All Classes and Interfaces

Class	Description
AbstractEvaluator<T extends Output<T>,C extends MetricContext<T>,E extends Evaluation<T>,M extends EvaluationMetric<T,C>>	Base class for evaluators.
AbstractSequenceEvaluator<T extends Output<T>,C extends MetricContext<T>,E extends SequenceEvaluation<T>,M extends EvaluationMetric<T,C>>	Base class for sequence evaluators.
AggregateConfigurableDataSource<T extends Output<T>>	Aggregates multiple ConfigurableDataSources, uses AggregateDataSource.IterationOrder to control the iteration order.
AggregateConfigurableDataSource.AggregateConfigurableDataSourceProvenance	Provenance for the AggregateConfigurableDataSource.
AggregateDataSource<T extends Output<T>>	Aggregates multiple DataSources, uses AggregateDataSource.IterationOrder to control the iteration order.
AggregateDataSource.AggregateDataSourceProvenance	Provenance for the AggregateDataSource.
AggregateDataSource.IterationOrder	Specifies the iteration order of the inner sources.
ArrayExample<T extends Output<T>>	An Example backed by two arrays, one of String and one of double.
BaggingTrainer<T extends Output<T>>	A Trainer that wraps another trainer and produces a bagged ensemble.
BinaryFeaturesExample<T extends Output<T>>	An Example backed by a single array of feature names.
BinningTransformation	A Transformation which bins values.
BinningTransformation.BinningTransformationProvenance	Provenance for BinningTransformation.
BinningTransformation.BinningType	The allowed binning types.
CategoricalIDInfo	Same as a CategoricalInfo, but with an additional int id field.
CategoricalInfo	Stores information about Categorical features.
ConfigurableDataSource<T extends Output<T>>	It's a DataSource that's also Configurable.
ConfiguredDataSourceProvenance	A tag interface for configurable data source provenance.
CrossValidation<T extends Output<T>,E extends Evaluation<T>>	A class that does k-fold cross-validation.
DataProvenance	Tag interface for data sources provenances.
Dataset<T extends Output<T>>	A class for sets of data, which are used to train and evaluate classifiers.
DatasetProvenance	Base class for dataset provenance.
DatasetView<T extends Output<T>>	DatasetView provides an immutable view on another Dataset that only exposes selected examples.
DatasetView.DatasetViewProvenance	Provenance for the DatasetView.
DataSource<T extends Output<T>>	A interface for things that can be given to a Dataset's constructor.
DataSourceProvenance	Data source provenance.
DescriptiveStats	Descriptive statistics calculated across a list of doubles.
EmptyDatasetProvenance	An empty DatasetProvenance, should not be used except by the provenance removal system.
EmptyDataSourceProvenance	An empty DataSourceProvenance, should not be used except by the provenance removal system.
EmptyTrainerProvenance	An empty TrainerProvenance, should not be used except by the provenance removal system.
EnsembleCombiner<T extends Output<T>>	An interface for combining predictions.
EnsembleExcuse<T extends Output<T>>	An Excuse which has a List of excuses for each of the ensemble members.
EnsembleModel<T extends Output<T>>	A model which contains a list of other Models.
EnsembleModelProvenance	Model provenance for ensemble models.
Evaluation<T extends Output<T>>	An immutable evaluation of a specific model and dataset.
EvaluationAggregator	Aggregates metrics from a list of evaluations, or a list of models and datasets.
EvaluationMetric<T extends Output<T>,C extends MetricContext<T>>	A metric that can be calculated for the specified output type.
EvaluationMetric.Average	Specifies what form of average to use for a EvaluationMetric.
EvaluationProvenance	Provenance for evaluations.
EvaluationRenderer<T extends Output<T>,E extends Evaluation<T>>	Renders an Evaluation into a String.
Evaluator<T extends Output<T>,E extends Evaluation<T>>	An evaluation factory which produces immutable Evaluations of a given Dataset using the given Model.
Example<T extends Output<T>>	An example used for training and evaluation.
Excuse<T extends Output<T>>	Holds an Example, a Prediction and a Map from String to List of Pairs that contains the per output explanation.
Feature	A class for features.
FeatureMap	A map from Strings to VariableInfo objects storing information about a feature.
HashCodeHasher	Hashes names using String.hashCode().
HashCodeHasher.HashCodeHasherProvenance	Provenance for the HashCodeHasher.
HashedFeatureMap	A FeatureMap used by the HashingTrainer to provide feature name hashing and guarantee that the Model does not contain feature name information, but still works with unhashed features names.
Hasher	An abstract base class for hash functions used to hash the names of features.
HashingOptions	An Options implementation which provides CLI arguments for the model hashing functionality.
HashingOptions.ModelHashingType	Supported types of hashes in CLI programs.
HashingSequenceTrainer<T extends Output<T>>	A SequenceTrainer that hashes all the feature names on the way in.
HashingSequenceTrainer.HashingSequenceTrainerProvenance	Provenance for HashingSequenceTrainer.
HashingTrainer<T extends Output<T>>	A Trainer which hashes the Dataset before the Model is produced.
HTMLOutput	Utilities for nice HTML output that can be put in wikis and such.
IDFTransformation	A feature transformation that computes the IDF for features and then transforms them with a TF-IDF weighting.
IDFTransformation.IDFTransformationProvenance	Provenance for IDFTransformation.
IDXDataSource<T extends Output<T>>	A DataSource which can read IDX formatted data (i.e., MNIST).
IDXDataSource.IDXData	Java side representation for an IDX file.
IDXDataSource.IDXDataSourceProvenance	Provenance class for IDXDataSource.
IDXDataSource.IDXType	The possible IDX input formats.
ImmutableDataset<T extends Output<T>>	This is a Dataset which has an ImmutableFeatureMap to store the feature information.
ImmutableFeatureMap	ImmutableFeatureMap is used when unknown features should not be added to the FeatureMap.
ImmutableOutputInfo<T extends Output<T>>	An OutputInfo that is fixed, and contains an id number for each valid output.
ImmutableSequenceDataset<T extends Output<T>>	This is a SequenceDataset which has an ImmutableFeatureMap to store the feature information.
IncrementalTrainer<T extends Output<T>,U extends Model<T>>	An interface for incremental training of Models.
IndependentSequenceModel<T extends Output<T>>	A SequenceModel which independently predicts each element of the sequence.
IndependentSequenceTrainer<T extends Output<T>>	Trains a sequence model by training a regular model to independently predict every example in each sequence.
IndexedArrayExample<T extends Output<T>>	A version of ArrayExample which also has the id numbers.
IndexedArrayExample.FeatureTuple	A tuple of the feature name, id and value.
IntDoublePair	A Pair of a primitive int and a primitive double.
KFoldSplitter<T extends Output<T>>	A k-fold splitter to be used in cross-validation.
KFoldSplitter.TrainTestFold<T extends Output<T>>	Stores a train/test split for a dataset.
LibSVMDataSource<T extends Output<T>>	A DataSource which can read LibSVM formatted data.
LibSVMDataSource.LibSVMDataSourceProvenance	The provenance for a LibSVMDataSource.
LinearScalingTransformation	A Transformation which takes an observed distribution and rescales it so all values are between the desired min and max.
LinearScalingTransformation.LinearScalingTransformationProvenance	Provenance for LinearScalingTransformation.
ListDataSource<T extends Output<T>>	A data source which wraps up a list of Examples along with their DataSourceProvenance and an OutputFactory.
ListExample<T extends Output<T>>	This class will not be performant until value types are available in Java.
MeanStdDevTransformation	A Transformation which takes an observed distribution and rescales it so it has the desired mean and standard deviation.
MeanStdDevTransformation.MeanStdDevTransformationProvenance	Provenance for MeanStdDevTransformation.
MeanVarianceAccumulator	An accumulator for online calculation of the mean and variance of a stream of doubles.
Merger	An interface which can merge double values.
MessageDigestHasher	Hashes Strings using the supplied MessageDigest type.
MessageDigestHasher.MessageDigestHasherProvenance	Provenance for MessageDigestHasher.
MetricContext<T extends Output<T>>	The context for a metric or set of metrics.
MetricID<T extends Output<T>>	Just an easier-to-read alias for Pair<MetricTarget<T>, String>.
MetricTarget<T extends Output<T>>	Used by a given EvaluationMetric to determine whether it should compute its value for a specific Output value or whether it should average them.
MinimumCardinalityDataset<T extends Output<T>>	This class creates a pruned dataset in which low frequency features that occur less than the provided minimum cardinality have been removed.
MinimumCardinalityDataset.MinimumCardinalityDatasetProvenance	Provenance for MinimumCardinalityDataset.
MinimumCardinalitySequenceDataset<T extends Output<T>>	This class creates a pruned dataset in which low frequency features that occur less than the provided minimum cardinality have been removed.
MinimumCardinalitySequenceDataset.MinimumCardinalitySequenceDatasetProvenance	Provenance for MinimumCardinalitySequenceDataset.
Model<T extends Output<T>>	A prediction model, which is used to predict outputs for unseen instances.
ModelExplorer	A command line interface for loading in models and inspecting their feature and output spaces.
ModelExplorer.ModelExplorerOptions	CLI options for ModelExplorer.
ModelProvenance	Contains provenance information for an instance of a Model.
ModHashCodeHasher	Hashes names using String.hashCode(), then reduces the dimension.
ModHashCodeHasher.ModHashCodeHasherProvenance	Provenance for the ModHashCodeHasher.
MurmurHash3	The MurmurHash3 algorithm was created by Austin Appleby and placed in the public domain.
MurmurHash3.LongPair	128 bits of state
MutableDataset<T extends Output<T>>	A MutableDataset is a Dataset with a MutableFeatureMap which grows over time.
MutableFeatureMap	A feature map that can record new feature value observations.
MutableOutputInfo<T extends Output<T>>	A mutable OutputInfo that can record observed output values.
MutableSequenceDataset<T extends Output<T>>	A MutableSequenceDataset is a SequenceDataset with a MutableFeatureMap which grows over time.
OnlineEvaluator<T extends Output<T>,E extends Evaluation<T>>	An evaluator which aggregates predictions and produces Evaluations covering all the Predictions it has seen or created.
ONNXExportable	An interface which denotes this Model can be exported as an ONNX model.
Output<T extends Output<T>>	Output is the root interface for the supported prediction types.
OutputFactory<T extends Output<T>>	An interface associated with a specific Output, which can generate the appropriate Output subclass, and OutputInfo subclass.
OutputFactoryProvenance	A tag provenance for an output factory.
OutputInfo<T extends Output<T>>	Tracks relevant properties of the appropriate Output subclass.
Prediction<T extends Output<T>>	A prediction made by a Model.
RealIDInfo	Same as a RealInfo, but with an additional int id field.
RealInfo	Stores information about real valued features.
SequenceDataset<T extends Output<T>>	A class for sets of data, which are used to train and evaluate classifiers.
SequenceDataSource<T extends Output<T>>	A interface for things that can be given to a SequenceDataset's constructor.
SequenceEvaluation<T extends Output<T>>	An immutable evaluation of a specific sequence model and dataset.
SequenceEvaluator<T extends Output<T>,E extends SequenceEvaluation<T>>	An evaluation factory which produces immutable SequenceEvaluations of a given SequenceDataset using the given SequenceModel.
SequenceExample<T extends Output<T>>	A sequence of examples, used for sequence classification.
SequenceModel<T extends Output<T>>	A prediction model, which is used to predict outputs for unseen instances.
SequenceModelExplorer	A CLI for interacting with a SequenceModel.
SequenceModelExplorer.SequenceModelExplorerOptions	Command line options.
SequenceTrainer<T extends Output<T>>	An interface for things that can train sequence prediction models.
SimpleDataSourceProvenance	This class stores a String describing the data source, along with a timestamp.
SimpleTransform	This is used for stateless functions such as exp, log, addition or multiplication by a constant.
SimpleTransform.Operation	Operations understood by this Transformation.
SimpleTransform.SimpleTransformProvenance	Provenance for SimpleTransform.
SkeletalTrainerProvenance	The skeleton of a TrainerProvenance that extracts the configured parameters.
SkeletalVariableInfo	Contains information about a feature and can be stored in the feature map in a Dataset.
SparseModel<T extends Output<T>>	A model which uses a subset of the features it knows about to make predictions.
SparseTrainer<T extends Output<T>>	Denotes this trainer emits a SparseModel.
TimestampedTrainerProvenance	A TrainerProvenance with a timestamp, used when there was no trainer involved in model construction (e.g., creating an EnsembleModel from existing models).
Trainer<T extends Output<T>>	An interface for things that can train predictive models.
TrainerProvenance	A tag interface for trainer provenances.
TrainerProvenanceImpl	An implementation of TrainerProvenance that delegates everything to SkeletalTrainerProvenance.
TrainTestSplitter<T extends Output<T>>	Splits data into training and testing sets.
TrainTestSplitter.SplitDataSourceProvenance	Provenance for a split data source.
Transformation	An interface representing a class of transformations which can be applied to a feature.
TransformationMap	A carrier type for a set of transformations to be applied to a Dataset.
TransformationMap.TransformationList	A carrier type as OLCUT does not support nested generics.
TransformationProvenance	A tag interface for provenances in the transformation system.
TransformedModel<T extends Output<T>>	Wraps a Model with it's TransformerMap so all Examples are transformed appropriately before the model makes predictions.
Transformer	A fitted Transformation which can apply a transform to the input value.
TransformerMap	A collection of Transformers which can be applied to a Dataset or Example.
TransformerMap.TransformerMapProvenance	Provenance for TransformerMap.
TransformStatistics	An interface for the statistics that need to be collected for a specific Transformation on a single feature.
TransformTrainer<T extends Output<T>>	A Trainer which encapsulates another trainer plus a TransformationMap object to apply to each Dataset before training each Model.
Tribuo	This class stores the current Tribuo version, along with other compile time information.
Util	Ye olde util class.
VariableIDInfo	Adds an id number to a VariableInfo.
VariableInfo	A VariableInfo subclass contains information about a feature and its observed values.
WeightedEnsembleModel<T extends Output<T>>	An ensemble model that uses weights to combine the ensemble member predictions.
WeightedExamples	Tag interface denoting that a Trainer can use example weights.