Index (Tribuo 4.1.1 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 Z

A

a - Variable in class org.tribuo.util.infotheory.impl.CachedTriple
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(AbstractCARTTrainer<T>) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer.AbstractCARTTrainerProvenance: Deprecated.
AbstractCARTTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer.AbstractCARTTrainerProvenance: Deprecated.
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
AbstractLinearSGDModel<T extends Output<T>> - Class in org.tribuo.common.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> - Class in org.tribuo.common.sgd: A trainer for a linear model which uses SGD.
AbstractLinearSGDTrainer(StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDTrainer: Constructs an SGD trainer for a linear model.
AbstractLinearSGDTrainer() - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDTrainer: For olcut.
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
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(StochasticGradientOptimiser, int, int, int, long, boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDTrainer: Constructs an SGD trainer.
AbstractSGDTrainer(boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDTrainer: Base constructor called by subclass no-args constructors used by OLCUT.
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(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(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() - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: The overall accuracy of the evaluation.
accuracy(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: The per label accuracy of the evaluation.
accuracy() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
accuracy(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
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(double, double) - Constructor for class org.tribuo.math.optimisers.AdaDelta: It's recommended to keep rho at 0.95.
AdaDelta(double) - Constructor for class org.tribuo.math.optimisers.AdaDelta: Keeps rho at 0.95, passes through epsilon.
AdaDelta() - Constructor for class org.tribuo.math.optimisers.AdaDelta: Sets rho to 0.95 and epsilon to 1e-6.
AdaGrad - Class in org.tribuo.math.optimisers: An implementation of the AdaGrad gradient optimiser.
AdaGrad(double, double) - Constructor for class org.tribuo.math.optimisers.AdaGrad
AdaGrad(double) - Constructor for class org.tribuo.math.optimisers.AdaGrad: Sets epsilon to 1e-6.
AdaGradRDA - Class in org.tribuo.math.optimisers: An implementation of the AdaGrad gradient optimiser with regularized dual averaging.
AdaGradRDA(double, double, double, double, int) - Constructor for class org.tribuo.math.optimisers.AdaGradRDA
AdaGradRDA(double, double) - Constructor for class org.tribuo.math.optimisers.AdaGradRDA
Adam - Class in org.tribuo.math.optimisers: An implementation of the Adam gradient optimiser.
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(double, double) - Constructor for class org.tribuo.math.optimisers.Adam: Sets betaOne to 0.9 and betaTwo to 0.999
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.
add(Feature) - Method in class org.tribuo.Example: Adds a feature.
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>, 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(String, double) - Method in class org.tribuo.impl.ArrayExample: Adds a single feature.
add(Feature) - Method in class org.tribuo.impl.ArrayExample
add(String) - Method in class org.tribuo.impl.BinaryFeaturesExample: Adds a single feature with a value of 1.
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(int, int, double) - Method in class org.tribuo.math.la.DenseMatrix
add(int, int, double) - Method in class org.tribuo.math.la.DenseSparseMatrix
add(SGDVector) - Method in class org.tribuo.math.la.DenseVector: Adds other to this vector, producing a new DenseVector.
add(int, double) - Method in class org.tribuo.math.la.DenseVector
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, double) - Method in interface org.tribuo.math.la.SGDVector: Adds value to the element at index.
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(int, double) - Method in class org.tribuo.math.la.SparseVector
add(Example<T>) - Method in class org.tribuo.MutableDataset: Adds an example to the dataset, which observes the output and each feature value.
add(String, double) - Method in class org.tribuo.MutableFeatureMap: Adds an occurrence of a feature with a given name.
add(int) - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
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>, 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(SequenceExample<T>) - Method in class org.tribuo.sequence.MutableSequenceDataset: Adds a SequenceExample to this dataset.
add(double) - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that adds the operand to each value.
add(Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
add(int, Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
add() - Static method in interface org.tribuo.util.Merger: A merger which adds the elements.
addAcrossDim1(int[], double) - Method in class org.tribuo.math.la.DenseMatrix
addAcrossDim2(int[], double) - Method in class org.tribuo.math.la.DenseMatrix
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 Example<T>>) - Method in class org.tribuo.MutableDataset: Adds all the Examples in the supplied collection to this dataset.
addAll(Collection<SequenceExample<T>>) - Method in class org.tribuo.sequence.MutableSequenceDataset: Adds all the SequenceExamples in the supplied collection to this dataset.
addAll(Collection<? extends Row<T>>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
addAll(int, Collection<? extends Row<T>>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
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.
addExample(Example<T>) - Method in class org.tribuo.sequence.SequenceExample: Adds an Example to this sequence.
adjustedMI() - Method in interface org.tribuo.clustering.evaluation.ClusteringEvaluation: Measures the adjusted normalized mutual information between the predicted ids and the supplied ids.
adjustedMI(ClusteringMetric.Context) - Static method in enum 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 in org.tribuo.datasource: Specifies the iteration order of the inner sources.
AggregateDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance
algorithm - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
algorithm - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
algorithm - Variable in class org.tribuo.regression.slm.TrainTest.SLMOptions
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.provenance.DatasetProvenance
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 in org.tribuo.classification.experiments: Types of algorithms supported.
alpha - Variable in class org.tribuo.regression.slm.TrainTest.SLMOptions
alpha - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
alpha - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
alphas - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults
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_EVENT - Static variable in class org.tribuo.anomaly.AnomalyFactory: The anomalous 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
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(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 in org.tribuo.anomaly.evaluation: Default metrics for evaluating anomaly detection.
apply(E) - Method in interface org.tribuo.evaluation.EvaluationRenderer: Convert the evaluation to a string.
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.
applyCase(String) - Method in enum org.tribuo.data.text.impl.CasingPreprocessor.CasingOperation: Apply the appropriate casing operation.
applyOptimiser(Graph, Operand<T>, Map<String, Float>) - Method in enum 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.
ARCH_STRING - Static variable in class org.tribuo.provenance.ModelProvenance
archString - Variable in class org.tribuo.provenance.ModelProvenance
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.
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.
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
ArrayExample<T extends Output<T>> - Class in org.tribuo.impl: An Example backed by two arrays, one of String and one of double.
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, float) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output and a weight.
ArrayExample(T, Map<String, Object>) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output and the metadata.
ArrayExample(T) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output.
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(Example<T>) - Constructor for class org.tribuo.impl.ArrayExample: Copy constructor.
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
ASSIGN_PLACEHOLDER - Static variable in class org.tribuo.interop.tensorflow.TensorFlowUtil
ATTENTION_MASK - Static variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
auc(double[], double[]) - Static method in class org.tribuo.util.Util: Calculates the area under the curve, bounded below by the x axis.
AUCROC(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Area under the ROC curve.
AUCROC(Label, List<Prediction<Label>>) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics: Area under the ROC curve.
AUCROC(MetricTarget<Label>, List<Prediction<Label>>) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics: Area under the ROC curve.
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.
averagedExplainedVariance() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: The average explained variance across all dimensions.
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(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(MetricTarget<Label>, List<Prediction<Label>>) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics
averagedPrecision(Label, List<Prediction<Label>>) - Static method in enum 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

B

b - Variable in class org.tribuo.util.infotheory.impl.CachedTriple
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
BaggingTrainer(Trainer<T>, EnsembleCombiner<T>, int, long) - Constructor for class org.tribuo.ensemble.BaggingTrainer
balancedErrorRate() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the balanced error rate, i.e., the mean of the per label recalls.
balancedErrorRate(ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the balanced error rate, i.e., the mean of the recalls.
balancedErrorRate() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
balancedErrorRate() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
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
batchSize - Variable in class org.tribuo.interop.tensorflow.TensorFlowModel
batchSize - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
begin - Variable in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence
begin - Variable in class org.tribuo.classification.sgd.crf.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<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 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
BIAS_FEATURE - Static variable in class org.tribuo.Model: Used to denote the bias feature in a linear model.
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.
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(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, float) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and a weight.
BinaryFeaturesExample(T, Map<String, Object>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and the metadata.
BinaryFeaturesExample(T) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output.
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(Example<T>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Copy constructor.
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.
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
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.BinningType - Enum in org.tribuo.transform.transformations: The allowed binning types.
BinningTransformationProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.transform.transformations.BinningTransformation.BinningTransformationProvenance
breakIteratorOptions - Variable in class org.tribuo.util.tokens.options.CoreTokenizerOptions
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
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.
buff - Variable in class org.tribuo.util.tokens.universal.Range
BUILD_TIMESTAMP - Static variable in class org.tribuo.Tribuo: The build timestamp.
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.
buildRandomTree(int[], SplittableRandom) - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode: Builds a CART tree with randomly chosen split points.
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).

C

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

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

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

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

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

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

Generate the counts for a single vector.

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.

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.

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.

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

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 in org.tribuo.classification.dtree

The impurity algorithm.

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

Creates a CART Trainer.

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

Creates a CART Trainer.

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

CARTJointRegressionTrainer - Class in org.tribuo.regression.rtree

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

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.

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.

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

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

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

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

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

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

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

CARTRegressionTrainer - Class in org.tribuo.regression.rtree

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

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.

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.

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

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

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

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 in org.tribuo.data.text.impl

The possible casing operations.

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.

cdf - Variable in class org.tribuo.CategoricalInfo

The CDF to sample from.

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

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

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

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

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

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

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

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 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.

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

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

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

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.

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.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

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

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

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.

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

ClusteringMetrics - Enum 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.

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

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

Calculates the sum of the specified column.

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

Returns the dense vector containing each column sum.

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

combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>, float[]) - 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.VotingCombiner

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.

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

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

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

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

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

Compute the result of this metric from the input context.

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

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.

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

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

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

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

ConfigurableTrainTest - Class in org.tribuo.data

Build and run a predictor for a standard dataset.

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

ConfiguredDataSourceProvenance - Interface in org.tribuo.provenance

A tag interface for configurable data source provenance.

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.

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

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

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

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<T3>) - Static method in class org.tribuo.util.infotheory.impl.TripleDistribution

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>, List<Double>) - Static method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution

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

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

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

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

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

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

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>, WeightCountTuple>) - Static method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution

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

Creates an ImmutableOutputInfo from the supplied mapping.

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

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

Does this example contain a feature with id i.

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?

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

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

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

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.

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

Converts the byte into the enum.

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

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

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

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

Converts a SGDVector representing the features into a TensorMap.

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

Transform implicitly pads unseen values with zero.

convert(List<? extends 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.ImageConverter

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

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

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.

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

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

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

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

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

Converts an examples into a DMatrix.

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

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

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

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

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

Converts the output of the external model into a Prediction.

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