer<T>, OrtSession.SessionOptions, String, String) - Static method in class org.tribuo.interop.onnx.ONNXExternalModel

Creates an ONNXExternalModel by loading the model from disk.

createOnnxModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, ExampleTransformer, OutputTransformer<T>, OrtSession.SessionOptions, Path, String) - Static method in class org.tribuo.interop.onnx.ONNXExternalModel

Creates an ONNXExternalModel by loading the model from disk.

createOutputInfo(OutputFactory<T>, Map<T, Integer>) - Static method in class org.tribuo.interop.ExternalModel

Creates an output info from a set of outputs.

createParameters(int, int, SplittableRandom) - Method in class org.tribuo.common.sgd.AbstractFMTrainer

Constructs the trainable parameters object, in this case a FMParameters containing a weight matrix for the feature weights and a series of weight matrices for the factorized feature representation.

createParameters(int, int, SplittableRandom) - Method in class org.tribuo.common.sgd.AbstractLinearSGDTrainer

Constructs the trainable parameters object, in this case a LinearParameters containing a single weight matrix.

createParameters(int, int, SplittableRandom) - Method in class org.tribuo.common.sgd.AbstractSGDTrainer

Constructs the trainable parameters object.

createQuartileTrainer(double) - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer

Creates a trainer which create models which return the value at the specified fraction of the sorted training data.

createSparseVector(int, int[], double[]) - Static method in class org.tribuo.math.la.SparseVector

Defensively copies the input, and checks that the indices are sorted.

createSparseVector(int, Map<Integer, Double>) - Static method in class org.tribuo.math.la.SparseVector

Builds a SparseVector from a map.

createSparseVector(Example<T>, ImmutableFeatureMap, boolean) - Static method in class org.tribuo.math.la.SparseVector

Builds a SparseVector from an Example.

createSplitFunction(boolean) - Static method in class org.tribuo.util.tokens.impl.wordpiece.WordpieceBasicTokenizer

Creates a SplitFunctionTokenizer.SplitFunction that is used by the super class SplitFunctionTokenizer to determine how and where the tokenizer splits the input.

createSplitNode() - Method in class org.tribuo.common.tree.AbstractTrainingNode

Transforms an AbstractTrainingNode into a SplitNode

createStats() - Method in interface org.tribuo.transform.Transformation

Creates the statistics object for this Transformation.

createStats() - Method in class org.tribuo.transform.transformations.BinningTransformation

createStats() - Method in class org.tribuo.transform.transformations.IDFTransformation

createStats() - Method in class org.tribuo.transform.transformations.LinearScalingTransformation

createStats() - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation

createStats() - Method in class org.tribuo.transform.transformations.SimpleTransform

Returns itself.

createStratifiedTrainer(long) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer

Creates a trainer which creates models which return random labels sampled from the training label distribution.

createSupplier(Tokenizer) - Static method in interface org.tribuo.util.tokens.Tokenizer

Creates a supplier from the specified tokenizer by cloning it.

createTensorflowModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, String, FeatureConverter, OutputConverter<T>, String) - Static method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel

Creates a TensorflowFrozenExternalModel by loading in a frozen graph.

Creates a TensorflowSavedModelExternalModel by loading in a SavedModelBundle.

createThreadLocal(Tokenizer) - Static method in interface org.tribuo.util.tokens.Tokenizer

Creates a thread local source of tokenizers by making a Tokenizer supplier using Tokenizer.createSupplier(Tokenizer).

createTransformers(TransformationMap) - Method in class org.tribuo.Dataset

Takes a TransformationMap and converts it into a TransformerMap by observing all the values in this dataset.

createTransformers(TransformationMap, boolean) - Method in class org.tribuo.Dataset

Takes a TransformationMap and converts it into a TransformerMap by observing all the values in this dataset.

createUniformTrainer(long) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer

Creates a trainer which creates models which return random labels sampled uniformly from the labels seen at training time.

createView(Dataset<T>, Predicate<Example<T>>, String) - Static method in class org.tribuo.dataset.DatasetView

Creates a view from the supplied dataset, using the specified predicate to test if each example should be in this view.

createWeightedBootstrapView(Dataset<T>, int, long, float[]) - Static method in class org.tribuo.dataset.DatasetView

Generates a DatasetView bootstrapped from the supplied Dataset using the supplied example weights.

createWeightedBootstrapView(Dataset<T>, int, long, float[], ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.dataset.DatasetView

Generates a DatasetView bootstrapped from the supplied Dataset using the supplied example weights.

createWhitespaceTokenizer() - Static method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer

Creates a tokenizer that splits on whitespace.

createWithEmptyOutputs(List<? extends List<? extends Feature>>, OutputFactory<T>) - Static method in class org.tribuo.sequence.SequenceExample

Creates a SequenceExample using OutputFactory.getUnknownOutput() as the output for each sequence element.

createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, String) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel

Creates an XGBoostExternalModel from the supplied model on disk.

createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, Path) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel

Creates an XGBoostExternalModel from the supplied model on disk.

createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, Booster, URL) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel

Deprecated.

As the URL argument must always be valid. To wrap an in-memory booster use XGBoostExternalModel.createXGBoostModel(OutputFactory, Map, Map, XGBoostOutputConverter, Booster, Map).

createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, Booster, Map<String, Provenance>) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel

Creates an XGBoostExternalModel from the supplied in-memory XGBoost Booster.

CREATION_TIME - Static variable in class org.tribuo.provenance.impl.TimestampedTrainerProvenance

The name of the provenance field storing the model creation time.

CRFModel - Class in org.tribuo.classification.sgd.crf

An inference time model for a linear chain CRF trained using SGD.

CRFModel.ConfidenceType - Enum Class in org.tribuo.classification.sgd.crf

The type of subsequence level confidence to predict.

CRFOptions - Class in org.tribuo.classification.sgd.crf

CLI options for training a linear chain CRF model.

CRFOptions() - Constructor for class org.tribuo.classification.sgd.crf.CRFOptions

CRFOptions() - Constructor for class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions

CRFParameters - Class in org.tribuo.classification.sgd.crf

A Parameters for training a CRF using SGD.

CRFTrainer - Class in org.tribuo.classification.sgd.crf

A trainer for CRFs using SGD.

CRFTrainer(StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer

Creates a CRFTrainer which uses SGD to learn the parameters.

CRFTrainer(StochasticGradientOptimiser, int, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer

Sets the minibatch size to 1.

CRFTrainer(StochasticGradientOptimiser, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer

Sets the minibatch size to 1 and the logging interval to 100.

crossValidation - Variable in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions

Cross-validate the output metrics.

CrossValidation<T extends Output<T>,E extends Evaluation<T>> - Class in org.tribuo.evaluation

A class that does k-fold cross-validation.

CrossValidation(Trainer<T>, Dataset<T>, Evaluator<T, E>, int) - Constructor for class org.tribuo.evaluation.CrossValidation

Builds a k-fold cross-validation loop.

CrossValidation(Trainer<T>, Dataset<T>, Evaluator<T, E>, int, long) - Constructor for class org.tribuo.evaluation.CrossValidation

Builds a k-fold cross-validation loop.

CSV - Enum constant in enum class org.tribuo.data.DataOptions.InputFormat

Simple numeric CSV file.

CSVDataSource<T extends Output<T>> - Class in org.tribuo.data.csv

A DataSource for loading separable data from a text file (e.g., CSV, TSV) and applying FieldProcessors to it.

CSVDataSource(URI, RowProcessor<T>, boolean) - Constructor for class org.tribuo.data.csv.CSVDataSource

Creates a CSVDataSource using the specified RowProcessor to process the data.

CSVDataSource(URI, RowProcessor<T>, boolean, char) - Constructor for class org.tribuo.data.csv.CSVDataSource

Creates a CSVDataSource using the specified RowProcessor to process the data.

CSVDataSource(URI, RowProcessor<T>, boolean, char, char) - Constructor for class org.tribuo.data.csv.CSVDataSource

Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.

CSVDataSource(URI, RowProcessor<T>, boolean, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVDataSource

Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.

CSVDataSource(Path, RowProcessor<T>, boolean) - Constructor for class org.tribuo.data.csv.CSVDataSource

Creates a CSVDataSource using the specified RowProcessor to process the data.

CSVDataSource(Path, RowProcessor<T>, boolean, char) - Constructor for class org.tribuo.data.csv.CSVDataSource

Creates a CSVDataSource using the specified RowProcessor to process the data.

CSVDataSource(Path, RowProcessor<T>, boolean, char, char) - Constructor for class org.tribuo.data.csv.CSVDataSource

Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.

CSVDataSource(Path, RowProcessor<T>, boolean, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVDataSource

Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.

CSVDataSource.CSVDataSourceProvenance - Class in org.tribuo.data.csv

Provenance for CSVDataSource.

CSVDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance

Deserialization constructor.

CSVIterator - Class in org.tribuo.data.csv

An iterator over a CSV file.

CSVIterator(Reader) - Constructor for class org.tribuo.data.csv.CSVIterator

Builds a CSVIterator for the supplied Reader.

CSVIterator(Reader, char, char) - Constructor for class org.tribuo.data.csv.CSVIterator

Builds a CSVIterator for the supplied Reader.

CSVIterator(Reader, char, char, String[]) - Constructor for class org.tribuo.data.csv.CSVIterator

Builds a CSVIterator for the supplied Reader.

CSVIterator(Reader, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVIterator

Builds a CSVIterator for the supplied Reader.

CSVIterator(URI) - Constructor for class org.tribuo.data.csv.CSVIterator

Builds a CSVIterator for the supplied URI.

CSVIterator(URI, char, char) - Constructor for class org.tribuo.data.csv.CSVIterator

Builds a CSVIterator for the supplied URI.

CSVIterator(URI, char, char, String[]) - Constructor for class org.tribuo.data.csv.CSVIterator

Builds a CSVIterator for the supplied URI.

CSVIterator(URI, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVIterator

Builds a CSVIterator for the supplied URI.

CSVLoader<T extends Output<T>> - Class in org.tribuo.data.csv

Load a DataSource/Dataset from a CSV file.

CSVLoader(char, char, OutputFactory<T>) - Constructor for class org.tribuo.data.csv.CSVLoader

Creates a CSVLoader using the supplied separator, quote and output factory.

CSVLoader(char, OutputFactory<T>) - Constructor for class org.tribuo.data.csv.CSVLoader

Creates a CSVLoader using the supplied separator and output factory.

CSVLoader(OutputFactory<T>) - Constructor for class org.tribuo.data.csv.CSVLoader

Creates a CSVLoader using the supplied output factory.

CSVLoader.CSVLoaderProvenance - Class in org.tribuo.data.csv

Deprecated.

Deprecated in 4.2 as CSVLoader now returns a CSVDataSource. This provenance is kept so older models can still load correctly.

CSVLoaderProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance

Deprecated.

Deserialization constructor.

csvQuoteChar - Variable in class org.tribuo.data.DataOptions

Quote character in the CSV file.

csvResponseName - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions

Response name in the csv file.

csvResponseName - Variable in class org.tribuo.data.DataOptions

Response name in the csv file.

CSVSaver - Class in org.tribuo.data.csv

Saves a Dataset in CSV format suitable for loading by CSVLoader.

CSVSaver() - Constructor for class org.tribuo.data.csv.CSVSaver

Builds a CSV saver using the default separator and quote from CSVIterator.

CSVSaver(char, char) - Constructor for class org.tribuo.data.csv.CSVSaver

Builds a CSV saver using the supplied separator and quote.

cumulativeSum(boolean[]) - Static method in class org.tribuo.util.Util

Produces a cumulative sum array.

cumulativeSum(double[]) - Static method in class org.tribuo.util.Util

Produces a cumulative sum array.

CURRENT_VERSION - Static variable in class org.tribuo.anomaly.libsvm.LibSVMAnomalyModel