Index (Tribuo 4.2.2 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 Y 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 class org.tribuo.regression.sgd.fm.TrainTest.LossEnum: Uses AbsoluteLoss.
ABSOLUTE - Enum constant in enum class org.tribuo.regression.sgd.TrainTest.LossEnum: Uses AbsoluteLoss.
AbsoluteLoss - Class in org.tribuo.regression.sgd.objectives: Absolute loss (i.e., l1).
AbsoluteLoss() - Constructor for class org.tribuo.regression.sgd.objectives.AbsoluteLoss: Constructs an absolute loss.
AbstractCARTTrainer<T extends Output<T>> - Class in org.tribuo.common.tree: Base class for Trainer's that use an approximation of the CART algorithm to build a decision tree.
AbstractCARTTrainer(int, float, float, float, boolean, long) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer: After calls to this superconstructor subclasses must call postConfig().
AbstractCARTTrainer.AbstractCARTTrainerProvenance - Class in org.tribuo.common.tree: Deprecated.
AbstractCARTTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.common.tree.AbstractCARTTrainer.AbstractCARTTrainerProvenance: Deprecated.

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

Constructs a provenance for the host AbstractCARTTrainer.
AbstractEvaluator<T extends Output<T>,C extends MetricContext<T>,E extends Evaluation<T>,M extends EvaluationMetric<T,C>> - Class in org.tribuo.evaluation: Base class for evaluators.
AbstractEvaluator() - Constructor for class org.tribuo.evaluation.AbstractEvaluator
AbstractFMModel<T extends Output<T>> - Class in org.tribuo.common.sgd: A quadratic factorization machine model trained using SGD.
AbstractFMModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, FMParameters, boolean) - Constructor for class org.tribuo.common.sgd.AbstractFMModel: Constructs a factorization machine model trained via SGD.
AbstractFMTrainer<T extends Output<T>,U,V extends AbstractFMModel<T>> - Class in org.tribuo.common.sgd: A trainer for a quadratic factorization machine model which uses SGD.
AbstractFMTrainer() - Constructor for class org.tribuo.common.sgd.AbstractFMTrainer: For olcut.
AbstractFMTrainer(StochasticGradientOptimiser, int, int, int, long, int, double) - Constructor for class org.tribuo.common.sgd.AbstractFMTrainer: Constructs an SGD trainer for a factorization machine.
AbstractLinearSGDModel<T extends Output<T>> - Class in org.tribuo.common.sgd: A linear model trained using SGD.
AbstractLinearSGDModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, LinearParameters, boolean) - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDModel: Constructs a linear model trained via SGD.
AbstractLinearSGDTrainer<T extends Output<T>,U,V extends AbstractLinearSGDModel<T>> - Class in org.tribuo.common.sgd: A trainer for a linear model which uses SGD.
AbstractLinearSGDTrainer() - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDTrainer: For olcut.
AbstractLinearSGDTrainer(StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.common.sgd.AbstractLinearSGDTrainer: Constructs an SGD trainer for a linear model.
AbstractSequenceEvaluator<T extends Output<T>,C extends MetricContext<T>,E extends SequenceEvaluation<T>,M extends EvaluationMetric<T,C>> - Class in org.tribuo.sequence: Base class for sequence evaluators.
AbstractSequenceEvaluator() - Constructor for class org.tribuo.sequence.AbstractSequenceEvaluator
AbstractSGDModel<T extends Output<T>> - Class in org.tribuo.common.sgd: A model trained using SGD.
AbstractSGDModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, FeedForwardParameters, boolean, boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDModel: Constructs a linear model trained via SGD.
AbstractSGDModel.PredAndActive - Class in org.tribuo.common.sgd: A nominal tuple used to capture the prediction and the number of active features used by the model.
AbstractSGDTrainer<T extends Output<T>,U,V extends Model<T>,X extends FeedForwardParameters> - Class in org.tribuo.common.sgd: A trainer for a model which uses SGD.
AbstractSGDTrainer(boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDTrainer: Base constructor called by subclass no-args constructors used by OLCUT.
AbstractSGDTrainer(StochasticGradientOptimiser, int, int, int, long, boolean) - Constructor for class org.tribuo.common.sgd.AbstractSGDTrainer: Constructs an SGD trainer.
AbstractTrainingNode<T extends Output<T>> - Class in org.tribuo.common.tree: Base class for decision tree nodes used at training time.
AbstractTrainingNode(int, int, AbstractTrainingNode.LeafDeterminer) - Constructor for class org.tribuo.common.tree.AbstractTrainingNode: Builds an abstract training node.
AbstractTrainingNode.LeafDeterminer - Class in org.tribuo.common.tree: Contains parameters needed to determine whether a node is a leaf.
accuracy() - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: The overall accuracy of the evaluation.
accuracy() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The accuracy.
accuracy(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: The per label accuracy of the evaluation.
accuracy(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the accuracy for this label.
accuracy(EvaluationMetric.Average, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the accuracy using the specified average type and confusion matrix.
accuracy(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the accuracy given this confusion matrix.
accuracy(T, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates a per label accuracy given this confusion matrix.
ACCURACY - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics: The accuracy.
ADABOOST - Enum constant in enum class org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Creates an AdaBoostTrainer.
AdaBoostTrainer - Class in org.tribuo.classification.ensemble: Implements Adaboost.SAMME one of the more popular algorithms for multiclass boosting.
AdaBoostTrainer(Trainer<Label>, int) - Constructor for class org.tribuo.classification.ensemble.AdaBoostTrainer: Constructs an adaboost trainer using the supplied weak learner trainer and the specified number of boosting rounds.
AdaBoostTrainer(Trainer<Label>, int, long) - Constructor for class org.tribuo.classification.ensemble.AdaBoostTrainer: Constructs an adaboost trainer using the supplied weak learner trainer, the specified number of boosting rounds and the supplied seed.
AdaDelta - Class in org.tribuo.math.optimisers: An implementation of the AdaDelta gradient optimiser.
AdaDelta() - Constructor for class org.tribuo.math.optimisers.AdaDelta: Sets rho to 0.95 and epsilon to 1e-6.
AdaDelta(double) - Constructor for class org.tribuo.math.optimisers.AdaDelta: Keeps rho at 0.95, passes through epsilon.
AdaDelta(double, double) - Constructor for class org.tribuo.math.optimisers.AdaDelta: It's recommended to keep rho at 0.95.
ADADELTA - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser: The AdaDelta optimiser.
ADADELTA - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType: The AdaDelta optimiser.
AdaGrad - Class in org.tribuo.math.optimisers: An implementation of the AdaGrad gradient optimiser.
AdaGrad(double) - Constructor for class org.tribuo.math.optimisers.AdaGrad: Creates an AdaGrad optimiser using the specified initial learning rate.
AdaGrad(double, double) - Constructor for class org.tribuo.math.optimisers.AdaGrad: Creates an AdaGrad optimiser using the specified learning rate and epsilon.
AdaGrad(double, double, double) - Constructor for class org.tribuo.math.optimisers.AdaGrad: Creates an AdaGrad optimiser using the specified learning rate, epsilon and initial accumulator value.
ADAGRAD - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser: The AdaGrad optimiser.
ADAGRAD - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType: The AdaGrad optimiser.
ADAGRADDA - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser: The AdaGrad Dual Averaging optimiser.
AdaGradRDA - Class in org.tribuo.math.optimisers: An implementation of the AdaGrad gradient optimiser with regularized dual averaging.
AdaGradRDA(double, double) - Constructor for class org.tribuo.math.optimisers.AdaGradRDA: Creates an AdaGradRDA optimiser with the specified parameter values.
AdaGradRDA(double, double, double, double, int) - Constructor for class org.tribuo.math.optimisers.AdaGradRDA: Creates an AdaGradRDA optimiser with the specified parameter values.
ADAGRADRDA - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType: The AdaGrad Regularised Dual Averaging optimiser.
Adam - Class in org.tribuo.math.optimisers: An implementation of the Adam gradient optimiser.
Adam() - Constructor for class org.tribuo.math.optimisers.Adam: Sets initialLearningRate to 0.001, betaOne to 0.9, betaTwo to 0.999, epsilon to 1e-6.
Adam(double, double) - Constructor for class org.tribuo.math.optimisers.Adam: Sets betaOne to 0.9 and betaTwo to 0.999
Adam(double, double, double, double) - Constructor for class org.tribuo.math.optimisers.Adam: It's highly recommended not to modify these parameters, use one of the other constructors.
ADAM - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser: The Adam optimiser.
ADAM - Enum constant in enum class org.tribuo.math.optimisers.GradientOptimiserOptions.StochasticGradientOptimiserType: The Adam optimiser.
ADAMAX - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser: The Adamax optimiser.
add - Enum constant in enum class org.tribuo.transform.transformations.SimpleTransform.Operation: Adds the specified constant.
add() - Static method in interface org.tribuo.util.Merger: A merger which adds the elements.
add(double) - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that adds the operand to each value.
add(int) - Method in class org.tribuo.regression.rtree.impl.InvertedFeature: Adds an index where the feature value occurs.
add(int, double) - Method in class org.tribuo.math.la.DenseVector
add(int, double) - Method in interface org.tribuo.math.la.SGDVector: Adds value to the element at index.
add(int, double) - Method in class org.tribuo.math.la.SparseVector
add(int, int, double) - Method in class org.tribuo.math.la.DenseMatrix
add(int, int, double) - Method in class org.tribuo.math.la.DenseSparseMatrix
add(int, int, double) - Method in interface org.tribuo.math.la.Matrix: Adds the argument value to the value at the supplied index.
add(int, Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
add(String) - Method in class org.tribuo.impl.BinaryFeaturesExample: Adds a single feature with a value of 1.
add(String, double) - Method in class org.tribuo.impl.ArrayExample: Adds a single feature.
add(String, double) - Method in class org.tribuo.MutableFeatureMap: Adds an occurrence of a feature with a given name.
add(Example<T>) - Method in class org.tribuo.ImmutableDataset: Adds an Example to the dataset, which will remove features with unknown names.
add(Example<T>) - Method in class org.tribuo.MutableDataset: Adds an example to the dataset, which observes the output and each feature value.
add(Example<T>, Merger) - Method in class org.tribuo.ImmutableDataset: Adds a Example to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids (merging duplicate ids).
add(Feature) - Method in class org.tribuo.Example: Adds a feature.
add(Feature) - Method in class org.tribuo.impl.ArrayExample
add(Feature) - Method in class org.tribuo.impl.BinaryFeaturesExample: Adds a feature to this example.
add(Feature) - Method in class org.tribuo.impl.IndexedArrayExample
add(Feature) - Method in class org.tribuo.impl.ListExample
add(SGDVector) - Method in class org.tribuo.math.la.DenseVector: Adds other to this vector, producing a new DenseVector.
add(SGDVector) - Method in interface org.tribuo.math.la.SGDVector: Adds other to this vector, producing a new SGDVector.
add(SGDVector) - Method in class org.tribuo.math.la.SparseVector: Adds other to this vector, producing a new SGDVector.
add(SequenceExample<T>) - Method in class org.tribuo.sequence.ImmutableSequenceDataset: Adds a SequenceExample to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids.
add(SequenceExample<T>) - Method in class org.tribuo.sequence.MutableSequenceDataset: Adds a SequenceExample to this dataset.
add(SequenceExample<T>, Merger) - Method in class org.tribuo.sequence.ImmutableSequenceDataset: Adds a SequenceExample to the dataset, which will insert feature ids, remove unknown features and sort the examples by the feature ids.
add(Row<T>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
ADD - Enum constant in enum class org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation: Adds the scores.
ADD - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators: Element-wise addition with broadcasting.
addAcrossDim1(int[], double) - Method in class org.tribuo.math.la.DenseMatrix: Adds the specified value to the specified elements across dimension 1.
addAcrossDim2(int[], double) - Method in class org.tribuo.math.la.DenseMatrix: Adds the specified value to the specified elements across dimension 2.
addAll(int, Collection<? extends Row<T>>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
addAll(Collection<? extends Example<T>>) - Method in class org.tribuo.MutableDataset: Adds all the Examples in the supplied collection to this dataset.
addAll(Collection<? extends Feature>) - Method in class org.tribuo.Example: Adds a collection of features.
addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.ArrayExample
addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.BinaryFeaturesExample: Adds a collection of features to this example.
addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.IndexedArrayExample
addAll(Collection<? extends Feature>) - Method in class org.tribuo.impl.ListExample
addAll(Collection<? extends Row<T>>) - Method in class org.tribuo.util.infotheory.impl.RowList: Unsupported.
addAll(Collection<SequenceExample<T>>) - Method in class org.tribuo.sequence.MutableSequenceDataset: Adds all the SequenceExamples in the supplied collection to this dataset.
addAllAttribute(Iterable<? extends OnnxMl.AttributeProto>) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: Additional named attributes.
addAllDim(Iterable<? extends OnnxMl.TensorShapeProto.Dimension>) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder: repeated .onnx.TensorShapeProto.Dimension dim = 1;
addAllDims(Iterable<? extends Long>) - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder: The shape of the underlying dense-tensor: [dim_1, dim_2, ...
addAllDims(Iterable<? extends Long>) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: The shape of the tensor.
addAllDoubleData(Iterable<? extends Double>) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For double Complex128 tensors are encoded as a single array of doubles, with the real components appearing in odd numbered positions, and the corresponding imaginary component appearing in the subsequent even numbered position.
addAllExternalData(Iterable<? extends OnnxMl.StringStringEntryProto>) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: Data can be stored inside the protobuf file using type-specific fields or raw_data.
addAllFloatData(Iterable<? extends Float>) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For float and complex64 values Complex64 tensors are encoded as a single array of floats, with the real components appearing in odd numbered positions, and the corresponding imaginary component appearing in the subsequent even numbered position.
addAllFloats(Iterable<? extends Float>) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of floats
addAllGraphs(Iterable<? extends OnnxMl.GraphProto>) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of graph
addAllInitializationBinding(Iterable<? extends OnnxMl.StringStringEntryProto>) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
addAllInitializer(Iterable<? extends OnnxMl.TensorProto>) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: A list of named tensor values, used to specify constant inputs of the graph.
addAllInput(Iterable<? extends OnnxMl.ValueInfoProto>) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The inputs and outputs of the graph.
addAllInput(Iterable<String>) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: namespace Value
addAllInt32Data(Iterable<? extends Integer>) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For int32, uint8, int8, uint16, int16, bool, and float16 values float16 values must be bit-wise converted to an uint16_t prior to writing to the buffer.
addAllInt64Data(Iterable<? extends Long>) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For int64.
addAllInts(Iterable<? extends Long>) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of ints
addAllMetadataProps(Iterable<? extends OnnxMl.StringStringEntryProto>) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Named metadata values; keys should be distinct.
addAllNode(Iterable<? extends OnnxMl.NodeProto>) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The nodes in the graph, sorted topologically.
addAllOpsetImport(Iterable<? extends OnnxMl.OperatorSetIdProto>) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: The OperatorSets this model relies on.
addAllOutput(Iterable<? extends OnnxMl.ValueInfoProto>) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: repeated .onnx.ValueInfoProto output = 12;
addAllOutput(Iterable<String>) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: namespace Value
addAllQuantizationAnnotation(Iterable<? extends OnnxMl.TensorAnnotation>) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: This field carries information to indicate the mapping among a tensor and its quantization parameter tensors.
addAllQuantParameterTensorNames(Iterable<? extends OnnxMl.StringStringEntryProto>) - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder: <key, value> pairs to annotate tensor specified by <tensor_name> above.
addAllSparseInitializer(Iterable<? extends OnnxMl.SparseTensorProto>) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Initializers (see above) stored in sparse format.
addAllSparseTensors(Iterable<? extends OnnxMl.SparseTensorProto>) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of sparse tensors
addAllStringData(Iterable<? extends ByteString>) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For strings.
addAllStrings(Iterable<? extends ByteString>) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of UTF-8 strings
addAllTensors(Iterable<? extends OnnxMl.TensorProto>) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of tensors
addAllTrainingInfo(Iterable<? extends OnnxMl.TrainingInfoProto>) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Training-specific information.
addAllUint64Data(Iterable<? extends Long>) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For uint64 and uint32 values When this field is present, the data_type field MUST be UINT32 or UINT64
addAllUpdateBinding(Iterable<? extends OnnxMl.StringStringEntryProto>) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: Gradient-based training is usually an iterative procedure.
addAllValueInfo(Iterable<? extends OnnxMl.ValueInfoProto>) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Information for the values in the graph.
addAttribute(int, OnnxMl.AttributeProto) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: Additional named attributes.
addAttribute(int, OnnxMl.AttributeProto.Builder) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: Additional named attributes.
addAttribute(OnnxMl.AttributeProto) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: Additional named attributes.
addAttribute(OnnxMl.AttributeProto.Builder) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: Additional named attributes.
addAttributeBuilder() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: Additional named attributes.
addAttributeBuilder(int) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: Additional named attributes.
addBias - Variable in class org.tribuo.common.sgd.AbstractSGDModel
addBias - Variable in class org.tribuo.common.sgd.AbstractSGDTrainer
addChar() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer: Add a character to the buffer that we're building for a token.
addDim(int, OnnxMl.TensorShapeProto.Dimension) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder: repeated .onnx.TensorShapeProto.Dimension dim = 1;
addDim(int, OnnxMl.TensorShapeProto.Dimension.Builder) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder: repeated .onnx.TensorShapeProto.Dimension dim = 1;
addDim(OnnxMl.TensorShapeProto.Dimension) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder: repeated .onnx.TensorShapeProto.Dimension dim = 1;
addDim(OnnxMl.TensorShapeProto.Dimension.Builder) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder: repeated .onnx.TensorShapeProto.Dimension dim = 1;
addDimBuilder() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder: repeated .onnx.TensorShapeProto.Dimension dim = 1;
addDimBuilder(int) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder: repeated .onnx.TensorShapeProto.Dimension dim = 1;
addDims(long) - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder: The shape of the underlying dense-tensor: [dim_1, dim_2, ...
addDims(long) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: The shape of the tensor.
addDoubleData(double) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For double Complex128 tensors are encoded as a single array of doubles, with the real components appearing in odd numbered positions, and the corresponding imaginary component appearing in the subsequent even numbered position.
addExample(Example<T>) - Method in class org.tribuo.sequence.SequenceExample: Adds an Example to this sequence.
addExternalData(int, OnnxMl.StringStringEntryProto) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: Data can be stored inside the protobuf file using type-specific fields or raw_data.
addExternalData(int, OnnxMl.StringStringEntryProto.Builder) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: Data can be stored inside the protobuf file using type-specific fields or raw_data.
addExternalData(OnnxMl.StringStringEntryProto) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: Data can be stored inside the protobuf file using type-specific fields or raw_data.
addExternalData(OnnxMl.StringStringEntryProto.Builder) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: Data can be stored inside the protobuf file using type-specific fields or raw_data.
addExternalDataBuilder() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: Data can be stored inside the protobuf file using type-specific fields or raw_data.
addExternalDataBuilder(int) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: Data can be stored inside the protobuf file using type-specific fields or raw_data.
addFloatData(float) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For float and complex64 values Complex64 tensors are encoded as a single array of floats, with the real components appearing in odd numbered positions, and the corresponding imaginary component appearing in the subsequent even numbered position.
addFloats(float) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of floats
addGraphs(int, OnnxMl.GraphProto) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of graph
addGraphs(int, OnnxMl.GraphProto.Builder) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of graph
addGraphs(OnnxMl.GraphProto) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of graph
addGraphs(OnnxMl.GraphProto.Builder) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of graph
addGraphsBuilder() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of graph
addGraphsBuilder(int) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of graph
addInitializationBinding(int, OnnxMl.StringStringEntryProto) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
addInitializationBinding(int, OnnxMl.StringStringEntryProto.Builder) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
addInitializationBinding(OnnxMl.StringStringEntryProto) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
addInitializationBinding(OnnxMl.StringStringEntryProto.Builder) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
addInitializationBindingBuilder() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
addInitializationBindingBuilder(int) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
addInitializer(int, OnnxMl.TensorProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: A list of named tensor values, used to specify constant inputs of the graph.
addInitializer(int, OnnxMl.TensorProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: A list of named tensor values, used to specify constant inputs of the graph.
addInitializer(OnnxMl.TensorProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: A list of named tensor values, used to specify constant inputs of the graph.
addInitializer(OnnxMl.TensorProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: A list of named tensor values, used to specify constant inputs of the graph.
addInitializerBuilder() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: A list of named tensor values, used to specify constant inputs of the graph.
addInitializerBuilder(int) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: A list of named tensor values, used to specify constant inputs of the graph.
addInput(int, OnnxMl.ValueInfoProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The inputs and outputs of the graph.
addInput(int, OnnxMl.ValueInfoProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The inputs and outputs of the graph.
addInput(OnnxMl.ValueInfoProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The inputs and outputs of the graph.
addInput(OnnxMl.ValueInfoProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The inputs and outputs of the graph.
addInput(String) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: namespace Value
addInputBuilder() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The inputs and outputs of the graph.
addInputBuilder(int) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The inputs and outputs of the graph.
addInputBytes(ByteString) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: namespace Value
addInt32Data(int) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For int32, uint8, int8, uint16, int16, bool, and float16 values float16 values must be bit-wise converted to an uint16_t prior to writing to the buffer.
addInt64Data(long) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For int64.
addInts(long) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of ints
addMetadataProps(int, OnnxMl.StringStringEntryProto) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Named metadata values; keys should be distinct.
addMetadataProps(int, OnnxMl.StringStringEntryProto.Builder) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Named metadata values; keys should be distinct.
addMetadataProps(OnnxMl.StringStringEntryProto) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Named metadata values; keys should be distinct.
addMetadataProps(OnnxMl.StringStringEntryProto.Builder) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Named metadata values; keys should be distinct.
addMetadataPropsBuilder() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Named metadata values; keys should be distinct.
addMetadataPropsBuilder(int) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Named metadata values; keys should be distinct.
addNode(int, OnnxMl.NodeProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The nodes in the graph, sorted topologically.
addNode(int, OnnxMl.NodeProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The nodes in the graph, sorted topologically.
addNode(OnnxMl.NodeProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The nodes in the graph, sorted topologically.
addNode(OnnxMl.NodeProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The nodes in the graph, sorted topologically.
addNodeBuilder() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The nodes in the graph, sorted topologically.
addNodeBuilder(int) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: The nodes in the graph, sorted topologically.
addOpsetImport(int, OnnxMl.OperatorSetIdProto) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: The OperatorSets this model relies on.
addOpsetImport(int, OnnxMl.OperatorSetIdProto.Builder) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: The OperatorSets this model relies on.
addOpsetImport(OnnxMl.OperatorSetIdProto) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: The OperatorSets this model relies on.
addOpsetImport(OnnxMl.OperatorSetIdProto.Builder) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: The OperatorSets this model relies on.
addOpsetImportBuilder() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: The OperatorSets this model relies on.
addOpsetImportBuilder(int) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: The OperatorSets this model relies on.
addOutput(int, OnnxMl.ValueInfoProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: repeated .onnx.ValueInfoProto output = 12;
addOutput(int, OnnxMl.ValueInfoProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: repeated .onnx.ValueInfoProto output = 12;
addOutput(OnnxMl.ValueInfoProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: repeated .onnx.ValueInfoProto output = 12;
addOutput(OnnxMl.ValueInfoProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: repeated .onnx.ValueInfoProto output = 12;
addOutput(String) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: namespace Value
addOutputBuilder() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: repeated .onnx.ValueInfoProto output = 12;
addOutputBuilder(int) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: repeated .onnx.ValueInfoProto output = 12;
addOutputBytes(ByteString) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder: namespace Value
addQuantizationAnnotation(int, OnnxMl.TensorAnnotation) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: This field carries information to indicate the mapping among a tensor and its quantization parameter tensors.
addQuantizationAnnotation(int, OnnxMl.TensorAnnotation.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: This field carries information to indicate the mapping among a tensor and its quantization parameter tensors.
addQuantizationAnnotation(OnnxMl.TensorAnnotation) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: This field carries information to indicate the mapping among a tensor and its quantization parameter tensors.
addQuantizationAnnotation(OnnxMl.TensorAnnotation.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: This field carries information to indicate the mapping among a tensor and its quantization parameter tensors.
addQuantizationAnnotationBuilder() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: This field carries information to indicate the mapping among a tensor and its quantization parameter tensors.
addQuantizationAnnotationBuilder(int) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: This field carries information to indicate the mapping among a tensor and its quantization parameter tensors.
addQuantParameterTensorNames(int, OnnxMl.StringStringEntryProto) - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder: <key, value> pairs to annotate tensor specified by <tensor_name> above.
addQuantParameterTensorNames(int, OnnxMl.StringStringEntryProto.Builder) - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder: <key, value> pairs to annotate tensor specified by <tensor_name> above.
addQuantParameterTensorNames(OnnxMl.StringStringEntryProto) - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder: <key, value> pairs to annotate tensor specified by <tensor_name> above.
addQuantParameterTensorNames(OnnxMl.StringStringEntryProto.Builder) - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder: <key, value> pairs to annotate tensor specified by <tensor_name> above.
addQuantParameterTensorNamesBuilder() - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder: <key, value> pairs to annotate tensor specified by <tensor_name> above.
addQuantParameterTensorNamesBuilder(int) - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder: <key, value> pairs to annotate tensor specified by <tensor_name> above.
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.OperatorSetIdProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.StringStringEntryProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Segment.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Map.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Opaque.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Sequence.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Tensor.Builder
addRepeatedField(Descriptors.FieldDescriptor, Object) - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto.Builder
addSparseInitializer(int, OnnxMl.SparseTensorProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Initializers (see above) stored in sparse format.
addSparseInitializer(int, OnnxMl.SparseTensorProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Initializers (see above) stored in sparse format.
addSparseInitializer(OnnxMl.SparseTensorProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Initializers (see above) stored in sparse format.
addSparseInitializer(OnnxMl.SparseTensorProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Initializers (see above) stored in sparse format.
addSparseInitializerBuilder() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Initializers (see above) stored in sparse format.
addSparseInitializerBuilder(int) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Initializers (see above) stored in sparse format.
addSparseTensors(int, OnnxMl.SparseTensorProto) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of sparse tensors
addSparseTensors(int, OnnxMl.SparseTensorProto.Builder) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of sparse tensors
addSparseTensors(OnnxMl.SparseTensorProto) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of sparse tensors
addSparseTensors(OnnxMl.SparseTensorProto.Builder) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of sparse tensors
addSparseTensorsBuilder() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of sparse tensors
addSparseTensorsBuilder(int) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of sparse tensors
addStringData(ByteString) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For strings.
addStrings(ByteString) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of UTF-8 strings
addTensors(int, OnnxMl.TensorProto) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of tensors
addTensors(int, OnnxMl.TensorProto.Builder) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of tensors
addTensors(OnnxMl.TensorProto) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of tensors
addTensors(OnnxMl.TensorProto.Builder) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of tensors
addTensorsBuilder() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of tensors
addTensorsBuilder(int) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder: list of tensors
addTrainingInfo(int, OnnxMl.TrainingInfoProto) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Training-specific information.
addTrainingInfo(int, OnnxMl.TrainingInfoProto.Builder) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Training-specific information.
addTrainingInfo(OnnxMl.TrainingInfoProto) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Training-specific information.
addTrainingInfo(OnnxMl.TrainingInfoProto.Builder) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Training-specific information.
addTrainingInfoBuilder() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Training-specific information.
addTrainingInfoBuilder(int) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder: Training-specific information.
addUint64Data(long) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder: For uint64 and uint32 values When this field is present, the data_type field MUST be UINT32 or UINT64
addUpdateBinding(int, OnnxMl.StringStringEntryProto) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: Gradient-based training is usually an iterative procedure.
addUpdateBinding(int, OnnxMl.StringStringEntryProto.Builder) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: Gradient-based training is usually an iterative procedure.
addUpdateBinding(OnnxMl.StringStringEntryProto) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: Gradient-based training is usually an iterative procedure.
addUpdateBinding(OnnxMl.StringStringEntryProto.Builder) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: Gradient-based training is usually an iterative procedure.
addUpdateBindingBuilder() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: Gradient-based training is usually an iterative procedure.
addUpdateBindingBuilder(int) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder: Gradient-based training is usually an iterative procedure.
addValueInfo(int, OnnxMl.ValueInfoProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Information for the values in the graph.
addValueInfo(int, OnnxMl.ValueInfoProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Information for the values in the graph.
addValueInfo(OnnxMl.ValueInfoProto) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Information for the values in the graph.
addValueInfo(OnnxMl.ValueInfoProto.Builder) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Information for the values in the graph.
addValueInfoBuilder() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Information for the values in the graph.
addValueInfoBuilder(int) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder: Information for the values in the graph.
ADJUSTED_MI - Enum constant in enum class org.tribuo.clustering.evaluation.ClusteringMetrics: The normalized mutual information adjusted for chance.
adjustedMI() - Method in interface org.tribuo.clustering.evaluation.ClusteringEvaluation: Measures the adjusted normalized mutual information between the predicted ids and the supplied ids.
adjustedMI(ClusteringMetric.Context) - Static method in enum class org.tribuo.clustering.evaluation.ClusteringMetrics: Calculates the adjusted normalized mutual information between two clusterings.
advance() - Method in class org.tribuo.util.tokens.impl.BreakIteratorTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.NonTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.ShapeTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.SplitFunctionTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.SplitPatternTokenizer
advance() - Method in class org.tribuo.util.tokens.impl.wordpiece.WordpieceTokenizer
advance() - Method in interface org.tribuo.util.tokens.Tokenizer: Advances the tokenizer to the next token.
advance() - Method in class org.tribuo.util.tokens.universal.UniversalTokenizer
aggregate(List<Feature>) - Method in interface org.tribuo.data.text.FeatureAggregator: Aggregates feature values with the same names.
aggregate(List<Feature>) - Method in class org.tribuo.data.text.impl.AverageAggregator
aggregate(List<Feature>) - Method in class org.tribuo.data.text.impl.SumAggregator
aggregate(List<Feature>) - Method in class org.tribuo.data.text.impl.UniqueAggregator
AggregateConfigurableDataSource<T extends Output<T>> - Class in org.tribuo.datasource: Aggregates multiple ConfigurableDataSources, uses AggregateDataSource.IterationOrder to control the iteration order.
AggregateConfigurableDataSource(List<ConfigurableDataSource<T>>) - Constructor for class org.tribuo.datasource.AggregateConfigurableDataSource: Creates an aggregate data source which will iterate the provided sources in the order of the list (i.e., using AggregateDataSource.IterationOrder.SEQUENTIAL.
AggregateConfigurableDataSource(List<ConfigurableDataSource<T>>, AggregateDataSource.IterationOrder) - Constructor for class org.tribuo.datasource.AggregateConfigurableDataSource: Creates an aggregate data source using the supplied sources and iteration order.
AggregateConfigurableDataSource.AggregateConfigurableDataSourceProvenance - Class in org.tribuo.datasource: Provenance for the AggregateConfigurableDataSource.
AggregateConfigurableDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.datasource.AggregateConfigurableDataSource.AggregateConfigurableDataSourceProvenance: Deserialization constructor.
AggregateDataSource<T extends Output<T>> - Class in org.tribuo.datasource: Aggregates multiple DataSources, uses AggregateDataSource.IterationOrder to control the iteration order.
AggregateDataSource(List<DataSource<T>>) - Constructor for class org.tribuo.datasource.AggregateDataSource: Creates an aggregate data source which will iterate the provided sources in the order of the list (i.e., using AggregateDataSource.IterationOrder.SEQUENTIAL.
AggregateDataSource(List<DataSource<T>>, AggregateDataSource.IterationOrder) - Constructor for class org.tribuo.datasource.AggregateDataSource: Creates an aggregate data source using the supplied sources and iteration order.
AggregateDataSource.AggregateDataSourceProvenance - Class in org.tribuo.datasource: Provenance for the AggregateDataSource.
AggregateDataSource.IterationOrder - Enum Class in org.tribuo.datasource: Specifies the iteration order of the inner sources.
AggregateDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance: Deserialization constructor.
ai.onnx.proto - package ai.onnx.proto
algorithm - Variable in class org.tribuo.classification.experiments.AllTrainerOptions: Type of learner (or base learner).
algorithm - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions: Type of SVR.
algorithm - Variable in class org.tribuo.regression.slm.TrainTest.SLMOptions: Choose the training algorithm (stepwise forward selection or least angle regression).
ALGORITHM_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TrainingInfoProto
ALL - Enum constant in enum class org.tribuo.json.StripProvenance.ProvenanceTypes: Selects all provenance stored in the model.
ALL_OUTPUTS - Static variable in class org.tribuo.Model: Used in getTopFeatures when the Model doesn't support per output feature lists.
AllClassificationOptions() - Constructor for class org.tribuo.classification.experiments.TrainTest.AllClassificationOptions
allProvenances() - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance
allProvenances() - Method in class org.tribuo.dataset.MinimumCardinalityDataset.MinimumCardinalityDatasetProvenance
allProvenances() - Method in class org.tribuo.provenance.DatasetProvenance: Returns a list of all the provenances.
allProvenances() - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset.MinimumCardinalitySequenceDatasetProvenance
AllTrainerOptions - Class in org.tribuo.classification.experiments: Aggregates all the classification algorithms.
AllTrainerOptions() - Constructor for class org.tribuo.classification.experiments.AllTrainerOptions
AllTrainerOptions.AlgorithmType - Enum Class in org.tribuo.classification.experiments: Types of algorithms supported.
alpha - Variable in class org.tribuo.regression.slm.TrainTest.SLMOptions: Regularisation strength in the Elastic Net.
alpha - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions: L1 regularization term for weights (default 0).
alpha - Variable in class org.tribuo.regression.xgboost.XGBoostOptions: L1 regularization term for weights (default 0).
alphas - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults: The alpha values array from forward propagation.
annotateGraph(Graph, Session) - Static method in class org.tribuo.interop.tensorflow.TensorFlowUtil: Annotates a graph with an extra placeholder and assign operation for each VariableV2.
ANOMALOUS - Enum constant in enum class org.tribuo.anomaly.Event.EventType: An anomalous event, with id 1.
ANOMALOUS_EVENT - Static variable in class org.tribuo.anomaly.AnomalyFactory: The anomalous event.
ANOMALY_DETECTION - Enum constant in enum class org.tribuo.interop.oci.OCIUtil.OCIModelType: Anomaly detection, maps to Tribuo's Event.
anomalyCount - Variable in class org.tribuo.anomaly.AnomalyInfo: The number of anomalous events observed.
AnomalyDataGenerator - Class in org.tribuo.anomaly.example: Generates three example train and test datasets, used for unit testing.
AnomalyDataGenerator() - Constructor for class org.tribuo.anomaly.example.AnomalyDataGenerator
AnomalyEvaluation - Interface in org.tribuo.anomaly.evaluation: An Evaluation for anomaly detection Events.
AnomalyEvaluator - Class in org.tribuo.anomaly.evaluation: An Evaluator for anomaly detection Events.
AnomalyEvaluator() - Constructor for class org.tribuo.anomaly.evaluation.AnomalyEvaluator
AnomalyFactory - Class in org.tribuo.anomaly: A factory for generating events.
AnomalyFactory() - Constructor for class org.tribuo.anomaly.AnomalyFactory
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 Class in org.tribuo.anomaly.evaluation: Default metrics for evaluating anomaly detection.
apply(int, int, CharSequence) - Method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer.SplitCharactersSplitterFunction
apply(int, int, CharSequence) - Method in interface org.tribuo.util.tokens.impl.SplitFunctionTokenizer.SplitFunction: Applies the split function.
apply(E) - Method in interface org.tribuo.evaluation.EvaluationRenderer: Convert the evaluation to a string.
apply(ONNXOperators) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperators, List, String), using this ONNXRef as the argument to inputs.
apply(ONNXOperators, String) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperators, List, String), using this ONNXRef as the argument to inputs.
apply(ONNXOperators, List<String>, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperators, List, List, Map), using this ONNXRef as the argument to inputs.
apply(ONNXOperators, List<ONNXRef<?>>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperators, List, String, Map), using this ONNXRef as the first argument to inputs, with otherInputs append as subsequent arguments.
apply(ONNXOperators, List<ONNXRef<?>>, String) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperators, List, String, Map), using this ONNXRef as the argument to inputs, with otherInputs append as subsequent arguments.
apply(ONNXOperators, List<ONNXRef<?>>, List<String>, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperators, List, List, Map), using this ONNXRef as the first argument to inputs, with otherInputs append as subsequent arguments.
apply(ONNXOperators, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperators, List, String, Map), using this ONNXRef as the argument to inputs.
apply(ONNXOperators, ONNXRef<?>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperators, List, String, Map), passing this ONNXRef and other as a length 2 list to inputs.
apply(ONNXOperators, ONNXRef<?>, String) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperators, List, String), passing this ONNXRef and other as a length 2 list to inputs.
apply(ONNXOperators, ONNXRef<?>, Map<String, Object>) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.operation(ONNXOperators, List, String, Map), passing this ONNXRef and other as a length 2 list to inputs.
applyCase(String) - Method in enum class org.tribuo.data.text.impl.CasingPreprocessor.CasingOperation: Apply the appropriate casing operation.
applyOptimiser(Graph, Operand<T>, Map<String, Float>) - Method in enum class org.tribuo.interop.tensorflow.GradientOptimiser: Applies the optimiser to the graph and returns the optimiser step operation.
applyTransformerList(double, List<Transformer>) - Static method in class org.tribuo.transform.TransformerMap: Applies a List of Transformers to the supplied double value, returning the transformed value.
APPROX - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.TreeMethod: Approximate greedy algorithm, using a quantile sketch of the data and a gradient histogram.
ARCH_STRING - Static variable in class org.tribuo.provenance.ModelProvenance
archString - Variable in class org.tribuo.provenance.ModelProvenance
argmax(List<R>, Function<R, Double>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Calculates the argmax of a metric across the supplied evaluations.
argmax(List<T>) - Static method in class org.tribuo.util.Util: Find the index of the maximum value in a list.
argmax(EvaluationMetric<T, C>, List<? extends Model<T>>, Dataset<T>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Calculates the argmax of a metric across the supplied models (i.e., the index of the model which performed the best).
argmax(EvaluationMetric<T, C>, Model<T>, List<? extends Dataset<T>>) - Static method in class org.tribuo.evaluation.EvaluationAggregator: Calculates the argmax of a metric across the supplied datasets.
argmin(List<T>) - Static method in class org.tribuo.util.Util: Find the index of the minimum value in a list.
array - Variable in class org.tribuo.common.tree.impl.IntArrayContainer: The array of ints.
array(String, double[]) - Method in class org.tribuo.util.onnx.ONNXContext: Creates a float tensor for this ONNXContext, populated according to parameters.
array(String, double[], boolean) - Method in class org.tribuo.util.onnx.ONNXContext: Creates a tensor for this ONNXContext, populated according to parameters.
array(String, float[]) - Method in class org.tribuo.util.onnx.ONNXContext: Creates a float tensor for this ONNXContext, populated according to parameters.
array(String, int[]) - Method in class org.tribuo.util.onnx.ONNXContext: Creates an int tensor for this ONNXContext, populated according to parameters.
array(String, long[]) - Method in class org.tribuo.util.onnx.ONNXContext: Creates a long tensor for this ONNXContext, populated according to parameters.
ARRAY_FEATURE_EXTRACTOR - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators: Array feature extractor, selects the indices specified by the second tensor from the last dimension of the first tensor.
arrayBuilder(ONNXContext, String, double[]) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a TensorProto containing the array.
arrayBuilder(ONNXContext, String, double[], boolean) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a TensorProto containing the array.
arrayBuilder(ONNXContext, String, float[]) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a TensorProto containing the array.
arrayBuilder(ONNXContext, String, int[]) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a TensorProto containing the array.
arrayBuilder(ONNXContext, String, long[]) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a TensorProto containing the array.
ArrayExample<T extends Output<T>> - Class in org.tribuo.impl: An Example backed by two arrays, one of String and one of double.
ArrayExample(Example<T>) - Constructor for class org.tribuo.impl.ArrayExample: Copy constructor.
ArrayExample(T) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output.
ArrayExample(T, float) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output and a weight.
ArrayExample(T, float, int) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output and a weight, with an initial size for the feature arrays.
ArrayExample(T, float, Map<String, Object>) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output, a weight and the metadata.
ArrayExample(T, String[], double[]) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output, an array of names and an array of values.
ArrayExample(T, List<? extends Feature>) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output and a list of features.
ArrayExample(T, Map<String, Object>) - Constructor for class org.tribuo.impl.ArrayExample: Constructs an example from an output and the metadata.
ArrayExample(T, Example, float) - Constructor for class org.tribuo.impl.ArrayExample: Clones an example's features, but uses the supplied output and weight.
asMap() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
asMap() - Method in interface org.tribuo.evaluation.Evaluation: Get a map of all the metrics stored in this evaluation.
asMap() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
asMap() - Method in interface org.tribuo.sequence.SequenceEvaluation: Get a map of all the metrics stored in this evaluation.
ASSIGN_OP - Static variable in class org.tribuo.interop.tensorflow.TensorFlowUtil: The name of the assignment op.
ASSIGN_PLACEHOLDER - Static variable in class org.tribuo.interop.tensorflow.TensorFlowUtil: The name given to the assignment operation from the placeholders.
assignTo(Ret) - Method in class org.tribuo.util.onnx.ONNXRef: Convenience method that calls ONNXContext.assignTo(ONNXRef, ONNXRef), using this ONNXRef as the argument to input.
assignTo(RHS, LHS) - Method in class org.tribuo.util.onnx.ONNXContext: Creates an ONNXOperators.IDENTITY node connecting input to output, effectively permitting assignment of values.
ATTENTION_MASK - Static variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor: Input name for the attention mask.
ATTRIBUTE_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.NodeProto
attributes - Variable in enum class org.tribuo.util.onnx.ONNXOperators: The operator attributes.
auc(double[], double[]) - Static method in class org.tribuo.util.Util: Calculates the area under the curve, bounded below by the x axis.
AUCROC - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics: The area under the receiver-operator curve (ROC).
AUCROC(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Area under the ROC curve.
AUCROC(Label, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics: Area under the ROC curve.
AUCROC(MetricTarget<Label>, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics: Area under the ROC curve.
AUTO - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.TreeMethod: XGBoost chooses between XGBoostTrainer.TreeMethod.EXACT and XGBoostTrainer.TreeMethod.APPROX depending on dataset size.
AverageAggregator - Class in org.tribuo.data.text.impl: A feature aggregator that averages feature values across a feature list.
AverageAggregator() - Constructor for class org.tribuo.data.text.impl.AverageAggregator
averageAUCROC(boolean) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Area under the ROC curve averaged across labels.
AVERAGED_PRECISION - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics: The averaged precision.
averagedExplainedVariance() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: The average explained variance across all dimensions.
averagedPrecision(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil: Summarises a Precision-Recall Curve by taking the weighted mean of the precisions at a given threshold, where the weight is the recall achieved at that threshold.
averagedPrecision(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Summarises a Precision-Recall Curve by taking the weighted mean of the precisions at a given threshold, where the weight is the recall achieved at that threshold.
averagedPrecision(Label, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics
averagedPrecision(MetricTarget<Label>, List<Prediction<Label>>) - Static method in enum class org.tribuo.classification.evaluation.LabelMetrics
averageMAE() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: The average Mean Absolute Error across all dimensions.
averageR2() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: The average R2 across all dimensions.
averageRMSE() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation: The average RMSE across all dimensions.
AveragingCombiner - Class in org.tribuo.regression.ensemble: A combiner which performs a weighted or unweighted average of the predicted regressors independently across the output dimensions.
AveragingCombiner() - Constructor for class org.tribuo.regression.ensemble.AveragingCombiner: Constructs an averaging combiner.

B

b - Variable in class org.tribuo.util.infotheory.impl.CachedTriple
backRef - Variable in class org.tribuo.util.onnx.ONNXRef
BAGGING - Enum constant in enum class org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Creates a BaggingTrainer.
BaggingTrainer<T extends Output<T>> - Class in org.tribuo.ensemble: A Trainer that wraps another trainer and produces a bagged ensemble.
BaggingTrainer() - Constructor for class org.tribuo.ensemble.BaggingTrainer: For the configuration system.
BaggingTrainer(Trainer<T>, EnsembleCombiner<T>, int) - Constructor for class org.tribuo.ensemble.BaggingTrainer: Constructs a bagging trainer with the supplied parameters using Trainer.DEFAULT_SEED as the RNG seed.
BaggingTrainer(Trainer<T>, EnsembleCombiner<T>, int, long) - Constructor for class org.tribuo.ensemble.BaggingTrainer: Constructs a bagging trainer with the supplied parameters.
BALANCED_ERROR_RATE - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics: The balanced error rate, i.e., the mean of the per class recalls.
BALANCED_ERROR_RATE - Enum constant in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics: The balanced error rate, i.e., the mean of the per class recalls.
balancedErrorRate() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the balanced error rate, i.e., the mean of the per label recalls.
balancedErrorRate() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the balanced error rate.
balancedErrorRate() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
balancedErrorRate(ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the balanced error rate, i.e., the mean of the recalls.
bandwidth - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions: Model bandwidth in MBps.
bandwidth - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelDeploymentConfig: The bandwidth for the load balancer in MBps.
BasicPipeline - Class in org.tribuo.data.text.impl: An example implementation of TextPipeline.
BasicPipeline(Tokenizer, int) - Constructor for class org.tribuo.data.text.impl.BasicPipeline: Constructs a basic text pipeline which tokenizes the input and generates word n-gram features in the range 1 to ngram.
batchSize - Variable in class org.tribuo.interop.tensorflow.TensorFlowModel
batchSize - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions: Minibatch size.
begin - Variable in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence: The subsequence start index.
begin - Variable in class org.tribuo.classification.sgd.crf.Chunk: The starting point of this chunk.
BEGIN_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorProto.Segment
beliefPropagation(ChainHelper.ChainCliqueValues) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper: Runs belief propagation on a linear chain CRF.
bert - Variable in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor.BERTFeatureExtractorOptions: BERTFeatureExtractor instance
BERTFeatureExtractor<T extends Output<T>> - Class in org.tribuo.interop.onnx.extractors: Builds examples and sequence examples using features from BERT.
BERTFeatureExtractor(OutputFactory<T>, Path, Path) - Constructor for class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor: Constructs a BERTFeatureExtractor.
BERTFeatureExtractor(OutputFactory<T>, Path, Path, BERTFeatureExtractor.OutputPooling, int, boolean) - Constructor for class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor: Constructs a BERTFeatureExtractor.
BERTFeatureExtractor.BERTFeatureExtractorOptions - Class in org.tribuo.interop.onnx.extractors: CLI options for running BERT.
BERTFeatureExtractor.OutputPooling - Enum Class in org.tribuo.interop.onnx.extractors: The type of output pooling to perform.
BERTFeatureExtractorOptions() - Constructor for class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor.BERTFeatureExtractorOptions
betas - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults: The beta values array from backward propagation.
BFLOAT16 - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType: Non-IEEE floating-point format based on IEEE754 single-precision floating-point number truncated to 16 bits.
BFLOAT16_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType: Non-IEEE floating-point format based on IEEE754 single-precision floating-point number truncated to 16 bits.
BIAS_FEATURE - Static variable in class org.tribuo.Model: Used to denote the bias feature in a linear model.
binarise - Enum constant in enum class org.tribuo.transform.transformations.SimpleTransform.Operation: Binarises the output around 1.0.
binarise() - Static method in class org.tribuo.transform.transformations.SimpleTransform: Generate a SimpleTransform that sets negative and zero values to zero and positive values to one.
BINARISED_CATEGORICAL - Enum constant in enum class org.tribuo.data.columnar.FieldProcessor.GeneratedFeatureType: Categoricals binarised into separate features.
BINARY_CLASSIFICATION - Enum constant in enum class org.tribuo.interop.oci.OCIUtil.OCIModelType: Binary classification.
binaryAUCROC(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil: Calculates the area under the receiver operator characteristic curve, i.e., the AUC of the ROC curve.
BinaryCrossEntropy - Class in org.tribuo.multilabel.sgd.objectives: A multilabel version of binary cross entropy loss which expects logits.
BinaryCrossEntropy() - Constructor for class org.tribuo.multilabel.sgd.objectives.BinaryCrossEntropy: Constructs a BinaryCrossEntropy objective.
BinaryFeaturesExample<T extends Output<T>> - Class in org.tribuo.impl: An Example backed by a single array of feature names.
BinaryFeaturesExample(Example<T>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Copy constructor.
BinaryFeaturesExample(T) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output.
BinaryFeaturesExample(T, float) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and a weight.
BinaryFeaturesExample(T, float, int) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and a weight, with an initial size for the feature arrays.
BinaryFeaturesExample(T, float, Map<String, Object>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output, a weight and the metadata.
BinaryFeaturesExample(T, String[]) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and an array of names.
BinaryFeaturesExample(T, List<? extends Feature>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and a list of features.
BinaryFeaturesExample(T, Map<String, Object>) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Constructs an example from an output and the metadata.
BinaryFeaturesExample(T, Example, float) - Constructor for class org.tribuo.impl.BinaryFeaturesExample: Clones an example's features, but uses the supplied output and weight.
BinaryResponseProcessor<T extends Output<T>> - Class in org.tribuo.data.columnar.processors.response: A ResponseProcessor that takes a single value of the field as the positive class and all other values as the negative class.
BinaryResponseProcessor(String, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
BinaryResponseProcessor(List<String>, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
BinaryResponseProcessor(List<String>, List<String>, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
BinaryResponseProcessor(List<String>, List<String>, OutputFactory<T>, boolean) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
BinaryResponseProcessor(List<String>, List<String>, OutputFactory<T>, String, String, boolean) - Constructor for class org.tribuo.data.columnar.processors.response.BinaryResponseProcessor: Constructs a binary response processor which emits a positive value for a single string and a negative value for all other field values.
binarySearch(List<? extends Comparable<? super T>>, T) - Static method in class org.tribuo.util.Util: A binary search function.
binarySearch(List<? extends Comparable<? super T>>, T, int, int) - Static method in class org.tribuo.util.Util: A binary search function.
binarySearch(List<? extends T>, int, ToIntFunction<T>) - Static method in class org.tribuo.util.Util: A binary search function.
binarySparseTrainTest() - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a pair of datasets with sparse features and unknown features in the test data.
binarySparseTrainTest(double) - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a pair of datasets with sparse features and unknown features in the test data.
BinningTransformation - Class in org.tribuo.transform.transformations: A Transformation which bins values.
BinningTransformation.BinningTransformationProvenance - Class in org.tribuo.transform.transformations: Provenance for BinningTransformation.
BinningTransformation.BinningType - Enum Class in org.tribuo.transform.transformations: The allowed binning types.
BinningTransformationProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.transform.transformations.BinningTransformation.BinningTransformationProvenance: Deserialization constructor.
BOOL - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType: bool
BOOL_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType: bool
BREAK_ITERATOR - Enum constant in enum class org.tribuo.util.tokens.options.CoreTokenizerOptions.CoreTokenizerType: Creates a BreakIteratorTokenizer.
breakIteratorOptions - Variable in class org.tribuo.util.tokens.options.CoreTokenizerOptions: Options for the break iterator tokenizer.
BreakIteratorTokenizer - Class in org.tribuo.util.tokens.impl: A tokenizer wrapping a BreakIterator instance.
BreakIteratorTokenizer(Locale) - Constructor for class org.tribuo.util.tokens.impl.BreakIteratorTokenizer: Constructs a BreakIteratorTokenizer using the specified locale.
BreakIteratorTokenizerOptions - Class in org.tribuo.util.tokens.options: CLI options for a BreakIteratorTokenizer.
BreakIteratorTokenizerOptions() - Constructor for class org.tribuo.util.tokens.options.BreakIteratorTokenizerOptions
broadcastIntersectAndAddInPlace(SGDVector, boolean) - Method in class org.tribuo.math.la.DenseMatrix: Broadcasts the input vector and adds it to each row/column of the matrix.
buff - Variable in class org.tribuo.util.tokens.universal.Range: The character buffer.
build() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.OperatorSetIdProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.StringStringEntryProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Segment.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Map.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Opaque.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Sequence.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor.Builder
build() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Tensor.Builder
build() - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto.Builder
build(Object) - Method in class org.tribuo.util.onnx.ONNXAttribute: Builds the attribute proto using the supplied value.
build(ONNXContext, String[], String) - Method in enum class org.tribuo.util.onnx.ONNXOperators: Builds this node based on the supplied inputs and output.
build(ONNXContext, String[], String[]) - Method in enum class org.tribuo.util.onnx.ONNXOperators: Builds this node based on the supplied inputs and outputs.
build(ONNXContext, String[], String[], Map<String, Object>) - Method in enum class org.tribuo.util.onnx.ONNXOperators: Builds this node based on the supplied inputs and outputs.
build(ONNXContext, String[], String, Map<String, Object>) - Method in enum class org.tribuo.util.onnx.ONNXOperators: Builds this node based on the supplied inputs and output.
build(ONNXContext, String, String) - Method in enum class org.tribuo.util.onnx.ONNXOperators: Builds this node based on the supplied inputs and output.
build(ONNXContext, String, String[]) - Method in enum class org.tribuo.util.onnx.ONNXOperators: Builds this node based on the supplied input and outputs.
build(ONNXContext, String, String[], Map<String, Object>) - Method in enum class org.tribuo.util.onnx.ONNXOperators: Builds this node based on the supplied input and outputs.
build(ONNXContext, String, String, Map<String, Object>) - Method in enum class org.tribuo.util.onnx.ONNXOperators: Builds this node based on the supplied inputs and output.
BUILD_TIMESTAMP - Static variable in class org.tribuo.Tribuo: The build timestamp.
buildGraph() - Method in class org.tribuo.util.onnx.ONNXContext: Builds the ONNX graph represented by this context.
buildLeNetGraph(String, int, int, int) - Static method in class org.tribuo.interop.tensorflow.example.CNNExamples: Builds a LeNet 5 style CNN (usually used for MNIST).
buildMLPGraph(String, int, int[], int) - Static method in class org.tribuo.interop.tensorflow.example.MLPExamples: Builds an MLP which expects the supplied number of inputs, has hiddenSizes.length hidden layers, before emitting numOutput outputs.
buildModel(ONNXContext, String, long, M) - Static method in interface org.tribuo.ONNXExportable: Creates an ONNX model protobuf for the supplied context.
buildPartial() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.OperatorSetIdProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.StringStringEntryProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Segment.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Map.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Opaque.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Sequence.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Tensor.Builder
buildPartial() - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto.Builder
buildRandomTree(int[], SplittableRandom) - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode: Builds a CART tree with randomly chosen split points.
buildRuntimeYaml(String, String) - Static method in class org.tribuo.interop.oci.OCIUtil: Builds the runtime.yaml String from the supplied arguments, throwing IllegalArgumentException if they are invalid.
buildTensorTypeNode(ONNXShape, OnnxMl.TensorProto.DataType) - Static method in class org.tribuo.util.onnx.ONNXUtils: Builds a type proto for the specified shape and tensor type.
buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode: Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.common.tree.AbstractTrainingNode: Builds next level of a tree.
buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode: Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
buildTree(int[], SplittableRandom, boolean) - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode: Builds a tree according to CART (as it does not do multi-way splits on categorical values like C4.5).
BYTE - Enum constant in enum class org.tribuo.datasource.IDXDataSource.IDXType: A signed byte.

C

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

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

Original SVM algorithm.

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

A pair of things with a cached hashcode.

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

Constructs a CachedPair.

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

A triple of things.

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

Constructs a CachedTriple.

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

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

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

Generate the counts for a single vector.

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

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

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

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

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

Overridden hashcode.

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

Generate the counts for a single vector.

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

The calendar quarter of the year.

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

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

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

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

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

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

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

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

Builds a CART model.

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

Creates a CARTClassificationTrainer.

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

Creates a CARTRegressionTrainer which treats each regression dimension independently.

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

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

CARTClassificationOptions - Class in org.tribuo.classification.dtree

Options for building a classification tree trainer.

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

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

The impurity algorithm.

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

Type of decision tree algorithm.

CARTClassificationTrainer - Class in org.tribuo.classification.dtree

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

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

Creates a CART Trainer.

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

Creates a CART trainer.

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

Creates a CART Trainer.

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

Creates a CART Trainer.

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

Creates a CART Trainer.

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

Creates a CART Trainer.

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

Impurity measure to use.

CARTJointRegressionTrainer - Class in org.tribuo.regression.rtree

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

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

Creates a CART Trainer.

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

Creates a CART Trainer.

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

Creates a CART Trainer.

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

Creates a CART Trainer.

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

Creates a CART Trainer.

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

Maximum depth in the decision tree.

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

Minimum child weight.

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

Minimum impurity decrease.

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

The CART trainer options.

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

Options for CART trainers.

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

Prints the decision tree.

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

Whether to choose split points for features at random.

CARTRegressionTrainer - Class in org.tribuo.regression.rtree

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

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

Creates a CART trainer.

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

Creates a CART trainer.

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

Creates a CART Trainer.

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

Creates a CART Trainer.

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

RNG seed.

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

Fraction of features in split.

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

Tree algorithm to use (options are CART).

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

A document preprocessor which uppercases or lowercases the input.

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

Construct a casing preprocessor.

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

The possible casing operations.

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

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

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

Cast input to specified type.

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

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

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

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

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

Unordered categorical features with the values converted into doubles.

CategoricalIDInfo - Class in org.tribuo

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

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

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

CategoricalInfo - Class in org.tribuo

Stores information about Categorical features.

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

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

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

Constructs a deep copy of the supplied categorical info.

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

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

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

The string used in the feature name for negative labels.

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

The string used in the feature name for positive labels.

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

The prefix for classifier chain added features.

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

The joiner character for classifier chain added features.

CCEnsembleTrainer - Class in org.tribuo.multilabel.ensemble

A trainer for an ensemble of randomly ordered Classifier Chains.

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

Constructs a classifier chain ensemble trainer.

cdf - Variable in class org.tribuo.CategoricalInfo

The CDF to sample from.

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

Number of centroids in K-Means.

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

Number of clusters to infer.

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

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

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

Belief Propagation results.

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

Clique scores within a chain.

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

Viterbi output from a linear chain.

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

CheckerboardDataSource - Class in org.tribuo.classification.example

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

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

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

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

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

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

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

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

Path to the checkpoint base directory.

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

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

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

Constructs a chunk.

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

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

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

Default classification token name.

ClassificationEnsembleOptions - Class in org.tribuo.classification.ensemble

Options for building a classification ensemble.

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

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

The type of ensemble.

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

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

ClassifierChainModel - Class in org.tribuo.multilabel.baseline

A Classifier Chain Model.

ClassifierChainTrainer - Class in org.tribuo.multilabel.baseline

A trainer for a Classifier Chain.

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

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

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

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

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

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

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

A decision tree node used at training time.

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

Constructor which creates the inverted file.

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

The tensor class name.

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

clear() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.OperatorSetIdProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.StringStringEntryProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Segment.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Map.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Opaque.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Sequence.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Tensor.Builder

clear() - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto.Builder

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

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

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

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

Clears the features from this example.

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

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

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

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

Clears all the feature observations.

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

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

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

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

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

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

Unsupported.

clearAlgorithm() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder

This field represents a training algorithm step.

clearAttribute() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

Additional named attributes.

clearBegin() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Segment.Builder

optional int64 begin = 1;

clearDataLocation() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

If value not set, data is stored in raw_data (if set) otherwise in type-specified field.

clearDataType() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

The data type of the tensor.

clearDenotation() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder

Standard denotation can optionally be used to denote tensor dimensions with standard semantic descriptions to ensure that operations are applied to the correct axis of a tensor.

clearDenotation() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

An optional denotation can be used to denote the whole type with a standard semantic description as to what is stored inside.

clearDim() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder

repeated .onnx.TensorShapeProto.Dimension dim = 1;

clearDimParam() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder

namespace Shape

clearDims() - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder

The shape of the underlying dense-tensor: [dim_1, dim_2, ...

clearDims() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

The shape of the tensor.

clearDimValue() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder

int64 dim_value = 1;

clearDocString() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

A human-readable documentation for this attribute.

clearDocString() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

A human-readable documentation for this graph.

clearDocString() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

A human-readable documentation for this model.

clearDocString() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

A human-readable documentation for this node.

clearDocString() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

A human-readable documentation for this tensor.

clearDocString() - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto.Builder

A human-readable documentation for this value.

clearDomain() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

Domain name of the model.

clearDomain() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

The domain of the OperatorSet that specifies the operator named by op_type.

clearDomain() - Method in class ai.onnx.proto.OnnxMl.OperatorSetIdProto.Builder

The domain of the operator set being identified.

clearDomain() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Opaque.Builder

When missing, the domain is the same as the model's.

clearDoubleData() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

For double Complex128 tensors are encoded as a single array of doubles, with the real components appearing in odd numbered positions, and the corresponding imaginary component appearing in the subsequent even numbered position.

clearElemType() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Sequence.Builder

The type and optional shape of each element of the sequence.

clearElemType() - Method in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor.Builder

This field MUST NOT have the value of UNDEFINED This field MUST have a valid TensorProto.DataType value This field MUST be present for this version of the IR.

clearElemType() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Tensor.Builder

This field MUST NOT have the value of UNDEFINED This field MUST have a valid TensorProto.DataType value This field MUST be present for this version of the IR.

clearEnd() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Segment.Builder

optional int64 end = 2;

clearExternalData() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

Data can be stored inside the protobuf file using type-specific fields or raw_data.

clearF() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

Exactly ONE of the following fields must be present for this version of the IR

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.OperatorSetIdProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.StringStringEntryProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Segment.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Map.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Opaque.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Sequence.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Tensor.Builder

clearField(Descriptors.FieldDescriptor) - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto.Builder

clearFloatData() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

For float and complex64 values Complex64 tensors are encoded as a single array of floats, with the real components appearing in odd numbered positions, and the corresponding imaginary component appearing in the subsequent even numbered position.

clearFloats() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

list of floats

clearG() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

graph

clearGraph() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

The parameterized graph that is evaluated to execute the model.

clearGraphs() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

list of graph

clearI() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

int

clearIndices() - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder

The indices of the non-default values, which may be stored in one of two formats.

clearInitialization() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder

This field describes a graph to compute the initial tensors upon starting the training process.

clearInitializationBinding() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder

This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.

clearInitializer() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

A list of named tensor values, used to specify constant inputs of the graph.

clearInput() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

The inputs and outputs of the graph.

clearInput() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

namespace Value

clearInt32Data() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

For int32, uint8, int8, uint16, int16, bool, and float16 values float16 values must be bit-wise converted to an uint16_t prior to writing to the buffer.

clearInt64Data() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

For int64.

clearInts() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

list of ints

clearIrVersion() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

The version of the IR this model targets.

clearKey() - Method in class ai.onnx.proto.OnnxMl.StringStringEntryProto.Builder

optional string key = 1;

clearKeyType() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Map.Builder

This field MUST have a valid TensorProto.DataType value This field MUST be present for this version of the IR.

clearMapType() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

The type of a map.

clearMetadataProps() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

Named metadata values; keys should be distinct.

clearModelVersion() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

The version of the graph encoded.

clearName() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

The name field MUST be present for this version of the IR.

clearName() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

The name of the graph.

clearName() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

An optional identifier for this node in a graph.

clearName() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

Optionally, a name for the tensor.

clearName() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Opaque.Builder

The name is optional but significant when provided.

clearName() - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto.Builder

This field MUST be present in this version of the IR.

clearNode() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

The nodes in the graph, sorted topologically.

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.OperatorSetIdProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.StringStringEntryProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Segment.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Map.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Opaque.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Sequence.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Tensor.Builder

clearOneof(Descriptors.OneofDescriptor) - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto.Builder

clearOpaqueType() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

.onnx.TypeProto.Opaque opaque_type = 7;

clearOpsetImport() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

The OperatorSets this model relies on.

clearOpType() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

The symbolic identifier of the Operator to execute.

clearOutput() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

repeated .onnx.ValueInfoProto output = 12;

clearOutput() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

namespace Value

clearProducerName() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

The name of the framework or tool used to generate this model.

clearProducerVersion() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

The version of the framework or tool used to generate this model.

clearQuantizationAnnotation() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

This field carries information to indicate the mapping among a tensor and its quantization parameter tensors.

clearQuantParameterTensorNames() - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder

<key, value> pairs to annotate tensor specified by <tensor_name> above.

clearRawData() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

Serializations can either use one of the fields above, or use this raw bytes field.

clearRefAttrName() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function.

clearS() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

UTF-8 string

clearSegment() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

optional .onnx.TensorProto.Segment segment = 3;

clearSequenceType() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

The type of a sequence.

clearShape() - Method in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor.Builder

optional .onnx.TensorShapeProto shape = 2;

clearShape() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Tensor.Builder

optional .onnx.TensorShapeProto shape = 2;

clearSparseInitializer() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

Initializers (see above) stored in sparse format.

clearSparseTensor() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

sparse tensor value

clearSparseTensors() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

list of sparse tensors

clearSparseTensorType() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

.onnx.TypeProto.SparseTensor sparse_tensor_type = 8;

clearStringData() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

For strings.

clearStrings() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

list of UTF-8 strings

clearT() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

tensor value

clearTensorName() - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder

optional string tensor_name = 1;

clearTensors() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

list of tensors

clearTensorType() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

The type of a tensor.

clearTrainingInfo() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

Training-specific information.

clearType() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

The type field MUST be present for this version of the IR.

clearType() - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto.Builder

This field MUST be present in this version of the IR for inputs and outputs of the top-level graph.

clearUint64Data() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

For uint64 and uint32 values When this field is present, the data_type field MUST be UINT32 or UINT64

clearUpdateBinding() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder

Gradient-based training is usually an iterative procedure.

clearValue() - Method in class ai.onnx.proto.OnnxMl.StringStringEntryProto.Builder

optional string value = 2;

clearValue() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder

clearValue() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

clearValueInfo() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

Information for the values in the graph.

clearValues() - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder

The sequence of non-default values are encoded as a tensor of shape [NNZ].

clearValueType() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Map.Builder

This field MUST be present for this version of the IR.

clearVersion() - Method in class ai.onnx.proto.OnnxMl.OperatorSetIdProto.Builder

The version of the operator set being identified.

clone() - Method in class ai.onnx.proto.OnnxMl.AttributeProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.GraphProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.ModelProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.OperatorSetIdProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.StringStringEntryProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TensorProto.Segment.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Map.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Opaque.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Sequence.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.TypeProto.Tensor.Builder

clone() - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto.Builder

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

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

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

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

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

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

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

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

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

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

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

Clones a tokenizer with it's configuration.

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

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

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

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

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

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

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

Close the session and graph if they exist.

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

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

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

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

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

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

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

Closes a collection of Tensors.

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

Returns the CLS embedding.

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

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

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

Output name for the classification token output.

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

ClusterID - Class in org.tribuo.clustering

A clustering id.

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

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

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

Creates a ClusterID with the specified id number and score.

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

Clustering, maps to Tribuo's ClusterID.

ClusteringDataGenerator - Class in org.tribuo.clustering.example

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

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

ClusteringEvaluation - Interface in org.tribuo.clustering.evaluation

An Evaluation for clustering tasks.

ClusteringEvaluator - Class in org.tribuo.clustering.evaluation

A Evaluator for clustering using ClusterIDs.

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

ClusteringFactory - Class in org.tribuo.clustering

A factory for making ClusterID related classes.

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

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

ClusteringFactory.ClusteringFactoryProvenance - Class in org.tribuo.clustering

Provenance for ClusteringFactory.

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

Rebuilds a clustering factory provenance from the marshalled form.

ClusteringInfo - Class in org.tribuo.clustering

The base class for a ClusterID OutputInfo.

ClusteringMetric - Class in org.tribuo.clustering.evaluation

A metric for evaluating clustering problems.

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

Constructs a clustering metric using the supplied parameters.

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

Default metrics for evaluating clusterings.

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

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

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

Static factory methods which produce Convolutional Neural Network architectures.

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

Intercept in kernel function.

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

A CSV file parsed using a configured RowProcessor.

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

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

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

For OLCUT.

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

Constructs a columnar data source with the specified parameters.

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

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

ColumnarFeature - Class in org.tribuo.data.columnar

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

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

Constructs a ColumnarFeature from the field name.

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

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

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

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

ColumnarIterator - Class in org.tribuo.data.columnar

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

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

Constructs a ColumnarIterator wrapped around a buffering spliterator.

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

Constructs a ColumnarIterator wrapped around a buffering spliterator.

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

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

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

Returns the dense vector containing each column sum.

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

Calculates the sum of the specified column.

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

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

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

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

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

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

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

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

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

Combine the predictions.

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

Combine the supplied predictions.

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

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

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

Comma separator.

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

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

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

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

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

Compartment ID.

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

OCI compartment ID.

CompletelyConfigurableTrainTest - Class in org.tribuo.data

Build and run a predictor for a standard dataset.

CompletelyConfigurableTrainTest.ConfigurableTrainTestOptions - Class in org.tribuo.data

Command line options.

COMPLEX128 - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType

complex with float64 real and imaginary components

COMPLEX128_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType

complex with float64 real and imaginary components

COMPLEX64 - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType

complex with float32 real and imaginary components

COMPLEX64_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType

complex with float32 real and imaginary components

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

Compute the result of this metric from the input context.

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

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

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

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

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

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

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

Computes each metric given the context.

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

Computes each metric given the context.

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

Concatenates tensors.

ConcentricCirclesDataSource - Class in org.tribuo.classification.example

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

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

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

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

OCI DS conda environment name.

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

The Conda environment name.

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

OCI DS conda environment path in object storage.

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

The Conda environment path in object storage.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

ConfidencePredictingSequenceModel - Class in org.tribuo.classification.sequence

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

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

Constructs a ConfidencePredictingSequenceModel with the supplied parameters.

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

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

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

It's a DataSource that's also Configurable.

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

ConfigurableTrainTest - Class in org.tribuo.classification.experiments

Build and run a classifier for a standard dataset.

ConfigurableTrainTest - Class in org.tribuo.data

Build and run a predictor for a standard dataset.

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

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

Command line options.

ConfigurableTrainTest.ConfigurableTrainTestOptions - Class in org.tribuo.data

Command line options.

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

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

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

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

ConfiguredDataSourceProvenance - Interface in org.tribuo.provenance

A tag interface for configurable data source provenance.

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

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

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

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

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

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

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

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

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

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

A confusion matrix for Classifiables.

ConfusionMetrics - Class in org.tribuo.classification.evaluation

Static functions for computing classification metrics based on a ConfusionMatrix.

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

Returns a confusion matrix formatted String for display.

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

The string used as the field name of conjunction features.

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

Connection string to the SQL database

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

Creates a float scalar constant for this ONNXContext.

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

Creates a long scalar constant for this ONNXContext.

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

Returns the supplied label for all inputs.

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

Returns the specified constant value.

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

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

SGD with a constant learning rate.

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

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

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

Runs constrained belief propagation on a linear chain CRF.

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

Generates the counts for two vectors.

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

Generates the counts for two vectors.

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

Constructs a TripleDistribution from three lists of the same length.

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

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

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

Constructs a distribution from a joint count.

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

Constructs a distribution from a joint count.

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

Constructs a joint distribution from the counts.

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

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

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

Constructs a TripleDistribution by marginalising the supplied joint distribution.

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

Constructs a TripleDistribution by marginalising the supplied joint distribution.

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

Constructs a TripleDistribution by marginalising the supplied joint distribution.

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

Constructs a WeightedTripleDistribution by marginalising the supplied joint distribution.

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

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

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

Unlike the other info types, clustering directly uses the integer IDs as the stored value, so this mapping discards the cluster IDs and just uses the supplied integers.

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

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

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

Creates an ImmutableOutputInfo from the supplied mapping.

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

Does this example contain a feature with id i.

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

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

Does this MultiLabel contain this string?

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

Does this MultiLabel contain this Label?

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

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

Test if the metadata contains the supplied key.

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

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

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

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

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

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

Converts the byte into the enum.

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

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

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

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

Transform implicitly pads unseen values with zero.

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

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

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

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

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

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

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

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

Transform implicitly pads unseen values with zero.

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

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

Converts a SGDVector representing the features into a TensorMap.

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

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

Deprecated.

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

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

Deprecated.

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

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

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

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

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

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

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.oci.OCIModel

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

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

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

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

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

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

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

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

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

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

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

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

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