Index (Tribuo 4.0.2 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
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

a - Variable in class org.tribuo.util.infotheory.impl.CachedTriple
ABSOLUTE - Enum constant in enum org.tribuo.regression.sgd.TrainTest.LossEnum
AbsoluteLoss - Class in org.tribuo.regression.sgd.objectives: Absolute loss (i.e., l1).
AbsoluteLoss() - Constructor for class org.tribuo.regression.sgd.objectives.AbsoluteLoss
AbstractCARTTrainer<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, 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.
AbstractCARTTrainerProvenance(AbstractCARTTrainer<T>) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer.AbstractCARTTrainerProvenance: Deprecated.
AbstractEvaluator<T,C,E,M> - Class in org.tribuo.evaluation: Base class for evaluators.
AbstractEvaluator() - Constructor for class org.tribuo.evaluation.AbstractEvaluator
AbstractSequenceEvaluator<T,C,E,M> - Class in org.tribuo.sequence: Base class for sequence evaluators.
AbstractSequenceEvaluator() - Constructor for class org.tribuo.sequence.AbstractSequenceEvaluator
AbstractTrainingNode<T> - Class in org.tribuo.common.tree: Base class for decision tree nodes used at training time.
AbstractTrainingNode(int, int) - Constructor for class org.tribuo.common.tree.AbstractTrainingNode: Builds an abstract training node.
accuracy() - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: The overall accuracy of the evaluation.
accuracy() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
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
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 org.tribuo.classification.evaluation.LabelMetrics: The accuracy.
ADABOOST - Enum constant in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType
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 org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
AdaGrad - Class in org.tribuo.math.optimisers: An implementation of the AdaGrad gradient optimiser.
AdaGrad(double) - Constructor for class org.tribuo.math.optimisers.AdaGrad: Sets epsilon to 1e-6.
AdaGrad(double, double) - Constructor for class org.tribuo.math.optimisers.AdaGrad
ADAGRAD - Enum constant in enum org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
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
AdaGradRDA(double, double, double, double, int) - Constructor for class org.tribuo.math.optimisers.AdaGradRDA
ADAGRADRDA - Enum constant in enum org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
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 org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
add - Enum constant in enum 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
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 org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation
addAcrossDim1(int[], double) - Method in class org.tribuo.math.la.DenseMatrix
addAcrossDim2(int[], double) - Method in class org.tribuo.math.la.DenseMatrix
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.
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.
ADJUSTED_MI - Enum constant in enum 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(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.SplitCharactersTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
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
AggregateDataSource<T> - Class in org.tribuo.datasource: Aggregates multiple DataSources, and round-robins the iterators.
AggregateDataSource(List<DataSource<T>>) - Constructor for class org.tribuo.datasource.AggregateDataSource
AggregateDataSource.AggregateDataSourceProvenance - Class in org.tribuo.datasource: Provenance for the AggregateDataSource.
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.LARSOptions
ALL - Enum constant in enum 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.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
alpha - Variable in class org.tribuo.regression.slm.TrainTest.LARSOptions
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 - Enum constant in enum org.tribuo.anomaly.Event.EventType: An anomalous event, with id 1.
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.
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.
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
ArrayExample<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
ASSIGN_PLACEHOLDER - Static variable in class org.tribuo.interop.tensorflow.TensorflowUtil
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 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 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.
AVERAGED_PRECISION - Enum constant in enum 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 org.tribuo.classification.evaluation.LabelMetrics
averagedPrecision(MetricTarget<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
BAGGING - Enum constant in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType
BaggingTrainer<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
BALANCED_ERROR_RATE - Enum constant in enum 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 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
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.
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.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.
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 - Enum constant in enum 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 org.tribuo.data.columnar.FieldProcessor.GeneratedFeatureType: Categoricals binarised into separate features.
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.
BinaryFeaturesExample<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> - 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
BREAK_ITERATOR - Enum constant in enum org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType
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.
buildTree(int[]) - 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[]) - Method in class org.tribuo.common.tree.AbstractTrainingNode
buildTree(int[]) - 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[]) - 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 org.tribuo.datasource.IDXDataSource.IDXType

C

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

C_SVC - Enum constant in enum 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

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(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.

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 org.tribuo.classification.dtree.CARTClassificationOptions.TreeType

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

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

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

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

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

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, 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

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, RegressorImpurity, boolean, long) - 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

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

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, RegressorImpurity, long) - 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

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

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.

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

ChainBPResults(double, DenseVector[], DenseVector[], ChainHelper.ChainCliqueValues) - Constructor for class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults

ChainCliqueValues(DenseVector[], DenseMatrix) - Constructor for class org.tribuo.classification.sgd.crf.ChainHelper.ChainCliqueValues

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.

ChainViterbiResults(double, int[], ChainHelper.ChainCliqueValues) - Constructor for class org.tribuo.classification.sgd.crf.ChainHelper.ChainViterbiResults

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> - Interface in org.tribuo.classification

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

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

ClassificationOptions<TRAINER> - Interface in org.tribuo.classification

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

ClassifierEvaluation<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>) - Constructor for class org.tribuo.classification.dtree.impl.ClassifierTrainingNode

Constructor which creates the inverted file.

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.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.SplitPatternTokenizer

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.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.TensorflowCheckpointModel

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

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

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

closeTensorList(List<Tensor<?>>) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil

Closes a list of Tensors.

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.

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

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

ColumnarDataSource<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> - 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() - Method in class org.tribuo.math.la.DenseMatrix

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

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<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

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

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

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

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(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

conditionalMI(WeightedTripleDistribution<T1, T2, T3>) - 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> - 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

ConfigurableTrainTest.ConfigurableTrainTestOptions - Class in org.tribuo.data

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(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> - 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

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

Returns the supplied label for all inputs.

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

Returns the specified constant value.

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

CONSTRAINED_BP - Enum constant in enum 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

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<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

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

Converts the byte into the enum.

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

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

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

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

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

convertDataset(Dataset<T>, Function<T, Float>) - 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.TensorflowExternalModel

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.TensorflowExternalModel

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.TensorflowExternalModel

Converts a tensor into a prediction.

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

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(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.

convertTensorToArray(Tensor<?>) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil

Extracts the appropriate type of primitive array from a Tensor.

convertTensorToScalar(Tensor<?>) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil

Converts a Tensor into a scalar object, boxing the primitive types.

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

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

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

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.

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.ImmutableClusteringInfo

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

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 class org.tribuo.math.la.DenseMatrix

Copies the matrix.

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

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 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 it's 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.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

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

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.kernel.KernelSVMModel

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

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.onnx.ONNXExternalModel

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

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

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

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

Copies a model, replacing it's provenance and name with the supplied values.

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

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.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.

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

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 in org.tribuo.util.tokens.options

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

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

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

Cosine similarity as a distance measure.

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

Cosine similarity used as a distance measure.

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

Calculates the cosine distance of two vectors.

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

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

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

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

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

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

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

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.

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.

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

Defensively copies the values before construction.

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.

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<T>, List<Booster>, XGBoostOutputConverter<T>) - Method in class org.tribuo.common.xgboost.XGBoostTrainer

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<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.

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.

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.

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.

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.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

createTensorflowModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, String, String, ExampleTransformer<T>, OutputTransformer<T>, String) - Static method in class org.tribuo.interop.tensorflow.TensorflowExternalModel

Creates a TensorflowExternalModel by loading in a frozen graph.

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

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

Creates an XGBoostExternalModel from the supplied model.

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

An inference time model for a CRF trained using SGD.

CRFModel.ConfidenceType - Enum 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

CrossValidation<T,E> - 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 org.tribuo.data.DataOptions.InputFormat

CSVDataSource<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(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.CSVDataSourceProvenance - Class in org.tribuo.data.csv

Provenance for CSVDataSource.

CSVDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance

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> - 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

Provenance for CSVs loaded by CSVLoader.

CSVLoaderProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance

csvQuoteChar - Variable in class org.tribuo.data.DataOptions

csvResponseName - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions

csvResponseName - Variable in class org.tribuo.data.DataOptions

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.

currentRow - Variable in class org.tribuo.data.columnar.ColumnarIterator

D

DART - Enum constant in enum org.tribuo.common.xgboost.XGBoostTrainer.BoosterType: A gradient boosted decision tree using dropout.
data - Variable in class org.tribuo.common.xgboost.XGBoostTrainer.DMatrixTuple
data - Variable in class org.tribuo.data.text.TextDataSource: The actual data read out of the text file.
data - Variable in class org.tribuo.Dataset: The data in this data set.
data - Variable in class org.tribuo.sequence.SequenceDataset: The data in this data set.
DataOptions - Class in org.tribuo.data: Options for working with training and test data in a CLI.
DataOptions() - Constructor for class org.tribuo.data.DataOptions
DataOptions.Delimiter - Enum in org.tribuo.data: The delimiters supported by CSV files in this options object.
DataOptions.InputFormat - Enum in org.tribuo.data: The input formats supported by this options object.
DataProvenance - Interface in org.tribuo.provenance: Tag interface for data sources provenances.
Dataset<T> - Class in org.tribuo: A class for sets of data, which are used to train and evaluate classifiers.
Dataset(DataSource<T>) - Constructor for class org.tribuo.Dataset: Creates a dataset.
Dataset(DataProvenance, OutputFactory<T>) - Constructor for class org.tribuo.Dataset: Creates a dataset.
DATASET - Enum constant in enum org.tribuo.json.StripProvenance.ProvenanceTypes: Select the dataset provenance.
DATASET - Static variable in class org.tribuo.provenance.ModelProvenance
DatasetExplorer - Class in org.tribuo.data: A CLI for exploring a serialised Dataset.
DatasetExplorer() - Constructor for class org.tribuo.data.DatasetExplorer
DatasetExplorer.DatasetExplorerOptions - Class in org.tribuo.data
DatasetExplorerOptions() - Constructor for class org.tribuo.data.DatasetExplorer.DatasetExplorerOptions
datasetName - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
datasetProvenance - Variable in class org.tribuo.provenance.ModelProvenance
DatasetProvenance - Class in org.tribuo.provenance: Base class for dataset provenance.
DatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.DatasetProvenance
DatasetProvenance(DataProvenance, ListProvenance<ObjectProvenance>, String, boolean, boolean, int, int, int) - Constructor for class org.tribuo.provenance.DatasetProvenance
DatasetProvenance(DataProvenance, ListProvenance<ObjectProvenance>, Dataset<T>) - Constructor for class org.tribuo.provenance.DatasetProvenance
DatasetProvenance(DataProvenance, ListProvenance<ObjectProvenance>, SequenceDataset<T>) - Constructor for class org.tribuo.provenance.DatasetProvenance
DatasetView<T> - Class in org.tribuo.dataset: DatasetView provides an immutable view on another Dataset that only exposes selected examples.
DatasetView(Dataset<T>, int[], String) - Constructor for class org.tribuo.dataset.DatasetView: Creates a DatasetView which includes the supplied indices from the dataset.
DatasetView(Dataset<T>, int[], ImmutableFeatureMap, ImmutableOutputInfo<T>, String) - Constructor for class org.tribuo.dataset.DatasetView: Creates a DatasetView which includes the supplied indices from the dataset.
DatasetView.DatasetViewProvenance - Class in org.tribuo.dataset: Provenance for the DatasetView.
DatasetViewProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.dataset.DatasetView.DatasetViewProvenance
dataSource - Variable in class org.tribuo.data.PreprocessAndSerialize.PreprocessAndSerializeOptions
DataSource<T> - Interface in org.tribuo: A interface for things that can be given to a Dataset's constructor.
DATASOURCE_CREATION_TIME - Static variable in interface org.tribuo.provenance.DataSourceProvenance
DataSourceProvenance - Interface in org.tribuo.provenance: Data source provenance.
DateExtractor - Class in org.tribuo.data.columnar.extractors: Extracts the field value and translates it to a LocalDate based on the specified DateTimeFormatter.
DateExtractor(String, String, String) - Constructor for class org.tribuo.data.columnar.extractors.DateExtractor: Constructs a date extractor that emits a LocalDate by applying the supplied format to the specified field.
DateExtractor(String, String, DateTimeFormatter) - Constructor for class org.tribuo.data.columnar.extractors.DateExtractor: Deprecated.
dbConfig - Variable in class org.tribuo.data.sql.SQLToCSV.SQLToCSVOptions
DecisionTreeOptions() - Constructor for class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
DecisionTreeTrainer<T> - Interface in org.tribuo.common.tree: A tag interface for a Trainer so the random forests trainer can check if it's actually a tree.
decode(Tensor<?>, List<SequenceExample<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputTransformer: Decode graph output tensors corresponding to a batch of input sequences.
decode(Tensor<?>, SequenceExample<T>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputTransformer: Decode a tensor of graph output into a list of predictions for the input sequence.
deepCopy() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
deepCopy() - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Returns a deep copy of this tree feature.
DEFAULT - Enum constant in enum org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions.ViterbiLabelFeatures
DEFAULT_BATCH_SIZE - Static variable in class org.tribuo.interop.ExternalModel: Default batch size for external model batch predictions.
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.impl.TripleDistribution
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.InformationTheory
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory
DEFAULT_METADATA_SIZE - Static variable in class org.tribuo.Example: The default initial size of the metadata map.
DEFAULT_NAME - Static variable in class org.tribuo.regression.Regressor
DEFAULT_RESPONSE - Static variable in class org.tribuo.data.csv.CSVSaver
DEFAULT_SCORE - Static variable in class org.tribuo.anomaly.Event: The default score of events.
DEFAULT_SEED - Static variable in interface org.tribuo.Trainer: Default seed used to initialise RNGs.
DEFAULT_SIZE - Static variable in class org.tribuo.common.tree.AbstractTrainingNode: Default buffer size used in the split operation.
DEFAULT_SIZE - Static variable in class org.tribuo.impl.ArrayExample
DEFAULT_SIZE - Static variable in class org.tribuo.impl.BinaryFeaturesExample
DEFAULT_SPLIT_CHAR - Static variable in class org.tribuo.regression.RegressionFactory
DEFAULT_SPLIT_CHARACTERS - Static variable in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
DEFAULT_SPLIT_EXCEPTING_IN_DIGITS_CHARACTERS - Static variable in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
DEFAULT_WEIGHT - Static variable in class org.tribuo.Example: The default weight.
DEFAULT_WEIGHT - Static variable in class org.tribuo.sequence.SequenceExample
DefaultFeatureExtractor - Class in org.tribuo.classification.sequence.viterbi: A label feature extractor that produces several kinds of label-based features.
DefaultFeatureExtractor() - Constructor for class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
DefaultFeatureExtractor(int, int, boolean, boolean, boolean) - Constructor for class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
degree - Variable in class org.tribuo.regression.libsvm.TrainTest.LibLinearOptions
delimiter - Variable in class org.tribuo.data.DataOptions
DELTA - Static variable in class org.tribuo.math.la.VectorTuple
DemoOptions() - Constructor for class org.tribuo.util.infotheory.example.InformationTheoryDemo.DemoOptions
dense - Variable in class org.tribuo.MutableDataset: Denotes if this dataset contains implicit zeros or not.
DENSE - Enum constant in enum org.tribuo.interop.tensorflow.TrainTest.InputType
DenseMatrix - Class in org.tribuo.math.la: A dense matrix, backed by a primitive array.
DenseMatrix(int, int) - Constructor for class org.tribuo.math.la.DenseMatrix
DenseMatrix(DenseMatrix) - Constructor for class org.tribuo.math.la.DenseMatrix
DenseMatrix(Matrix) - Constructor for class org.tribuo.math.la.DenseMatrix
DenseSparseMatrix - Class in org.tribuo.math.la: A matrix which is dense in the first dimension and sparse in the second.
DenseSparseMatrix(List<SparseVector>) - Constructor for class org.tribuo.math.la.DenseSparseMatrix
DenseSparseMatrix(DenseSparseMatrix) - Constructor for class org.tribuo.math.la.DenseSparseMatrix
denseTrainTest() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator: Makes a simple dataset for training and testing.
denseTrainTest() - Static method in class org.tribuo.classification.example.LabelledDataGenerator
denseTrainTest() - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator
denseTrainTest() - Static method in class org.tribuo.regression.example.RegressionDataGenerator
denseTrainTest(double) - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator: Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
denseTrainTest(double) - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
denseTrainTest(double) - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator: Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
denseTrainTest(double) - Static method in class org.tribuo.regression.example.RegressionDataGenerator: Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
DenseTransformer - Class in org.tribuo.interop.onnx: Converts a sparse Tribuo example into a dense float vector, then wraps it in an OnnxTensor.
DenseTransformer<T> - Class in org.tribuo.interop.tensorflow: Converts a sparse example into a dense float vector, then wraps it in a Tensor.
DenseTransformer() - Constructor for class org.tribuo.interop.onnx.DenseTransformer
DenseTransformer() - Constructor for class org.tribuo.interop.tensorflow.DenseTransformer
DenseVector - Class in org.tribuo.math.la: A dense vector, backed by a double array.
DenseVector(double[]) - Constructor for class org.tribuo.math.la.DenseVector: Does not defensively copy the input, used internally.
DenseVector(int) - Constructor for class org.tribuo.math.la.DenseVector
DenseVector(int, double) - Constructor for class org.tribuo.math.la.DenseVector
DenseVector(DenseVector) - Constructor for class org.tribuo.math.la.DenseVector
densify() - Method in class org.tribuo.MutableDataset: Iterates through the examples, converting implicit zeros into explicit zeros.
densify(List<String>) - Method in class org.tribuo.Example: Converts all implicit zeros into explicit zeros based on the supplied feature names.
densify(List<String>) - Method in class org.tribuo.impl.ArrayExample
densify(List<String>) - Method in class org.tribuo.impl.BinaryFeaturesExample
densify(List<String>) - Method in class org.tribuo.impl.IndexedArrayExample
densify(List<String>) - Method in class org.tribuo.impl.ListExample
densify(FeatureMap) - Method in class org.tribuo.Example: Converts all implicit zeros into explicit zeros based on the supplied feature map.
densify(FeatureMap) - Method in class org.tribuo.impl.BinaryFeaturesExample
depth - Variable in class org.tribuo.common.tree.AbstractTrainingNode
depth - Variable in class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
depth - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
depth - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
DESCRIPTION - Static variable in class org.tribuo.provenance.SimpleDataSourceProvenance
DescriptiveStats - Class in org.tribuo.evaluation: Descriptive statistics calculated across a list of doubles.
DescriptiveStats() - Constructor for class org.tribuo.evaluation.DescriptiveStats
DescriptiveStats(List<Double>) - Constructor for class org.tribuo.evaluation.DescriptiveStats
deserialise(Session, Map<String, Object>) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil: Writes a map containing the name of each Tensorflow VariableV2 and the associated parameter array into the supplied session.
difference(SparseVector) - Method in class org.tribuo.math.la.SparseVector: Generates an array of the indices that are active in this vector but are not present in other.
differencesIndices(double[]) - Static method in class org.tribuo.util.Util: Returns an array containing the indices where values are different.
differencesIndices(double[], double) - Static method in class org.tribuo.util.Util: Returns an array containing the indices where values are different.
dim1 - Variable in class org.tribuo.math.la.DenseMatrix
dim2 - Variable in class org.tribuo.math.la.DenseMatrix
dimensions - Variable in class org.tribuo.regression.impl.SkeletalIndependentRegressionModel
dimensions - Variable in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel
DimensionTuple(String, double) - Constructor for class org.tribuo.regression.Regressor.DimensionTuple: Creates a dimension tuple from the supplied name and value.
DimensionTuple(String, double, double) - Constructor for class org.tribuo.regression.Regressor.DimensionTuple: Creates a dimension tuple from the supplied name, value and variance.
directory - Variable in class org.tribuo.classification.experiments.RunAll.RunAllOptions
DirectoryFileSource<T> - Class in org.tribuo.data.text: A data source for a somewhat-common format for text classification datasets: a top level directory that contains a number of subdirectories.
DirectoryFileSource() - Constructor for class org.tribuo.data.text.DirectoryFileSource: for olcut
DirectoryFileSource(Path, OutputFactory<T>, TextFeatureExtractor<T>, DocumentPreprocessor...) - Constructor for class org.tribuo.data.text.DirectoryFileSource
DirectoryFileSource(OutputFactory<T>, TextFeatureExtractor<T>, DocumentPreprocessor...) - Constructor for class org.tribuo.data.text.DirectoryFileSource: Creates a data source that will use the given feature extractor and document preprocessors on the data read from the files in the directories representing classes.
DirectoryFileSource.DirectoryFileSourceProvenance - Class in org.tribuo.data.text: Provenance for DirectoryFileSource.
DirectoryFileSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
distance - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
distance - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
distance(SGDVector, DoubleUnaryOperator, DoubleUnaryOperator) - Method in class org.tribuo.math.la.SparseVector
DISTANCE_DELTA - Static variable in class org.tribuo.classification.explanations.lime.LIMEBase
div - Enum constant in enum org.tribuo.transform.transformations.SimpleTransform.Operation: Divides by the specified constant.
div(double) - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that divides each value by the operand.
DMatrixTuple(DMatrix, int[], Example<T>[]) - Constructor for class org.tribuo.common.xgboost.XGBoostTrainer.DMatrixTuple
DocumentPreprocessor - Interface in org.tribuo.data.text: An interface for things that can pre-process documents before they are broken into features.
dot(SGDVector) - Method in class org.tribuo.math.la.DenseVector
dot(SGDVector) - Method in interface org.tribuo.math.la.SGDVector: Calculates the dot product between this vector and other.
dot(SGDVector) - Method in class org.tribuo.math.la.SparseVector
dot(SGDVector) - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
DOUBLE - Enum constant in enum org.tribuo.datasource.IDXDataSource.IDXType
DoubleExtractor - Class in org.tribuo.data.columnar.extractors: Extracts the field value and converts it to a double.
DoubleExtractor(String) - Constructor for class org.tribuo.data.columnar.extractors.DoubleExtractor: Extracts a double value from the supplied field name.
DoubleExtractor(String, String) - Constructor for class org.tribuo.data.columnar.extractors.DoubleExtractor: Extracts a double value from the supplied field name.
DoubleFieldProcessor - Class in org.tribuo.data.columnar.processors.field: Processes a column that contains a real value.
DoubleFieldProcessor(String) - Constructor for class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor: Constructs a field processor which extracts a single double valued feature from the specified field name.
dropInvalidExamples - Variable in class org.tribuo.ImmutableDataset: If true, instead of throwing an exception when an invalid Example is encountered, this Dataset will log a warning and drop it.
DTYPE - Static variable in class org.tribuo.interop.tensorflow.TensorflowUtil
DummyClassifierModel - Class in org.tribuo.classification.baseline: A model which performs dummy classifications (e.g., constant output, uniform sampled labels, stratified sampled labels).
DummyClassifierTrainer - Class in org.tribuo.classification.baseline: A trainer for simple baseline classifiers.
DummyClassifierTrainer.DummyType - Enum in org.tribuo.classification.baseline: Types of dummy classifier.
DummyRegressionModel - Class in org.tribuo.regression.baseline: A model which performs dummy regressions (e.g., constant output, gaussian sampled output, mean value, median, quartile).
DummyRegressionTrainer - Class in org.tribuo.regression.baseline: A trainer for simple baseline regressors.
DummyRegressionTrainer.DummyRegressionTrainerProvenance - Class in org.tribuo.regression.baseline: Deprecated.
DummyRegressionTrainer.DummyType - Enum in org.tribuo.regression.baseline: Types of dummy regression model.
DummyRegressionTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance: Deprecated.

Constructs a provenance from the marshalled form.
DummyRegressionTrainerProvenance(DummyRegressionTrainer) - Constructor for class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance: Deprecated.

Constructs a provenance from the host.

E

ELASTICNET - Enum constant in enum org.tribuo.regression.slm.TrainTest.SLMType
ElasticNetCDTrainer - Class in org.tribuo.regression.slm: An ElasticNet trainer that uses co-ordinate descent.
ElasticNetCDTrainer(double, double) - Constructor for class org.tribuo.regression.slm.ElasticNetCDTrainer
ElasticNetCDTrainer(double, double, double, int, boolean, long) - Constructor for class org.tribuo.regression.slm.ElasticNetCDTrainer
ElasticNetCDTrainer(double, double, long) - Constructor for class org.tribuo.regression.slm.ElasticNetCDTrainer
elements - Variable in class org.tribuo.math.la.DenseVector
EmptyDatasetProvenance - Class in org.tribuo.provenance.impl: An empty DatasetProvenance, should not be used except by the provenance removal system.
EmptyDatasetProvenance() - Constructor for class org.tribuo.provenance.impl.EmptyDatasetProvenance
EmptyDatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.EmptyDatasetProvenance
EmptyDataSourceProvenance - Class in org.tribuo.provenance.impl: An empty DataSourceProvenance, should not be used except by the provenance removal system.
EmptyDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.EmptyDataSourceProvenance
emptyExample() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator: Generates an example with no features.
emptyExample() - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates an example with no features.
emptyExample() - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator: Generates an example with no features.
emptyExample() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator: Generates an example with no features.
emptyExample() - Static method in class org.tribuo.regression.example.RegressionDataGenerator: Generates an example with no features.
emptyMultiDimExample() - Static method in class org.tribuo.regression.example.RegressionDataGenerator: Generates an example with no features.
EmptyTrainerProvenance - Class in org.tribuo.provenance.impl: An empty TrainerProvenance, should not be used except by the provenance removal system.
EmptyTrainerProvenance() - Constructor for class org.tribuo.provenance.impl.EmptyTrainerProvenance
EmptyTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.EmptyTrainerProvenance
encode(List<SequenceExample<T>>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceExampleTransformer: Encodes a batch of examples as a feed dict.
encode(List<SequenceExample<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputTransformer: Encodes a batch of labels as a feed dict.
encode(SequenceExample<T>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceExampleTransformer: Encodes an example as a feed dict.
encode(SequenceExample<T>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputTransformer: Encodes an example's label as a feed dict.
end - Variable in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence
end - Variable in class org.tribuo.util.tokens.Token
end - Variable in class org.tribuo.util.tokens.universal.Range
ensemble - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
EnsembleCombiner<T> - Interface in org.tribuo.ensemble: An interface for combining predictions.
EnsembleExcuse<T> - Class in org.tribuo.ensemble: An Excuse which has a List of excuses for each of the ensemble members.
EnsembleExcuse(Example<T>, Prediction<T>, Map<String, List<Pair<String, Double>>>, List<Excuse<T>>) - Constructor for class org.tribuo.ensemble.EnsembleExcuse
EnsembleModel<T> - Class in org.tribuo.ensemble: A model which contains a list of other Models.
EnsembleModel(String, EnsembleModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Model<T>>) - Constructor for class org.tribuo.ensemble.EnsembleModel
EnsembleModelProvenance - Class in org.tribuo.provenance: Model provenance for ensemble models.
EnsembleModelProvenance(String, OffsetDateTime, DatasetProvenance, TrainerProvenance, ListProvenance<? extends ModelProvenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
EnsembleModelProvenance(String, OffsetDateTime, DatasetProvenance, TrainerProvenance, Map<String, Provenance>, ListProvenance<? extends ModelProvenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
EnsembleModelProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
ensembleName() - Method in class org.tribuo.common.tree.RandomForestTrainer
ensembleName() - Method in class org.tribuo.ensemble.BaggingTrainer
ensembleOptions - Variable in class org.tribuo.classification.liblinear.TrainTest.TrainTestOptions
ensembleOptions - Variable in class org.tribuo.classification.libsvm.TrainTest.TrainTestOptions
ensembleOptions - Variable in class org.tribuo.classification.mnb.TrainTest.TrainTestOptions
ensembleOptions - Variable in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
ensembleSize - Variable in class org.tribuo.classification.ensemble.ClassificationEnsembleOptions
ensembleSize - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
ensembleSize - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
entropy(List<T>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon entropy, using histogram probability estimators.
Entropy - Class in org.tribuo.classification.dtree.impurity: A log_e entropy impurity measure.
Entropy() - Constructor for class org.tribuo.classification.dtree.impurity.Entropy
ENTROPY - Enum constant in enum org.tribuo.classification.dtree.CARTClassificationOptions.ImpurityType
entrySet() - Method in class org.tribuo.transform.TransformerMap: Get the feature names and associated list of transformers.
EPOCH - Static variable in class org.tribuo.interop.tensorflow.TensorflowTrainer
epochs - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
epochs - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
epochs - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
epochs - Variable in class org.tribuo.regression.sgd.TrainTest.SGDOptions
epsilon - Variable in class org.tribuo.common.liblinear.LibLinearTrainer
epsilon - Variable in class org.tribuo.math.optimisers.GradientOptimiserOptions
epsilon - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
EPSILON - Static variable in class org.tribuo.transform.transformations.SimpleTransform
EPSILON_SVR - Enum constant in enum org.tribuo.regression.libsvm.SVMRegressionType.SVMMode: epsilon-insensitive SVR.
EQUAL_FREQUENCY - Enum constant in enum org.tribuo.transform.transformations.BinningTransformation.BinningType
EQUAL_WIDTH - Enum constant in enum org.tribuo.transform.transformations.BinningTransformation.BinningType
equalFrequency(int) - Static method in class org.tribuo.transform.transformations.BinningTransformation: Returns a BinningTransformation which generates bins which contain the same amount of training data that is, each bin has an equal probability of occurrence in the training data.
equals(Object) - Method in class org.tribuo.anomaly.AnomalyFactory.AnomalyFactoryProvenance
equals(Object) - Method in class org.tribuo.anomaly.Event
equals(Object) - Method in class org.tribuo.classification.evaluation.LabelMetric
equals(Object) - Method in class org.tribuo.classification.Label
equals(Object) - Method in class org.tribuo.classification.LabelFactory
equals(Object) - Method in class org.tribuo.classification.LabelFactory.LabelFactoryProvenance
equals(Object) - Method in class org.tribuo.clustering.ClusterID
equals(Object) - Method in class org.tribuo.clustering.ClusteringFactory.ClusteringFactoryProvenance
equals(Object) - Method in class org.tribuo.clustering.ClusteringFactory
equals(Object) - Method in class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance
equals(Object) - Method in class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance
equals(Object) - Method in class org.tribuo.data.sql.SQLDataSource.SQLDataSourceProvenance
equals(Object) - Method in class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
equals(Object) - Method in class org.tribuo.data.text.impl.SimpleStringDataSource.SimpleStringDataSourceProvenance
equals(Object) - Method in class org.tribuo.data.text.impl.SimpleTextDataSource.SimpleTextDataSourceProvenance
equals(Object) - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance
equals(Object) - Method in class org.tribuo.dataset.MinimumCardinalityDataset.MinimumCardinalityDatasetProvenance
equals(Object) - Method in class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance
equals(Object) - Method in class org.tribuo.datasource.LibSVMDataSource.LibSVMDataSourceProvenance
equals(Object) - Method in class org.tribuo.evaluation.DescriptiveStats
equals(Object) - Method in class org.tribuo.evaluation.metrics.MetricTarget
equals(Object) - Method in class org.tribuo.evaluation.TrainTestSplitter.SplitDataSourceProvenance
equals(Object) - Method in class org.tribuo.Feature
equals(Object) - Method in class org.tribuo.hash.HashCodeHasher.HashCodeHasherProvenance
equals(Object) - Method in class org.tribuo.hash.MessageDigestHasher.MessageDigestHasherProvenance
equals(Object) - Method in class org.tribuo.hash.ModHashCodeHasher.ModHashCodeHasherProvenance
equals(Object) - Method in class org.tribuo.impl.ArrayExample
equals(Object) - Method in class org.tribuo.impl.BinaryFeaturesExample
equals(Object) - Method in class org.tribuo.impl.IndexedArrayExample
equals(Object) - Method in class org.tribuo.impl.ListExample
equals(Object) - Method in class org.tribuo.interop.ExternalTrainerProvenance
equals(Object) - Method in class org.tribuo.json.JsonDataSource.JsonDataSourceProvenance
equals(Object) - Method in class org.tribuo.math.la.DenseMatrix
equals(Object) - Method in class org.tribuo.math.la.DenseSparseMatrix
equals(Object) - Method in class org.tribuo.math.la.DenseVector: Equals is defined mathematically, that is two SGDVectors are equal iff they have the same indices and the same values at those indices.
equals(Object) - Method in class org.tribuo.math.la.MatrixTuple
equals(Object) - Method in class org.tribuo.math.la.SparseVector: Equals is defined mathematically, that is two SGDVectors are equal iff they have the same indices and the same values at those indices.
equals(Object) - Method in class org.tribuo.math.la.VectorTuple
equals(Object) - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric
equals(Object) - Method in class org.tribuo.multilabel.MultiLabel
equals(Object) - Method in class org.tribuo.multilabel.MultiLabelFactory
equals(Object) - Method in class org.tribuo.multilabel.MultiLabelFactory.MultiLabelFactoryProvenance
equals(Object) - Method in class org.tribuo.provenance.DatasetProvenance
equals(Object) - Method in class org.tribuo.provenance.EvaluationProvenance
equals(Object) - Method in class org.tribuo.provenance.impl.EmptyDataSourceProvenance
equals(Object) - Method in class org.tribuo.provenance.impl.EmptyTrainerProvenance
equals(Object) - Method in class org.tribuo.provenance.ModelProvenance
equals(Object) - Method in class org.tribuo.provenance.SimpleDataSourceProvenance
equals(Object) - Method in class org.tribuo.provenance.SkeletalTrainerProvenance
equals(Object) - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance: Deprecated.
equals(Object) - Method in class org.tribuo.regression.RegressionFactory
equals(Object) - Method in class org.tribuo.regression.RegressionFactory.RegressionFactoryProvenance
equals(Object) - Method in class org.tribuo.regression.Regressor.DimensionTuple
equals(Object) - Method in class org.tribuo.regression.Regressor: Regressors are equal if they have the same number of dimensions and equal dimension names.
equals(Object) - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset.MinimumCardinalitySequenceDatasetProvenance
equals(Object) - Method in class org.tribuo.SkeletalVariableInfo
equals(Object) - Method in class org.tribuo.transform.TransformationMap.TransformationList
equals(Object) - Method in class org.tribuo.transform.transformations.BinningTransformation.BinningTransformationProvenance
equals(Object) - Method in class org.tribuo.transform.transformations.LinearScalingTransformation.LinearScalingTransformationProvenance
equals(Object) - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation.MeanStdDevTransformationProvenance
equals(Object) - Method in class org.tribuo.transform.transformations.SimpleTransform.SimpleTransformProvenance
equals(Object) - Method in class org.tribuo.transform.TransformerMap.TransformerMapProvenance
equals(Object) - Method in class org.tribuo.util.infotheory.impl.CachedPair
equals(Object) - Method in class org.tribuo.util.infotheory.impl.CachedTriple
equals(Object) - Method in class org.tribuo.util.infotheory.impl.Row
equals(Object) - Method in class org.tribuo.util.infotheory.impl.WeightCountTuple
equalWidth(int) - Static method in class org.tribuo.transform.transformations.BinningTransformation: Returns a BinningTransformation which generates fixed equal width bins between the observed min and max values.
eta - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
eta - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
EUCLIDEAN - Enum constant in enum org.tribuo.clustering.kmeans.KMeansTrainer.Distance: Euclidean (or l2) distance.
euclideanDistance(SGDVector) - Method in class org.tribuo.math.la.DenseVector: The l2 or euclidean distance between this vector and the other vector.
euclideanDistance(SGDVector) - Method in interface org.tribuo.math.la.SGDVector: The l2 or euclidean distance between this vector and the other vector.
euclideanDistance(SGDVector) - Method in class org.tribuo.math.la.SparseVector
EV - Enum constant in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates the Explained Variance of the predictions.
evaluate() - Method in class org.tribuo.evaluation.CrossValidation: Performs k fold cross validation, returning the k evaluations.
evaluate() - Method in class org.tribuo.evaluation.OnlineEvaluator: Creates an Evaluation containing all the current predictions.
evaluate(Model<T>, List<Prediction<T>>, List<T>, DataProvenance) - Method in interface org.tribuo.evaluation.Evaluator: Evaluates the model performance using the supplied predictions, returning an immutable Evaluation of the appropriate type.
evaluate(Model<T>, List<Prediction<T>>, DataProvenance) - Method in class org.tribuo.evaluation.AbstractEvaluator: Produces an evaluation for the supplied model and predictions by aggregating the appropriate statistics.
evaluate(Model<T>, List<Prediction<T>>, DataProvenance) - Method in interface org.tribuo.evaluation.Evaluator: Evaluates the model performance using the supplied predictions, returning an immutable Evaluation of the appropriate type.
evaluate(Model<T>, Dataset<T>) - Method in class org.tribuo.evaluation.AbstractEvaluator: Produces an evaluation for the supplied model and dataset, by calling Model.predict(org.tribuo.Example<T>) to create the predictions, then aggregating the appropriate statistics.
evaluate(Model<T>, Dataset<T>) - Method in interface org.tribuo.evaluation.Evaluator: Evaluates the dataset using the supplied model, returning an immutable Evaluation of the appropriate type.
evaluate(Model<T>, DataSource<T>) - Method in class org.tribuo.evaluation.AbstractEvaluator: Produces an evaluation for the supplied model and datasource, by calling Model.predict(org.tribuo.Example<T>) to create the predictions, then aggregating the appropriate statistics.
evaluate(Model<T>, DataSource<T>) - Method in interface org.tribuo.evaluation.Evaluator: Evaluates the dataset using the supplied model, returning an immutable Evaluation of the appropriate type.
evaluate(SequenceModel<T>, List<List<Prediction<T>>>, DataProvenance) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator: Produces an evaluation for the supplied model and predictions by aggregating the appropriate statistics.
evaluate(SequenceModel<T>, List<List<Prediction<T>>>, DataProvenance) - Method in interface org.tribuo.sequence.SequenceEvaluator: Evaluates the supplied model and predictions by aggregating the appropriate statistics.
evaluate(SequenceModel<T>, SequenceDataset<T>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator: Produces an evaluation for the supplied model and dataset, by calling SequenceModel.predict(org.tribuo.sequence.SequenceExample<T>) to create the predictions, then aggregating the appropriate statistics.
evaluate(SequenceModel<T>, SequenceDataset<T>) - Method in interface org.tribuo.sequence.SequenceEvaluator: Evaluates the dataset using the supplied model, returning an immutable evaluation.
evaluate(SequenceModel<T>, SequenceDataSource<T>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator: Produces an evaluation for the supplied model and datasource, by calling SequenceModel.predict(org.tribuo.sequence.SequenceExample<T>) to create the predictions, then aggregating the appropriate statistics.
evaluate(SequenceModel<T>, SequenceDataSource<T>) - Method in interface org.tribuo.sequence.SequenceEvaluator: Evaluates the datasource using the supplied model, returning an immutable evaluation.
Evaluation<T> - Interface in org.tribuo.evaluation: An immutable evaluation of a specific model and dataset.
EvaluationAggregator - Class in org.tribuo.evaluation: Aggregates metrics from a list of evaluations, or a list of models and datasets.
EvaluationMetric<T,C> - Interface in org.tribuo.evaluation.metrics: A metric that can be calculated for the specified output type.
EvaluationMetric.Average - Enum in org.tribuo.evaluation.metrics: Specifies what form of average to use for a EvaluationMetric.
EvaluationProvenance - Class in org.tribuo.provenance: Provenance for evaluations.
EvaluationProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.EvaluationProvenance
EvaluationProvenance(ModelProvenance, DataProvenance) - Constructor for class org.tribuo.provenance.EvaluationProvenance
EvaluationRenderer<T,E> - Interface in org.tribuo.evaluation: Renders an Evaluation into a String.
evaluator - Static variable in class org.tribuo.classification.explanations.lime.LIMEBase
Evaluator<T,E> - Interface in org.tribuo.evaluation: An evaluation factory which produces immutable Evaluations of a given Dataset using the given Model.
Event - Class in org.tribuo.anomaly: An Output representing either an Event.EventType.ANOMALOUS or an Event.EventType.EXPECTED event.
Event(Event.EventType) - Constructor for class org.tribuo.anomaly.Event: Constructs a new event of the specified type with the default score of Event.DEFAULT_SCORE.
Event(Event.EventType, double) - Constructor for class org.tribuo.anomaly.Event: Constructs a new event of the specified type and score.
Event.EventType - Enum in org.tribuo.anomaly: The type of event.
Example<T> - Class in org.tribuo: An example used for training and evaluation.
Example(Example<T>) - Constructor for class org.tribuo.Example: Copies the output, weight and metadata into this example.
Example(T) - Constructor for class org.tribuo.Example: Construct an empty example using the supplied output and Example.DEFAULT_WEIGHT as the weight.
Example(T, float) - Constructor for class org.tribuo.Example: Construct an empty example using the supplied output and weight.
Example(T, float, Map<String, Object>) - Constructor for class org.tribuo.Example: Construct an empty example using the supplied output, weight and metadata.
Example(T, Map<String, Object>) - Constructor for class org.tribuo.Example: Construct an empty example using the supplied output, metadata and Example.DEFAULT_WEIGHT as the weight.
ExampleArray(SparseVector[], int[], double[]) - Constructor for class org.tribuo.classification.sgd.Util.ExampleArray
examples - Variable in class org.tribuo.common.xgboost.XGBoostTrainer.DMatrixTuple
exampleToNodes(Example<T>, ImmutableFeatureMap, List<FeatureNode>) - Static method in class org.tribuo.common.liblinear.LibLinearTrainer: Converts a Tribuo Example into a liblinear FeatureNode array, including a bias feature.
exampleToNodes(Example<T>, ImmutableFeatureMap, List<svm_node>) - Static method in class org.tribuo.common.libsvm.LibSVMTrainer: Convert the example into an array of svm_node which represents a sparse feature vector.
exampleTransformer - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceModel
exampleTransformer - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
ExampleTransformer - Interface in org.tribuo.interop.onnx: Transforms a SparseVector, extracting the features from it as a OnnxTensor.
ExampleTransformer<T> - Interface in org.tribuo.interop.tensorflow: TensorFlow support is experimental, and may change without a major version bump.
excuse(String) - Method in class org.tribuo.Excuse: Returns the features involved in this excuse.
Excuse<T> - Class in org.tribuo: Holds an Example, a Prediction and a Map from String to List of Pairs that contains the per output explanation.
Excuse(Example<T>, Prediction<T>, Map<String, List<Pair<String, Double>>>) - Constructor for class org.tribuo.Excuse: Constructs an excuse for the prediction of the supplied example, using the feature weights.
exp - Enum constant in enum org.tribuo.transform.transformations.SimpleTransform.Operation: Exponentiates the inputs
exp() - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that applies Math.exp(double).
expandRegexMapping(Collection<String>) - Method in class org.tribuo.data.columnar.RowProcessor: Uses similar logic to TransformationMap.validateTransformations(org.tribuo.FeatureMap) to check the regexes against the supplied list of field names.
expandRegexMapping(ImmutableFeatureMap) - Method in class org.tribuo.data.columnar.RowProcessor: Uses similar logic to TransformationMap.validateTransformations(org.tribuo.FeatureMap) to check the regexes against the supplied feature map.
expandRegexMapping(Model<T>) - Method in class org.tribuo.data.columnar.RowProcessor: Uses similar logic to TransformationMap.validateTransformations(org.tribuo.FeatureMap) to check the regexes against the ImmutableFeatureMap contained in the supplied Model.
EXPECTED - Enum constant in enum org.tribuo.anomaly.Event.EventType: An expected event, with id 0.
EXPECTED_EVENT - Static variable in class org.tribuo.anomaly.AnomalyFactory: The expected event.
expectedCount - Variable in class org.tribuo.anomaly.AnomalyInfo: The number of expected events observed.
explain(CommandInterpreter, String[]) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
explain(String) - Method in class org.tribuo.classification.explanations.lime.LIMEText
explain(String) - Method in interface org.tribuo.classification.explanations.TextExplainer: Converts the supplied text into an Example, and generates an explanation of the contained Model's prediction.
explain(Map<String, String>) - Method in interface org.tribuo.classification.explanations.ColumnarExplainer: Explains the supplied data.
explain(Map<String, String>) - Method in class org.tribuo.classification.explanations.lime.LIMEColumnar
explain(Example<Label>) - Method in class org.tribuo.classification.explanations.lime.LIMEBase
explain(Example<Label>) - Method in interface org.tribuo.classification.explanations.TabularExplainer: Explain why the supplied Example is classified a certain way.
explainedVariance() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: Calculatest the explained variance for all dimensions.
explainedVariance(MetricTarget<Regressor>, RegressionSufficientStatistics) - Static method in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates the explained variance based on the supplied statistics.
explainedVariance(Regressor) - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: Calculates the explained variance of the ground truth using the predictions for the supplied dimension.
explainedVariance(Regressor, RegressionSufficientStatistics) - Static method in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates the explained variance based on the supplied statistics for a single dimension.
explainWithSamples(Map<String, String>) - Method in class org.tribuo.classification.explanations.lime.LIMEColumnar
explainWithSamples(Example<Label>) - Method in class org.tribuo.classification.explanations.lime.LIMEBase
Explanation<T> - Interface in org.tribuo.classification.explanations: An explanation knows what features are used, what the explaining Model is and what the original Model's prediction is.
explanationTrainer - Variable in class org.tribuo.classification.explanations.lime.LIMEBase
expNormalize(double) - Method in class org.tribuo.math.la.DenseVector: An optimisation for the exponential normalizer when you already know the normalization constant.
ExpNormalizer - Class in org.tribuo.math.util: Normalizes the exponential values of the input array.
ExpNormalizer() - Constructor for class org.tribuo.math.util.ExpNormalizer
ExternalDatasetProvenance - Class in org.tribuo.interop: A dummy provenance used to describe the dataset of external models.
ExternalDatasetProvenance(String, OutputFactory<T>, boolean, int, int) - Constructor for class org.tribuo.interop.ExternalDatasetProvenance
ExternalDatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.interop.ExternalDatasetProvenance
ExternalModel<T,U,V> - Class in org.tribuo.interop: This is the base class for third party models which are trained externally and loaded into Tribuo for prediction.
ExternalModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, boolean, Map<String, Integer>) - Constructor for class org.tribuo.interop.ExternalModel
ExternalModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, int[], int[], boolean) - Constructor for class org.tribuo.interop.ExternalModel
externalPrediction(OnnxTensor) - Method in class org.tribuo.interop.onnx.ONNXExternalModel: Runs the session to make a prediction.
externalPrediction(DMatrix) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel
externalPrediction(Tensor<?>) - Method in class org.tribuo.interop.tensorflow.TensorflowExternalModel: Runs the session to make a prediction.
externalPrediction(U) - Method in class org.tribuo.interop.ExternalModel: Runs the external model's prediction function.
ExternalTrainerProvenance - Class in org.tribuo.interop: A dummy provenance for a model trained outside Tribuo.
ExternalTrainerProvenance(URL) - Constructor for class org.tribuo.interop.ExternalTrainerProvenance: Creates an external trainer provenance, storing the location and pulling in the timestamp and file hash.
ExternalTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.interop.ExternalTrainerProvenance: Used by the provenance serialization system.
extract(ColumnarIterator.Row) - Method in class org.tribuo.data.columnar.extractors.IndexExtractor
extract(ColumnarIterator.Row) - Method in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor
extract(ColumnarIterator.Row) - Method in interface org.tribuo.data.columnar.FieldExtractor: Returns Optional which is filled if extraction succeeded.
extract(T, String) - Method in class org.tribuo.data.text.impl.TextFeatureExtractorImpl
extract(T, String) - Method in interface org.tribuo.data.text.TextFeatureExtractor: Extracts an example from the supplied input text and output object.
extractData(Dataset<Event>, ImmutableOutputInfo<Event>, ImmutableFeatureMap) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer
extractData(Dataset<Label>, ImmutableOutputInfo<Label>, ImmutableFeatureMap) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationTrainer
extractData(Dataset<Label>, ImmutableOutputInfo<Label>, ImmutableFeatureMap) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationTrainer
extractData(Dataset<Regressor>, ImmutableOutputInfo<Regressor>, ImmutableFeatureMap) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionTrainer
extractData(Dataset<Regressor>, ImmutableOutputInfo<Regressor>, ImmutableFeatureMap) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionTrainer
extractData(Dataset<T>, ImmutableOutputInfo<T>, ImmutableFeatureMap) - Method in class org.tribuo.common.liblinear.LibLinearTrainer: Extracts the features and Outputs in LibLinear's format.
extractData(Dataset<T>, ImmutableOutputInfo<T>, ImmutableFeatureMap) - Method in class org.tribuo.common.libsvm.LibSVMTrainer: Extracts the features and Outputs in LibLinear's format.
extractFeatures(List<Label>, double) - Method in class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
extractFeatures(List<Label>, double) - Method in interface org.tribuo.classification.sequence.viterbi.LabelFeatureExtractor: Generates features based on the previously produced labels.
extractFeatures(List<Label>, double) - Method in class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
extractField(String) - Method in class org.tribuo.data.columnar.extractors.DateExtractor
extractField(String) - Method in class org.tribuo.data.columnar.extractors.DoubleExtractor
extractField(String) - Method in class org.tribuo.data.columnar.extractors.FloatExtractor
extractField(String) - Method in class org.tribuo.data.columnar.extractors.IdentityExtractor
extractField(String) - Method in class org.tribuo.data.columnar.extractors.IntExtractor
extractField(String) - Method in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor: Extracts the field value, or returns Optional.empty() if it failed to parse.
extractNames(OutputInfo<Regressor>) - Static method in class org.tribuo.regression.Regressor: Extracts the names from the supplied Regressor domain in their canonical order.
extractor - Variable in class org.tribuo.data.text.DirectoryFileSource: The extractor that we'll use to turn text into examples.
extractor - Variable in class org.tribuo.data.text.TextDataSource: The extractor that we'll use to turn text into examples.
extractOutput(Event) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer: Converts an output into a double for use in training.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.sql.SQLDataSource.SQLDataSourceProvenance
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.text.impl.SimpleStringDataSource.SimpleStringDataSourceProvenance
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.text.impl.SimpleTextDataSource.SimpleTextDataSourceProvenance
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.datasource.LibSVMDataSource.LibSVMDataSourceProvenance
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.json.JsonDataSource.JsonDataSourceProvenance
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.provenance.SkeletalTrainerProvenance
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.regression.example.GaussianDataSource.GaussianDataSourceProvenance: Extracts the relevant provenance information fields for this class.
extractTFProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer.TensorflowSequenceTrainerProvenance
extractTFProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer.TensorflowCheckpointTrainerProvenance
extractTFProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.interop.tensorflow.TensorflowTrainer.TensorflowTrainerProvenance

F

f1(double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Computes the F_1 score.
f1(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
f1(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Computes the F_1 score.
f1(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
f1(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the F_1 score, i.e., the harmonic mean of the precision and recall.
F1 - Enum constant in enum org.tribuo.anomaly.evaluation.AnomalyMetrics: The F_1 score, i.e., the harmonic mean of the precision and the recall.
F1 - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The F_1 score, i.e., the harmonic mean of the precision and the recall.
F1 - Enum constant in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics: The F_1 score, i.e., the harmonic mean of the precision and the recall.
Feature - Class in org.tribuo: A class for features.
Feature(String, double) - Constructor for class org.tribuo.Feature: Creates an immutable feature.
FEATURE_TYPE - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
FEATURE_VALUE - Static variable in class org.tribuo.data.columnar.processors.field.IdentityProcessor: The value of the emitted features.
FeatureAggregator - Interface in org.tribuo.data.text: An interface for aggregating feature values into other values.
featureBackwardMapping - Variable in class org.tribuo.interop.ExternalModel
featureForwardMapping - Variable in class org.tribuo.interop.ExternalModel
FeatureHasher - Class in org.tribuo.data.text.impl: Hashes the feature names to reduce the dimensionality.
FeatureHasher(int) - Constructor for class org.tribuo.data.text.impl.FeatureHasher
featureIDMap - Variable in class org.tribuo.ImmutableDataset: A map from feature names to IDs for the features found in this dataset.
featureIDMap - Variable in class org.tribuo.Model: The features this model knows about.
featureIDMap - Variable in class org.tribuo.sequence.ImmutableSequenceDataset: A map from feature names to IDs for the features found in this dataset.
featureIDMap - Variable in class org.tribuo.sequence.SequenceModel
featureIDs - Variable in class org.tribuo.impl.IndexedArrayExample
featureInfo(CommandInterpreter, String) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
featureInfo(CommandInterpreter, String) - Method in class org.tribuo.data.DatasetExplorer
featureInfo(CommandInterpreter, String) - Method in class org.tribuo.ModelExplorer: Shows a specific feature's information.
featureInfo(CommandInterpreter, String) - Method in class org.tribuo.sequence.SequenceModelExplorer
featureIterator() - Method in class org.tribuo.sequence.SequenceExample: Creates an iterator over every feature in this sequence.
featureMap - Variable in class org.tribuo.MutableDataset: A map from feature names to feature info objects.
featureMap - Variable in class org.tribuo.sequence.MutableSequenceDataset: A map from feature names to IDs for the features found in this dataset.
FeatureMap - Class in org.tribuo: A map from Strings to VariableInfo objects storing information about a feature.
FeatureMap() - Constructor for class org.tribuo.FeatureMap: Constructs an empty feature map.
FeatureMap(Map<String, ? extends VariableInfo>) - Constructor for class org.tribuo.FeatureMap: Constructs a feature map wrapping the supplied map.
FeatureMap(FeatureMap) - Constructor for class org.tribuo.FeatureMap: Constructs a deep copy of the supplied feature map.
featureNameComparator() - Static method in class org.tribuo.Feature: A comparator using the lexicographic ordering of feature names.
featureNames - Variable in class org.tribuo.impl.ArrayExample
featureNames - Variable in class org.tribuo.impl.BinaryFeaturesExample
FeatureProcessor - Interface in org.tribuo.data.columnar: Takes a list of columnar features and adds new features or removes existing features.
features - Variable in class org.tribuo.classification.sgd.Util.ExampleArray
features - Variable in class org.tribuo.classification.sgd.Util.SequenceExampleArray
FEATURES_FILE_MODIFIED_TIME - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
FEATURES_RESOURCE_HASH - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
FeatureTransformer - Interface in org.tribuo.data.text: A feature transformer maps a list of features to a new list of features Useful for example to apply the hashing trick to a set of features
FeatureTuple() - Constructor for class org.tribuo.impl.IndexedArrayExample.FeatureTuple
FeatureTuple(String, int, double) - Constructor for class org.tribuo.impl.IndexedArrayExample.FeatureTuple
featureValues - Variable in class org.tribuo.impl.ArrayExample
FieldExtractor<T> - Interface in org.tribuo.data.columnar: Extracts a value from a field to be placed in an Example's metadata field.
fieldName - Variable in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor
FieldProcessor - Interface in org.tribuo.data.columnar: An interface for things that process the columns in a data set.
FieldProcessor.GeneratedFeatureType - Enum in org.tribuo.data.columnar: The types of generated features.
fieldProcessorMap - Variable in class org.tribuo.data.columnar.RowProcessor
FieldResponseProcessor<T> - Class in org.tribuo.data.columnar.processors.response: A response processor that returns the value in a given field.
FieldResponseProcessor(String, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor: Constructs a response processor which passes the field value through the output factory.
fields - Variable in class org.tribuo.data.columnar.ColumnarIterator
FILE_MODIFIED_TIME - Static variable in interface org.tribuo.provenance.DataSourceProvenance
fileCompleter() - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
fileCompleter() - Method in class org.tribuo.data.DatasetExplorer
fileCompleter() - Method in class org.tribuo.ModelExplorer: Completers for files.
fileCompleter() - Method in class org.tribuo.sequence.SequenceModelExplorer
fill(double) - Method in class org.tribuo.math.la.DenseVector: Fills this DenseVector with value.
fill(int[]) - Method in class org.tribuo.common.tree.impl.IntArrayContainer: Overwrites values from the supplied array into this array.
fill(IntArrayContainer) - Method in class org.tribuo.common.tree.impl.IntArrayContainer: Overwrites values in this array with the supplied array.
finalise() - Method in class org.tribuo.math.optimisers.AdaGradRDA
finalise() - Method in class org.tribuo.math.optimisers.ParameterAveraging: This sets the parameters to their average value.
finalise() - Method in class org.tribuo.math.optimisers.Pegasos
finalise() - Method in interface org.tribuo.math.StochasticGradientOptimiser: Finalises the gradient optimisation, setting the parameters to their correct values.
FIRST - Enum constant in enum org.tribuo.data.columnar.processors.feature.UniqueProcessor.UniqueType: Select the first feature value in the list.
FIRST - Enum constant in enum org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector
firstCount - Variable in class org.tribuo.util.infotheory.impl.PairDistribution
firstDimensionName - Static variable in class org.tribuo.regression.example.RegressionDataGenerator
fixSize() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
fixSize() - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Fixes the size of each InvertedFeature's inner arrays.
FLOAT - Enum constant in enum org.tribuo.datasource.IDXDataSource.IDXType
FloatExtractor - Class in org.tribuo.data.columnar.extractors: Extracts the field value and converts it to a float.
FloatExtractor(String) - Constructor for class org.tribuo.data.columnar.extractors.FloatExtractor: Extracts a float value from the supplied field name.
FloatExtractor(String, String) - Constructor for class org.tribuo.data.columnar.extractors.FloatExtractor: Extracts a float value from the supplied field name.
fmix32(int) - Static method in class org.tribuo.util.MurmurHash3
fmix64(long) - Static method in class org.tribuo.util.MurmurHash3
fn() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro averaged number of false negatives.
fn() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The total number of false negatives.
fn() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
fn() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
fn(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
fn(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
fn(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Returns the number of false negatives, possibly averaged depending on the metric target.
fn(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
fn(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
fn(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the number of false negatives, i.e., the number of times the true label was incorrectly predicted as another label.
fn(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of false negatives for the supplied label.
FN - Enum constant in enum org.tribuo.anomaly.evaluation.AnomalyMetrics: The number of false negatives.
FN - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The number of false negatives.
FN - Enum constant in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics: The number of false negatives.
foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseMatrix
foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseSparseMatrix
foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseVector
foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.SparseVector
foreachInPlace(DoubleUnaryOperator) - Method in interface org.tribuo.math.la.Tensor: Applies a DoubleUnaryOperator elementwise to this Tensor.
forEachRemaining(Consumer<? super ColumnarIterator.Row>) - Method in class org.tribuo.data.columnar.ColumnarIterator
formatDuration(long, long) - Static method in class org.tribuo.util.Util: Formats a duration given two times in milliseconds.
forTarget(MetricTarget<Label>) - Method in enum org.tribuo.classification.evaluation.LabelMetrics: Gets the LabelMetric wrapped around the supplied MetricTarget.
forTarget(MetricTarget<ClusterID>) - Method in enum org.tribuo.clustering.evaluation.ClusteringMetrics
forTarget(MetricTarget<MultiLabel>) - Method in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics
fp() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro average of the number of false positives across all the labels, i.e., the total number of false positives.
fp() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The total number of false positives.
fp() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
fp() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
fp(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
fp(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
fp(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Returns the number of false positives, possibly averaged depending on the metric target.
fp(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
fp(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
fp(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the number of false positives, i.e., the number of times this label was predicted but it was not the true label..
fp(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of false positives for the supplied label.
FP - Enum constant in enum org.tribuo.anomaly.evaluation.AnomalyMetrics: The number of false positives.
FP - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The number of false positives.
FP - Enum constant in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics: The number of false positives.
fpr - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.ROC
fraction - Variable in class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
fractionFeaturesInSplit - Variable in class org.tribuo.common.tree.AbstractCARTTrainer: Number of features to sample per split.
frequencyBasedSample(Random, long) - Method in class org.tribuo.CategoricalInfo: Samples a value from this feature according to the frequency of observation.
frequencyBasedSample(SplittableRandom, long) - Method in class org.tribuo.CategoricalInfo: Samples a value from this feature according to the frequency of observation.
fscore(double, double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Computes the Fscore.
fscore(MetricTarget<T>, ConfusionMatrix<T>, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Computes the Fscore.
fullEquals(Event) - Method in class org.tribuo.anomaly.Event
fullEquals(Label) - Method in class org.tribuo.classification.Label
fullEquals(ClusterID) - Method in class org.tribuo.clustering.ClusterID
fullEquals(MultiLabel) - Method in class org.tribuo.multilabel.MultiLabel
fullEquals(Regressor) - Method in class org.tribuo.regression.Regressor.DimensionTuple
fullEquals(Regressor) - Method in class org.tribuo.regression.Regressor
fullEquals(T) - Method in interface org.tribuo.Output: Compares other to this output.
FULLY_WEIGHTED_VOTING - Enum constant in enum org.tribuo.common.nearest.KNNClassifierOptions.EnsembleCombinerType
FullyWeightedVotingCombiner - Class in org.tribuo.classification.ensemble: A combiner which performs a weighted or unweighted vote across the predicted labels.
FullyWeightedVotingCombiner() - Constructor for class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner

G

gamma - Variable in class org.tribuo.regression.libsvm.TrainTest.LibLinearOptions
gamma - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
gamma - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
GAMMA - Enum constant in enum org.tribuo.regression.xgboost.XGBoostRegressionTrainer.RegressionType: Gamma loss function.
gatherAcrossDim1(int[]) - Method in class org.tribuo.math.la.DenseMatrix
gatherAcrossDim2(int[]) - Method in class org.tribuo.math.la.DenseMatrix
GAUSSIAN - Enum constant in enum org.tribuo.regression.baseline.DummyRegressionTrainer.DummyType: Samples from a Gaussian using the means and variances from the training data.
gaussianAnomaly() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator: Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
gaussianAnomaly(long, double) - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator: Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
gaussianClusters(long, long) - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator: Generates a dataset drawn from a mixture of 5 2d gaussians.
GaussianDataSource - Class in org.tribuo.regression.example: Generates a single dimensional output drawn from N(slope*x + intercept,variance).
GaussianDataSource(int, float, float, float, float, float, long) - Constructor for class org.tribuo.regression.example.GaussianDataSource: Generates a single dimensional output drawn from N(slope*x + intercept,variance).
GaussianDataSource.GaussianDataSourceProvenance - Class in org.tribuo.regression.example: Provenance for GaussianDataSource.
GaussianDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.regression.example.GaussianDataSource.GaussianDataSourceProvenance: Constructs a provenance from the marshalled form.
GBTREE - Enum constant in enum org.tribuo.common.xgboost.XGBoostTrainer.BoosterType: A gradient boosted decision tree.
general - Variable in class org.tribuo.classification.experiments.ConfigurableTrainTest.ConfigurableTrainTestOptions
general - Variable in class org.tribuo.classification.experiments.RunAll.RunAllOptions
general - Variable in class org.tribuo.classification.experiments.TrainTest.AllClassificationOptions
general - Variable in class org.tribuo.classification.liblinear.TrainTest.TrainTestOptions
general - Variable in class org.tribuo.classification.libsvm.TrainTest.TrainTestOptions
general - Variable in class org.tribuo.classification.mnb.TrainTest.TrainTestOptions
general - Variable in class org.tribuo.classification.sgd.kernel.TrainTest.TrainTestOptions
general - Variable in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
general - Variable in class org.tribuo.classification.xgboost.TrainTest.TrainTestOptions
general - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
general - Variable in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
general - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
general - Variable in class org.tribuo.regression.libsvm.TrainTest.LibLinearOptions
general - Variable in class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
general - Variable in class org.tribuo.regression.sgd.TrainTest.SGDOptions
general - Variable in class org.tribuo.regression.slm.TrainTest.LARSOptions
general - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
generalOptions - Variable in class org.tribuo.classification.dtree.TrainTest.TrainTestOptions
generateBootstrap() - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance: Generates the indices from this DatasetViewProvenance by rerunning the bootstrap sample.
generateBootstrapIndices(int, Random) - Static method in class org.tribuo.util.Util: Draws a bootstrap sample of indices.
generateBootstrapIndices(int, SplittableRandom) - Static method in class org.tribuo.util.Util: Draws a bootstrap sample of indices.
generateCDF(double[]) - Static method in class org.tribuo.util.Util: Generates a cumulative distribution function from the supplied probability mass function.
generateCDF(float[]) - Static method in class org.tribuo.util.Util: Generates a cumulative distribution function from the supplied probability mass function.
generateCDF(long[], long) - Static method in class org.tribuo.util.Util: Generates a cumulative distribution function from the supplied probability mass function.
generateCorrelated(int, int, double, double) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo: These correlations don't map to mutual information values, as if xyDraw is above xyCorrelation then the draw is completely random.
generateDataset() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
generateDataset(int, float, float, float, float, float, long) - Static method in class org.tribuo.regression.example.GaussianDataSource: Generates a single dimensional output drawn from N(slope*x + intercept,variance).
generateEmptyExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator: This generates a sequence example with no examples.
generateExample(long, Map<String, String>, boolean) - Method in class org.tribuo.data.columnar.RowProcessor: Generate an Example from the supplied row.
generateExample(Map<String, String>, boolean) - Method in class org.tribuo.data.columnar.RowProcessor: Generate an Example from the supplied row.
generateExample(ColumnarIterator.Row, boolean) - Method in class org.tribuo.data.columnar.RowProcessor: Generate an Example from the supplied row.
generateFeatureName(String, String) - Static method in class org.tribuo.data.columnar.ColumnarFeature: Generates a feature name based on the field name.
generateFeatureName(String, String, String) - Static method in class org.tribuo.data.columnar.ColumnarFeature: Generates a feature name used for conjunction features.
generateFeatures(Map<String, String>) - Method in class org.tribuo.data.columnar.RowProcessor: Generates the features from the supplied row.
generateGorillaA() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
generateGorillaB() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
generateGorillaDataset(int) - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
generateHashedFeatureMap(FeatureMap, Hasher) - Static method in class org.tribuo.hash.HashedFeatureMap: Converts a standard FeatureMap by hashing each entry using the supplied hash function Hasher.
generateIDs(List<? extends VariableInfo>) - Static method in class org.tribuo.ImmutableFeatureMap: Generates the feature ids by sorting the features with the String comparator, then sequentially numbering them.
generateIDs(FeatureMap) - Static method in class org.tribuo.ImmutableFeatureMap: Generates the feature ids by sorting the features with the String comparator, then sequentially numbering them.
generateImmutableOutputInfo() - Method in class org.tribuo.anomaly.AnomalyInfo
generateImmutableOutputInfo() - Method in class org.tribuo.classification.LabelInfo
generateImmutableOutputInfo() - Method in class org.tribuo.clustering.ClusteringInfo
generateImmutableOutputInfo() - Method in class org.tribuo.multilabel.MultiLabelInfo
generateImmutableOutputInfo() - Method in interface org.tribuo.OutputInfo: Generates an ImmutableOutputInfo which has a copy of the data in this OutputInfo, but also has id values and is immutable.
generateImmutableOutputInfo() - Method in class org.tribuo.regression.RegressionInfo
generateInfo() - Method in class org.tribuo.anomaly.AnomalyFactory
generateInfo() - Method in class org.tribuo.classification.LabelFactory: Generates an empty MutableLabelInfo.
generateInfo() - Method in class org.tribuo.clustering.ClusteringFactory
generateInfo() - Method in class org.tribuo.multilabel.MultiLabelFactory
generateInfo() - Method in interface org.tribuo.OutputFactory: Generates the appropriate MutableOutputInfo so the output values can be tracked by a Dataset or other aggregate.
generateInfo() - Method in class org.tribuo.regression.RegressionFactory
generateInvalidExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator: This generates a sequence example with features that are unused by the training data.
generateLabelString(Set<Label>) - Static method in class org.tribuo.multilabel.MultiLabelFactory: Generates a comma separated string of labels from a Set of Label.
generateMetadata(ColumnarIterator.Row) - Method in class org.tribuo.data.columnar.RowProcessor: Generates the example metadata from the supplied row and index.
generateMutableOutputInfo() - Method in class org.tribuo.anomaly.AnomalyInfo
generateMutableOutputInfo() - Method in class org.tribuo.classification.LabelInfo
generateMutableOutputInfo() - Method in class org.tribuo.clustering.ClusteringInfo
generateMutableOutputInfo() - Method in class org.tribuo.multilabel.MultiLabelInfo
generateMutableOutputInfo() - Method in interface org.tribuo.OutputInfo: Generates a mutable copy of this OutputInfo.
generateMutableOutputInfo() - Method in class org.tribuo.regression.RegressionInfo
generateOtherInvalidExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator: This generates a sequence example where the first example has no features.
generateOutput(V) - Method in class org.tribuo.anomaly.AnomalyFactory
generateOutput(V) - Method in class org.tribuo.classification.LabelFactory: Generates the Label string by calling toString on the input.
generateOutput(V) - Method in class org.tribuo.clustering.ClusteringFactory: Generates a ClusterID by calling toString on the input, then calling Integer.parseInt.
generateOutput(V) - Method in class org.tribuo.multilabel.MultiLabelFactory: Parses the MultiLabel value either by toStringing the input and calling MultiLabel.parseString(java.lang.String) or if it's a Collection iterating over the elements calling toString on each element in turn and using MultiLabel.parseElement(java.lang.String).
generateOutput(V) - Method in interface org.tribuo.OutputFactory: Parses the V and generates the appropriate Output value.
generateOutput(V) - Method in class org.tribuo.regression.RegressionFactory: Parses the MultipleRegression value either by toStringing the input and calling Regressor.parseString(java.lang.String) or if it's a collection iterating over the elements calling toString on each element in turn and using Regressor.parseElement(int, java.lang.String).
generateOutputs(List<V>) - Method in interface org.tribuo.OutputFactory: Generate a list of outputs from the supplied list of inputs.
generatePlaceholderName(String) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil: Creates a name for a placeholder based on the supplied variable name.
generatePRCurve(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil: Calculates the Precision Recall curve for a single label.
generateRealInfo() - Method in class org.tribuo.CategoricalIDInfo: Generates a RealIDInfo that matches this CategoricalInfo and also contains an id number.
generateRealInfo() - Method in class org.tribuo.CategoricalInfo: Generates a RealInfo using the currently observed counts to calculate the min, max, mean and variance.
generateROCCurve(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil: Calculates the binary ROC for a single label.
generatesProbabilities - Variable in class org.tribuo.Model: Does this model generate probability distributions in the output.
generatesProbabilities() - Method in class org.tribuo.classification.xgboost.XGBoostClassificationConverter
generatesProbabilities() - Method in interface org.tribuo.common.xgboost.XGBoostOutputConverter: Does this converter produce probabilities?
generatesProbabilities() - Method in class org.tribuo.interop.onnx.LabelTransformer
generatesProbabilities() - Method in interface org.tribuo.interop.onnx.OutputTransformer: Does this OutputTransformer generate probabilities.
generatesProbabilities() - Method in class org.tribuo.interop.onnx.RegressorTransformer
generatesProbabilities() - Method in class org.tribuo.interop.tensorflow.LabelTransformer
generatesProbabilities() - Method in interface org.tribuo.interop.tensorflow.OutputTransformer: Does this OutputTransformer generate probabilities.
generatesProbabilities() - Method in class org.tribuo.interop.tensorflow.RegressorTransformer
generatesProbabilities() - Method in class org.tribuo.Model: Does this model generate probabilistic predictions.
generatesProbabilities() - Method in class org.tribuo.regression.xgboost.XGBoostRegressionConverter
generatesProbabilities(CommandInterpreter) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
generatesProbabilities(CommandInterpreter) - Method in class org.tribuo.ModelExplorer: Checks if the model generates probabilities.
generateTestData() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
generateTrainData() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
generateTransformer() - Method in class org.tribuo.transform.transformations.SimpleTransform: Returns itself.
generateTransformer() - Method in interface org.tribuo.transform.TransformStatistics: Generates the appropriate Transformer from the collected statistics.
generateUniform(int, int) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo: Generates a sample from a uniform distribution over the integers.
generateUniformFloatVector(int, float) - Static method in class org.tribuo.util.Util
generateUniformVector(int, double) - Static method in class org.tribuo.util.Util
generateUniformVector(int, float) - Static method in class org.tribuo.util.Util
generateWeightedIndicesSample(int, double[], Random) - Static method in class org.tribuo.util.Util: Generates a sample of indices weighted by the provided weights.
generateWeightedIndicesSample(int, double[], SplittableRandom) - Static method in class org.tribuo.util.Util: Generates a sample of indices weighted by the provided weights.
generateWeightedIndicesSample(int, float[], Random) - Static method in class org.tribuo.util.Util: Generates a sample of indices weighted by the provided weights.
generateWeightedIndicesSample(int, float[], SplittableRandom) - Static method in class org.tribuo.util.Util: Generates a sample of indices weighted by the provided weights.
generateWeightedIndicesSampleWithoutReplacement(int, double[], Random) - Static method in class org.tribuo.util.Util: Generates a sample of indices weighted by the provided weights without replacement.
generateWeightedIndicesSampleWithoutReplacement(int, float[], Random) - Static method in class org.tribuo.util.Util: Generates a sample of indices weighted by the provided weights without replacement.
generateWeightsString() - Method in class org.tribuo.classification.sgd.crf.CRFModel: Generates a human readable string containing all the weights in this model.
generateXOR(int) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo: Generates a sample from a three variable XOR function.
get() - Method in class org.tribuo.classification.sgd.crf.CRFParameters
get() - Method in class org.tribuo.math.LinearParameters
get() - Method in interface org.tribuo.math.Parameters: Get a reference to the underlying Tensor array.
get(int) - Method in class org.tribuo.ImmutableFeatureMap: Gets the VariableIDInfo for this id number.
get(int) - Method in class org.tribuo.math.la.DenseVector
get(int) - Method in interface org.tribuo.math.la.SGDVector: Gets an element from this vector.
get(int) - Method in class org.tribuo.math.la.SparseVector
get(int) - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
get(int) - Method in class org.tribuo.sequence.SequenceExample: Gets the example found at the specified index.
get(int) - Method in class org.tribuo.util.infotheory.impl.RowList
get(int, int) - Method in class org.tribuo.math.la.DenseMatrix
get(int, int) - Method in class org.tribuo.math.la.DenseSparseMatrix
get(int, int) - Method in interface org.tribuo.math.la.Matrix: Gets an element from this Matrix.
get(int, int) - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
get(String) - Method in class org.tribuo.FeatureMap: Gets the variable info associated with that feature name, or null if it's unknown.
get(String) - Method in class org.tribuo.hash.HashedFeatureMap
get(String) - Method in class org.tribuo.ImmutableFeatureMap: Gets the VariableIDInfo for this name.
get(MetricID<T>) - Method in interface org.tribuo.evaluation.Evaluation: Gets the value associated with the specific metric.
get(MetricID<T>) - Method in interface org.tribuo.sequence.SequenceEvaluation: Gets the value associated with the specific metric.
getA() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
getAB() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
getABCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
getABCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
getAC() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
getACCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
getACCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
getACount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
getACount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
getActiveFeatures() - Method in interface org.tribuo.classification.explanations.Explanation: Returns the names of the active features in this explanation.
getActiveFeatures() - Method in class org.tribuo.classification.explanations.lime.LIMEExplanation
getActiveFeatures() - Method in class org.tribuo.SparseModel: Return an immutable view on the active features for each dimension.
getAnomalyCount() - Method in class org.tribuo.anomaly.AnomalyInfo: The number of anomalous events observed.
getAverageTarget() - Method in class org.tribuo.evaluation.metrics.MetricTarget: Returns the average this metric computes, or Optional.empty() if it targets an output.
getB() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
getBatchSize() - Method in class org.tribuo.interop.ExternalModel: Gets the current testing batch size.
getBatchSize() - Method in class org.tribuo.interop.tensorflow.TensorflowModel: Gets the current testing batch size.
getBC() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
getBCCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
getBCCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
getBCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
getBCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
getBias(int) - Method in class org.tribuo.classification.sgd.crf.CRFParameters
getC() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
getCCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
getCCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
getCentroidVectors() - Method in class org.tribuo.clustering.kmeans.KMeansModel: Returns a copy of the centroids.
getClassName() - Method in class org.tribuo.anomaly.AnomalyFactory.AnomalyFactoryProvenance
getClassName() - Method in class org.tribuo.classification.LabelFactory.LabelFactoryProvenance
getClassName() - Method in class org.tribuo.clustering.ClusteringFactory.ClusteringFactoryProvenance
getClassName() - Method in class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance
getClassName() - Method in class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance
getClassName() - Method in class org.tribuo.evaluation.TrainTestSplitter.SplitDataSourceProvenance
getClassName() - Method in class org.tribuo.hash.HashCodeHasher.HashCodeHasherProvenance
getClassName() - Method in class org.tribuo.hash.MessageDigestHasher.MessageDigestHasherProvenance
getClassName() - Method in class org.tribuo.hash.ModHashCodeHasher.ModHashCodeHasherProvenance
getClassName() - Method in class org.tribuo.interop.ExternalTrainerProvenance
getClassName() - Method in class org.tribuo.multilabel.MultiLabelFactory.MultiLabelFactoryProvenance
getClassName() - Method in class org.tribuo.provenance.DatasetProvenance
getClassName() - Method in class org.tribuo.provenance.EvaluationProvenance
getClassName() - Method in class org.tribuo.provenance.impl.EmptyDataSourceProvenance
getClassName() - Method in class org.tribuo.provenance.impl.EmptyTrainerProvenance
getClassName() - Method in class org.tribuo.provenance.ModelProvenance
getClassName() - Method in class org.tribuo.provenance.SimpleDataSourceProvenance
getClassName() - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance: Deprecated.
getClassName() - Method in class org.tribuo.regression.RegressionFactory.RegressionFactoryProvenance
getClassName() - Method in class org.tribuo.transform.transformations.BinningTransformation.BinningTransformationProvenance
getClassName() - Method in class org.tribuo.transform.transformations.LinearScalingTransformation.LinearScalingTransformationProvenance
getClassName() - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation.MeanStdDevTransformationProvenance
getClassName() - Method in class org.tribuo.transform.transformations.SimpleTransform.SimpleTransformProvenance
getClassName() - Method in class org.tribuo.transform.TransformerMap.TransformerMapProvenance
getCliqueValues(SparseVector[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Generates the local scores and tuples them with the label - label transition weights.
getCM() - Method in class org.tribuo.classification.evaluation.LabelMetric.Context
getColumn(int) - Method in class org.tribuo.math.la.DenseMatrix
getColumnEntry() - Method in class org.tribuo.data.columnar.ColumnarFeature: Gets the columnEntry (i.e., the feature name produced by the FieldExtractor without the fieldName).
getColumnNames() - Method in class org.tribuo.data.columnar.RowProcessor: The set of column names this will use for the feature processing.
getConfiguredParameters() - Method in class org.tribuo.hash.HashCodeHasher.HashCodeHasherProvenance
getConfiguredParameters() - Method in class org.tribuo.hash.MessageDigestHasher.MessageDigestHasherProvenance
getConfiguredParameters() - Method in class org.tribuo.hash.ModHashCodeHasher.ModHashCodeHasherProvenance
getConfiguredParameters() - Method in class org.tribuo.interop.ExternalTrainerProvenance
getConfiguredParameters() - Method in class org.tribuo.provenance.impl.EmptyTrainerProvenance
getConfiguredParameters() - Method in interface org.tribuo.provenance.OutputFactoryProvenance
getConfiguredParameters() - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance: Deprecated.
getConfiguredParameters() - Method in class org.tribuo.regression.RegressionFactory.RegressionFactoryProvenance
getConfiguredParameters() - Method in class org.tribuo.transform.transformations.BinningTransformation.BinningTransformationProvenance
getConfiguredParameters() - Method in class org.tribuo.transform.transformations.LinearScalingTransformation.LinearScalingTransformationProvenance
getConfiguredParameters() - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation.MeanStdDevTransformationProvenance
getConfiguredParameters() - Method in class org.tribuo.transform.transformations.SimpleTransform.SimpleTransformProvenance
getConfusionMatrix() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the underlying confusion matrix.
getConfusionMatrix() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the confusion matrix backing this evaluation.
getConfusionMatrix() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
getConnection() - Method in class org.tribuo.data.sql.SQLDBConfig: Constructs a connection based on the object fields.
getCount() - Method in class org.tribuo.SkeletalVariableInfo: Returns the occurrence count of this feature.
getCount() - Method in interface org.tribuo.VariableInfo: The occurrence count of this feature.
getData() - Method in class org.tribuo.dataset.DatasetView
getData() - Method in class org.tribuo.Dataset: Gets the examples as an unmodifiable list.
getData() - Method in class org.tribuo.sequence.SequenceDataset: Returns an unmodifiable view on the data.
getDatasetProvenance() - Method in class org.tribuo.provenance.ModelProvenance: The training dataset provenance.
getDataType() - Method in class org.tribuo.datasource.IDXDataSource: The type of the features that were loaded in.
getDepth() - Method in class org.tribuo.common.tree.AbstractTrainingNode: The depth of this node in the tree.
getDescription() - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
getDescription() - Method in class org.tribuo.data.columnar.RowProcessor: Returns a description of the row processor and it's fields.
getDescription() - Method in class org.tribuo.data.DatasetExplorer
getDescription() - Method in class org.tribuo.ModelExplorer
getDescription() - Method in class org.tribuo.sequence.SequenceModelExplorer
getDigestSupplier(String) - Static method in class org.tribuo.hash.MessageDigestHasher: Creates a supplier for the specified hash type.
getDimension(String) - Method in class org.tribuo.regression.Regressor.DimensionTuple
getDimension(String) - Method in class org.tribuo.regression.Regressor: Returns a dimension tuple for the requested dimension, or optional empty if it's not valid.
getDimension1Size() - Method in class org.tribuo.math.la.DenseMatrix
getDimension1Size() - Method in class org.tribuo.math.la.DenseSparseMatrix
getDimension1Size() - Method in interface org.tribuo.math.la.Matrix: The size of the first dimension.
getDimension2Size() - Method in class org.tribuo.math.la.DenseMatrix
getDimension2Size() - Method in class org.tribuo.math.la.DenseSparseMatrix
getDimension2Size() - Method in interface org.tribuo.math.la.Matrix: The size of the second dimension.
getDimensionNamesString() - Method in class org.tribuo.regression.Regressor.DimensionTuple
getDimensionNamesString() - Method in class org.tribuo.regression.Regressor: Returns a comma separated list of the dimension names.
getDimensionNamesString(char) - Method in class org.tribuo.regression.Regressor: Returns a delimiter separated list of the dimension names.
getDistribution() - Method in class org.tribuo.common.tree.LeafNode: Gets the distribution over scores in this node.
getDomain() - Method in class org.tribuo.anomaly.AnomalyInfo: Returns the set of possible Events.
getDomain() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: Returns the classification domain that this confusion matrix operates over.
getDomain() - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
getDomain() - Method in class org.tribuo.classification.ImmutableLabelInfo: Returns the set of possible Labels that this LabelInfo has seen.
getDomain() - Method in class org.tribuo.classification.LabelInfo: Returns the set of possible Labels that this LabelInfo has seen.
getDomain() - Method in class org.tribuo.clustering.ClusteringInfo
getDomain() - Method in class org.tribuo.clustering.ImmutableClusteringInfo
getDomain() - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
getDomain() - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
getDomain() - Method in class org.tribuo.multilabel.MultiLabelInfo: Returns a set of MultiLabel, where each has a single Label inside it.
getDomain() - Method in interface org.tribuo.OutputInfo: Returns a set of Output which represent the space of possible values the Output has taken.
getDomain() - Method in class org.tribuo.regression.ImmutableRegressionInfo
getDomain() - Method in class org.tribuo.regression.RegressionInfo: Returns a set containing a Regressor for each dimension with the minimum value observed.
getDropInvalidExamples() - Method in class org.tribuo.ImmutableDataset: Returns true if this immutable dataset dropped any invalid examples on construction.
getEmptyCopy() - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Returns a 3 element Tensor array.
getEmptyCopy() - Method in class org.tribuo.math.LinearParameters: This returns a DenseMatrix the same size as the Parameters.
getEmptyCopy() - Method in interface org.tribuo.math.Parameters: Generates an empty copy of the underlying Tensor array.
getEnd() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
getEnd() - Method in class org.tribuo.util.tokens.impl.NonTokenizer
getEnd() - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer
getEnd() - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
getEnd() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
getEnd() - Method in interface org.tribuo.util.tokens.Tokenizer: Gets the ending offset (exclusive) of the current token in the character sequence
getEnd() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
getEvaluation() - Method in class org.tribuo.classification.explanations.lime.LIMEExplanation: Gets the evaluator which scores how close the sparse model's predictions are to the complex model's predictions.
getEvaluator() - Method in class org.tribuo.anomaly.AnomalyFactory
getEvaluator() - Method in class org.tribuo.classification.LabelFactory
getEvaluator() - Method in class org.tribuo.clustering.ClusteringFactory
getEvaluator() - Method in class org.tribuo.multilabel.MultiLabelFactory
getEvaluator() - Method in interface org.tribuo.OutputFactory: Gets an Evaluator suitable for measuring performance of predictions for the Output subclass.
getEvaluator() - Method in class org.tribuo.regression.RegressionFactory
getEventCount(Event.EventType) - Method in class org.tribuo.anomaly.AnomalyInfo: Gets the count of the supplied EventType.
getExample() - Method in class org.tribuo.Excuse: The example being excused.
getExample() - Method in class org.tribuo.Prediction: Returns the example itself.
getExample(int) - Method in class org.tribuo.dataset.DatasetView
getExample(int) - Method in class org.tribuo.Dataset: Gets the example at the supplied index.
getExample(int) - Method in class org.tribuo.sequence.SequenceDataset: Gets the example at the specified index, or throws IllegalArgumentException if the index is out of bounds.
getExampleIndices() - Method in class org.tribuo.dataset.DatasetView: Returns a copy of the indicies used in this view.
getExampleSize() - Method in class org.tribuo.Prediction: Returns the number of features in the example.
getExcuse(Example<Label>) - Method in class org.tribuo.classification.baseline.DummyClassifierModel
getExcuse(Example<Label>) - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesModel
getExcuse(Example<Label>) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel
getExcuse(Example<Label>) - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel
getExcuse(Example<ClusterID>) - Method in class org.tribuo.clustering.kmeans.KMeansModel
getExcuse(Example<MultiLabel>) - Method in class org.tribuo.multilabel.baseline.IndependentMultiLabelModel
getExcuse(Example<Regressor>) - Method in class org.tribuo.regression.baseline.DummyRegressionModel
getExcuse(Example<Regressor>) - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
getExcuse(Example<Regressor>) - Method in class org.tribuo.regression.sgd.linear.LinearSGDModel
getExcuse(Example<Regressor>) - Method in class org.tribuo.regression.slm.SparseLinearModel
getExcuse(Example<T>) - Method in class org.tribuo.common.liblinear.LibLinearModel: This call is expensive as it copies out the weight matrix from the LibLinear model.
getExcuse(Example<T>) - Method in class org.tribuo.common.libsvm.LibSVMModel
getExcuse(Example<T>) - Method in class org.tribuo.common.nearest.KNNModel
getExcuse(Example<T>) - Method in class org.tribuo.common.tree.TreeModel
getExcuse(Example<T>) - Method in class org.tribuo.common.xgboost.XGBoostModel
getExcuse(Example<T>) - Method in class org.tribuo.ensemble.EnsembleModel
getExcuse(Example<T>) - Method in class org.tribuo.ensemble.WeightedEnsembleModel
getExcuse(Example<T>) - Method in class org.tribuo.interop.ExternalModel: By default third party models don't return excuses.
getExcuse(Example<T>) - Method in class org.tribuo.interop.tensorflow.TensorflowCheckpointModel: Deep learning models don't do excuses.
getExcuse(Example<T>) - Method in class org.tribuo.interop.tensorflow.TensorflowModel: Deep learning models don't do excuses.
getExcuse(Example<T>) - Method in class org.tribuo.Model: Generates an excuse for an example.
getExcuse(Example<T>) - Method in class org.tribuo.transform.TransformedModel
getExcuses(Iterable<Example<T>>) - Method in class org.tribuo.common.liblinear.LibLinearModel
getExcuses(Iterable<Example<T>>) - Method in class org.tribuo.Model: Generates an excuse for each example.
getExpectedCount() - Method in class org.tribuo.anomaly.AnomalyInfo: The number of expected events observed.
getF1() - Method in interface org.tribuo.anomaly.evaluation.AnomalyEvaluation: Returns the F_1 score of the anomalous events, i.e., the harmonic mean of the precision and the recall.
getFalseNegatives() - Method in interface org.tribuo.anomaly.evaluation.AnomalyEvaluation: Returns the number of false negatives, i.e., anomalous events classified as expected.
getFalsePositives() - Method in interface org.tribuo.anomaly.evaluation.AnomalyEvaluation: Returns the number of false positives, i.e., expected events classified as anomalous.
getFeature() - Method in class org.tribuo.regression.rtree.impl.TreeFeature
getFeatureID() - Method in class org.tribuo.common.tree.SplitNode: Gets the feature ID that this node uses for splitting.
getFeatureIDMap() - Method in class org.tribuo.Dataset: Returns or generates an ImmutableFeatureMap.
getFeatureIDMap() - Method in class org.tribuo.ImmutableDataset
getFeatureIDMap() - Method in class org.tribuo.Model: Gets the feature domain.
getFeatureIDMap() - Method in class org.tribuo.MutableDataset
getFeatureIDMap() - Method in class org.tribuo.sequence.ImmutableSequenceDataset
getFeatureIDMap() - Method in class org.tribuo.sequence.MutableSequenceDataset
getFeatureIDMap() - Method in class org.tribuo.sequence.SequenceDataset: An immutable view on the feature map.
getFeatureIDMap() - Method in class org.tribuo.sequence.SequenceModel: Gets the feature domain.
getFeatureMap() - Method in class org.tribuo.dataset.DatasetView
getFeatureMap() - Method in class org.tribuo.Dataset: Returns this dataset's FeatureMap.
getFeatureMap() - Method in class org.tribuo.ImmutableDataset
getFeatureMap() - Method in class org.tribuo.MutableDataset
getFeatureMap() - Method in class org.tribuo.sequence.ImmutableSequenceDataset
getFeatureMap() - Method in class org.tribuo.sequence.MutableSequenceDataset
getFeatureMap() - Method in class org.tribuo.sequence.SequenceDataset: The feature map.
getFeatureProcessors() - Method in class org.tribuo.data.columnar.RowProcessor: Returns the set of FeatureProcessors this RowProcessor uses.
getFeatures() - Method in class org.tribuo.common.tree.TreeModel: Returns the set of features which are split on in this tree.
getFeatures() - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
getFeatureTransformations() - Method in class org.tribuo.transform.TransformationMap: Gets the map of feature specific transformations.
getFeatureType() - Method in interface org.tribuo.data.columnar.FieldProcessor: Returns the feature type this FieldProcessor generates.
getFeatureType() - Method in class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
getFeatureType() - Method in class org.tribuo.data.columnar.processors.field.IdentityProcessor
getFeatureType() - Method in class org.tribuo.data.columnar.processors.field.RegexFieldProcessor
getFeatureType() - Method in class org.tribuo.data.columnar.processors.field.TextFieldProcessor
getFeatureWeights() - Method in class org.tribuo.classification.liblinear.LibLinearClassificationModel
getFeatureWeights() - Method in class org.tribuo.common.liblinear.LibLinearModel: Extracts the feature weights from the models.
getFeatureWeights() - Method in class org.tribuo.regression.liblinear.LibLinearRegressionModel
getFeatureWeights(int) - Method in class org.tribuo.classification.sgd.crf.CRFModel: Get a copy of the weights for feature featureID.
getFeatureWeights(int) - Method in class org.tribuo.classification.sgd.crf.CRFParameters
getFeatureWeights(String) - Method in class org.tribuo.classification.sgd.crf.CRFModel: Get a copy of the weights for feature named featureName.
getFieldName() - Method in class org.tribuo.data.columnar.ColumnarFeature: Gets the field name.
getFieldName() - Method in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor: Gets the field name this extractor operates on.
getFieldName() - Method in interface org.tribuo.data.columnar.FieldProcessor: Gets the field name this FieldProcessor uses.
getFieldName() - Method in class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
getFieldName() - Method in class org.tribuo.data.columnar.processors.field.IdentityProcessor
getFieldName() - Method in class org.tribuo.data.columnar.processors.field.RegexFieldProcessor
getFieldName() - Method in class org.tribuo.data.columnar.processors.field.TextFieldProcessor
getFieldName() - Method in class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor
getFieldName() - Method in class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
getFieldName() - Method in class org.tribuo.data.columnar.processors.response.QuartileResponseProcessor
getFieldName() - Method in interface org.tribuo.data.columnar.ResponseProcessor: Gets the field name this ResponseProcessor uses.
getFieldProcessors() - Method in class org.tribuo.data.columnar.RowProcessor: Returns the map of FieldProcessors this RowProcessor uses.
getFields() - Method in class org.tribuo.data.columnar.ColumnarIterator: The immutable list of field names.
getFields() - Method in class org.tribuo.data.columnar.ColumnarIterator.Row
getFirstCount() - Method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution
getFirstFieldName() - Method in class org.tribuo.data.columnar.ColumnarFeature: If it's a conjunction feature, return the first field name.
getFlatDataset() - Method in class org.tribuo.sequence.SequenceDataset: Returns a view on this SequenceDataset which aggregates all the examples and ignores the sequence structure.
getFractionFeaturesInSplit() - Method in class org.tribuo.common.tree.AbstractCARTTrainer
getFractionFeaturesInSplit() - Method in interface org.tribuo.common.tree.DecisionTreeTrainer: Returns the feature subsampling rate.
getGamma() - Method in class org.tribuo.common.libsvm.SVMParameters
getGlobalTransformations() - Method in class org.tribuo.transform.TransformationMap: Gets the global transformations in this TransformationMap.
getGreaterThan() - Method in class org.tribuo.common.tree.SplitNode: The node used if the value is greater than the splitValue.
getHashedTrainer(Trainer<T>) - Method in class org.tribuo.hash.HashingOptions: Gets the trainer wrapped in a hashing trainer.
getHasher() - Method in class org.tribuo.hash.HashingOptions: Get the specified hasher.
getHyperparameterFeed() - Method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
getID() - Method in enum org.tribuo.anomaly.Event.EventType: Returns the id of the event.
getID() - Method in class org.tribuo.CategoricalIDInfo
getID() - Method in class org.tribuo.clustering.ClusterID: Gets the cluster id number.
getID() - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric: The metric ID, a combination of the metric target and metric name.
getID() - Method in class org.tribuo.RealIDInfo
getID() - Method in interface org.tribuo.VariableIDInfo: The id number associated with this variable.
getID(String) - Method in class org.tribuo.hash.HashedFeatureMap: Gets the id number for this feature, returns -1 if it's unknown.
getID(String) - Method in class org.tribuo.ImmutableFeatureMap: Gets the id number for this feature, returns -1 if it's unknown.
getID(Event) - Method in class org.tribuo.anomaly.ImmutableAnomalyInfo
getID(Label) - Method in class org.tribuo.classification.ImmutableLabelInfo
getID(ClusterID) - Method in class org.tribuo.clustering.ImmutableClusteringInfo
getID(MultiLabel) - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
getID(Regressor) - Method in class org.tribuo.regression.ImmutableRegressionInfo
getID(T) - Method in interface org.tribuo.ImmutableOutputInfo: Return the id number associated with this output, or -1 if the output is unknown.
getIdx(int) - Method in class org.tribuo.impl.IndexedArrayExample: Gets the feature at internal index i.
getImpl() - Method in enum org.tribuo.classification.evaluation.LabelMetrics: Returns the implementing function for this metric.
getImpl() - Method in enum org.tribuo.clustering.evaluation.ClusteringMetrics
getImpl() - Method in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics
getImpurity() - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode
getImpurity() - Method in class org.tribuo.common.tree.LeafNode
getImpurity() - Method in interface org.tribuo.common.tree.Node: The impurity score of this node.
getImpurity() - Method in class org.tribuo.common.tree.SplitNode
getImpurity() - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode
getImpurity() - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode
getIndex() - Method in class org.tribuo.data.columnar.ColumnarIterator.Row
getInnerExcuses() - Method in class org.tribuo.ensemble.EnsembleExcuse: The individual ensemble member's excuses.
getInnerModels() - Method in class org.tribuo.common.liblinear.LibLinearModel: Returns an unmodifiable list containing a copy of each model.
getInnerModels() - Method in class org.tribuo.common.libsvm.LibSVMModel: Returns an unmodifiable copy of the underlying list of libsvm models.
getInnerModels() - Method in class org.tribuo.common.xgboost.XGBoostModel: Returns an unmodifiable list containing a copy of each model.
getInstanceProvenance() - Method in class org.tribuo.provenance.ModelProvenance: Provenance for the specific training run which created this model.
getInstanceValues() - Method in class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance
getInstanceValues() - Method in class org.tribuo.data.sql.SQLDataSource.SQLDataSourceProvenance
getInstanceValues() - Method in class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
getInstanceValues() - Method in class org.tribuo.data.text.impl.SimpleStringDataSource.SimpleStringDataSourceProvenance
getInstanceValues() - Method in class org.tribuo.data.text.impl.SimpleTextDataSource.SimpleTextDataSourceProvenance
getInstanceValues() - Method in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
getInstanceValues() - Method in class org.tribuo.datasource.LibSVMDataSource.LibSVMDataSourceProvenance
getInstanceValues() - Method in class org.tribuo.interop.ExternalTrainerProvenance
getInstanceValues() - Method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer.TensorflowSequenceTrainerProvenance
getInstanceValues() - Method in class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer.TensorflowCheckpointTrainerProvenance
getInstanceValues() - Method in class org.tribuo.interop.tensorflow.TensorflowTrainer.TensorflowTrainerProvenance
getInstanceValues() - Method in class org.tribuo.json.JsonDataSource.JsonDataSourceProvenance
getInstanceValues() - Method in class org.tribuo.provenance.SkeletalTrainerProvenance
getInvocationCount() - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer
getInvocationCount() - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer
getInvocationCount() - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesTrainer
getInvocationCount() - Method in class org.tribuo.classification.sequence.viterbi.ViterbiTrainer
getInvocationCount() - Method in class org.tribuo.classification.sgd.crf.CRFTrainer
getInvocationCount() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
getInvocationCount() - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
getInvocationCount() - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
getInvocationCount() - Method in class org.tribuo.common.liblinear.LibLinearTrainer
getInvocationCount() - Method in class org.tribuo.common.libsvm.LibSVMTrainer
getInvocationCount() - Method in class org.tribuo.common.nearest.KNNTrainer
getInvocationCount() - Method in class org.tribuo.common.tree.AbstractCARTTrainer
getInvocationCount() - Method in class org.tribuo.common.xgboost.XGBoostTrainer
getInvocationCount() - Method in class org.tribuo.ensemble.BaggingTrainer
getInvocationCount() - Method in class org.tribuo.hash.HashingTrainer
getInvocationCount() - Method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
getInvocationCount() - Method in class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer
getInvocationCount() - Method in class org.tribuo.interop.tensorflow.TensorflowTrainer
getInvocationCount() - Method in class org.tribuo.multilabel.baseline.IndependentMultiLabelTrainer
getInvocationCount() - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer
getInvocationCount() - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer
getInvocationCount() - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer
getInvocationCount() - Method in class org.tribuo.regression.sgd.linear.LinearSGDTrainer
getInvocationCount() - Method in class org.tribuo.regression.slm.ElasticNetCDTrainer
getInvocationCount() - Method in class org.tribuo.regression.slm.SLMTrainer
getInvocationCount() - Method in class org.tribuo.sequence.HashingSequenceTrainer
getInvocationCount() - Method in interface org.tribuo.sequence.SequenceTrainer: Returns the number of times the train method has been invoked.
getInvocationCount() - Method in interface org.tribuo.Trainer: The number of times this trainer instance has had it's train method invoked.
getInvocationCount() - Method in class org.tribuo.transform.TransformTrainer
getJointCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
getJointCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
getJointCounts() - Method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution
getK() - Method in class org.tribuo.evaluation.CrossValidation: Returns the number of folds.
getKernelType() - Method in class org.tribuo.common.libsvm.SVMParameters
getKernelType(int) - Static method in enum org.tribuo.common.libsvm.KernelType: Converts the LibSVM int id into the enum value.
getLabel() - Method in class org.tribuo.classification.Label: Gets the name of this label.
getLabelCount(int) - Method in class org.tribuo.classification.ImmutableLabelInfo: Returns the number of times the supplied id was observed before this LabelInfo was frozen.
getLabelCount(int) - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo: Gets the count of the label occurrence for the specified id number, or 0 if it's unknown.
getLabelCount(String) - Method in class org.tribuo.classification.LabelInfo: Gets the count of the supplied label, or 0 if the label is unknown.
getLabelCount(String) - Method in class org.tribuo.multilabel.MultiLabelInfo: Get the number of times this String was observed, or 0 if unknown.
getLabelCount(Label) - Method in class org.tribuo.classification.LabelInfo: Gets the count of the supplied label, or 0 if the label is unknown.
getLabelCount(Label) - Method in class org.tribuo.multilabel.MultiLabelInfo: Get the number of times this Label was observed, or 0 if unknown.
getLabelSet() - Method in class org.tribuo.multilabel.MultiLabel: The set of labels contained in this multilabel.
getLabelString() - Method in class org.tribuo.multilabel.MultiLabel: Returns a comma separated string representing the labels in this multilabel instance.
getLanguageTag() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer: Returns the locale string this tokenizer uses.
getLessThanOrEqual() - Method in class org.tribuo.common.tree.SplitNode: The node used if the value is less than or equal to the splitValue.
getLinearDecaySGD(double) - Static method in class org.tribuo.math.optimisers.SGD: Generates an SGD optimiser with a linearly decaying learning rate initialised to learningRate.
getLinearDecaySGD(double, double, SGD.Momentum) - Static method in class org.tribuo.math.optimisers.SGD: Generates an SGD optimiser with a linearly decaying learning rate initialised to learningRate, with momentum.
getLocalScores(SparseVector[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Generate the local scores (i.e., the linear classifier for each token).
getLongLittleEndian(byte[], int) - Static method in class org.tribuo.util.MurmurHash3: Gets a long from a byte buffer in little endian byte order.
getLoss() - Method in class org.tribuo.classification.sgd.linear.LinearSGDOptions: Returns the loss function specified in the arguments.
getLossOp - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
getLowerMedian() - Method in class org.tribuo.data.columnar.processors.response.Quartile: Returns the lower quartile value.
getMax() - Method in class org.tribuo.evaluation.DescriptiveStats: Calculates the max of the values.
getMax() - Method in class org.tribuo.RealInfo: Gets the maximum observed value.
getMax(String) - Method in class org.tribuo.regression.RegressionInfo: Gets the maximum value this RegressionInfo has seen, or NaN if it's not seen that dimension.
getMaxFeatureID() - Method in class org.tribuo.datasource.LibSVMDataSource: Gets the maximum feature ID found.
getMaxTokenLength() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
getMean() - Method in class org.tribuo.evaluation.DescriptiveStats: Calculates the mean of the values.
getMean() - Method in class org.tribuo.RealInfo: Gets the sample mean.
getMean(String) - Method in class org.tribuo.regression.RegressionInfo: Gets the mean value this RegressionInfo has seen, or NaN if it's not seen that dimension.
getMedian() - Method in class org.tribuo.data.columnar.processors.response.Quartile: Returns the median value.
getMemberProvenance() - Method in class org.tribuo.provenance.EnsembleModelProvenance
getMetadata() - Method in class org.tribuo.Example: Returns a copy of this example's metadata.
getMetadataName() - Method in class org.tribuo.data.columnar.extractors.IndexExtractor
getMetadataName() - Method in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor: Gets the metadata key name.
getMetadataName() - Method in interface org.tribuo.data.columnar.FieldExtractor: Gets the metadata key name.
getMetadataTypes() - Method in class org.tribuo.data.columnar.ColumnarDataSource: Returns the metadata keys and value types that are created by this DataSource.
getMetadataTypes() - Method in class org.tribuo.data.columnar.RowProcessor: Returns the metadata keys and value types that are extracted by this RowProcessor.
getMetadataValue(String) - Method in class org.tribuo.Example: Gets the associated metadata value for this key, if it exists.
getMin() - Method in class org.tribuo.evaluation.DescriptiveStats: Calculates the min of the values.
getMin() - Method in class org.tribuo.RealInfo: Gets the minimum observed value.
getMin(String) - Method in class org.tribuo.regression.RegressionInfo: Gets the minimum value this RegressionInfo has seen, or NaN if it's not seen anything.
getMinCardinality() - Method in class org.tribuo.dataset.MinimumCardinalityDataset: The minimum cardinality threshold for the features.
getMinCardinality() - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset: The minimum cardinality threshold for the features.
getModel() - Method in interface org.tribuo.classification.explanations.Explanation: Returns the explanining model.
getModel() - Method in class org.tribuo.classification.explanations.lime.LIMEExplanation
getModel() - Method in class org.tribuo.common.libsvm.LibSVMModel: Deprecated.
getModel() - Method in class org.tribuo.evaluation.metrics.MetricContext: Gets the Model used by this context.
getModelClassName() - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer: Returns the class name of the model that this class produces.
getModelClassName() - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer: Returns the class name of the model that this class produces.
getModelDump() - Method in class org.tribuo.common.xgboost.XGBoostModel: Returns the string model dumps from each Booster.
getModelProvenance() - Method in class org.tribuo.provenance.EvaluationProvenance: The model provenance.
getModels() - Method in class org.tribuo.ensemble.EnsembleModel: Returns an unmodifiable view on the ensemble members.
getN() - Method in class org.tribuo.evaluation.DescriptiveStats: Returns the number of values.
getName() - Method in class org.tribuo.anomaly.evaluation.AnomalyMetric
getName() - Method in class org.tribuo.classification.evaluation.LabelMetric
getName() - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
getName() - Method in class org.tribuo.clustering.evaluation.ClusteringMetric
getName() - Method in class org.tribuo.data.DatasetExplorer
getName() - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric: The name of this metric.
getName() - Method in class org.tribuo.Feature: Returns the feature name.
getName() - Method in class org.tribuo.Model: Returns the model name.
getName() - Method in class org.tribuo.ModelExplorer
getName() - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric
getName() - Method in class org.tribuo.regression.evaluation.RegressionMetric
getName() - Method in class org.tribuo.regression.Regressor.DimensionTuple: Returns the name.
getName() - Method in class org.tribuo.sequence.SequenceModel: Gets the model name.
getName() - Method in class org.tribuo.sequence.SequenceModelExplorer
getName() - Method in class org.tribuo.SkeletalVariableInfo: Returns the name of the feature.
getName() - Method in interface org.tribuo.VariableInfo: The name of this feature.
getNames() - Method in class org.tribuo.regression.Regressor: The names of the dimensions.
getNameSet() - Method in class org.tribuo.multilabel.MultiLabel: The set of strings that represent the labels in this multilabel.
getNativeType() - Method in class org.tribuo.anomaly.libsvm.SVMAnomalyType
getNativeType() - Method in class org.tribuo.classification.libsvm.SVMClassificationType
getNativeType() - Method in enum org.tribuo.common.libsvm.KernelType: Gets LibSVM's int id.
getNativeType() - Method in interface org.tribuo.common.libsvm.SVMType: The LibSVM int id for the algorithm.
getNativeType() - Method in class org.tribuo.regression.libsvm.SVMRegressionType
getNextNode(SparseVector) - Method in class org.tribuo.common.tree.AbstractTrainingNode
getNextNode(SparseVector) - Method in class org.tribuo.common.tree.LeafNode
getNextNode(SparseVector) - Method in interface org.tribuo.common.tree.Node: Returns the next node in the tree based on the supplied example, or null if it's a leaf.
getNextNode(SparseVector) - Method in class org.tribuo.common.tree.SplitNode: Return the appropriate child node.
getNormalizer() - Method in interface org.tribuo.classification.sgd.LabelObjective: Generates a new VectorNormalizer which normalizes the predictions into [0,1].
getNormalizer() - Method in class org.tribuo.classification.sgd.objectives.Hinge: Returns a new NoopNormalizer.
getNormalizer() - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass
getNumActiveFeatures() - Method in class org.tribuo.Prediction: Returns the number of features used in the prediction.
getNumberOfSupportVectors() - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyModel: Returns the number of support vectors.
getNumberOfSupportVectors() - Method in class org.tribuo.classification.libsvm.LibSVMClassificationModel
getNumberOfSupportVectors() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel: Returns the number of support vectors used.
getNumberOfSupportVectors() - Method in class org.tribuo.regression.libsvm.LibSVMRegressionModel: Returns the support vectors used for each dimension.
getNumExamples() - Method in class org.tribuo.common.tree.AbstractTrainingNode: The number of training examples in this node.
getNumExamples() - Method in class org.tribuo.provenance.DatasetProvenance: The number of examples.
getNumExamplesRemoved() - Method in class org.tribuo.dataset.MinimumCardinalityDataset: The number of examples removed due to a lack of features.
getNumExamplesRemoved() - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset: The number of examples removed due to a lack of features.
getNumFeatures() - Method in class org.tribuo.provenance.DatasetProvenance: The number of features.
getNumModels() - Method in class org.tribuo.ensemble.EnsembleModel: The number of ensemble members.
getNumNamespaces() - Method in interface org.tribuo.data.columnar.FieldProcessor: Binarised categoricals can be namespaced, where the field name is appended with "#<non-negative-int>" to denote the namespace.
getNumOutputs() - Method in class org.tribuo.provenance.DatasetProvenance: The number of output dimensions.
getObservationCount(double) - Method in class org.tribuo.CategoricalInfo: Gets the number of times a specific value was observed, and zero if this value is unknown.
getOptimiser() - Method in class org.tribuo.math.optimisers.GradientOptimiserOptions: Gets the configured gradient optimiser.
getOptionsDescription() - Method in class org.tribuo.classification.dtree.CARTClassificationOptions
getOptionsDescription() - Method in class org.tribuo.classification.dtree.TrainTest.TrainTestOptions
getOptionsDescription() - Method in class org.tribuo.classification.experiments.ConfigurableTrainTest.ConfigurableTrainTestOptions
getOptionsDescription() - Method in class org.tribuo.classification.experiments.RunAll.RunAllOptions
getOptionsDescription() - Method in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
getOptionsDescription() - Method in class org.tribuo.classification.experiments.TrainTest.AllClassificationOptions
getOptionsDescription() - Method in class org.tribuo.classification.liblinear.LibLinearOptions
getOptionsDescription() - Method in class org.tribuo.classification.liblinear.TrainTest.TrainTestOptions
getOptionsDescription() - Method in class org.tribuo.classification.libsvm.LibSVMOptions
getOptionsDescription() - Method in class org.tribuo.classification.libsvm.TrainTest.TrainTestOptions
getOptionsDescription() - Method in class org.tribuo.classification.mnb.TrainTest.TrainTestOptions
getOptionsDescription() - Method in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
getOptionsDescription() - Method in class org.tribuo.classification.sgd.kernel.TrainTest.TrainTestOptions
getOptionsDescription() - Method in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
getOptionsDescription() - Method in class org.tribuo.classification.xgboost.TrainTest.TrainTestOptions
getOptionsDescription() - Method in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
getOptionsDescription() - Method in class org.tribuo.common.nearest.KNNClassifierOptions
getOptionsDescription() - Method in class org.tribuo.data.CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions
getOptionsDescription() - Method in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
getOptionsDescription() - Method in class org.tribuo.data.DataOptions
getOptionsDescription() - Method in class org.tribuo.data.text.SplitTextData.TrainTestSplitOptions
getOptionsDescription() - Method in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
getOptionsDescription() - Method in class org.tribuo.json.StripProvenance.StripProvenanceOptions
getOptionsDescription() - Method in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
getOptionsDescription() - Method in class org.tribuo.regression.libsvm.TrainTest.LibLinearOptions
getOptionsDescription() - Method in class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
getOptionsDescription() - Method in class org.tribuo.regression.sgd.TrainTest.SGDOptions
getOptionsDescription() - Method in class org.tribuo.regression.slm.TrainTest.LARSOptions
getOptionsDescription() - Method in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
getOptionsDescription() - Method in class org.tribuo.util.infotheory.example.InformationTheoryDemo.DemoOptions
getOutput() - Method in class org.tribuo.common.tree.LeafNode: Gets the output in this node.
getOutput() - Method in class org.tribuo.Example: Gets the example's Output.
getOutput() - Method in class org.tribuo.Prediction: Returns the predicted output.
getOutput(int) - Method in class org.tribuo.anomaly.ImmutableAnomalyInfo
getOutput(int) - Method in class org.tribuo.classification.ImmutableLabelInfo
getOutput(int) - Method in class org.tribuo.clustering.ImmutableClusteringInfo
getOutput(int) - Method in interface org.tribuo.ImmutableOutputInfo: Returns the output associated with this id, or null if the id is unknown.
getOutput(int) - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
getOutput(int) - Method in class org.tribuo.regression.ImmutableRegressionInfo
getOutputFactory() - Method in class org.tribuo.data.columnar.ColumnarDataSource
getOutputFactory() - Method in class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor
getOutputFactory() - Method in class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
getOutputFactory() - Method in class org.tribuo.data.columnar.processors.response.QuartileResponseProcessor
getOutputFactory() - Method in interface org.tribuo.data.columnar.ResponseProcessor: Gets the OutputFactory this ResponseProcessor uses.
getOutputFactory() - Method in class org.tribuo.data.text.DirectoryFileSource
getOutputFactory() - Method in class org.tribuo.data.text.TextDataSource: Returns the output factory used to convert the text input into an Output.
getOutputFactory() - Method in class org.tribuo.Dataset: Gets the output factory this dataset contains.
getOutputFactory() - Method in class org.tribuo.datasource.AggregateDataSource
getOutputFactory() - Method in interface org.tribuo.DataSource: Returns the OutputFactory associated with this Output subclass.
getOutputFactory() - Method in class org.tribuo.datasource.IDXDataSource
getOutputFactory() - Method in class org.tribuo.datasource.LibSVMDataSource
getOutputFactory() - Method in class org.tribuo.datasource.ListDataSource
getOutputFactory() - Method in class org.tribuo.regression.example.GaussianDataSource
getOutputFactory() - Method in class org.tribuo.sequence.SequenceDataset: Gets the output factory.
getOutputFactory() - Method in interface org.tribuo.sequence.SequenceDataSource: Gets the OutputFactory which was used to generate the Outputs in this SequenceDataSource.
getOutputID() - Method in class org.tribuo.impl.IndexedArrayExample: Gets the output id dimension number.
getOutputIDInfo() - Method in class org.tribuo.Dataset: Returns or generates an ImmutableOutputInfo.
getOutputIDInfo() - Method in class org.tribuo.ImmutableDataset
getOutputIDInfo() - Method in class org.tribuo.Model: Gets the output domain.
getOutputIDInfo() - Method in class org.tribuo.MutableDataset
getOutputIDInfo() - Method in class org.tribuo.sequence.ImmutableSequenceDataset
getOutputIDInfo() - Method in class org.tribuo.sequence.MutableSequenceDataset
getOutputIDInfo() - Method in class org.tribuo.sequence.SequenceDataset: An immutable view on the output info in this dataset.
getOutputIDInfo() - Method in class org.tribuo.sequence.SequenceModel: Gets the output domain.
getOutputInfo() - Method in class org.tribuo.dataset.DatasetView
getOutputInfo() - Method in class org.tribuo.Dataset: Returns this dataset's OutputInfo.
getOutputInfo() - Method in class org.tribuo.ImmutableDataset
getOutputInfo() - Method in class org.tribuo.MutableDataset
getOutputInfo() - Method in class org.tribuo.sequence.ImmutableSequenceDataset
getOutputInfo() - Method in class org.tribuo.sequence.MutableSequenceDataset
getOutputInfo() - Method in class org.tribuo.sequence.SequenceDataset: The output info in this dataset.
getOutputs() - Method in class org.tribuo.dataset.DatasetView: Gets the set of outputs that occur in the examples in this dataset.
getOutputs() - Method in class org.tribuo.Dataset: Gets the set of outputs that occur in the examples in this dataset.
getOutputs() - Method in class org.tribuo.ImmutableDataset
getOutputs() - Method in class org.tribuo.MutableDataset: Gets the set of possible outputs in this dataset.
getOutputs() - Method in class org.tribuo.sequence.ImmutableSequenceDataset
getOutputs() - Method in class org.tribuo.sequence.MutableSequenceDataset
getOutputs() - Method in class org.tribuo.sequence.SequenceDataset: Gets the set of labels that occur in the examples in this dataset.
getOutputScores() - Method in class org.tribuo.Prediction: Gets the output scores for each output.
getOutputTarget() - Method in class org.tribuo.evaluation.metrics.MetricTarget: Returns the Output this metric targets, or Optional.empty() if it's an average.
getParameters() - Method in class org.tribuo.common.libsvm.SVMParameters
getPos() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
getPrecision() - Method in interface org.tribuo.anomaly.evaluation.AnomalyEvaluation: Returns the precision of the anomalous events, i.e., true positives divided by the number of predicted positives.
getPrediction() - Method in interface org.tribuo.classification.explanations.Explanation: The original model's prediction which is being explained.
getPrediction() - Method in class org.tribuo.classification.explanations.lime.LIMEExplanation
getPrediction() - Method in class org.tribuo.Excuse: Returns the prediction being excused.
getPrediction(int, Example<T>) - Method in class org.tribuo.common.tree.LeafNode: Constructs a new prediction object based on this node's scores.
getPredictions() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the flattened predictions.
getPredictions() - Method in interface org.tribuo.evaluation.Evaluation: Gets the predictions stored in this evaluation.
getPredictions() - Method in class org.tribuo.evaluation.metrics.MetricContext: Gets the predictions used by this context.
getPredictions() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
getProvenance() - Method in class org.tribuo.anomaly.AnomalyFactory
getProvenance() - Method in class org.tribuo.anomaly.libsvm.SVMAnomalyType
getProvenance() - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer
getProvenance() - Method in class org.tribuo.classification.dtree.CARTClassificationTrainer
getProvenance() - Method in class org.tribuo.classification.dtree.impurity.Entropy
getProvenance() - Method in class org.tribuo.classification.dtree.impurity.GiniIndex
getProvenance() - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer
getProvenance() - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
getProvenance() - Method in class org.tribuo.classification.ensemble.VotingCombiner
getProvenance() - Method in class org.tribuo.classification.LabelFactory
getProvenance() - Method in class org.tribuo.classification.liblinear.LinearClassificationType
getProvenance() - Method in class org.tribuo.classification.libsvm.SVMClassificationType
getProvenance() - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesTrainer
getProvenance() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
getProvenance() - Method in class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
getProvenance() - Method in class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
getProvenance() - Method in class org.tribuo.classification.sequence.viterbi.ViterbiTrainer
getProvenance() - Method in class org.tribuo.classification.sgd.crf.CRFTrainer
getProvenance() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
getProvenance() - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
getProvenance() - Method in class org.tribuo.classification.sgd.objectives.Hinge
getProvenance() - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass
getProvenance() - Method in class org.tribuo.classification.xgboost.XGBoostClassificationTrainer
getProvenance() - Method in class org.tribuo.clustering.ClusteringFactory
getProvenance() - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
getProvenance() - Method in class org.tribuo.common.liblinear.LibLinearTrainer
getProvenance() - Method in class org.tribuo.common.libsvm.LibSVMTrainer
getProvenance() - Method in class org.tribuo.common.nearest.KNNTrainer
getProvenance() - Method in class org.tribuo.data.columnar.extractors.DateExtractor
getProvenance() - Method in class org.tribuo.data.columnar.extractors.DoubleExtractor
getProvenance() - Method in class org.tribuo.data.columnar.extractors.FloatExtractor
getProvenance() - Method in class org.tribuo.data.columnar.extractors.IdentityExtractor
getProvenance() - Method in class org.tribuo.data.columnar.extractors.IndexExtractor
getProvenance() - Method in class org.tribuo.data.columnar.extractors.IntExtractor
getProvenance() - Method in class org.tribuo.data.columnar.processors.feature.UniqueProcessor
getProvenance() - Method in class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
getProvenance() - Method in class org.tribuo.data.columnar.processors.field.IdentityProcessor
getProvenance() - Method in class org.tribuo.data.columnar.processors.field.RegexFieldProcessor
getProvenance() - Method in class org.tribuo.data.columnar.processors.field.TextFieldProcessor
getProvenance() - Method in class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor
getProvenance() - Method in class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
getProvenance() - Method in class org.tribuo.data.columnar.processors.response.Quartile
getProvenance() - Method in class org.tribuo.data.columnar.processors.response.QuartileResponseProcessor
getProvenance() - Method in class org.tribuo.data.columnar.RowProcessor
getProvenance() - Method in class org.tribuo.data.csv.CSVDataSource
getProvenance() - Method in class org.tribuo.data.sql.SQLDataSource
getProvenance() - Method in class org.tribuo.data.sql.SQLDBConfig
getProvenance() - Method in class org.tribuo.data.text.DirectoryFileSource
getProvenance() - Method in class org.tribuo.data.text.impl.AverageAggregator
getProvenance() - Method in class org.tribuo.data.text.impl.BasicPipeline
getProvenance() - Method in class org.tribuo.data.text.impl.FeatureHasher
getProvenance() - Method in class org.tribuo.data.text.impl.NgramProcessor
getProvenance() - Method in class org.tribuo.data.text.impl.SimpleTextDataSource
getProvenance() - Method in class org.tribuo.data.text.impl.SumAggregator
getProvenance() - Method in class org.tribuo.data.text.impl.TextFeatureExtractorImpl
getProvenance() - Method in class org.tribuo.data.text.impl.TokenPipeline
getProvenance() - Method in class org.tribuo.data.text.impl.UniqueAggregator
getProvenance() - Method in class org.tribuo.dataset.DatasetView
getProvenance() - Method in class org.tribuo.dataset.MinimumCardinalityDataset
getProvenance() - Method in class org.tribuo.datasource.AggregateDataSource
getProvenance() - Method in class org.tribuo.datasource.IDXDataSource
getProvenance() - Method in class org.tribuo.datasource.LibSVMDataSource
getProvenance() - Method in class org.tribuo.datasource.ListDataSource
getProvenance() - Method in class org.tribuo.ensemble.BaggingTrainer
getProvenance() - Method in class org.tribuo.ensemble.EnsembleModel
getProvenance() - Method in class org.tribuo.hash.HashCodeHasher
getProvenance() - Method in class org.tribuo.hash.HashingTrainer
getProvenance() - Method in class org.tribuo.hash.MessageDigestHasher
getProvenance() - Method in class org.tribuo.hash.ModHashCodeHasher
getProvenance() - Method in class org.tribuo.ImmutableDataset
getProvenance() - Method in class org.tribuo.interop.onnx.DenseTransformer
getProvenance() - Method in class org.tribuo.interop.onnx.ImageTransformer
getProvenance() - Method in class org.tribuo.interop.onnx.LabelTransformer
getProvenance() - Method in class org.tribuo.interop.onnx.RegressorTransformer
getProvenance() - Method in class org.tribuo.interop.tensorflow.DenseTransformer
getProvenance() - Method in class org.tribuo.interop.tensorflow.ImageTransformer
getProvenance() - Method in class org.tribuo.interop.tensorflow.LabelTransformer
getProvenance() - Method in class org.tribuo.interop.tensorflow.RegressorTransformer
getProvenance() - Method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
getProvenance() - Method in class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer
getProvenance() - Method in class org.tribuo.interop.tensorflow.TensorflowTrainer
getProvenance() - Method in class org.tribuo.json.JsonDataSource
getProvenance() - Method in class org.tribuo.math.kernel.Linear
getProvenance() - Method in class org.tribuo.math.kernel.Polynomial
getProvenance() - Method in class org.tribuo.math.kernel.RBF
getProvenance() - Method in class org.tribuo.math.kernel.Sigmoid
getProvenance() - Method in class org.tribuo.math.optimisers.AdaDelta
getProvenance() - Method in class org.tribuo.math.optimisers.AdaGrad
getProvenance() - Method in class org.tribuo.math.optimisers.AdaGradRDA
getProvenance() - Method in class org.tribuo.math.optimisers.Adam
getProvenance() - Method in class org.tribuo.math.optimisers.ParameterAveraging
getProvenance() - Method in class org.tribuo.math.optimisers.Pegasos
getProvenance() - Method in class org.tribuo.math.optimisers.RMSProp
getProvenance() - Method in class org.tribuo.math.optimisers.SGD
getProvenance() - Method in class org.tribuo.Model
getProvenance() - Method in class org.tribuo.multilabel.baseline.IndependentMultiLabelTrainer
getProvenance() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
getProvenance() - Method in class org.tribuo.multilabel.MultiLabelFactory
getProvenance() - Method in class org.tribuo.MutableDataset
getProvenance() - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer
getProvenance() - Method in class org.tribuo.regression.ensemble.AveragingCombiner
getProvenance() - Method in class org.tribuo.regression.example.GaussianDataSource
getProvenance() - Method in class org.tribuo.regression.liblinear.LinearRegressionType
getProvenance() - Method in class org.tribuo.regression.libsvm.SVMRegressionType
getProvenance() - Method in class org.tribuo.regression.RegressionFactory
getProvenance() - Method in class org.tribuo.regression.rtree.CARTJointRegressionTrainer
getProvenance() - Method in class org.tribuo.regression.rtree.CARTRegressionTrainer
getProvenance() - Method in class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
getProvenance() - Method in class org.tribuo.regression.rtree.impurity.MeanSquaredError
getProvenance() - Method in class org.tribuo.regression.sgd.linear.LinearSGDTrainer
getProvenance() - Method in class org.tribuo.regression.sgd.objectives.AbsoluteLoss
getProvenance() - Method in class org.tribuo.regression.sgd.objectives.Huber
getProvenance() - Method in class org.tribuo.regression.sgd.objectives.SquaredLoss
getProvenance() - Method in class org.tribuo.regression.slm.ElasticNetCDTrainer
getProvenance() - Method in class org.tribuo.regression.slm.SLMTrainer
getProvenance() - Method in class org.tribuo.regression.xgboost.XGBoostRegressionTrainer
getProvenance() - Method in class org.tribuo.sequence.HashingSequenceTrainer
getProvenance() - Method in class org.tribuo.sequence.ImmutableSequenceDataset
getProvenance() - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset
getProvenance() - Method in class org.tribuo.sequence.MutableSequenceDataset
getProvenance() - Method in class org.tribuo.sequence.SequenceModel
getProvenance() - Method in class org.tribuo.transform.TransformationMap
getProvenance() - Method in class org.tribuo.transform.TransformationMap.TransformationList
getProvenance() - Method in class org.tribuo.transform.transformations.BinningTransformation
getProvenance() - Method in class org.tribuo.transform.transformations.LinearScalingTransformation
getProvenance() - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation
getProvenance() - Method in class org.tribuo.transform.transformations.SimpleTransform
getProvenance() - Method in class org.tribuo.transform.TransformerMap
getProvenance() - Method in class org.tribuo.transform.TransformTrainer
getProvenance() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
getProvenance() - Method in class org.tribuo.util.tokens.impl.NonTokenizer
getProvenance() - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer
getProvenance() - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
getProvenance() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
getProvenance() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
getRecall() - Method in interface org.tribuo.anomaly.evaluation.AnomalyEvaluation: Returns the recall of the anomalous events, i.e., true positives divided by the number of positives.
getReference() - Method in interface org.tribuo.math.la.MatrixIterator: Gets the MatrixTuple reference that this iterator updates.
getReference() - Method in interface org.tribuo.math.la.VectorIterator: Gets the reference to the VectorTuple this iterator updates.
getRemoved() - Method in class org.tribuo.dataset.MinimumCardinalityDataset: The feature names that were removed.
getRemoved() - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset: The feature names that were removed.
getResponseProcessor() - Method in class org.tribuo.data.columnar.RowProcessor: Returns the response processor this RowProcessor uses.
getRMSE(String) - Method in class org.tribuo.classification.explanations.lime.LIMEExplanation: Get the RMSE of a specific dimension of the explanation model.
getRow() - Method in class org.tribuo.data.columnar.ColumnarIterator: Returns the next row of data based on internal state stored by the implementor, or Optional.empty() if there is no more data.
getRow() - Method in class org.tribuo.data.csv.CSVIterator
getRow() - Method in class org.tribuo.data.sql.ResultSetIterator
getRow() - Method in class org.tribuo.json.JsonFileIterator
getRow(int) - Method in class org.tribuo.math.la.DenseMatrix
getRow(int) - Method in class org.tribuo.math.la.DenseSparseMatrix
getRow(int) - Method in interface org.tribuo.math.la.Matrix: Extract a row as an SGDVector.
getRowData() - Method in class org.tribuo.data.columnar.ColumnarIterator.Row
getScore() - Method in class org.tribuo.anomaly.Event: Get a real valued score for this label.
getScore() - Method in class org.tribuo.classification.Label: Get a real valued score for this label.
getScore() - Method in class org.tribuo.clustering.ClusterID: Get a real valued score for this ClusterID.
getScore() - Method in class org.tribuo.multilabel.MultiLabel: The overall score for this set of labels.
getScoreAggregation() - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
getScores() - Method in class org.tribuo.Excuse: Returns the scores for all outputs and the relevant feature values.
getSecondCount() - Method in class org.tribuo.util.infotheory.impl.WeightedPairDistribution
getSecondFieldName() - Method in class org.tribuo.data.columnar.ColumnarFeature: If it's a conjunction feature, return the second field name.
getSequenceModel() - Method in class org.tribuo.evaluation.metrics.MetricContext: Gets the SequenceModel used by this context.
getSequenceTrainer() - Method in class org.tribuo.classification.sgd.crf.CRFOptions: Returns the configured CRF trainer.
getSequenceTrainer(Trainer<Label>) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions
getSerializableForm(boolean) - Method in class org.tribuo.anomaly.Event: Returns "EventType" or "EventType,score=eventScore".
getSerializableForm(boolean) - Method in class org.tribuo.classification.Label: Returns "labelName" or "labelName,score=labelScore".
getSerializableForm(boolean) - Method in class org.tribuo.clustering.ClusterID: Returns "id" or "id,score=idScore".
getSerializableForm(boolean) - Method in class org.tribuo.multilabel.MultiLabel: For a MultiLabel with label set = {a, b, c}, outputs a string of the form:
getSerializableForm(boolean) - Method in interface org.tribuo.Output: Generates a String suitable for writing to a csv or json file.
getSerializableForm(boolean) - Method in class org.tribuo.regression.Regressor.DimensionTuple
getSerializableForm(boolean) - Method in class org.tribuo.regression.Regressor
getShape() - Method in class org.tribuo.math.la.DenseMatrix
getShape() - Method in class org.tribuo.math.la.DenseSparseMatrix
getShape() - Method in class org.tribuo.math.la.DenseVector
getShape() - Method in class org.tribuo.math.la.SparseVector
getShape() - Method in interface org.tribuo.math.la.Tensor: Returns an int array specifying the shape of this Tensor.
getSimpleSGD(double) - Static method in class org.tribuo.math.optimisers.SGD: Generates an SGD optimiser with a constant learning rate set to learningRate.
getSimpleSGD(double, double, SGD.Momentum) - Static method in class org.tribuo.math.optimisers.SGD: Generates an SGD optimiser with a constant learning rate set to learningRate, with momentum.
getSolverType() - Method in class org.tribuo.classification.liblinear.LinearClassificationType
getSolverType() - Method in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType
getSolverType() - Method in interface org.tribuo.common.liblinear.LibLinearType: Returns the liblinear enum type.
getSolverType() - Method in class org.tribuo.regression.liblinear.LinearRegressionType
getSolverType() - Method in enum org.tribuo.regression.liblinear.LinearRegressionType.LinearType: Returns the liblinear enum.
getSourceDescription() - Method in class org.tribuo.Dataset: A String description of this dataset.
getSourceDescription() - Method in class org.tribuo.sequence.SequenceDataset: Returns the description of the source provenance.
getSourceProvenance() - Method in class org.tribuo.Dataset: The provenance of the data this Dataset contains.
getSourceProvenance() - Method in class org.tribuo.provenance.DatasetProvenance: The input data provenance.
getSourceProvenance() - Method in class org.tribuo.sequence.SequenceDataset: Returns the source provenance.
getSplitCharacters() - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer: Returns a copy of the split characters.
getSplitPatternRegex() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer: Gets the String form of the regex in use.
getSplitXDigitsCharacters() - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer: Returns a copy of the split characters except inside digits.
getSqrtDecaySGD(double) - Static method in class org.tribuo.math.optimisers.SGD: Generates an SGD optimiser with a sqrt decaying learning rate initialised to learningRate.
getSqrtDecaySGD(double, double, SGD.Momentum) - Static method in class org.tribuo.math.optimisers.SGD: Generates an SGD optimiser with a sqrt decaying learning rate initialised to learningRate, with momentum.
getStackSize() - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
getStandardDeviation() - Method in class org.tribuo.evaluation.DescriptiveStats: Calculates the standard deviation of the values.
getStart() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
getStart() - Method in class org.tribuo.util.tokens.impl.NonTokenizer
getStart() - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer
getStart() - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
getStart() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
getStart() - Method in interface org.tribuo.util.tokens.Tokenizer: Gets the starting character offset of the current token in the character sequence
getStart() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
getStatement() - Method in class org.tribuo.data.sql.SQLDBConfig: Constructs a statement based on the object fields.
getSvmType() - Method in class org.tribuo.common.libsvm.SVMParameters
getTarget() - Method in class org.tribuo.anomaly.evaluation.AnomalyMetric
getTarget() - Method in class org.tribuo.classification.evaluation.LabelMetric
getTarget() - Method in class org.tribuo.clustering.evaluation.ClusteringMetric
getTarget() - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric: The target for this metric instance.
getTarget() - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric
getTarget() - Method in class org.tribuo.regression.evaluation.RegressionMetric
getTest() - Method in class org.tribuo.evaluation.TrainTestSplitter: Gets the testing datasource.
getTestDatasetProvenance() - Method in class org.tribuo.provenance.EvaluationProvenance: The test dataset provenance.
getText() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
getText() - Method in class org.tribuo.util.tokens.impl.NonTokenizer
getText() - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer
getText() - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
getText() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
getText() - Method in interface org.tribuo.util.tokens.Tokenizer: Gets the text of the current token, as a string
getText() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
getToken() - Method in interface org.tribuo.util.tokens.Tokenizer: Generates a Token object from the current state of the tokenizer.
getTokenizer() - Method in class org.tribuo.util.tokens.options.BreakIteratorTokenizerOptions
getTokenizer() - Method in class org.tribuo.util.tokens.options.CoreTokenizerOptions
getTokenizer() - Method in class org.tribuo.util.tokens.options.SplitCharactersTokenizerOptions
getTokenizer() - Method in class org.tribuo.util.tokens.options.SplitPatternTokenizerOptions
getTokenizer() - Method in interface org.tribuo.util.tokens.options.TokenizerOptions: Creates the appropriately configured tokenizer.
getTopFeatures(int) - Method in class org.tribuo.classification.baseline.DummyClassifierModel
getTopFeatures(int) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationModel
getTopFeatures(int) - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesModel
getTopFeatures(int) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
getTopFeatures(int) - Method in class org.tribuo.classification.sgd.crf.CRFModel
getTopFeatures(int) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel
getTopFeatures(int) - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel
getTopFeatures(int) - Method in class org.tribuo.clustering.kmeans.KMeansModel
getTopFeatures(int) - Method in class org.tribuo.common.libsvm.LibSVMModel
getTopFeatures(int) - Method in class org.tribuo.common.nearest.KNNModel
getTopFeatures(int) - Method in class org.tribuo.common.tree.TreeModel
getTopFeatures(int) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel
getTopFeatures(int) - Method in class org.tribuo.common.xgboost.XGBoostModel
getTopFeatures(int) - Method in class org.tribuo.ensemble.EnsembleModel
getTopFeatures(int) - Method in class org.tribuo.interop.onnx.ONNXExternalModel
getTopFeatures(int) - Method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceModel: Returns an empty map, as the top features are not well defined for most Tensorflow models.
getTopFeatures(int) - Method in class org.tribuo.interop.tensorflow.TensorflowCheckpointModel: Deep learning models don't do feature rankings.
getTopFeatures(int) - Method in class org.tribuo.interop.tensorflow.TensorflowExternalModel
getTopFeatures(int) - Method in class org.tribuo.interop.tensorflow.TensorflowModel: Deep learning models don't do feature rankings.
getTopFeatures(int) - Method in class org.tribuo.Model: Gets the top n features associated with this model.
getTopFeatures(int) - Method in class org.tribuo.multilabel.baseline.IndependentMultiLabelModel: This aggregates the top features from each of the models.
getTopFeatures(int) - Method in class org.tribuo.regression.baseline.DummyRegressionModel
getTopFeatures(int) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionModel
getTopFeatures(int) - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
getTopFeatures(int) - Method in class org.tribuo.regression.sgd.linear.LinearSGDModel
getTopFeatures(int) - Method in class org.tribuo.regression.slm.SparseLinearModel
getTopFeatures(int) - Method in class org.tribuo.sequence.SequenceModel: Gets the top n features associated with this model.
getTopFeatures(int) - Method in class org.tribuo.transform.TransformedModel
getTopLabels(Map<String, Label>) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
getTopLabels(Map<String, Label>, int) - Static method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
getTotalObservations() - Method in class org.tribuo.anomaly.ImmutableAnomalyInfo
getTotalObservations() - Method in class org.tribuo.classification.ImmutableLabelInfo
getTotalObservations() - Method in class org.tribuo.clustering.ImmutableClusteringInfo
getTotalObservations() - Method in interface org.tribuo.ImmutableOutputInfo: Returns the total number of observed outputs seen by this ImmutableOutputInfo.
getTotalObservations() - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
getTotalObservations() - Method in class org.tribuo.regression.ImmutableRegressionInfo
getTrain() - Method in class org.tribuo.evaluation.TrainTestSplitter: Gets the training data source.
getTrainer() - Method in interface org.tribuo.classification.ClassificationOptions: Constructs the trainer based on the provided arguments.
getTrainer() - Method in class org.tribuo.classification.dtree.CARTClassificationOptions
getTrainer() - Method in class org.tribuo.classification.experiments.AllTrainerOptions
getTrainer() - Method in class org.tribuo.classification.liblinear.LibLinearOptions
getTrainer() - Method in class org.tribuo.classification.libsvm.LibSVMOptions
getTrainer() - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesOptions
getTrainer() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMOptions
getTrainer() - Method in class org.tribuo.classification.sgd.linear.LinearSGDOptions
getTrainer() - Method in class org.tribuo.classification.xgboost.XGBoostOptions
getTrainer() - Method in class org.tribuo.clustering.kmeans.KMeansOptions
getTrainer() - Method in class org.tribuo.common.nearest.KNNClassifierOptions
getTrainer() - Method in class org.tribuo.regression.xgboost.XGBoostOptions: Gets the configured XGBoostRegressionTrainer.
getTrainerProvenance() - Method in class org.tribuo.provenance.ModelProvenance: The trainer provenance.
getTrainingTime() - Method in class org.tribuo.provenance.ModelProvenance: The training timestamp.
getTransformationProvenance() - Method in class org.tribuo.provenance.DatasetProvenance: The transformation provenances, in application order.
getTribuoVersion() - Method in class org.tribuo.provenance.DatasetProvenance: The Tribuo version used to create this dataset.
getTribuoVersion() - Method in class org.tribuo.provenance.EvaluationProvenance: The Tribuo version used to create this dataset.
getTribuoVersion() - Method in class org.tribuo.provenance.ModelProvenance: The Tribuo version used to create this dataset.
getTribuoVersion() - Method in class org.tribuo.provenance.SkeletalTrainerProvenance: The Tribuo version.
getTrueNegatives() - Method in interface org.tribuo.anomaly.evaluation.AnomalyEvaluation: Returns the number of true negatives, i.e., expected events classified as events.
getTruePositives() - Method in interface org.tribuo.anomaly.evaluation.AnomalyEvaluation: Returns the number of true positives, i.e., anomalous events classified as anomalous.
getType() - Method in class org.tribuo.anomaly.Event: Gets the event type.
getType() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
getType() - Method in class org.tribuo.util.tokens.impl.NonTokenizer
getType() - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer
getType() - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
getType() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
getType() - Method in interface org.tribuo.util.tokens.Tokenizer: Gets the type of the current token.
getType() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
getUniqueObservations() - Method in class org.tribuo.CategoricalInfo: Gets the number of unique values this CategoricalInfo has observed.
getUnknownCount() - Method in class org.tribuo.anomaly.AnomalyInfo
getUnknownCount() - Method in class org.tribuo.classification.LabelInfo
getUnknownCount() - Method in class org.tribuo.clustering.ClusteringInfo
getUnknownCount() - Method in class org.tribuo.multilabel.MultiLabelInfo
getUnknownCount() - Method in interface org.tribuo.OutputInfo: Returns the number of unknown Output instances (generated by OutputFactory.getUnknownOutput()) that this OutputInfo has seen.
getUnknownCount() - Method in class org.tribuo.regression.RegressionInfo
getUnknownOutput() - Method in class org.tribuo.anomaly.AnomalyFactory
getUnknownOutput() - Method in class org.tribuo.classification.LabelFactory
getUnknownOutput() - Method in class org.tribuo.clustering.ClusteringFactory
getUnknownOutput() - Method in class org.tribuo.multilabel.MultiLabelFactory
getUnknownOutput() - Method in interface org.tribuo.OutputFactory: Returns the singleton unknown output of type T which can be used for prediction time examples.
getUnknownOutput() - Method in class org.tribuo.regression.RegressionFactory
getUpperMedian() - Method in class org.tribuo.data.columnar.processors.response.Quartile: The upper quartile value.
getValue() - Method in class org.tribuo.Feature: Returns the feature value.
getValue() - Method in class org.tribuo.regression.Regressor.DimensionTuple: Returns the value.
getValues() - Method in class org.tribuo.regression.Regressor: Returns the regression values.
getValueType() - Method in class org.tribuo.data.columnar.extractors.DateExtractor
getValueType() - Method in class org.tribuo.data.columnar.extractors.DoubleExtractor
getValueType() - Method in class org.tribuo.data.columnar.extractors.FloatExtractor
getValueType() - Method in class org.tribuo.data.columnar.extractors.IdentityExtractor
getValueType() - Method in class org.tribuo.data.columnar.extractors.IndexExtractor
getValueType() - Method in class org.tribuo.data.columnar.extractors.IntExtractor
getValueType() - Method in interface org.tribuo.data.columnar.FieldExtractor: Gets the class of the value produced by this extractor.
getVariance() - Method in class org.tribuo.evaluation.DescriptiveStats: Calculates the sample variance of the values.
getVariance() - Method in class org.tribuo.RealInfo: Gets the sample variance.
getVariance() - Method in class org.tribuo.regression.Regressor.DimensionTuple: Returns the variance.
getVariance(String) - Method in class org.tribuo.regression.RegressionInfo: Gets the variance this RegressionInfo has seen, or NaN if it's not seen that dimension.
getVariances() - Method in class org.tribuo.regression.Regressor: The variances of the regressed values, if known.
getWeight() - Method in class org.tribuo.Example: Gets the example's weight.
getWeight() - Method in class org.tribuo.sequence.SequenceExample: Gets the weight of this sequence.
getWeight(int, int) - Method in class org.tribuo.classification.sgd.crf.CRFParameters
getWeightMatrix() - Method in class org.tribuo.math.LinearParameters: Returns the weight matrix.
getWeights() - Method in class org.tribuo.regression.slm.SparseLinearModel: Gets a copy of the model parameters.
getWeightsCopy() - Method in class org.tribuo.regression.sgd.linear.LinearSGDModel
GINI - Enum constant in enum org.tribuo.classification.dtree.CARTClassificationOptions.ImpurityType
GiniIndex - Class in org.tribuo.classification.dtree.impurity: The Gini index impurity measure.
GiniIndex() - Constructor for class org.tribuo.classification.dtree.impurity.GiniIndex
GradientOptimiserOptions - Class in org.tribuo.math.optimisers: CLI options for configuring a gradient optimiser.
GradientOptimiserOptions() - Constructor for class org.tribuo.math.optimisers.GradientOptimiserOptions
GradientOptimiserOptions.StochasticGradientOptimiserType - Enum in org.tribuo.math.optimisers: Type of the gradient optimisers available in CLIs.
gradientOptions - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
gradientOptions - Variable in class org.tribuo.regression.sgd.TrainTest.SGDOptions
gradients(Pair<Double, SGDVector>, SparseVector) - Method in class org.tribuo.math.LinearParameters: Generate the gradients for a particular feature vector given the loss and the per output gradients.
GRAPH_HASH - Static variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer.TensorflowSequenceTrainerProvenance
GRAPH_HASH - Static variable in class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer.TensorflowCheckpointTrainerProvenance
GRAPH_HASH - Static variable in class org.tribuo.interop.tensorflow.TensorflowTrainer.TensorflowTrainerProvenance
GRAPH_LAST_MOD - Static variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer.TensorflowSequenceTrainerProvenance
GRAPH_LAST_MOD - Static variable in class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer.TensorflowCheckpointTrainerProvenance
GRAPH_LAST_MOD - Static variable in class org.tribuo.interop.tensorflow.TensorflowTrainer.TensorflowTrainerProvenance
graphPath - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
greaterThan - Variable in class org.tribuo.common.tree.AbstractTrainingNode
GROUPS - Enum constant in enum org.tribuo.data.columnar.processors.field.RegexFieldProcessor.Mode
grow(int) - Method in class org.tribuo.common.tree.impl.IntArrayContainer: Grows the backing array, copying the elements.
growArray() - Method in class org.tribuo.impl.ArrayExample: Grows the backing arrays by size+1.
growArray() - Method in class org.tribuo.impl.BinaryFeaturesExample: Grows the backing arrays by size+1.
growArray(int) - Method in class org.tribuo.impl.ArrayExample: Grows the backing arrays storing the names and values.
growArray(int) - Method in class org.tribuo.impl.BinaryFeaturesExample: Grows the backing arrays storing the names.
growArray(int) - Method in class org.tribuo.impl.IndexedArrayExample
gStatistic - Variable in class org.tribuo.util.infotheory.InformationTheory.GTestStatistics
gTest(List<T1>, List<T2>, Set<List<T3>>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the GTest statistics for the input variables conditioned on the set.
GTestStatistics(double, int, double) - Constructor for class org.tribuo.util.infotheory.InformationTheory.GTestStatistics

H

hadamardProductInPlace(Tensor) - Method in interface org.tribuo.math.la.Tensor: Same as Tensor.hadamardProductInPlace(org.tribuo.math.la.Tensor, java.util.function.DoubleUnaryOperator), but applies the identity function.
hadamardProductInPlace(Tensor, DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseMatrix
hadamardProductInPlace(Tensor, DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseSparseMatrix: Only implemented for DenseMatrix.
hadamardProductInPlace(Tensor, DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseVector
hadamardProductInPlace(Tensor, DoubleUnaryOperator) - Method in class org.tribuo.math.la.SparseVector
hadamardProductInPlace(Tensor, DoubleUnaryOperator) - Method in interface org.tribuo.math.la.Tensor: Updates this Tensor with the Hadamard product (i.e., a term by term multiply) of this and other.
handleChar() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer: Handle a character to add to the token buffer.
handleDoc(String) - Method in class org.tribuo.data.text.TextDataSource: A method that can be overridden to do different things to each document that we've read.
hash(String) - Method in class org.tribuo.hash.HashCodeHasher
hash(String) - Method in class org.tribuo.hash.Hasher: Hashes the supplied input using the hashing function.
hash(String) - Method in class org.tribuo.hash.MessageDigestHasher
hash(String) - Method in class org.tribuo.hash.ModHashCodeHasher
hashCode() - Method in class org.tribuo.anomaly.AnomalyFactory.AnomalyFactoryProvenance
hashCode() - Method in class org.tribuo.anomaly.Event
hashCode() - Method in class org.tribuo.classification.evaluation.LabelMetric
hashCode() - Method in class org.tribuo.classification.Label
hashCode() - Method in class org.tribuo.classification.LabelFactory
hashCode() - Method in class org.tribuo.classification.LabelFactory.LabelFactoryProvenance
hashCode() - Method in class org.tribuo.clustering.ClusterID
hashCode() - Method in class org.tribuo.clustering.ClusteringFactory.ClusteringFactoryProvenance
hashCode() - Method in class org.tribuo.clustering.ClusteringFactory
hashCode() - Method in class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance
hashCode() - Method in class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance
hashCode() - Method in class org.tribuo.data.sql.SQLDataSource.SQLDataSourceProvenance
hashCode() - Method in class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
hashCode() - Method in class org.tribuo.data.text.impl.SimpleStringDataSource.SimpleStringDataSourceProvenance
hashCode() - Method in class org.tribuo.data.text.impl.SimpleTextDataSource.SimpleTextDataSourceProvenance
hashCode() - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance
hashCode() - Method in class org.tribuo.dataset.MinimumCardinalityDataset.MinimumCardinalityDatasetProvenance
hashCode() - Method in class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance
hashCode() - Method in class org.tribuo.datasource.LibSVMDataSource.LibSVMDataSourceProvenance
hashCode() - Method in class org.tribuo.evaluation.DescriptiveStats
hashCode() - Method in class org.tribuo.evaluation.metrics.MetricTarget
hashCode() - Method in class org.tribuo.evaluation.TrainTestSplitter.SplitDataSourceProvenance
hashCode() - Method in class org.tribuo.Feature
hashCode() - Method in class org.tribuo.hash.HashCodeHasher.HashCodeHasherProvenance
hashCode() - Method in class org.tribuo.hash.MessageDigestHasher.MessageDigestHasherProvenance
hashCode() - Method in class org.tribuo.hash.ModHashCodeHasher.ModHashCodeHasherProvenance
hashCode() - Method in class org.tribuo.impl.ArrayExample
hashCode() - Method in class org.tribuo.impl.BinaryFeaturesExample
hashCode() - Method in class org.tribuo.impl.IndexedArrayExample
hashCode() - Method in class org.tribuo.impl.ListExample
hashCode() - Method in class org.tribuo.interop.ExternalTrainerProvenance
hashCode() - Method in class org.tribuo.json.JsonDataSource.JsonDataSourceProvenance
hashCode() - Method in class org.tribuo.math.la.DenseMatrix
hashCode() - Method in class org.tribuo.math.la.DenseSparseMatrix
hashCode() - Method in class org.tribuo.math.la.DenseVector
hashCode() - Method in class org.tribuo.math.la.MatrixTuple
hashCode() - Method in class org.tribuo.math.la.SparseVector
hashCode() - Method in class org.tribuo.math.la.VectorTuple
hashCode() - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric
hashCode() - Method in class org.tribuo.multilabel.MultiLabel
hashCode() - Method in class org.tribuo.multilabel.MultiLabelFactory
hashCode() - Method in class org.tribuo.multilabel.MultiLabelFactory.MultiLabelFactoryProvenance
hashCode() - Method in class org.tribuo.provenance.DatasetProvenance
hashCode() - Method in class org.tribuo.provenance.EvaluationProvenance
hashCode() - Method in class org.tribuo.provenance.impl.EmptyDataSourceProvenance
hashCode() - Method in class org.tribuo.provenance.impl.EmptyTrainerProvenance
hashCode() - Method in class org.tribuo.provenance.ModelProvenance
hashCode() - Method in class org.tribuo.provenance.SimpleDataSourceProvenance
hashCode() - Method in class org.tribuo.provenance.SkeletalTrainerProvenance
hashCode() - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance: Deprecated.
hashCode() - Method in class org.tribuo.regression.RegressionFactory
hashCode() - Method in class org.tribuo.regression.RegressionFactory.RegressionFactoryProvenance
hashCode() - Method in class org.tribuo.regression.Regressor.DimensionTuple: All regressors have a hashcode based on only the dimension names.
hashCode() - Method in class org.tribuo.regression.Regressor: Regressor's hashcode is based on the hash of the dimension names.
hashCode() - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset.MinimumCardinalitySequenceDatasetProvenance
hashCode() - Method in class org.tribuo.SkeletalVariableInfo
hashCode() - Method in class org.tribuo.transform.TransformationMap.TransformationList
hashCode() - Method in class org.tribuo.transform.transformations.BinningTransformation.BinningTransformationProvenance
hashCode() - Method in class org.tribuo.transform.transformations.LinearScalingTransformation.LinearScalingTransformationProvenance
hashCode() - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation.MeanStdDevTransformationProvenance
hashCode() - Method in class org.tribuo.transform.transformations.SimpleTransform.SimpleTransformProvenance
hashCode() - Method in class org.tribuo.transform.TransformerMap.TransformerMapProvenance
hashCode() - Method in class org.tribuo.util.infotheory.impl.CachedPair: Overridden hashcode.
hashCode() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
hashCode() - Method in class org.tribuo.util.infotheory.impl.Row
hashCode() - Method in class org.tribuo.util.infotheory.impl.WeightCountTuple
HashCodeHasher - Class in org.tribuo.hash: Hashes names using String.hashCode().
HashCodeHasher(String) - Constructor for class org.tribuo.hash.HashCodeHasher
HashCodeHasher.HashCodeHasherProvenance - Class in org.tribuo.hash: Provenance for the HashCodeHasher.
HashCodeHasherProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.hash.HashCodeHasher.HashCodeHasherProvenance
hashDim - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
hashDim - Variable in class org.tribuo.data.DataOptions
HashedFeatureMap - Class in org.tribuo.hash: 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 - Class in org.tribuo.hash: An abstract base class for hash functions used to hash the names of features.
Hasher() - Constructor for class org.tribuo.hash.Hasher
hashFeatureMap(Dataset<T>, Hasher) - Static method in class org.tribuo.ImmutableDataset: Creates an immutable shallow copy of the supplied dataset, using the hasher to generate a HashedFeatureMap which transparently maps from the feature name to the hashed variant.
hashingOptions - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
HashingOptions - Class in org.tribuo.hash: An Options implementation which provides CLI arguments for the model hashing functionality.
HashingOptions() - Constructor for class org.tribuo.hash.HashingOptions
HashingOptions.ModelHashingType - Enum in org.tribuo.hash: Supported types of hashes in CLI programs.
HashingSequenceTrainer<T> - Class in org.tribuo.sequence: A SequenceTrainer that hashes all the feature names on the way in.
HashingSequenceTrainer(SequenceTrainer<T>, Hasher) - Constructor for class org.tribuo.sequence.HashingSequenceTrainer
HashingSequenceTrainer.HashingSequenceTrainerProvenance - Class in org.tribuo.sequence: Provenance for HashingSequenceTrainer.
HashingSequenceTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.sequence.HashingSequenceTrainer.HashingSequenceTrainerProvenance
HashingTrainer<T> - Class in org.tribuo.hash: A Trainer which hashes the Dataset before the Model is produced.
HashingTrainer(Trainer<T>, Hasher) - Constructor for class org.tribuo.hash.HashingTrainer
hashType - Variable in class org.tribuo.json.StripProvenance.StripProvenanceOptions
hasNext() - Method in class org.tribuo.data.columnar.ColumnarIterator
hasProbabilities() - Method in class org.tribuo.Prediction: Are the scores probabilities?
HC - Enum constant in enum org.tribuo.hash.HashingOptions.ModelHashingType
HeapMerger - Class in org.tribuo.math.util: Merges each SparseVector separately using a PriorityQueue as a heap.
HeapMerger() - Constructor for class org.tribuo.math.util.HeapMerger
Hinge - Class in org.tribuo.classification.sgd.objectives: Hinge loss, scores the correct value margin and any incorrect predictions -margin.
Hinge() - Constructor for class org.tribuo.classification.sgd.objectives.Hinge: Construct a hinge objective with a margin of 1.0.
Hinge(double) - Constructor for class org.tribuo.classification.sgd.objectives.Hinge: Construct a hinge objective with the supplied margin.
HINGE - Enum constant in enum org.tribuo.classification.sgd.linear.LinearSGDOptions.LossEnum
HTMLOutput - Class in org.tribuo.util: Utilities for nice HTML output that can be put in wikis and such.
Huber - Class in org.tribuo.regression.sgd.objectives: Huber loss, i.e., a mixture of l2 and l1 losses.
Huber() - Constructor for class org.tribuo.regression.sgd.objectives.Huber
Huber(double) - Constructor for class org.tribuo.regression.sgd.objectives.Huber
HUBER - Enum constant in enum org.tribuo.regression.sgd.TrainTest.LossEnum

I

i - Variable in class org.tribuo.math.la.MatrixTuple
id - Variable in class org.tribuo.impl.IndexedArrayExample.FeatureTuple
IdentityExtractor - Class in org.tribuo.data.columnar.extractors: Extracts the field value and emits it as a String.
IdentityExtractor(String) - Constructor for class org.tribuo.data.columnar.extractors.IdentityExtractor: Extracts the String value from the supplied field.
IdentityExtractor(String, String) - Constructor for class org.tribuo.data.columnar.extractors.IdentityExtractor: Extracts the String value from the supplied field.
IdentityProcessor - Class in org.tribuo.data.columnar.processors.field: A FieldProcessor which converts the field name and value into a feature with a value of IdentityProcessor.FEATURE_VALUE.
IdentityProcessor(String) - Constructor for class org.tribuo.data.columnar.processors.field.IdentityProcessor: Constructs a field processor which emits a single feature with a specific value and uses the field name and field value as the feature name.
idIterator() - Method in class org.tribuo.impl.IndexedArrayExample: Iterator over the feature ids and values.
idMap - Variable in class org.tribuo.ImmutableFeatureMap: The map from id numbers to the feature infos.
IDXDataSource<T> - Class in org.tribuo.datasource: A DataSource which can read IDX formatted data (i.e., MNIST).
IDXDataSource(Path, Path, OutputFactory<T>) - Constructor for class org.tribuo.datasource.IDXDataSource: Constructs an IDXDataSource from the supplied paths.
IDXDataSource.IDXData - Class in org.tribuo.datasource: Java side representation for an IDX file.
IDXDataSource.IDXDataSourceProvenance - Class in org.tribuo.datasource: Provenance class for IDXDataSource.
IDXDataSource.IDXType - Enum in org.tribuo.datasource: The possible IDX input formats.
IDXDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
IMAGE - Enum constant in enum org.tribuo.interop.tensorflow.TrainTest.InputType
imageFormat - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
ImageTransformer - Class in org.tribuo.interop.onnx: Image transformer.
ImageTransformer<T> - Class in org.tribuo.interop.tensorflow: Image transformer.
ImageTransformer(int, int, int) - Constructor for class org.tribuo.interop.onnx.ImageTransformer
ImageTransformer(int, int, int) - Constructor for class org.tribuo.interop.tensorflow.ImageTransformer
ImmutableAnomalyInfo - Class in org.tribuo.anomaly: An ImmutableOutputInfo object for Events.
ImmutableClusteringInfo - Class in org.tribuo.clustering: An ImmutableOutputInfo object for ClusterIDs.
ImmutableClusteringInfo(Map<Integer, MutableLong>) - Constructor for class org.tribuo.clustering.ImmutableClusteringInfo
ImmutableClusteringInfo(ClusteringInfo) - Constructor for class org.tribuo.clustering.ImmutableClusteringInfo
ImmutableDataset<T> - Class in org.tribuo: This is a Dataset which has an ImmutableFeatureMap to store the feature information.
ImmutableDataset(Iterable<Example<T>>, DataProvenance, OutputFactory<T>, FeatureMap, OutputInfo<T>, boolean) - Constructor for class org.tribuo.ImmutableDataset: Creates a dataset from a data source.
ImmutableDataset(Iterable<Example<T>>, DataProvenance, OutputFactory<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>, boolean) - Constructor for class org.tribuo.ImmutableDataset: Creates a dataset from a data source.
ImmutableDataset(DataSource<T>, FeatureMap, OutputInfo<T>, boolean) - Constructor for class org.tribuo.ImmutableDataset: Creates a dataset from a data source.
ImmutableDataset(DataSource<T>, Model<T>, boolean) - Constructor for class org.tribuo.ImmutableDataset: Creates a dataset from a data source.
ImmutableDataset(DataProvenance, OutputFactory<T>) - Constructor for class org.tribuo.ImmutableDataset: If you call this it's your job to setup outputMap, featureIDMap and fill it with examples.
ImmutableDataset(DataProvenance, OutputFactory<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Constructor for class org.tribuo.ImmutableDataset: This is dangerous, and should not be used unless you've overridden everything in ImmutableDataset.
ImmutableFeatureMap - Class in org.tribuo: ImmutableFeatureMap is used when unknown features should not be added to the FeatureMap.
ImmutableFeatureMap() - Constructor for class org.tribuo.ImmutableFeatureMap: Constructs a new empty immutable feature map.
ImmutableFeatureMap(List<VariableInfo>) - Constructor for class org.tribuo.ImmutableFeatureMap: Constructs a new immutable feature map copying the supplied variable infos and generating appropriate ID numbers.
ImmutableFeatureMap(FeatureMap) - Constructor for class org.tribuo.ImmutableFeatureMap: Constructs a new immutable version which is a deep copy of the supplied feature map, generating new ID numbers.
ImmutableLabelInfo - Class in org.tribuo.classification: An ImmutableOutputInfo object for Labels.
ImmutableMultiLabelInfo - Class in org.tribuo.multilabel: An ImmutableOutputInfo for working with multi-label tasks.
ImmutableOutputInfo<T> - Interface in org.tribuo: An OutputInfo that is fixed, and contains an id number for each valid output.
ImmutableRegressionInfo - Class in org.tribuo.regression: A ImmutableOutputInfo for Regressors.
ImmutableSequenceDataset<T> - Class in org.tribuo.sequence: This is a SequenceDataset which has an ImmutableFeatureMap to store the feature information.
ImmutableSequenceDataset(Iterable<SequenceExample<T>>, DataProvenance, FeatureMap, OutputInfo<T>, OutputFactory<T>) - Constructor for class org.tribuo.sequence.ImmutableSequenceDataset: Creates a dataset from a data source.
ImmutableSequenceDataset(Iterable<SequenceExample<T>>, DataProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, OutputFactory<T>) - Constructor for class org.tribuo.sequence.ImmutableSequenceDataset: Creates a dataset from a data source.
ImmutableSequenceDataset(DataProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Constructor for class org.tribuo.sequence.ImmutableSequenceDataset: This is dangerous, and should not be used unless you've overridden everything in ImmutableSequenceDataset.
ImmutableSequenceDataset(DataProvenance, OutputFactory<T>) - Constructor for class org.tribuo.sequence.ImmutableSequenceDataset: If you call this it's your job to setup outputIDInfo and featureIDMap.
ImmutableSequenceDataset(SequenceDataSource<T>, FeatureMap, OutputInfo<T>) - Constructor for class org.tribuo.sequence.ImmutableSequenceDataset
ImmutableSequenceDataset(SequenceDataSource<T>, SequenceModel<T>) - Constructor for class org.tribuo.sequence.ImmutableSequenceDataset
impurity - Variable in class org.tribuo.regression.rtree.impurity.RegressorImpurity.ImpurityTuple
impurity(double[]) - Method in interface org.tribuo.classification.dtree.impurity.LabelImpurity: Calculates the impurity assuming the inputs are counts.
impurity(float[]) - Method in interface org.tribuo.classification.dtree.impurity.LabelImpurity: Calculates the impurity assuming the input are fractional counts.
impurity(float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
impurity(float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanSquaredError
impurity(float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the impurity based on the supplied weights and targets.
impurity(int[]) - Method in interface org.tribuo.classification.dtree.impurity.LabelImpurity: Calculates the impurity assuming the input are counts.
impurity(int[], float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in the indices array.
impurity(int[], int, float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in the indices array.
impurity(List<int[]>, float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in all the indices arrays.
impurity(Map<String, Double>) - Method in interface org.tribuo.classification.dtree.impurity.LabelImpurity: Takes a Map for weighted counts.
impurity(IntArrayContainer, float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in the indices container.
impurityNormed(double[]) - Method in class org.tribuo.classification.dtree.impurity.Entropy
impurityNormed(double[]) - Method in class org.tribuo.classification.dtree.impurity.GiniIndex
impurityNormed(double[]) - Method in interface org.tribuo.classification.dtree.impurity.LabelImpurity: Calculates the impurity, assuming it's input is a normalized probability distribution.
impurityTuple(int[], int, float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
impurityTuple(int[], int, float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanSquaredError
impurityTuple(int[], int, float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in the indices array.
impurityTuple(List<int[]>, float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
impurityTuple(List<int[]>, float[], float[]) - Method in class org.tribuo.regression.rtree.impurity.MeanSquaredError
impurityTuple(List<int[]>, float[], float[]) - Method in interface org.tribuo.regression.rtree.impurity.RegressorImpurity: Calculates the weighted impurity of the targets specified in all the indices arrays.
ImpurityTuple(float, float) - Constructor for class org.tribuo.regression.rtree.impurity.RegressorImpurity.ImpurityTuple
impurityType - Variable in class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
impurityWeighted(double[]) - Method in interface org.tribuo.classification.dtree.impurity.LabelImpurity: Calculates the impurity assuming the inputs are weighted counts normalizing by their sum.
impurityWeighted(float[]) - Method in interface org.tribuo.classification.dtree.impurity.LabelImpurity: Calculates the impurity assuming the input are weighted counts, normalizing by their sum.
incr - Variable in class org.tribuo.util.tokens.universal.Range
incrementalTrain(Dataset<T>, U) - Method in interface org.tribuo.IncrementalTrainer: Incrementally trains the supplied model with the new data.
IncrementalTrainer<T,U> - Interface in org.tribuo: An interface for incremental training of Models.
IndependentMultiLabelModel - Class in org.tribuo.multilabel.baseline: A Model which wraps n binary models, where n is the size of the MultiLabel domain.
IndependentMultiLabelTrainer - Class in org.tribuo.multilabel.baseline: Trains n independent binary Models, each of which predicts a single Label.
IndependentMultiLabelTrainer(Trainer<Label>) - Constructor for class org.tribuo.multilabel.baseline.IndependentMultiLabelTrainer
IndependentRegressionTreeModel - Class in org.tribuo.regression.rtree: A Model wrapped around a list of decision tree root Nodes used to generate independent predictions for each dimension in a regression.
index - Variable in class org.tribuo.math.la.VectorTuple
index - Variable in class org.tribuo.util.IntDoublePair
IndexedArrayExample<T> - Class in org.tribuo.impl: A version of ArrayExample which also has the id numbers.
IndexedArrayExample(Example<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Constructor for class org.tribuo.impl.IndexedArrayExample: This constructor removes unknown features.
IndexedArrayExample(IndexedArrayExample<T>) - Constructor for class org.tribuo.impl.IndexedArrayExample: Copy constructor.
IndexedArrayExample.FeatureTuple - Class in org.tribuo.impl: A tuple of the feature name, id and value.
IndexExtractor - Class in org.tribuo.data.columnar.extractors: An Extractor with special casing for loading the index from a Row.
IndexExtractor() - Constructor for class org.tribuo.data.columnar.extractors.IndexExtractor: Extracts the index writing to the default metadata field name Example.NAME.
IndexExtractor(String) - Constructor for class org.tribuo.data.columnar.extractors.IndexExtractor: Extracts the index, writing to the supplied metadata field name.
indexOf(Object) - Method in class org.tribuo.util.infotheory.impl.RowList
indexOfMax() - Method in class org.tribuo.math.la.DenseVector
indexOfMax() - Method in interface org.tribuo.math.la.SGDVector: Returns the index of the maximum value.
indexOfMax() - Method in class org.tribuo.math.la.SparseVector
indexOfMax() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
indices - Variable in class org.tribuo.Dataset: The indices of the shuffled order.
indices - Variable in class org.tribuo.math.la.SparseVector
indices() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
InformationTheory - Class in org.tribuo.util.infotheory: A class of (discrete) information theoretic functions.
InformationTheory.GTestStatistics - Class in org.tribuo.util.infotheory: An immutable named tuple containing the statistics from a G test.
InformationTheoryDemo - Class in org.tribuo.util.infotheory.example: Demo showing how to calculate various mutual informations and entropies.
InformationTheoryDemo() - Constructor for class org.tribuo.util.infotheory.example.InformationTheoryDemo
InformationTheoryDemo.DemoOptions - Class in org.tribuo.util.infotheory.example
InformationTheoryDemo.DistributionType - Enum in org.tribuo.util.infotheory.example
INIT - Static variable in class org.tribuo.interop.tensorflow.TensorflowTrainer
initialise(Parameters) - Method in class org.tribuo.math.optimisers.AdaDelta
initialise(Parameters) - Method in class org.tribuo.math.optimisers.AdaGrad
initialise(Parameters) - Method in class org.tribuo.math.optimisers.AdaGradRDA
initialise(Parameters) - Method in class org.tribuo.math.optimisers.Adam
initialise(Parameters) - Method in class org.tribuo.math.optimisers.ParameterAveraging
initialise(Parameters) - Method in class org.tribuo.math.optimisers.Pegasos
initialise(Parameters) - Method in class org.tribuo.math.optimisers.RMSProp
initialise(Parameters) - Method in class org.tribuo.math.optimisers.SGD
initialise(Parameters) - Method in interface org.tribuo.math.StochasticGradientOptimiser: Initialises the gradient optimiser.
initialiseCentroids(int, Dataset<ClusterID>, ImmutableFeatureMap, SplittableRandom) - Static method in class org.tribuo.clustering.kmeans.KMeansTrainer: Initialisation method called at the start of each train call.
initialLearningRate - Variable in class org.tribuo.math.optimisers.SGD
initOp - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceModel
initOp - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
innerGetExcuse(Example<Label>, double[][]) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationModel: The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.
innerGetExcuse(Example<Regressor>, double[][]) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionModel: The call to model.getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.
innerGetExcuse(Example<T>, double[][]) - Method in class org.tribuo.common.liblinear.LibLinearModel: The call to getFeatureWeights in the public methods copies the weights array so this inner method exists to save the copy in getExcuses.
innerModel - Variable in class org.tribuo.classification.explanations.lime.LIMEBase
innerPredict(Iterable<Example<T>>) - Method in class org.tribuo.common.nearest.KNNModel: Uses the model to predict the output for multiple examples.
innerPredict(Iterable<Example<T>>) - Method in class org.tribuo.interop.ExternalModel
innerPredict(Iterable<Example<T>>) - Method in class org.tribuo.interop.tensorflow.TensorflowModel
innerPredict(Iterable<Example<T>>) - Method in class org.tribuo.Model: Called by the base implementations of Model.predict(Iterable) and Model.predict(Dataset).
INNERTHREADPOOL - Enum constant in enum org.tribuo.common.nearest.KNNModel.Backend: Uses a thread pool at the inner level (i.e., the whole thread pool works on each prediction).
innerTrainer - Variable in class org.tribuo.classification.ensemble.AdaBoostTrainer
innerTrainer - Variable in class org.tribuo.ensemble.BaggingTrainer
inPlaceAdd(double[], double[]) - Static method in class org.tribuo.util.Util
inPlaceAdd(float[], float[]) - Static method in class org.tribuo.util.Util
inplaceNormalizeToDistribution(double[]) - Static method in class org.tribuo.util.Util
inplaceNormalizeToDistribution(float[]) - Static method in class org.tribuo.util.Util
inPlaceSubtract(double[], double[]) - Static method in class org.tribuo.util.Util
inPlaceSubtract(float[], float[]) - Static method in class org.tribuo.util.Util
INPUT_NAME - Static variable in class org.tribuo.interop.tensorflow.TensorflowModel
inputFormat - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
inputFormat - Variable in class org.tribuo.data.DataOptions
inputModel - Variable in class org.tribuo.json.StripProvenance.StripProvenanceOptions
inputPath - Variable in class org.tribuo.data.sql.SQLToCSV.SQLToCSVOptions
inputPath - Variable in class org.tribuo.data.text.SplitTextData.TrainTestSplitOptions
inputType - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
INSTANCE - Enum constant in enum org.tribuo.json.StripProvenance.ProvenanceTypes: Select any instance provenance from the specific training run that created this model.
INSTANCE_VALUES - Static variable in class org.tribuo.provenance.ModelProvenance
instanceProvenance - Variable in class org.tribuo.provenance.ModelProvenance
INT - Enum constant in enum org.tribuo.datasource.IDXDataSource.IDXType
IntArrayContainer - Class in org.tribuo.common.tree.impl: An array container which maintains the array and the size.
IntArrayContainer(int) - Constructor for class org.tribuo.common.tree.impl.IntArrayContainer: Constructs a new int array container with the specified initial backing array size.
IntDoublePair - Class in org.tribuo.util: A Pair of a primitive int and a primitive double.
IntDoublePair(int, double) - Constructor for class org.tribuo.util.IntDoublePair
intersectAndAddInPlace(Tensor) - Method in interface org.tribuo.math.la.Tensor: Same as Tensor.intersectAndAddInPlace(org.tribuo.math.la.Tensor, java.util.function.DoubleUnaryOperator), but applies the identity function.
intersectAndAddInPlace(Tensor, DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseMatrix
intersectAndAddInPlace(Tensor, DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseSparseMatrix: Only implemented for DenseMatrix.
intersectAndAddInPlace(Tensor, DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseVector
intersectAndAddInPlace(Tensor, DoubleUnaryOperator) - Method in class org.tribuo.math.la.SparseVector
intersectAndAddInPlace(Tensor, DoubleUnaryOperator) - Method in interface org.tribuo.math.la.Tensor: Updates this Tensor by adding all the values from the intersection with other.
intersectAndAddInPlace(Tensor, DoubleUnaryOperator) - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
intersectAndAddInPlace(Tensor, DoubleUnaryOperator) - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
intersection(SparseVector) - Method in class org.tribuo.math.la.SparseVector: Generates an array of the indices that are active in both this vector and other
IntExtractor - Class in org.tribuo.data.columnar.extractors: Extracts the field value and converts it to a int.
IntExtractor(String) - Constructor for class org.tribuo.data.columnar.extractors.IntExtractor: Extracts a int value from the supplied field name.
IntExtractor(String, String) - Constructor for class org.tribuo.data.columnar.extractors.IntExtractor: Extracts a int value from the supplied field name.
invalidMultiDimSparseExample() - Static method in class org.tribuo.regression.example.RegressionDataGenerator: Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
invalidSparseExample() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator: Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
invalidSparseExample() - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
invalidSparseExample() - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator: Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
invalidSparseExample() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator: Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
invalidSparseExample() - Static method in class org.tribuo.regression.example.RegressionDataGenerator: Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
invertData(Dataset<Regressor>) - Static method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode: Inverts a training dataset from row major to column major.
InvertedFeature - Class in org.tribuo.regression.rtree.impl: Internal datastructure for implementing a decision tree.
InvertedFeature(double, int) - Constructor for class org.tribuo.regression.rtree.impl.InvertedFeature
InvertedFeature(double, int[]) - Constructor for class org.tribuo.regression.rtree.impl.InvertedFeature
IS_SEQUENCE - Static variable in interface org.tribuo.provenance.TrainerProvenance
IS_SNAPSHOT - Static variable in class org.tribuo.Tribuo: Is this a snapshot build.
IS_TRAINING - Static variable in class org.tribuo.interop.tensorflow.TensorflowTrainer
isAnomaly() - Method in class org.tribuo.anomaly.libsvm.SVMAnomalyType
isAnomaly() - Method in class org.tribuo.classification.libsvm.SVMClassificationType
isAnomaly() - Method in interface org.tribuo.common.libsvm.SVMType: Is this an anomaly detection algorithm.
isAnomaly() - Method in class org.tribuo.regression.libsvm.SVMRegressionType
isBinary(Feature) - Static method in class org.tribuo.impl.BinaryFeaturesExample
isClassification() - Method in class org.tribuo.anomaly.libsvm.SVMAnomalyType
isClassification() - Method in class org.tribuo.classification.liblinear.LinearClassificationType
isClassification() - Method in class org.tribuo.classification.libsvm.SVMClassificationType
isClassification() - Method in interface org.tribuo.common.liblinear.LibLinearType: Is this class a Classification algorithm?
isClassification() - Method in interface org.tribuo.common.libsvm.SVMType: Is this a classification algorithm.
isClassification() - Method in class org.tribuo.regression.liblinear.LinearRegressionType
isClassification() - Method in class org.tribuo.regression.libsvm.SVMRegressionType
isConfigured() - Method in class org.tribuo.data.columnar.RowProcessor: Returns true if the regexes have been expanded into field processors.
isDense() - Method in class org.tribuo.MutableDataset: Is the dataset dense (i.e., do all features in the domain have a value in each example).
isDense() - Method in class org.tribuo.provenance.DatasetProvenance: Is the Dataset dense?
isDigit(char) - Static method in class org.tribuo.util.tokens.universal.UniversalTokenizer: A quick check for whether a character is a digit.
isEmpty() - Method in class org.tribuo.util.infotheory.impl.RowList
isGenerateNgrams() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
isGenerateUnigrams() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
isLeaf() - Method in class org.tribuo.common.tree.AbstractTrainingNode
isLeaf() - Method in class org.tribuo.common.tree.LeafNode
isLeaf() - Method in interface org.tribuo.common.tree.Node: Is it a leaf node?
isLeaf() - Method in class org.tribuo.common.tree.SplitNode
isLetterOrDigit(char) - Static method in class org.tribuo.util.tokens.universal.UniversalTokenizer: A quick check for whether a character should be kept in a word or should be removed from the word if it occurs at one of the ends.
isNgram(char) - Static method in class org.tribuo.util.tokens.universal.UniversalTokenizer: A quick check for a character in a language that may not separate words with whitespace (includes Arabic, CJK, and Thai).
isNu() - Method in class org.tribuo.anomaly.libsvm.SVMAnomalyType
isNu() - Method in class org.tribuo.classification.libsvm.SVMClassificationType
isNu() - Method in interface org.tribuo.common.libsvm.SVMType: Is this a nu-SVM.
isNu() - Method in class org.tribuo.regression.libsvm.SVMRegressionType
isProbabilistic() - Method in interface org.tribuo.classification.sgd.LabelObjective: Does the objective function score probabilities or not?
isProbabilistic() - Method in class org.tribuo.classification.sgd.objectives.Hinge: Returns false.
isProbabilistic() - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass: Returns true.
isRegression() - Method in class org.tribuo.anomaly.libsvm.SVMAnomalyType
isRegression() - Method in class org.tribuo.classification.liblinear.LinearClassificationType
isRegression() - Method in class org.tribuo.classification.libsvm.SVMClassificationType
isRegression() - Method in interface org.tribuo.common.liblinear.LibLinearType: Is this class a Regression algorithm?
isRegression() - Method in interface org.tribuo.common.libsvm.SVMType: Is this a regression algorithm.
isRegression() - Method in class org.tribuo.regression.liblinear.LinearRegressionType
isRegression() - Method in class org.tribuo.regression.libsvm.SVMRegressionType
isSampled() - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance: Is this view from a bootstrap sample.
isSequence() - Method in class org.tribuo.provenance.DatasetProvenance: Is it a sequence dataset?
isSequence() - Method in class org.tribuo.provenance.SkeletalTrainerProvenance: Is this a sequence trainer.
isSplitCharacter(char) - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer: Is this character a split character for this tokenizer instance.
isSplitXDigitCharacter(char) - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer: Is this character a split character except inside a digit for this tokenizer instance.
isWeighted() - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance: Is this view a weighted bootstrap sample.
isWhitespace(char) - Static method in class org.tribuo.util.tokens.universal.UniversalTokenizer: A quick check for whether a character is whitespace.
isZeroIndexed() - Method in class org.tribuo.datasource.LibSVMDataSource: Returns true if this dataset is zero indexed, false otherwise (i.e., it starts from 1).
iteration - Variable in class org.tribuo.math.optimisers.SGD
iterations - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
iterations - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
iterations - Variable in class org.tribuo.regression.slm.TrainTest.LARSOptions
iterator() - Method in class org.tribuo.anomaly.ImmutableAnomalyInfo
iterator() - Method in class org.tribuo.classification.ImmutableLabelInfo
iterator() - Method in class org.tribuo.clustering.ImmutableClusteringInfo
iterator() - Method in class org.tribuo.data.columnar.ColumnarDataSource
iterator() - Method in class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance
iterator() - Method in class org.tribuo.data.text.DirectoryFileSource
iterator() - Method in class org.tribuo.data.text.TextDataSource
iterator() - Method in class org.tribuo.dataset.DatasetView
iterator() - Method in class org.tribuo.Dataset
iterator() - Method in class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance
iterator() - Method in class org.tribuo.datasource.AggregateDataSource
iterator() - Method in class org.tribuo.datasource.IDXDataSource
iterator() - Method in class org.tribuo.datasource.LibSVMDataSource
iterator() - Method in class org.tribuo.datasource.ListDataSource
iterator() - Method in class org.tribuo.evaluation.TrainTestSplitter.SplitDataSourceProvenance
iterator() - Method in class org.tribuo.FeatureMap
iterator() - Method in class org.tribuo.impl.ArrayExample
iterator() - Method in class org.tribuo.impl.BinaryFeaturesExample
iterator() - Method in class org.tribuo.impl.ListExample
iterator() - Method in class org.tribuo.math.la.DenseMatrix
iterator() - Method in class org.tribuo.math.la.DenseSparseMatrix
iterator() - Method in class org.tribuo.math.la.DenseVector
iterator() - Method in class org.tribuo.math.la.SparseVector
iterator() - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
iterator() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
iterator() - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
iterator() - Method in class org.tribuo.provenance.DatasetProvenance
iterator() - Method in class org.tribuo.provenance.EnsembleModelProvenance
iterator() - Method in class org.tribuo.provenance.EvaluationProvenance
iterator() - Method in class org.tribuo.provenance.impl.EmptyDataSourceProvenance
iterator() - Method in class org.tribuo.provenance.ModelProvenance
iterator() - Method in class org.tribuo.provenance.SimpleDataSourceProvenance
iterator() - Method in class org.tribuo.regression.example.GaussianDataSource
iterator() - Method in class org.tribuo.regression.ImmutableRegressionInfo
iterator() - Method in class org.tribuo.regression.Regressor.DimensionTuple
iterator() - Method in class org.tribuo.regression.Regressor
iterator() - Method in class org.tribuo.regression.rtree.impl.TreeFeature
iterator() - Method in class org.tribuo.sequence.SequenceDataset
iterator() - Method in class org.tribuo.sequence.SequenceExample
iterator() - Method in class org.tribuo.transform.TransformerMap.TransformerMapProvenance
iterator() - Method in class org.tribuo.util.infotheory.impl.RowList

J

j - Variable in class org.tribuo.math.la.MatrixTuple
JOINER - Static variable in class org.tribuo.data.columnar.ColumnarFeature
jointCounts - Variable in class org.tribuo.util.infotheory.impl.PairDistribution
jointEntropy(ArrayList<T1>, ArrayList<T2>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the Shannon/Guiasu weighted joint entropy of two arrays, using histogram probability estimators.
jointEntropy(List<T1>, List<T2>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the Shannon joint entropy of two arrays, using histogram probability estimators.
jointMI(List<T1>, List<T2>, List<T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon joint mutual information, using histogram probability estimators.
jointMI(List<T1>, List<T2>, List<T3>, List<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted joint mutual information, using histogram probability estimators.
jointMI(TripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon joint mutual information, using histogram probability estimators.
jointMI(TripleDistribution<T1, T2, T3>, Map<?, Double>, WeightedInformationTheory.VariableSelector) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory
jointMI(WeightedTripleDistribution<T1, T2, T3>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory
JointRegressorTrainingNode - Class in org.tribuo.regression.rtree.impl: A decision tree node used at training time.
JointRegressorTrainingNode(RegressorImpurity, Dataset<Regressor>, boolean) - Constructor for class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode: Constructor which creates the inverted file.
JsonDataSource<T> - Class in org.tribuo.json: A DataSource for loading data from a JSON text file and applying FieldProcessors to it.
JsonDataSource(URI, RowProcessor<T>, boolean) - Constructor for class org.tribuo.json.JsonDataSource: Creates a JsonDataSource using the specified RowProcessor to process the data.
JsonDataSource(Path, RowProcessor<T>, boolean) - Constructor for class org.tribuo.json.JsonDataSource: Creates a JsonDataSource using the specified RowProcessor to process the data.
JsonDataSource.JsonDataSourceProvenance - Class in org.tribuo.json: Provenance for JsonDataSource.
JsonDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.json.JsonDataSource.JsonDataSourceProvenance
JsonFileIterator - Class in org.tribuo.json: An iterator for JSON format files converting them into a format suitable for RowProcessor.
JsonFileIterator(Reader) - Constructor for class org.tribuo.json.JsonFileIterator: Builds a JsonFileIterator for the supplied Reader.
JsonFileIterator(URI) - Constructor for class org.tribuo.json.JsonFileIterator: Builds a CSVIterator for the supplied URI.
JsonUtil - Class in org.tribuo.json: Utilities for interacting with JSON objects or text representations.

K

Kernel - Interface in org.tribuo.math.kernel: An interface for a Mercer kernel function.
kernelDegree - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions
kernelDist(double, double) - Static method in class org.tribuo.classification.explanations.lime.LIMEBase: Calculates an RBF kernel of a specific width.
kernelEpochs - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions
kernelGamma - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions
kernelIntercept - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions
kernelKernel - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions
kernelLambda - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions
kernelLoggingInterval - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions
KernelSVMModel - Class in org.tribuo.classification.sgd.kernel: The inference time version of a kernel model trained using Pegasos.
kernelSVMOptions - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
KernelSVMOptions - Class in org.tribuo.classification.sgd.kernel: Options for using the KernelSVMTrainer.
KernelSVMOptions() - Constructor for class org.tribuo.classification.sgd.kernel.KernelSVMOptions
KernelSVMOptions.KernelEnum - Enum in org.tribuo.classification.sgd.kernel: The kernel types.
KernelSVMTrainer - Class in org.tribuo.classification.sgd.kernel: A trainer for a kernelised model using the Pegasos optimiser.
KernelSVMTrainer(Kernel, double, int, int, long) - Constructor for class org.tribuo.classification.sgd.kernel.KernelSVMTrainer: Constructs a trainer for a kernel SVM model.
KernelSVMTrainer(Kernel, double, int, long) - Constructor for class org.tribuo.classification.sgd.kernel.KernelSVMTrainer: Constructs a trainer for a kernel SVM model.
kernelType - Variable in class org.tribuo.common.libsvm.SVMParameters
kernelType - Variable in class org.tribuo.regression.libsvm.TrainTest.LibLinearOptions
KernelType - Enum in org.tribuo.common.libsvm: Kernel types from libsvm.
kernelWidth - Variable in class org.tribuo.classification.explanations.lime.LIMEBase
keySet() - Method in class org.tribuo.FeatureMap: Returns all the feature names in the domain.
KFoldSplitter<T> - Class in org.tribuo.evaluation: A k-fold splitter to be used in cross-validation.
KFoldSplitter(int) - Constructor for class org.tribuo.evaluation.KFoldSplitter: Builds a k-fold splitter using Trainer.DEFAULT_SEED as the seed.
KFoldSplitter(int, long) - Constructor for class org.tribuo.evaluation.KFoldSplitter: Builds a k-fold splitter.
KFoldSplitter.TrainTestFold<T> - Class in org.tribuo.evaluation: Stores a train/test split for a dataset.
KMeansModel - Class in org.tribuo.clustering.kmeans: A K-Means model with a selectable distance function.
KMeansOptions - Class in org.tribuo.clustering.kmeans: OLCUT Options for the K-Means implementation.
KMeansOptions() - Constructor for class org.tribuo.clustering.kmeans.KMeansOptions
KMeansOptions() - Constructor for class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
KMeansTrainer - Class in org.tribuo.clustering.kmeans: A K-Means trainer, which generates a K-means clustering of the supplied data.
KMeansTrainer(int, int, KMeansTrainer.Distance, int, long) - Constructor for class org.tribuo.clustering.kmeans.KMeansTrainer: Constructs a K-Means trainer using the supplied parameters.
KMeansTrainer.Distance - Enum in org.tribuo.clustering.kmeans: Possible distance functions.
KNN - Enum constant in enum org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType
knnBackend - Variable in class org.tribuo.common.nearest.KNNClassifierOptions
KNNClassifierOptions - Class in org.tribuo.common.nearest: CLI Options for training a k-nearest neighbour predictor.
KNNClassifierOptions() - Constructor for class org.tribuo.common.nearest.KNNClassifierOptions
KNNClassifierOptions.EnsembleCombinerType - Enum in org.tribuo.common.nearest: The type of combination function.
knnDistance - Variable in class org.tribuo.common.nearest.KNNClassifierOptions
knnEnsembleCombiner - Variable in class org.tribuo.common.nearest.KNNClassifierOptions
knnK - Variable in class org.tribuo.common.nearest.KNNClassifierOptions
KNNModel<T> - Class in org.tribuo.common.nearest: A k-nearest neighbours model.
KNNModel.Backend - Enum in org.tribuo.common.nearest: The parallel backend for batch predictions.
knnNumThreads - Variable in class org.tribuo.common.nearest.KNNClassifierOptions
knnOptions - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
KNNTrainer<T> - Class in org.tribuo.common.nearest: A Trainer for k-nearest neighbour models.
KNNTrainer(int, KNNTrainer.Distance, int, EnsembleCombiner<T>, KNNModel.Backend) - Constructor for class org.tribuo.common.nearest.KNNTrainer: Creates a K-NN trainer using the supplied parameters.
KNNTrainer.Distance - Enum in org.tribuo.common.nearest: The available distance functions.

L

L1 - Enum constant in enum org.tribuo.clustering.kmeans.KMeansTrainer.Distance: L1 (or Manhattan) distance.
L1 - Enum constant in enum org.tribuo.common.nearest.KNNTrainer.Distance: L1 (or Manhattan) distance.
l1Distance(SGDVector) - Method in class org.tribuo.math.la.DenseVector: The l1 or Manhattan distance between this vector and the other vector.
l1Distance(SGDVector) - Method in interface org.tribuo.math.la.SGDVector: The l1 or Manhattan distance between this vector and the other vector.
l1Distance(SGDVector) - Method in class org.tribuo.math.la.SparseVector
L1R_L2LOSS_SVC - Enum constant in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType: L1-regularized L2-loss support vector classification
L1R_LR - Enum constant in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType: L1-regularized logistic regression
l1Ratio - Variable in class org.tribuo.regression.slm.TrainTest.LARSOptions
L2 - Enum constant in enum org.tribuo.common.nearest.KNNTrainer.Distance: L2 (or Euclidean) distance.
l2Distance(SGDVector) - Method in interface org.tribuo.math.la.SGDVector: Synonym for euclideanDistance.
L2R_L1LOSS_SVC_DUAL - Enum constant in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType: L2-regularized L1-loss support vector classification (dual)
L2R_L1LOSS_SVR_DUAL - Enum constant in enum org.tribuo.regression.liblinear.LinearRegressionType.LinearType: L2-regularized L1-loss support vector regression (dual)
L2R_L2LOSS_SVC - Enum constant in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType: L2-regularized L2-loss support vector classification (primal)
L2R_L2LOSS_SVC_DUAL - Enum constant in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType: L2-regularized L2-loss support vector classification (dual)
L2R_L2LOSS_SVR - Enum constant in enum org.tribuo.regression.liblinear.LinearRegressionType.LinearType: L2-regularized L2-loss support vector regression (primal)
L2R_L2LOSS_SVR_DUAL - Enum constant in enum org.tribuo.regression.liblinear.LinearRegressionType.LinearType: L2-regularized L2-loss support vector regression (dual)
L2R_LR - Enum constant in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType: L2-regularized logistic regression (primal)
L2R_LR_DUAL - Enum constant in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType: L2-regularized logistic regression (dual)
label - Variable in class org.tribuo.classification.Label: The name of the label.
Label - Class in org.tribuo.classification: An immutable multi-class classification label.
Label(String) - Constructor for class org.tribuo.classification.Label: Builds a label with the sentinel score of Double.NaN.
Label(String, double) - Constructor for class org.tribuo.classification.Label: Builds a label with the supplied string and score.
LabelConfusionMatrix - Class in org.tribuo.classification.evaluation: A confusion matrix for Labels.
LabelConfusionMatrix(ImmutableOutputInfo<Label>, List<Prediction<Label>>) - Constructor for class org.tribuo.classification.evaluation.LabelConfusionMatrix: Creates a confusion matrix from the supplied predictions and label info.
LabelConfusionMatrix(Model<Label>, List<Prediction<Label>>) - Constructor for class org.tribuo.classification.evaluation.LabelConfusionMatrix: Creates a confusion matrix from the supplied predictions, using the label info from the supplied model.
labelCounts - Variable in class org.tribuo.classification.LabelInfo: The occurrence counts of each label.
labelCounts - Variable in class org.tribuo.multilabel.MultiLabelInfo
LabelEvaluation - Interface in org.tribuo.classification.evaluation: Adds multi-class classification specific metrics to ClassifierEvaluation.
LabelEvaluationUtil - Class in org.tribuo.classification.evaluation: Static utility functions for calculating performance metrics on Labels.
LabelEvaluationUtil.PRCurve - Class in org.tribuo.classification.evaluation: Stores the Precision-Recall curve as three arrays: the precisions, the recalls, and the thresholds associated with those values.
LabelEvaluationUtil.ROC - Class in org.tribuo.classification.evaluation: Stores the ROC curve as three arrays: the false positive rate, the true positive rate, and the thresholds associated with those rates.
LabelEvaluator - Class in org.tribuo.classification.evaluation: An Evaluator for Labels.
LabelEvaluator() - Constructor for class org.tribuo.classification.evaluation.LabelEvaluator
LabelFactory - Class in org.tribuo.classification: A factory for making Label related classes.
LabelFactory() - Constructor for class org.tribuo.classification.LabelFactory: Constructs a label factory.
LabelFactory.LabelFactoryProvenance - Class in org.tribuo.classification: Provenance for LabelFactory.
LabelFactoryProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.classification.LabelFactory.LabelFactoryProvenance: Constructor used by the provenance serialization system.
LabelFeatureExtractor - Interface in org.tribuo.classification.sequence.viterbi: A class for featurising labels from previous steps in Viterbi.
LabelImpurity - Interface in org.tribuo.classification.dtree.impurity: Calculates a tree impurity score based on label counts, weighted label counts or a probability distribution.
LabelInfo - Class in org.tribuo.classification: The base class for information about multi-class classification Labels.
LabelledDataGenerator - Class in org.tribuo.classification.example: Generates three example train and test datasets, used for unit testing.
LabelMetric - Class in org.tribuo.classification.evaluation: A EvaluationMetric for Labels which calculates the value based on a ConfusionMatrix.
LabelMetric(MetricTarget<Label>, String, ToDoubleBiFunction<MetricTarget<Label>, LabelMetric.Context>) - Constructor for class org.tribuo.classification.evaluation.LabelMetric: Construct a new LabelMetric for the supplied metric target, using the supplied function.
LabelMetric.Context - Class in org.tribuo.classification.evaluation: The context for a LabelMetric is a ConfusionMatrix.
LabelMetrics - Enum in org.tribuo.classification.evaluation: An enum of the default LabelMetrics supported by the multi-class classification evaluation package.
LabelObjective - Interface in org.tribuo.classification.sgd: An interface for single label prediction objectives.
labels - Variable in class org.tribuo.classification.LabelInfo: The label domain.
labels - Variable in class org.tribuo.classification.sgd.crf.Chunk
labels - Variable in class org.tribuo.classification.sgd.Util.ExampleArray
labels - Variable in class org.tribuo.classification.sgd.Util.SequenceExampleArray
labels - Variable in class org.tribuo.multilabel.MultiLabelInfo
LabelSequenceEvaluation - Class in org.tribuo.classification.sequence: A class that can be used to evaluate a sequence label classification model element wise on a given set of data.
LabelSequenceEvaluation(Map<MetricID<Label>, Double>, LabelMetric.Context, EvaluationProvenance) - Constructor for class org.tribuo.classification.sequence.LabelSequenceEvaluation
LabelSequenceEvaluator - Class in org.tribuo.classification.sequence: A sequence evaluator for labels.
LabelSequenceEvaluator() - Constructor for class org.tribuo.classification.sequence.LabelSequenceEvaluator
LabelTransformer - Class in org.tribuo.interop.onnx: Can convert an OnnxValue into a Prediction or a Label.
LabelTransformer - Class in org.tribuo.interop.tensorflow: Can convert a Label into a Tensor containing a 32-bit integer and can convert a vector of 32-bit floats into a Prediction or a Label.
LabelTransformer() - Constructor for class org.tribuo.interop.onnx.LabelTransformer
LabelTransformer() - Constructor for class org.tribuo.interop.tensorflow.LabelTransformer
lambda - Variable in class org.tribuo.math.optimisers.GradientOptimiserOptions
lambda - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
lambda - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
languageTag - Variable in class org.tribuo.util.tokens.options.BreakIteratorTokenizerOptions
LARS - Enum constant in enum org.tribuo.regression.slm.TrainTest.SLMType
LARSLASSO - Enum constant in enum org.tribuo.regression.slm.TrainTest.SLMType
LARSLassoTrainer - Class in org.tribuo.regression.slm: A trainer for a lasso linear regression model which uses LARS to construct the model.
LARSLassoTrainer() - Constructor for class org.tribuo.regression.slm.LARSLassoTrainer: Constructs a lasso LARS trainer that selects all the features.
LARSLassoTrainer(int) - Constructor for class org.tribuo.regression.slm.LARSLassoTrainer: Constructs a lasso LARS trainer for a linear model.
LARSOptions() - Constructor for class org.tribuo.regression.slm.TrainTest.LARSOptions
LARSTrainer - Class in org.tribuo.regression.slm: A trainer for a linear regression model which uses least angle regression.
LARSTrainer() - Constructor for class org.tribuo.regression.slm.LARSTrainer: Constructs a least angle regression trainer that selects all the features.
LARSTrainer(int) - Constructor for class org.tribuo.regression.slm.LARSTrainer: Constructs a least angle regression trainer for a linear model.
LAST - Enum constant in enum org.tribuo.data.columnar.processors.feature.UniqueProcessor.UniqueType: Select the last feature value in the list.
lastIndexOf(Object) - Method in class org.tribuo.util.infotheory.impl.RowList
LeafNode<T> - Class in org.tribuo.common.tree: An immutable leaf Node that can create a prediction.
LeafNode(double, T, Map<String, T>, boolean) - Constructor for class org.tribuo.common.tree.LeafNode: Constructs a leaf node.
learningRate - Variable in class org.tribuo.math.optimisers.GradientOptimiserOptions
learningRate() - Method in class org.tribuo.math.optimisers.SGD: Override to provide a function which calculates the learning rate.
leftMultiply(SGDVector) - Method in class org.tribuo.math.la.DenseMatrix
leftMultiply(SGDVector) - Method in class org.tribuo.math.la.DenseSparseMatrix
leftMultiply(SGDVector) - Method in interface org.tribuo.math.la.Matrix: Multiplies this Matrix by a SGDVector returning a vector of the appropriate size.
leftMultiply(SGDVector) - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
len - Variable in class org.tribuo.util.tokens.universal.Range
length() - Method in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence: Returns the number of elements in this subsequence.
length() - Method in class org.tribuo.util.tokens.Token: The number of characters in this token.
length() - Method in class org.tribuo.util.tokens.universal.Range
lessThanOrEqual - Variable in class org.tribuo.common.tree.AbstractTrainingNode
LIBLINEAR - Enum constant in enum org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType
LibLinearClassificationModel - Class in org.tribuo.classification.liblinear: A Model which wraps a LibLinear-java classification model.
LibLinearClassificationTrainer - Class in org.tribuo.classification.liblinear: A Trainer which wraps a liblinear-java classifier trainer.
LibLinearClassificationTrainer() - Constructor for class org.tribuo.classification.liblinear.LibLinearClassificationTrainer: Creates a trainer using the default values (L2R_L2LOSS_SVC_DUAL, 1, 0.1).
LibLinearClassificationTrainer(LinearClassificationType, double, double) - Constructor for class org.tribuo.classification.liblinear.LibLinearClassificationTrainer: Creates a trainer for a LibLinearClassificationModel.
LibLinearClassificationTrainer(LinearClassificationType, double, int, double) - Constructor for class org.tribuo.classification.liblinear.LibLinearClassificationTrainer: Creates a trainer for a LibLinear model
LibLinearModel<T> - Class in org.tribuo.common.liblinear: A Model which wraps a LibLinear-java model.
LibLinearModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, boolean, List<Model>) - Constructor for class org.tribuo.common.liblinear.LibLinearModel: Constructs a LibLinear model from the supplied arguments.
liblinearOptions - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
libLinearOptions - Variable in class org.tribuo.classification.liblinear.TrainTest.TrainTestOptions
LibLinearOptions - Class in org.tribuo.classification.liblinear: Command line options for working with a classification liblinear model.
LibLinearOptions() - Constructor for class org.tribuo.classification.liblinear.LibLinearOptions
LibLinearOptions() - Constructor for class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
LibLinearOptions() - Constructor for class org.tribuo.regression.libsvm.TrainTest.LibLinearOptions
libLinearParams - Variable in class org.tribuo.common.liblinear.LibLinearTrainer
LibLinearRegressionModel - Class in org.tribuo.regression.liblinear: A Model which wraps a LibLinear-java model.
LibLinearRegressionTrainer - Class in org.tribuo.regression.liblinear: A Trainer which wraps a liblinear-java regression trainer.
LibLinearRegressionTrainer() - Constructor for class org.tribuo.regression.liblinear.LibLinearRegressionTrainer: Creates a trainer using the default values (L2R_L2LOSS_SVR, 1, 1000, 0.1, 0.1).
LibLinearRegressionTrainer(LinearRegressionType) - Constructor for class org.tribuo.regression.liblinear.LibLinearRegressionTrainer
LibLinearRegressionTrainer(LinearRegressionType, double, int, double, double) - Constructor for class org.tribuo.regression.liblinear.LibLinearRegressionTrainer: Creates a trainer for a LibLinear model
LibLinearTrainer<T> - Class in org.tribuo.common.liblinear: A Trainer which wraps a liblinear-java trainer.
LibLinearTrainer() - Constructor for class org.tribuo.common.liblinear.LibLinearTrainer
LibLinearTrainer(LibLinearType<T>, double, int, double) - Constructor for class org.tribuo.common.liblinear.LibLinearTrainer: Creates a trainer for a LibLinear model
LibLinearTrainer(LibLinearType<T>, double, int, double, double) - Constructor for class org.tribuo.common.liblinear.LibLinearTrainer: Creates a trainer for a LibLinear model
LibLinearType<T> - Interface in org.tribuo.common.liblinear: A carrier type for the liblinear algorithm type.
LIBSVM - Enum constant in enum org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType
LIBSVM - Enum constant in enum org.tribuo.data.DataOptions.InputFormat
LibSVMAnomalyModel - Class in org.tribuo.anomaly.libsvm: A anomaly detection model that uses an underlying libSVM model to make the predictions.
LibSVMAnomalyTrainer - Class in org.tribuo.anomaly.libsvm: A trainer for anomaly models that uses LibSVM.
LibSVMAnomalyTrainer() - Constructor for class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer: For OLCUT.
LibSVMAnomalyTrainer(SVMParameters<Event>) - Constructor for class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer: Creates a one-class LibSVM trainer using the supplied parameters.
LibSVMClassificationModel - Class in org.tribuo.classification.libsvm: A classification model that uses an underlying LibSVM model to make the predictions.
LibSVMClassificationTrainer - Class in org.tribuo.classification.libsvm: A trainer for classification models that uses LibSVM.
LibSVMClassificationTrainer() - Constructor for class org.tribuo.classification.libsvm.LibSVMClassificationTrainer
LibSVMClassificationTrainer(SVMParameters<Label>) - Constructor for class org.tribuo.classification.libsvm.LibSVMClassificationTrainer
LibSVMDataSource<T> - Class in org.tribuo.datasource: A DataSource which can read LibSVM formatted data.
LibSVMDataSource(URL, OutputFactory<T>) - Constructor for class org.tribuo.datasource.LibSVMDataSource: Constructs a LibSVMDataSource from the supplied URL and output factory.
LibSVMDataSource(URL, OutputFactory<T>, boolean, int) - Constructor for class org.tribuo.datasource.LibSVMDataSource: Constructs a LibSVMDataSource from the supplied URL and output factory.
LibSVMDataSource(Path, OutputFactory<T>) - Constructor for class org.tribuo.datasource.LibSVMDataSource: Constructs a LibSVMDataSource from the supplied path and output factory.
LibSVMDataSource(Path, OutputFactory<T>, boolean, int) - Constructor for class org.tribuo.datasource.LibSVMDataSource: Constructs a LibSVMDataSource from the supplied path and output factory.
LibSVMDataSource.LibSVMDataSourceProvenance - Class in org.tribuo.datasource: The provenance for a LibSVMDataSource.
LibSVMDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.datasource.LibSVMDataSource.LibSVMDataSourceProvenance: Constructs a provenance during unmarshalling.
LibSVMModel<T> - Class in org.tribuo.common.libsvm: A model that uses an underlying libSVM model to make the predictions.
LibSVMModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, boolean, List<svm_model>) - Constructor for class org.tribuo.common.libsvm.LibSVMModel: Constructs a LibSVMModel from the supplied arguments.
libsvmOptions - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
libsvmOptions - Variable in class org.tribuo.classification.libsvm.TrainTest.TrainTestOptions
LibSVMOptions - Class in org.tribuo.classification.libsvm: CLI options for training a LibSVM classification model.
LibSVMOptions() - Constructor for class org.tribuo.classification.libsvm.LibSVMOptions
LibSVMRegressionModel - Class in org.tribuo.regression.libsvm: A regression model that uses an underlying libSVM model to make the predictions.
LibSVMRegressionTrainer - Class in org.tribuo.regression.libsvm: A trainer for regression models that uses LibSVM.
LibSVMRegressionTrainer() - Constructor for class org.tribuo.regression.libsvm.LibSVMRegressionTrainer: For olcut.
LibSVMRegressionTrainer(SVMParameters<Regressor>) - Constructor for class org.tribuo.regression.libsvm.LibSVMRegressionTrainer
LibSVMTrainer<T> - Class in org.tribuo.common.libsvm: A trainer that will train using libsvm's Java implementation.
LibSVMTrainer() - Constructor for class org.tribuo.common.libsvm.LibSVMTrainer: For olcut.
LibSVMTrainer(SVMParameters<T>) - Constructor for class org.tribuo.common.libsvm.LibSVMTrainer: Constructs a LibSVMTrainer from the parameters.
LIMEBase - Class in org.tribuo.classification.explanations.lime: LIMEBase merges the lime_base.py and lime_tabular.py implementations, and deals with simple matrices of numerical or categorical data.
LIMEBase(SplittableRandom, Model<Label>, SparseTrainer<Regressor>, int) - Constructor for class org.tribuo.classification.explanations.lime.LIMEBase: Constructs a LIME explainer for a model which uses tabular data (i.e., no special treatment for text features).
LIMEColumnar - Class in org.tribuo.classification.explanations.lime: Uses the columnar data processing infrastructure to mix text and tabular data.
LIMEColumnar(SplittableRandom, Model<Label>, SparseTrainer<Regressor>, int, RowProcessor<Label>, Tokenizer) - Constructor for class org.tribuo.classification.explanations.lime.LIMEColumnar: Constructs a LIME explainer for a model which uses the columnar data processing system.
LIMEExplanation - Class in org.tribuo.classification.explanations.lime: An Explanation using LIME.
LIMEExplanation(SparseModel<Regressor>, Prediction<Label>, RegressionEvaluation) - Constructor for class org.tribuo.classification.explanations.lime.LIMEExplanation
LIMEText - Class in org.tribuo.classification.explanations.lime: Uses a Tribuo TextFeatureExtractor to explain the prediction for a given piece of text.
LIMEText(SplittableRandom, Model<Label>, SparseTrainer<Regressor>, int, TextFeatureExtractor<Label>, Tokenizer) - Constructor for class org.tribuo.classification.explanations.lime.LIMEText: Constructs a LIME explainer for a model which uses text data.
LIMETextCLI - Class in org.tribuo.classification.explanations.lime: A CLI for interacting with LIMEText.
LIMETextCLI() - Constructor for class org.tribuo.classification.explanations.lime.LIMETextCLI
LIMETextCLI.LIMETextCLIOptions - Class in org.tribuo.classification.explanations.lime
LIMETextCLIOptions() - Constructor for class org.tribuo.classification.explanations.lime.LIMETextCLI.LIMETextCLIOptions
Linear - Class in org.tribuo.math.kernel: A linear kernel, u.dot(v).
Linear() - Constructor for class org.tribuo.math.kernel.Linear: A linear kernel, u.dot(v).
LINEAR - Enum constant in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum
LINEAR - Enum constant in enum org.tribuo.common.libsvm.KernelType: A linear kernel function (i.e., a dot product).
LINEAR - Enum constant in enum org.tribuo.common.xgboost.XGBoostTrainer.BoosterType: A boosted linear model.
LINEAR - Enum constant in enum org.tribuo.regression.xgboost.XGBoostRegressionTrainer.RegressionType: Squared error loss function.
LinearClassificationType - Class in org.tribuo.classification.liblinear: The carrier type for liblinear classification modes.
LinearClassificationType(LinearClassificationType.LinearType) - Constructor for class org.tribuo.classification.liblinear.LinearClassificationType
LinearClassificationType.LinearType - Enum in org.tribuo.classification.liblinear: The different model types available for classification.
LinearParameters - Class in org.tribuo.math: A Parameters for producing single label linear models.
LinearParameters(int, int) - Constructor for class org.tribuo.math.LinearParameters: Constructor.
LinearRegressionType - Class in org.tribuo.regression.liblinear: The carrier type for liblinear linear regression modes.
LinearRegressionType(LinearRegressionType.LinearType) - Constructor for class org.tribuo.regression.liblinear.LinearRegressionType
LinearRegressionType.LinearType - Enum in org.tribuo.regression.liblinear: The type of linear regression algorithm.
LinearScalingTransformation - Class in org.tribuo.transform.transformations: A Transformation which takes an observed distribution and rescales it so all values are between the desired min and max.
LinearScalingTransformation() - Constructor for class org.tribuo.transform.transformations.LinearScalingTransformation: Defaults to zero - one.
LinearScalingTransformation(double, double) - Constructor for class org.tribuo.transform.transformations.LinearScalingTransformation
LinearScalingTransformation.LinearScalingTransformationProvenance - Class in org.tribuo.transform.transformations: Provenance for LinearScalingTransformation.
LinearScalingTransformationProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.transform.transformations.LinearScalingTransformation.LinearScalingTransformationProvenance
LINEARSGD - Enum constant in enum org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
LinearSGDModel - Class in org.tribuo.classification.sgd.linear: The inference time version of a linear model trained using SGD.
LinearSGDModel - Class in org.tribuo.regression.sgd.linear: The inference time version of a linear model trained using SGD.
linearSGDOptions - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
LinearSGDOptions - Class in org.tribuo.classification.sgd.linear: CLI options for training a linear classifier.
LinearSGDOptions() - Constructor for class org.tribuo.classification.sgd.linear.LinearSGDOptions
LinearSGDOptions.LossEnum - Enum in org.tribuo.classification.sgd.linear: Available loss types.
LinearSGDTrainer - Class in org.tribuo.classification.sgd.linear: A trainer for a linear model which uses SGD.
LinearSGDTrainer - Class in org.tribuo.regression.sgd.linear: A trainer for a linear regression model which uses SGD.
LinearSGDTrainer(LabelObjective, StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.classification.sgd.linear.LinearSGDTrainer: Constructs an SGD trainer for a linear model.
LinearSGDTrainer(LabelObjective, StochasticGradientOptimiser, int, int, long) - Constructor for class org.tribuo.classification.sgd.linear.LinearSGDTrainer: Sets the minibatch size to 1.
LinearSGDTrainer(LabelObjective, StochasticGradientOptimiser, int, long) - Constructor for class org.tribuo.classification.sgd.linear.LinearSGDTrainer: Sets the minibatch size to 1 and the logging interval to 1000.
LinearSGDTrainer(RegressionObjective, StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.regression.sgd.linear.LinearSGDTrainer: Constructs an SGD trainer for a linear model.
LinearSGDTrainer(RegressionObjective, StochasticGradientOptimiser, int, int, long) - Constructor for class org.tribuo.regression.sgd.linear.LinearSGDTrainer: Sets the minibatch size to 1.
LinearSGDTrainer(RegressionObjective, StochasticGradientOptimiser, int, long) - Constructor for class org.tribuo.regression.sgd.linear.LinearSGDTrainer: Sets the minibatch size to 1 and the logging interval to 1000.
list - Variable in class org.tribuo.transform.TransformationMap.TransformationList
ListDataSource<T> - Class in org.tribuo.datasource: A data source which wraps up a list of Examples along with their DataSourceProvenance and an OutputFactory.
ListDataSource(List<Example<T>>, OutputFactory<T>, DataSourceProvenance) - Constructor for class org.tribuo.datasource.ListDataSource
ListExample<T> - Class in org.tribuo.impl: This class will not be performant until value types are available in Java.
ListExample(Example<T>) - Constructor for class org.tribuo.impl.ListExample
ListExample(T) - Constructor for class org.tribuo.impl.ListExample
ListExample(T, float) - Constructor for class org.tribuo.impl.ListExample
ListExample(T, String[], double[]) - Constructor for class org.tribuo.impl.ListExample
ListExample(T, List<? extends Feature>) - Constructor for class org.tribuo.impl.ListExample
listIterator() - Method in class org.tribuo.util.infotheory.impl.RowList
listIterator(int) - Method in class org.tribuo.util.infotheory.impl.RowList
load(Path, String) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv file then wraps it in a dataset.
load(Path, String, String[]) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv file then wraps it in a dataset.
load(Path, Path, OutputFactory<Label>) - Static method in class org.tribuo.interop.tensorflow.TrainTest
load(Path, Set<String>) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv file then wraps it in a dataset.
load(Path, Set<String>, String[]) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv file then wraps it in a dataset.
load(Test.ConfigurableTestOptions) - Static method in class org.tribuo.classification.experiments.Test
load(OutputFactory<T>) - Method in class org.tribuo.data.DataOptions
loadDataset(CommandInterpreter, File) - Method in class org.tribuo.data.DatasetExplorer
loadDataSource(URL, String) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv path.
loadDataSource(URL, String, String[]) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv path.
loadDataSource(URL, Set<String>) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv path.
loadDataSource(URL, Set<String>, String[]) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv path.
loadDataSource(Path, String) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv path.
loadDataSource(Path, String, String[]) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv path.
loadDataSource(Path, Set<String>) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv path.
loadDataSource(Path, Set<String>, String[]) - Method in class org.tribuo.data.csv.CSVLoader: Loads a DataSource from the specified csv path.
loadModel(CommandInterpreter, File) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
loadModel(CommandInterpreter, File) - Method in class org.tribuo.ModelExplorer: Loads a model.
loadModel(CommandInterpreter, File) - Method in class org.tribuo.sequence.SequenceModelExplorer
localValues - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainCliqueValues
log - Enum constant in enum org.tribuo.transform.transformations.SimpleTransform.Operation: Logs the inputs (base_e)
log() - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that applies Math.log(double).
LOG - Enum constant in enum org.tribuo.classification.sgd.linear.LinearSGDOptions.LossEnum
LOG_2 - Static variable in class org.tribuo.util.infotheory.InformationTheory
LOG_2 - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory
LOG_BASE - Static variable in class org.tribuo.util.infotheory.InformationTheory: Sets the base of the logarithm used in the information theoretic calculations.
LOG_BASE - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory: Sets the base of the logarithm used in the information theoretic calculations.
LOG_E - Static variable in class org.tribuo.util.infotheory.InformationTheory
LOG_E - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory
loggingInterval - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
loggingInterval - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
loggingInterval - Variable in class org.tribuo.regression.sgd.TrainTest.SGDOptions
LogisticRegressionTrainer - Class in org.tribuo.classification.sgd.linear: A logistic regression trainer that uses a reasonable objective, optimiser, number of epochs and minibatch size.
LogisticRegressionTrainer() - Constructor for class org.tribuo.classification.sgd.linear.LogisticRegressionTrainer
logModel - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
LogMulticlass - Class in org.tribuo.classification.sgd.objectives: A multiclass version of the log loss.
LogMulticlass() - Constructor for class org.tribuo.classification.sgd.objectives.LogMulticlass
logVector(Logger, Level, double[]) - Static method in class org.tribuo.util.Util
logVector(Logger, Level, float[]) - Static method in class org.tribuo.util.Util
logZ - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults
LongPair() - Constructor for class org.tribuo.util.MurmurHash3.LongPair
lookup(String) - Method in class org.tribuo.Example: Returns the Feature in this Example which has the supplied name, if it's present.
lookup(String) - Method in class org.tribuo.impl.ArrayExample
lookup(String) - Method in class org.tribuo.impl.BinaryFeaturesExample
lookup(String) - Method in class org.tribuo.impl.ListExample
loss - Variable in class org.tribuo.regression.sgd.TrainTest.SGDOptions
loss(DenseVector, SGDVector) - Method in class org.tribuo.regression.sgd.objectives.AbsoluteLoss
loss(DenseVector, SGDVector) - Method in class org.tribuo.regression.sgd.objectives.Huber
loss(DenseVector, SGDVector) - Method in class org.tribuo.regression.sgd.objectives.SquaredLoss
loss(DenseVector, SGDVector) - Method in interface org.tribuo.regression.sgd.RegressionObjective: Scores a prediction, returning the loss.

M

m - Variable in class org.tribuo.FeatureMap: Map from the feature names to their info.
MACRO - Enum constant in enum org.tribuo.evaluation.metrics.EvaluationMetric.Average
macroAveragedF1() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged F_1 across all the labels.
macroAveragedF1() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
macroAveragedF1() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
macroAveragedPrecision() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged precision.
macroAveragedPrecision() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
macroAveragedPrecision() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
macroAveragedRecall() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged recall.
macroAveragedRecall() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
macroAveragedRecall() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
macroAverageTarget() - Static method in class org.tribuo.evaluation.metrics.MetricTarget: Get the singleton MetricTarget which contains the EvaluationMetric.Average.MACRO.
macroFN() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged number of false negatives.
macroFN() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
macroFN() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
macroFP() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged number of false positives, averaged across the labels.
macroFP() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
macroFP() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
macroTN() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged number of true negatives.
macroTN() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
macroTN() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
macroTP() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged number of true positives, averaged across the labels.
macroTP() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
macroTP() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
mae() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: Calculates the Mean Absolute Error for all dimensions.
mae(MetricTarget<Regressor>, RegressionSufficientStatistics) - Static method in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates the Mean Absolute Error based on the supplied statistics.
mae(Regressor) - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: Calculates the Mean Absolute Error for that dimension.
mae(Regressor, RegressionSufficientStatistics) - Static method in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates the Mean Absolute Error based on the supplied statistics for a single dimension.
MAE - Enum constant in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates the Mean Absolute Error of the predictions.
MAE - Enum constant in enum org.tribuo.regression.rtree.TrainTest.ImpurityType
main(String[]) - Static method in class org.tribuo.classification.dtree.TrainTest
main(String[]) - Static method in class org.tribuo.classification.experiments.ConfigurableTrainTest
main(String[]) - Static method in class org.tribuo.classification.experiments.RunAll
main(String[]) - Static method in class org.tribuo.classification.experiments.Test
main(String[]) - Static method in class org.tribuo.classification.experiments.TrainTest
main(String[]) - Static method in class org.tribuo.classification.explanations.lime.LIMETextCLI
main(String[]) - Static method in class org.tribuo.classification.liblinear.TrainTest
main(String[]) - Static method in class org.tribuo.classification.libsvm.TrainTest
main(String[]) - Static method in class org.tribuo.classification.mnb.TrainTest
main(String[]) - Static method in class org.tribuo.classification.sgd.crf.SeqTest
main(String[]) - Static method in class org.tribuo.classification.sgd.kernel.TrainTest
main(String[]) - Static method in class org.tribuo.classification.sgd.TrainTest
main(String[]) - Static method in class org.tribuo.classification.xgboost.TrainTest
main(String[]) - Static method in class org.tribuo.clustering.kmeans.TrainTest
main(String[]) - Static method in class org.tribuo.data.CompletelyConfigurableTrainTest
main(String[]) - Static method in class org.tribuo.data.ConfigurableTrainTest
main(String[]) - Static method in class org.tribuo.data.DatasetExplorer
main(String[]) - Static method in class org.tribuo.data.PreprocessAndSerialize
main(String[]) - Static method in class org.tribuo.data.sql.SQLToCSV: Reads an SQL query from the standard input and writes the results of the query to the standard output.
main(String[]) - Static method in class org.tribuo.data.text.SplitTextData
main(String[]) - Static method in class org.tribuo.interop.tensorflow.TrainTest
main(String[]) - Static method in class org.tribuo.json.StripProvenance
main(String[]) - Static method in class org.tribuo.ModelExplorer: Entry point.
main(String[]) - Static method in class org.tribuo.regression.liblinear.TrainTest
main(String[]) - Static method in class org.tribuo.regression.libsvm.TrainTest
main(String[]) - Static method in class org.tribuo.regression.rtree.TrainTest
main(String[]) - Static method in class org.tribuo.regression.sgd.TrainTest
main(String[]) - Static method in class org.tribuo.regression.slm.TrainTest
main(String[]) - Static method in class org.tribuo.regression.xgboost.TrainTest
main(String[]) - Static method in class org.tribuo.sequence.SequenceModelExplorer
main(String[]) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo
MAJOR_VERSION - Static variable in class org.tribuo.Tribuo: The major version number.
makeIDInfo(int) - Method in class org.tribuo.CategoricalIDInfo
makeIDInfo(int) - Method in class org.tribuo.CategoricalInfo
makeIDInfo(int) - Method in class org.tribuo.RealIDInfo
makeIDInfo(int) - Method in class org.tribuo.RealInfo
makeIDInfo(int) - Method in interface org.tribuo.VariableInfo: Generates a VariableIDInfo subclass which represents the same feature.
makeTokens() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer: Make one or more tokens from our current collected characters.
map(String, List<Feature>) - Method in interface org.tribuo.data.text.FeatureTransformer: Transforms features into a new list of features
map(String, List<Feature>) - Method in class org.tribuo.data.text.impl.FeatureHasher
mapScore - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainViterbiResults
mapValues - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainViterbiResults
MATCH_ALL - Enum constant in enum org.tribuo.data.columnar.processors.field.RegexFieldProcessor.Mode
MATCH_CONTAINS - Enum constant in enum org.tribuo.data.columnar.processors.field.RegexFieldProcessor.Mode
Matrix - Interface in org.tribuo.math.la: Interface for 2 dimensional Tensors.
MatrixHeapMerger - Class in org.tribuo.math.util: Merges each DenseSparseMatrix using a PriorityQueue as a heap on the MatrixIterator.
MatrixHeapMerger() - Constructor for class org.tribuo.math.util.MatrixHeapMerger
MatrixIterator - Interface in org.tribuo.math.la: A Comparable Iterator over MatrixTuples.
matrixMultiply(Matrix) - Method in class org.tribuo.math.la.DenseMatrix
matrixMultiply(Matrix) - Method in class org.tribuo.math.la.DenseSparseMatrix
matrixMultiply(Matrix) - Method in interface org.tribuo.math.la.Matrix: Multiplies this Matrix by another Matrix returning a matrix of the appropriate size.
matrixMultiply(Matrix, boolean, boolean) - Method in class org.tribuo.math.la.DenseMatrix
matrixMultiply(Matrix, boolean, boolean) - Method in class org.tribuo.math.la.DenseSparseMatrix
matrixMultiply(Matrix, boolean, boolean) - Method in interface org.tribuo.math.la.Matrix: Multiplies this Matrix by another Matrix returning a matrix of the appropriate size.
MatrixTuple - Class in org.tribuo.math.la: A mutable tuple used to avoid allocation when iterating a matrix.
MatrixTuple() - Constructor for class org.tribuo.math.la.MatrixTuple
MatrixTuple(int, int, int) - Constructor for class org.tribuo.math.la.MatrixTuple
max - Variable in class org.tribuo.RealInfo: The maximum observed feature value.
max() - Static method in interface org.tribuo.util.Merger: A merger which takes the maximum element.
MAX - Enum constant in enum org.tribuo.data.columnar.processors.feature.UniqueProcessor.UniqueType: Select the maximum feature value in the list.
maxDepth - Variable in class org.tribuo.common.tree.AbstractCARTTrainer: Maximum tree depth.
maxIterations - Variable in class org.tribuo.common.liblinear.LibLinearTrainer
maxIterations - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
maxMap - Variable in class org.tribuo.regression.RegressionInfo
maxNumFeatures - Variable in class org.tribuo.regression.slm.SLMTrainer
maxNumFeatures - Variable in class org.tribuo.regression.slm.TrainTest.LARSOptions
maxTokenLength - Variable in class org.tribuo.util.tokens.universal.UniversalTokenizer: The length of the longest token that we will generate.
maxValue() - Method in class org.tribuo.math.la.DenseVector
maxValue() - Method in interface org.tribuo.math.la.SGDVector: Returns the maximum value.
maxValue() - Method in class org.tribuo.math.la.SparseVector
maxValue() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
MCSVM_CS - Enum constant in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType: multi-class support vector classification by Crammer and Singer
mean - Variable in class org.tribuo.RealInfo: The feature mean.
mean(double[]) - Static method in class org.tribuo.util.Util: Returns the mean of the input array.
mean(double[], int) - Static method in class org.tribuo.util.Util
mean(Collection<V>) - Static method in class org.tribuo.util.Util
MEAN - Enum constant in enum org.tribuo.regression.baseline.DummyRegressionTrainer.DummyType: Returns the mean of the training data outputs.
MeanAbsoluteError - Class in org.tribuo.regression.rtree.impurity: Measures the mean absolute error over a set of inputs.
MeanAbsoluteError() - Constructor for class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
meanAndVariance(double[]) - Static method in class org.tribuo.util.Util: Returns the mean and variance of the input.
meanAndVariance(double[], int) - Static method in class org.tribuo.util.Util: Returns the mean and variance of the input's first length elements.
meanMap - Variable in class org.tribuo.regression.RegressionInfo
MeanSquaredError - Class in org.tribuo.regression.rtree.impurity: Measures the mean squared error over a set of inputs.
MeanSquaredError() - Constructor for class org.tribuo.regression.rtree.impurity.MeanSquaredError
MeanStdDevTransformation - Class in org.tribuo.transform.transformations: A Transformation which takes an observed distribution and rescales it so it has the desired mean and standard deviation.
MeanStdDevTransformation() - Constructor for class org.tribuo.transform.transformations.MeanStdDevTransformation: Defaults to zero mean, one std dev.
MeanStdDevTransformation(double, double) - Constructor for class org.tribuo.transform.transformations.MeanStdDevTransformation
MeanStdDevTransformation.MeanStdDevTransformationProvenance - Class in org.tribuo.transform.transformations: Provenance for MeanStdDevTransformation.
MeanStdDevTransformationProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.transform.transformations.MeanStdDevTransformation.MeanStdDevTransformationProvenance
measureDistance(ImmutableFeatureMap, long, SparseVector, SparseVector) - Static method in class org.tribuo.classification.explanations.lime.LIMEBase: Measures the distance between an input point and a sampled point.
MEDIAN - Enum constant in enum org.tribuo.regression.baseline.DummyRegressionTrainer.DummyType: Returns the median of the training data outputs.
MEMBERS - Static variable in class org.tribuo.provenance.EnsembleModelProvenance
merge(double, double) - Method in interface org.tribuo.util.Merger: Merges first and second.
merge(List<int[]>, IntArrayContainer, IntArrayContainer) - Static method in class org.tribuo.common.tree.impl.IntArrayContainer: Merges the list of int arrays into a single int array, using the two supplied buffers.
merge(List<SparseVector>, int, int[], double[]) - Static method in class org.tribuo.math.util.HeapMerger: Merges a list of sparse vectors into a single sparse vector, summing the values.
merge(IntArrayContainer, int[], IntArrayContainer) - Static method in class org.tribuo.common.tree.impl.IntArrayContainer: Merges input and otherArray writing to output.
merge(DenseSparseMatrix[]) - Method in class org.tribuo.math.util.HeapMerger
merge(DenseSparseMatrix[]) - Method in class org.tribuo.math.util.MatrixHeapMerger
merge(DenseSparseMatrix[]) - Method in interface org.tribuo.math.util.Merger: Merges an array of DenseSparseMatrix into a single DenseSparseMatrix.
merge(SparseVector[]) - Method in class org.tribuo.math.util.HeapMerger
merge(SparseVector[]) - Method in class org.tribuo.math.util.MatrixHeapMerger
merge(SparseVector[]) - Method in interface org.tribuo.math.util.Merger: Merges an array of SparseVector into a single SparseVector.
merge(Tensor[][], int) - Method in class org.tribuo.classification.sgd.crf.CRFParameters
merge(Tensor[][], int) - Method in class org.tribuo.math.LinearParameters
merge(Tensor[][], int) - Method in interface org.tribuo.math.Parameters: Merge together an array of gradient arrays.
Merger - Interface in org.tribuo.math.util: An interface for merging an array of DenseSparseMatrix into a single DenseSparseMatrix.
Merger - Interface in org.tribuo.util: An interface which can merge double values.
MessageDigestHasher - Class in org.tribuo.hash: Hashes Strings using the supplied MessageDigest type.
MessageDigestHasher(String, String) - Constructor for class org.tribuo.hash.MessageDigestHasher
MessageDigestHasher.MessageDigestHasherProvenance - Class in org.tribuo.hash: Provenance for MessageDigestHasher.
MessageDigestHasherProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.hash.MessageDigestHasher.MessageDigestHasherProvenance
metadata - Variable in class org.tribuo.Example: The example metadata.
metadataName - Variable in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor
MetricContext<T> - Class in org.tribuo.evaluation.metrics: The context for a metric or set of metrics.
MetricContext(Model<T>, List<Prediction<T>>) - Constructor for class org.tribuo.evaluation.metrics.MetricContext
MetricContext(SequenceModel<T>, List<Prediction<T>>) - Constructor for class org.tribuo.evaluation.metrics.MetricContext
MetricID<T> - Class in org.tribuo.evaluation.metrics: Just an easier-to-read alias for Pair<MetricTarget<T>, String>.
MetricID(MetricTarget<T>, String) - Constructor for class org.tribuo.evaluation.metrics.MetricID
MetricTarget<T> - Class in org.tribuo.evaluation.metrics: Used by a given EvaluationMetric to determine whether it should compute its value for a specific Output value or whether it should average them.
MetricTarget(EvaluationMetric.Average) - Constructor for class org.tribuo.evaluation.metrics.MetricTarget: Builds a metric target for an average.
MetricTarget(T) - Constructor for class org.tribuo.evaluation.metrics.MetricTarget: Builds a metric target for an output.
mi(ArrayList<T1>, ArrayList<T2>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete weighted mutual information, using histogram probability estimators.
mi(List<T1>, List<T2>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon mutual information, using histogram probability estimators.
mi(Set<List<T1>>, Set<List<T2>>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the mutual information between the two sets of random variables.
mi(PairDistribution<T1, T2>) - Static method in class org.tribuo.util.infotheory.InformationTheory: Calculates the discrete Shannon mutual information, using histogram probability estimators.
mi(PairDistribution<T1, T2>, Map<?, Double>, WeightedInformationTheory.VariableSelector) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory
mi(WeightedPairDistribution<T1, T2>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory
MICRO - Enum constant in enum org.tribuo.evaluation.metrics.EvaluationMetric.Average
microAveragedF1() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro averaged F_1 across all labels.
microAveragedF1() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
microAveragedF1() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
microAveragedPrecision() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro averaged precision.
microAveragedPrecision() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
microAveragedPrecision() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
microAveragedRecall() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro averaged recall.
microAveragedRecall() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
microAveragedRecall() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
microAverageTarget() - Static method in class org.tribuo.evaluation.metrics.MetricTarget: Get the singleton MetricTarget which contains the EvaluationMetric.Average.MICRO.
min - Variable in class org.tribuo.RealInfo: The minimum observed feature value.
min() - Static method in interface org.tribuo.util.Merger: A merger which takes the minimum element.
MIN - Enum constant in enum org.tribuo.data.columnar.processors.feature.UniqueProcessor.UniqueType: Select the minimum feature value in the list.
MIN_EXAMPLES - Static variable in class org.tribuo.common.tree.AbstractCARTTrainer: Default minimum weight of examples allowed in a leaf node.
MIN_LENGTH - Static variable in class org.tribuo.hash.Hasher: The minimum length of the salt.
minChildWeight - Variable in class org.tribuo.common.tree.AbstractCARTTrainer: Minimum weight of examples allowed in a leaf.
minChildWeight - Variable in class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
minCount - Variable in class org.tribuo.data.CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions
minCount - Variable in class org.tribuo.data.DataOptions
minCount(CommandInterpreter, int) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
minCount(CommandInterpreter, int) - Method in class org.tribuo.data.DatasetExplorer
minCount(CommandInterpreter, int) - Method in class org.tribuo.ModelExplorer: Shows the number of features which occurred more than min count times.
minCount(CommandInterpreter, int) - Method in class org.tribuo.sequence.SequenceModelExplorer
minibatchSize - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
minibatchSize - Variable in class org.tribuo.regression.sgd.TrainTest.SGDOptions
MinimumCardinalityDataset<T> - Class in org.tribuo.dataset: This class creates a pruned dataset in which low frequency features that occur less than the provided minimum cardinality have been removed.
MinimumCardinalityDataset(Dataset<T>, int) - Constructor for class org.tribuo.dataset.MinimumCardinalityDataset
MinimumCardinalityDataset.MinimumCardinalityDatasetProvenance - Class in org.tribuo.dataset: Provenance for MinimumCardinalityDataset.
MinimumCardinalityDatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.dataset.MinimumCardinalityDataset.MinimumCardinalityDatasetProvenance
MinimumCardinalitySequenceDataset<T> - Class in org.tribuo.sequence: This class creates a pruned dataset in which low frequency features that occur less than the provided minimum cardinality have been removed.
MinimumCardinalitySequenceDataset(SequenceDataset<T>, int) - Constructor for class org.tribuo.sequence.MinimumCardinalitySequenceDataset
MinimumCardinalitySequenceDataset.MinimumCardinalitySequenceDatasetProvenance - Class in org.tribuo.sequence: Provenance for MinimumCardinalitySequenceDataset.
MinimumCardinalitySequenceDatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.sequence.MinimumCardinalitySequenceDataset.MinimumCardinalitySequenceDatasetProvenance
minMap - Variable in class org.tribuo.regression.RegressionInfo
MINOR_VERSION - Static variable in class org.tribuo.Tribuo: The minor version number.
minValue() - Method in class org.tribuo.math.la.DenseVector
minValue() - Method in interface org.tribuo.math.la.SGDVector: Returns the minimum value.
minValue() - Method in class org.tribuo.math.la.SparseVector
minValue() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
minWeight - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
minWeight - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
mkTrainingNode(Dataset<Label>) - Method in class org.tribuo.classification.dtree.CARTClassificationTrainer
mkTrainingNode(Dataset<Regressor>) - Method in class org.tribuo.regression.rtree.CARTJointRegressionTrainer
mkTrainingNode(Dataset<Regressor>) - Method in class org.tribuo.regression.rtree.CARTRegressionTrainer
mkTrainingNode(Dataset<T>) - Method in class org.tribuo.common.tree.AbstractCARTTrainer
MNB - Enum constant in enum org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType
mnbAlpha - Variable in class org.tribuo.classification.mnb.MultinomialNaiveBayesOptions
mnbOptions - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
mnbOptions - Variable in class org.tribuo.classification.mnb.TrainTest.TrainTestOptions
MOD - Enum constant in enum org.tribuo.hash.HashingOptions.ModelHashingType
model - Variable in class org.tribuo.common.xgboost.XGBoostExternalModel: Transient as we rely upon the native serialisation mechanism to bytes rather than Java serializing the Booster.
Model<T> - Class in org.tribuo: A prediction model, which is used to predict outputs for unseen instances.
Model(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, boolean) - Constructor for class org.tribuo.Model: Constructs a new model, storing the supplied fields.
MODEL_FILENAME - Static variable in class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer
modelDescription(CommandInterpreter) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
modelDescription(CommandInterpreter) - Method in class org.tribuo.sequence.SequenceModelExplorer
ModelExplorer - Class in org.tribuo: A command line interface for loading in models and inspecting their feature and output spaces.
ModelExplorer() - Constructor for class org.tribuo.ModelExplorer: Builds a new model explorer shell.
ModelExplorer.ModelExplorerOptions - Class in org.tribuo: CLI options for ModelExplorer.
ModelExplorerOptions() - Constructor for class org.tribuo.ModelExplorer.ModelExplorerOptions
modelFilename - Variable in class org.tribuo.classification.explanations.lime.LIMETextCLI.LIMETextCLIOptions
modelFilename - Variable in class org.tribuo.data.DatasetExplorer.DatasetExplorerOptions
modelFilename - Variable in class org.tribuo.ModelExplorer.ModelExplorerOptions: Model file to load.
modelFilename - Variable in class org.tribuo.sequence.SequenceModelExplorer.SequenceModelExplorerOptions
modelHashingAlgorithm - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
modelHashingAlgorithm - Variable in class org.tribuo.hash.HashingOptions
modelHashingSalt - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
modelHashingSalt - Variable in class org.tribuo.hash.HashingOptions
modelPath - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
modelProvenance(CommandInterpreter) - Method in class org.tribuo.ModelExplorer: Displays the model provenance.
ModelProvenance - Class in org.tribuo.provenance: Contains provenance information for an instance of a Model.
ModelProvenance(String, OffsetDateTime, DatasetProvenance, TrainerProvenance) - Constructor for class org.tribuo.provenance.ModelProvenance
ModelProvenance(String, OffsetDateTime, DatasetProvenance, TrainerProvenance, Map<String, Provenance>) - Constructor for class org.tribuo.provenance.ModelProvenance
ModelProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.ModelProvenance
models - Variable in class org.tribuo.common.liblinear.LibLinearModel: The list of LibLinear models.
models - Variable in class org.tribuo.common.libsvm.LibSVMModel: The LibSVM models.
models - Variable in class org.tribuo.common.xgboost.XGBoostModel: The XGBoost4J Boosters.
models - Variable in class org.tribuo.ensemble.EnsembleModel
ModHashCodeHasher - Class in org.tribuo.hash: Hashes names using String.hashCode(), then reduces the dimension.
ModHashCodeHasher(int, String) - Constructor for class org.tribuo.hash.ModHashCodeHasher
ModHashCodeHasher(String) - Constructor for class org.tribuo.hash.ModHashCodeHasher
ModHashCodeHasher.ModHashCodeHasherProvenance - Class in org.tribuo.hash: Provenance for the ModHashCodeHasher.
ModHashCodeHasherProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.hash.ModHashCodeHasher.ModHashCodeHasherProvenance
momentum - Variable in class org.tribuo.math.optimisers.GradientOptimiserOptions
MOST_FREQUENT - Enum constant in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Returns the most frequent training label.
MSE - Enum constant in enum org.tribuo.regression.rtree.TrainTest.ImpurityType
mStep(ForkJoinPool, DenseVector[], Map<Integer, List<Integer>>, SparseVector[], double[]) - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
mul - Enum constant in enum org.tribuo.transform.transformations.SimpleTransform.Operation: Multiplies by the specified constant.
mul(double) - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that multiplies each value by the operand.
multiDimDenseTrainTest() - Static method in class org.tribuo.regression.example.RegressionDataGenerator
multiDimDenseTrainTest(double) - Static method in class org.tribuo.regression.example.RegressionDataGenerator: Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
multiDimSparseTrainTest() - Static method in class org.tribuo.regression.example.RegressionDataGenerator
multiDimSparseTrainTest(double) - Static method in class org.tribuo.regression.example.RegressionDataGenerator: Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
MultiLabel - Class in org.tribuo.multilabel: A class for multi-label classification.
MultiLabel(String) - Constructor for class org.tribuo.multilabel.MultiLabel: Builds a MultiLabel with a single String label.
MultiLabel(Set<Label>) - Constructor for class org.tribuo.multilabel.MultiLabel: Builds a MultiLabel object from a Set of Labels.
MultiLabel(Set<Label>, double) - Constructor for class org.tribuo.multilabel.MultiLabel: Builds a MultiLabel object from a Set of Labels, when the whole set has a score as well as (optionally) the individual labels.
MultiLabel(Label) - Constructor for class org.tribuo.multilabel.MultiLabel: Builds a MultiLabel from a single Label.
MultiLabelConfusionMatrix - Class in org.tribuo.multilabel.evaluation: A ConfusionMatrix which accepts MultiLabels.
MultiLabelConfusionMatrix(Model<MultiLabel>, List<Prediction<MultiLabel>>) - Constructor for class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
MultiLabelDataGenerator - Class in org.tribuo.multilabel.example: Generates three example train and test datasets, used for unit testing.
MultiLabelEvaluation - Interface in org.tribuo.multilabel.evaluation: A MultiLabel specific ClassifierEvaluation.
MultiLabelEvaluationImpl - Class in org.tribuo.multilabel.evaluation: The implementation of a MultiLabelEvaluation using the default metrics.
MultiLabelEvaluator - Class in org.tribuo.multilabel.evaluation: An Evaluator for MultiLabel problems.
MultiLabelEvaluator() - Constructor for class org.tribuo.multilabel.evaluation.MultiLabelEvaluator
MultiLabelFactory - Class in org.tribuo.multilabel: A factory for generating MultiLabel objects and their associated OutputInfo and Evaluator objects.
MultiLabelFactory() - Constructor for class org.tribuo.multilabel.MultiLabelFactory: Construct a MultiLabelFactory.
MultiLabelFactory.MultiLabelFactoryProvenance - Class in org.tribuo.multilabel: Provenance for MultiLabelFactory.
MultiLabelFactoryProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.multilabel.MultiLabelFactory.MultiLabelFactoryProvenance: Constructs a multi-label factory provenance from the empty marshalled form.
MultiLabelInfo - Class in org.tribuo.multilabel: The base class for information about MultiLabel outputs.
MultiLabelMetric - Class in org.tribuo.multilabel.evaluation: A EvaluationMetric for evaluating MultiLabel problems.
MultiLabelMetric(MetricTarget<MultiLabel>, String, BiFunction<MetricTarget<MultiLabel>, MultiLabelMetric.Context, Double>) - Constructor for class org.tribuo.multilabel.evaluation.MultiLabelMetric
MultiLabelMetrics - Enum in org.tribuo.multilabel.evaluation: An enum of the default MultiLabelMetrics supported by the multi-label classification evaluation package.
MultinomialNaiveBayesModel - Class in org.tribuo.classification.mnb: A Model for multinomial Naive Bayes with Laplace smoothing.
MultinomialNaiveBayesOptions - Class in org.tribuo.classification.mnb: CLI options for a multinomial naive bayes model.
MultinomialNaiveBayesOptions() - Constructor for class org.tribuo.classification.mnb.MultinomialNaiveBayesOptions
MultinomialNaiveBayesTrainer - Class in org.tribuo.classification.mnb: A Trainer which trains a multinomial Naive Bayes model with Laplace smoothing.
MultinomialNaiveBayesTrainer() - Constructor for class org.tribuo.classification.mnb.MultinomialNaiveBayesTrainer
MultinomialNaiveBayesTrainer(double) - Constructor for class org.tribuo.classification.mnb.MultinomialNaiveBayesTrainer
MULTIPLY - Enum constant in enum org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation
MULTIPLY - Enum constant in enum org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType: Belief Propagation
multiplyWeights(List<Prediction<Label>>, List) - Static method in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel: A scoring method which multiplies together the per prediction scores.
MurmurHash3 - Class in org.tribuo.util: The MurmurHash3 algorithm was created by Austin Appleby and placed in the public domain.
MurmurHash3() - Constructor for class org.tribuo.util.MurmurHash3
murmurhash3_x64_128(byte[], int, int, int, MurmurHash3.LongPair) - Static method in class org.tribuo.util.MurmurHash3: Returns the MurmurHash3_x64_128 hash, placing the result in "out".
murmurhash3_x86_32(byte[], int, int, int) - Static method in class org.tribuo.util.MurmurHash3: Returns the MurmurHash3_x86_32 hash.
murmurhash3_x86_32(CharSequence, int, int, int) - Static method in class org.tribuo.util.MurmurHash3: Returns the MurmurHash3_x86_32 hash of the UTF-8 bytes of the String without actually encoding the string to a temporary buffer.
MurmurHash3.LongPair - Class in org.tribuo.util: 128 bits of state
MutableAnomalyInfo - Class in org.tribuo.anomaly: An MutableOutputInfo object for Events.
MutableClusteringInfo - Class in org.tribuo.clustering: A mutable ClusteringInfo.
MutableDataset<T> - Class in org.tribuo: A MutableDataset is a Dataset with a MutableFeatureMap which grows over time.
MutableDataset(Iterable<Example<T>>, DataProvenance, OutputFactory<T>) - Constructor for class org.tribuo.MutableDataset: Creates a dataset from a data source.
MutableDataset(DataSource<T>) - Constructor for class org.tribuo.MutableDataset: Creates a dataset from a data source.
MutableDataset(DataProvenance, OutputFactory<T>) - Constructor for class org.tribuo.MutableDataset: Creates an empty dataset.
MutableFeatureMap - Class in org.tribuo: A feature map that can record new feature value observations.
MutableFeatureMap() - Constructor for class org.tribuo.MutableFeatureMap: Creates an empty feature map which converts high cardinality categorical variable infos into reals.
MutableFeatureMap(boolean) - Constructor for class org.tribuo.MutableFeatureMap: Creates an empty feature map which can optionally convert high cardinality categorical variable infos into reals.
MutableLabelInfo - Class in org.tribuo.classification: A mutable LabelInfo.
MutableLabelInfo(LabelInfo) - Constructor for class org.tribuo.classification.MutableLabelInfo: Constructs a mutable deep copy of the supplied label info.
MutableMultiLabelInfo - Class in org.tribuo.multilabel: A MutableOutputInfo for working with multi-label tasks.
MutableMultiLabelInfo(MultiLabelInfo) - Constructor for class org.tribuo.multilabel.MutableMultiLabelInfo: Construct a MutableMultiLabelInfo with it's state copied from another MultiLabelInfo.
MutableOutputInfo<T> - Interface in org.tribuo: A mutable OutputInfo that can record observed output values.
MutableRegressionInfo - Class in org.tribuo.regression: A MutableOutputInfo for Regressors.
MutableRegressionInfo(RegressionInfo) - Constructor for class org.tribuo.regression.MutableRegressionInfo
MutableSequenceDataset<T> - Class in org.tribuo.sequence: A MutableSequenceDataset is a SequenceDataset with a MutableFeatureMap which grows over time.
MutableSequenceDataset(Iterable<SequenceExample<T>>, DataProvenance, OutputFactory<T>) - Constructor for class org.tribuo.sequence.MutableSequenceDataset: Creates a dataset from a data source.
MutableSequenceDataset(DataProvenance, OutputFactory<T>) - Constructor for class org.tribuo.sequence.MutableSequenceDataset: Creates an empty sequence dataset.
MutableSequenceDataset(ImmutableSequenceDataset<T>) - Constructor for class org.tribuo.sequence.MutableSequenceDataset
MutableSequenceDataset(SequenceDataSource<T>) - Constructor for class org.tribuo.sequence.MutableSequenceDataset

N

name - Variable in class org.tribuo.Feature: The feature name.
name - Variable in class org.tribuo.impl.IndexedArrayExample.FeatureTuple
name - Variable in class org.tribuo.Model: The model's name.
name - Variable in class org.tribuo.sequence.SequenceModel
name - Variable in class org.tribuo.SkeletalVariableInfo: The name of the feature.
NAME - Static variable in class org.tribuo.Example: By convention the example name is stored using this metadata key.
nameFeature(String, int) - Method in class org.tribuo.classification.explanations.lime.LIMEText: Generate the feature name by combining the word and index.
nameFeature(String, String, int) - Method in class org.tribuo.classification.explanations.lime.LIMEColumnar: Generate the feature name by combining the word and index.
NAMESPACE - Static variable in interface org.tribuo.data.columnar.FieldProcessor: The namespacing separator.
NEGATIVE_LABEL - Static variable in class org.tribuo.multilabel.MultiLabel: A Label representing the binary negative label.
NEGATIVE_LABEL_STRING - Static variable in class org.tribuo.multilabel.MultiLabel
NESTEROV - Enum constant in enum org.tribuo.math.optimisers.SGD.Momentum: Nesterov momentum.
newBooleanArray(long[]) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil: Creates a new primitive boolean array of up to 8 dimensions, using the supplied shape.
newByteArray(long[]) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil: Creates a new primitive byte array of up to 8 dimensions, using the supplied shape.
newCapacity(int) - Method in class org.tribuo.impl.ArrayExample: Returns a capacity at least as large as the given minimum capacity.
newCapacity(int) - Method in class org.tribuo.impl.BinaryFeaturesExample: Returns a capacity at least as large as the given minimum capacity.
newDoubleArray(long[]) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil: Creates a new primitive double array of up to 8 dimensions, using the supplied shape.
newFloatArray(long[]) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil: Creates a new primitive float array of up to 8 dimensions, using the supplied shape.
newIntArray(long[]) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil: Creates a new primitive int array of up to 8 dimensions, using the supplied shape.
newLongArray(long[]) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil: Creates a new primitive long array of up to 8 dimensions, using the supplied shape.
newWeights(SLMTrainer.SLMState) - Method in class org.tribuo.regression.slm.LARSLassoTrainer
newWeights(SLMTrainer.SLMState) - Method in class org.tribuo.regression.slm.LARSTrainer
newWeights(SLMTrainer.SLMState) - Method in class org.tribuo.regression.slm.SLMTrainer
next() - Method in class org.tribuo.data.columnar.ColumnarIterator
ngram - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
ngram - Variable in class org.tribuo.data.DataOptions
NGRAM - Enum constant in enum org.tribuo.util.tokens.Token.TokenType
NgramProcessor - Class in org.tribuo.data.text.impl: A text processor that will generate token ngrams of a particular size.
NgramProcessor(Tokenizer, int, double) - Constructor for class org.tribuo.data.text.impl.NgramProcessor: Creates a processor that will generate token ngrams of size n.
Node<T> - Interface in org.tribuo.common.tree: A node in a decision tree.
NON - Enum constant in enum org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType
NONE - Enum constant in enum org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions.ViterbiLabelFeatures
NONE - Enum constant in enum org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType: No confidence predction.
NONE - Enum constant in enum org.tribuo.hash.HashingOptions.ModelHashingType
NONE - Enum constant in enum org.tribuo.math.optimisers.SGD.Momentum: No momentum.
NonTokenizer - Class in org.tribuo.util.tokens.impl: A convenience class for when you are required to provide a tokenizer but you don't actually want to split up the text into tokens.
NonTokenizer() - Constructor for class org.tribuo.util.tokens.impl.NonTokenizer
NoopFeatureExtractor - Class in org.tribuo.classification.sequence.viterbi: A label feature extractor that doesn't produce any label based features.
NoopFeatureExtractor() - Constructor for class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
NoopNormalizer - Class in org.tribuo.math.util: NoopNormalizer returns a copy of the input, without normalizing it.
NoopNormalizer() - Constructor for class org.tribuo.math.util.NoopNormalizer
normaliseWeights(Map<T, WeightCountTuple>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Normalizes the weights in the map, i.e., divides each weight by it's count.
normalize - Variable in class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
normalize - Variable in class org.tribuo.regression.slm.SLMTrainer
normalize(double[]) - Method in class org.tribuo.math.util.ExpNormalizer
normalize(double[]) - Method in class org.tribuo.math.util.NoopNormalizer
normalize(double[]) - Method in class org.tribuo.math.util.Normalizer
normalize(double[]) - Method in interface org.tribuo.math.util.VectorNormalizer: Normalizes the input array in some fashion specified by the class.
normalize(VectorNormalizer) - Method in class org.tribuo.math.la.DenseVector
normalize(VectorNormalizer) - Method in interface org.tribuo.math.la.SGDVector: Normalizes the vector using the supplied vector normalizer.
normalize(VectorNormalizer) - Method in class org.tribuo.math.la.SparseVector
NORMALIZED_MI - Enum constant in enum org.tribuo.clustering.evaluation.ClusteringMetrics: The normalized mutual information between the two clusterings
normalizedMI() - Method in interface org.tribuo.clustering.evaluation.ClusteringEvaluation: Calculates the normalized MI between the ground truth clustering ids and the predicted ones.
normalizedMI(ClusteringMetric.Context) - Static method in enum org.tribuo.clustering.evaluation.ClusteringMetrics: Calculates the normalized mutual information between two clusterings.
Normalizer - Class in org.tribuo.math.util: Normalizes, but first subtracts the minimum value (to ensure positivity).
Normalizer() - Constructor for class org.tribuo.math.util.Normalizer
normalizeRows(VectorNormalizer) - Method in class org.tribuo.math.la.DenseMatrix
normalizeToDistribution(double[]) - Static method in class org.tribuo.util.Util
normalizeToDistribution(float[]) - Static method in class org.tribuo.util.Util
nsplits - Variable in class org.tribuo.evaluation.KFoldSplitter
NU_SVC - Enum constant in enum org.tribuo.classification.libsvm.SVMClassificationType.SVMMode: Classification SVM, optimization in dual space.
NU_SVR - Enum constant in enum org.tribuo.regression.libsvm.SVMRegressionType.SVMMode: optimization in dual space.
numActiveElements() - Method in class org.tribuo.math.la.DenseVector
numActiveElements() - Method in interface org.tribuo.math.la.SGDVector: Returns the number of non-zero elements (on construction, an element could be set to zero and it would still remain active).
numActiveElements() - Method in class org.tribuo.math.la.SparseVector
numActiveElements(int) - Method in class org.tribuo.math.la.DenseMatrix
numActiveElements(int) - Method in class org.tribuo.math.la.DenseSparseMatrix
numActiveElements(int) - Method in interface org.tribuo.math.la.Matrix: The number of non-zero elements in that row.
numExamples - Variable in class org.tribuo.common.tree.AbstractTrainingNode
numExamples(CommandInterpreter) - Method in class org.tribuo.data.DatasetExplorer
numFeatures(CommandInterpreter) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
numFeatures(CommandInterpreter) - Method in class org.tribuo.data.DatasetExplorer
numFeatures(CommandInterpreter) - Method in class org.tribuo.ModelExplorer: Displays the number of features.
numFeatures(CommandInterpreter) - Method in class org.tribuo.sequence.SequenceModelExplorer
numFolds - Variable in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
numMembers - Variable in class org.tribuo.classification.ensemble.AdaBoostTrainer
numMembers - Variable in class org.tribuo.ensemble.BaggingTrainer
numSamples - Variable in class org.tribuo.classification.explanations.lime.LIMEBase
numStates - Variable in class org.tribuo.util.infotheory.InformationTheory.GTestStatistics
numThreads - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
numThreads - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
numThreads - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
numThreads - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
numTrainingExamples - Variable in class org.tribuo.classification.explanations.lime.LIMEBase
numTrees - Variable in class org.tribuo.common.xgboost.XGBoostTrainer
numValidFeatures - Variable in class org.tribuo.common.xgboost.XGBoostTrainer.DMatrixTuple

O

observe(double) - Method in class org.tribuo.CategoricalInfo
observe(double) - Method in class org.tribuo.RealInfo
observe(double) - Method in class org.tribuo.SkeletalVariableInfo: Records the value.
observe(List<Prediction<T>>) - Method in class org.tribuo.evaluation.OnlineEvaluator: Records all the supplied predictions.
observe(Event) - Method in class org.tribuo.anomaly.MutableAnomalyInfo
observe(Label) - Method in class org.tribuo.classification.MutableLabelInfo
observe(ClusterID) - Method in class org.tribuo.clustering.MutableClusteringInfo
observe(MultiLabel) - Method in class org.tribuo.multilabel.MutableMultiLabelInfo: Throws IllegalStateException if the MultiLabel contains a Label which has a "," in it.
observe(Prediction<T>) - Method in class org.tribuo.evaluation.OnlineEvaluator: Records the supplied prediction.
observe(Regressor) - Method in class org.tribuo.regression.MutableRegressionInfo
observe(T) - Method in interface org.tribuo.MutableOutputInfo: Records an output value or statistics thereof.
observedCount - Variable in class org.tribuo.CategoricalInfo: The count of the observed value if it's only seen a single one.
observedValue - Variable in class org.tribuo.CategoricalInfo: The observed value if it's only seen a single one.
observeSparse() - Method in class org.tribuo.transform.transformations.SimpleTransform: No-op on this TransformStatistics.
observeSparse() - Method in interface org.tribuo.transform.TransformStatistics: Observes a sparse (i.e., zero) value.
observeSparse(int) - Method in class org.tribuo.transform.transformations.SimpleTransform: No-op on this TransformStatistics.
observeSparse(int) - Method in interface org.tribuo.transform.TransformStatistics: Observes count sparse values.
observeValue(double) - Method in class org.tribuo.transform.transformations.SimpleTransform: No-op on this TransformStatistics.
observeValue(double) - Method in interface org.tribuo.transform.TransformStatistics: Observes a value and updates the statistics.
observeValue(double, int) - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Observes a value for this feature.
ONE_CLASS - Enum constant in enum org.tribuo.anomaly.libsvm.SVMAnomalyType.SVMMode: Anomaly detection SVM.
oneNorm() - Method in class org.tribuo.math.la.DenseVector
oneNorm() - Method in interface org.tribuo.math.la.SGDVector: Calculates the Manhattan norm for this vector.
oneNorm() - Method in class org.tribuo.math.la.SparseVector
OnlineEvaluator<T,E> - Class in org.tribuo.evaluation: An evaluator which aggregates predictions and produces Evaluations covering all the Predictions it has seen or created.
OnlineEvaluator(Evaluator<T, E>, Model<T>, DataProvenance) - Constructor for class org.tribuo.evaluation.OnlineEvaluator: Constructs an OnlineEvaluator which accumulates predictions.
ONNXExternalModel<T> - Class in org.tribuo.interop.onnx: A Tribuo wrapper around a ONNX model.
org.tribuo - package org.tribuo: Provides the core interfaces and classes for using Tribuo.
org.tribuo.anomaly - package org.tribuo.anomaly: Provides classes and infrastructure for anomaly detection problems.
org.tribuo.anomaly.evaluation - package org.tribuo.anomaly.evaluation: Evaluation classes for anomaly detection.
org.tribuo.anomaly.example - package org.tribuo.anomaly.example: Provides a anomaly data generator used for testing implementations.
org.tribuo.anomaly.libsvm - package org.tribuo.anomaly.libsvm: Provides an interface to LibSVM for anomaly detection problems.
org.tribuo.classification - package org.tribuo.classification: Provides classes and infrastructure for multiclass classification problems.
org.tribuo.classification.baseline - package org.tribuo.classification.baseline: Provides simple baseline multiclass classifiers.
org.tribuo.classification.dtree - package org.tribuo.classification.dtree: Provides implementations of decision trees for classification problems.
org.tribuo.classification.dtree.impl - package org.tribuo.classification.dtree.impl: Provides internal implementation classes for classification decision trees.
org.tribuo.classification.dtree.impurity - package org.tribuo.classification.dtree.impurity: Provides classification impurity metrics for decision trees.
org.tribuo.classification.ensemble - package org.tribuo.classification.ensemble: Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
org.tribuo.classification.evaluation - package org.tribuo.classification.evaluation: Evaluation classes for multi-class classification.
org.tribuo.classification.example - package org.tribuo.classification.example: Provides a multiclass data generator used for testing implementations.
org.tribuo.classification.experiments - package org.tribuo.classification.experiments: Provides a set of main methods for interacting with classification tasks.
org.tribuo.classification.explanations - package org.tribuo.classification.explanations: Provides core infrastructure for local model based explanations.
org.tribuo.classification.explanations.lime - package org.tribuo.classification.explanations.lime: Provides an implementation of LIME (Locally Interpretable Model Explanations).
org.tribuo.classification.liblinear - package org.tribuo.classification.liblinear: Provides an interface to LibLinear-java for classification problems.
org.tribuo.classification.libsvm - package org.tribuo.classification.libsvm: Provides an interface to LibSVM for classification problems.
org.tribuo.classification.mnb - package org.tribuo.classification.mnb: Provides an implementation of multinomial naive bayes (i.e., naive bayes for non-negative count data).
org.tribuo.classification.sequence - package org.tribuo.classification.sequence: Provides infrastructure for SequenceModels which emit Labels at each step of the sequence.
org.tribuo.classification.sequence.example - package org.tribuo.classification.sequence.example: Provides a classification sequence data generator for smoke testing implementations.
org.tribuo.classification.sequence.viterbi - package org.tribuo.classification.sequence.viterbi: Provides an implementation of Viterbi for generating structured outputs, which can sit on top of any Label based classification model.
org.tribuo.classification.sgd - package org.tribuo.classification.sgd: Provides infrastructure for Stochastic Gradient Descent for classification problems.
org.tribuo.classification.sgd.crf - package org.tribuo.classification.sgd.crf: Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
org.tribuo.classification.sgd.kernel - package org.tribuo.classification.sgd.kernel: Provides a SGD implementation of a Kernel SVM using the Pegasos algorithm.
org.tribuo.classification.sgd.linear - package org.tribuo.classification.sgd.linear: Provides an implementation of a classification linear model using Stochastic Gradient Descent.
org.tribuo.classification.sgd.objectives - package org.tribuo.classification.sgd.objectives: Provides classification loss functions for Stochastic Gradient Descent.
org.tribuo.classification.xgboost - package org.tribuo.classification.xgboost: Provides an interface to XGBoost for classification problems.
org.tribuo.clustering - package org.tribuo.clustering: Provides classes and infrastructure for working with clustering problems.
org.tribuo.clustering.evaluation - package org.tribuo.clustering.evaluation: Evaluation classes for clustering.
org.tribuo.clustering.example - package org.tribuo.clustering.example: Provides a clustering data generator used for testing implementations.
org.tribuo.clustering.kmeans - package org.tribuo.clustering.kmeans: Provides a multithreaded implementation of K-Means, with a configurable distance function.
org.tribuo.common.liblinear - package org.tribuo.common.liblinear: Provides base classes for using liblinear from Tribuo.
org.tribuo.common.libsvm - package org.tribuo.common.libsvm: The base interface to LibSVM.
org.tribuo.common.nearest - package org.tribuo.common.nearest: Provides a K-Nearest Neighbours implementation which works across all Tribuo Output types.
org.tribuo.common.tree - package org.tribuo.common.tree: Provides common functionality for building decision trees, irrespective of the predicted Output.
org.tribuo.common.tree.impl - package org.tribuo.common.tree.impl: Provides internal implementation classes for building decision trees.
org.tribuo.common.xgboost - package org.tribuo.common.xgboost: Provides abstract classes for interfacing with XGBoost abstracting away all the Output dependent parts.
org.tribuo.data - package org.tribuo.data: Provides classes for loading in data from disk, processing it into examples, and splitting datasets for things like cross-validation and train-test splits.
org.tribuo.data.columnar - package org.tribuo.data.columnar: Provides classes for processing columnar data and generating Examples.
org.tribuo.data.columnar.extractors - package org.tribuo.data.columnar.extractors: Provides implementations of FieldExtractor.
org.tribuo.data.columnar.processors.feature - package org.tribuo.data.columnar.processors.feature: Provides implementations of FeatureProcessor.
org.tribuo.data.columnar.processors.field - package org.tribuo.data.columnar.processors.field: Provides implementations of FieldProcessor.
org.tribuo.data.columnar.processors.response - package org.tribuo.data.columnar.processors.response: Provides implementations of ResponseProcessor.
org.tribuo.data.csv - package org.tribuo.data.csv: Provides classes which can load columnar data (using a RowProcessor) from a CSV (or other character delimited format) file.
org.tribuo.data.sql - package org.tribuo.data.sql: Provides classes which can load columnar data (using a RowProcessor) from a SQL source.
org.tribuo.data.text - package org.tribuo.data.text: Provides interfaces for converting text inputs into Features and Examples.
org.tribuo.data.text.impl - package org.tribuo.data.text.impl: Provides implementations of text data processors.
org.tribuo.dataset - package org.tribuo.dataset: Provides utility datasets which subsample or otherwise transform the wrapped dataset.
org.tribuo.datasource - package org.tribuo.datasource: Simple data sources for ingesting or aggregating data.
org.tribuo.ensemble - package org.tribuo.ensemble: Provides an interface for model prediction combinations, two base classes for ensemble models, a base class for ensemble excuses, and a Bagging implementation.
org.tribuo.evaluation - package org.tribuo.evaluation: Evaluation base classes, along with code for train/test splits and cross validation.
org.tribuo.evaluation.metrics - package org.tribuo.evaluation.metrics: This package contains the infrastructure classes for building evaluation metrics.
org.tribuo.hash - package org.tribuo.hash: Provides the base interface and implementations of the Model hashing which obscures the feature names stored in a model.
org.tribuo.impl - package org.tribuo.impl: Provides implementations of base classes and interfaces from org.tribuo.
org.tribuo.interop - package org.tribuo.interop: This package contains the abstract implementation of an external model trained by something outside of Tribuo.
org.tribuo.interop.onnx - package org.tribuo.interop.onnx: This package contains a Tribuo wrapper around the ONNX Runtime.
org.tribuo.interop.tensorflow - package org.tribuo.interop.tensorflow: Provides an interface to Tensorflow, allowing the training of non-sequential models using any supported Tribuo output type.
org.tribuo.interop.tensorflow.sequence - package org.tribuo.interop.tensorflow.sequence: Provides an interface for working with Tensorflow sequence models, using Tribuo's SequenceModel abstraction.
org.tribuo.json - package org.tribuo.json: Provides interop with JSON formatted data, along with tools for interacting with JSON provenance objects.
org.tribuo.math - package org.tribuo.math: Contains the implementation of Tribuo's math library, it's gradient descent optimisers, kernels and a set of math related utils.
org.tribuo.math.kernel - package org.tribuo.math.kernel: Provides a Kernel interface for Mercer kernels, along with implementations of standard kernels.
org.tribuo.math.la - package org.tribuo.math.la: Provides a linear algebra system used for numerical operations in Tribuo.
org.tribuo.math.optimisers - package org.tribuo.math.optimisers: Provides implementations of StochasticGradientOptimiser.
org.tribuo.math.optimisers.util - package org.tribuo.math.optimisers.util: Provides some utility tensors for use in gradient optimisers.
org.tribuo.math.util - package org.tribuo.math.util: Provides math related util classes.
org.tribuo.multilabel - package org.tribuo.multilabel: Provides classes and infrastructure for working with multi-label classification problems.
org.tribuo.multilabel.baseline - package org.tribuo.multilabel.baseline: Provides an implementation of independent multi-label classification that wraps a Label Trainer and uses it to make independent predictions of each label.
org.tribuo.multilabel.evaluation - package org.tribuo.multilabel.evaluation: Evaluation classes for multi-label classification using MultiLabel.
org.tribuo.multilabel.example - package org.tribuo.multilabel.example: Provides a multi-label data generator for testing implementations.
org.tribuo.provenance - package org.tribuo.provenance: Provides Tribuo specific infrastructure for the Provenance system which tracks models and datasets.
org.tribuo.provenance.impl - package org.tribuo.provenance.impl: Provides internal implementations for empty provenance classes and TrainerProvenance.
org.tribuo.regression - package org.tribuo.regression: Provides classes and infrastructure for regression problems with single or multiple output dimensions.
org.tribuo.regression.baseline - package org.tribuo.regression.baseline: Provides simple baseline regression predictors.
org.tribuo.regression.ensemble - package org.tribuo.regression.ensemble: Provides EnsembleCombiner implementations for working with multi-output regression problems.
org.tribuo.regression.evaluation - package org.tribuo.regression.evaluation: Evaluation classes for single or multi-dimensional regression.
org.tribuo.regression.example - package org.tribuo.regression.example: Provides some example regression data generators for testing implementations.
org.tribuo.regression.impl - package org.tribuo.regression.impl: Provides skeletal implementations of Regressor Trainer that can wrap a single dimension trainer/model and produce one prediction per dimension independently.
org.tribuo.regression.liblinear - package org.tribuo.regression.liblinear: Provides an interface to liblinear for regression problems.
org.tribuo.regression.libsvm - package org.tribuo.regression.libsvm: Provides an interface to LibSVM for regression problems.
org.tribuo.regression.rtree - package org.tribuo.regression.rtree: Provides an implementation of decision trees for regression problems.
org.tribuo.regression.rtree.impl - package org.tribuo.regression.rtree.impl: Provides internal implementation classes for the regression trees.
org.tribuo.regression.rtree.impurity - package org.tribuo.regression.rtree.impurity: Provides implementations of regression tree impurity metrics.
org.tribuo.regression.sgd - package org.tribuo.regression.sgd: Provides infrastructure for Stochastic Gradient Descent based regression models.
org.tribuo.regression.sgd.linear - package org.tribuo.regression.sgd.linear: Provides an implementation of linear regression using Stochastic Gradient Descent.
org.tribuo.regression.sgd.objectives - package org.tribuo.regression.sgd.objectives: Provides regression loss functions for Stochastic Gradient Descent.
org.tribuo.regression.slm - package org.tribuo.regression.slm: Provides implementations of sparse linear regression using various forms of regularisation penalty.
org.tribuo.regression.xgboost - package org.tribuo.regression.xgboost: Provides an interface to XGBoost for regression problems.
org.tribuo.sequence - package org.tribuo.sequence: Provides core classes for working with sequences of Examples.
org.tribuo.tests - package org.tribuo.tests: This package provides helper classes for Tribuo's unit tests.
org.tribuo.transform - package org.tribuo.transform: Provides infrastructure for applying transformations to a Dataset.
org.tribuo.transform.transformations - package org.tribuo.transform.transformations: Provides implementations of standard transformations like binning, scaling, taking logs and exponents.
org.tribuo.util - package org.tribuo.util: Provides utilities which don't have other Tribuo dependencies.
org.tribuo.util.infotheory - package org.tribuo.util.infotheory: This package provides static classes of information theoretic functions.
org.tribuo.util.infotheory.example - package org.tribuo.util.infotheory.example: This package provides demos for the information theoretic function classes in org.tribuo.util.infotheory.
org.tribuo.util.infotheory.impl - package org.tribuo.util.infotheory.impl: This package provides the implementations and helper classes for the information theoretic functions in org.tribuo.util.infotheory.
org.tribuo.util.tokens - package org.tribuo.util.tokens: Core definitions for tokenization.
org.tribuo.util.tokens.impl - package org.tribuo.util.tokens.impl: Simple fixed rule tokenizers.
org.tribuo.util.tokens.options - package org.tribuo.util.tokens.options: OLCUT Options implementations which can construct Tokenizers of various types.
org.tribuo.util.tokens.universal - package org.tribuo.util.tokens.universal
outer(SGDVector) - Method in class org.tribuo.math.la.DenseVector
outer(SGDVector) - Method in interface org.tribuo.math.la.SGDVector: Generates the matrix representing the outer product between the two vectors.
outer(SGDVector) - Method in class org.tribuo.math.la.SparseVector: This generates the outer product when dotted with another SparseVector.
output - Variable in class org.tribuo.data.PreprocessAndSerialize.PreprocessAndSerializeOptions
output - Variable in class org.tribuo.Example: The output associated with this example.
Output<T> - Interface in org.tribuo: Output is the root interface for the supported prediction types.
OUTPUT_FACTORY - Static variable in interface org.tribuo.provenance.DataSourceProvenance
OUTPUT_FILE_MODIFIED_TIME - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
OUTPUT_NAME - Static variable in class org.tribuo.interop.tensorflow.TensorflowModel
OUTPUT_RESOURCE_HASH - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
outputCountsIterable() - Method in class org.tribuo.anomaly.AnomalyInfo
outputCountsIterable() - Method in class org.tribuo.classification.LabelInfo
outputCountsIterable() - Method in class org.tribuo.clustering.ClusteringInfo
outputCountsIterable() - Method in class org.tribuo.multilabel.MultiLabelInfo
outputCountsIterable() - Method in interface org.tribuo.OutputInfo: An Iterable over the possible outputs and the number of times they were observed.
outputCountsIterable() - Method in class org.tribuo.regression.RegressionInfo
outputFactory - Variable in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
outputFactory - Variable in class org.tribuo.data.text.DirectoryFileSource: The factory that converts a String into an Output.
outputFactory - Variable in class org.tribuo.data.text.TextDataSource: The factory that converts a String into an Output.
outputFactory - Variable in class org.tribuo.Dataset: A factory for making OutputInfo and Output of the appropriate type.
outputFactory - Variable in class org.tribuo.sequence.SequenceDataset: A factory for making OutputInfo and Output of the appropriate type.
OutputFactory<T> - Interface in org.tribuo: An interface associated with a specific Output, which can generate the appropriate Output subclass, and OutputInfo subclass.
OutputFactoryProvenance - Interface in org.tribuo.provenance: A tag provenance for an output factory.
outputID - Variable in class org.tribuo.impl.IndexedArrayExample
outputIDInfo - Variable in class org.tribuo.ImmutableDataset: Output information, and id numbers for outputs found in this dataset.
outputIDInfo - Variable in class org.tribuo.Model: The outputs this model predicts.
outputIDInfo - Variable in class org.tribuo.sequence.ImmutableSequenceDataset: A map from labels to IDs for the labels found in this dataset.
outputIDMap - Variable in class org.tribuo.sequence.SequenceModel
outputInfo - Variable in class org.tribuo.sequence.MutableSequenceDataset: A map from labels to IDs for the labels found in this dataset.
outputInfo(CommandInterpreter) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
outputInfo(CommandInterpreter) - Method in class org.tribuo.data.DatasetExplorer
outputInfo(CommandInterpreter) - Method in class org.tribuo.ModelExplorer: Displays the output info.
outputInfo(CommandInterpreter) - Method in class org.tribuo.sequence.SequenceModelExplorer
OutputInfo<T> - Interface in org.tribuo: Tracks relevant properties of the appropriate Output subclass.
outputMap - Variable in class org.tribuo.MutableDataset: Information about the outputs in this dataset.
outputModel - Variable in class org.tribuo.json.StripProvenance.StripProvenanceOptions
outputPath - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
outputPath - Variable in class org.tribuo.data.CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions
outputPath - Variable in class org.tribuo.data.DataOptions
outputPath - Variable in class org.tribuo.data.sql.SQLToCSV.SQLToCSVOptions
outputPath - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
outputRequired - Variable in class org.tribuo.data.columnar.ColumnarDataSource
outputTransformer - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceModel
outputTransformer - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
OutputTransformer<T> - Interface in org.tribuo.interop.onnx: Converts an OnnxValue into an Output or a Prediction.
OutputTransformer<T> - Interface in org.tribuo.interop.tensorflow: TensorFlow support is experimental, and may change without a major version bump.
overallCount - Variable in class org.tribuo.regression.RegressionInfo

P

pairDescendingValueComparator() - Static method in class org.tribuo.util.IntDoublePair: Compare pairs by value.
PairDistribution<T1,T2> - Class in org.tribuo.util.infotheory.impl: A count distribution over CachedPair objects.
PairDistribution(long, LinkedHashMap<CachedPair<T1, T2>, MutableLong>, LinkedHashMap<T1, MutableLong>, LinkedHashMap<T2, MutableLong>) - Constructor for class org.tribuo.util.infotheory.impl.PairDistribution
PairDistribution(long, Map<CachedPair<T1, T2>, MutableLong>, Map<T1, MutableLong>, Map<T2, MutableLong>) - Constructor for class org.tribuo.util.infotheory.impl.PairDistribution
pairIndexComparator() - Static method in class org.tribuo.util.IntDoublePair: Compare pairs by index.
pairValueComparator() - Static method in class org.tribuo.util.IntDoublePair: Compare pairs by value.
paramAve - Variable in class org.tribuo.math.optimisers.GradientOptimiserOptions
ParameterAveraging - Class in org.tribuo.math.optimisers: Averages the parameters across a gradient run.
ParameterAveraging(StochasticGradientOptimiser) - Constructor for class org.tribuo.math.optimisers.ParameterAveraging: Adds parameter averaging around a gradient optimiser.
parameters - Variable in class org.tribuo.common.libsvm.LibSVMTrainer: The SVM parameters suitable for use by LibSVM.
parameters - Variable in class org.tribuo.common.libsvm.SVMParameters
parameters - Variable in class org.tribuo.common.xgboost.XGBoostTrainer
Parameters - Interface in org.tribuo.math: An interface to a Tensor[] array which accepts updates to the parameters.
paramName - Variable in enum org.tribuo.common.xgboost.XGBoostTrainer.BoosterType
paramName - Variable in enum org.tribuo.regression.xgboost.XGBoostRegressionTrainer.RegressionType
parseElement(int, String) - Static method in class org.tribuo.regression.Regressor: Parses a string of the form:
parseElement(String) - Static method in class org.tribuo.multilabel.MultiLabel: Parses a string of the form:
parseLine(String, int) - Method in class org.tribuo.data.text.impl.SimpleTextDataSource
parseString(String) - Static method in class org.tribuo.multilabel.MultiLabel: Parses a string of the form: dimension-name=output,...,dimension-name=output where output must be readable by Boolean.parseBoolean(String).
parseString(String) - Static method in class org.tribuo.regression.Regressor: Parses a string of the form:
parseString(String, char) - Static method in class org.tribuo.multilabel.MultiLabel: Parses a string of the form:
parseString(String, char) - Static method in class org.tribuo.regression.Regressor: Parses a string of the form:
partialExpandRegexMapping(Collection<String>) - Method in class org.tribuo.data.columnar.RowProcessor: Caveat Implementor! This method contains the logic of RowProcessor.expandRegexMapping(org.tribuo.Model<T>) without any of the checks that ensure the RowProcessor is in a valid state.
password - Variable in class org.tribuo.data.sql.SQLToCSV.SQLToCSVOptions
path - Variable in class org.tribuo.data.text.TextDataSource: The path that data was read from.
Pegasos - Class in org.tribuo.math.optimisers: An implementation of the Pegasos gradient optimiser used primarily for solving the SVM problem.
Pegasos(double, double) - Constructor for class org.tribuo.math.optimisers.Pegasos
PEGASOS - Enum constant in enum org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
PLACEHOLDER - Static variable in class org.tribuo.interop.tensorflow.TensorflowUtil
POINT_VERSION - Static variable in class org.tribuo.Tribuo: The patch release number.
POLY - Enum constant in enum org.tribuo.common.libsvm.KernelType: A polynomial kernel of the form (gamma*u'*v + coef0)^degree
Polynomial - Class in org.tribuo.math.kernel: A polynomial kernel, (gamma*u.dot(v) + intercept)^degree.
Polynomial(double, double, double) - Constructor for class org.tribuo.math.kernel.Polynomial: A polynomial kernel, (gamma*u.dot(v) + intercept)^degree.
POLYNOMIAL - Enum constant in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum
postConfig() - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer
postConfig() - Method in class org.tribuo.classification.liblinear.LibLinearClassificationTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.classification.libsvm.LibSVMClassificationTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.classification.sgd.crf.CRFTrainer
postConfig() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
postConfig() - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
postConfig() - Method in class org.tribuo.classification.xgboost.XGBoostClassificationTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
postConfig() - Method in class org.tribuo.common.liblinear.LibLinearTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.common.libsvm.LibSVMTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.common.nearest.KNNTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.common.tree.AbstractCARTTrainer
postConfig() - Method in class org.tribuo.common.tree.RandomForestTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.common.xgboost.XGBoostTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.columnar.extractors.DateExtractor: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.columnar.processors.field.RegexFieldProcessor: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.columnar.RowProcessor: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.csv.CSVDataSource: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.sql.SQLDBConfig: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.text.impl.BasicPipeline: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.text.impl.NgramProcessor: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.text.impl.SimpleStringDataSource: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.text.impl.SimpleTextDataSource: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.data.text.impl.TokenPipeline: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.datasource.IDXDataSource: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.datasource.LibSVMDataSource: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.ensemble.BaggingTrainer
postConfig() - Method in class org.tribuo.hash.MessageDigestHasher: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.hash.ModHashCodeHasher: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.interop.onnx.ImageTransformer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.interop.tensorflow.ImageTransformer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
postConfig() - Method in class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.interop.tensorflow.TensorflowTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.json.JsonDataSource: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.math.optimisers.RMSProp: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.regression.example.GaussianDataSource: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer
postConfig() - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer
postConfig() - Method in class org.tribuo.regression.liblinear.LibLinearRegressionTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.regression.libsvm.LibSVMRegressionTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.regression.RegressionFactory: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.regression.sgd.linear.LinearSGDTrainer
postConfig() - Method in class org.tribuo.regression.sgd.objectives.Huber: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.regression.slm.ElasticNetCDTrainer
postConfig() - Method in class org.tribuo.regression.xgboost.XGBoostRegressionTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.transform.TransformationMap: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.transform.transformations.BinningTransformation: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.transform.transformations.LinearScalingTransformation: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.transform.transformations.SimpleTransform: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer: Used by the OLCUT configuration system, and should not be called by external code.
PRCurve(double[], double[], double[]) - Constructor for class org.tribuo.classification.evaluation.LabelEvaluationUtil.PRCurve
precision - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.PRCurve
precision(double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the precision based upon the supplied statistics.
precision(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
precision(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the precision for this metric target.
precision(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
precision(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the precision of this label, i.e., the number of true positives divided by the number of true positives plus false positives.
PRECISION - Enum constant in enum org.tribuo.anomaly.evaluation.AnomalyMetrics: The precision, i.e., the true positives divided by the predicted positives.
PRECISION - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The precision, i.e., the number of true positives divided by the number of predicted positives.
PRECISION - Enum constant in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics: The precision, i.e., the number of true positives divided by the number of predicted positives.
precisionRecallCurve(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Calculates the Precision Recall curve for a single label.
precisionRecallCurve(Label, List<Prediction<Label>>) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics
predict(CommandInterpreter, String[]) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
predict(Iterable<Example<T>>) - Method in class org.tribuo.common.xgboost.XGBoostModel: Uses the model to predict the label for multiple examples.
predict(Iterable<Example<T>>) - Method in class org.tribuo.Model: Uses the model to predict the output for multiple examples.
predict(Iterable<SequenceExample<T>>) - Method in class org.tribuo.sequence.SequenceModel: Uses the model to predict the output for multiple examples.
predict(Dataset<T>) - Method in class org.tribuo.common.xgboost.XGBoostModel: Uses the model to predict the labels for multiple examples contained in a data set.
predict(Dataset<T>) - Method in class org.tribuo.Model: Uses the model to predict the outputs for multiple examples contained in a data set.
predict(Dataset<T>) - Method in class org.tribuo.transform.TransformedModel
predict(Example<Event>) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyModel
predict(Example<Label>) - Method in class org.tribuo.classification.baseline.DummyClassifierModel
predict(Example<Label>) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationModel
predict(Example<Label>) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationModel
predict(Example<Label>) - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesModel
predict(Example<Label>) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel
predict(Example<Label>) - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel
predict(Example<ClusterID>) - Method in class org.tribuo.clustering.kmeans.KMeansModel
predict(Example<MultiLabel>) - Method in class org.tribuo.multilabel.baseline.IndependentMultiLabelModel
predict(Example<Regressor>) - Method in class org.tribuo.regression.baseline.DummyRegressionModel
predict(Example<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionModel
predict(Example<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel
predict(Example<Regressor>) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionModel
predict(Example<Regressor>) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionModel
predict(Example<Regressor>) - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
predict(Example<Regressor>) - Method in class org.tribuo.regression.sgd.linear.LinearSGDModel
predict(Example<T>) - Method in class org.tribuo.common.nearest.KNNModel
predict(Example<T>) - Method in class org.tribuo.common.tree.TreeModel
predict(Example<T>) - Method in class org.tribuo.common.xgboost.XGBoostModel
predict(Example<T>) - Method in class org.tribuo.ensemble.WeightedEnsembleModel
predict(Example<T>) - Method in class org.tribuo.interop.ExternalModel
predict(Example<T>) - Method in class org.tribuo.interop.tensorflow.TensorflowCheckpointModel
predict(Example<T>) - Method in class org.tribuo.interop.tensorflow.TensorflowModel
predict(Example<T>) - Method in class org.tribuo.Model: Uses the model to predict the output for a single example.
predict(Example<T>) - Method in class org.tribuo.transform.TransformedModel
predict(SparseVector) - Method in class org.tribuo.math.LinearParameters: Generates an unnormalised prediction by leftMultiply'ing the weights with the incoming features.
predict(SparseVector[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Generate a prediction using Viterbi.
predict(SequenceDataset<Label>) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
predict(SequenceDataset<T>) - Method in class org.tribuo.sequence.SequenceModel: Uses the model to predict the labels for multiple examples contained in a data set.
predict(SequenceExample<Label>) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
predict(SequenceExample<Label>) - Method in class org.tribuo.classification.sgd.crf.CRFModel
predict(SequenceExample<T>) - Method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceModel
predict(SequenceExample<T>) - Method in class org.tribuo.sequence.SequenceModel: Uses the model to predict the output for a single example.
predictAndObserve(Iterable<Example<T>>) - Method in class org.tribuo.evaluation.OnlineEvaluator: Feeds the examples to the model, records the predictions and returns them.
predictAndObserve(Example<T>) - Method in class org.tribuo.evaluation.OnlineEvaluator: Feeds the example to the model, records the prediction and returns it.
predictConfidenceUsingCBP(SparseVector[], List<Chunk>) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: This predicts per chunk confidence using the constrained forward backward algorithm from Culotta and McCallum 2004.
Prediction<T> - Class in org.tribuo: A prediction made by a Model.
Prediction(Prediction<T>, int, Example<T>) - Constructor for class org.tribuo.Prediction: Constructs a prediction from the supplied arguments.
Prediction(T, int, Example<T>) - Constructor for class org.tribuo.Prediction: Constructs a prediction from the supplied arguments.
Prediction(T, Map<String, T>, int, Example<T>, boolean) - Constructor for class org.tribuo.Prediction: Constructs a prediction from the supplied arguments.
predictionPath - Variable in class org.tribuo.classification.experiments.ConfigurableTrainTest.ConfigurableTrainTestOptions
predictionPath - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
predictMarginals(SparseVector[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Generate a prediction using Belief Propagation.
predictOp - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceModel
predictOp - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
PreprocessAndSerialize - Class in org.tribuo.data: Reads in a Datasource, processes all the data, and writes it out as a serialized dataset.
PreprocessAndSerialize.PreprocessAndSerializeOptions - Class in org.tribuo.data
PreprocessAndSerializeOptions() - Constructor for class org.tribuo.data.PreprocessAndSerialize.PreprocessAndSerializeOptions
preprocessors - Variable in class org.tribuo.data.text.DirectoryFileSource: Document preprocessors that should be run on the documents that make up this data set.
preprocessors - Variable in class org.tribuo.data.text.TextDataSource: Document preprocessors that should be run on the documents that make up this data set.
preTrainingHook(Session, SequenceDataset<T>) - Method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
printFeatureMap(Map<String, List<Pair<String, Double>>>, List<String>, PrintStream) - Static method in class org.tribuo.util.HTMLOutput
printTree - Variable in class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
probability - Variable in class org.tribuo.util.infotheory.InformationTheory.GTestStatistics
process(String) - Method in interface org.tribuo.data.columnar.FieldProcessor: Processes the field value and generates a (possibly empty) list of ColumnarFeatures.
process(String) - Method in class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
process(String) - Method in class org.tribuo.data.columnar.processors.field.IdentityProcessor
process(String) - Method in class org.tribuo.data.columnar.processors.field.RegexFieldProcessor
process(String) - Method in class org.tribuo.data.columnar.processors.field.TextFieldProcessor
process(String) - Method in class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor
process(String) - Method in class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
process(String) - Method in class org.tribuo.data.columnar.processors.response.QuartileResponseProcessor
process(String) - Method in interface org.tribuo.data.columnar.ResponseProcessor: Returns Optional.empty() if it failed to process out a response.
process(String) - Method in class org.tribuo.data.text.impl.NgramProcessor
process(String) - Method in interface org.tribuo.data.text.TextProcessor: Extracts features from the supplied text.
process(String, String) - Method in class org.tribuo.data.text.impl.BasicPipeline
process(String, String) - Method in class org.tribuo.data.text.impl.NgramProcessor
process(String, String) - Method in class org.tribuo.data.text.impl.TokenPipeline
process(String, String) - Method in interface org.tribuo.data.text.TextPipeline: Extracts a list of features from the supplied text, using the tag to prepend the feature names.
process(String, String) - Method in interface org.tribuo.data.text.TextProcessor: Extracts features from the supplied text.
process(List<ColumnarFeature>) - Method in interface org.tribuo.data.columnar.FeatureProcessor: Processes a list of ColumnarFeatures, transforming it by adding conjunctions or removing unnecessary features.
process(List<ColumnarFeature>) - Method in class org.tribuo.data.columnar.processors.feature.UniqueProcessor
processDoc(String) - Method in interface org.tribuo.data.text.DocumentPreprocessor: Processes the content of part of a document stored as a string, returning a new string.
processWeights(List<String>) - Static method in class org.tribuo.classification.experiments.ConfigurableTrainTest: Converts the weight text input format into an object suitable for use in a Trainer.
protobufPath - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
provenance - Variable in class org.tribuo.data.text.impl.SimpleTextDataSource
provenance - Variable in class org.tribuo.Model: The model provenance.
provenanceFile - Variable in class org.tribuo.json.StripProvenance.StripProvenanceOptions
provenanceOutput - Variable in class org.tribuo.Model: The cached toString of the model provenance.
provenanceOutput - Variable in class org.tribuo.sequence.SequenceModel
punct(char, int) - Method in class org.tribuo.util.tokens.universal.Range
PUNCTUATION - Enum constant in enum org.tribuo.util.tokens.Token.TokenType
put(VariableInfo) - Method in class org.tribuo.MutableFeatureMap: Adds a variable info into the feature map.

Q

Quartile - Class in org.tribuo.data.columnar.processors.response: A quartile to split data into 4 chunks.
Quartile(double, double, double) - Constructor for class org.tribuo.data.columnar.processors.response.Quartile: Constructs a quartile with the specified values.
QUARTILE - Enum constant in enum org.tribuo.regression.baseline.DummyRegressionTrainer.DummyType: Returns the training data output at the specified fraction of the sorted output.
QuartileResponseProcessor<T> - Class in org.tribuo.data.columnar.processors.response: Processes the response into quartiles and emits them as classification outputs.
QuartileResponseProcessor(String, String, Quartile, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.QuartileResponseProcessor: Constructs a repsonse processor which emits 4 distinct bins for the output factory to process.
quiet - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
quiet - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
QUOTE - Static variable in class org.tribuo.data.csv.CSVIterator: Default quote character.

R

r2() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: Calculates R2 for all dimensions.
r2(MetricTarget<Regressor>, RegressionSufficientStatistics) - Static method in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates R^2 based on the supplied statistics.
r2(Regressor) - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: Calculates R2 for the supplied dimension.
r2(Regressor, RegressionSufficientStatistics) - Static method in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates R^2 based on the supplied statistics for a single dimension.
R2 - Enum constant in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates the R^2 of the predictions.
RANDOM - Enum constant in enum org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType
RandomForestTrainer<T> - Class in org.tribuo.common.tree: A trainer which produces a random forest.
RandomForestTrainer(DecisionTreeTrainer<T>, EnsembleCombiner<T>, int) - Constructor for class org.tribuo.common.tree.RandomForestTrainer: Constructs a RandomForestTrainer with the default seed Trainer.DEFAULT_SEED.
RandomForestTrainer(DecisionTreeTrainer<T>, EnsembleCombiner<T>, int, long) - Constructor for class org.tribuo.common.tree.RandomForestTrainer: Constructs a RandomForestTrainer with the supplied seed, trainer, combining function and number of members.
randperm(int, Random) - Static method in class org.tribuo.util.Util: Shuffles the indices in the range [0,size).
randperm(int, SplittableRandom) - Static method in class org.tribuo.util.Util: Shuffles the indices in the range [0,size).
randpermInPlace(int[], Random) - Static method in class org.tribuo.util.Util: Shuffles the input.
randpermInPlace(int[], SplittableRandom) - Static method in class org.tribuo.util.Util: Shuffles the input.
Range - Class in org.tribuo.util.tokens.universal: A range currently being segmented.
rawLines - Variable in class org.tribuo.data.text.impl.SimpleStringDataSource: Used because OLCUT doesn't support generic Iterables.
RBF - Class in org.tribuo.math.kernel: A Radial Basis Function (RBF) kernel, exp(-gamma*|u-v|^2).
RBF - Enum constant in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum
RBF - Enum constant in enum org.tribuo.common.libsvm.KernelType: An RBF kernel of the form exp(-gamma*|u-v|^2)
RBF(double) - Constructor for class org.tribuo.math.kernel.RBF: A Radial Basis Function (RBF) kernel, exp(-gamma*|u-v|^2).
read() - Method in class org.tribuo.data.text.impl.SimpleStringDataSource
read() - Method in class org.tribuo.data.text.impl.SimpleTextDataSource
read() - Method in class org.tribuo.data.text.TextDataSource: Reads the data from the Path.
REAL - Enum constant in enum org.tribuo.data.columnar.FieldProcessor.GeneratedFeatureType: Real valued features.
RealIDInfo - Class in org.tribuo: Same as a RealInfo, but with an additional int id field.
RealIDInfo(String, int, double, double, double, double, int) - Constructor for class org.tribuo.RealIDInfo: Constructs a real id info from the supplied arguments.
RealIDInfo(RealInfo, int) - Constructor for class org.tribuo.RealIDInfo: Constructs a deep copy of the supplied real info and id.
RealInfo - Class in org.tribuo: Stores information about real valued features.
RealInfo(String) - Constructor for class org.tribuo.RealInfo: Creates an empty real info with the supplied name.
RealInfo(String, int) - Constructor for class org.tribuo.RealInfo: Creates a real info with the supplied starting conditions.
RealInfo(String, int, double, double, double, double) - Constructor for class org.tribuo.RealInfo: Creates a real info with the supplied starting conditions.
RealInfo(RealInfo) - Constructor for class org.tribuo.RealInfo: Copy constructor.
RealInfo(RealInfo, String) - Constructor for class org.tribuo.RealInfo: Copy constructor which renames the feature.
rebuild(OrtSession.SessionOptions) - Method in class org.tribuo.interop.onnx.ONNXExternalModel: Closes the session and rebuilds it using the supplied options.
recall - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.PRCurve
recall(double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the recall based upon the supplied statistics.
recall(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
recall(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the recall for this metric target.
recall(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
recall(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the recall of this label, i.e., the number of true positives divided by the number of true positives plus false negatives.
RECALL - Enum constant in enum org.tribuo.anomaly.evaluation.AnomalyMetrics: The recall, i.e., the true positives divided by the ground truth positives.
RECALL - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The recall, i.e., the number of true positives divided by the number of ground truth positives.
RECALL - Enum constant in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics: The recall, i.e., the number of true positives divided by the number of ground truth positives.
reduce(double, DoubleUnaryOperator, DoubleBinaryOperator) - Method in class org.tribuo.math.la.DenseVector: Performs a reduction from left to right of this vector.
reduceByName(Merger) - Method in class org.tribuo.Example: Merges features with the same name using the supplied Merger.
reduceByName(Merger) - Method in class org.tribuo.impl.ArrayExample
reduceByName(Merger) - Method in class org.tribuo.impl.BinaryFeaturesExample
reduceByName(Merger) - Method in class org.tribuo.impl.IndexedArrayExample
reduceByName(Merger) - Method in class org.tribuo.impl.ListExample
reduceByName(Merger) - Method in class org.tribuo.sequence.SequenceExample: Reduces the features in each example using the supplied Merger.
RegexFieldProcessor - Class in org.tribuo.data.columnar.processors.field: A FieldProcessor which applies a regex to a field and generates ColumnarFeatures based on the matches.
RegexFieldProcessor(String, String, EnumSet<RegexFieldProcessor.Mode>) - Constructor for class org.tribuo.data.columnar.processors.field.RegexFieldProcessor: Constructs a field processor which emits features when the field value matches the supplied regex.
RegexFieldProcessor(String, Pattern, EnumSet<RegexFieldProcessor.Mode>) - Constructor for class org.tribuo.data.columnar.processors.field.RegexFieldProcessor: Constructs a field processor which emits features when the field value matches the supplied regex.
RegexFieldProcessor.Mode - Enum in org.tribuo.data.columnar.processors.field: Matching mode.
regexMappingProcessors - Variable in class org.tribuo.data.columnar.RowProcessor
RegressionDataGenerator - Class in org.tribuo.regression.example: Generates two example train and test datasets, used for unit testing.
RegressionDataGenerator() - Constructor for class org.tribuo.regression.example.RegressionDataGenerator
RegressionEvaluation - Interface in org.tribuo.regression.evaluation: Defines methods that calculate regression performance.
RegressionEvaluator - Class in org.tribuo.regression.evaluation: A Evaluator for multi-dimensional regression using Regressors.
RegressionEvaluator() - Constructor for class org.tribuo.regression.evaluation.RegressionEvaluator: By default, don't use example weights.
RegressionEvaluator(boolean) - Constructor for class org.tribuo.regression.evaluation.RegressionEvaluator: Construct an evaluator.
regressionFactory - Static variable in class org.tribuo.classification.explanations.lime.LIMEBase
RegressionFactory - Class in org.tribuo.regression: A factory for creating Regressors and RegressionInfos.
RegressionFactory() - Constructor for class org.tribuo.regression.RegressionFactory: Builds a regression factory using the default split character RegressionFactory.DEFAULT_SPLIT_CHAR.
RegressionFactory(char) - Constructor for class org.tribuo.regression.RegressionFactory: Sets the split character used to parse Regressor instances from Strings.
RegressionFactory.RegressionFactoryProvenance - Class in org.tribuo.regression: Provenance for RegressionFactory.
RegressionFactoryProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.regression.RegressionFactory.RegressionFactoryProvenance: Constructs a provenance from it's marshalled form.
RegressionInfo - Class in org.tribuo.regression: The base class for regression information using Regressors.
RegressionMetric - Class in org.tribuo.regression.evaluation: A EvaluationMetric for Regressors which calculates the metric based on a the true values and the predicted values.
RegressionMetric(MetricTarget<Regressor>, String, ToDoubleBiFunction<MetricTarget<Regressor>, RegressionMetric.Context>) - Constructor for class org.tribuo.regression.evaluation.RegressionMetric: Construct a new RegressionMetric for the supplied metric target, using the supplied function.
RegressionMetric(MetricTarget<Regressor>, String, ToDoubleBiFunction<MetricTarget<Regressor>, RegressionMetric.Context>, boolean) - Constructor for class org.tribuo.regression.evaluation.RegressionMetric: Construct a new RegressionMetric for the supplied metric target, using the supplied function.
RegressionMetrics - Enum in org.tribuo.regression.evaluation: An enum of the default RegressionMetrics supported by the multi-dimensional regression evaluation package.
RegressionObjective - Interface in org.tribuo.regression.sgd: An interface for regression objectives.
RegressionSufficientStatistics - Class in org.tribuo.regression.evaluation: The sufficient statistics for regression metrics (i.e., each prediction and each true value).
RegressionSufficientStatistics(ImmutableOutputInfo<Regressor>, List<Prediction<Regressor>>, boolean) - Constructor for class org.tribuo.regression.evaluation.RegressionSufficientStatistics
Regressor - Class in org.tribuo.regression: An Output for n-dimensional real valued regression.
Regressor(String[], double[]) - Constructor for class org.tribuo.regression.Regressor: Constructs a regressor from the supplied named values.
Regressor(String[], double[], double[]) - Constructor for class org.tribuo.regression.Regressor: Constructs a regressor from the supplied named values.
Regressor(String, double) - Constructor for class org.tribuo.regression.Regressor: Constructs a regressor containing a single dimension, using Double.NaN as the variance.
Regressor(String, double, double) - Constructor for class org.tribuo.regression.Regressor: Constructs a regressor containing a single dimension.
Regressor(Regressor.DimensionTuple[]) - Constructor for class org.tribuo.regression.Regressor: Constructs a regressor from the supplied dimension tuples.
Regressor.DimensionTuple - Class in org.tribuo.regression: A Regressor which contains a single dimension, used internally when the model implementation doesn't natively support multi-dimensional regression outputs.
RegressorImpurity - Interface in org.tribuo.regression.rtree.impurity: Calculates a tree impurity score based on the regression targets.
RegressorImpurity.ImpurityTuple - Class in org.tribuo.regression.rtree.impurity: Tuple class for the impurity and summed weight.
RegressorTrainingNode - Class in org.tribuo.regression.rtree.impl: A decision tree node used at training time.
RegressorTrainingNode(RegressorImpurity, RegressorTrainingNode.InvertedData, int, String, int, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>) - Constructor for class org.tribuo.regression.rtree.impl.RegressorTrainingNode
RegressorTrainingNode.InvertedData - Class in org.tribuo.regression.rtree.impl: Tuple containing an inverted dataset (i.e., feature-wise not exmaple-wise).
RegressorTransformer - Class in org.tribuo.interop.onnx: Can convert an OnnxValue into a Prediction or Regressor.
RegressorTransformer - Class in org.tribuo.interop.tensorflow: Can convert a Regressor to a Tensor containing a 32-bit float and a Tensor into a Prediction or Regressor.
RegressorTransformer() - Constructor for class org.tribuo.interop.onnx.RegressorTransformer
RegressorTransformer() - Constructor for class org.tribuo.interop.tensorflow.RegressorTransformer
remove(int) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
remove(Object) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
removeAll(Collection<?>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
removeFeatures(List<Feature>) - Method in class org.tribuo.Example: Removes all features in this list from the Example.
removeFeatures(List<Feature>) - Method in class org.tribuo.impl.ArrayExample
removeFeatures(List<Feature>) - Method in class org.tribuo.impl.BinaryFeaturesExample
removeFeatures(List<Feature>) - Method in class org.tribuo.impl.IndexedArrayExample
removeFeatures(List<Feature>) - Method in class org.tribuo.impl.ListExample
removeFeatures(List<Feature>) - Method in class org.tribuo.sequence.SequenceExample: Removes the features in the supplied list from each example contained in this sequence.
removeOther(IntArrayContainer, int[], IntArrayContainer) - Static method in class org.tribuo.common.tree.impl.IntArrayContainer: Copies from input to output excluding the values in otherArray.
removeProvenances - Variable in class org.tribuo.json.StripProvenance.StripProvenanceOptions
rename(String) - Method in class org.tribuo.CategoricalIDInfo
rename(String) - Method in class org.tribuo.CategoricalInfo
rename(String) - Method in class org.tribuo.RealIDInfo
rename(String) - Method in class org.tribuo.RealInfo
rename(String) - Method in interface org.tribuo.VariableInfo: Rename generates a fresh VariableInfo with the new name.
reset() - Method in class org.tribuo.math.optimisers.AdaDelta
reset() - Method in class org.tribuo.math.optimisers.AdaGrad
reset() - Method in class org.tribuo.math.optimisers.AdaGradRDA
reset() - Method in class org.tribuo.math.optimisers.Adam
reset() - Method in class org.tribuo.math.optimisers.ParameterAveraging
reset() - Method in class org.tribuo.math.optimisers.Pegasos
reset() - Method in class org.tribuo.math.optimisers.RMSProp
reset() - Method in class org.tribuo.math.optimisers.SGD
reset() - Method in interface org.tribuo.math.StochasticGradientOptimiser: Resets the optimiser so it's ready to optimise a new Parameters.
reset(CharSequence) - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
reset(CharSequence) - Method in class org.tribuo.util.tokens.impl.NonTokenizer
reset(CharSequence) - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer
reset(CharSequence) - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
reset(CharSequence) - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
reset(CharSequence) - Method in interface org.tribuo.util.tokens.Tokenizer: Resets the tokenizer so that it operates on a new sequence of characters.
reset(CharSequence) - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer: Reset state of tokenizer to clean slate.
reshape(int[]) - Method in class org.tribuo.math.la.DenseMatrix
reshape(int[]) - Method in class org.tribuo.math.la.DenseSparseMatrix
reshape(int[]) - Method in class org.tribuo.math.la.DenseVector
reshape(int[]) - Method in class org.tribuo.math.la.SparseVector
reshape(int[]) - Method in interface org.tribuo.math.la.Tensor: Reshapes the Tensor to the supplied shape.
RESOURCE_HASH - Static variable in interface org.tribuo.provenance.DataSourceProvenance
Resources - Class in org.tribuo.tests: Utils for working with classpath resources at test time.
responseProcessor - Variable in class org.tribuo.data.columnar.RowProcessor
ResponseProcessor<T> - Interface in org.tribuo.data.columnar: An interface that will take the response field and produce an Output.
ResultSetIterator - Class in org.tribuo.data.sql: An iterator over a ResultSet returned from JDBC.
ResultSetIterator(ResultSet) - Constructor for class org.tribuo.data.sql.ResultSetIterator
ResultSetIterator(ResultSet, int) - Constructor for class org.tribuo.data.sql.ResultSetIterator
retainAll(Collection<?>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
RF - Enum constant in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType
rho - Variable in class org.tribuo.math.optimisers.GradientOptimiserOptions
rho - Variable in class org.tribuo.math.optimisers.SGD
rightMultiply(SGDVector) - Method in class org.tribuo.math.la.DenseMatrix
rightMultiply(SGDVector) - Method in class org.tribuo.math.la.DenseSparseMatrix: rightMultiply is very inefficient on DenseSparseMatrix due to the storage format.
rightMultiply(SGDVector) - Method in interface org.tribuo.math.la.Matrix: Multiplies this Matrix by a SGDVector returning a vector of the appropriate size.
rmse() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: Calculates the RMSE for all dimensions.
rmse(MetricTarget<Regressor>, RegressionSufficientStatistics) - Static method in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates the RMSE based on the supplied statistics.
rmse(Regressor) - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: Calculates the Root Mean Squared Error (i.e., the square root of the average squared errors across all data points) for the supplied dimension.
rmse(Regressor, RegressionSufficientStatistics) - Static method in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates the RMSE based on the supplied statistics for a single dimension.
RMSE - Enum constant in enum org.tribuo.regression.evaluation.RegressionMetrics: Calculates the Root Mean Squared Error of the predictions.
RMSProp - Class in org.tribuo.math.optimisers: An implementation of the RMSProp gradient optimiser.
RMSProp(double, double) - Constructor for class org.tribuo.math.optimisers.RMSProp
RMSProp(double, double, double, double) - Constructor for class org.tribuo.math.optimisers.RMSProp
RMSPROP - Enum constant in enum org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
rng - Variable in class org.tribuo.classification.ensemble.AdaBoostTrainer
rng - Variable in class org.tribuo.classification.explanations.lime.LIMEBase
rng - Variable in class org.tribuo.common.tree.AbstractCARTTrainer
rng - Variable in class org.tribuo.ensemble.BaggingTrainer
rng - Variable in class org.tribuo.evaluation.KFoldSplitter
rng - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
ROC(double[], double[], double[]) - Constructor for class org.tribuo.classification.evaluation.LabelEvaluationUtil.ROC
Row<T> - Class in org.tribuo.util.infotheory.impl: A row of values from a RowList.
Row(long, List<String>, Map<String, String>) - Constructor for class org.tribuo.data.columnar.ColumnarIterator.Row
rowIterator() - Method in class org.tribuo.data.columnar.ColumnarDataSource: The iterator that emits ColumnarIterator.Row objects from the underlying data source.
rowIterator() - Method in class org.tribuo.data.csv.CSVDataSource
rowIterator() - Method in class org.tribuo.data.sql.SQLDataSource
rowIterator() - Method in class org.tribuo.json.JsonDataSource
RowList<T> - Class in org.tribuo.util.infotheory.impl: An implementation of a List which wraps a set of lists.
RowList(Set<List<T>>) - Constructor for class org.tribuo.util.infotheory.impl.RowList
rowProcessor - Variable in class org.tribuo.data.columnar.ColumnarDataSource
rowProcessor - Variable in class org.tribuo.data.DataOptions
RowProcessor<T> - Class in org.tribuo.data.columnar: A processor which takes a Map of String to String and returns an Example.
RowProcessor() - Constructor for class org.tribuo.data.columnar.RowProcessor: For olcut.
RowProcessor(List<FieldExtractor<?>>, FieldExtractor<Float>, ResponseProcessor<T>, Map<String, FieldProcessor>, Map<String, FieldProcessor>, Set<FeatureProcessor>) - Constructor for class org.tribuo.data.columnar.RowProcessor: Constructs a RowProcessor using the supplied responseProcessor to extract the response variable, and the supplied fieldProcessorMap to control which fields are parsed and how they are parsed.
RowProcessor(List<FieldExtractor<?>>, FieldExtractor<Float>, ResponseProcessor<T>, Map<String, FieldProcessor>, Set<FeatureProcessor>) - Constructor for class org.tribuo.data.columnar.RowProcessor: Constructs a RowProcessor using the supplied responseProcessor to extract the response variable, and the supplied fieldProcessorMap to control which fields are parsed and how they are parsed.
RowProcessor(ResponseProcessor<T>, Map<String, FieldProcessor>) - Constructor for class org.tribuo.data.columnar.RowProcessor: Constructs a RowProcessor using the supplied responseProcessor to extract the response variable, and the supplied fieldProcessorMap to control which fields are parsed and how they are parsed.
RowProcessor(ResponseProcessor<T>, Map<String, FieldProcessor>, Set<FeatureProcessor>) - Constructor for class org.tribuo.data.columnar.RowProcessor: Constructs a RowProcessor using the supplied responseProcessor to extract the response variable, and the supplied fieldProcessorMap to control which fields are parsed and how they are parsed.
rowScaleInPlace(DenseVector) - Method in class org.tribuo.math.la.DenseMatrix
rowScaleInPlace(DenseVector) - Method in class org.tribuo.math.la.DenseSparseMatrix
rowScaleInPlace(DenseVector) - Method in interface org.tribuo.math.la.Matrix: Scales each row by the appropriate value in the DenseVector.
rowSum() - Method in class org.tribuo.math.la.DenseMatrix
rowSum() - Method in class org.tribuo.math.la.DenseSparseMatrix
rowSum() - Method in interface org.tribuo.math.la.Matrix: Generates a DenseVector representing the sum of each row.
rowSum(int) - Method in class org.tribuo.math.la.DenseMatrix
rType - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
rType - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
run(ConfigurationManager, DataOptions, Trainer<Label>) - Static method in class org.tribuo.classification.TrainTestHelper: This method trains a model on the specified training data, and evaluates it on the specified test data.
RunAll - Class in org.tribuo.classification.experiments: Trains and tests a model using the supplied data, for each trainer inside a configuration file.
RunAll() - Constructor for class org.tribuo.classification.experiments.RunAll
RunAll.RunAllOptions - Class in org.tribuo.classification.experiments
RunAllOptions() - Constructor for class org.tribuo.classification.experiments.RunAll.RunAllOptions

S

sampleData(String, List<Token>) - Method in class org.tribuo.classification.explanations.lime.LIMEText: Samples a new dataset from the input text.
sampleFromCDF(double[], Random) - Static method in class org.tribuo.util.Util: Samples an index from the supplied cdf.
sampleFromCDF(double[], SplittableRandom) - Static method in class org.tribuo.util.Util: Samples an index from the supplied cdf.
sampleInts(Random, int, int) - Static method in class org.tribuo.util.Util
samplePoint(Random, ImmutableFeatureMap, long, SparseVector) - Static method in class org.tribuo.classification.explanations.lime.LIMEBase: Samples a single example from the supplied feature map and input vector.
SAMPLES_RATIO - Static variable in class org.tribuo.util.infotheory.InformationTheory
SAMPLES_RATIO - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory
sampleStandardDeviation(Collection<V>) - Static method in class org.tribuo.util.Util
sampleVariance(Collection<V>) - Static method in class org.tribuo.util.Util
save(Path, boolean) - Method in class org.tribuo.datasource.IDXDataSource.IDXData: Writes out this IDXData to the specified path.
save(Path, Dataset<T>, String) - Method in class org.tribuo.data.csv.CSVSaver: Saves the dataset to the specified path.
save(Path, Dataset<T>, Set<String>) - Method in class org.tribuo.data.csv.CSVSaver: Saves the dataset to the specified path.
saveCSV(CommandInterpreter, String) - Method in class org.tribuo.data.DatasetExplorer
saveModel(Path, Model<Label>) - Static method in class org.tribuo.interop.tensorflow.TrainTest
saveModel(Model<T>) - Method in class org.tribuo.data.DataOptions
scalarAddInPlace(double) - Method in interface org.tribuo.math.la.Tensor: Adds scalar to each element of this Tensor.
scale(double) - Method in class org.tribuo.math.la.DenseVector
scale(double) - Method in interface org.tribuo.math.la.SGDVector: Generates a new vector with each element scaled by coefficient.
scale(double) - Method in class org.tribuo.math.la.SparseVector
scaleInPlace(double) - Method in interface org.tribuo.math.la.Tensor: Scales each element of this Tensor by coefficient.
scaleInPlace(double) - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
scaleInPlace(double) - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
score - Variable in class org.tribuo.classification.Label: The score of the label.
scoreChunks(SequenceExample<Label>, List<Chunk>) - Method in class org.tribuo.classification.sgd.crf.CRFModel: Scores the chunks using constrained belief propagation.
scoreDimension(int, SparseVector) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionModel: Makes a prediction for a single dimension.
scoreDimension(int, SparseVector) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel: Makes a prediction for a single dimension.
scoreDimension(int, SparseVector) - Method in class org.tribuo.regression.slm.SparseLinearModel
scores - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults
scores - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainViterbiResults
scoreSubsequences(SequenceExample<Label>, List<Prediction<Label>>, List) - Method in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel: The scoring function for the subsequences.
scoreSubsequences(SequenceExample<Label>, List<Prediction<Label>>, List) - Method in class org.tribuo.classification.sgd.crf.CRFModel
SECOND - Enum constant in enum org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector
secondCount - Variable in class org.tribuo.util.infotheory.impl.PairDistribution
secondDimensionName - Static variable in class org.tribuo.regression.example.RegressionDataGenerator
seed - Variable in class org.tribuo.classification.ensemble.AdaBoostTrainer
seed - Variable in class org.tribuo.classification.ensemble.ClassificationEnsembleOptions
seed - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
seed - Variable in class org.tribuo.common.tree.AbstractCARTTrainer
seed - Variable in class org.tribuo.data.DataOptions
seed - Variable in class org.tribuo.data.text.SplitTextData.TrainTestSplitOptions
seed - Variable in class org.tribuo.ensemble.BaggingTrainer
seed - Variable in class org.tribuo.evaluation.KFoldSplitter
seed - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
SEMICOLON - Enum constant in enum org.tribuo.data.DataOptions.Delimiter
SEPARATOR - Static variable in class org.tribuo.data.csv.CSVIterator: Default separator character.
SeqTest - Class in org.tribuo.classification.sgd.crf: Build and run a sequence classifier on a generated dataset.
SeqTest() - Constructor for class org.tribuo.classification.sgd.crf.SeqTest
SeqTest.CRFOptions - Class in org.tribuo.classification.sgd.crf
SequenceDataGenerator - Class in org.tribuo.classification.sequence.example: A data generator for smoke testing sequence label models.
SequenceDataset<T> - Class in org.tribuo.sequence: A class for sets of data, which are used to train and evaluate classifiers.
SequenceDataset(DataProvenance, OutputFactory<T>) - Constructor for class org.tribuo.sequence.SequenceDataset
SequenceDataSource<T> - Interface in org.tribuo.sequence: A interface for things that can be given to a SequenceDataset's constructor.
SequenceEvaluation<T> - Interface in org.tribuo.sequence: An immutable evaluation of a specific sequence model and dataset.
SequenceEvaluator<T,E> - Interface in org.tribuo.sequence: An evaluation factory which produces immutable SequenceEvaluations of a given SequenceDataset using the given SequenceModel.
SequenceExample<T> - Class in org.tribuo.sequence: A sequence of examples, used for sequence classification.
SequenceExample() - Constructor for class org.tribuo.sequence.SequenceExample: Creates an empty sequence example.
SequenceExample(List<Example<T>>) - Constructor for class org.tribuo.sequence.SequenceExample: Creates a sequence example from the list of examples.
SequenceExample(List<Example<T>>, float) - Constructor for class org.tribuo.sequence.SequenceExample: Creates a sequence example from the list of examples, setting the weight.
SequenceExample(List<T>, List<? extends List<? extends Feature>>) - Constructor for class org.tribuo.sequence.SequenceExample: Creates a sequence example from the supplied outputs and list of list of features.
SequenceExample(List<T>, List<? extends List<? extends Feature>>, boolean) - Constructor for class org.tribuo.sequence.SequenceExample
SequenceExample(List<T>, List<? extends List<? extends Feature>>, float) - Constructor for class org.tribuo.sequence.SequenceExample: Creates a sequence example from the supplied weight, outputs and list of list of features.
SequenceExample(List<T>, List<? extends List<? extends Feature>>, float, boolean) - Constructor for class org.tribuo.sequence.SequenceExample
SequenceExample(SequenceExample<T>) - Constructor for class org.tribuo.sequence.SequenceExample: Creates a deep copy of the supplied sequence example.
SequenceExampleArray(SparseVector[][], int[][], double[]) - Constructor for class org.tribuo.classification.sgd.Util.SequenceExampleArray
SequenceExampleTransformer<T> - Interface in org.tribuo.interop.tensorflow.sequence: Converts a sequence example into a feed dict suitable for Tensorflow.
SequenceModel<T> - Class in org.tribuo.sequence: A prediction model, which is used to predict outputs for unseen instances.
SequenceModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Constructor for class org.tribuo.sequence.SequenceModel
SequenceModelExplorer - Class in org.tribuo.sequence: A CLI for interacting with a SequenceModel.
SequenceModelExplorer() - Constructor for class org.tribuo.sequence.SequenceModelExplorer
SequenceModelExplorer.SequenceModelExplorerOptions - Class in org.tribuo.sequence
SequenceModelExplorerOptions() - Constructor for class org.tribuo.sequence.SequenceModelExplorer.SequenceModelExplorerOptions
SequenceOutputTransformer<T> - Interface in org.tribuo.interop.tensorflow.sequence: Converts a Tensorflow output tensor into a list of predictions, and a Tribuo sequence example into a Tensorflow tensor suitable for training.
SequenceTrainer<T> - Interface in org.tribuo.sequence: An interface for things that can train sequence prediction models.
serialise(Graph, Session) - Static method in class org.tribuo.interop.tensorflow.TensorflowUtil: Extracts a Map containing the name of each Tensorflow VariableV2 and the associated parameter array.
SERIALIZED - Enum constant in enum org.tribuo.data.DataOptions.InputFormat
set(char[], int, int) - Method in class org.tribuo.util.tokens.universal.Range
set(char, char, int) - Method in class org.tribuo.util.tokens.universal.Range
set(char, int) - Method in class org.tribuo.util.tokens.universal.Range
set(int, double) - Method in class org.tribuo.math.la.DenseVector
set(int, double) - Method in interface org.tribuo.math.la.SGDVector: Sets the index to the value.
set(int, double) - Method in class org.tribuo.math.la.SparseVector
set(int, int, double) - Method in class org.tribuo.math.la.DenseMatrix
set(int, int, double) - Method in class org.tribuo.math.la.DenseSparseMatrix
set(int, int, double) - Method in interface org.tribuo.math.la.Matrix: Sets an element at the supplied location.
set(int, Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
set(Feature) - Method in class org.tribuo.Example: Overwrites the feature with the matching name.
set(Feature) - Method in class org.tribuo.impl.ArrayExample
set(Feature) - Method in class org.tribuo.impl.BinaryFeaturesExample
set(Feature) - Method in class org.tribuo.impl.ListExample
set(Tensor[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters
set(Tensor[]) - Method in class org.tribuo.math.LinearParameters
set(Tensor[]) - Method in interface org.tribuo.math.Parameters: Set the underlying Tensor array to newWeights.
setBatchSize(int) - Method in class org.tribuo.interop.ExternalModel: Sets a new batch size.
setBatchSize(int) - Method in class org.tribuo.interop.tensorflow.TensorflowModel: Sets a new batch size.
setCacheSize(double) - Method in class org.tribuo.common.libsvm.SVMParameters
setCoeff(double) - Method in class org.tribuo.common.libsvm.SVMParameters
setConfidenceType(CRFModel.ConfidenceType) - Method in class org.tribuo.classification.sgd.crf.CRFModel: Sets the inference method used for confidence prediction.
setCost(double) - Method in class org.tribuo.common.libsvm.SVMParameters
setDegree(int) - Method in class org.tribuo.common.libsvm.SVMParameters
setElements(DenseVector) - Method in class org.tribuo.math.la.DenseVector: Sets all the elements of this vector to be the same as other.
setEpsilon(double) - Method in class org.tribuo.common.libsvm.SVMParameters
setFieldName(String) - Method in class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Deprecated.
setFieldName(String) - Method in class org.tribuo.data.columnar.processors.response.FieldResponseProcessor: Deprecated.
setFieldName(String) - Method in class org.tribuo.data.columnar.processors.response.QuartileResponseProcessor: Deprecated.
setFieldName(String) - Method in interface org.tribuo.data.columnar.ResponseProcessor: Deprecated.
Response processors should be immutable; downstream objects assume that they are Set the field name this ResponseProcessor uses.
setGamma(double) - Method in class org.tribuo.common.libsvm.SVMParameters
setGenerateNgrams(boolean) - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
setGenerateUnigrams(boolean) - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
setLabelOrder(List<Label>) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix: Sets the label order used in LabelConfusionMatrix.toString().
setLabelWeights(Map<Label, Float>) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationTrainer
setLabelWeights(Map<Label, Float>) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationTrainer
setLabelWeights(Map<Label, Float>) - Method in interface org.tribuo.classification.WeightedLabels: Sets the label weights used by this trainer.
setMaxTokenLength(int) - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
setMetadataValue(String, Object) - Method in class org.tribuo.Example: Puts the specified key, value pair into the metadata.
setName(String) - Method in class org.tribuo.Model: Sets the model name.
setName(String) - Method in class org.tribuo.sequence.SequenceModel: Sets the model name.
setNu(double) - Method in class org.tribuo.common.libsvm.SVMParameters
setNumFeatures(CommandInterpreter, int) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
setNumSamples(CommandInterpreter, int) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
setNumThreads(int) - Method in class org.tribuo.common.xgboost.XGBoostModel: Sets the number of threads to use at prediction time.
setProbability() - Method in class org.tribuo.common.libsvm.SVMParameters: Makes the model that is built provide probability estimates.
setSalt(String) - Method in class org.tribuo.hash.HashCodeHasher
setSalt(String) - Method in class org.tribuo.hash.HashedFeatureMap: The salt is not serialised with the Model.
setSalt(String) - Method in class org.tribuo.hash.Hasher: The salt is transient, it must be set **to the same value as it was trained with** after the Model is deserialized.
setSalt(String) - Method in class org.tribuo.hash.MessageDigestHasher
setSalt(String) - Method in class org.tribuo.hash.ModHashCodeHasher
setShuffle(boolean) - Method in class org.tribuo.classification.sgd.crf.CRFTrainer: Turn on or off shuffling of examples.
setShuffle(boolean) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer: Turn on or off shuffling of examples.
setShuffle(boolean) - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer: Turn on or off shuffling of examples.
setShuffle(boolean) - Method in class org.tribuo.regression.sgd.linear.LinearSGDTrainer: Turn on or off shuffling of examples.
setStoreIndices(boolean) - Method in class org.tribuo.dataset.DatasetView: Set to true to store the indices in the provenance system.
setType(Token.TokenType) - Method in class org.tribuo.util.tokens.universal.Range
setupParameters(ImmutableOutputInfo<Label>) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationTrainer
setupParameters(ImmutableOutputInfo<Label>) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationTrainer
setupParameters(ImmutableOutputInfo<T>) - Method in class org.tribuo.common.liblinear.LibLinearTrainer: Constructs the parameters.
setupParameters(ImmutableOutputInfo<T>) - Method in class org.tribuo.common.libsvm.LibSVMTrainer: Constructs the svm_parameter.
setWeight(float) - Method in class org.tribuo.Example: Sets the example's weight.
setWeight(float) - Method in class org.tribuo.sequence.SequenceExample: Sets the weight of this sequence.
setWeights(Map<T, Float>) - Method in class org.tribuo.MutableDataset: Sets the weights in each example according to their output.
SFS - Enum constant in enum org.tribuo.regression.slm.TrainTest.SLMType
SFSN - Enum constant in enum org.tribuo.regression.slm.TrainTest.SLMType
SGD - Class in org.tribuo.math.optimisers: An implementation of single learning rate SGD and optionally momentum.
SGD() - Constructor for class org.tribuo.math.optimisers.SGD: For olcut.
SGD_KERNEL - Enum constant in enum org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType
SGD_LINEAR - Enum constant in enum org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType
SGD.Momentum - Enum in org.tribuo.math.optimisers: Momentum types.
sgdEpochs - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions
sgdLoggingInterval - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions
sgdMinibatchSize - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions
sgdObjective - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions
SGDOptions() - Constructor for class org.tribuo.regression.sgd.TrainTest.SGDOptions
sgdType() - Method in class org.tribuo.math.optimisers.SGD: Override to specify the kind of SGD.
SGDVector - Interface in org.tribuo.math.la: Interface for 1 dimensional Tensors.
sgoOptions - Variable in class org.tribuo.classification.sgd.crf.CRFOptions
sgoOptions - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions
SHA1 - Enum constant in enum org.tribuo.hash.HashingOptions.ModelHashingType
SHA256 - Enum constant in enum org.tribuo.hash.HashingOptions.ModelHashingType
SHAPE - Enum constant in enum org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType
shapeCheck(Tensor, Tensor) - Static method in interface org.tribuo.math.la.Tensor
shapeSum(int[]) - Static method in interface org.tribuo.math.la.Tensor
ShapeTokenizer - Class in org.tribuo.util.tokens.impl: This tokenizer is loosely based on the notion of word shape which is a common feature used in NLP.
ShapeTokenizer() - Constructor for class org.tribuo.util.tokens.impl.ShapeTokenizer
shell - Variable in class org.tribuo.classification.explanations.lime.LIMETextCLI
shell - Variable in class org.tribuo.data.DatasetExplorer
shell - Variable in class org.tribuo.ModelExplorer: The command shell instance.
shell - Variable in class org.tribuo.sequence.SequenceModelExplorer
SHORT - Enum constant in enum org.tribuo.datasource.IDXDataSource.IDXType
showLabelStats(CommandInterpreter) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
showLabelStats(CommandInterpreter) - Method in class org.tribuo.data.DatasetExplorer
showProvenance(CommandInterpreter) - Method in class org.tribuo.data.DatasetExplorer
ShrinkingMatrix - Class in org.tribuo.math.optimisers.util: A subclass of DenseMatrix which shrinks the value every time a new value is added.
ShrinkingMatrix(DenseMatrix, double, boolean) - Constructor for class org.tribuo.math.optimisers.util.ShrinkingMatrix
ShrinkingMatrix(DenseMatrix, double, double) - Constructor for class org.tribuo.math.optimisers.util.ShrinkingMatrix
ShrinkingTensor - Interface in org.tribuo.math.optimisers.util: An interface which tags a Tensor with a convertToDense method.
ShrinkingVector - Class in org.tribuo.math.optimisers.util: A subclass of DenseVector which shrinks the value every time a new value is added.
ShrinkingVector(DenseVector, double, boolean) - Constructor for class org.tribuo.math.optimisers.util.ShrinkingVector
ShrinkingVector(DenseVector, double, double) - Constructor for class org.tribuo.math.optimisers.util.ShrinkingVector
shuffle - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
shuffle(boolean) - Method in class org.tribuo.Dataset: Shuffles the indices, or stops shuffling them.
shuffle(SparseVector[][], int[][], double[], SplittableRandom) - Static method in class org.tribuo.classification.sgd.Util: Shuffles a sequence of features, labels and weights, returning a tuple of the shuffled values.
shuffle(SparseVector[], int[], double[], SplittableRandom) - Static method in class org.tribuo.classification.sgd.Util: Shuffles the features, labels and weights returning a tuple of the shuffled inputs.
shuffleInPlace(SparseVector[][], int[][], double[], SplittableRandom) - Static method in class org.tribuo.classification.sgd.Util: In place shuffle used for sequence problems.
shuffleInPlace(SparseVector[], int[], double[], int[], SplittableRandom) - Static method in class org.tribuo.classification.sgd.Util: In place shuffle of the features, labels, weights and indices.
shuffleInPlace(SparseVector[], int[], double[], SplittableRandom) - Static method in class org.tribuo.classification.sgd.Util: In place shuffle of the features, labels and weights.
shuffleInPlace(SparseVector[], DenseVector[], double[], int[], SplittableRandom) - Static method in class org.tribuo.regression.sgd.Util: In place shuffle of the features, labels and weights.
shuffleInPlace(SparseVector[], DenseVector[], double[], SplittableRandom) - Static method in class org.tribuo.regression.sgd.Util: In place shuffle of the features, labels and weights.
Sigmoid - Class in org.tribuo.math.kernel: A sigmoid kernel, tanh(gamma*u.dot(v) + intercept).
Sigmoid(double, double) - Constructor for class org.tribuo.math.kernel.Sigmoid: A sigmoid kernel, tanh(gamma*u.dot(v) + intercept).
SIGMOID - Enum constant in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum
SIGMOID - Enum constant in enum org.tribuo.common.libsvm.KernelType: A sigmoid kernel of the form tanh(gamma*u'*v + coef0)
similarity(SparseVector, SparseVector) - Method in interface org.tribuo.math.kernel.Kernel: Calculates the similarity between two SparseVectors.
similarity(SparseVector, SparseVector) - Method in class org.tribuo.math.kernel.Linear
similarity(SparseVector, SparseVector) - Method in class org.tribuo.math.kernel.Polynomial
similarity(SparseVector, SparseVector) - Method in class org.tribuo.math.kernel.RBF
similarity(SparseVector, SparseVector) - Method in class org.tribuo.math.kernel.Sigmoid
SIMPLE_DEFAULT_PATTERN - Static variable in class org.tribuo.util.tokens.impl.SplitPatternTokenizer: The default split pattern, which is [\.,]?\s+.
SimpleDataSourceProvenance - Class in org.tribuo.provenance: This class stores a String describing the data source, along with a timestamp.
SimpleDataSourceProvenance(String, OffsetDateTime, OutputFactory<T>) - Constructor for class org.tribuo.provenance.SimpleDataSourceProvenance: This constructor initialises the provenance using the supplied description, time and output factory.
SimpleDataSourceProvenance(String, OutputFactory<T>) - Constructor for class org.tribuo.provenance.SimpleDataSourceProvenance: This constructor initialises the provenance using the current time in the system timezone.
SimpleDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.SimpleDataSourceProvenance: Used for provenance deserialization.
SimpleFieldExtractor<T> - Class in org.tribuo.data.columnar.extractors: Extracts a value from a single field to be placed in an Example's metadata field.
SimpleFieldExtractor() - Constructor for class org.tribuo.data.columnar.extractors.SimpleFieldExtractor: For olcut.
SimpleFieldExtractor(String) - Constructor for class org.tribuo.data.columnar.extractors.SimpleFieldExtractor: Constructs a simple field extractor which reads from the supplied field name and writes out to a metadata field with the same name.
SimpleFieldExtractor(String, String) - Constructor for class org.tribuo.data.columnar.extractors.SimpleFieldExtractor: Constructs a simple field extractor with the supplied field name and metadata field name.
SimpleStringDataSource<T> - Class in org.tribuo.data.text.impl: A version of SimpleTextDataSource that accepts an Iterable of Strings.
SimpleStringDataSource(List<String>, OutputFactory<T>, TextFeatureExtractor<T>) - Constructor for class org.tribuo.data.text.impl.SimpleStringDataSource
SimpleStringDataSource.SimpleStringDataSourceProvenance - Class in org.tribuo.data.text.impl: Provenance for SimpleStringDataSource.
SimpleStringDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.text.impl.SimpleStringDataSource.SimpleStringDataSourceProvenance
SimpleTextDataSource<T> - Class in org.tribuo.data.text.impl: A dataset for a simple data format for text classification experiments.
SimpleTextDataSource() - Constructor for class org.tribuo.data.text.impl.SimpleTextDataSource: for olcut
SimpleTextDataSource(File, OutputFactory<T>, TextFeatureExtractor<T>) - Constructor for class org.tribuo.data.text.impl.SimpleTextDataSource
SimpleTextDataSource(Path, OutputFactory<T>, TextFeatureExtractor<T>) - Constructor for class org.tribuo.data.text.impl.SimpleTextDataSource
SimpleTextDataSource(OutputFactory<T>, TextFeatureExtractor<T>) - Constructor for class org.tribuo.data.text.impl.SimpleTextDataSource
SimpleTextDataSource.SimpleTextDataSourceProvenance - Class in org.tribuo.data.text.impl: Provenance for SimpleTextDataSource.
SimpleTextDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.text.impl.SimpleTextDataSource.SimpleTextDataSourceProvenance
SimpleTransform - Class in org.tribuo.transform.transformations: This is used for stateless functions such as exp, log, addition or multiplication by a constant.
SimpleTransform.Operation - Enum in org.tribuo.transform.transformations: Operations understood by this Transformation.
SimpleTransform.SimpleTransformProvenance - Class in org.tribuo.transform.transformations: Provenance for SimpleTransform.
SimpleTransformProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.transform.transformations.SimpleTransform.SimpleTransformProvenance
SINGLE_DIM_NAME - Static variable in class org.tribuo.regression.example.RegressionDataGenerator
size - Variable in class org.tribuo.common.tree.impl.IntArrayContainer
size - Variable in class org.tribuo.ImmutableFeatureMap: The number of features.
size - Variable in class org.tribuo.impl.ArrayExample
size - Variable in class org.tribuo.impl.BinaryFeaturesExample
size() - Method in class org.tribuo.anomaly.AnomalyInfo: The number of possible event types (i.e., 2).
size() - Method in class org.tribuo.classification.LabelInfo: The number of unique Labels this LabelInfo has seen.
size() - Method in class org.tribuo.clustering.ClusteringInfo
size() - Method in class org.tribuo.dataset.DatasetView: Gets the size of the data set.
size() - Method in class org.tribuo.Dataset: Gets the size of the data set.
size() - Method in class org.tribuo.datasource.IDXDataSource: The number of examples loaded.
size() - Method in class org.tribuo.datasource.LibSVMDataSource: The number of examples.
size() - Method in class org.tribuo.datasource.ListDataSource: Number of examples.
size() - Method in class org.tribuo.Example: Return how many features are in this example.
size() - Method in class org.tribuo.FeatureMap: Returns the number of features in the domain.
size() - Method in class org.tribuo.ImmutableFeatureMap
size() - Method in class org.tribuo.impl.ArrayExample
size() - Method in class org.tribuo.impl.BinaryFeaturesExample
size() - Method in class org.tribuo.impl.ListExample
size() - Method in class org.tribuo.math.la.DenseVector
size() - Method in interface org.tribuo.math.la.SGDVector: Returns the dimensionality of this vector.
size() - Method in class org.tribuo.math.la.SparseVector
size() - Method in class org.tribuo.multilabel.MultiLabelInfo
size() - Method in interface org.tribuo.OutputInfo: Returns the number of possible values this OutputInfo knows about.
size() - Method in class org.tribuo.regression.RegressionInfo: The number of dimensions this OutputInfo has seen.
size() - Method in class org.tribuo.regression.Regressor.DimensionTuple
size() - Method in class org.tribuo.regression.Regressor: Returns the number of dimensions in this regressor.
size() - Method in class org.tribuo.sequence.SequenceDataset: Gets the size of the data set.
size() - Method in class org.tribuo.sequence.SequenceExample: Return how many examples are in this sequence.
size() - Method in class org.tribuo.util.infotheory.impl.RowList
SkeletalIndependentRegressionModel - Class in org.tribuo.regression.impl: A Model which wraps n independent regression models, where n is the size of the MultipleRegressor domain.
SkeletalIndependentRegressionModel(String, String[], ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>) - Constructor for class org.tribuo.regression.impl.SkeletalIndependentRegressionModel: models.size() must equal labelInfo.getDomain().size()
SkeletalIndependentRegressionSparseModel - Class in org.tribuo.regression.impl: A SparseModel which wraps n independent regression models, where n is the size of the MultipleRegressor domain.
SkeletalIndependentRegressionSparseModel(String, String[], ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, Map<String, List<String>>) - Constructor for class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel: models.size() must equal labelInfo.getDomain().size()
SkeletalIndependentRegressionSparseTrainer<T> - Class in org.tribuo.regression.impl: Base class for training n independent sparse models, one per dimension.
SkeletalIndependentRegressionSparseTrainer() - Constructor for class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer: for olcut.
SkeletalIndependentRegressionTrainer<T> - Class in org.tribuo.regression.impl: Trains n independent binary Models, each of which predicts a single Regressor.
SkeletalIndependentRegressionTrainer() - Constructor for class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer: for olcut.
SkeletalTrainerProvenance - Class in org.tribuo.provenance: The skeleton of a TrainerProvenance that extracts the configured parameters.
SkeletalTrainerProvenance(SkeletalConfiguredObjectProvenance.ExtractedInfo) - Constructor for class org.tribuo.provenance.SkeletalTrainerProvenance
SkeletalTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.SkeletalTrainerProvenance
SkeletalTrainerProvenance(SequenceTrainer<T>) - Constructor for class org.tribuo.provenance.SkeletalTrainerProvenance
SkeletalTrainerProvenance(Trainer<T>) - Constructor for class org.tribuo.provenance.SkeletalTrainerProvenance
SkeletalVariableInfo - Class in org.tribuo: Contains information about a feature and can be stored in the feature map in a Dataset.
SkeletalVariableInfo(String) - Constructor for class org.tribuo.SkeletalVariableInfo: Constructs a variable info with the supplied name.
SkeletalVariableInfo(String, int) - Constructor for class org.tribuo.SkeletalVariableInfo: Constructs a variable info with the supplied name and initial count.
SLMTrainer - Class in org.tribuo.regression.slm: A trainer for a sparse linear regression model.
SLMTrainer() - Constructor for class org.tribuo.regression.slm.SLMTrainer: For OLCUT.
SLMTrainer(boolean) - Constructor for class org.tribuo.regression.slm.SLMTrainer: Constructs a trainer for a sparse linear model using sequential forward selection.
SLMTrainer(boolean, int) - Constructor for class org.tribuo.regression.slm.SLMTrainer: Constructs a trainer for a sparse linear model using sequential forward selection.
sort() - Method in class org.tribuo.Example: Sorts the example by the string comparator.
sort() - Method in class org.tribuo.impl.ArrayExample: Sorts the feature list to maintain the lexicographic order invariant.
sort() - Method in class org.tribuo.impl.BinaryFeaturesExample: Sorts the feature list to maintain the lexicographic order invariant.
sort() - Method in class org.tribuo.impl.IndexedArrayExample
sort() - Method in class org.tribuo.impl.ListExample: Sorts the feature list to maintain the lexicographic order invariant.
sort() - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Sort the list using InvertedFeature's natural ordering.
sortedDifference(int[], int[]) - Static method in class org.tribuo.util.Util: Expects sorted input arrays.
sourceProvenance - Variable in class org.tribuo.Dataset: The provenance of the data source, extracted on construction.
sourceProvenance - Variable in class org.tribuo.sequence.SequenceDataset: The provenance of the data source, extracted on construction.
SparseLinearModel - Class in org.tribuo.regression.slm: The inference time version of a sparse linear regression model.
SparseModel<T> - Class in org.tribuo: A model which uses a subset of the features it knows about to make predictions.
SparseModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, boolean, Map<String, List<String>>) - Constructor for class org.tribuo.SparseModel: Constructs a sparse model from the supplied arguments.
SparseTrainer<T> - Interface in org.tribuo: Denotes this trainer emits a SparseModel.
sparseTrainTest() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator: Makes a simple dataset for training and testing.
sparseTrainTest() - Static method in class org.tribuo.classification.example.LabelledDataGenerator
sparseTrainTest() - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator
sparseTrainTest() - Static method in class org.tribuo.regression.example.RegressionDataGenerator
sparseTrainTest(double) - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator: Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
sparseTrainTest(double) - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
sparseTrainTest(double) - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator: Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
sparseTrainTest(double) - Static method in class org.tribuo.regression.example.RegressionDataGenerator: Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
SparseVector - Class in org.tribuo.math.la: A sparse vector.
SparseVector(int, int[], double) - Constructor for class org.tribuo.math.la.SparseVector
sparsify() - Method in class org.tribuo.math.la.DenseVector: Generates a SparseVector representation from this dense vector, removing all values with absolute value below VectorTuple.DELTA.
sparsify(double) - Method in class org.tribuo.math.la.DenseVector: Generates a SparseVector representation from this dense vector, removing all values with absolute value below the supplied tolerance.
split - Variable in class org.tribuo.common.tree.AbstractTrainingNode
split(int[], int[], IntArrayContainer, IntArrayContainer) - Method in class org.tribuo.regression.rtree.impl.TreeFeature: Splits this tree feature into two.
split(CharSequence) - Method in interface org.tribuo.util.tokens.Tokenizer: Uses this tokenizer to split a string into it's component substrings.
split(IntArrayContainer, IntArrayContainer) - Method in class org.tribuo.regression.rtree.impl.InvertedFeature: Relies upon allLeftIndices being sorted in ascending order.
split(Dataset<T>, boolean) - Method in class org.tribuo.evaluation.KFoldSplitter: Splits a dataset into k consecutive folds; for each fold, the remaining k-1 folds form the training set.
SPLIT_CHARACTERS - Enum constant in enum org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType
SPLIT_PATTERN - Enum constant in enum org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType
splitChar - Variable in class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
SplitCharactersTokenizer - Class in org.tribuo.util.tokens.impl: This implementation of Tokenizer is instantiated with an array of characters that are considered split characters.
SplitCharactersTokenizer() - Constructor for class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
SplitCharactersTokenizer(char[], char[]) - Constructor for class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
splitCharactersTokenizerOptions - Variable in class org.tribuo.util.tokens.options.CoreTokenizerOptions
SplitCharactersTokenizerOptions - Class in org.tribuo.util.tokens.options: CLI options for a SplitCharactersTokenizer.
SplitCharactersTokenizerOptions() - Constructor for class org.tribuo.util.tokens.options.SplitCharactersTokenizerOptions
splitChars - Variable in class org.tribuo.util.tokens.options.SplitCharactersTokenizerOptions
SplitDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.evaluation.TrainTestSplitter.SplitDataSourceProvenance
splitFraction - Variable in class org.tribuo.data.text.SplitTextData.TrainTestSplitOptions
splitID - Variable in class org.tribuo.common.tree.AbstractTrainingNode
SplitNode<T> - Class in org.tribuo.common.tree: An immutable Node with a split and two child nodes.
SplitNode(double, int, double, Node<T>, Node<T>) - Constructor for class org.tribuo.common.tree.SplitNode: Constructs a split node with the specified split value, feature id, impurity and child nodes.
SplitPatternTokenizer - Class in org.tribuo.util.tokens.impl: This implementation of Tokenizer is instantiated with a regular expression pattern which determines how to split a string into tokens.
SplitPatternTokenizer() - Constructor for class org.tribuo.util.tokens.impl.SplitPatternTokenizer: Initializes a case insensitive tokenizer with the pattern [\.,]?\s+
SplitPatternTokenizer(String) - Constructor for class org.tribuo.util.tokens.impl.SplitPatternTokenizer: Constructs a splitting tokenizer using the supplied regex.
splitPatternTokenizerOptions - Variable in class org.tribuo.util.tokens.options.CoreTokenizerOptions
SplitPatternTokenizerOptions - Class in org.tribuo.util.tokens.options: CLI options for a SplitPatternTokenizer.
SplitPatternTokenizerOptions() - Constructor for class org.tribuo.util.tokens.options.SplitPatternTokenizerOptions
SplitTextData - Class in org.tribuo.data.text: Splits data in our standard text format into training and testing portions.
SplitTextData() - Constructor for class org.tribuo.data.text.SplitTextData
SplitTextData.TrainTestSplitOptions - Class in org.tribuo.data.text
splitValue - Variable in class org.tribuo.common.tree.AbstractTrainingNode
splitValue() - Method in class org.tribuo.common.tree.SplitNode: The threshold value.
splitXDigitsChars - Variable in class org.tribuo.util.tokens.options.SplitCharactersTokenizerOptions
SQLDataSource<T> - Class in org.tribuo.data.sql: A DataSource for loading columnar data from a database and applying FieldProcessors to it.
SQLDataSource(String, SQLDBConfig, OutputFactory<T>, RowProcessor<T>, boolean) - Constructor for class org.tribuo.data.sql.SQLDataSource
SQLDataSource.SQLDataSourceProvenance - Class in org.tribuo.data.sql: Provenance for SQLDataSource.
SQLDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.sql.SQLDataSource.SQLDataSourceProvenance
SQLDBConfig - Class in org.tribuo.data.sql: N.B.
SQLDBConfig(String, String, String, String, String, Map<String, String>) - Constructor for class org.tribuo.data.sql.SQLDBConfig
SQLDBConfig(String, String, String, Map<String, String>) - Constructor for class org.tribuo.data.sql.SQLDBConfig
SQLDBConfig(String, Map<String, String>) - Constructor for class org.tribuo.data.sql.SQLDBConfig
SQLToCSV - Class in org.tribuo.data.sql: Read an SQL query in on the standard input, write a CSV file containing the results to the standard output.
SQLToCSV() - Constructor for class org.tribuo.data.sql.SQLToCSV
SQLToCSV.SQLToCSVOptions - Class in org.tribuo.data.sql
SQLToCSVOptions() - Constructor for class org.tribuo.data.sql.SQLToCSV.SQLToCSVOptions
SQRTSGD - Enum constant in enum org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType
SQUARED - Enum constant in enum org.tribuo.regression.sgd.TrainTest.LossEnum
SquaredLoss - Class in org.tribuo.regression.sgd.objectives: Squared loss, i.e., l2.
SquaredLoss() - Constructor for class org.tribuo.regression.sgd.objectives.SquaredLoss
STANDARD - Enum constant in enum org.tribuo.math.optimisers.SGD.Momentum: Standard momentum.
start - Variable in class org.tribuo.util.tokens.Token
start - Variable in class org.tribuo.util.tokens.universal.Range
startShell() - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI: Start the command shell
startShell() - Method in class org.tribuo.data.DatasetExplorer: Start the command shell
startShell() - Method in class org.tribuo.ModelExplorer: Start the command shell
startShell() - Method in class org.tribuo.sequence.SequenceModelExplorer: Start the command shell
STD_DEVS - Enum constant in enum org.tribuo.transform.transformations.BinningTransformation.BinningType
stdDevs(int) - Static method in class org.tribuo.transform.transformations.BinningTransformation: Returns a BinningTransformation which generates bins based on the observed standard deviation of the training data.
step(Tensor[], double) - Method in class org.tribuo.math.optimisers.AdaDelta
step(Tensor[], double) - Method in class org.tribuo.math.optimisers.AdaGrad
step(Tensor[], double) - Method in class org.tribuo.math.optimisers.AdaGradRDA
step(Tensor[], double) - Method in class org.tribuo.math.optimisers.Adam
step(Tensor[], double) - Method in class org.tribuo.math.optimisers.ParameterAveraging: This passes the gradient update to the inner optimiser, then updates the average weight values.
step(Tensor[], double) - Method in class org.tribuo.math.optimisers.Pegasos
step(Tensor[], double) - Method in class org.tribuo.math.optimisers.RMSProp
step(Tensor[], double) - Method in class org.tribuo.math.optimisers.SGD
step(Tensor[], double) - Method in interface org.tribuo.math.StochasticGradientOptimiser: Take a Tensor array of gradients and transform them according to the current weight and learning rates.
StochasticGradientOptimiser - Interface in org.tribuo.math: Interface for gradient based optimisation methods.
storeHash - Variable in class org.tribuo.json.StripProvenance.StripProvenanceOptions
storeIndicesInProvenance() - Method in class org.tribuo.dataset.DatasetView: Are the indices stored in the provenance system.
STRATIFIED - Enum constant in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Samples the label proprotional to the training label frequencies.
STREAMS - Enum constant in enum org.tribuo.common.nearest.KNNModel.Backend: Uses the streams API for parallelism when scoring a batch of predictions.
StripProvenance - Class in org.tribuo.json: A main class for stripping out and storing provenance from a model.
StripProvenance.ProvenanceTypes - Enum in org.tribuo.json: Types of provenance that can be removed.
StripProvenance.StripProvenanceOptions - Class in org.tribuo.json
StripProvenanceOptions() - Constructor for class org.tribuo.json.StripProvenance.StripProvenanceOptions
sub - Enum constant in enum org.tribuo.transform.transformations.SimpleTransform.Operation: Subtracts the specified constant.
sub(double) - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that subtracts the operand from each value.
subList(int, int) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
subsample - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
subsample - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
subsampleFeatures - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
subsampleFeatures - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
subSequence(int, int) - Method in class org.tribuo.util.tokens.universal.Range
Subsequence(int, int) - Constructor for class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence: Constructs a subsequence for the fixed range, exclusive of the end.
subtract(SGDVector) - Method in class org.tribuo.math.la.DenseVector: Subtracts other from this vector, producing a new DenseVector.
subtract(SGDVector) - Method in interface org.tribuo.math.la.SGDVector: Subtracts other from this vector, producing a new SGDVector.
subtract(SGDVector) - Method in class org.tribuo.math.la.SparseVector: Subtracts other from this vector, producing a new SGDVector.
sum() - Method in class org.tribuo.math.la.DenseVector
sum() - Method in interface org.tribuo.math.la.SGDVector: Calculates the sum of this vector.
sum() - Method in class org.tribuo.math.la.SparseVector
sum() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
sum(double[]) - Static method in class org.tribuo.util.Util
sum(double[], int) - Static method in class org.tribuo.util.Util
sum(float[]) - Static method in class org.tribuo.util.Util
sum(float[], int) - Static method in class org.tribuo.util.Util
sum(int[], float[]) - Static method in class org.tribuo.util.Util
sum(int[], int, float[]) - Static method in class org.tribuo.util.Util
sum(DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseVector
SUM - Enum constant in enum org.tribuo.data.columnar.processors.feature.UniqueProcessor.UniqueType: Add together all the feature values.
SumAggregator - Class in org.tribuo.data.text.impl: A feature aggregator that aggregates occurrence counts across a number of feature lists.
SumAggregator() - Constructor for class org.tribuo.data.text.impl.SumAggregator
sumLogProbs(double[]) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper: Sums the log probabilities.
sumLogProbs(DenseVector) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper: Sums the log probabilities.
summarize(List<? extends EvaluationMetric<T, C>>, Model<T>, List<Prediction<T>>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Summarize model performance on dataset across several metrics.
summarize(List<? extends EvaluationMetric<T, C>>, Model<T>, Dataset<T>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Summarize model performance on dataset across several metrics.
summarize(List<R>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Summarize all fields of a list of evaluations.
summarize(List<R>, ToDoubleFunction<R>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Summarize a single field of an evaluation across several evaluations.
summarize(Evaluator<T, R>, List<? extends Model<T>>, Dataset<T>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Summarize performance using the supplied evaluator across several models on one dataset.
summarize(Evaluator<T, R>, Model<T>, List<? extends Dataset<T>>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Summarize performance according to evaluator for a single model across several datasets.
summarize(EvaluationMetric<T, C>, List<? extends Model<T>>, Dataset<T>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Summarize performance w.r.t.
summarize(EvaluationMetric<T, C>, Model<T>, List<? extends Dataset<T>>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Summarize a model's performance w.r.t.
sumOverOutputs(ImmutableOutputInfo<T>, ToDoubleFunction<T>) - Static method in interface org.tribuo.classification.evaluation.ConfusionMatrix: Sums the supplied getter over the domain.
sumSquares - Variable in class org.tribuo.RealInfo: The sum of the squared feature values (used to compute the variance).
sumSquaresMap - Variable in class org.tribuo.regression.RegressionInfo
support() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of examples this confusion matrix has seen.
support() - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
support() - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
support(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
support(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
support(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of examples with this true label this confusion matrix has seen.
SVMAnomalyType - Class in org.tribuo.anomaly.libsvm: The carrier type for LibSVM anomaly detection modes.
SVMAnomalyType(SVMAnomalyType.SVMMode) - Constructor for class org.tribuo.anomaly.libsvm.SVMAnomalyType: Constructs an SVM anomaly type wrapping the SVM algorithm choice.
SVMAnomalyType.SVMMode - Enum in org.tribuo.anomaly.libsvm: Valid SVM modes for anomaly detection.
SVMClassificationType - Class in org.tribuo.classification.libsvm: The carrier type for LibSVM classification modes.
SVMClassificationType(SVMClassificationType.SVMMode) - Constructor for class org.tribuo.classification.libsvm.SVMClassificationType
SVMClassificationType.SVMMode - Enum in org.tribuo.classification.libsvm: The classification model types.
svmCoefficient - Variable in class org.tribuo.classification.libsvm.LibSVMOptions
svmDegree - Variable in class org.tribuo.classification.libsvm.LibSVMOptions
svmGamma - Variable in class org.tribuo.classification.libsvm.LibSVMOptions
svmKernel - Variable in class org.tribuo.classification.libsvm.LibSVMOptions
SVMParameters<T> - Class in org.tribuo.common.libsvm: A container for SVM parameters and the kernel.
SVMParameters(SVMType<T>, KernelType) - Constructor for class org.tribuo.common.libsvm.SVMParameters
svmParamsToString(svm_parameter) - Static method in class org.tribuo.common.libsvm.SVMParameters: A sensible toString for svm_parameter.
SVMRegressionType - Class in org.tribuo.regression.libsvm: The carrier type for LibSVM regression modes.
SVMRegressionType(SVMRegressionType.SVMMode) - Constructor for class org.tribuo.regression.libsvm.SVMRegressionType
SVMRegressionType.SVMMode - Enum in org.tribuo.regression.libsvm: Type of regression SVM.
svmType - Variable in class org.tribuo.classification.libsvm.LibSVMOptions
svmType - Variable in class org.tribuo.common.libsvm.LibSVMTrainer: The type of SVM algorithm.
svmType - Variable in class org.tribuo.common.libsvm.SVMParameters
svmType - Variable in class org.tribuo.regression.libsvm.TrainTest.LibLinearOptions
SVMType<T> - Interface in org.tribuo.common.libsvm: A carrier type for the SVM type.

T

TAB - Enum constant in enum org.tribuo.data.DataOptions.Delimiter
TabularExplainer<T> - Interface in org.tribuo.classification.explanations: An explainer for tabular data.
TAG_VERSION - Static variable in class org.tribuo.Tribuo: Any tag on the version number, e.g., SNAPSHOT, ALPHA, etc.
TARGET - Static variable in class org.tribuo.interop.tensorflow.TensorflowTrainer
Tensor - Interface in org.tribuo.math.la: An interface for Tensors, currently Vectors and Matrices.
TensorflowCheckpointModel<T> - Class in org.tribuo.interop.tensorflow: TensorFlow support is experimental, and may change without a major version bump.
TensorflowCheckpointTrainer<T> - Class in org.tribuo.interop.tensorflow: Trainer for Tensorflow.
TensorflowCheckpointTrainer(Path, Path, ExampleTransformer<T>, OutputTransformer<T>, int, int) - Constructor for class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer: Builds a trainer using the supplied graph and arguments.
TensorflowCheckpointTrainer.TensorflowCheckpointTrainerProvenance - Class in org.tribuo.interop.tensorflow
TensorflowCheckpointTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer.TensorflowCheckpointTrainerProvenance
TensorflowExternalModel<T> - Class in org.tribuo.interop.tensorflow: A Tribuo wrapper around a Tensorflow frozen model.
TensorflowModel<T> - Class in org.tribuo.interop.tensorflow: This model encapsulates a simple model with a single input tensor (labelled TensorflowModel.INPUT_NAME), and produces a single output tensor (labelled TensorflowModel.OUTPUT_NAME).
TensorflowOptions() - Constructor for class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
TensorflowSequenceModel<T> - Class in org.tribuo.interop.tensorflow.sequence: A Tensorflow model which implements SequenceModel, suitable for use in sequential prediction tasks.
TensorflowSequenceTrainer<T> - Class in org.tribuo.interop.tensorflow.sequence: A trainer for SequenceModels which use an underlying Tensorflow graph.
TensorflowSequenceTrainer(Path, SequenceExampleTransformer<T>, SequenceOutputTransformer<T>, int, int, int, long, String, String, String, String) - Constructor for class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
TensorflowSequenceTrainer.TensorflowSequenceTrainerProvenance - Class in org.tribuo.interop.tensorflow.sequence
TensorflowSequenceTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer.TensorflowSequenceTrainerProvenance
TensorflowTrainer<T> - Class in org.tribuo.interop.tensorflow: Trainer for Tensorflow.
TensorflowTrainer(byte[], ExampleTransformer<T>, OutputTransformer<T>, int, int, int) - Constructor for class org.tribuo.interop.tensorflow.TensorflowTrainer: Constructs a Trainer for a tensorflow graph.
TensorflowTrainer(Path, ExampleTransformer<T>, OutputTransformer<T>, int, int, int) - Constructor for class org.tribuo.interop.tensorflow.TensorflowTrainer: Constructs a Trainer for a tensorflow graph.
TensorflowTrainer.TensorflowTrainerProvenance - Class in org.tribuo.interop.tensorflow
TensorflowTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.interop.tensorflow.TensorflowTrainer.TensorflowTrainerProvenance
TensorflowUtil - Class in org.tribuo.interop.tensorflow: Helper functions for working with Tensorflow.
TensorflowUtil() - Constructor for class org.tribuo.interop.tensorflow.TensorflowUtil
termCounting - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
termCounting - Variable in class org.tribuo.data.DataOptions
terminationCriterion - Variable in class org.tribuo.common.liblinear.LibLinearTrainer
terminationCriterion - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
test - Variable in class org.tribuo.evaluation.KFoldSplitter.TrainTestFold
Test - Class in org.tribuo.classification.experiments: Test a classifier for a standard dataset.
Test() - Constructor for class org.tribuo.classification.experiments.Test
Test.ConfigurableTestOptions - Class in org.tribuo.classification.experiments
testBatchSize - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
testDataset - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
testingPath - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
testingPath - Variable in class org.tribuo.data.DataOptions
testingPath - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
testSource - Variable in class org.tribuo.data.CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions
text - Variable in class org.tribuo.util.tokens.Token
TEXT - Enum constant in enum org.tribuo.data.columnar.FieldProcessor.GeneratedFeatureType: Text features.
TEXT - Enum constant in enum org.tribuo.data.DataOptions.InputFormat
TextDataSource<T> - Class in org.tribuo.data.text: A base class for textual data sets.
TextDataSource() - Constructor for class org.tribuo.data.text.TextDataSource: for olcut
TextDataSource(File, OutputFactory<T>, TextFeatureExtractor<T>, DocumentPreprocessor...) - Constructor for class org.tribuo.data.text.TextDataSource
TextDataSource(Path, OutputFactory<T>, TextFeatureExtractor<T>, DocumentPreprocessor...) - Constructor for class org.tribuo.data.text.TextDataSource: Creates a text data set by reading it from a path.
TextExplainer<T> - Interface in org.tribuo.classification.explanations: An explainer for text data.
TextFeatureExtractor<T> - Interface in org.tribuo.data.text: An interface for things that take text and turn them into examples that we can use to train or evaluate a classifier.
TextFeatureExtractorImpl<T> - Class in org.tribuo.data.text.impl: An implementation of TextFeatureExtractor that takes a TextPipeline and generates ListExample.
TextFeatureExtractorImpl(TextPipeline) - Constructor for class org.tribuo.data.text.impl.TextFeatureExtractorImpl
TextFieldProcessor - Class in org.tribuo.data.columnar.processors.field: A FieldProcessor which takes a text field and runs a TextPipeline on it to generate features.
TextFieldProcessor(String, TextPipeline) - Constructor for class org.tribuo.data.columnar.processors.field.TextFieldProcessor: Constructs a field processor which uses the supplied text pipeline to process the field value.
TextPipeline - Interface in org.tribuo.data.text: A pipeline that takes a String and returns a List of Features.
TextProcessingException - Exception Class in org.tribuo.data.text: An exception thrown by the text processing system.
TextProcessingException(String) - Constructor for exception class org.tribuo.data.text.TextProcessingException
TextProcessingException(String, Throwable) - Constructor for exception class org.tribuo.data.text.TextProcessingException
TextProcessingException(Throwable) - Constructor for exception class org.tribuo.data.text.TextProcessingException
TextProcessor - Interface in org.tribuo.data.text: A TextProcessor takes some text and optionally a feature tag and generates a list of Features from that text.
THIRD - Enum constant in enum org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector
THREADPOOL - Enum constant in enum org.tribuo.common.nearest.KNNModel.Backend: Uses a thread pool at the outer level (i.e., one thread per prediction).
threshold - Enum constant in enum org.tribuo.transform.transformations.SimpleTransform.Operation: Min and max thresholds applied to the input.
threshold(double, double) - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that sets values below min to min, and values above max to max.
THRESHOLD - Static variable in class org.tribuo.CategoricalInfo: The default threshold for converting a categorical info into a RealInfo.
THRESHOLD - Static variable in class org.tribuo.interop.onnx.DenseTransformer: Feature size beyond which a warning is generated (as ONNX requires dense features and large feature spaces are memory hungry).
THRESHOLD - Static variable in class org.tribuo.interop.tensorflow.DenseTransformer: Feature size beyond which a warning is generated (as ONNX requires dense features and large feature spaces are memory hungry).
thresholds - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.PRCurve
thresholds - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.ROC
time - Variable in class org.tribuo.provenance.ModelProvenance
tn() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the total number of true negatives.
tn() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The total number of true negatives.
tn() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
tn() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
tn(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
tn(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
tn(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Returns the number of true negatives, possibly averaged depending on the metric target.
tn(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
tn(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
tn(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the number of true negatives for that label, i.e., the number of times it wasn't predicted, and was not the true label.
tn(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of true negatives for the supplied label.
TN - Enum constant in enum org.tribuo.anomaly.evaluation.AnomalyMetrics: The number of true negatives.
TN - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The number of true negatives.
TN - Enum constant in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics: The number of true negatives.
toArray() - Method in class org.tribuo.math.la.DenseVector: Generates a copy of the values in this DenseVector.
toArray() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
toArray() - Method in class org.tribuo.util.infotheory.impl.RowList
toArray(U[]) - Method in class org.tribuo.util.infotheory.impl.RowList
toDenseArray() - Method in class org.tribuo.math.la.SparseVector
toDoubleArray(float[]) - Static method in class org.tribuo.util.Util: Convert an array of floats to an array of doubles.
toFloatArray(double[]) - Static method in class org.tribuo.util.Util: Convert an array of doubles to an array of floats.
toFormattedString(LabelEvaluation) - Static method in interface org.tribuo.classification.evaluation.LabelEvaluation: This method produces a nicely formatted String output, with appropriate tabs and newlines, suitable for display on a terminal.
toHTML() - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix: Emits a HTML table representation of the Confusion Matrix.
toHTML() - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Returns a HTML formatted String representing this evaluation.
toHTML() - Method in class org.tribuo.Feature: Returns the feature name formatted as a table cell.
toHTML(Pair<String, Double>) - Static method in class org.tribuo.util.HTMLOutput
toHTML(LabelEvaluation) - Static method in interface org.tribuo.classification.evaluation.LabelEvaluation: This method produces a HTML formatted String output, with appropriate tabs and newlines, suitable for integation into a webpage.
Token - Class in org.tribuo.util.tokens: A single token extracted from a String.
Token(String, int, int) - Constructor for class org.tribuo.util.tokens.Token: Constructs a token.
Token(String, int, int, Token.TokenType) - Constructor for class org.tribuo.util.tokens.Token: Constructs a token.
Token.TokenType - Enum in org.tribuo.util.tokens: Tokenizers may product multiple kinds of tokens, depending on the application to which they're being put.
TokenizationException - Exception Class in org.tribuo.util.tokens: Wraps exceptions thrown by tokenizers.
TokenizationException(String) - Constructor for exception class org.tribuo.util.tokens.TokenizationException
TokenizationException(String, Throwable) - Constructor for exception class org.tribuo.util.tokens.TokenizationException
TokenizationException(Throwable) - Constructor for exception class org.tribuo.util.tokens.TokenizationException
tokenize(CharSequence) - Method in interface org.tribuo.util.tokens.Tokenizer: Uses this tokenizer to tokenize a string and return the list of tokens that were generated.
Tokenizer - Interface in org.tribuo.util.tokens: An interface for things that tokenize text: breaking it into words according to some set of rules.
TokenizerOptions - Interface in org.tribuo.util.tokens.options: CLI Options for creating a tokenizer.
TokenPipeline - Class in org.tribuo.data.text.impl: A pipeline for generating ngram features.
TokenPipeline(Tokenizer, int, boolean) - Constructor for class org.tribuo.data.text.impl.TokenPipeline: Creates a new token pipeline.
TokenPipeline(Tokenizer, int, boolean, int) - Constructor for class org.tribuo.data.text.impl.TokenPipeline: Creates a new token pipeline.
tolerance - Static variable in interface org.tribuo.math.optimisers.util.ShrinkingTensor
TOLERANCE - Static variable in class org.tribuo.regression.Regressor
toMaxLabels(List<Prediction<T>>) - Static method in class org.tribuo.sequence.SequenceModel
topFeatures(CommandInterpreter, int) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
topFeatures(CommandInterpreter, int) - Method in class org.tribuo.ModelExplorer: Displays the top n features.
topFeatures(CommandInterpreter, int) - Method in class org.tribuo.sequence.SequenceModelExplorer
toPrimitiveDouble(List<Double>) - Static method in class org.tribuo.util.Util
toPrimitiveDoubleFromInteger(List<Integer>) - Static method in class org.tribuo.util.Util
toPrimitiveFloat(List<Float>) - Static method in class org.tribuo.util.Util
toPrimitiveInt(List<Integer>) - Static method in class org.tribuo.util.Util
toPrimitiveLong(List<Long>) - Static method in class org.tribuo.util.Util
toReadableString() - Method in class org.tribuo.anomaly.AnomalyInfo
toReadableString() - Method in class org.tribuo.classification.ImmutableLabelInfo
toReadableString() - Method in class org.tribuo.classification.MutableLabelInfo
toReadableString() - Method in class org.tribuo.clustering.ClusteringInfo
toReadableString() - Method in class org.tribuo.FeatureMap: Same as the toString, but ordered by name, and with newlines.
toReadableString() - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
toReadableString() - Method in class org.tribuo.multilabel.MutableMultiLabelInfo
toReadableString() - Method in interface org.tribuo.OutputInfo: Generates a String form of this OutputInfo.
toReadableString() - Method in class org.tribuo.regression.ImmutableRegressionInfo
toReadableString() - Method in class org.tribuo.regression.MutableRegressionInfo
toString() - Method in class org.tribuo.anomaly.AnomalyFactory.AnomalyFactoryProvenance
toString() - Method in class org.tribuo.anomaly.Event
toString() - Method in class org.tribuo.CategoricalIDInfo
toString() - Method in class org.tribuo.CategoricalInfo
toString() - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer
toString() - Method in class org.tribuo.classification.dtree.CARTClassificationTrainer
toString() - Method in class org.tribuo.classification.dtree.impurity.Entropy
toString() - Method in class org.tribuo.classification.dtree.impurity.GiniIndex
toString() - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer
toString() - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
toString() - Method in class org.tribuo.classification.ensemble.VotingCombiner
toString() - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
toString() - Method in class org.tribuo.classification.evaluation.LabelMetric
toString() - Method in class org.tribuo.classification.explanations.lime.LIMEExplanation
toString() - Method in class org.tribuo.classification.Label
toString() - Method in class org.tribuo.classification.LabelFactory.LabelFactoryProvenance
toString() - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesTrainer
toString() - Method in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence
toString() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
toString() - Method in class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
toString() - Method in class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
toString() - Method in class org.tribuo.classification.sequence.viterbi.ViterbiTrainer
toString() - Method in class org.tribuo.classification.sgd.crf.CRFTrainer
toString() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
toString() - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
toString() - Method in class org.tribuo.classification.sgd.objectives.Hinge
toString() - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass
toString() - Method in class org.tribuo.clustering.ClusterID
toString() - Method in class org.tribuo.clustering.ClusteringFactory.ClusteringFactoryProvenance
toString() - Method in class org.tribuo.clustering.evaluation.ClusteringMetric
toString() - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
toString() - Method in class org.tribuo.common.liblinear.LibLinearTrainer
toString() - Method in class org.tribuo.common.libsvm.LibSVMTrainer
toString() - Method in class org.tribuo.common.libsvm.SVMParameters
toString() - Method in class org.tribuo.common.nearest.KNNTrainer
toString() - Method in class org.tribuo.common.tree.LeafNode
toString() - Method in class org.tribuo.common.tree.RandomForestTrainer
toString() - Method in class org.tribuo.common.tree.SplitNode
toString() - Method in class org.tribuo.common.tree.TreeModel
toString() - Method in class org.tribuo.common.xgboost.XGBoostTrainer
toString() - Method in class org.tribuo.data.columnar.ColumnarIterator.Row
toString() - Method in class org.tribuo.data.columnar.extractors.DateExtractor
toString() - Method in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor
toString() - Method in class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
toString() - Method in class org.tribuo.data.columnar.processors.field.IdentityProcessor
toString() - Method in class org.tribuo.data.columnar.processors.field.RegexFieldProcessor
toString() - Method in class org.tribuo.data.columnar.processors.field.TextFieldProcessor
toString() - Method in class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor
toString() - Method in class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
toString() - Method in class org.tribuo.data.columnar.processors.response.Quartile
toString() - Method in class org.tribuo.data.columnar.processors.response.QuartileResponseProcessor
toString() - Method in class org.tribuo.data.columnar.RowProcessor
toString() - Method in class org.tribuo.data.csv.CSVDataSource
toString() - Method in class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance
toString() - Method in class org.tribuo.data.sql.SQLDataSource
toString() - Method in class org.tribuo.data.sql.SQLDBConfig
toString() - Method in class org.tribuo.data.text.DirectoryFileSource
toString() - Method in class org.tribuo.data.text.impl.BasicPipeline
toString() - Method in class org.tribuo.data.text.impl.SimpleStringDataSource
toString() - Method in class org.tribuo.data.text.impl.TextFeatureExtractorImpl
toString() - Method in class org.tribuo.data.text.impl.TokenPipeline
toString() - Method in class org.tribuo.data.text.TextDataSource
toString() - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance: This toString doesn't put the indices in the string, as it's likely to be huge.
toString() - Method in class org.tribuo.dataset.DatasetView
toString() - Method in class org.tribuo.Dataset
toString() - Method in class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance
toString() - Method in class org.tribuo.datasource.AggregateDataSource
toString() - Method in class org.tribuo.datasource.IDXDataSource
toString() - Method in class org.tribuo.datasource.LibSVMDataSource
toString() - Method in class org.tribuo.datasource.ListDataSource
toString() - Method in class org.tribuo.ensemble.BaggingTrainer
toString() - Method in class org.tribuo.evaluation.DescriptiveStats
toString() - Method in class org.tribuo.evaluation.metrics.MetricID
toString() - Method in class org.tribuo.evaluation.metrics.MetricTarget
toString() - Method in class org.tribuo.evaluation.TrainTestSplitter.SplitDataSourceProvenance
toString() - Method in class org.tribuo.Feature
toString() - Method in class org.tribuo.FeatureMap
toString() - Method in class org.tribuo.hash.HashCodeHasher.HashCodeHasherProvenance
toString() - Method in class org.tribuo.hash.HashCodeHasher
toString() - Method in class org.tribuo.hash.MessageDigestHasher.MessageDigestHasherProvenance
toString() - Method in class org.tribuo.hash.MessageDigestHasher
toString() - Method in class org.tribuo.hash.ModHashCodeHasher.ModHashCodeHasherProvenance
toString() - Method in class org.tribuo.hash.ModHashCodeHasher
toString() - Method in class org.tribuo.ImmutableDataset
toString() - Method in class org.tribuo.impl.ArrayExample
toString() - Method in class org.tribuo.impl.BinaryFeaturesExample
toString() - Method in class org.tribuo.impl.ListExample
toString() - Method in class org.tribuo.interop.ExternalTrainerProvenance
toString() - Method in class org.tribuo.interop.onnx.DenseTransformer
toString() - Method in class org.tribuo.interop.onnx.ImageTransformer
toString() - Method in class org.tribuo.interop.onnx.LabelTransformer
toString() - Method in class org.tribuo.interop.onnx.RegressorTransformer
toString() - Method in class org.tribuo.interop.tensorflow.DenseTransformer
toString() - Method in class org.tribuo.interop.tensorflow.ImageTransformer
toString() - Method in class org.tribuo.interop.tensorflow.LabelTransformer
toString() - Method in class org.tribuo.interop.tensorflow.RegressorTransformer
toString() - Method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
toString() - Method in class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer
toString() - Method in class org.tribuo.interop.tensorflow.TensorflowTrainer
toString() - Method in class org.tribuo.json.JsonDataSource
toString() - Method in class org.tribuo.math.kernel.Linear
toString() - Method in class org.tribuo.math.kernel.Polynomial
toString() - Method in class org.tribuo.math.kernel.RBF
toString() - Method in class org.tribuo.math.kernel.Sigmoid
toString() - Method in class org.tribuo.math.la.DenseMatrix
toString() - Method in class org.tribuo.math.la.DenseSparseMatrix
toString() - Method in class org.tribuo.math.la.DenseVector
toString() - Method in class org.tribuo.math.la.MatrixTuple
toString() - Method in class org.tribuo.math.la.SparseVector
toString() - Method in class org.tribuo.math.la.VectorTuple
toString() - Method in class org.tribuo.math.optimisers.AdaDelta
toString() - Method in class org.tribuo.math.optimisers.AdaGrad
toString() - Method in class org.tribuo.math.optimisers.AdaGradRDA
toString() - Method in class org.tribuo.math.optimisers.Adam
toString() - Method in class org.tribuo.math.optimisers.ParameterAveraging
toString() - Method in class org.tribuo.math.optimisers.Pegasos
toString() - Method in class org.tribuo.math.optimisers.RMSProp
toString() - Method in class org.tribuo.math.optimisers.SGD
toString() - Method in class org.tribuo.Model
toString() - Method in class org.tribuo.multilabel.baseline.IndependentMultiLabelTrainer
toString() - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
toString() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
toString() - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric
toString() - Method in class org.tribuo.multilabel.MultiLabel
toString() - Method in class org.tribuo.multilabel.MultiLabelFactory.MultiLabelFactoryProvenance
toString() - Method in class org.tribuo.MutableDataset
toString() - Method in class org.tribuo.Prediction
toString() - Method in class org.tribuo.provenance.DatasetProvenance
toString() - Method in class org.tribuo.provenance.EnsembleModelProvenance
toString() - Method in class org.tribuo.provenance.EvaluationProvenance
toString() - Method in class org.tribuo.provenance.impl.EmptyDatasetProvenance
toString() - Method in class org.tribuo.provenance.impl.EmptyDataSourceProvenance
toString() - Method in class org.tribuo.provenance.impl.EmptyTrainerProvenance
toString() - Method in class org.tribuo.provenance.ModelProvenance
toString() - Method in class org.tribuo.provenance.SimpleDataSourceProvenance
toString() - Method in class org.tribuo.RealIDInfo
toString() - Method in class org.tribuo.RealInfo
toString() - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance: Deprecated.
toString() - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer
toString() - Method in class org.tribuo.regression.ensemble.AveragingCombiner
toString() - Method in class org.tribuo.regression.ImmutableRegressionInfo
toString() - Method in class org.tribuo.regression.MutableRegressionInfo
toString() - Method in class org.tribuo.regression.RegressionFactory.RegressionFactoryProvenance
toString() - Method in class org.tribuo.regression.Regressor.DimensionTuple
toString() - Method in class org.tribuo.regression.Regressor
toString() - Method in class org.tribuo.regression.rtree.CARTJointRegressionTrainer
toString() - Method in class org.tribuo.regression.rtree.CARTRegressionTrainer
toString() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
toString() - Method in class org.tribuo.regression.rtree.impl.TreeFeature
toString() - Method in class org.tribuo.regression.rtree.impurity.MeanAbsoluteError
toString() - Method in class org.tribuo.regression.rtree.impurity.MeanSquaredError
toString() - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
toString() - Method in class org.tribuo.regression.sgd.linear.LinearSGDTrainer
toString() - Method in class org.tribuo.regression.sgd.objectives.AbsoluteLoss
toString() - Method in class org.tribuo.regression.sgd.objectives.Huber
toString() - Method in class org.tribuo.regression.sgd.objectives.SquaredLoss
toString() - Method in class org.tribuo.regression.slm.ElasticNetCDTrainer
toString() - Method in class org.tribuo.regression.slm.LARSLassoTrainer
toString() - Method in class org.tribuo.regression.slm.LARSTrainer
toString() - Method in class org.tribuo.regression.slm.SLMTrainer
toString() - Method in class org.tribuo.sequence.HashingSequenceTrainer
toString() - Method in class org.tribuo.sequence.ImmutableSequenceDataset
toString() - Method in class org.tribuo.sequence.MutableSequenceDataset
toString() - Method in class org.tribuo.sequence.SequenceDataset
toString() - Method in class org.tribuo.sequence.SequenceModel
toString() - Method in class org.tribuo.SkeletalVariableInfo
toString() - Method in class org.tribuo.transform.TransformationMap
toString() - Method in class org.tribuo.transform.transformations.BinningTransformation
toString() - Method in class org.tribuo.transform.transformations.LinearScalingTransformation
toString() - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation
toString() - Method in class org.tribuo.transform.transformations.SimpleTransform
toString() - Method in class org.tribuo.transform.TransformerMap
toString() - Method in class org.tribuo.transform.TransformerMap.TransformerMapProvenance
toString() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
toString() - Method in class org.tribuo.util.infotheory.impl.Row
toString() - Method in class org.tribuo.util.infotheory.InformationTheory.GTestStatistics
toString() - Method in class org.tribuo.util.IntDoublePair
toString() - Method in class org.tribuo.util.tokens.Token
toString() - Method in class org.tribuo.util.tokens.universal.Range
totalCount - Variable in class org.tribuo.multilabel.MultiLabelInfo
totalObservations - Variable in class org.tribuo.CategoricalInfo: The total number of observations (including zeros).
totalSize() - Method in class org.tribuo.evaluation.TrainTestSplitter: The total amount of data in train and test combined.
tp() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro average of the number of true positives across all the labels, i.e., the total number of true positives.
tp() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The total number of true positives.
tp() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
tp() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
tp(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
tp(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
tp(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Returns the number of true positives, possibly averaged depending on the metric target.
tp(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
tp(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
tp(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the number of true positives, i.e., the number of times the label was correctly predicted.
tp(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of true positives for the supplied label.
TP - Enum constant in enum org.tribuo.anomaly.evaluation.AnomalyMetrics: The number of true positives.
TP - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The number of true positives.
TP - Enum constant in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics: The number of true positives.
tpr - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.ROC
train - Variable in class org.tribuo.evaluation.KFoldSplitter.TrainTestFold
train(Dataset<Event>, Map<String, Provenance>) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer
train(Dataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer
train(Dataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer: If the trainer implements WeightedExamples then do boosting by weighting, otherwise do boosting by sampling.
train(Dataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesTrainer
train(Dataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
train(Dataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
train(Dataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.xgboost.XGBoostClassificationTrainer
train(Dataset<ClusterID>) - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
train(Dataset<ClusterID>, Map<String, Provenance>) - Method in class org.tribuo.clustering.kmeans.KMeansTrainer
train(Dataset<MultiLabel>, Map<String, Provenance>) - Method in class org.tribuo.multilabel.baseline.IndependentMultiLabelTrainer
train(Dataset<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer
train(Dataset<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer
train(Dataset<Regressor>, Map<String, Provenance>) - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer
train(Dataset<Regressor>, Map<String, Provenance>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer
train(Dataset<Regressor>, Map<String, Provenance>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer
train(Dataset<Regressor>, Map<String, Provenance>) - Method in class org.tribuo.regression.rtree.CARTRegressionTrainer
train(Dataset<Regressor>, Map<String, Provenance>) - Method in class org.tribuo.regression.sgd.linear.LinearSGDTrainer
train(Dataset<Regressor>, Map<String, Provenance>) - Method in class org.tribuo.regression.slm.ElasticNetCDTrainer
train(Dataset<Regressor>, Map<String, Provenance>) - Method in class org.tribuo.regression.slm.SLMTrainer: Trains a sparse linear model.
train(Dataset<Regressor>, Map<String, Provenance>) - Method in class org.tribuo.regression.xgboost.XGBoostRegressionTrainer
train(Dataset<T>) - Method in class org.tribuo.common.libsvm.LibSVMTrainer
train(Dataset<T>) - Method in class org.tribuo.common.tree.AbstractCARTTrainer
train(Dataset<T>) - Method in interface org.tribuo.SparseTrainer: Trains a sparse predictive model using the examples in the given data set.
train(Dataset<T>) - Method in interface org.tribuo.Trainer: Trains a predictive model using the examples in the given data set.
train(Dataset<T>, Map<String, Provenance>) - Method in class org.tribuo.common.liblinear.LibLinearTrainer
train(Dataset<T>, Map<String, Provenance>) - Method in class org.tribuo.common.libsvm.LibSVMTrainer
train(Dataset<T>, Map<String, Provenance>) - Method in class org.tribuo.common.nearest.KNNTrainer
train(Dataset<T>, Map<String, Provenance>) - Method in class org.tribuo.common.tree.AbstractCARTTrainer
train(Dataset<T>, Map<String, Provenance>) - Method in class org.tribuo.ensemble.BaggingTrainer
train(Dataset<T>, Map<String, Provenance>) - Method in class org.tribuo.hash.HashingTrainer: This clones the Dataset, hashes each of the examples and rewrites their feature ids before passing it to the inner trainer.
train(Dataset<T>, Map<String, Provenance>) - Method in class org.tribuo.interop.tensorflow.TensorflowCheckpointTrainer
train(Dataset<T>, Map<String, Provenance>) - Method in class org.tribuo.interop.tensorflow.TensorflowTrainer
train(Dataset<T>, Map<String, Provenance>) - Method in interface org.tribuo.SparseTrainer: Trains a sparse predictive model using the examples in the given data set.
train(Dataset<T>, Map<String, Provenance>) - Method in interface org.tribuo.Trainer: Trains a predictive model using the examples in the given data set.
train(Dataset<T>, Map<String, Provenance>) - Method in class org.tribuo.transform.TransformTrainer
train(SequenceDataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiTrainer: The viterbi train method is unique because it delegates to a regular Model train method, but before it does, it adds features derived from preceding labels.
train(SequenceDataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.sgd.crf.CRFTrainer
train(SequenceDataset<T>) - Method in interface org.tribuo.sequence.SequenceTrainer: Trains a sequence prediction model using the examples in the given data set.
train(SequenceDataset<T>, Map<String, Provenance>) - Method in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
train(SequenceDataset<T>, Map<String, Provenance>) - Method in class org.tribuo.sequence.HashingSequenceTrainer: This clones the SequenceDataset, hashes each of the examples and rewrites their feature ids before passing it to the inner trainer.
train(SequenceDataset<T>, Map<String, Provenance>) - Method in interface org.tribuo.sequence.SequenceTrainer: Trains a sequence prediction model using the examples in the given data set.
TRAIN - Static variable in class org.tribuo.interop.tensorflow.TensorflowTrainer
TRAIN_INVOCATION_COUNT - Static variable in interface org.tribuo.provenance.TrainerProvenance
trainDataset - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
trainDimension(double[], SparseVector[], float[], SplittableRandom) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer: Trains a single dimension of the possibly multiple dimensions.
trainDimension(double[], SparseVector[], float[], SplittableRandom) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer: Trains a single dimension of the possibly multiple dimensions.
trainer - Variable in class org.tribuo.classification.experiments.ConfigurableTrainTest.ConfigurableTrainTestOptions
trainer - Variable in class org.tribuo.data.CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions
trainer - Variable in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
Trainer<T> - Interface in org.tribuo: An interface for things that can train predictive models.
TRAINER - Enum constant in enum org.tribuo.json.StripProvenance.ProvenanceTypes: Select the trainer provenance.
TRAINER - Static variable in class org.tribuo.provenance.ModelProvenance
trainerOptions - Variable in class org.tribuo.classification.experiments.TrainTest.AllClassificationOptions
trainerOptions - Variable in class org.tribuo.classification.sgd.kernel.TrainTest.TrainTestOptions
trainerOptions - Variable in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
trainerProvenance - Variable in class org.tribuo.provenance.ModelProvenance
TrainerProvenance - Interface in org.tribuo.provenance: A tag interface for trainer provenances.
TrainerProvenanceImpl - Class in org.tribuo.provenance.impl: An implementation of TrainerProvenance that delegates everything to SkeletalTrainerProvenance.
TrainerProvenanceImpl(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.TrainerProvenanceImpl: Construct a TrainerProvenance by extracting the necessary fields from the supplied map.
TrainerProvenanceImpl(SequenceTrainer<T>) - Constructor for class org.tribuo.provenance.impl.TrainerProvenanceImpl: Construct a TrainerProvenance by reading all the configurable parameters along with the train call count.
TrainerProvenanceImpl(Trainer<T>) - Constructor for class org.tribuo.provenance.impl.TrainerProvenanceImpl: Construct a TrainerProvenance by reading all the configurable parameters along with the train call count.
trainerType - Variable in class org.tribuo.common.liblinear.LibLinearTrainer
trainExplainer(Example<Regressor>, List<Example<Regressor>>) - Method in class org.tribuo.classification.explanations.lime.LIMEBase: Trains the explanation model using the supplied sampled data and the input example.
TRAINING_LOSS - Static variable in class org.tribuo.interop.tensorflow.TensorflowTrainer
TRAINING_TIME - Static variable in class org.tribuo.provenance.ModelProvenance
trainingPath - Variable in class org.tribuo.data.DataOptions
trainingPath - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
trainInvocationCounter - Variable in class org.tribuo.classification.ensemble.AdaBoostTrainer
trainInvocationCounter - Variable in class org.tribuo.common.tree.AbstractCARTTrainer
trainInvocationCounter - Variable in class org.tribuo.common.xgboost.XGBoostTrainer
trainInvocationCounter - Variable in class org.tribuo.ensemble.BaggingTrainer
trainInvocationCounter - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
trainInvocationCounter - Variable in class org.tribuo.regression.slm.SLMTrainer
trainModels(Parameter, int, FeatureNode[][], double[][]) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationTrainer
trainModels(Parameter, int, FeatureNode[][], double[][]) - Method in class org.tribuo.common.liblinear.LibLinearTrainer: Train all the liblinear instances necessary for this dataset.
trainModels(Parameter, int, FeatureNode[][], double[][]) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionTrainer
trainModels(svm_parameter, int, svm_node[][], double[][]) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer
trainModels(svm_parameter, int, svm_node[][], double[][]) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationTrainer
trainModels(svm_parameter, int, svm_node[][], double[][]) - Method in class org.tribuo.common.libsvm.LibSVMTrainer: Train all the liblinear instances necessary for this dataset.
trainModels(svm_parameter, int, svm_node[][], double[][]) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionTrainer
trainOp - Variable in class org.tribuo.interop.tensorflow.sequence.TensorflowSequenceTrainer
trainPath - Variable in class org.tribuo.data.text.SplitTextData.TrainTestSplitOptions
trainSingleModel(Dataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>, SplittableRandom, Map<String, Provenance>) - Method in class org.tribuo.ensemble.BaggingTrainer
trainSource - Variable in class org.tribuo.data.CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions
TrainTest - Class in org.tribuo.classification.dtree: Build and run a decision tree classifier for a standard dataset.
TrainTest - Class in org.tribuo.classification.experiments: Build and run a classifier for a standard dataset.
TrainTest - Class in org.tribuo.classification.liblinear: Build and run a liblinear-java classifier for a standard dataset.
TrainTest - Class in org.tribuo.classification.libsvm: Build and run a LibSVM classifier for a standard dataset.
TrainTest - Class in org.tribuo.classification.mnb: Build and run a multinomial naive bayes classifier for a standard dataset.
TrainTest - Class in org.tribuo.classification.sgd.kernel: Build and run a kernel SVM classifier for a standard dataset.
TrainTest - Class in org.tribuo.classification.sgd: Build and run a classifier for a standard dataset using LinearSGDTrainer.
TrainTest - Class in org.tribuo.classification.xgboost: Build and run an XGBoost classifier for a standard dataset.
TrainTest - Class in org.tribuo.clustering.kmeans: Build and run a k-means clustering model for a standard dataset.
TrainTest - Class in org.tribuo.interop.tensorflow: Build and run a Tensorflow multi-class classifier for a standard dataset.
TrainTest - Class in org.tribuo.regression.liblinear: Build and run a LibLinear regressor for a standard dataset.
TrainTest - Class in org.tribuo.regression.libsvm: Build and run a LibSVM regressor for a standard dataset.
TrainTest - Class in org.tribuo.regression.rtree: Build and run a regression tree for a standard dataset.
TrainTest - Class in org.tribuo.regression.sgd: Build and run a linear regression for a standard dataset.
TrainTest - Class in org.tribuo.regression.slm: Build and run a sparse linear regression model for a standard dataset.
TrainTest - Class in org.tribuo.regression.xgboost: Build and run an XGBoost regressor for a standard dataset.
TrainTest() - Constructor for class org.tribuo.classification.dtree.TrainTest
TrainTest() - Constructor for class org.tribuo.classification.experiments.TrainTest
TrainTest() - Constructor for class org.tribuo.classification.liblinear.TrainTest
TrainTest() - Constructor for class org.tribuo.classification.libsvm.TrainTest
TrainTest() - Constructor for class org.tribuo.classification.mnb.TrainTest
TrainTest() - Constructor for class org.tribuo.classification.sgd.kernel.TrainTest
TrainTest() - Constructor for class org.tribuo.classification.sgd.TrainTest
TrainTest() - Constructor for class org.tribuo.classification.xgboost.TrainTest
TrainTest() - Constructor for class org.tribuo.clustering.kmeans.TrainTest
TrainTest() - Constructor for class org.tribuo.interop.tensorflow.TrainTest
TrainTest() - Constructor for class org.tribuo.regression.liblinear.TrainTest
TrainTest() - Constructor for class org.tribuo.regression.libsvm.TrainTest
TrainTest() - Constructor for class org.tribuo.regression.rtree.TrainTest
TrainTest() - Constructor for class org.tribuo.regression.sgd.TrainTest
TrainTest() - Constructor for class org.tribuo.regression.slm.TrainTest
TrainTest() - Constructor for class org.tribuo.regression.xgboost.TrainTest
TrainTest.AllClassificationOptions - Class in org.tribuo.classification.experiments
TrainTest.DecisionTreeOptions - Class in org.tribuo.regression.rtree
TrainTest.ImpurityType - Enum in org.tribuo.regression.rtree
TrainTest.InputType - Enum in org.tribuo.interop.tensorflow
TrainTest.KMeansOptions - Class in org.tribuo.clustering.kmeans: Options for the K-Means CLI.
TrainTest.LARSOptions - Class in org.tribuo.regression.slm
TrainTest.LibLinearOptions - Class in org.tribuo.regression.liblinear
TrainTest.LibLinearOptions - Class in org.tribuo.regression.libsvm
TrainTest.LossEnum - Enum in org.tribuo.regression.sgd
TrainTest.SGDOptions - Class in org.tribuo.regression.sgd
TrainTest.SLMType - Enum in org.tribuo.regression.slm
TrainTest.TensorflowOptions - Class in org.tribuo.interop.tensorflow
TrainTest.TrainTestOptions - Class in org.tribuo.classification.dtree
TrainTest.TrainTestOptions - Class in org.tribuo.classification.liblinear
TrainTest.TrainTestOptions - Class in org.tribuo.classification.libsvm
TrainTest.TrainTestOptions - Class in org.tribuo.classification.mnb
TrainTest.TrainTestOptions - Class in org.tribuo.classification.sgd.kernel
TrainTest.TrainTestOptions - Class in org.tribuo.classification.sgd
TrainTest.TrainTestOptions - Class in org.tribuo.classification.xgboost
TrainTest.TreeType - Enum in org.tribuo.regression.rtree
TrainTest.XGBoostOptions - Class in org.tribuo.regression.xgboost
TrainTestHelper - Class in org.tribuo.classification: This class provides static methods used by the demo classes in each classification backend.
TrainTestOptions() - Constructor for class org.tribuo.classification.dtree.TrainTest.TrainTestOptions
TrainTestOptions() - Constructor for class org.tribuo.classification.liblinear.TrainTest.TrainTestOptions
TrainTestOptions() - Constructor for class org.tribuo.classification.libsvm.TrainTest.TrainTestOptions
TrainTestOptions() - Constructor for class org.tribuo.classification.mnb.TrainTest.TrainTestOptions
TrainTestOptions() - Constructor for class org.tribuo.classification.sgd.kernel.TrainTest.TrainTestOptions
TrainTestOptions() - Constructor for class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
TrainTestOptions() - Constructor for class org.tribuo.classification.xgboost.TrainTest.TrainTestOptions
TrainTestSplitOptions() - Constructor for class org.tribuo.data.text.SplitTextData.TrainTestSplitOptions
TrainTestSplitter<T> - Class in org.tribuo.evaluation: Splits data into training and testing sets.
TrainTestSplitter(DataSource<T>) - Constructor for class org.tribuo.evaluation.TrainTestSplitter: Creates a splitter that splits a dataset 70/30 train and test using a default seed.
TrainTestSplitter(DataSource<T>, double, long) - Constructor for class org.tribuo.evaluation.TrainTestSplitter: Creates a splitter that will split the given data set into a training and testing set.
TrainTestSplitter(DataSource<T>, long) - Constructor for class org.tribuo.evaluation.TrainTestSplitter: Creates a splitter that splits a dataset 70/30 train and test.
TrainTestSplitter.SplitDataSourceProvenance - Class in org.tribuo.evaluation: Provenance for a split data source.
transform(double) - Method in class org.tribuo.transform.transformations.SimpleTransform: Apply the operation to the input.
transform(double) - Method in interface org.tribuo.transform.Transformer: Applies the transformation to the supplied input value.
transform(OrtEnvironment, List<SparseVector>) - Method in class org.tribuo.interop.onnx.DenseTransformer
transform(OrtEnvironment, List<SparseVector>) - Method in interface org.tribuo.interop.onnx.ExampleTransformer: Converts a list of SparseVectors representing a batch of features into a OnnxTensor.
transform(OrtEnvironment, List<SparseVector>) - Method in class org.tribuo.interop.onnx.ImageTransformer
transform(OrtEnvironment, SparseVector) - Method in class org.tribuo.interop.onnx.DenseTransformer
transform(OrtEnvironment, SparseVector) - Method in interface org.tribuo.interop.onnx.ExampleTransformer: Converts a SparseVector representing the features into a OnnxTensor.
transform(OrtEnvironment, SparseVector) - Method in class org.tribuo.interop.onnx.ImageTransformer
transform(List<Example<Label>>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelTransformer
transform(List<Example<Regressor>>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorTransformer
transform(List<Example<T>>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.DenseTransformer
transform(List<Example<T>>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.ExampleTransformer: Converts a batch of Examples into a single Tensor suitable for supplying as an input to a graph.
transform(List<Example<T>>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.ImageTransformer: Transform implicitly pads unseen values with zero.
transform(List<Example<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputTransformer: Converts a list of Example into a Tensor representing all the outputs in the list.
transform(List<SparseVector>) - Method in class org.tribuo.interop.tensorflow.DenseTransformer
transform(List<SparseVector>) - Method in interface org.tribuo.interop.tensorflow.ExampleTransformer: Converts a list of SparseVectors representing a batch of features into a Tensor.
transform(List<SparseVector>) - Method in class org.tribuo.interop.tensorflow.ImageTransformer
transform(Label, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelTransformer
transform(Example<T>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.DenseTransformer
transform(Example<T>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.ExampleTransformer: Converts an Example into a Tensor suitable for supplying as an input to a graph.
transform(Example<T>, ImmutableFeatureMap) - Method in class org.tribuo.interop.tensorflow.ImageTransformer: Transform implicitly pads unseen values with zero.
transform(SparseVector) - Method in class org.tribuo.interop.tensorflow.DenseTransformer
transform(SparseVector) - Method in interface org.tribuo.interop.tensorflow.ExampleTransformer: Converts a SparseVector representing the features into a Tensor.
transform(SparseVector) - Method in class org.tribuo.interop.tensorflow.ImageTransformer
transform(Regressor, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorTransformer
transform(TransformerMap) - Method in class org.tribuo.Example: Transforms this example by applying the transformations from the supplied TransformerMap.
transform(TransformerMap) - Method in class org.tribuo.impl.ArrayExample
transform(TransformerMap) - Method in class org.tribuo.impl.BinaryFeaturesExample
transform(TransformerMap) - Method in class org.tribuo.impl.ListExample
transform(TransformerMap) - Method in class org.tribuo.MutableDataset: Applies all the transformations from the TransformerMap to this dataset.
transform(T, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputTransformer: Converts an Output into a Tensor representing it's output.
Transformation - Interface in org.tribuo.transform: An interface representing a class of transformations which can be applied to a feature.
TransformationList(List<Transformation>) - Constructor for class org.tribuo.transform.TransformationMap.TransformationList
transformationMap - Variable in class org.tribuo.data.CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions
transformationMap - Variable in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
TransformationMap - Class in org.tribuo.transform: A carrier type for a set of transformations to be applied to a Dataset.
TransformationMap(List<Transformation>) - Constructor for class org.tribuo.transform.TransformationMap
TransformationMap(List<Transformation>, Map<String, List<Transformation>>) - Constructor for class org.tribuo.transform.TransformationMap
TransformationMap(Map<String, List<Transformation>>) - Constructor for class org.tribuo.transform.TransformationMap
TransformationMap.TransformationList - Class in org.tribuo.transform: A carrier type as OLCUT does not support nested generics.
TransformationProvenance - Interface in org.tribuo.transform: A tag interface for provenances in the transformation system.
transformDataset(Dataset<T>) - Method in class org.tribuo.transform.TransformerMap: Copies the supplied dataset and applies the transformers to each example in it.
transformDataset(Dataset<T>, boolean) - Method in class org.tribuo.transform.TransformerMap: Copies the supplied dataset and applies the transformers to each example in it.
TransformedModel<T> - Class in org.tribuo.transform: Wraps a Model with it's TransformerMap so all Examples are transformed appropriately before the model makes predictions.
Transformer - Interface in org.tribuo.transform: A fitted Transformation which can apply a transform to the input value.
TransformerMap - Class in org.tribuo.transform: A collection of Transformers which can be applied to a Dataset or Example.
TransformerMap(Map<String, List<Transformer>>, DatasetProvenance, ConfiguredObjectProvenance) - Constructor for class org.tribuo.transform.TransformerMap: Constructs a transformer map which encapsulates a set of transformers that can be applied to features.
TransformerMap.TransformerMapProvenance - Class in org.tribuo.transform: Provenance for TransformerMap.
TransformerMapProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.transform.TransformerMap.TransformerMapProvenance
transformExample(Example<T>) - Method in class org.tribuo.transform.TransformerMap: Copies the supplied example and applies the transformers to it.
transformExample(Example<T>, List<String>) - Method in class org.tribuo.transform.TransformerMap: Copies the supplied example and applies the transformers to it.
transformOutput(Prediction<Label>) - Static method in class org.tribuo.classification.explanations.lime.LIMEBase: Transforms a Prediction for a multiclass problem into a Regressor output which represents the probability for each class.
transformProvenances - Variable in class org.tribuo.MutableDataset: The provenances of the transformations applied to this dataset.
TransformStatistics - Interface in org.tribuo.transform: An interface for the statistics that need to be collected for a specific Transformation on a single feature.
transformToBatchOutput(List<OnnxValue>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.onnx.LabelTransformer
transformToBatchOutput(List<OnnxValue>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.onnx.RegressorTransformer
transformToBatchOutput(List<OnnxValue>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.onnx.OutputTransformer: Converts a OnnxValue containing multiple outputs into a list of Outputs.
transformToBatchOutput(Tensor<?>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelTransformer
transformToBatchOutput(Tensor<?>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorTransformer
transformToBatchOutput(Tensor<?>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputTransformer: Converts a Tensor containing multiple outputs into a list of Outputs.
transformToBatchPrediction(List<OnnxValue>, ImmutableOutputInfo<Label>, int[], List<Example<Label>>) - Method in class org.tribuo.interop.onnx.LabelTransformer
transformToBatchPrediction(List<OnnxValue>, ImmutableOutputInfo<Regressor>, int[], List<Example<Regressor>>) - Method in class org.tribuo.interop.onnx.RegressorTransformer
transformToBatchPrediction(List<OnnxValue>, ImmutableOutputInfo<T>, int[], List<Example<T>>) - Method in interface org.tribuo.interop.onnx.OutputTransformer: Converts a OnnxValue containing multiple outputs into a list of Predictions.
transformToBatchPrediction(Tensor<?>, ImmutableOutputInfo<Label>, int[], List<Example<Label>>) - Method in class org.tribuo.interop.tensorflow.LabelTransformer
transformToBatchPrediction(Tensor<?>, ImmutableOutputInfo<Regressor>, int[], List<Example<Regressor>>) - Method in class org.tribuo.interop.tensorflow.RegressorTransformer
transformToBatchPrediction(Tensor<?>, ImmutableOutputInfo<T>, int[], List<Example<T>>) - Method in interface org.tribuo.interop.tensorflow.OutputTransformer: Converts a Tensor containing multiple outputs into a list of Predictions.
transformToOutput(List<OnnxValue>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.onnx.LabelTransformer
transformToOutput(List<OnnxValue>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.onnx.RegressorTransformer
transformToOutput(List<OnnxValue>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.onnx.OutputTransformer: Converts a OnnxValue into the specified output type.
transformToOutput(Tensor<?>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelTransformer
transformToOutput(Tensor<?>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorTransformer
transformToOutput(Tensor<?>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputTransformer: Converts a Tensor into the specified output type.
transformToPrediction(List<OnnxValue>, ImmutableOutputInfo<Label>, int, Example<Label>) - Method in class org.tribuo.interop.onnx.LabelTransformer
transformToPrediction(List<OnnxValue>, ImmutableOutputInfo<Regressor>, int, Example<Regressor>) - Method in class org.tribuo.interop.onnx.RegressorTransformer
transformToPrediction(List<OnnxValue>, ImmutableOutputInfo<T>, int, Example<T>) - Method in interface org.tribuo.interop.onnx.OutputTransformer: Converts a OnnxValue into a Prediction.
transformToPrediction(Tensor<?>, ImmutableOutputInfo<Label>, int, Example<Label>) - Method in class org.tribuo.interop.tensorflow.LabelTransformer
transformToPrediction(Tensor<?>, ImmutableOutputInfo<Regressor>, int, Example<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorTransformer
transformToPrediction(Tensor<?>, ImmutableOutputInfo<T>, int, Example<T>) - Method in interface org.tribuo.interop.tensorflow.OutputTransformer: Converts a Tensor into a Prediction.
TransformTrainer<T> - Class in org.tribuo.transform: A Trainer which encapsulates another trainer plus a TransformationMap object to apply to each Dataset before training each Model.
TransformTrainer(Trainer<T>, TransformationMap) - Constructor for class org.tribuo.transform.TransformTrainer: Creates a trainer which transforms the data before training, and stores the transformers along with the trained model in a TransformedModel.
TransformTrainer(Trainer<T>, TransformationMap, boolean) - Constructor for class org.tribuo.transform.TransformTrainer: Creates a trainer which transforms the data before training, and stores the transformers along with the trained model in a TransformedModel.
transitionValues - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainCliqueValues
transpose() - Method in class org.tribuo.math.la.DenseMatrix
transpose(Dataset<T>) - Static method in class org.tribuo.math.la.SparseVector: Converts a dataset of row-major examples into an array of column-major sparse vectors.
transpose(Dataset<T>, ImmutableFeatureMap) - Static method in class org.tribuo.math.la.SparseVector: Converts a dataset of row-major examples into an array of column-major sparse vectors.
transpose(SparseVector[]) - Static method in class org.tribuo.math.la.SparseVector: Transposes an array of sparse vectors from row-major to column-major or vice versa.
TreeFeature - Class in org.tribuo.regression.rtree.impl: An inverted feature, which stores a reference to all the values of this feature.
TreeFeature(int) - Constructor for class org.tribuo.regression.rtree.impl.TreeFeature
TreeModel<T> - Class in org.tribuo.common.tree: A Model wrapped around a decision tree root Node.
TreeModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, boolean, Map<String, List<String>>) - Constructor for class org.tribuo.common.tree.TreeModel: Constructs a trained decision tree model.
treeType - Variable in class org.tribuo.regression.rtree.TrainTest.DecisionTreeOptions
Tribuo - Class in org.tribuo: This class stores the current Tribuo version, along with other compile time information.
Tribuo() - Constructor for class org.tribuo.Tribuo
TRIBUO_VERSION_STRING - Static variable in interface org.tribuo.provenance.TrainerProvenance
TripleDistribution<T1,T2,T3> - Class in org.tribuo.util.infotheory.impl: Generates the counts for a triplet of vectors.
TripleDistribution(long, 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>) - Constructor for class org.tribuo.util.infotheory.impl.TripleDistribution
tryAdvance(Consumer<? super ColumnarIterator.Row>) - Method in class org.tribuo.data.columnar.ColumnarIterator
TWEEDIE - Enum constant in enum org.tribuo.regression.xgboost.XGBoostRegressionTrainer.RegressionType: Tweedie loss function.
twoNorm() - Method in class org.tribuo.math.la.DenseMatrix
twoNorm() - Method in class org.tribuo.math.la.DenseSparseMatrix
twoNorm() - Method in class org.tribuo.math.la.DenseVector
twoNorm() - Method in interface org.tribuo.math.la.SGDVector: Calculates the euclidean norm for this vector.
twoNorm() - Method in class org.tribuo.math.la.SparseVector
twoNorm() - Method in interface org.tribuo.math.la.Tensor: Calculates the euclidean norm for this vector.
twoNorm() - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
twoNorm() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
type - Variable in class org.tribuo.classification.ensemble.ClassificationEnsembleOptions
type - Variable in class org.tribuo.util.infotheory.example.InformationTheoryDemo.DemoOptions
type - Variable in class org.tribuo.util.tokens.Token
type - Variable in class org.tribuo.util.tokens.universal.Range

U

UBYTE - Enum constant in enum org.tribuo.datasource.IDXDataSource.IDXType
UNASSIGNED - Static variable in class org.tribuo.clustering.ClusterID
UNASSIGNED_CLUSTER_ID - Static variable in class org.tribuo.clustering.ClusteringFactory
UNIFORM - Enum constant in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Samples uniformly from the label domain.
uniformSample(SplittableRandom) - Method in class org.tribuo.CategoricalInfo
uniformSample(SplittableRandom) - Method in class org.tribuo.RealInfo
uniformSample(SplittableRandom) - Method in interface org.tribuo.VariableInfo: Sample a value uniformly from the range of this variable.
UniqueAggregator - Class in org.tribuo.data.text.impl: Aggregates feature tokens, generating unique features.
UniqueAggregator() - Constructor for class org.tribuo.data.text.impl.UniqueAggregator
UniqueAggregator(double) - Constructor for class org.tribuo.data.text.impl.UniqueAggregator
UniqueProcessor - Class in org.tribuo.data.columnar.processors.feature: Processes a feature list, aggregating all the feature values with the same name.
UniqueProcessor(UniqueProcessor.UniqueType) - Constructor for class org.tribuo.data.columnar.processors.feature.UniqueProcessor: Creates a UniqueProcessor using the specified reduction operation.
UniqueProcessor.UniqueType - Enum in org.tribuo.data.columnar.processors.feature: The type of reduction operation to perform.
UNIVERSAL - Enum constant in enum org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType
UniversalTokenizer - Class in org.tribuo.util.tokens.universal: This class was originally written for the purpose of document indexing in an information retrieval context (principally used in Sun Labs' Minion search engine).
UniversalTokenizer() - Constructor for class org.tribuo.util.tokens.universal.UniversalTokenizer: Constructs a universal tokenizer which doesn't send punctuation.
UniversalTokenizer(boolean) - Constructor for class org.tribuo.util.tokens.universal.UniversalTokenizer
UNKNOWN - Enum constant in enum org.tribuo.anomaly.Event.EventType: An unknown (i.e., unlabelled) event, with id -1.
UNKNOWN - Static variable in class org.tribuo.classification.Label: The name of the unknown label (i.e., an unlabelled output).
UNKNOWN_EVENT - Static variable in class org.tribuo.anomaly.AnomalyFactory: The unknown event.
UNKNOWN_LABEL - Static variable in class org.tribuo.classification.LabelFactory: The singleton unknown label, used for unlablled examples.
UNKNOWN_MULTILABEL - Static variable in class org.tribuo.multilabel.MultiLabelFactory
UNKNOWN_MULTIPLE_REGRESSOR - Static variable in class org.tribuo.regression.RegressionFactory
UNKNOWN_REGRESSOR - Static variable in class org.tribuo.regression.RegressionFactory
unknownCount - Variable in class org.tribuo.anomaly.AnomalyInfo: The number of unknown events observed (i.e., those without labels).
unknownCount - Variable in class org.tribuo.classification.LabelInfo: The number of unknown labels this LabelInfo has seen.
unknownCount - Variable in class org.tribuo.clustering.ClusteringInfo
unknownCount - Variable in class org.tribuo.multilabel.MultiLabelInfo
unknownCount - Variable in class org.tribuo.regression.RegressionInfo
unpack(int[]) - Method in class org.tribuo.classification.sgd.crf.Chunk
update(Tensor[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters
update(Tensor[]) - Method in class org.tribuo.math.LinearParameters
update(Tensor[]) - Method in interface org.tribuo.math.Parameters: Apply gradients to the parameters.
usage() - Static method in class org.tribuo.sequence.SequenceModelExplorer
useBias() - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer: Returns true if the SparseVector should be constructed with a bias feature.
useBias() - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer: Returns true if the SparseVector should be constructed with a bias feature.
useMomentum - Variable in class org.tribuo.math.optimisers.SGD
username - Variable in class org.tribuo.data.sql.SQLToCSV.SQLToCSVOptions
utf8Charset - Static variable in class org.tribuo.hash.MessageDigestHasher
Util - Class in org.tribuo.classification.sgd: SGD utilities.
Util - Class in org.tribuo.regression.sgd: Utilities.
Util - Class in org.tribuo.util: Ye olde util class.
Util() - Constructor for class org.tribuo.classification.sgd.Util
Util() - Constructor for class org.tribuo.regression.sgd.Util
Util.ExampleArray - Class in org.tribuo.classification.sgd: A nominal tuple.
Util.SequenceExampleArray - Class in org.tribuo.classification.sgd: A nominal tuple.

V

val1 - Variable in class org.tribuo.util.MurmurHash3.LongPair
val2 - Variable in class org.tribuo.util.MurmurHash3.LongPair
validate(Class<? extends Output<?>>) - Method in class org.tribuo.Model: Validates that this Model does in fact support the supplied output type.
validate(Class<? extends Output<?>>) - Method in class org.tribuo.sequence.SequenceModel: Validates that this Model does in fact support the supplied output type.
validateExample() - Method in class org.tribuo.Example: Checks the example to see if all the feature names are unique, the feature values are not NaN, and there is at least one feature.
validateExample() - Method in class org.tribuo.impl.ArrayExample
validateExample() - Method in class org.tribuo.impl.BinaryFeaturesExample
validateExample() - Method in class org.tribuo.impl.ListExample
validateExample() - Method in class org.tribuo.sequence.SequenceExample: Checks that each Example in this sequence is valid.
validateMapping(Map<T, Integer>) - Static method in interface org.tribuo.OutputFactory: Validates that the mapping can be used as an output info, i.e.
validateSalt(String) - Static method in class org.tribuo.hash.Hasher: Salt validation is currently a test to see if the string is longer than Hasher.MIN_LENGTH.
validateTransformations(FeatureMap) - Method in class org.tribuo.transform.TransformationMap: Checks that a given transformation set doesn't have conflicts when applied to the supplied featureMap.
validationPath - Variable in class org.tribuo.data.text.SplitTextData.TrainTestSplitOptions
value - Variable in enum org.tribuo.data.DataOptions.Delimiter
value - Variable in enum org.tribuo.datasource.IDXDataSource.IDXType: The encoded byte value.
value - Variable in class org.tribuo.Feature: The feature value.
value - Variable in class org.tribuo.impl.IndexedArrayExample.FeatureTuple
value - Variable in class org.tribuo.math.la.MatrixTuple
value - Variable in class org.tribuo.math.la.VectorTuple
value - Variable in class org.tribuo.regression.rtree.impl.InvertedFeature
value - Variable in class org.tribuo.util.IntDoublePair
valueAndGradient(int, SGDVector) - Method in interface org.tribuo.classification.sgd.LabelObjective: Scores a prediction, returning the loss and a vector of per label gradients.
valueAndGradient(int, SGDVector) - Method in class org.tribuo.classification.sgd.objectives.Hinge: Returns a Pair of Double and SparseVector.
valueAndGradient(int, SGDVector) - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass: Returns a Pair of Double and the supplied prediction vector.
valueAndGradient(SparseVector[], int[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Generates predictions based on the input features and labels, then scores those predictions to produce a loss for the example and a gradient update.
valueCounts - Variable in class org.tribuo.CategoricalInfo: The occurrence counts of each value.
valueOf(String) - Static method in enum org.tribuo.anomaly.evaluation.AnomalyMetrics: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.anomaly.Event.EventType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.anomaly.libsvm.SVMAnomalyType.SVMMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.dtree.CARTClassificationOptions.ImpurityType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.dtree.CARTClassificationOptions.TreeType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.libsvm.SVMClassificationType.SVMMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions.ViterbiLabelFeatures: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.sgd.linear.LinearSGDOptions.LossEnum: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.clustering.evaluation.ClusteringMetrics: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.clustering.kmeans.KMeansTrainer.Distance: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.common.libsvm.KernelType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.common.nearest.KNNClassifierOptions.EnsembleCombinerType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.common.nearest.KNNModel.Backend: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.common.nearest.KNNTrainer.Distance: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.common.xgboost.XGBoostTrainer.BoosterType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.data.columnar.FieldProcessor.GeneratedFeatureType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.data.columnar.processors.feature.UniqueProcessor.UniqueType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.data.columnar.processors.field.RegexFieldProcessor.Mode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.data.DataOptions.Delimiter: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.data.DataOptions.InputFormat: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.datasource.IDXDataSource.IDXType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.evaluation.metrics.EvaluationMetric.Average: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.hash.HashingOptions.ModelHashingType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.interop.tensorflow.TrainTest.InputType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.json.StripProvenance.ProvenanceTypes: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.math.optimisers.SGD.Momentum: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.regression.baseline.DummyRegressionTrainer.DummyType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.regression.evaluation.RegressionMetrics: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.regression.liblinear.LinearRegressionType.LinearType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.regression.libsvm.SVMRegressionType.SVMMode: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.regression.rtree.TrainTest.ImpurityType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.regression.rtree.TrainTest.TreeType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.regression.sgd.TrainTest.LossEnum: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.regression.slm.TrainTest.SLMType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.regression.xgboost.XGBoostRegressionTrainer.RegressionType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.transform.transformations.BinningTransformation.BinningType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.transform.transformations.SimpleTransform.Operation: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.util.tokens.Token.TokenType: Returns the enum constant of this type with the specified name.
values - Variable in class org.tribuo.CategoricalInfo: The values array.
values - Variable in class org.tribuo.math.la.DenseMatrix
values - Variable in class org.tribuo.math.la.SparseVector
values() - Static method in enum org.tribuo.anomaly.evaluation.AnomalyMetrics: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.anomaly.Event.EventType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.anomaly.libsvm.SVMAnomalyType.SVMMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.dtree.CARTClassificationOptions.ImpurityType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.dtree.CARTClassificationOptions.TreeType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.evaluation.LabelMetrics: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.liblinear.LinearClassificationType.LinearType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.libsvm.SVMClassificationType.SVMMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions.ViterbiLabelFeatures: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.sgd.linear.LinearSGDOptions.LossEnum: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.clustering.evaluation.ClusteringMetrics: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.clustering.kmeans.KMeansTrainer.Distance: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.common.libsvm.KernelType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.common.nearest.KNNClassifierOptions.EnsembleCombinerType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.common.nearest.KNNModel.Backend: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.common.nearest.KNNTrainer.Distance: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.common.xgboost.XGBoostTrainer.BoosterType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.data.columnar.FieldProcessor.GeneratedFeatureType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.data.columnar.processors.feature.UniqueProcessor.UniqueType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.data.columnar.processors.field.RegexFieldProcessor.Mode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.data.DataOptions.Delimiter: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.data.DataOptions.InputFormat: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.datasource.IDXDataSource.IDXType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Method in class org.tribuo.evaluation.DescriptiveStats: Returns a copy of the values.
values() - Static method in enum org.tribuo.evaluation.metrics.EvaluationMetric.Average: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.hash.HashingOptions.ModelHashingType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.interop.tensorflow.TrainTest.InputType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.json.StripProvenance.ProvenanceTypes: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.math.optimisers.SGD.Momentum: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.multilabel.evaluation.MultiLabelMetrics: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.regression.baseline.DummyRegressionTrainer.DummyType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.regression.evaluation.RegressionMetrics: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.regression.liblinear.LinearRegressionType.LinearType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.regression.libsvm.SVMRegressionType.SVMMode: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.regression.rtree.TrainTest.ImpurityType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.regression.rtree.TrainTest.TreeType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.regression.sgd.TrainTest.LossEnum: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.regression.slm.TrainTest.SLMType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.regression.xgboost.XGBoostRegressionTrainer.RegressionType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.transform.transformations.BinningTransformation.BinningType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.transform.transformations.SimpleTransform.Operation: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.util.tokens.Token.TokenType: Returns an array containing the constants of this enum type, in the order they are declared.
VARIABLE_V2 - Static variable in class org.tribuo.interop.tensorflow.TensorflowUtil
VariableIDInfo - Interface in org.tribuo: Adds an id number to a VariableInfo.
VariableInfo - Interface in org.tribuo: A VariableInfo subclass contains information about a feature and its observed values.
variance() - Method in interface org.tribuo.math.la.SGDVector: Calculates the variance of this vector.
variance(double) - Method in class org.tribuo.math.la.DenseVector
variance(double) - Method in interface org.tribuo.math.la.SGDVector: Calculates the variance of this vector based on the supplied mean.
variance(double) - Method in class org.tribuo.math.la.SparseVector
VectorIterator - Interface in org.tribuo.math.la: A Comparable Iterator over VectorTuples.
vectorNorm(double[]) - Static method in class org.tribuo.util.Util
VectorNormalizer - Interface in org.tribuo.math.util: A functional interface that generates a normalized version of a double array.
VectorTuple - Class in org.tribuo.math.la: A mutable tuple used to avoid allocation when iterating a vector.
VectorTuple() - Constructor for class org.tribuo.math.la.VectorTuple
VectorTuple(int, int) - Constructor for class org.tribuo.math.la.VectorTuple
VERSION - Static variable in class org.tribuo.Tribuo: The full Tribuo version string.
versionString - Variable in class org.tribuo.provenance.ModelProvenance
viterbi(ChainHelper.ChainCliqueValues) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper: Runs Viterbi on a linear chain CRF.
ViterbiModel - Class in org.tribuo.classification.sequence.viterbi: An implementation of a viterbi model.
ViterbiModel.ScoreAggregation - Enum in org.tribuo.classification.sequence.viterbi: Types of label score aggregation.
ViterbiTrainer - Class in org.tribuo.classification.sequence.viterbi: Builds a Viterbi model using the supplied Trainer.
ViterbiTrainer(Trainer<Label>, LabelFeatureExtractor, int, ViterbiModel.ScoreAggregation) - Constructor for class org.tribuo.classification.sequence.viterbi.ViterbiTrainer
ViterbiTrainer(Trainer<Label>, LabelFeatureExtractor, ViterbiModel.ScoreAggregation) - Constructor for class org.tribuo.classification.sequence.viterbi.ViterbiTrainer
ViterbiTrainerOptions - Class in org.tribuo.classification.sequence.viterbi: Options for building a viterbi trainer.
ViterbiTrainerOptions() - Constructor for class org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions
ViterbiTrainerOptions.ViterbiLabelFeatures - Enum in org.tribuo.classification.sequence.viterbi
VOTING - Enum constant in enum org.tribuo.common.nearest.KNNClassifierOptions.EnsembleCombinerType
VotingCombiner - Class in org.tribuo.classification.ensemble: A combiner which performs a weighted or unweighted vote across the predicted labels.
VotingCombiner() - Constructor for class org.tribuo.classification.ensemble.VotingCombiner

W

WARNING_THRESHOLD - Static variable in class org.tribuo.interop.onnx.DenseTransformer: Number of times the feature size warning should be printed.
WARNING_THRESHOLD - Static variable in class org.tribuo.interop.tensorflow.DenseTransformer: Number of times the feature size warning should be printed.
weight - Variable in class org.tribuo.Example: The weight associated with this example.
weight - Variable in class org.tribuo.regression.rtree.impurity.RegressorImpurity.ImpurityTuple
weight - Variable in class org.tribuo.util.infotheory.impl.WeightCountTuple
WeightCountTuple - Class in org.tribuo.util.infotheory.impl: An mutable tuple of a double and a long.
WeightCountTuple() - Constructor for class org.tribuo.util.infotheory.impl.WeightCountTuple
WeightCountTuple(double, long) - Constructor for class org.tribuo.util.infotheory.impl.WeightCountTuple
weightedConditionalEntropy(ArrayList<T1>, ArrayList<T2>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete Shannon/Guiasu Weighted Conditional Entropy of two arrays, using histogram probability estimators.
WeightedEnsembleModel<T> - Class in org.tribuo.ensemble: An ensemble model that uses weights to combine the ensemble member predictions.
WeightedEnsembleModel(String, EnsembleModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Model<T>>, EnsembleCombiner<T>) - Constructor for class org.tribuo.ensemble.WeightedEnsembleModel
WeightedEnsembleModel(String, EnsembleModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Model<T>>, EnsembleCombiner<T>, float[]) - Constructor for class org.tribuo.ensemble.WeightedEnsembleModel
weightedEntropy(ArrayList<T>, ArrayList<Double>) - Static method in class org.tribuo.util.infotheory.WeightedInformationTheory: Calculates the discrete Shannon/Guiasu Weighted Entropy, using histogram probability estimators.
WeightedExamples - Interface in org.tribuo: Tag interface denoting that a Trainer can use example weights.
WeightedInformationTheory - Class in org.tribuo.util.infotheory: A class of (discrete) weighted information theoretic functions.
WeightedInformationTheory.VariableSelector - Enum in org.tribuo.util.infotheory: Chooses which variable is the one with associated weights.
WeightedLabels - Interface in org.tribuo.classification: Tag interface denoting the Trainer can use label weights.
weightedMean(double[], double[]) - Static method in class org.tribuo.util.Util: Returns the weighted mean of the input.
weightedMean(double[], float[], int) - Static method in class org.tribuo.util.Util
WeightedPairDistribution<T1,T2> - Class in org.tribuo.util.infotheory.impl: Generates the counts for a pair of vectors.
WeightedPairDistribution(long, LinkedHashMap<CachedPair<T1, T2>, WeightCountTuple>, LinkedHashMap<T1, WeightCountTuple>, LinkedHashMap<T2, WeightCountTuple>) - Constructor for class org.tribuo.util.infotheory.impl.WeightedPairDistribution
WeightedPairDistribution(long, Map<CachedPair<T1, T2>, WeightCountTuple>, Map<T1, WeightCountTuple>, Map<T2, WeightCountTuple>) - Constructor for class org.tribuo.util.infotheory.impl.WeightedPairDistribution
weightedSum(double[], float[], int) - Static method in class org.tribuo.util.Util
WeightedTripleDistribution<T1,T2,T3> - Class in org.tribuo.util.infotheory.impl: Generates the counts for a triplet of vectors.
WeightedTripleDistribution(long, Map<CachedTriple<T1, T2, T3>, WeightCountTuple>, Map<CachedPair<T1, T2>, WeightCountTuple>, Map<CachedPair<T1, T3>, WeightCountTuple>, Map<CachedPair<T2, T3>, WeightCountTuple>, Map<T1, WeightCountTuple>, Map<T2, WeightCountTuple>, Map<T3, WeightCountTuple>) - Constructor for class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
weightExtractor - Variable in class org.tribuo.data.columnar.RowProcessor
weights - Variable in class org.tribuo.classification.experiments.ConfigurableTrainTest.ConfigurableTrainTestOptions
weights - Variable in class org.tribuo.classification.sgd.Util.ExampleArray
weights - Variable in class org.tribuo.classification.sgd.Util.SequenceExampleArray
weights - Variable in class org.tribuo.ensemble.WeightedEnsembleModel
WHITESPACE - Enum constant in enum org.tribuo.util.tokens.Token.TokenType
WIDTH_CONSTANT - Static variable in class org.tribuo.classification.explanations.lime.LIMEBase
WORD - Enum constant in enum org.tribuo.util.tokens.Token.TokenType
wrapFeatures(String, List<Feature>) - Static method in class org.tribuo.data.columnar.processors.field.TextFieldProcessor: Convert the Features from a text pipeline into ColumnarFeatures with the right field name.
wrapTrainer(Trainer<Label>) - Method in class org.tribuo.classification.ensemble.ClassificationEnsembleOptions
writeLibSVMFormat(Dataset<T>, PrintStream, boolean, Function<T, Number>) - Static method in class org.tribuo.datasource.LibSVMDataSource: Writes out a dataset in LibSVM format.

X

xbgAlpha - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
xgbEnsembleSize - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
xgbEta - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
xgbGamma - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
xgbLambda - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
xgbMaxDepth - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
xgbMinWeight - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
xgbNumThreads - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
XGBOOST - Enum constant in enum org.tribuo.classification.experiments.AllTrainerOptions.AlgorithmType
XGBoostClassificationConverter - Class in org.tribuo.classification.xgboost: Converts XGBoost outputs into Label Predictions.
XGBoostClassificationConverter() - Constructor for class org.tribuo.classification.xgboost.XGBoostClassificationConverter
XGBoostClassificationTrainer - Class in org.tribuo.classification.xgboost: A Trainer which wraps the XGBoost training procedure.
XGBoostClassificationTrainer() - Constructor for class org.tribuo.classification.xgboost.XGBoostClassificationTrainer: For olcut.
XGBoostClassificationTrainer(int) - Constructor for class org.tribuo.classification.xgboost.XGBoostClassificationTrainer
XGBoostClassificationTrainer(int, double, double, int, double, double, double, double, double, int, boolean, long) - Constructor for class org.tribuo.classification.xgboost.XGBoostClassificationTrainer: Create an XGBoost trainer.
XGBoostClassificationTrainer(int, int, boolean) - Constructor for class org.tribuo.classification.xgboost.XGBoostClassificationTrainer
XGBoostClassificationTrainer(int, Map<String, Object>) - Constructor for class org.tribuo.classification.xgboost.XGBoostClassificationTrainer: This gives direct access to the XGBoost parameter map.
XGBoostExternalModel<T> - Class in org.tribuo.common.xgboost: A Model which wraps around a XGBoost.Booster which was trained by a system other than Tribuo.
XGBoostModel<T> - Class in org.tribuo.common.xgboost: A Model which wraps around a XGBoost.Booster.
xgboostOptions - Variable in class org.tribuo.classification.xgboost.TrainTest.TrainTestOptions
xgBoostOptions - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
XGBoostOptions - Class in org.tribuo.classification.xgboost: CLI options for training an XGBoost classifier.
XGBoostOptions - Class in org.tribuo.regression.xgboost: CLI options for configuring an XGBoost regression trainer.
XGBoostOptions() - Constructor for class org.tribuo.classification.xgboost.XGBoostOptions
XGBoostOptions() - Constructor for class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
XGBoostOptions() - Constructor for class org.tribuo.regression.xgboost.XGBoostOptions
XGBoostOutputConverter<T> - Interface in org.tribuo.common.xgboost: Converts the output of XGBoost into the appropriate prediction type.
XGBoostRegressionConverter - Class in org.tribuo.regression.xgboost: Converts XGBoost outputs into Regressor Predictions.
XGBoostRegressionConverter() - Constructor for class org.tribuo.regression.xgboost.XGBoostRegressionConverter
XGBoostRegressionTrainer - Class in org.tribuo.regression.xgboost: A Trainer which wraps the XGBoost training procedure.
XGBoostRegressionTrainer(int) - Constructor for class org.tribuo.regression.xgboost.XGBoostRegressionTrainer
XGBoostRegressionTrainer(XGBoostRegressionTrainer.RegressionType, int) - Constructor for class org.tribuo.regression.xgboost.XGBoostRegressionTrainer
XGBoostRegressionTrainer(XGBoostRegressionTrainer.RegressionType, int, double, double, int, double, double, double, double, double, int, boolean, long) - Constructor for class org.tribuo.regression.xgboost.XGBoostRegressionTrainer: Create an XGBoost trainer.
XGBoostRegressionTrainer(XGBoostRegressionTrainer.RegressionType, int, int, boolean) - Constructor for class org.tribuo.regression.xgboost.XGBoostRegressionTrainer
XGBoostRegressionTrainer(XGBoostRegressionTrainer.RegressionType, int, Map<String, Object>) - Constructor for class org.tribuo.regression.xgboost.XGBoostRegressionTrainer: This gives direct access to the XGBoost parameter map.
XGBoostRegressionTrainer.RegressionType - Enum in org.tribuo.regression.xgboost: Types of regression loss.
XGBoostTrainer<T> - Class in org.tribuo.common.xgboost: A Trainer which wraps the XGBoost training procedure.
XGBoostTrainer() - Constructor for class org.tribuo.common.xgboost.XGBoostTrainer: For olcut.
XGBoostTrainer(int) - Constructor for class org.tribuo.common.xgboost.XGBoostTrainer
XGBoostTrainer(int, double, double, int, double, double, double, double, double, int, boolean, long) - Constructor for class org.tribuo.common.xgboost.XGBoostTrainer: Create an XGBoost trainer.
XGBoostTrainer(int, int, boolean) - Constructor for class org.tribuo.common.xgboost.XGBoostTrainer
XGBoostTrainer(int, Map<String, Object>) - Constructor for class org.tribuo.common.xgboost.XGBoostTrainer: This gives direct access to the XGBoost parameter map.
XGBoostTrainer.BoosterType - Enum in org.tribuo.common.xgboost: The type of XGBoost model.
XGBoostTrainer.DMatrixTuple<T> - Class in org.tribuo.common.xgboost: Tuple of a DMatrix, the number of valid features in each example, and the examples themselves.
XGBoostTrainer.XGBoostTrainerProvenance - Class in org.tribuo.common.xgboost: Deprecated.
XGBoostTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.common.xgboost.XGBoostTrainer.XGBoostTrainerProvenance: Deprecated.
XGBoostTrainerProvenance(XGBoostTrainer<T>) - Constructor for class org.tribuo.common.xgboost.XGBoostTrainer.XGBoostTrainerProvenance: Deprecated.
xgbQuiet - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
xgbSubsample - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
xgbSubsampleFeatures - Variable in class org.tribuo.classification.xgboost.XGBoostOptions
XOR - Enum constant in enum org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType

Z

zeroIndexed - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
zipArraysCached(ArrayList<T1>, ArrayList<T2>) - Static method in class org.tribuo.util.infotheory.impl.CachedPair: Takes two arrays and zips them together into an array of CachedPairs.

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