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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
ABSOLUTE - Enum constant in enum class org.tribuo.regression.sgd.TrainTest.LossEnum
AbsoluteLoss - Class in org.tribuo.regression.sgd.objectives
Absolute loss (i.e., l1).
AbsoluteLoss() - Constructor for class org.tribuo.regression.sgd.objectives.AbsoluteLoss
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
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<U>, 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<U>, 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
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
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<U>) - 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
convert(SequenceExample<T>, ImmutableFeatureMap) - Static method in class org.tribuo.classification.sgd.crf.CRFModel
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
Converts a tensor into a prediction.
convertOutput(List<OnnxValue>, int, Example<T>) - Method in class org.tribuo.interop.onnx.ONNXExternalModel
Converts a tensor into a prediction.
convertOutput(Tensor, int[], List<Example<T>>) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
Converts a tensor into a prediction.
convertOutput(Tensor, int, Example<T>) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
Converts a tensor into a prediction.
convertOutput(ImmutableOutputInfo<Label>, List<float[]>, int, Example<Label>) - Method in class org.tribuo.classification.xgboost.XGBoostClassificationConverter
 
convertOutput(ImmutableOutputInfo<Regressor>, List<float[]>, int, Example<Regressor>) - Method in class org.tribuo.regression.xgboost.XGBoostRegressionConverter
 
convertOutput(ImmutableOutputInfo<T>, List<float[]>, int, Example<T>) - Method in interface org.tribuo.common.xgboost.XGBoostOutputConverter
Converts a list of float arrays from XGBoost Boosters into a Tribuo Prediction.
convertOutput(TensorMap, int[], List<Example<T>>) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
Converts a tensor into a prediction.
convertOutput(TensorMap, int, Example<T>) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
Converts a tensor into a prediction.
convertOutput(DenseMatrix, int[], List<Example<Label>>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.oci.OCILabelConverter
 
convertOutput(DenseMatrix, int[], List<Example<MultiLabel>>, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.oci.OCIMultiLabelConverter
 
convertOutput(DenseMatrix, int[], List<Example<Regressor>>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.oci.OCIRegressorConverter
 
convertOutput(DenseMatrix, int[], List<Example<T>>) - Method in class org.tribuo.interop.oci.OCIModel
 
convertOutput(DenseMatrix, int[], List<Example<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.oci.OCIOutputConverter
Converts a dense matrix into a list of predictions of the appropriate type.
convertOutput(DenseMatrix, int, Example<T>) - Method in class org.tribuo.interop.oci.OCIModel
 
convertOutput(DenseVector, int, Example<Label>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.oci.OCILabelConverter
 
convertOutput(DenseVector, int, Example<MultiLabel>, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.oci.OCIMultiLabelConverter
 
convertOutput(DenseVector, int, Example<Regressor>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.oci.OCIRegressorConverter
 
convertOutput(DenseVector, int, Example<T>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.oci.OCIOutputConverter
Converts a dense vector into a single prediction of the appropriate type.
convertOutput(V, int[], List<Example<T>>) - Method in class org.tribuo.interop.ExternalModel
Converts the output of the external model into a list of Predictions.
convertOutput(V, int, Example<T>) - Method in class org.tribuo.interop.ExternalModel
Converts the output of the external model into a Prediction.
convertSingleExample(Example<T>, ImmutableFeatureMap, ArrayList<Float>, ArrayList<Integer>, ArrayList<Long>, long) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
Writes out the features from an example into the three supplied ArrayLists.
convertSparseVector(SparseVector) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
Used when predicting with an externally trained XGBoost model.
convertSparseVectors(List<SparseVector>) - Static method in class org.tribuo.common.xgboost.XGBoostTrainer
Used when predicting with an externally trained XGBoost model.
convertToBatchOutput(Tensor, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
 
convertToBatchOutput(Tensor, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
 
convertToBatchOutput(Tensor, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
 
convertToBatchOutput(Tensor, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
Converts a Tensor containing multiple outputs into a list of Outputs.
convertToBatchPrediction(Tensor, ImmutableOutputInfo<Label>, int[], List<Example<Label>>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
 
convertToBatchPrediction(Tensor, ImmutableOutputInfo<MultiLabel>, int[], List<Example<MultiLabel>>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
 
convertToBatchPrediction(Tensor, ImmutableOutputInfo<Regressor>, int[], List<Example<Regressor>>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
 
convertToBatchPrediction(Tensor, ImmutableOutputInfo<T>, int[], List<Example<T>>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
Converts a Tensor containing multiple outputs into a list of Predictions.
convertToCheckpointModel(String, String) - Method in class org.tribuo.interop.tensorflow.TensorFlowNativeModel
Creates a TensorFlowCheckpointModel version of this model.
convertToDense() - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
 
convertToDense() - Method in interface org.tribuo.math.optimisers.util.ShrinkingTensor
Converts the tensor into a dense tensor.
convertToDense() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
 
convertToDenseVector(ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.multilabel.MultiLabel
Converts this MultiLabel into a DenseVector using the indices from the output info.
convertToMap(ObjectNode) - Static method in class org.tribuo.json.JsonUtil
Converts a Json node into a Map from String to String for use in downstream processing by RowProcessor.
convertToNativeModel() - Method in class org.tribuo.interop.tensorflow.TensorFlowCheckpointModel
Creates a TensorFlowNativeModel version of this model.
convertToOutput(Tensor, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
 
convertToOutput(Tensor, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
 
convertToOutput(Tensor, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
 
convertToOutput(Tensor, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
Converts a Tensor into the specified output type.
convertToPrediction(Tensor, ImmutableOutputInfo<Label>, int, Example<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
 
convertToPrediction(Tensor, ImmutableOutputInfo<MultiLabel>, int, Example<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
 
convertToPrediction(Tensor, ImmutableOutputInfo<Regressor>, int, Example<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
 
convertToPrediction(Tensor, ImmutableOutputInfo<T>, int, Example<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
Converts a Tensor into a Prediction.
convertToSparseVector(ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.multilabel.MultiLabel
Converts this MultiLabel into a SparseVector using the indices from the output info.
convertToTensor(List<Example<Label>>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
 
convertToTensor(List<Example<MultiLabel>>, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
 
convertToTensor(List<Example<Regressor>>, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
 
convertToTensor(List<Example<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
Converts a list of Example into a Tensor representing all the outputs in the list.
convertToTensor(Label, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.tensorflow.LabelConverter
 
convertToTensor(MultiLabel, ImmutableOutputInfo<MultiLabel>) - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
 
convertToTensor(Regressor, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.interop.tensorflow.RegressorConverter
 
convertToTensor(T, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.OutputConverter
Converts an Output into a Tensor representing it's output.
convertToVector(SequenceExample<Label>, ImmutableFeatureMap, ImmutableOutputInfo<Label>) - Static method in class org.tribuo.classification.sgd.crf.CRFModel
Converts a SequenceExample into an array of SGDVectors and labels suitable for CRF prediction.
convertToVector(SequenceExample<T>, ImmutableFeatureMap) - Static method in class org.tribuo.classification.sgd.crf.CRFModel
Converts a SequenceExample into an array of SGDVectors suitable for CRF prediction.
convertTree() - Method in class org.tribuo.classification.dtree.impl.ClassifierTrainingNode
Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.
convertTree() - Method in class org.tribuo.common.tree.AbstractTrainingNode
Converts a tree from a training representation to the final inference time representation.
convertTree() - Method in class org.tribuo.regression.rtree.impl.JointRegressorTrainingNode
Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.
convertTree() - Method in class org.tribuo.regression.rtree.impl.RegressorTrainingNode
Generates a test time tree (made of SplitNode and LeafNode) from the tree rooted at this node.
copy() - Method in class org.tribuo.anomaly.AnomalyInfo
 
copy() - Method in class org.tribuo.anomaly.Event
 
copy() - Method in class org.tribuo.anomaly.ImmutableAnomalyInfo
 
copy() - Method in class org.tribuo.anomaly.MutableAnomalyInfo
 
copy() - Method in class org.tribuo.CategoricalIDInfo
 
copy() - Method in class org.tribuo.CategoricalInfo
 
copy() - Method in class org.tribuo.classification.ImmutableLabelInfo
 
copy() - Method in class org.tribuo.classification.Label
 
copy() - Method in class org.tribuo.classification.LabelInfo
 
copy() - Method in class org.tribuo.classification.MutableLabelInfo
 
copy() - Method in class org.tribuo.clustering.ClusterID
 
copy() - Method in class org.tribuo.clustering.ClusteringInfo
 
copy() - Method in class org.tribuo.clustering.ImmutableClusteringInfo
 
copy() - Method in class org.tribuo.clustering.MutableClusteringInfo
 
copy() - Method in class org.tribuo.common.sgd.FMParameters
 
copy() - Method in class org.tribuo.common.tree.AbstractTrainingNode
 
copy() - Method in class org.tribuo.common.tree.impl.IntArrayContainer
Returns a copy of the elements in use.
copy() - Method in class org.tribuo.common.tree.LeafNode
 
copy() - Method in interface org.tribuo.common.tree.Node
Copies the node and it's children.
copy() - Method in class org.tribuo.common.tree.SplitNode
 
copy() - Method in class org.tribuo.data.columnar.RowProcessor
Deprecated.
In a future release this API will change, in the meantime this is the correct way to get a row processor with clean state.

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

     RowProcessor rp;
     Dataset ds1 = new MutableDataset(new CSVDataSource(csvfile1, rp));
     Dataset ds2 = new MutableDataset(new CSVDataSource(csvfile2, rp)); // this may fail due to state in rp
 
This method returns a RowProcessor with clean state and the same configuration as this row processor.
copy() - Method in class org.tribuo.Example
Returns a deep copy of this Example.
copy() - Method in class org.tribuo.impl.ArrayExample
 
copy() - Method in class org.tribuo.impl.BinaryFeaturesExample
 
copy() - Method in class org.tribuo.impl.IndexedArrayExample
 
copy() - Method in class org.tribuo.impl.ListExample
 
copy() - Method in interface org.tribuo.math.FeedForwardParameters
Returns a copy of the parameters.
copy() - Method in class org.tribuo.math.la.DenseMatrix
Copies the matrix.
copy() - Method in class org.tribuo.math.la.DenseSparseMatrix
 
copy() - Method in class org.tribuo.math.la.DenseVector
 
copy() - Method in interface org.tribuo.math.la.Matrix
Copies the matrix.
copy() - Method in interface org.tribuo.math.la.SGDVector
Returns a deep copy of this vector.
copy() - Method in class org.tribuo.math.la.SparseVector
 
copy() - Method in interface org.tribuo.math.la.Tensor
Returns a copy of this Tensor.
copy() - Method in class org.tribuo.math.LinearParameters
 
copy() - Method in class org.tribuo.math.optimisers.AdaDelta
 
copy() - Method in class org.tribuo.math.optimisers.AdaGrad
 
copy() - Method in class org.tribuo.math.optimisers.AdaGradRDA
 
copy() - Method in class org.tribuo.math.optimisers.Adam
 
copy() - Method in class org.tribuo.math.optimisers.ParameterAveraging
 
copy() - Method in class org.tribuo.math.optimisers.Pegasos
 
copy() - Method in class org.tribuo.math.optimisers.RMSProp
 
copy() - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
 
copy() - Method in interface org.tribuo.math.StochasticGradientOptimiser
Copies a gradient optimiser with it's configuration.
copy() - Method in class org.tribuo.Model
Copies a model, returning a deep copy of any mutable state, and a shallow copy otherwise.
copy() - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
 
copy() - Method in class org.tribuo.multilabel.MultiLabel
 
copy() - Method in class org.tribuo.multilabel.MultiLabelInfo
 
copy() - Method in class org.tribuo.multilabel.MutableMultiLabelInfo
 
copy() - Method in interface org.tribuo.Output
Deep copy of the output up to it's immutable state.
copy() - Method in interface org.tribuo.OutputInfo
Generates a copy of this OutputInfo, including it's mutability.
copy() - Method in class org.tribuo.RealIDInfo
 
copy() - Method in class org.tribuo.RealInfo
 
copy() - Method in class org.tribuo.regression.ImmutableRegressionInfo
 
copy() - Method in class org.tribuo.regression.MutableRegressionInfo
 
copy() - Method in class org.tribuo.regression.RegressionInfo
 
copy() - Method in class org.tribuo.regression.Regressor
 
copy() - Method in class org.tribuo.regression.Regressor.DimensionTuple
 
copy() - Method in class org.tribuo.sequence.SequenceExample
Returns a deep copy of this SequenceExample.
copy() - Method in class org.tribuo.SparseModel
 
copy() - Method in interface org.tribuo.VariableInfo
Returns a copy of this variable info.
copy(String) - Method in interface org.tribuo.data.columnar.FieldProcessor
Returns a copy of this FieldProcessor bound to the supplied newFieldName.
copy(String) - Method in class org.tribuo.data.columnar.processors.field.DateFieldProcessor
 
copy(String) - Method in class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
 
copy(String) - Method in class org.tribuo.data.columnar.processors.field.IdentityProcessor
 
copy(String) - Method in class org.tribuo.data.columnar.processors.field.RegexFieldProcessor
 
copy(String) - Method in class org.tribuo.data.columnar.processors.field.TextFieldProcessor
Note: the copy shares the text pipeline with the original.
copy(String, EnsembleModelProvenance, List<Model<T>>) - Method in class org.tribuo.ensemble.EnsembleModel
Copies this ensemble model.
copy(String, EnsembleModelProvenance, List<Model<T>>) - Method in class org.tribuo.ensemble.WeightedEnsembleModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.anomaly.liblinear.LibLinearAnomalyModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.classification.baseline.DummyClassifierModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.classification.mnb.MultinomialNaiveBayesModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.classification.sgd.fm.FMClassificationModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.clustering.hdbscan.HdbscanModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.clustering.kmeans.KMeansModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.common.nearest.KNNModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.common.tree.TreeModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.common.xgboost.XGBoostModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.ensemble.EnsembleModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.interop.oci.OCIModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.interop.onnx.ONNXExternalModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowCheckpointModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowNativeModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.Model
Copies a model, replacing its provenance and name with the supplied values.
copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.baseline.ClassifierChainModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.baseline.IndependentMultiLabelModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.sgd.fm.FMMultiLabelModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.multilabel.sgd.linear.LinearSGDModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.regression.baseline.DummyRegressionModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.regression.rtree.IndependentRegressionTreeModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.regression.sgd.fm.FMRegressionModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.regression.sgd.linear.LinearSGDModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.regression.slm.SparseLinearModel
 
copy(String, ModelProvenance) - Method in class org.tribuo.transform.TransformedModel
 
copyDataset(Dataset<T>) - Static method in class org.tribuo.ImmutableDataset
Creates an immutable deep copy of the supplied dataset.
copyDataset(Dataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.ImmutableDataset
Creates an immutable deep copy of the supplied dataset, using a different feature and output map.
copyDataset(Dataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>, Merger) - Static method in class org.tribuo.ImmutableDataset
Creates an immutable deep copy of the supplied dataset.
copyDataset(SequenceDataset<T>) - Static method in class org.tribuo.sequence.ImmutableSequenceDataset
Creates an immutable deep copy of the supplied dataset.
copyDataset(SequenceDataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.sequence.ImmutableSequenceDataset
Creates an immutable deep copy of the supplied dataset, using a different feature and output map.
copyDataset(SequenceDataset<T>, ImmutableFeatureMap, ImmutableOutputInfo<T>, Merger) - Static method in class org.tribuo.sequence.ImmutableSequenceDataset
Creates an immutable deep copy of the supplied dataset.
copyModel(Model) - Static method in class org.tribuo.common.liblinear.LibLinearModel
Copies the model by writing it out to a String and loading it back in.
copyModel(svm_model) - Static method in class org.tribuo.common.libsvm.LibSVMModel
Copies an svm_model, as it does not provide a copy method.
copyParameters(svm_parameter) - Static method in class org.tribuo.common.libsvm.SVMParameters
Deep copy of the svm_parameters including the arrays.
copyResourceToTmp(String) - Static method in class org.tribuo.tests.Resources
Copies a classpath resource to a temporary file.
copyValues(int) - Method in class org.tribuo.impl.ArrayExample
Returns a copy of the feature values array at the specific size.
CoreTokenizerOptions - Class in org.tribuo.util.tokens.options
CLI Options for all the tokenizers in the core package.
CoreTokenizerOptions() - Constructor for class org.tribuo.util.tokens.options.CoreTokenizerOptions
 
CoreTokenizerOptions.CoreTokenizerType - Enum Class in org.tribuo.util.tokens.options
Tokenizer type.
coreTokenizerType - Variable in class org.tribuo.util.tokens.options.CoreTokenizerOptions
Type of tokenizer
CORRELATED - Enum constant in enum class org.tribuo.util.infotheory.example.InformationTheoryDemo.DistributionType
Correlated data.
COSINE - Enum constant in enum class org.tribuo.clustering.hdbscan.HdbscanTrainer.Distance
Cosine similarity as a distance measure.
COSINE - Enum constant in enum class org.tribuo.clustering.kmeans.KMeansTrainer.Distance
Cosine similarity as a distance measure.
COSINE - Enum constant in enum class org.tribuo.common.nearest.KNNTrainer.Distance
Cosine similarity used as a distance measure.
cosineDistance(SGDVector) - Method in interface org.tribuo.math.la.SGDVector
Calculates the cosine distance of two vectors.
cosineSimilarity(SGDVector) - Method in interface org.tribuo.math.la.SGDVector
Calculates the cosine similarity of two vectors.
cost - Variable in class org.tribuo.common.liblinear.LibLinearTrainer
 
cost - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
Cost penalty for SVM.
count - Variable in class org.tribuo.SkeletalVariableInfo
How often the feature occurs in the dataset.
count - Variable in class org.tribuo.util.infotheory.impl.PairDistribution
The number of samples this distribution has seen.
count - Variable in class org.tribuo.util.infotheory.impl.TripleDistribution
The number of samples in this distribution.
count - Variable in class org.tribuo.util.infotheory.impl.WeightCountTuple
The current count.
count - Variable in class org.tribuo.util.infotheory.impl.WeightedPairDistribution
The sample count.
count - Variable in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
The sample count.
countMap - Variable in class org.tribuo.regression.RegressionInfo
 
CREATE_AND_DEPLOY - Enum constant in enum class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions.Mode
Create a Model artifact, upload it to OCI and create a Model Deployment.
createBootstrapView(Dataset<T>, int, long) - Static method in class org.tribuo.dataset.DatasetView
Generates a DatasetView bootstrapped from the supplied Dataset.
createBootstrapView(Dataset<T>, int, long, ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.dataset.DatasetView
Generates a DatasetView bootstrapped from the supplied Dataset.
createConstantTrainer(double) - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer
Creates a trainer which create models which return a fixed value.
createConstantTrainer(String) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer
Creates a trainer which creates models which return a fixed label.
createContext(Model<Event>, List<Prediction<Event>>) - Method in class org.tribuo.anomaly.evaluation.AnomalyEvaluator
 
createContext(Model<Event>, List<Prediction<Event>>) - Method in class org.tribuo.anomaly.evaluation.AnomalyMetric
 
createContext(Model<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.evaluation.LabelEvaluator
 
createContext(Model<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.evaluation.LabelMetric
 
createContext(Model<ClusterID>, List<Prediction<ClusterID>>) - Method in class org.tribuo.clustering.evaluation.ClusteringEvaluator
 
createContext(Model<ClusterID>, List<Prediction<ClusterID>>) - Method in class org.tribuo.clustering.evaluation.ClusteringMetric
 
createContext(Model<MultiLabel>, List<Prediction<MultiLabel>>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluator
 
createContext(Model<MultiLabel>, List<Prediction<MultiLabel>>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric
 
createContext(Model<Regressor>, List<Prediction<Regressor>>) - Method in class org.tribuo.regression.evaluation.RegressionEvaluator
 
createContext(Model<Regressor>, List<Prediction<Regressor>>) - Method in class org.tribuo.regression.evaluation.RegressionMetric
 
createContext(Model<T>, List<Prediction<T>>) - Method in class org.tribuo.evaluation.AbstractEvaluator
Create the context needed for evaluation.
createContext(Model<T>, List<Prediction<T>>) - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric
Creates the context this metric uses to compute it's value.
createContext(Model<T>, Dataset<T>) - Method in interface org.tribuo.evaluation.metrics.EvaluationMetric
Creates the metric context used to compute this metric's value, generating Predictions for each Example in the supplied dataset.
createContext(SequenceModel<Label>, List<List<Prediction<Label>>>) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator
 
createContext(SequenceModel<T>, List<List<Prediction<T>>>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
Create the context needed for evaluation.
createDeepCopy(Dataset<T>) - Static method in class org.tribuo.MutableDataset
Creates a deep copy of the supplied Dataset which is mutable.
createDenseMatrix(double[][]) - Static method in class org.tribuo.math.la.DenseMatrix
Defensively copies the values before construction.
createDenseVector(double[]) - Static method in class org.tribuo.math.la.DenseVector
Defensively copies the values before construction.
createDenseVector(Example<T>, ImmutableFeatureMap, boolean) - Static method in class org.tribuo.math.la.DenseVector
Builds a DenseVector from an Example.
createEnsembleFromExistingModels(String, List<Model<T>>, EnsembleCombiner<T>) - Static method in class org.tribuo.ensemble.WeightedEnsembleModel
Creates an ensemble from existing models.
createEnsembleFromExistingModels(String, List<Model<T>>, EnsembleCombiner<T>, float[]) - Static method in class org.tribuo.ensemble.WeightedEnsembleModel
Creates an ensemble from existing models.
createEvaluation(C, Map<MetricID<T>, Double>, EvaluationProvenance) - Method in class org.tribuo.evaluation.AbstractEvaluator
Create an evaluation for the given results
createEvaluation(C, Map<MetricID<T>, Double>, EvaluationProvenance) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
Create an evaluation for the given results
createEvaluation(AnomalyMetric.Context, Map<MetricID<Event>, Double>, EvaluationProvenance) - Method in class org.tribuo.anomaly.evaluation.AnomalyEvaluator
 
createEvaluation(LabelMetric.Context, Map<MetricID<Label>, Double>, EvaluationProvenance) - Method in class org.tribuo.classification.evaluation.LabelEvaluator
 
createEvaluation(LabelMetric.Context, Map<MetricID<Label>, Double>, EvaluationProvenance) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator
 
createEvaluation(ClusteringMetric.Context, Map<MetricID<ClusterID>, Double>, EvaluationProvenance) - Method in class org.tribuo.clustering.evaluation.ClusteringEvaluator
 
createEvaluation(MultiLabelMetric.Context, Map<MetricID<MultiLabel>, Double>, EvaluationProvenance) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluator
 
createEvaluation(RegressionMetric.Context, Map<MetricID<Regressor>, Double>, EvaluationProvenance) - Method in class org.tribuo.regression.evaluation.RegressionEvaluator
 
createFeatureMap(Set<String>) - Static method in class org.tribuo.interop.ExternalModel
Creates an immutable feature map from a set of feature names.
createFeatures(Example<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionModel
Creates the feature vector.
createFeatures(Example<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel
Creates the feature vector.
createFeatures(Example<Regressor>) - Method in class org.tribuo.regression.slm.SparseLinearModel
Creates the feature vector.
createFromPairList(List<Pair<String, Boolean>>) - Static method in class org.tribuo.multilabel.MultiLabel
Creates a MultiLabel from a list of dimensions.
createFromPairList(List<Pair<String, Double>>) - Static method in class org.tribuo.regression.Regressor
Creates a Regressor from a list of dimension tuples.
createFromSparseVectors(SparseVector[]) - Static method in class org.tribuo.math.la.DenseSparseMatrix
Defensively copies the values.
createGaussianTrainer(long) - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer
Creates a trainer which create models which sample the output from a gaussian distribution fit to the training data.
createIDXData(IDXDataSource.IDXType, int[], double[]) - Static method in class org.tribuo.datasource.IDXDataSource.IDXData
Constructs an IDXData, validating the input and defensively copying it.
createLabel(Label) - Method in class org.tribuo.multilabel.MultiLabel
Creates a binary label from this multilabel.
createMeanTrainer() - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer
Creates a trainer which create models which return the mean of the training data.
createMedianTrainer() - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer
Creates a trainer which create models which return the median of the training data.
createMetrics(Model<Event>) - Method in class org.tribuo.anomaly.evaluation.AnomalyEvaluator
 
createMetrics(Model<Label>) - Method in class org.tribuo.classification.evaluation.LabelEvaluator
 
createMetrics(Model<ClusterID>) - Method in class org.tribuo.clustering.evaluation.ClusteringEvaluator
 
createMetrics(Model<MultiLabel>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluator
 
createMetrics(Model<Regressor>) - Method in class org.tribuo.regression.evaluation.RegressionEvaluator
 
createMetrics(Model<T>) - Method in class org.tribuo.evaluation.AbstractEvaluator
Creates the appropriate set of metrics for this model, by querying for it's OutputInfo.
createMetrics(SequenceModel<Label>) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator
 
createMetrics(SequenceModel<T>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
Creates the appropriate set of metrics for this model, by querying for it's OutputInfo.
createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, FMParameters) - Method in class org.tribuo.classification.sgd.fm.FMClassificationTrainer
 
createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, LinearParameters) - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
 
createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<MultiLabel>, FMParameters) - Method in class org.tribuo.multilabel.sgd.fm.FMMultiLabelTrainer
 
createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<MultiLabel>, LinearParameters) - Method in class org.tribuo.multilabel.sgd.linear.LinearSGDTrainer
 
createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, FMParameters) - Method in class org.tribuo.regression.sgd.fm.FMRegressionTrainer
 
createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, LinearParameters) - Method in class org.tribuo.regression.sgd.linear.LinearSGDTrainer
 
createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Booster>, XGBoostOutputConverter<T>) - Method in class org.tribuo.common.xgboost.XGBoostTrainer
 
createModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, X) - Method in class org.tribuo.common.sgd.AbstractSGDTrainer
Creates the appropriate model subclass for this subclass of AbstractSGDTrainer.
createModel(Path, ModelProvenance, OCIUtil.OCIModelType, DataScienceClient, ObjectMapper, OCIUtil.OCIModelArtifactConfig) - Static method in class org.tribuo.interop.oci.OCIUtil
Creates an OCI DS model and uploads the model artifact.
createModel(Map<String, T>, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer
Constructs the appropriate subclass of SkeletalIndependentRegressionModel for this trainer.
createModel(Map<String, T>, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>) - Method in class org.tribuo.regression.impl.SkeletalIndependentRegressionTrainer
Constructs the appropriate subclass of SkeletalIndependentRegressionModel for this trainer.
createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Event>, List<Model>) - Method in class org.tribuo.anomaly.liblinear.LibLinearAnomalyTrainer
 
createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Event>, List<svm_model>) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer
 
createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, List<Model>) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationTrainer
 
createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>, List<svm_model>) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationTrainer
 
createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, List<Model>) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionTrainer
 
createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Regressor>, List<svm_model>) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionTrainer
 
createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Model>) - Method in class org.tribuo.common.liblinear.LibLinearTrainer
Construct the appropriate subtype of LibLinearModel for the prediction task.
createModel(ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<svm_model>) - Method in class org.tribuo.common.libsvm.LibSVMTrainer
Construct the appropriate subtype of LibSVMModel for the prediction task.
createModel(U, DataScienceClient, ObjectMapper, OCIUtil.OCIModelArtifactConfig) - Static method in class org.tribuo.interop.oci.OCIUtil
Creates an OCI DS model and uploads the model artifact.
createModelArtifact(Path, OCIUtil.OCIModelArtifactConfig) - Static method in class org.tribuo.interop.oci.OCIUtil
Creates the OCI DS model artifact zip file.
createMostFrequentTrainer() - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer
Creates a trainer which creates models which return a fixed label, the one which was most frequent in the training data.
createObjectMapper() - Static method in class org.tribuo.interop.oci.OCIUtil
Creates an ObjectMapper capable of parsing the OCI DS json.
createOCIModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, Path, String, String, OCIOutputConverter<T>) - Static method in class org.tribuo.interop.oci.OCIModel
Creates an OCIModel by wrapping an OCI DS Model Deployment endpoint.
createOCIModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, Path, String, OCIOutputConverter<T>) - Static method in class org.tribuo.interop.oci.OCIModel
Creates an OCIModel by wrapping an OCI DS Model Deployment endpoint.
createOnlineEvaluator(Model<T>, DataProvenance) - Method in interface org.tribuo.evaluation.Evaluator
Creates an online evaluator that maintains a list of all the predictions it has seen and can evaluate them upon request.
createOnnxModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, ExampleTransformer, OutputTransformer<T>, OrtSession.SessionOptions, String, String) - Static method in class org.tribuo.interop.onnx.ONNXExternalModel
Creates an ONNXExternalModel by loading the model from disk.
createOnnxModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, ExampleTransformer, OutputTransformer<T>, OrtSession.SessionOptions, Path, String) - Static method in class org.tribuo.interop.onnx.ONNXExternalModel
Creates an ONNXExternalModel by loading the model from disk.
createOutputInfo(OutputFactory<T>, Map<T, Integer>) - Static method in class org.tribuo.interop.ExternalModel
Creates an output info from a set of outputs.
createParameters(int, int, SplittableRandom) - Method in class org.tribuo.common.sgd.AbstractFMTrainer
Constructs the trainable parameters object, in this case a FMParameters containing a weight matrix for the feature weights and a series of weight matrices for the factorized feature representation.
createParameters(int, int, SplittableRandom) - Method in class org.tribuo.common.sgd.AbstractLinearSGDTrainer
Constructs the trainable parameters object, in this case a LinearParameters containing a single weight matrix.
createParameters(int, int, SplittableRandom) - Method in class org.tribuo.common.sgd.AbstractSGDTrainer
Constructs the trainable parameters object.
createQuartileTrainer(double) - Static method in class org.tribuo.regression.baseline.DummyRegressionTrainer
Creates a trainer which create models which return the value at the specified fraction of the sorted training data.
createSparseVector(int, int[], double[]) - Static method in class org.tribuo.math.la.SparseVector
Defensively copies the input, and checks that the indices are sorted.
createSparseVector(int, Map<Integer, Double>) - Static method in class org.tribuo.math.la.SparseVector
Builds a SparseVector from a map.
createSparseVector(Example<T>, ImmutableFeatureMap, boolean) - Static method in class org.tribuo.math.la.SparseVector
Builds a SparseVector from an Example.
createSplitFunction(boolean) - Static method in class org.tribuo.util.tokens.impl.wordpiece.WordpieceBasicTokenizer
Creates a SplitFunctionTokenizer.SplitFunction that is used by the super class SplitFunctionTokenizer to determine how and where the tokenizer splits the input.
createSplitNode() - Method in class org.tribuo.common.tree.AbstractTrainingNode
Transforms an AbstractTrainingNode into a SplitNode
createStats() - Method in interface org.tribuo.transform.Transformation
Creates the statistics object for this Transformation.
createStats() - Method in class org.tribuo.transform.transformations.BinningTransformation
 
createStats() - Method in class org.tribuo.transform.transformations.IDFTransformation
 
createStats() - Method in class org.tribuo.transform.transformations.LinearScalingTransformation
 
createStats() - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation
 
createStats() - Method in class org.tribuo.transform.transformations.SimpleTransform
Returns itself.
createStratifiedTrainer(long) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer
Creates a trainer which creates models which return random labels sampled from the training label distribution.
createSupplier(Tokenizer) - Static method in interface org.tribuo.util.tokens.Tokenizer
Creates a supplier from the specified tokenizer by cloning it.
createTensorflowModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, String, String, FeatureConverter, OutputConverter<T>, String) - Static method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
Creates a TensorflowFrozenExternalModel by loading in a frozen graph.
createTensorflowModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, String, FeatureConverter, OutputConverter<T>, String) - Static method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
Creates a TensorflowSavedModelExternalModel by loading in a SavedModelBundle.
createThreadLocal(Tokenizer) - Static method in interface org.tribuo.util.tokens.Tokenizer
Creates a thread local source of tokenizers by making a Tokenizer supplier using Tokenizer.createSupplier(Tokenizer).
createTransformers(TransformationMap) - Method in class org.tribuo.Dataset
Takes a TransformationMap and converts it into a TransformerMap by observing all the values in this dataset.
createTransformers(TransformationMap, boolean) - Method in class org.tribuo.Dataset
Takes a TransformationMap and converts it into a TransformerMap by observing all the values in this dataset.
createUniformTrainer(long) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer
Creates a trainer which creates models which return random labels sampled uniformly from the labels seen at training time.
createView(Dataset<T>, Predicate<Example<T>>, String) - Static method in class org.tribuo.dataset.DatasetView
Creates a view from the supplied dataset, using the specified predicate to test if each example should be in this view.
createWeightedBootstrapView(Dataset<T>, int, long, float[]) - Static method in class org.tribuo.dataset.DatasetView
Generates a DatasetView bootstrapped from the supplied Dataset using the supplied example weights.
createWeightedBootstrapView(Dataset<T>, int, long, float[], ImmutableFeatureMap, ImmutableOutputInfo<T>) - Static method in class org.tribuo.dataset.DatasetView
Generates a DatasetView bootstrapped from the supplied Dataset using the supplied example weights.
createWhitespaceTokenizer() - Static method in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
Creates a tokenizer that splits on whitespace.
createWithEmptyOutputs(List<? extends List<? extends Feature>>, OutputFactory<T>) - Static method in class org.tribuo.sequence.SequenceExample
Creates a SequenceExample using OutputFactory.getUnknownOutput() as the output for each sequence element.
createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, String) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel
Creates an XGBoostExternalModel from the supplied model on disk.
createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, Path) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel
Creates an XGBoostExternalModel from the supplied model on disk.
createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, Booster, URL) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel
Deprecated.
As the URL argument must always be valid. To wrap an in-memory booster use XGBoostExternalModel.createXGBoostModel(OutputFactory, Map, Map, XGBoostOutputConverter, Booster, Map).
createXGBoostModel(OutputFactory<T>, Map<String, Integer>, Map<T, Integer>, XGBoostOutputConverter<T>, Booster, Map<String, Provenance>) - Static method in class org.tribuo.common.xgboost.XGBoostExternalModel
Creates an XGBoostExternalModel from the supplied in-memory XGBoost Booster.
CREATION_TIME - Static variable in class org.tribuo.provenance.impl.TimestampedTrainerProvenance
The name of the provenance field storing the model creation time.
CRFModel - Class in org.tribuo.classification.sgd.crf
An inference time model for a linear chain CRF trained using SGD.
CRFModel.ConfidenceType - Enum Class in org.tribuo.classification.sgd.crf
The type of subsequence level confidence to predict.
CRFOptions - Class in org.tribuo.classification.sgd.crf
CLI options for training a linear chain CRF model.
CRFOptions() - Constructor for class org.tribuo.classification.sgd.crf.CRFOptions
 
CRFOptions() - Constructor for class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
 
CRFParameters - Class in org.tribuo.classification.sgd.crf
A Parameters for training a CRF using SGD.
CRFTrainer - Class in org.tribuo.classification.sgd.crf
A trainer for CRFs using SGD.
CRFTrainer(StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer
Creates a CRFTrainer which uses SGD to learn the parameters.
CRFTrainer(StochasticGradientOptimiser, int, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer
Sets the minibatch size to 1.
CRFTrainer(StochasticGradientOptimiser, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer
Sets the minibatch size to 1 and the logging interval to 100.
crossValidation - Variable in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
Cross-validate the output metrics.
CrossValidation<T extends Output<T>,E extends Evaluation<T>> - Class in org.tribuo.evaluation
A class that does k-fold cross-validation.
CrossValidation(Trainer<T>, Dataset<T>, Evaluator<T, E>, int) - Constructor for class org.tribuo.evaluation.CrossValidation
Builds a k-fold cross-validation loop.
CrossValidation(Trainer<T>, Dataset<T>, Evaluator<T, E>, int, long) - Constructor for class org.tribuo.evaluation.CrossValidation
Builds a k-fold cross-validation loop.
CSV - Enum constant in enum class org.tribuo.data.DataOptions.InputFormat
Simple numeric CSV file.
CSVDataSource<T extends Output<T>> - Class in org.tribuo.data.csv
A DataSource for loading separable data from a text file (e.g., CSV, TSV) and applying FieldProcessors to it.
CSVDataSource(URI, RowProcessor<T>, boolean) - Constructor for class org.tribuo.data.csv.CSVDataSource
Creates a CSVDataSource using the specified RowProcessor to process the data.
CSVDataSource(URI, RowProcessor<T>, boolean, char) - Constructor for class org.tribuo.data.csv.CSVDataSource
Creates a CSVDataSource using the specified RowProcessor to process the data.
CSVDataSource(URI, RowProcessor<T>, boolean, char, char) - Constructor for class org.tribuo.data.csv.CSVDataSource
Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.
CSVDataSource(URI, RowProcessor<T>, boolean, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVDataSource
Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.
CSVDataSource(Path, RowProcessor<T>, boolean) - Constructor for class org.tribuo.data.csv.CSVDataSource
Creates a CSVDataSource using the specified RowProcessor to process the data.
CSVDataSource(Path, RowProcessor<T>, boolean, char) - Constructor for class org.tribuo.data.csv.CSVDataSource
Creates a CSVDataSource using the specified RowProcessor to process the data.
CSVDataSource(Path, RowProcessor<T>, boolean, char, char) - Constructor for class org.tribuo.data.csv.CSVDataSource
Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.
CSVDataSource(Path, RowProcessor<T>, boolean, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVDataSource
Creates a CSVDataSource using the specified RowProcessor to process the data, and the supplied separator and quote characters to read the input data file.
CSVDataSource.CSVDataSourceProvenance - Class in org.tribuo.data.csv
Provenance for CSVDataSource.
CSVDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance
Deserialization constructor.
CSVIterator - Class in org.tribuo.data.csv
An iterator over a CSV file.
CSVIterator(Reader) - Constructor for class org.tribuo.data.csv.CSVIterator
Builds a CSVIterator for the supplied Reader.
CSVIterator(Reader, char, char) - Constructor for class org.tribuo.data.csv.CSVIterator
Builds a CSVIterator for the supplied Reader.
CSVIterator(Reader, char, char, String[]) - Constructor for class org.tribuo.data.csv.CSVIterator
Builds a CSVIterator for the supplied Reader.
CSVIterator(Reader, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVIterator
Builds a CSVIterator for the supplied Reader.
CSVIterator(URI) - Constructor for class org.tribuo.data.csv.CSVIterator
Builds a CSVIterator for the supplied URI.
CSVIterator(URI, char, char) - Constructor for class org.tribuo.data.csv.CSVIterator
Builds a CSVIterator for the supplied URI.
CSVIterator(URI, char, char, String[]) - Constructor for class org.tribuo.data.csv.CSVIterator
Builds a CSVIterator for the supplied URI.
CSVIterator(URI, char, char, List<String>) - Constructor for class org.tribuo.data.csv.CSVIterator
Builds a CSVIterator for the supplied URI.
CSVLoader<T extends Output<T>> - Class in org.tribuo.data.csv
Load a DataSource/Dataset from a CSV file.
CSVLoader(char, char, OutputFactory<T>) - Constructor for class org.tribuo.data.csv.CSVLoader
Creates a CSVLoader using the supplied separator, quote and output factory.
CSVLoader(char, OutputFactory<T>) - Constructor for class org.tribuo.data.csv.CSVLoader
Creates a CSVLoader using the supplied separator and output factory.
CSVLoader(OutputFactory<T>) - Constructor for class org.tribuo.data.csv.CSVLoader
Creates a CSVLoader using the supplied output factory.
CSVLoader.CSVLoaderProvenance - Class in org.tribuo.data.csv
Deprecated.
Deprecated in 4.2 as CSVLoader now returns a CSVDataSource. This provenance is kept so older models can still load correctly.
CSVLoaderProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance
Deprecated.
Deserialization constructor.
csvQuoteChar - Variable in class org.tribuo.data.DataOptions
Quote character in the CSV file.
csvResponseName - Variable in class org.tribuo.classification.experiments.Test.ConfigurableTestOptions
Response name in the csv file.
csvResponseName - Variable in class org.tribuo.data.DataOptions
Response name in the csv file.
CSVSaver - Class in org.tribuo.data.csv
Saves a Dataset in CSV format suitable for loading by CSVLoader.
CSVSaver() - Constructor for class org.tribuo.data.csv.CSVSaver
Builds a CSV saver using the default separator and quote from CSVIterator.
CSVSaver(char, char) - Constructor for class org.tribuo.data.csv.CSVSaver
Builds a CSV saver using the supplied separator and quote.
cumulativeSum(boolean[]) - Static method in class org.tribuo.util.Util
Produces a cumulative sum array.
cumulativeSum(double[]) - Static method in class org.tribuo.util.Util
Produces a cumulative sum array.
currentRow - Variable in class org.tribuo.data.columnar.ColumnarIterator
The current row.

D

DART - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.BoosterType
A gradient boosted decision tree using dropout.
data - Variable in class org.tribuo.common.xgboost.XGBoostTrainer.DMatrixTuple
 
data - Variable in class org.tribuo.data.text.TextDataSource
The actual data read out of the text file.
data - Variable in class org.tribuo.Dataset
The data in this data set.
data - Variable in class org.tribuo.interop.tensorflow.TensorFlowUtil.TensorTuple
The tensor data.
data - Variable in class org.tribuo.sequence.SequenceDataset
The data in this data set.
DATA_LOCATION_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorProto
 
DATA_TYPE_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorProto
 
DataOptions - Class in org.tribuo.data
Options for working with training and test data in a CLI.
DataOptions() - Constructor for class org.tribuo.data.DataOptions
 
DataOptions.Delimiter - Enum Class in org.tribuo.data
The delimiters supported by CSV files in this options object.
DataOptions.InputFormat - Enum Class in org.tribuo.data
The input formats supported by this options object.
DataProvenance - Interface in org.tribuo.provenance
Tag interface for data sources provenances.
Dataset<T extends Output<T>> - Class in org.tribuo
A class for sets of data, which are used to train and evaluate classifiers.
Dataset(DataSource<T>) - Constructor for class org.tribuo.Dataset
Creates a dataset.
Dataset(DataProvenance, OutputFactory<T>) - Constructor for class org.tribuo.Dataset
Creates a dataset.
DATASET - Enum constant in enum class org.tribuo.json.StripProvenance.ProvenanceTypes
Select the dataset provenance.
DATASET - Static variable in class org.tribuo.provenance.ModelProvenance
 
DatasetExplorer - Class in org.tribuo.data
A CLI for exploring a serialised Dataset.
DatasetExplorer() - Constructor for class org.tribuo.data.DatasetExplorer
Constructs a dataset explorer.
DatasetExplorer.DatasetExplorerOptions - Class in org.tribuo.data
Command line options.
DatasetExplorerOptions() - Constructor for class org.tribuo.data.DatasetExplorer.DatasetExplorerOptions
 
datasetName - Variable in class org.tribuo.classification.sequence.SeqTrainTest.SeqTrainTestOptions
Name of the example dataset, options are {gorilla}.
datasetName - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
Name of the example dataset, options are {gorilla}.
datasetPath - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions
Path to the serialized dataset to score.
datasetProvenance - Variable in class org.tribuo.provenance.ModelProvenance
 
DatasetProvenance - Class in org.tribuo.provenance
Base class for dataset provenance.
DatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.DatasetProvenance
Deserialization constructor.
DatasetProvenance(DataProvenance, ListProvenance<ObjectProvenance>, String, boolean, boolean, int, int, int) - Constructor for class org.tribuo.provenance.DatasetProvenance
Constructs a dataset provenance using the supplied information.
DatasetProvenance(DataProvenance, ListProvenance<ObjectProvenance>, Dataset<T>) - Constructor for class org.tribuo.provenance.DatasetProvenance
Creates a dataset provenance from the supplied dataset.
DatasetProvenance(DataProvenance, ListProvenance<ObjectProvenance>, SequenceDataset<T>) - Constructor for class org.tribuo.provenance.DatasetProvenance
Creates a dataset provenance from the supplied sequence dataset.
DatasetView<T extends Output<T>> - Class in org.tribuo.dataset
DatasetView provides an immutable view on another Dataset that only exposes selected examples.
DatasetView(Dataset<T>, int[], String) - Constructor for class org.tribuo.dataset.DatasetView
Creates a DatasetView which includes the supplied indices from the dataset.
DatasetView(Dataset<T>, int[], ImmutableFeatureMap, ImmutableOutputInfo<T>, String) - Constructor for class org.tribuo.dataset.DatasetView
Creates a DatasetView which includes the supplied indices from the dataset.
DatasetView.DatasetViewProvenance - Class in org.tribuo.dataset
Provenance for the DatasetView.
DatasetViewProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.dataset.DatasetView.DatasetViewProvenance
Deserialization constructor.
dataSource - Variable in class org.tribuo.data.PreprocessAndSerialize.PreprocessAndSerializeOptions
Datasource to load from a config file
DataSource<T extends Output<T>> - Interface in org.tribuo
A interface for things that can be given to a Dataset's constructor.
DATASOURCE_CREATION_TIME - Static variable in interface org.tribuo.provenance.DataSourceProvenance
The name of the provenance field for the datasource timestamp.
DataSourceProvenance - Interface in org.tribuo.provenance
Data source provenance.
DateExtractor - Class in org.tribuo.data.columnar.extractors
Extracts the field value and translates it to a LocalDate based on the specified DateTimeFormatter.
DateExtractor(String, String, String) - Constructor for class org.tribuo.data.columnar.extractors.DateExtractor
Constructs a date extractor that emits a LocalDate by applying the supplied format to the specified field.
DateExtractor(String, String, String, String, String) - Constructor for class org.tribuo.data.columnar.extractors.DateExtractor
Constructs a date extractor that emits a LocalDate by applying the supplied format to the specified field.
DateExtractor(String, String, DateTimeFormatter) - Constructor for class org.tribuo.data.columnar.extractors.DateExtractor
Deprecated.
DateFieldProcessor - Class in org.tribuo.data.columnar.processors.field
Processes a column that contains a date value.
DateFieldProcessor(String, EnumSet<DateFieldProcessor.DateFeatureType>, String) - Constructor for class org.tribuo.data.columnar.processors.field.DateFieldProcessor
Constructs a field processor which parses a date from the specified field name using the supplied format string then extracts date features according to the supplied EnumSet.
DateFieldProcessor(String, EnumSet<DateFieldProcessor.DateFeatureType>, String, String, String) - Constructor for class org.tribuo.data.columnar.processors.field.DateFieldProcessor
Constructs a field processor which parses a date from the specified field name using the supplied format string then extracts date features according to the supplied EnumSet.
DateFieldProcessor.DateFeatureType - Enum Class in org.tribuo.data.columnar.processors.field
The types of date features which can be extracted.
DAY - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
The day.
DAY_OF_QUARTER - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
The day of the quarter.
DAY_OF_WEEK - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
The day of the week in ISO 8601.
DAY_OF_YEAR - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
The day of the year.
dbConfig - Variable in class org.tribuo.data.sql.SQLToCSV.SQLToCSVOptions
Name of the DBConfig to use
DEBUG - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.LoggingVerbosity
All the logging.
DecisionTreeTrainer<T extends Output<T>> - Interface in org.tribuo.common.tree
A tag interface for a Trainer so the random forests trainer can check if it's actually a tree.
decode(Tensor, List<SequenceExample<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputConverter
Decode graph output tensors corresponding to a batch of input sequences.
decode(Tensor, SequenceExample<T>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputConverter
Decode a tensor of graph output into a list of predictions for the input sequence.
deepCopy() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
Copies this inverted feature.
deepCopy() - Method in class org.tribuo.regression.rtree.impl.TreeFeature
Returns a deep copy of this tree feature.
DEFAULT - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorProto.DataLocation
DEFAULT = 0;
DEFAULT - Enum constant in enum class org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions.ViterbiLabelFeatures
The default label features.
DEFAULT_BATCH_SIZE - Static variable in class org.tribuo.interop.ExternalModel
Default batch size for external model batch predictions.
DEFAULT_COST - Static variable in class org.tribuo.regression.sgd.objectives.Huber
The default cost beyond which the function is linear.
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.impl.TripleDistribution
The default map size to initialise the marginalised count maps with.
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
The default map size.
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.InformationTheory
The initial size of the various maps.
DEFAULT_MAP_SIZE - Static variable in class org.tribuo.util.infotheory.WeightedInformationTheory
The initial size of the various maps.
DEFAULT_METADATA_SIZE - Static variable in class org.tribuo.Example
The default initial size of the metadata map.
DEFAULT_NAME - Static variable in class org.tribuo.regression.Regressor
Default name used for dimensions which are unnamed when parsed from Strings.
DEFAULT_RESPONSE - Static variable in class org.tribuo.data.csv.CSVSaver
The default response column name.
DEFAULT_SCORE - Static variable in class org.tribuo.anomaly.Event
The default score of events.
DEFAULT_SEED - Static variable in interface org.tribuo.Trainer
Default seed used to initialise RNGs.
DEFAULT_SIZE - Static variable in class org.tribuo.common.tree.AbstractTrainingNode
Default buffer size used in the split operation.
DEFAULT_SIZE - Static variable in class org.tribuo.impl.ArrayExample
Default initial size of the backing arrays.
DEFAULT_SIZE - Static variable in class org.tribuo.impl.BinaryFeaturesExample
Default initial size of the backing arrays.
DEFAULT_SPLIT_CHAR - Static variable in class org.tribuo.regression.RegressionFactory
The default character to split the string form of a multidimensional regressor.
DEFAULT_SPLIT_CHARACTERS - Static variable in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
The default split characters.
DEFAULT_SPLIT_EXCEPTING_IN_DIGITS_CHARACTERS - Static variable in class org.tribuo.util.tokens.impl.SplitCharactersTokenizer
The default characters which don't cause splits inside digits.
DEFAULT_THRESHOLD - Static variable in class org.tribuo.interop.oci.OCIMultiLabelConverter
The default threshold for conversion into a label.
DEFAULT_THRESHOLD - Static variable in class org.tribuo.interop.onnx.MultiLabelTransformer
The default threshold for conversion into a label.
DEFAULT_UNKNOWN_TOKEN - Static variable in class org.tribuo.util.tokens.impl.wordpiece.Wordpiece
The default unknown token string.
DEFAULT_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.TensorProto.DataLocation
DEFAULT = 0;
DEFAULT_WEIGHT - Static variable in class org.tribuo.Example
The default weight.
DEFAULT_WEIGHT - Static variable in class org.tribuo.sequence.SequenceExample
The default sequence example weight.
DefaultFeatureExtractor - Class in org.tribuo.classification.sequence.viterbi
A label feature extractor that produces several kinds of label-based features.
DefaultFeatureExtractor() - Constructor for class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
Constructs a default feature extractor for bigrams and trigrams using the past 3 outcomes.
DefaultFeatureExtractor(int, int, boolean, boolean, boolean) - Constructor for class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
Constructs a default feature extractor using the supplied parameters.
degree - Variable in class org.tribuo.regression.libsvm.TrainTest.LibSVMOptions
Degree in polynomial kernel.
delimiter - Variable in class org.tribuo.data.DataOptions
Delimiter
DELTA - Static variable in class org.tribuo.math.la.VectorTuple
The tolerance for equality in value comparisons.
DemoLabelDataSource - Class in org.tribuo.classification.example
The base class for the 2d binary classification data sources in org.tribuo.classification.example.
DemoLabelDataSource.DemoLabelDataSourceProvenance - Class in org.tribuo.classification.example
Provenance for DemoLabelDataSource.
DemoLabelDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.classification.example.DemoLabelDataSource.DemoLabelDataSourceProvenance
Constructs a provenance from the marshalled form.
DemoOptions() - Constructor for class org.tribuo.util.infotheory.example.InformationTheoryDemo.DemoOptions
 
DENOTATION_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension
 
DENOTATION_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TypeProto
 
dense - Variable in class org.tribuo.MutableDataset
Denotes if this dataset contains implicit zeros or not.
dense - Variable in class org.tribuo.sequence.MutableSequenceDataset
Does this dataset have a dense feature space.
DENSE - Enum constant in enum class org.tribuo.interop.tensorflow.TrainTest.InputType
Dense feature extractor.
DenseFeatureConverter - Class in org.tribuo.interop.tensorflow
Converts a sparse example into a dense float vector, then wraps it in a TFloat32.
DenseFeatureConverter(String) - Constructor for class org.tribuo.interop.tensorflow.DenseFeatureConverter
Builds a DenseFeatureConverter, setting the input name.
DenseMatrix - Class in org.tribuo.math.la
A dense matrix, backed by a primitive array.
DenseMatrix(int, int) - Constructor for class org.tribuo.math.la.DenseMatrix
Creates a dense matrix full of zeros.
DenseMatrix(DenseMatrix) - Constructor for class org.tribuo.math.la.DenseMatrix
Copies the supplied matrix.
DenseMatrix(Matrix) - Constructor for class org.tribuo.math.la.DenseMatrix
Copies the supplied matrix, densifying it if it's sparse.
DenseSparseMatrix - Class in org.tribuo.math.la
A matrix which is dense in the first dimension and sparse in the second.
DenseSparseMatrix(int, int) - Constructor for class org.tribuo.math.la.DenseSparseMatrix
Creates a DenseSparseMatrix with no values or indices.
DenseSparseMatrix(List<SparseVector>) - Constructor for class org.tribuo.math.la.DenseSparseMatrix
Constructs a DenseSparseMatrix out of the supplied sparse vector list.
DenseSparseMatrix(DenseSparseMatrix) - Constructor for class org.tribuo.math.la.DenseSparseMatrix
Creates a new DenseSparseMatrix by deep copying the supplied DenseSparseMatrix.
denseTrainTest() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator
Makes a simple dataset for training and testing.
denseTrainTest() - Static method in class org.tribuo.classification.example.LabelledDataGenerator
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
denseTrainTest() - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
denseTrainTest() - Static method in class org.tribuo.regression.example.RegressionDataGenerator
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
denseTrainTest(double) - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
denseTrainTest(double) - Static method in class org.tribuo.classification.example.LabelledDataGenerator
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
denseTrainTest(double) - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 clusters, {0,1,2,3}.
denseTrainTest(double) - Static method in class org.tribuo.regression.example.RegressionDataGenerator
Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}.
DenseTransformer - Class in org.tribuo.interop.onnx
Converts a sparse Tribuo example into a dense float vector, then wraps it in an OnnxTensor.
DenseTransformer() - Constructor for class org.tribuo.interop.onnx.DenseTransformer
 
DenseVector - Class in org.tribuo.math.la
A dense vector, backed by a double array.
DenseVector(double[]) - Constructor for class org.tribuo.math.la.DenseVector
Does not defensively copy the input, used internally.
DenseVector(int) - Constructor for class org.tribuo.math.la.DenseVector
Creates an empty dense vector of the specified size.
DenseVector(int, double) - Constructor for class org.tribuo.math.la.DenseVector
Creates a dense vector of the specified size where each element is initialised to the specified value.
DenseVector(DenseVector) - Constructor for class org.tribuo.math.la.DenseVector
Copy constructor.
densify() - Method in class org.tribuo.math.la.SparseVector
Returns a dense vector copying this sparse vector.
densify() - Method in class org.tribuo.MutableDataset
Iterates through the examples, converting implicit zeros into explicit zeros.
densify() - Method in class org.tribuo.sequence.MutableSequenceDataset
Iterates through the examples, converting implicit zeros into explicit zeros.
densify(List<String>) - Method in class org.tribuo.Example
Converts all implicit zeros into explicit zeros based on the supplied feature names.
densify(List<String>) - Method in class org.tribuo.impl.ArrayExample
 
densify(List<String>) - Method in class org.tribuo.impl.BinaryFeaturesExample
 
densify(List<String>) - Method in class org.tribuo.impl.IndexedArrayExample
 
densify(List<String>) - Method in class org.tribuo.impl.ListExample
 
densify(FeatureMap) - Method in class org.tribuo.Example
Converts all implicit zeros into explicit zeros based on the supplied feature map.
densify(FeatureMap) - Method in class org.tribuo.impl.BinaryFeaturesExample
 
densify(FeatureMap) - Method in class org.tribuo.sequence.SequenceExample
Converts all implicit zeros into explicit zeros based on the supplied feature map.
deploy(OCIUtil.OCIModelDeploymentConfig, DataScienceClient, ObjectMapper) - Static method in class org.tribuo.interop.oci.OCIUtil
Creates a Model deployment from an uploaded model.
DEPLOY - Enum constant in enum class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions.Mode
Create a Model deployment.
deploymentName - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelDeploymentConfig
The deployment name.
depth - Variable in class org.tribuo.common.tree.AbstractTrainingNode
 
depth - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions
Maximum depth in the decision tree.
depth - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
Max tree depth (default 6, range (0,inf]).
depth - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
Max tree depth (default 6, range (0,inf]).
DESCRIPTION - Static variable in class org.tribuo.provenance.SimpleDataSourceProvenance
The description field in the provenance.
DescriptiveStats - Class in org.tribuo.evaluation
Descriptive statistics calculated across a list of doubles.
DescriptiveStats() - Constructor for class org.tribuo.evaluation.DescriptiveStats
Create an empty DescriptiveStats.
DescriptiveStats(List<Double>) - Constructor for class org.tribuo.evaluation.DescriptiveStats
Create a DescriptiveStats initialized with the supplied values.
difference(SparseVector) - Method in class org.tribuo.math.la.SparseVector
Generates an array of the indices that are active in this vector but are not present in other.
differencesIndices(double[]) - Static method in class org.tribuo.util.Util
Returns an array containing the indices where values are different.
differencesIndices(double[], double) - Static method in class org.tribuo.util.Util
Returns an array containing the indices where values are different.
diffProvenance(ModelProvenance, ModelProvenance) - Static method in class org.tribuo.reproducibility.ReproUtil
Creates a JSON String diff of two ModelProvenance objects.
DIM_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorShapeProto
 
DIM_PARAM - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.ValueCase
 
DIM_PARAM_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension
 
DIM_VALUE - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.ValueCase
 
DIM_VALUE_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension
 
dim1 - Variable in class org.tribuo.math.la.DenseMatrix
 
dim2 - Variable in class org.tribuo.math.la.DenseMatrix
 
DIMENSIONALITY_REDUCTION - Enum constant in enum class org.tribuo.interop.oci.OCIUtil.OCIModelType
Dimensionality reduction, no Tribuo mapping.
dimensions - Variable in class org.tribuo.regression.impl.SkeletalIndependentRegressionModel
 
dimensions - Variable in class org.tribuo.regression.impl.SkeletalIndependentRegressionSparseModel
 
DimensionTuple(String, double) - Constructor for class org.tribuo.regression.Regressor.DimensionTuple
Creates a dimension tuple from the supplied name and value.
DimensionTuple(String, double, double) - Constructor for class org.tribuo.regression.Regressor.DimensionTuple
Creates a dimension tuple from the supplied name, value and variance.
DIMS_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.SparseTensorProto
 
DIMS_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorProto
 
directory - Variable in class org.tribuo.classification.experiments.RunAll.RunAllOptions
Directory to write out the models and test reports.
DirectoryFileSource<T extends Output<T>> - Class in org.tribuo.data.text
A data source for a somewhat-common format for text classification datasets: a top level directory that contains a number of subdirectories.
DirectoryFileSource() - Constructor for class org.tribuo.data.text.DirectoryFileSource
for olcut
DirectoryFileSource(Path, OutputFactory<T>, TextFeatureExtractor<T>, DocumentPreprocessor...) - Constructor for class org.tribuo.data.text.DirectoryFileSource
Creates a data source that will use the given feature extractor and document preprocessors on the data read from the files in the directories representing classes.
DirectoryFileSource.DirectoryFileSourceProvenance - Class in org.tribuo.data.text
Provenance for DirectoryFileSource.
DirectoryFileSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
Deserialization constructor.
distance - Variable in class org.tribuo.clustering.kmeans.KMeansOptions
Distance function in K-Means.
distance - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
Distance function to use in the e step.
distance(SGDVector, DoubleUnaryOperator, DoubleUnaryOperator) - Method in class org.tribuo.math.la.SparseVector
Computes the distance between this vector and the other vector.
DISTANCE_DELTA - Static variable in class org.tribuo.classification.explanations.lime.LIMEBase
Delta to consider two distances equal.
distanceType - Variable in class org.tribuo.clustering.hdbscan.HdbscanOptions
Distance function in HDBSCAN*.
distributionEquals(Prediction<T>) - Method in class org.tribuo.Prediction
Checks that the other prediction has the same distribution as this prediction, using the Output.fullEquals(T) method.
div - Enum constant in enum class org.tribuo.transform.transformations.SimpleTransform.Operation
Divides by the specified constant.
div(double) - Static method in class org.tribuo.transform.transformations.SimpleTransform
Generate a SimpleTransform that divides each value by the operand.
DIV - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
Element-wise division with broadcasting.
DMatrixTuple(DMatrix, int[], Example<T>[]) - Constructor for class org.tribuo.common.xgboost.XGBoostTrainer.DMatrixTuple
 
DOC_STRING_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.AttributeProto
 
DOC_STRING_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.GraphProto
 
DOC_STRING_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.ModelProto
 
DOC_STRING_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.NodeProto
 
DOC_STRING_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorProto
 
DOC_STRING_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.ValueInfoProto
 
DocumentPreprocessor - Interface in org.tribuo.data.text
An interface for things that can pre-process documents before they are broken into features.
domain - Variable in enum class org.tribuo.util.onnx.ONNXOperators
The operator domain (used for the ML operators).
DOMAIN_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.ModelProto
 
DOMAIN_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.NodeProto
 
DOMAIN_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.OperatorSetIdProto
 
DOMAIN_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TypeProto.Opaque
 
dot(SGDVector) - Method in class org.tribuo.math.la.DenseVector
 
dot(SGDVector) - Method in interface org.tribuo.math.la.SGDVector
Calculates the dot product between this vector and other.
dot(SGDVector) - Method in class org.tribuo.math.la.SparseVector
 
dot(SGDVector) - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
 
DOUBLE - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType
DOUBLE = 11;
DOUBLE - Enum constant in enum class org.tribuo.datasource.IDXDataSource.IDXType
A 64-bit float.
DOUBLE_DATA_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorProto
 
DOUBLE_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType
DOUBLE = 11;
DoubleExtractor - Class in org.tribuo.data.columnar.extractors
Extracts the field value and converts it to a double.
DoubleExtractor(String) - Constructor for class org.tribuo.data.columnar.extractors.DoubleExtractor
Extracts a double value from the supplied field name.
DoubleExtractor(String, String) - Constructor for class org.tribuo.data.columnar.extractors.DoubleExtractor
Extracts a double value from the supplied field name.
DoubleFieldProcessor - Class in org.tribuo.data.columnar.processors.field
Processes a column that contains a real value.
DoubleFieldProcessor(String) - Constructor for class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
Constructs a field processor which extracts a single double valued feature from the specified field name.
DoubleFieldProcessor(String, boolean) - Constructor for class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
Constructs a field processor which extracts a single double valued feature from the specified field name.
DoubleFieldProcessor(String, boolean, boolean) - Constructor for class org.tribuo.data.columnar.processors.field.DoubleFieldProcessor
Constructs a field processor which extracts a single double valued feature from the specified field name.
doubleTensorBuilder(ONNXContext, String, List<Integer>, Consumer<DoubleBuffer>) - Static method in class org.tribuo.util.onnx.ONNXUtils
Generic method to create double OnnxMl.TensorProto instances.
dropInvalidExamples - Variable in class org.tribuo.ImmutableDataset
If true, instead of throwing an exception when an invalid Example is encountered, this Dataset will log a warning and drop it.
dsConfig - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelArtifactConfig
The OCI Data Science config.
dsConfig - Variable in class org.tribuo.interop.oci.OCIUtil.OCIModelDeploymentConfig
The OCI Data Science config.
DTYPE - Static variable in class org.tribuo.interop.tensorflow.TensorFlowUtil
The name of the data type.
DummyClassifierModel - Class in org.tribuo.classification.baseline
A model which performs dummy classifications (e.g., constant output, uniform sampled labels, stratified sampled labels).
DummyClassifierTrainer - Class in org.tribuo.classification.baseline
A trainer for simple baseline classifiers.
DummyClassifierTrainer.DummyType - Enum Class in org.tribuo.classification.baseline
Types of dummy classifier.
DummyRegressionModel - Class in org.tribuo.regression.baseline
A model which performs dummy regressions (e.g., constant output, gaussian sampled output, mean value, median, quartile).
DummyRegressionTrainer - Class in org.tribuo.regression.baseline
A trainer for simple baseline regressors.
DummyRegressionTrainer.DummyRegressionTrainerProvenance - Class in org.tribuo.regression.baseline
Deprecated.
DummyRegressionTrainer.DummyType - Enum Class in org.tribuo.regression.baseline
Types of dummy regression model.
DummyRegressionTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance
Deprecated.
Constructs a provenance from the marshalled form.
DummyRegressionTrainerProvenance(DummyRegressionTrainer) - Constructor for class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance
Deprecated.
Constructs a provenance from the host.

E

ELASTICNET - Enum constant in enum class org.tribuo.regression.slm.TrainTest.SLMType
ElasticNetCDTrainer - Class in org.tribuo.regression.slm
An ElasticNet trainer that uses co-ordinate descent.
ElasticNetCDTrainer(double, double) - Constructor for class org.tribuo.regression.slm.ElasticNetCDTrainer
Constructs an elastic net trainer using the supplied parameters, with a tolerance of 1e-4, max iterations of 500, randomising the feature choice and using Trainer.DEFAULT_SEED as the RNG seed.
ElasticNetCDTrainer(double, double, double, int, boolean, long) - Constructor for class org.tribuo.regression.slm.ElasticNetCDTrainer
Constructs an elastic net trainer using the supplied parameters.
ElasticNetCDTrainer(double, double, long) - Constructor for class org.tribuo.regression.slm.ElasticNetCDTrainer
Constructs an elastic net trainer using the supplied parameters, with a tolerance of 1e-4, max iterations of 500, and randomising the feature choice.
ELEM_TYPE_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TypeProto.Sequence
 
ELEM_TYPE_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor
 
ELEM_TYPE_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TypeProto.Tensor
 
elements - Variable in class org.tribuo.math.la.DenseVector
 
EmptyDatasetProvenance - Class in org.tribuo.provenance.impl
An empty DatasetProvenance, should not be used except by the provenance removal system.
EmptyDatasetProvenance() - Constructor for class org.tribuo.provenance.impl.EmptyDatasetProvenance
An empty dataset provenance.
EmptyDatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.EmptyDatasetProvenance
Deserialization constructor.
EmptyDataSourceProvenance - Class in org.tribuo.provenance.impl
An empty DataSourceProvenance, should not be used except by the provenance removal system.
EmptyDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.EmptyDataSourceProvenance
Deserialization constructor.
emptyExample() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator
Generates an example with no features.
emptyExample() - Static method in class org.tribuo.classification.example.LabelledDataGenerator
Generates an example with no features.
emptyExample() - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator
Generates an example with no features.
emptyExample() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
Generates an example with no features.
emptyExample() - Static method in class org.tribuo.regression.example.RegressionDataGenerator
Generates an example with no features.
emptyMultiDimExample() - Static method in class org.tribuo.regression.example.RegressionDataGenerator
Generates an example with no features.
EmptyResponseProcessor<T extends Output<T>> - Class in org.tribuo.data.columnar.processors.response
A ResponseProcessor that always emits an empty optional.
EmptyResponseProcessor(OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.EmptyResponseProcessor
Constructs a response processor which never emits a response.
EmptyTrainerProvenance - Class in org.tribuo.provenance.impl
An empty TrainerProvenance, should not be used except by the provenance removal system.
EmptyTrainerProvenance() - Constructor for class org.tribuo.provenance.impl.EmptyTrainerProvenance
Constructs an empty trainer provenance.
EmptyTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.impl.EmptyTrainerProvenance
Deserialization constructor.
encode(List<? extends SequenceExample<?>>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceFeatureConverter
Encodes a batch of examples as a feed dict.
encode(List<SequenceExample<T>>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputConverter
Encodes a batch of labels as a feed dict.
encode(SequenceExample<?>, ImmutableFeatureMap) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceFeatureConverter
Encodes an example as a feed dict.
encode(SequenceExample<T>, ImmutableOutputInfo<T>) - Method in interface org.tribuo.interop.tensorflow.sequence.SequenceOutputConverter
Encodes an example's label as a feed dict.
end - Variable in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence
The subsequence end index.
end - Variable in class org.tribuo.util.tokens.Token
The end index.
end - Variable in class org.tribuo.util.tokens.universal.Range
The end index.
END_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorProto.Segment
 
endpointDomain - Variable in class org.tribuo.interop.oci.OCIModelCLI.OCIModelOptions
The OCI endpoint domain.
ensemble - Variable in class org.tribuo.classification.experiments.AllTrainerOptions
Options for classifier ensembles.
EnsembleCombiner<T extends Output<T>> - Interface in org.tribuo.ensemble
An interface for combining predictions.
EnsembleExcuse<T extends Output<T>> - Class in org.tribuo.ensemble
An Excuse which has a List of excuses for each of the ensemble members.
EnsembleExcuse(Example<T>, Prediction<T>, Map<String, List<Pair<String, Double>>>, List<Excuse<T>>) - Constructor for class org.tribuo.ensemble.EnsembleExcuse
Constructs an ensemble excuse, comprising the excuses from each ensemble member, along with the feature weights.
EnsembleModel<T extends Output<T>> - Class in org.tribuo.ensemble
A model which contains a list of other Models.
EnsembleModel(String, EnsembleModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, List<Model<T>>) - Constructor for class org.tribuo.ensemble.EnsembleModel
Builds an EnsembleModel from the supplied model list.
EnsembleModelProvenance - Class in org.tribuo.provenance
Model provenance for ensemble models.
EnsembleModelProvenance(String, OffsetDateTime, DatasetProvenance, TrainerProvenance, ListProvenance<? extends ModelProvenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
Creates a provenance for an ensemble model tracking the class name, creation time, dataset provenance and trainer provenance along with the individual model provenances for each ensemble member.
EnsembleModelProvenance(String, OffsetDateTime, DatasetProvenance, TrainerProvenance, Map<String, Provenance>, boolean, ListProvenance<? extends ModelProvenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
Creates a provenance for an ensemble model tracking the class name, creation time, dataset provenance, trainer provenance and any instance specific provenance along with the individual model provenances for each ensemble member.
EnsembleModelProvenance(String, OffsetDateTime, DatasetProvenance, TrainerProvenance, Map<String, Provenance>, ListProvenance<? extends ModelProvenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
Creates a provenance for an ensemble model tracking the class name, creation time, dataset provenance, trainer provenance and any instance specific provenance along with the individual model provenances for each ensemble member.
EnsembleModelProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.EnsembleModelProvenance
Used by the provenance unmarshalling system.
ensembleName() - Method in class org.tribuo.common.tree.ExtraTreesTrainer
 
ensembleName() - Method in class org.tribuo.common.tree.RandomForestTrainer
 
ensembleName() - Method in class org.tribuo.ensemble.BaggingTrainer
Default name of the generated ensemble.
ensembleOptions - Variable in class org.tribuo.classification.liblinear.TrainTest.TrainTestOptions
The ensemble options.
ensembleOptions - Variable in class org.tribuo.classification.libsvm.TrainTest.TrainTestOptions
The ensemble options.
ensembleOptions - Variable in class org.tribuo.classification.mnb.TrainTest.TrainTestOptions
The ensemble options.
ensembleOptions - Variable in class org.tribuo.classification.sgd.fm.TrainTest.TrainTestOptions
 
ensembleOptions - Variable in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
The ensemble options.
ensembleSize - Variable in class org.tribuo.classification.ensemble.ClassificationEnsembleOptions
Number of base learners in the ensemble.
ensembleSize - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
Number of trees in the ensemble.
ensembleSize - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
Number of trees in the ensemble.
entropy(List<T>) - Static method in class org.tribuo.util.infotheory.InformationTheory
Calculates the discrete Shannon entropy, using histogram probability estimators.
Entropy - Class in org.tribuo.classification.dtree.impurity
A log_e entropy impurity measure.
Entropy() - Constructor for class org.tribuo.classification.dtree.impurity.Entropy
 
ENTROPY - Enum constant in enum class org.tribuo.classification.dtree.CARTClassificationOptions.ImpurityType
Uses Entropy.
entrySet() - Method in class org.tribuo.transform.TransformerMap
Get the feature names and associated list of transformers.
epochs - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
Number of SGD epochs.
epochs - Variable in class org.tribuo.common.sgd.AbstractSGDTrainer
 
epochs - Variable in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceTrainer
 
epochs - Variable in class org.tribuo.interop.tensorflow.TrainTest.TensorflowOptions
Number of gradient descent epochs.
epochs - Variable in class org.tribuo.regression.sgd.fm.TrainTest.FMRegressionOptions
Number of SGD epochs.
epochs - Variable in class org.tribuo.regression.sgd.TrainTest.SGDOptions
Number of SGD epochs.
epsilon - Variable in class org.tribuo.common.liblinear.LibLinearTrainer
 
epsilon - Variable in class org.tribuo.math.optimisers.GradientOptimiserOptions
Epsilon for AdaDelta, AdaGrad, AdaGradRDA, Adam.
epsilon - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
Regression value insensitivity for margin.
EPSILON - Static variable in class org.tribuo.transform.transformations.SimpleTransform
Epsilon for determining when two double values are the same.
EPSILON_SVR - Enum constant in enum class org.tribuo.regression.libsvm.SVMRegressionType.SVMMode
epsilon-insensitive SVR.
EQUAL_FREQUENCY - Enum constant in enum class org.tribuo.transform.transformations.BinningTransformation.BinningType
Creates bins of equal frequency (i.e., equal numbers of data points).
EQUAL_WIDTH - Enum constant in enum class org.tribuo.transform.transformations.BinningTransformation.BinningType
Creates bins of equal width over the data range.
equalFrequency(int) - Static method in class org.tribuo.transform.transformations.BinningTransformation
Returns a BinningTransformation which generates bins which contain the same amount of training data that is, each bin has an equal probability of occurrence in the training data.
equals(Object) - Method in class ai.onnx.proto.OnnxMl.AttributeProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.GraphProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.ModelProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.NodeProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.OperatorSetIdProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.SparseTensorProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.StringStringEntryProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TensorAnnotation
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TensorProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TensorProto.Segment
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TensorShapeProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Map
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Opaque
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Sequence
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.SparseTensor
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.TypeProto.Tensor
 
equals(Object) - Method in class ai.onnx.proto.OnnxMl.ValueInfoProto
 
equals(Object) - Method in class org.tribuo.anomaly.AnomalyFactory.AnomalyFactoryProvenance
 
equals(Object) - Method in class org.tribuo.anomaly.Event
 
equals(Object) - Method in class org.tribuo.classification.evaluation.LabelMetric
 
equals(Object) - Method in class org.tribuo.classification.Label
 
equals(Object) - Method in class org.tribuo.classification.LabelFactory
 
equals(Object) - Method in class org.tribuo.classification.LabelFactory.LabelFactoryProvenance
 
equals(Object) - Method in class org.tribuo.clustering.ClusterID
 
equals(Object) - Method in class org.tribuo.clustering.ClusteringFactory.ClusteringFactoryProvenance
 
equals(Object) - Method in class org.tribuo.clustering.ClusteringFactory
 
equals(Object) - Method in class org.tribuo.common.tree.LeafNode
 
equals(Object) - Method in class org.tribuo.common.tree.SplitNode
 
equals(Object) - Method in class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance
 
equals(Object) - Method in class org.tribuo.data.csv.CSVLoader.CSVLoaderProvenance
Deprecated.
 
equals(Object) - Method in class org.tribuo.data.sql.SQLDataSource.SQLDataSourceProvenance
 
equals(Object) - Method in class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
 
equals(Object) - Method in class org.tribuo.data.text.impl.SimpleStringDataSource.SimpleStringDataSourceProvenance
 
equals(Object) - Method in class org.tribuo.data.text.impl.SimpleTextDataSource.SimpleTextDataSourceProvenance
 
equals(Object) - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance
 
equals(Object) - Method in class org.tribuo.dataset.MinimumCardinalityDataset.MinimumCardinalityDatasetProvenance
 
equals(Object) - Method in class org.tribuo.datasource.AggregateDataSource.AggregateDataSourceProvenance
 
equals(Object) - Method in class org.tribuo.datasource.LibSVMDataSource.LibSVMDataSourceProvenance
 
equals(Object) - Method in class org.tribuo.evaluation.DescriptiveStats
 
equals(Object) - Method in class org.tribuo.evaluation.metrics.MetricTarget
 
equals(Object) - Method in class org.tribuo.evaluation.TrainTestSplitter.SplitDataSourceProvenance
 
equals(Object) - Method in class org.tribuo.Feature
 
equals(Object) - Method in class org.tribuo.hash.HashCodeHasher.HashCodeHasherProvenance
 
equals(Object) - Method in class org.tribuo.hash.MessageDigestHasher.MessageDigestHasherProvenance
 
equals(Object) - Method in class org.tribuo.hash.ModHashCodeHasher.ModHashCodeHasherProvenance
 
equals(Object) - Method in class org.tribuo.impl.ArrayExample
 
equals(Object) - Method in class org.tribuo.impl.BinaryFeaturesExample
 
equals(Object) - Method in class org.tribuo.impl.IndexedArrayExample
 
equals(Object) - Method in class org.tribuo.impl.ListExample
 
equals(Object) - Method in class org.tribuo.interop.ExternalTrainerProvenance
 
equals(Object) - Method in class org.tribuo.interop.oci.OCILabelConverter
 
equals(Object) - Method in class org.tribuo.interop.oci.OCIMultiLabelConverter
 
equals(Object) - Method in class org.tribuo.interop.tensorflow.TensorFlowTrainer.TensorFlowTrainerProvenance
 
equals(Object) - Method in class org.tribuo.json.JsonDataSource.JsonDataSourceProvenance
 
equals(Object) - Method in class org.tribuo.math.la.DenseMatrix
 
equals(Object) - Method in class org.tribuo.math.la.DenseSparseMatrix
 
equals(Object) - Method in class org.tribuo.math.la.DenseVector
Equals is defined mathematically, that is two SGDVectors are equal iff they have the same indices and the same values at those indices.
equals(Object) - Method in class org.tribuo.math.la.MatrixTuple
 
equals(Object) - Method in class org.tribuo.math.la.SparseVector
Equals is defined mathematically, that is two SGDVectors are equal iff they have the same indices and the same values at those indices.
equals(Object) - Method in class org.tribuo.math.la.VectorTuple
 
equals(Object) - Method in class org.tribuo.multilabel.evaluation.MultiLabelMetric
 
equals(Object) - Method in class org.tribuo.multilabel.ImmutableMultiLabelInfo
 
equals(Object) - Method in class org.tribuo.multilabel.MultiLabel
 
equals(Object) - Method in class org.tribuo.multilabel.MultiLabelFactory
 
equals(Object) - Method in class org.tribuo.multilabel.MultiLabelFactory.MultiLabelFactoryProvenance
 
equals(Object) - Method in class org.tribuo.multilabel.MultiLabelInfo
 
equals(Object) - Method in class org.tribuo.provenance.DatasetProvenance
 
equals(Object) - Method in class org.tribuo.provenance.EnsembleModelProvenance
 
equals(Object) - Method in class org.tribuo.provenance.EvaluationProvenance
 
equals(Object) - Method in class org.tribuo.provenance.impl.EmptyDataSourceProvenance
 
equals(Object) - Method in class org.tribuo.provenance.impl.EmptyTrainerProvenance
 
equals(Object) - Method in class org.tribuo.provenance.impl.TimestampedTrainerProvenance
 
equals(Object) - Method in class org.tribuo.provenance.ModelProvenance
 
equals(Object) - Method in class org.tribuo.provenance.SimpleDataSourceProvenance
 
equals(Object) - Method in class org.tribuo.provenance.SkeletalTrainerProvenance
 
equals(Object) - Method in class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyRegressionTrainerProvenance
Deprecated.
 
equals(Object) - Method in class org.tribuo.regression.RegressionFactory
 
equals(Object) - Method in class org.tribuo.regression.RegressionFactory.RegressionFactoryProvenance
 
equals(Object) - Method in class org.tribuo.regression.Regressor.DimensionTuple
 
equals(Object) - Method in class org.tribuo.regression.Regressor
Regressors are equal if they have the same number of dimensions and equal dimension names.
equals(Object) - Method in record class org.tribuo.reproducibility.ReproUtil.FeatureDiff
Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class org.tribuo.reproducibility.ReproUtil.ModelReproduction
Indicates whether some other object is "equal to" this one.
equals(Object) - Method in record class org.tribuo.reproducibility.ReproUtil.OutputDiff
Indicates whether some other object is "equal to" this one.
equals(Object) - Method in class org.tribuo.sequence.MinimumCardinalitySequenceDataset.MinimumCardinalitySequenceDatasetProvenance
 
equals(Object) - Method in class org.tribuo.SkeletalVariableInfo
 
equals(Object) - Method in class org.tribuo.transform.TransformationMap.TransformationList
 
equals(Object) - Method in class org.tribuo.transform.transformations.BinningTransformation.BinningTransformationProvenance
 
equals(Object) - Method in class org.tribuo.transform.transformations.LinearScalingTransformation.LinearScalingTransformationProvenance
 
equals(Object) - Method in class org.tribuo.transform.transformations.MeanStdDevTransformation.MeanStdDevTransformationProvenance
 
equals(Object) - Method in class org.tribuo.transform.transformations.SimpleTransform.SimpleTransformProvenance
 
equals(Object) - Method in class org.tribuo.transform.TransformerMap.TransformerMapProvenance
 
equals(Object) - Method in class org.tribuo.util.infotheory.impl.CachedPair
 
equals(Object) - Method in class org.tribuo.util.infotheory.impl.CachedTriple
 
equals(Object) - Method in class org.tribuo.util.infotheory.impl.Row
 
equals(Object) - Method in class org.tribuo.util.infotheory.impl.WeightCountTuple
 
equals(Object) - Method in class org.tribuo.util.IntDoublePair
 
equals(Object) - Method in class org.tribuo.util.MeanVarianceAccumulator
 
equalWidth(int) - Static method in class org.tribuo.transform.transformations.BinningTransformation
Returns a BinningTransformation which generates fixed equal width bins between the observed min and max values.
eta - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
Step size shrinkage parameter (default 0.3, range [0,1]).
eta - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
Step size shrinkage parameter (default 0.3, range [0,1]).
EUCLIDEAN - Enum constant in enum class org.tribuo.clustering.hdbscan.HdbscanTrainer.Distance
Euclidean (or l2) distance.
EUCLIDEAN - Enum constant in enum class org.tribuo.clustering.kmeans.KMeansTrainer.Distance
Euclidean (or l2) distance.
euclideanDistance(SGDVector) - Method in class org.tribuo.math.la.DenseVector
The l2 or euclidean distance between this vector and the other vector.
euclideanDistance(SGDVector) - Method in interface org.tribuo.math.la.SGDVector
The l2 or euclidean distance between this vector and the other vector.
euclideanDistance(SGDVector) - Method in class org.tribuo.math.la.SparseVector
 
EV - Enum constant in enum class org.tribuo.regression.evaluation.RegressionMetrics
Calculates the Explained Variance of the predictions.
evaluate() - Method in class org.tribuo.evaluation.CrossValidation
Performs k fold cross validation, returning the k evaluations.
evaluate() - Method in class org.tribuo.evaluation.OnlineEvaluator
Creates an Evaluation containing all the current predictions.
evaluate(Model<T>, List<Prediction<T>>, List<T>, DataProvenance) - Method in interface org.tribuo.evaluation.Evaluator
Evaluates the model performance using the supplied predictions, returning an immutable Evaluation of the appropriate type.
evaluate(Model<T>, List<Prediction<T>>, DataProvenance) - Method in class org.tribuo.evaluation.AbstractEvaluator
Produces an evaluation for the supplied model and predictions by aggregating the appropriate statistics.
evaluate(Model<T>, List<Prediction<T>>, DataProvenance) - Method in interface org.tribuo.evaluation.Evaluator
Evaluates the model performance using the supplied predictions, returning an immutable Evaluation of the appropriate type.
evaluate(Model<T>, Dataset<T>) - Method in class org.tribuo.evaluation.AbstractEvaluator
Produces an evaluation for the supplied model and dataset, by calling Model.predict(org.tribuo.Example<T>) to create the predictions, then aggregating the appropriate statistics.
evaluate(Model<T>, Dataset<T>) - Method in interface org.tribuo.evaluation.Evaluator
Evaluates the dataset using the supplied model, returning an immutable Evaluation of the appropriate type.
evaluate(Model<T>, DataSource<T>) - Method in class org.tribuo.evaluation.AbstractEvaluator
Produces an evaluation for the supplied model and datasource, by calling Model.predict(org.tribuo.Example<T>) to create the predictions, then aggregating the appropriate statistics.
evaluate(Model<T>, DataSource<T>) - Method in interface org.tribuo.evaluation.Evaluator
Evaluates the dataset using the supplied model, returning an immutable Evaluation of the appropriate type.
evaluate(SequenceModel<T>, List<List<Prediction<T>>>, DataProvenance) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
Produces an evaluation for the supplied model and predictions by aggregating the appropriate statistics.
evaluate(SequenceModel<T>, List<List<Prediction<T>>>, DataProvenance) - Method in interface org.tribuo.sequence.SequenceEvaluator
Evaluates the supplied model and predictions by aggregating the appropriate statistics.
evaluate(SequenceModel<T>, SequenceDataset<T>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
Produces an evaluation for the supplied model and dataset, by calling SequenceModel.predict(org.tribuo.sequence.SequenceExample<T>) to create the predictions, then aggregating the appropriate statistics.
evaluate(SequenceModel<T>, SequenceDataset<T>) - Method in interface org.tribuo.sequence.SequenceEvaluator
Evaluates the dataset using the supplied model, returning an immutable evaluation.
evaluate(SequenceModel<T>, SequenceDataSource<T>) - Method in class org.tribuo.sequence.AbstractSequenceEvaluator
Produces an evaluation for the supplied model and datasource, by calling SequenceModel.predict(org.tribuo.sequence.SequenceExample<T>) to create the predictions, then aggregating the appropriate statistics.
evaluate(SequenceModel<T>, SequenceDataSource<T>) - Method in interface org.tribuo.sequence.SequenceEvaluator
Evaluates the datasource using the supplied model, returning an immutable evaluation.
Evaluation<T extends Output<T>> - Interface in org.tribuo.evaluation
An immutable evaluation of a specific model and dataset.
EvaluationAggregator - Class in org.tribuo.evaluation
Aggregates metrics from a list of evaluations, or a list of models and datasets.
EvaluationMetric<T extends Output<T>,C extends MetricContext<T>> - Interface in org.tribuo.evaluation.metrics
A metric that can be calculated for the specified output type.
EvaluationMetric.Average - Enum Class in org.tribuo.evaluation.metrics
Specifies what form of average to use for a EvaluationMetric.
EvaluationProvenance - Class in org.tribuo.provenance
Provenance for evaluations.
EvaluationProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.provenance.EvaluationProvenance
Deserialization constructor.
EvaluationProvenance(ModelProvenance, DataProvenance) - Constructor for class org.tribuo.provenance.EvaluationProvenance
Constructs an evaluation provenance from the supplied provenances.
EvaluationRenderer<T extends Output<T>,E extends Evaluation<T>> - Interface in org.tribuo.evaluation
Renders an Evaluation into a String.
evaluator - Static variable in class org.tribuo.classification.explanations.lime.LIMEBase
 
Evaluator<T extends Output<T>,E extends Evaluation<T>> - Interface in org.tribuo.evaluation
An evaluation factory which produces immutable Evaluations of a given Dataset using the given Model.
EVEN_OR_ODD_DAY - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
The parity of the day of the year.
EVEN_OR_ODD_MONTH - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
The parity of the month.
EVEN_OR_ODD_WEEK - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
The parity of the week of the year as defined by ISO 8601.
EVEN_OR_ODD_YEAR - Enum constant in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
The parity of the year.
Event - Class in org.tribuo.anomaly
An Output representing either an Event.EventType.ANOMALOUS or an Event.EventType.EXPECTED event.
Event(Event.EventType) - Constructor for class org.tribuo.anomaly.Event
Constructs a new event of the specified type with the default score of Event.DEFAULT_SCORE.
Event(Event.EventType, double) - Constructor for class org.tribuo.anomaly.Event
Constructs a new event of the specified type and score.
Event.EventType - Enum Class in org.tribuo.anomaly
The type of event.
EXACT - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.TreeMethod
Exact greedy algorithm, enumerates all split candidates.
Example<T extends Output<T>> - Class in org.tribuo
An example used for training and evaluation.
Example(Example<T>) - Constructor for class org.tribuo.Example
Copies the output, weight and metadata into this example.
Example(T) - Constructor for class org.tribuo.Example
Construct an empty example using the supplied output and Example.DEFAULT_WEIGHT as the weight.
Example(T, float) - Constructor for class org.tribuo.Example
Construct an empty example using the supplied output and weight.
Example(T, float, Map<String, Object>) - Constructor for class org.tribuo.Example
Construct an empty example using the supplied output, weight and metadata.
Example(T, Map<String, Object>) - Constructor for class org.tribuo.Example
Construct an empty example using the supplied output, metadata and Example.DEFAULT_WEIGHT as the weight.
ExampleArray(SparseVector[], int[], double[]) - Constructor for class org.tribuo.classification.sgd.Util.ExampleArray
Constructs an example array.
examples - Variable in class org.tribuo.classification.example.DemoLabelDataSource
 
examples - Variable in class org.tribuo.common.xgboost.XGBoostTrainer.DMatrixTuple
 
exampleToNodes(Example<T>, ImmutableFeatureMap, List<FeatureNode>) - Static method in class org.tribuo.common.liblinear.LibLinearTrainer
Converts a Tribuo Example into a liblinear FeatureNode array, including a bias feature.
exampleToNodes(Example<T>, ImmutableFeatureMap, List<svm_node>) - Static method in class org.tribuo.common.libsvm.LibSVMTrainer
Convert the example into an array of svm_node which represents a sparse feature vector.
ExampleTransformer - Interface in org.tribuo.interop.onnx
Transforms a SparseVector, extracting the features from it as a OnnxTensor.
excuse(String) - Method in class org.tribuo.Excuse
Returns the features involved in this excuse.
Excuse<T extends Output<T>> - Class in org.tribuo
Holds an Example, a Prediction and a Map from String to List of Pairs that contains the per output explanation.
Excuse(Example<T>, Prediction<T>, Map<String, List<Pair<String, Double>>>) - Constructor for class org.tribuo.Excuse
Constructs an excuse for the prediction of the supplied example, using the feature weights.
exp - Enum constant in enum class org.tribuo.transform.transformations.SimpleTransform.Operation
Exponentiates the inputs
exp() - Static method in class org.tribuo.transform.transformations.SimpleTransform
Generate a SimpleTransform that applies Math.exp(double).
expandRegexMapping(Collection<String>) - Method in class org.tribuo.data.columnar.RowProcessor
Uses similar logic to TransformationMap.validateTransformations(org.tribuo.FeatureMap) to check the regexes against the supplied list of field names.
expandRegexMapping(ImmutableFeatureMap) - Method in class org.tribuo.data.columnar.RowProcessor
Uses similar logic to TransformationMap.validateTransformations(org.tribuo.FeatureMap) to check the regexes against the supplied feature map.
expandRegexMapping(Model<T>) - Method in class org.tribuo.data.columnar.RowProcessor
Uses similar logic to TransformationMap.validateTransformations(org.tribuo.FeatureMap) to check the regexes against the ImmutableFeatureMap contained in the supplied Model.
EXPECTED - Enum constant in enum class org.tribuo.anomaly.Event.EventType
An expected event, with id 0.
EXPECTED_EVENT - Static variable in class org.tribuo.anomaly.AnomalyFactory
The expected event.
expectedCount - Variable in class org.tribuo.anomaly.AnomalyInfo
The number of expected events observed.
explain(CommandInterpreter, String[]) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
Explains a text classification.
explain(String) - Method in class org.tribuo.classification.explanations.lime.LIMEText
 
explain(String) - Method in interface org.tribuo.classification.explanations.TextExplainer
Converts the supplied text into an Example, and generates an explanation of the contained Model's prediction.
explain(Map<String, String>) - Method in interface org.tribuo.classification.explanations.ColumnarExplainer
Explains the supplied data.
explain(Map<String, String>) - Method in class org.tribuo.classification.explanations.lime.LIMEColumnar
 
explain(Example<Label>) - Method in class org.tribuo.classification.explanations.lime.LIMEBase
 
explain(Example<Label>) - Method in interface org.tribuo.classification.explanations.TabularExplainer
Explain why the supplied Example is classified a certain way.
explainedVariance() - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation
Calculatest the explained variance for all dimensions.
explainedVariance(MetricTarget<Regressor>, RegressionSufficientStatistics) - Static method in enum class org.tribuo.regression.evaluation.RegressionMetrics
Calculates the explained variance based on the supplied statistics.
explainedVariance(Regressor) - Method in interface org.tribuo.regression.evaluation.RegressionEvaluation
Calculates the explained variance of the ground truth using the predictions for the supplied dimension.
explainedVariance(Regressor, RegressionSufficientStatistics) - Static method in enum class org.tribuo.regression.evaluation.RegressionMetrics
Calculates the explained variance based on the supplied statistics for a single dimension.
explainWithSamples(Map<String, String>) - Method in class org.tribuo.classification.explanations.lime.LIMEColumnar
 
explainWithSamples(Example<Label>) - Method in class org.tribuo.classification.explanations.lime.LIMEBase
 
Explanation<T extends Output<T>> - Interface in org.tribuo.classification.explanations
An explanation knows what features are used, what the explaining Model is and what the original Model's prediction is.
explanationTrainer - Variable in class org.tribuo.classification.explanations.lime.LIMEBase
 
expNormalize(double) - Method in class org.tribuo.math.la.DenseVector
An optimisation for the exponential normalizer when you already know the normalization constant.
ExpNormalizer - Class in org.tribuo.math.util
Normalizes the exponential values of the input array.
ExpNormalizer() - Constructor for class org.tribuo.math.util.ExpNormalizer
 
exportCombiner(ONNXNode) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
Exports this voting combiner to ONNX.
exportCombiner(ONNXNode) - Method in class org.tribuo.classification.ensemble.VotingCombiner
Exports this voting combiner to ONNX.
exportCombiner(ONNXNode) - Method in interface org.tribuo.ensemble.EnsembleCombiner
Exports this ensemble combiner into the ONNX context of its input.
exportCombiner(ONNXNode) - Method in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner
Exports this voting combiner to ONNX.
exportCombiner(ONNXNode) - Method in class org.tribuo.regression.ensemble.AveragingCombiner
Exports this averaging combiner, writing constructed nodes into the ONNXContext governing input and returning the leaf node of the combiner.
exportCombiner(ONNXNode, T) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
Exports this voting combiner to ONNX.
exportCombiner(ONNXNode, T) - Method in class org.tribuo.classification.ensemble.VotingCombiner
Exports this voting combiner to ONNX
exportCombiner(ONNXNode, T) - Method in class org.tribuo.multilabel.ensemble.MultiLabelVotingCombiner
Exports this voting combiner to ONNX
exportCombiner(ONNXNode, T) - Method in class org.tribuo.regression.ensemble.AveragingCombiner
Exports this averaging combiner, writing constructed nodes into the ONNXContext governing input and returning the leaf node of the combiner.
exportCombiner(ONNXNode, U) - Method in interface org.tribuo.ensemble.EnsembleCombiner
Exports this ensemble combiner into the ONNX context of its input.
exportModel(String) - Method in class org.tribuo.interop.tensorflow.TensorFlowModel
Exports this model as a SavedModelBundle, writing to the supplied directory.
exportNormalizer(ONNXNode) - Method in class org.tribuo.math.util.ExpNormalizer
Returns the ONNX softmax node over the 2nd dimension.
exportNormalizer(ONNXNode) - Method in class org.tribuo.math.util.NoopNormalizer
Returns its input.
exportNormalizer(ONNXNode) - Method in class org.tribuo.math.util.Normalizer
Applies ONNX ReduceMin, Sub, ReduceSum, and Div operations to input.
exportNormalizer(ONNXNode) - Method in class org.tribuo.math.util.SigmoidNormalizer
Returns the ONNX sigmoid node, operating independently over each element.
exportNormalizer(ONNXNode) - Method in interface org.tribuo.math.util.VectorNormalizer
Exports this normalizer to ONNX, returning the leaf of the appended graph and writing the nodes needed for normalization into the ONNXContext that input belongs to.
exportONNXModel(String, long) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationModel
 
exportONNXModel(String, long) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationModel
 
exportONNXModel(String, long) - Method in class org.tribuo.common.sgd.AbstractFMModel
Exports this Model as an ONNX protobuf.
exportONNXModel(String, long) - Method in class org.tribuo.common.sgd.AbstractLinearSGDModel
Exports this Model as an ONNX protobuf.
exportONNXModel(String, long) - Method in class org.tribuo.ensemble.WeightedEnsembleModel
Exports this EnsembleModel as an ONNX model.
exportONNXModel(String, long) - Method in interface org.tribuo.ONNXExportable
Exports this Model as an ONNX protobuf.
exportONNXModel(String, long) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionModel
 
exportONNXModel(String, long) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionModel
 
exportONNXModel(String, long) - Method in class org.tribuo.regression.slm.SparseLinearModel
 
EXTERNAL - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorProto.DataLocation
EXTERNAL = 1;
EXTERNAL_DATA_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorProto
 
EXTERNAL_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.TensorProto.DataLocation
EXTERNAL = 1;
ExternalDatasetProvenance - Class in org.tribuo.interop
A dummy provenance used to describe the dataset of external models.
ExternalDatasetProvenance(String, OutputFactory<T>, boolean, int, int) - Constructor for class org.tribuo.interop.ExternalDatasetProvenance
An empty provenance used as a placeholder for externally trained models.
ExternalDatasetProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.interop.ExternalDatasetProvenance
Deserialization constructor.
ExternalModel<T extends Output<T>,U,V> - Class in org.tribuo.interop
This is the base class for third party models which are trained externally and loaded into Tribuo for prediction.
ExternalModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, boolean, Map<String, Integer>) - Constructor for class org.tribuo.interop.ExternalModel
Constructs an external model from a model trained outside of Tribuo.
ExternalModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<T>, int[], int[], boolean) - Constructor for class org.tribuo.interop.ExternalModel
Constructs an external model from a model trained outside of Tribuo.
externalPrediction(OnnxTensor) - Method in class org.tribuo.interop.onnx.ONNXExternalModel
Runs the session to make a prediction.
externalPrediction(DMatrix) - Method in class org.tribuo.common.xgboost.XGBoostExternalModel
 
externalPrediction(TensorMap) - Method in class org.tribuo.interop.tensorflow.TensorFlowFrozenExternalModel
Runs the session to make a prediction.
externalPrediction(TensorMap) - Method in class org.tribuo.interop.tensorflow.TensorFlowSavedModelExternalModel
Runs the session to make a prediction.
externalPrediction(DenseMatrix) - Method in class org.tribuo.interop.oci.OCIModel
 
externalPrediction(U) - Method in class org.tribuo.interop.ExternalModel
Runs the external model's prediction function.
ExternalTrainerProvenance - Class in org.tribuo.interop
A dummy provenance for a model trained outside Tribuo.
ExternalTrainerProvenance(byte[]) - Constructor for class org.tribuo.interop.ExternalTrainerProvenance
Creates an external trainer provenance, computing the hash from the byte array and storing this instant as the timestamp, and the current working directory as the location.
ExternalTrainerProvenance(URL) - Constructor for class org.tribuo.interop.ExternalTrainerProvenance
Creates an external trainer provenance, storing the location and pulling in the timestamp and file hash.
ExternalTrainerProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.interop.ExternalTrainerProvenance
Used by the provenance serialization system.
EXTRA_TREES - Enum constant in enum class org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType
Creates an ExtraTreesTrainer.
extract(LocalDate) - Method in enum class org.tribuo.data.columnar.processors.field.DateFieldProcessor.DateFeatureType
Applies this enum's extraction function to the supplied date.
extract(ColumnarIterator.Row) - Method in class org.tribuo.data.columnar.extractors.IndexExtractor
 
extract(ColumnarIterator.Row) - Method in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor
 
extract(ColumnarIterator.Row) - Method in interface org.tribuo.data.columnar.FieldExtractor
Returns Optional which is filled if extraction succeeded.
extract(T, String) - Method in class org.tribuo.data.text.impl.TextFeatureExtractorImpl
 
extract(T, String) - Method in interface org.tribuo.data.text.TextFeatureExtractor
Extracts an example from the supplied input text and output object.
extract(T, String) - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
Tokenizes the input using the loaded tokenizer, truncates the token list if it's longer than maxLength - 2 (to account for [CLS] and [SEP] tokens), and then passes the token list to BERTFeatureExtractor.extractExample(java.util.List<java.lang.String>).
extractData(Dataset<Event>, ImmutableOutputInfo<Event>, ImmutableFeatureMap) - Method in class org.tribuo.anomaly.liblinear.LibLinearAnomalyTrainer
 
extractData(Dataset<Event>, ImmutableOutputInfo<Event>, ImmutableFeatureMap) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer
 
extractData(Dataset<Label>, ImmutableOutputInfo<Label>, ImmutableFeatureMap) - Method in class org.tribuo.classification.liblinear.LibLinearClassificationTrainer
 
extractData(Dataset<Label>, ImmutableOutputInfo<Label>, ImmutableFeatureMap) - Method in class org.tribuo.classification.libsvm.LibSVMClassificationTrainer
 
extractData(Dataset<Regressor>, ImmutableOutputInfo<Regressor>, ImmutableFeatureMap) - Method in class org.tribuo.regression.liblinear.LibLinearRegressionTrainer
 
extractData(Dataset<Regressor>, ImmutableOutputInfo<Regressor>, ImmutableFeatureMap) - Method in class org.tribuo.regression.libsvm.LibSVMRegressionTrainer
 
extractData(Dataset<T>, ImmutableOutputInfo<T>, ImmutableFeatureMap) - Method in class org.tribuo.common.liblinear.LibLinearTrainer
Extracts the features and Outputs in LibLinear's format.
extractData(Dataset<T>, ImmutableOutputInfo<T>, ImmutableFeatureMap) - Method in class org.tribuo.common.libsvm.LibSVMTrainer
Extracts the features and Outputs in LibSVM's format.
extractExample(List<String>) - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
Passes the tokens through BERT, replacing any unknown tokens with the [UNK] token.
extractExample(List<String>, T) - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
Passes the tokens through BERT, replacing any unknown tokens with the [UNK] token.
extractFeatures(List<Label>, double) - Method in class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
 
extractFeatures(List<Label>, double) - Method in interface org.tribuo.classification.sequence.viterbi.LabelFeatureExtractor
Generates features based on the previously produced labels.
extractFeatures(List<Label>, double) - Method in class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
 
extractField(String) - Method in class org.tribuo.data.columnar.extractors.DateExtractor
 
extractField(String) - Method in class org.tribuo.data.columnar.extractors.DoubleExtractor
 
extractField(String) - Method in class org.tribuo.data.columnar.extractors.FloatExtractor
 
extractField(String) - Method in class org.tribuo.data.columnar.extractors.IdentityExtractor
 
extractField(String) - Method in class org.tribuo.data.columnar.extractors.IntExtractor
 
extractField(String) - Method in class org.tribuo.data.columnar.extractors.OffsetDateTimeExtractor
 
extractField(String) - Method in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor
Extracts the field value, or returns Optional.empty() if it failed to parse.
extractMarshalledVariables(Graph, Session) - Static method in class org.tribuo.interop.tensorflow.TensorFlowUtil
Extracts a Map containing the name of each Tensorflow VariableV2 and the associated parameter array.
extractNames(OutputInfo<Regressor>) - Static method in class org.tribuo.regression.Regressor
Extracts the names from the supplied Regressor domain in their canonical order.
extractor - Variable in class org.tribuo.data.text.DirectoryFileSource
The extractor that we'll use to turn text into examples.
extractor - Variable in class org.tribuo.data.text.TextDataSource
The extractor that we'll use to turn text into examples.
extractOutput(Event) - Method in class org.tribuo.anomaly.libsvm.LibSVMAnomalyTrainer
Converts an output into a double for use in training.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.anomaly.example.GaussianAnomalyDataSource.GaussianAnomalyDataSourceProvenance
Extracts the relevant provenance information fields for this class.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.clustering.example.GaussianClusterDataSource.GaussianClusterDataSourceProvenance
Extracts the relevant provenance information fields for this class.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.csv.CSVDataSource.CSVDataSourceProvenance
Separates this class's non-configurable fields from the configurable fields.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.sql.SQLDataSource.SQLDataSourceProvenance
 
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.text.DirectoryFileSource.DirectoryFileSourceProvenance
Splits the provenance into configured and non-configured values.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.text.impl.SimpleStringDataSource.SimpleStringDataSourceProvenance
 
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.data.text.impl.SimpleTextDataSource.SimpleTextDataSourceProvenance
 
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.datasource.AggregateConfigurableDataSource.AggregateConfigurableDataSourceProvenance
Extracts the class name and host type fields from the provenance map.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
Separates out the configured and non-configured provenance values.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.datasource.LibSVMDataSource.LibSVMDataSourceProvenance
 
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.json.JsonDataSource.JsonDataSourceProvenance
Splits the provenance into configurable and non-configurable provenances.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.multilabel.example.MultiLabelGaussianDataSource.MultiLabelGaussianDataSourceProvenance
Extracts the relevant provenance information fields for this class.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.provenance.SkeletalTrainerProvenance
 
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.regression.example.GaussianDataSource.GaussianDataSourceProvenance
Extracts the relevant provenance information fields for this class.
extractProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.regression.example.NonlinearGaussianDataSource.NonlinearGaussianDataSourceProvenance
Extracts the relevant provenance information fields for this class.
extractSequenceExample(List<String>, boolean) - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
Passes the tokens through BERT, replacing any unknown tokens with the [UNK] token.
extractSequenceExample(List<String>, List<T>, boolean) - Method in class org.tribuo.interop.onnx.extractors.BERTFeatureExtractor
Passes the tokens through BERT, replacing any unknown tokens with the [UNK] token.
extractTFProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceTrainer.TensorFlowSequenceTrainerProvenance
 
extractTFProvenanceInfo(Map<String, Provenance>) - Static method in class org.tribuo.interop.tensorflow.TensorFlowTrainer.TensorFlowTrainerProvenance
 
ExtraTreesTrainer<T extends Output<T>> - Class in org.tribuo.common.tree
A trainer which produces an Extremely Randomized Tree Ensemble.
ExtraTreesTrainer(DecisionTreeTrainer<T>, EnsembleCombiner<T>, int) - Constructor for class org.tribuo.common.tree.ExtraTreesTrainer
Constructs an ExtraTreesTrainer with the default seed Trainer.DEFAULT_SEED.
ExtraTreesTrainer(DecisionTreeTrainer<T>, EnsembleCombiner<T>, int, long) - Constructor for class org.tribuo.common.tree.ExtraTreesTrainer
Constructs an ExtraTreesTrainer with the supplied seed, trainer, combining function and number of members.

F

F_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.AttributeProto
 
f1(double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
Computes the F_1 score.
f1(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
The F1 for this label.
f1(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
Computes the F_1 score.
f1(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
 
f1(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
Returns the F_1 score, i.e., the harmonic mean of the precision and recall.
F1 - Enum constant in enum class org.tribuo.anomaly.evaluation.AnomalyMetrics
The F_1 score, i.e., the harmonic mean of the precision and the recall.
F1 - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics
The F_1 score, i.e., the harmonic mean of the precision and the recall.
F1 - Enum constant in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics
The F_1 score, i.e., the harmonic mean of the precision and the recall.
factorizedDimSize - Variable in class org.tribuo.common.sgd.AbstractFMTrainer
 
factorSize - Variable in class org.tribuo.regression.sgd.fm.TrainTest.FMRegressionOptions
Factor size.
factory - Static variable in class org.tribuo.classification.example.DemoLabelDataSource
 
Feature - Class in org.tribuo
A class for features.
Feature(String, double) - Constructor for class org.tribuo.Feature
Creates an immutable feature.
FEATURE_TYPE - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
The name of the provenance field for the idx feature type.
FEATURE_VALUE - Static variable in class org.tribuo.data.columnar.processors.field.IdentityProcessor
The value of the emitted features.
FeatureAggregator - Interface in org.tribuo.data.text
An interface for aggregating feature values into other values.
featureBackwardMapping - Variable in class org.tribuo.interop.ExternalModel
 
featureConverter - Variable in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceModel
 
featureConverter - Variable in class org.tribuo.interop.tensorflow.sequence.TensorFlowSequenceTrainer
 
featureConverter - Variable in class org.tribuo.interop.tensorflow.TensorFlowModel
 
FeatureConverter - Interface in org.tribuo.interop.tensorflow
Transforms an Example or SGDVector, extracting the features from it as a TensorMap.
featureDiff() - Method in record class org.tribuo.reproducibility.ReproUtil.ModelReproduction
Returns the value of the featureDiff record component.
FeatureDiff(Set<String>, Set<String>) - Constructor for record class org.tribuo.reproducibility.ReproUtil.FeatureDiff
Creates an instance of a FeatureDiff record class.
featureForwardMapping - Variable in class org.tribuo.interop.ExternalModel
 
FeatureHasher - Class in org.tribuo.data.text.impl
Hashes the feature names to reduce the dimensionality.
FeatureHasher(int) - Constructor for class org.tribuo.data.text.impl.FeatureHasher
Constructs a feature hasher using the supplied hash dimension.
featureIDMap - Variable in class org.tribuo.ImmutableDataset
A map from feature names to IDs for the features found in this dataset.
featureIDMap - Variable in class org.tribuo.Model
The features this model knows about.
featureIDMap - Variable in class org.tribuo.sequence.ImmutableSequenceDataset
A map from feature names to IDs for the features found in this dataset.
featureIDMap - Variable in class org.tribuo.sequence.SequenceModel
 
featureIDs - Variable in class org.tribuo.impl.IndexedArrayExample
Feature id numbers from the internal featureMap.
featureInfo(CommandInterpreter, String) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
Shows information on a particular feature.
featureInfo(CommandInterpreter, String) - Method in class org.tribuo.data.DatasetExplorer
Shows information on a particular feature.
featureInfo(CommandInterpreter, String) - Method in class org.tribuo.ModelExplorer
Shows a specific feature's information.
featureInfo(CommandInterpreter, String) - Method in class org.tribuo.sequence.SequenceModelExplorer
Shows information on a particular feature.
featureIterator() - Method in class org.tribuo.sequence.SequenceExample
Creates an iterator over every feature in this sequence.
featureMap - Variable in class org.tribuo.MutableDataset
A map from feature names to feature info objects.
featureMap - Variable in class org.tribuo.sequence.MutableSequenceDataset
A map from feature names to IDs for the features found in this dataset.
FeatureMap - Class in org.tribuo
A map from Strings to VariableInfo objects storing information about a feature.
FeatureMap() - Constructor for class org.tribuo.FeatureMap
Constructs an empty feature map.
FeatureMap(Map<String, ? extends VariableInfo>) - Constructor for class org.tribuo.FeatureMap
Constructs a feature map wrapping the supplied map.
FeatureMap(FeatureMap) - Constructor for class org.tribuo.FeatureMap
Constructs a deep copy of the supplied feature map.
featureNameComparator() - Static method in class org.tribuo.Feature
A comparator using the lexicographic ordering of feature names.
featureNames - Variable in class org.tribuo.impl.ArrayExample
Feature names array.
featureNames - Variable in class org.tribuo.impl.BinaryFeaturesExample
Feature names array.
FeatureProcessor - Interface in org.tribuo.data.columnar
Takes a list of columnar features and adds new features or removes existing features.
features - Variable in class org.tribuo.classification.sgd.Util.ExampleArray
The examples encoded as sparse vectors.
features - Variable in class org.tribuo.classification.sgd.Util.SequenceExampleArray
The array of sequence example features.
FEATURES_FILE_MODIFIED_TIME - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
The name of the features file modified time provenance field.
FEATURES_RESOURCE_HASH - Static variable in class org.tribuo.datasource.IDXDataSource.IDXDataSourceProvenance
The name of the provenance field for the feature file hash.
FeatureTransformer - Interface in org.tribuo.data.text
A feature transformer maps a list of features to a new list of features Useful for example to apply the hashing trick to a set of features
FeatureTuple() - Constructor for class org.tribuo.impl.IndexedArrayExample.FeatureTuple
Constructs an empty feature tuple.
FeatureTuple(String, int, double) - Constructor for class org.tribuo.impl.IndexedArrayExample.FeatureTuple
Constructs a feature tuple using the specified values.
featureValues - Variable in class org.tribuo.impl.ArrayExample
Feature values array.
FeedForwardParameters - Interface in org.tribuo.math
A Parameters for models which make a single prediction like logistic regressions and neural networks.
feedInto(Session.Runner) - Method in class org.tribuo.interop.tensorflow.TensorMap
Feeds the tensors in this FeedDict into the runner.
FIELD_NAME - Static variable in class org.tribuo.data.columnar.processors.response.EmptyResponseProcessor
The field name this response processor looks for, which is ignored anyway as this processor always returns Optional.empty().
FieldExtractor<T> - Interface in org.tribuo.data.columnar
Extracts a value from a field to be placed in an Example's metadata field.
fieldName - Variable in class org.tribuo.data.columnar.extractors.SimpleFieldExtractor
 
FieldProcessor - Interface in org.tribuo.data.columnar
An interface for things that process the columns in a data set.
FieldProcessor.GeneratedFeatureType - Enum Class in org.tribuo.data.columnar
The types of generated features.
fieldProcessorMap - Variable in class org.tribuo.data.columnar.RowProcessor
 
FieldResponseProcessor<T extends Output<T>> - Class in org.tribuo.data.columnar.processors.response
A response processor that returns the value(s) in a given (set of) fields.
FieldResponseProcessor(String, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
Constructs a response processor which passes the field value through the output factory.
FieldResponseProcessor(List<String>, String, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
Constructs a response processor which passes the field value through the output factory.
FieldResponseProcessor(List<String>, List<String>, OutputFactory<T>) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
Constructs a response processor which passes the field value through the output factory.
FieldResponseProcessor(List<String>, List<String>, OutputFactory<T>, boolean) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
Constructs a response processor which passes the field value through the output factory.
FieldResponseProcessor(List<String>, List<String>, OutputFactory<T>, boolean, boolean) - Constructor for class org.tribuo.data.columnar.processors.response.FieldResponseProcessor
Constructs a response processor which passes the field value through the output factory.
fields - Variable in class org.tribuo.data.columnar.ColumnarIterator
The column headers for this iterator.
FILE_MODIFIED_TIME - Static variable in interface org.tribuo.provenance.DataSourceProvenance
The name of the provenance field for the file timestamp.
fileCompleter() - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
Completers for filenames.
fileCompleter() - Method in class org.tribuo.data.DatasetExplorer
The filename completer.
fileCompleter() - Method in class org.tribuo.ModelExplorer
Completers for files.
fileCompleter() - Method in class org.tribuo.sequence.SequenceModelExplorer
Completers for files.
fill(double) - Method in class org.tribuo.math.la.DenseVector
Fills this DenseVector with value.
fill(int[]) - Method in class org.tribuo.common.tree.impl.IntArrayContainer
Overwrites values from the supplied array into this array.
fill(IntArrayContainer) - Method in class org.tribuo.common.tree.impl.IntArrayContainer
Overwrites values in this array with the supplied array.
finalise() - Method in class org.tribuo.math.optimisers.AdaGradRDA
 
finalise() - Method in class org.tribuo.math.optimisers.ParameterAveraging
This sets the parameters to their average value.
finalise() - Method in class org.tribuo.math.optimisers.Pegasos
 
finalise() - Method in interface org.tribuo.math.StochasticGradientOptimiser
Finalises the gradient optimisation, setting the parameters to their correct values.
FIRST - Enum constant in enum class org.tribuo.data.columnar.processors.feature.UniqueProcessor.UniqueType
Select the first feature value in the list.
FIRST - Enum constant in enum class org.tribuo.util.infotheory.WeightedInformationTheory.VariableSelector
The first variable is weighted.
FIRST_CLASS - Static variable in class org.tribuo.classification.example.DemoLabelDataSource
The first class.
firstCount - Variable in class org.tribuo.util.infotheory.impl.PairDistribution
The first marginal distribution.
firstDimensionName - Static variable in class org.tribuo.regression.example.RegressionDataGenerator
Name of the first output dimension.
fixSize() - Method in class org.tribuo.regression.rtree.impl.InvertedFeature
Fixes the size of the backing array.
fixSize() - Method in class org.tribuo.regression.rtree.impl.TreeFeature
Fixes the size of each InvertedFeature's inner arrays.
FLOAT - Enum constant in enum class ai.onnx.proto.OnnxMl.AttributeProto.AttributeType
FLOAT = 1;
FLOAT - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType
Basic types.
FLOAT - Enum constant in enum class org.tribuo.datasource.IDXDataSource.IDXType
A 32-bit float.
FLOAT_DATA_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.TensorProto
 
FLOAT_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.AttributeProto.AttributeType
FLOAT = 1;
FLOAT_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType
Basic types.
FLOAT16 - Enum constant in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType
IEEE754 half-precision floating-point format (16 bits wide).
FLOAT16_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType
IEEE754 half-precision floating-point format (16 bits wide).
FloatExtractor - Class in org.tribuo.data.columnar.extractors
Extracts the field value and converts it to a float.
FloatExtractor(String) - Constructor for class org.tribuo.data.columnar.extractors.FloatExtractor
Extracts a float value from the supplied field name.
FloatExtractor(String, String) - Constructor for class org.tribuo.data.columnar.extractors.FloatExtractor
Extracts a float value from the supplied field name.
floatInput(int) - Method in class org.tribuo.util.onnx.ONNXContext
Creates an input node for this ONNXContext, with the name "input", of dimension [batch_size, featureDimension], and of type float32.
floatInput(String, int) - Method in class org.tribuo.util.onnx.ONNXContext
Creates an input node for this ONNXContext, with the given name, of dimension [batch_size, featureDimension], and of type float32.
floatMatrix(ONNXContext, String, Matrix, boolean) - Static method in class org.tribuo.math.onnx.ONNXMathUtils
Builds a ONNXInitializer containing the Matrix.
floatOutput(int) - Method in class org.tribuo.util.onnx.ONNXContext
Creates an output node for this ONNXContext, with the name "output", of dimension [batch_size, outputDimension], and of type float32.
floatOutput(String, int) - Method in class org.tribuo.util.onnx.ONNXContext
Creates an output node for this ONNXContext, with the given name, of dimension [batch_size, outputDimension], and of type float32.
FLOATS - Enum constant in enum class ai.onnx.proto.OnnxMl.AttributeProto.AttributeType
FLOATS = 6;
FLOATS_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.AttributeProto
 
FLOATS_VALUE - Static variable in enum class ai.onnx.proto.OnnxMl.AttributeProto.AttributeType
FLOATS = 6;
floatTensor(String, List<Integer>, Consumer<FloatBuffer>) - Method in class org.tribuo.util.onnx.ONNXContext
Creates a tensor for this ONNXContext, populated as ONNXUtils.floatTensorBuilder(ONNXContext, String, List, Consumer).
floatTensorBuilder(ONNXContext, String, List<Integer>, Consumer<FloatBuffer>) - Static method in class org.tribuo.util.onnx.ONNXUtils
Generic method to create float OnnxMl.TensorProto instances.
floatVector(ONNXContext, String, SGDVector) - Static method in class org.tribuo.math.onnx.ONNXMathUtils
Builds a ONNXInitializer containing the SGDVector.
FMClassificationModel - Class in org.tribuo.classification.sgd.fm
The inference time version of a factorization machine trained using SGD.
FMClassificationOptions - Class in org.tribuo.classification.sgd.fm
CLI options for training a factorization machine classifier.
FMClassificationOptions() - Constructor for class org.tribuo.classification.sgd.fm.FMClassificationOptions
 
FMClassificationOptions.LossEnum - Enum Class in org.tribuo.classification.sgd.fm
Available loss types.
FMClassificationTrainer - Class in org.tribuo.classification.sgd.fm
A trainer for a classification factorization machine using SGD.
FMClassificationTrainer(LabelObjective, StochasticGradientOptimiser, int, int, int, long, int, double) - Constructor for class org.tribuo.classification.sgd.fm.FMClassificationTrainer
Constructs an SGD trainer for a factorization machine.
FMClassificationTrainer(LabelObjective, StochasticGradientOptimiser, int, int, long, int, double) - Constructor for class org.tribuo.classification.sgd.fm.FMClassificationTrainer
Constructs an SGD trainer for a factorization machine.
FMClassificationTrainer(LabelObjective, StochasticGradientOptimiser, int, long, int, double) - Constructor for class org.tribuo.classification.sgd.fm.FMClassificationTrainer
Constructs an SGD trainer for a factorization machine.
fmEpochs - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
Number of SGD epochs.
fmEpochs - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
Number of SGD epochs.
fmFactorSize - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
Factor size.
fmFactorSize - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
Factor size.
fmix32(int) - Static method in class org.tribuo.util.MurmurHash3
32-bit mixing function.
fmix64(long) - Static method in class org.tribuo.util.MurmurHash3
64-bit mixing function.
fmLoggingInterval - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
Log the objective after n examples.
fmLoggingInterval - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
Log the objective after n examples.
fmMinibatchSize - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
Minibatch size.
fmMinibatchSize - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
Minibatch size.
FMMultiLabelModel - Class in org.tribuo.multilabel.sgd.fm
The inference time version of a multi-label factorization machine trained using SGD.
FMMultiLabelOptions - Class in org.tribuo.multilabel.sgd.fm
CLI options for training a linear classifier.
FMMultiLabelOptions() - Constructor for class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
 
FMMultiLabelOptions.LossEnum - Enum Class in org.tribuo.multilabel.sgd.fm
Available loss types.
FMMultiLabelTrainer - Class in org.tribuo.multilabel.sgd.fm
A trainer for a multi-label classification factorization machine using SGD.
FMMultiLabelTrainer(MultiLabelObjective, StochasticGradientOptimiser, int, int, int, long, int, double) - Constructor for class org.tribuo.multilabel.sgd.fm.FMMultiLabelTrainer
Constructs an SGD trainer for a multi-label factorization machine.
FMMultiLabelTrainer(MultiLabelObjective, StochasticGradientOptimiser, int, int, long, int, double) - Constructor for class org.tribuo.multilabel.sgd.fm.FMMultiLabelTrainer
Constructs an SGD trainer for a multi-label factorization machine.
FMMultiLabelTrainer(MultiLabelObjective, StochasticGradientOptimiser, int, long, int, double) - Constructor for class org.tribuo.multilabel.sgd.fm.FMMultiLabelTrainer
Constructs an SGD trainer for a multi-label factorization machine.
fmObjective - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
Loss function.
fmObjective - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
Loss function.
FMParameters - Class in org.tribuo.common.sgd
A Parameters for factorization machines.
FMParameters(SplittableRandom, int, int, int, double) - Constructor for class org.tribuo.common.sgd.FMParameters
Constructor.
FMRegressionModel - Class in org.tribuo.regression.sgd.fm
The inference time model of a regression factorization machine trained using SGD.
FMRegressionOptions() - Constructor for class org.tribuo.regression.sgd.fm.TrainTest.FMRegressionOptions
 
FMRegressionTrainer - Class in org.tribuo.regression.sgd.fm
A trainer for a regression factorization machine using SGD.
FMRegressionTrainer(RegressionObjective, StochasticGradientOptimiser, int, int, int, long, int, double, boolean) - Constructor for class org.tribuo.regression.sgd.fm.FMRegressionTrainer
Constructs an SGD trainer for a factorization machine.
FMRegressionTrainer(RegressionObjective, StochasticGradientOptimiser, int, int, long, int, double, boolean) - Constructor for class org.tribuo.regression.sgd.fm.FMRegressionTrainer
Constructs an SGD trainer for a factorization machine.
FMRegressionTrainer(RegressionObjective, StochasticGradientOptimiser, int, long, int, double, boolean) - Constructor for class org.tribuo.regression.sgd.fm.FMRegressionTrainer
Constructs an SGD trainer for a factorization machine.
fmVariance - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions
Variance of the initialization gaussian.
fmVariance - Variable in class org.tribuo.multilabel.sgd.fm.FMMultiLabelOptions
Variance of the initialization gaussian.
fn() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
Returns the micro averaged number of false negatives.
fn() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix
The total number of false negatives.
fn() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
Gets the micro averaged false negative count.
fn() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
 
fn(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
 
fn(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
The false negative count for this label.
fn(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
Returns the number of false negatives, possibly averaged depending on the metric target.
fn(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
 
fn(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
 
fn(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
Returns the number of false negatives, i.e., the number of times the true label was incorrectly predicted as another label.
fn(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix
The number of false negatives for the supplied label.
FN - Enum constant in enum class org.tribuo.anomaly.evaluation.AnomalyMetrics
The number of false negatives.
FN - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics
The number of false negatives.
FN - Enum constant in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics
The number of false negatives.
foreachIndexedInPlace(ToDoubleBiFunction<Integer, Double>) - Method in class org.tribuo.math.la.DenseVector
 
foreachIndexedInPlace(ToDoubleBiFunction<Integer, Double>) - Method in interface org.tribuo.math.la.SGDVector
Applies a ToDoubleBiFunction elementwise to this SGDVector.
foreachIndexedInPlace(ToDoubleBiFunction<Integer, Double>) - Method in class org.tribuo.math.la.SparseVector
Applies a ToDoubleBiFunction elementwise to this SGDVector.
foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseMatrix
 
foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseSparseMatrix
 
foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.DenseVector
 
foreachInPlace(DoubleUnaryOperator) - Method in class org.tribuo.math.la.SparseVector
Applies a DoubleUnaryOperator elementwise to this SGDVector.
foreachInPlace(DoubleUnaryOperator) - Method in interface org.tribuo.math.la.Tensor
Applies a DoubleUnaryOperator elementwise to this Tensor.
forEachRemaining(Consumer<? super ColumnarIterator.Row>) - Method in class org.tribuo.data.columnar.ColumnarIterator
 
formatDuration(long, long) - Static method in class org.tribuo.util.Util
Formats a duration given two times in milliseconds.
forNumber(int) - Static method in enum class ai.onnx.proto.OnnxMl.AttributeProto.AttributeType
 
forNumber(int) - Static method in enum class ai.onnx.proto.OnnxMl.TensorProto.DataLocation
 
forNumber(int) - Static method in enum class ai.onnx.proto.OnnxMl.TensorProto.DataType
 
forNumber(int) - Static method in enum class ai.onnx.proto.OnnxMl.TensorShapeProto.Dimension.ValueCase
 
forNumber(int) - Static method in enum class ai.onnx.proto.OnnxMl.TypeProto.ValueCase
 
forNumber(int) - Static method in enum class ai.onnx.proto.OnnxMl.Version
 
forTarget(MetricTarget<Label>) - Method in enum class org.tribuo.classification.evaluation.LabelMetrics
Gets the LabelMetric wrapped around the supplied MetricTarget.
forTarget(MetricTarget<ClusterID>) - Method in enum class org.tribuo.clustering.evaluation.ClusteringMetrics
Constructs the metric for the specified metric target.
forTarget(MetricTarget<MultiLabel>) - Method in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics
Get the metric for the supplied target.
fp() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
Returns the micro average of the number of false positives across all the labels, i.e., the total number of false positives.
fp() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix
The total number of false positives.
fp() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
Gets the micro averaged false positive count.
fp() - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
 
fp(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
 
fp(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
The false positive count for this label.
fp(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
Returns the number of false positives, possibly averaged depending on the metric target.
fp(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelConfusionMatrix
 
fp(MultiLabel) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
 
fp(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation
Returns the number of false positives, i.e., the number of times this label was predicted but it was not the true label..
fp(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix
The number of false positives for the supplied label.
FP - Enum constant in enum class org.tribuo.anomaly.evaluation.AnomalyMetrics
The number of false positives.
FP - Enum constant in enum class org.tribuo.classification.evaluation.LabelMetrics
The number of false positives.
FP - Enum constant in enum class org.tribuo.multilabel.evaluation.MultiLabelMetrics
The number of false positives.
fpr - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.ROC
The false positive rate at the corresponding threshold.
fraction - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions
Fraction of features in split.
fractionFeaturesInSplit - Variable in class org.tribuo.common.tree.AbstractCARTTrainer
Number of features to sample per split.
frequencyBasedSample(Random, long) - Method in class org.tribuo.CategoricalInfo
Samples a value from this feature according to the frequency of observation.
frequencyBasedSample(SplittableRandom, long) - Method in class org.tribuo.CategoricalInfo
Samples a value from this feature according to the frequency of observation.
fscore(double, double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
Computes the Fscore.
fscore(MetricTarget<T>, ConfusionMatrix<T>, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics
Computes the Fscore.
FTRL - Enum constant in enum class org.tribuo.interop.tensorflow.GradientOptimiser
The FTRL optimiser.
fullEquals(Event) - Method in class org.tribuo.anomaly.Event
 
fullEquals(Label) - Method in class org.tribuo.classification.Label
 
fullEquals(ClusterID) - Method in class org.tribuo.clustering.ClusterID
 
fullEquals(MultiLabel) - Method in class org.tribuo.multilabel.MultiLabel
 
fullEquals(Regressor) - Method in class org.tribuo.regression.Regressor.DimensionTuple
 
fullEquals(Regressor) - Method in class org.tribuo.regression.Regressor
 
fullEquals(T) - Method in interface org.tribuo.Output
Compares other to this output.
FULLY_WEIGHTED_VOTING - Enum constant in enum class org.tribuo.common.nearest.KNNClassifierOptions.EnsembleCombinerType
FullyWeightedVotingCombiner - Class in org.tribuo.classification.ensemble
A combiner which performs a weighted or unweighted vote across the predicted labels.
FullyWeightedVotingCombiner() - Constructor for class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
Constructs a weighted voting combiner.

G

G_FIELD_NUMBER - Static variable in class ai.onnx.proto.OnnxMl.AttributeProto
 
gamma - Variable in class org.tribuo.regression.libsvm.TrainTest.LibSVMOptions
Gamma value in kernel function.
gamma - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
Minimum loss reduction to make a split (default 0, range [0,inf]).
gamma - Variable in class org.tribuo.regression.xgboost.XGBoostOptions
Minimum loss reduction to make a split (default 0, range [0,inf]).
GAMMA - Enum constant in enum class org.tribuo.regression.xgboost.XGBoostRegressionTrainer.RegressionType
Gamma loss function.
GATHER - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
Gathers elements from the first argument (of rank r) indexed by the second argument (of rank q) producing a tensor of rank q + r - 1.
gatherAcrossDim1(int[]) - Method in class org.tribuo.math.la.DenseMatrix
Constructs a dense vector by gathering values across dimension 1.
gatherAcrossDim2(int[]) - Method in class org.tribuo.math.la.DenseMatrix
Constructs a dense vector by gathering values across dimension 2.
GAUSSIAN - Enum constant in enum class org.tribuo.regression.baseline.DummyRegressionTrainer.DummyType
Samples from a Gaussian using the means and variances from the training data.
gaussianAnomaly() - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator
Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
gaussianAnomaly(long, double) - Static method in class org.tribuo.anomaly.example.AnomalyDataGenerator
Generates two datasets, one without anomalies drawn from a single gaussian and the second drawn from a mixture of two gaussians, with the second tagged anomalous.
GaussianAnomalyDataSource - Class in org.tribuo.anomaly.example
Generates an anomaly detection dataset sampling each feature uniformly from a univariate Gaussian.
GaussianAnomalyDataSource(int, double[], double[], double[], double[], float, long) - Constructor for class org.tribuo.anomaly.example.GaussianAnomalyDataSource
Generates anomaly detection examples sampling each feature uniformly from a univariate Gaussian.
GaussianAnomalyDataSource(int, float, long) - Constructor for class org.tribuo.anomaly.example.GaussianAnomalyDataSource
Generates anomaly detection examples sampling each feature uniformly from a univariate Gaussian.
GaussianAnomalyDataSource.GaussianAnomalyDataSourceProvenance - Class in org.tribuo.anomaly.example
GaussianAnomalyDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.anomaly.example.GaussianAnomalyDataSource.GaussianAnomalyDataSourceProvenance
Constructs a provenance from the marshalled form.
GaussianClusterDataSource - Class in org.tribuo.clustering.example
Generates a clustering dataset drawn from a mixture of 5 Gaussians.
GaussianClusterDataSource(int, double[], double[], double[], double[], double[], double[], double[], double[], double[], double[], double[], long) - Constructor for class org.tribuo.clustering.example.GaussianClusterDataSource
Generates a clustering dataset drawn from a mixture of 5 Gaussians.
GaussianClusterDataSource(int, long) - Constructor for class org.tribuo.clustering.example.GaussianClusterDataSource
Generates a clustering dataset drawn from a mixture of 5 Gaussians.
GaussianClusterDataSource.GaussianClusterDataSourceProvenance - Class in org.tribuo.clustering.example
GaussianClusterDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.clustering.example.GaussianClusterDataSource.GaussianClusterDataSourceProvenance
Constructs a provenance from the marshalled form.
gaussianClusters(long, long) - Static method in class org.tribuo.clustering.example.ClusteringDataGenerator
Generates a dataset drawn from a mixture of 5 2d gaussians.
GaussianDataSource - Class in org.tribuo.regression.example
Generates a single dimensional output drawn from N(slope*x + intercept,variance).
GaussianDataSource(int, float, float, float, float, float, long) - Constructor for class org.tribuo.regression.example.GaussianDataSource
Generates a single dimensional output drawn from N(slope*x + intercept,variance).
GaussianDataSource.GaussianDataSourceProvenance - Class in org.tribuo.regression.example
Provenance for GaussianDataSource.
GaussianDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.regression.example.GaussianDataSource.GaussianDataSourceProvenance
Constructs a provenance from the marshalled form.
GaussianLabelDataSource - Class in org.tribuo.classification.example
A data source for two classes generated from separate Gaussians.
GaussianLabelDataSource(int, long, double[], double[], double[], double[]) - Constructor for class org.tribuo.classification.example.GaussianLabelDataSource
Constructs a data source which contains two classes where each class is sampled from a 2d Gaussian with the specified parameters.
GBTREE - Enum constant in enum class org.tribuo.common.xgboost.XGBoostTrainer.BoosterType
A gradient boosted decision tree.
GEMM - Enum constant in enum class org.tribuo.util.onnx.ONNXOperators
General Matrix Multiply: alpha*AB + beta*C.
general - Variable in class org.tribuo.classification.experiments.ConfigurableTrainTest.ConfigurableTrainTestOptions
Options for loading in data.
general - Variable in class org.tribuo.classification.experiments.RunAll.RunAllOptions
Options for loading in data.
general - Variable in class org.tribuo.classification.experiments.TrainTest.AllClassificationOptions
Options for loading in data.
general - Variable in class org.tribuo.classification.liblinear.TrainTest.TrainTestOptions
The data loading options.
general - Variable in class org.tribuo.classification.libsvm.TrainTest.TrainTestOptions
The data loading options.
general - Variable in class org.tribuo.classification.mnb.TrainTest.TrainTestOptions
The data loading options.
general - Variable in class org.tribuo.classification.sgd.fm.TrainTest.TrainTestOptions
 
general - Variable in class org.tribuo.classification.sgd.kernel.TrainTest.TrainTestOptions
The data loading options.
general - Variable in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
The data loading options.
general - Variable in class org.tribuo.classification.xgboost.TrainTest.TrainTestOptions
The data loading options.
general - Variable in class org.tribuo.clustering.hdbscan.TrainTest.HdbscanCLIOptions
The data loading options.
general - Variable in class org.tribuo.clustering.kmeans.TrainTest.KMeansOptions
The data loading options.
general - Variable in class org.tribuo.data.ConfigurableTrainTest.ConfigurableTrainTestOptions
Data loading options.
general - Variable in class org.tribuo.regression.liblinear.TrainTest.LibLinearOptions
The data loading options.
general - Variable in class org.tribuo.regression.libsvm.TrainTest.LibSVMOptions
The data loading options.
general - Variable in class org.tribuo.regression.rtree.TrainTest.RegressionTreeOptions
The data loading options.
general - Variable in class org.tribuo.regression.sgd.fm.TrainTest.FMRegressionOptions
The dataset loading options.
general - Variable in class org.tribuo.regression.sgd.TrainTest.SGDOptions
The dataset loading options.
general - Variable in class org.tribuo.regression.slm.TrainTest.SLMOptions
The data loading options.
general - Variable in class org.tribuo.regression.xgboost.TrainTest.XGBoostOptions
The data loading options.
generalOptions - Variable in class org.tribuo.classification.dtree.TrainTest.TrainTestOptions
The data loading options.
generate() - Method in class org.tribuo.classification.example.CheckerboardDataSource
 
generate() - Method in class org.tribuo.classification.example.ConcentricCirclesDataSource
 
generate() - Method in class org.tribuo.classification.example.DemoLabelDataSource
Generates the examples using the configured fields.
generate() - Method in class org.tribuo.classification.example.GaussianLabelDataSource
 
generate() - Method in class org.tribuo.classification.example.InterlockingCrescentsDataSource
 
generate() - Method in class org.tribuo.classification.example.NoisyInterlockingCrescentsDataSource
 
generateBootstrap() - Method in class org.tribuo.dataset.DatasetView.DatasetViewProvenance
Generates the indices from this DatasetViewProvenance by rerunning the bootstrap sample.
generateBootstrapIndices(int, Random) - Static method in class org.tribuo.util.Util
Draws a bootstrap sample of indices.
generateBootstrapIndices(int, SplittableRandom) - Static method in class org.tribuo.util.Util
Draws a bootstrap sample of indices.
generateCDF(double[]) - Static method in class org.tribuo.util.Util
Generates a cumulative distribution function from the supplied probability mass function.
generateCDF(float[]) - Static method in class org.tribuo.util.Util
Generates a cumulative distribution function from the supplied probability mass function.
generateCDF(long[], long) - Static method in class org.tribuo.util.Util
Generates a cumulative distribution function from the supplied probability mass function.
generateCorrelated(int, int, double, double) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo
These correlations don't map to mutual information values, as if xyDraw is above xyCorrelation then the draw is completely random.
generateDataset() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
Generate training and testing datasets.
generateDataset(int, double[], double[], double[], double[], double[], double[], double[], double[], double[], double[], double[], long) - Static method in class org.tribuo.clustering.example.GaussianClusterDataSource
Generates a clustering dataset drawn from a mixture of 5 Gaussians.
generateDataset(int, double[], double[], double[], double[], float, long) - Static method in class org.tribuo.anomaly.example.GaussianAnomalyDataSource
Generates an anomaly detection dataset sampling each feature uniformly from a univariate Gaussian.
generateDataset(int, float[], float[], float[], float[], boolean[], float, float[], float[], long) - Static method in class org.tribuo.multilabel.example.MultiLabelGaussianDataSource
Generates a multi-label output drawn from three gaussian functions.
generateDataset(int, float[], float, float, float, float, float, float, long) - Static method in class org.tribuo.regression.example.NonlinearGaussianDataSource
Generates a single dimensional output drawn from N(w_0*x_0 + w_1*x_1 + w_2*x_1*x_0 + w_3*x_1*x_1*x_1 + intercept,variance).
generateDataset(int, float, float, float, float, float, long) - Static method in class org.tribuo.regression.example.GaussianDataSource
Generates a single dimensional output drawn from N(slope*x + intercept,variance).
generateEmptyExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
This generates a sequence example with no examples.
generateExample(long, Map<String, String>, boolean) - Method in class org.tribuo.data.columnar.RowProcessor
Generate an Example from the supplied row.
generateExample(Map<String, String>, boolean) - Method in class org.tribuo.data.columnar.RowProcessor
Generate an Example from the supplied row.
generateExample(ColumnarIterator.Row, boolean) - Method in class org.tribuo.data.columnar.RowProcessor
Generate an Example from the supplied row.
generateFeatureName(String, String) - Static method in class org.tribuo.data.columnar.ColumnarFeature
Generates a feature name based on the field name and the name.
generateFeatureName(String, String, String) - Static method in class org.tribuo.data.columnar.ColumnarFeature
Generates a feature name used for conjunction features.
generateFeatures(Map<String, String>) - Method in class org.tribuo.data.columnar.RowProcessor
Generates the features from the supplied row.
generateGorillaA() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
Generates a sequence example with a mixture of features and three labels "O", "Status" and "Monkey".
generateGorillaB() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
Generates a sequence example with a mixture of features and three labels "O", "Status" and "Monkey".
generateGorillaDataset(int) - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
Generates a simple dataset consisting of numCopies repeats of two sequences.
generateHashedFeatureMap(FeatureMap, Hasher) - Static method in class org.tribuo.hash.HashedFeatureMap
Converts a standard FeatureMap by hashing each entry using the supplied hash function Hasher.
generateIDs(List<? extends VariableInfo>) - Static method in class org.tribuo.ImmutableFeatureMap
Generates the feature ids by sorting the features with the String comparator, then sequentially numbering them.
generateIDs(FeatureMap) - Static method in class org.tribuo.ImmutableFeatureMap
Generates the feature ids by sorting the features with the String comparator, then sequentially numbering them.
generateImmutableOutputInfo() - Method in class org.tribuo.anomaly.AnomalyInfo
 
generateImmutableOutputInfo() - Method in class org.tribuo.classification.LabelInfo
 
generateImmutableOutputInfo() - Method in class org.tribuo.clustering.ClusteringInfo
 
generateImmutableOutputInfo() - Method in class org.tribuo.multilabel.MultiLabelInfo
 
generateImmutableOutputInfo() - Method in interface org.tribuo.OutputInfo
Generates an ImmutableOutputInfo which has a copy of the data in this OutputInfo, but also has id values and is immutable.
generateImmutableOutputInfo() - Method in class org.tribuo.regression.RegressionInfo
 
generateInfo() - Method in class org.tribuo.anomaly.AnomalyFactory
 
generateInfo() - Method in class org.tribuo.classification.LabelFactory
Generates an empty MutableLabelInfo.
generateInfo() - Method in class org.tribuo.clustering.ClusteringFactory
 
generateInfo() - Method in class org.tribuo.multilabel.MultiLabelFactory
 
generateInfo() - Method in interface org.tribuo.OutputFactory
Generates the appropriate MutableOutputInfo so the output values can be tracked by a Dataset or other aggregate.
generateInfo() - Method in class org.tribuo.regression.RegressionFactory
 
generateInvalidExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
This generates a sequence example with features that are unused by the training data.
generateLabelString(Set<Label>) - Static method in class org.tribuo.multilabel.MultiLabelFactory
Generates a comma separated string of labels from a Set of Label.
generateMetadata(ColumnarIterator.Row) - Method in class org.tribuo.data.columnar.RowProcessor
Generates the example metadata from the supplied row and index.
generateMutableOutputInfo() - Method in class org.tribuo.anomaly.AnomalyInfo
 
generateMutableOutputInfo() - Method in class org.tribuo.classification.LabelInfo
 
generateMutableOutputInfo() - Method in class org.tribuo.clustering.ClusteringInfo
 
generateMutableOutputInfo() - Method in class org.tribuo.multilabel.MultiLabelInfo
 
generateMutableOutputInfo() - Method in interface org.tribuo.OutputInfo
Generates a mutable copy of this OutputInfo.
generateMutableOutputInfo() - Method in class org.tribuo.regression.RegressionInfo
 
generateOtherInvalidExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator
This generates a sequence example where the first example has no features.
generateOutput(V) - Method in class org.tribuo.anomaly.AnomalyFactory
 
generateOutput(V) - Method in class org.tribuo.classification.LabelFactory
Generates the Label string by calling toString on the input.
generateOutput(V) - Method in class org.tribuo.clustering.ClusteringFactory
Generates a ClusterID by calling toString on the input, then calling Integer.parseInt.
generateOutput(V) - Method in class org.tribuo.multilabel.MultiLabelFactory
Parses the MultiLabel value either by toStringing the input and calling MultiLabel.parseString(java.lang.String) or if it's a Collection iterating over the elements calling toString on each element in turn and using MultiLabel.parseElement(java.lang.String).
generateOutput(V) - Method in interface org.tribuo.OutputFactory
Parses the V and generates the appropriate Output value.
generateOutput(V) - Method in class org.tribuo.regression.RegressionFactory
Parses the Regressor value either by toStringing the input and calling Regressor.parseString(java.lang.String) or if it's a collection iterating over the elements calling toString on each element in turn and using Regressor.parseElement(int, java.lang.String).
generateOutputs(List<V>) - Method in interface org.tribuo.OutputFactory
Generate a list of outputs from the supplied list of inputs.
generatePlaceholderName(String) - Static method in class org.tribuo.interop.tensorflow.TensorFlowUtil
Creates a name for a placeholder based on the supplied variable name.
generatePRCurve(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil
Calculates the Precision Recall curve for a single label.
generateRealInfo() - Method in class org.tribuo.CategoricalIDInfo
Generates a RealIDInfo that matches this CategoricalInfo and also contains an id number.
generateRealInfo() - Method in class org.tribuo.CategoricalInfo
Generates a RealInfo using the currently observed counts to calculate the min, max, mean and variance.
generateROCCurve(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil
Calculates the binary ROC for a single label.
generatesProbabilities - Variable in class org.tribuo.interop.onnx.LabelTransformer
 
generatesProbabilities - Variable in class org.tribuo.Model
Does this model generate probability distributions in the output.
generatesProbabilities() - Method in class org.tribuo.classification.xgboost.XGBoostClassificationConverter
 
generatesProbabilities() - Method in interface org.tribuo.common.xgboost.XGBoostOutputConverter
Does this converter produce probabilities?
generatesProbabilities() - Method in class org.tribuo.interop.oci.OCILabelConverter
 
generatesProbabilities() - Method in class org.tribuo.interop.oci.OCIMultiLabelConverter
 
generatesProbabilities() - Method in interface org.tribuo.interop.oci.OCIOutputConverter
Does this OCIOutputConverter generate probabilities?
generatesProbabilities() - Method in class org.tribuo.interop.oci.OCIRegressorConverter
 
generatesProbabilities() - Method in class org.tribuo.interop.onnx.LabelTransformer
 
generatesProbabilities() - Method in class org.tribuo.interop.onnx.MultiLabelTransformer
 
generatesProbabilities() - Method in interface org.tribuo.interop.onnx.OutputTransformer
Does this OutputTransformer generate probabilities.
generatesProbabilities() - Method in class org.tribuo.interop.onnx.RegressorTransformer
 
generatesProbabilities() - Method in class org.tribuo.interop.tensorflow.LabelConverter
 
generatesProbabilities() - Method in class org.tribuo.interop.tensorflow.MultiLabelConverter
 
generatesProbabilities() - Method in interface org.tribuo.interop.tensorflow.OutputConverter
Does this OutputConverter generate probabilities.
generatesProbabilities() - Method in class org.tribuo.interop.tensorflow.RegressorConverter
 
generatesProbabilities() - Method in class org.tribuo.Model
Does this model generate probabilistic predictions.
generatesProbabilities() - Method in class org.tribuo.regression.xgboost.XGBoostRegressionConverter
 
generatesProbabilities(CommandInterpreter) - Method in class org.tribuo.classification.explanations.lime.LIMETextCLI
Does the model generate probabilities.
generatesProbabilities(CommandInterpreter) - Method in class org.tribuo.ModelExplorer
Checks if the model generates probabilities.
generateTestData() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
Simple test data for checking multi-label trainers.
generateTrainData() - Static method in class org.tribuo.multilabel.example.MultiLabelDataGenerator
Simple training data for checking multi-label trainers.
generateTransformer() - Method in class org.tribuo.transform.transformations.SimpleTransform
Returns itself.
generateTransformer() - Method in interface org.tribuo.transform.TransformStatistics
Generates the appropriate Transformer from the collected statistics.
generateUniform(int, int) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo
Generates a sample from a uniform distribution over the integers.
generateUniformFloatVector(int, float) - Static method in class org.tribuo.util.Util
Generates a float vector of the specified length filled with the specified value.
generateUniformVector(int, double) - Static method in class org.tribuo.util.Util
Generates an array of the specified length filled with the specified value.
generateUniformVector(int, float) - Static method in class org.tribuo.util.Util
Generates an array of the specified length filled with the specified value.
generateWeightedIndicesSample(int, double[], Random) - Static method in class org.tribuo.util.Util
Generates a sample of indices weighted by the provided weights.
generateWeightedIndicesSample(int, double[], SplittableRandom) - Static method in class org.tribuo.util.Util
Generates a sample of indices weighted by the provided weights.
generateWeightedIndicesSample(int, float[], Random) - Static method in class org.tribuo.util.Util
Generates a sample of indices weighted by the provided weights.
generateWeightedIndicesSample(int, float[], SplittableRandom) - Static method in class org.tribuo.util.Util
Generates a sample of indices weighted by the provided weights.
generateWeightedIndicesSampleWithoutReplacement(int, double[], Random) - Static method in class org.tribuo.util.Util
Generates a sample of indices weighted by the provided weights without replacement.
generateWeightedIndicesSampleWithoutReplacement(int, float[], Random) - Static method in class org.tribuo.util.Util
Generates a sample of indices weighted by the provided weights without replacement.
generateWeightsString() - Method in class org.tribuo.classification.sgd.crf.CRFModel
Generates a human readable string containing all the weights in this model.
generateXOR(int) - Static method in class org.tribuo.util.infotheory.example.InformationTheoryDemo
Generates a sample from a three variable XOR function.
get() - Method in class org.tribuo.classification.sgd.crf.CRFParameters
 
get() - Method in class org.tribuo.common.sgd.FMParameters
 
get() - Method in class org.tribuo.math.LinearParameters
 
get() - Method in interface org.tribuo.math.Parameters
Get a reference to the underlying Tensor array.
get(int) - Method in class org.tribuo.ImmutableFeatureMap
Gets the VariableIDInfo for this id number.
get(int) - Method in class org.tribuo.math.la.DenseVector
 
get(int) - Method in interface org.tribuo.math.la.SGDVector
Gets an element from this vector.
get(int) - Method in class org.tribuo.math.la.SparseVector
 
get(int) - Method in class org.tribuo.math.optimisers.util.ShrinkingVector
 
get(int) - Method in class org.tribuo.sequence.SequenceExample
Gets the example found at the specified index.
get(int) - Method in class org.tribuo.util.infotheory.impl.RowList
 
get(int, int) - Method in class org.tribuo.math.la.DenseMatrix
 
get(int, int) - Method in class org.tribuo.math.la.DenseSparseMatrix
 
get(int, int) - Method in interface org.tribuo.math.la.Matrix
Gets an element from this Matrix.
get(int, int) - Method in class org.tribuo.math.optimisers.util.ShrinkingMatrix
 
get(String) - Method in class org.tribuo.FeatureMap
Gets the variable info associated with that feature name, or null if it's unknown.
get(String) - Method in class org.tribuo.hash.HashedFeatureMap
 
get(String) - Method in class org.tribuo.ImmutableFeatureMap
Gets the VariableIDInfo for this name.
get(String) - Method in class org.tribuo.transform.TransformerMap
Gets the transformer associated with a given feature name.
get(MetricID<MultiLabel>) - Method in class org.tribuo.multilabel.evaluation.MultiLabelEvaluationImpl
 
get(MetricID<T>) - Method in interface org.tribuo.evaluation.Evaluation
Gets the value associated with the specific metric.
get(MetricID<T>) - Method in interface org.tribuo.sequence.SequenceEvaluation
Gets the value associated with the specific metric.
getA() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
Gets the first element.
getAB() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
Gets the pair of the first and second elements.
getABCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
The joint distribution over the first and second variables.
getABCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
The joint distribution over the first and second variables.
getAC() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
Gets the pair of the first and third elements.
getACCount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
The joint distribution over the first and third variables.
getACCount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
The joint distribution over the first and third variables.
getACount() - Method in class org.tribuo.util.infotheory.impl.TripleDistribution
The marginal distribution over the first variable.
getACount() - Method in class org.tribuo.util.infotheory.impl.WeightedTripleDistribution
The marginal distribution over the first variable.
getActiveFeatures() - Method in interface org.tribuo.classification.explanations.Explanation
Returns the names of the active features in this explanation.
getActiveFeatures() - Method in class org.tribuo.classification.explanations.lime.LIMEExplanation
 
getActiveFeatures() - Method in class org.tribuo.SparseModel
Return an immutable view on the active features for each dimension.
getAlgorithm() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder
This field represents a training algorithm step.
getAlgorithm() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto
This field represents a training algorithm step.
getAlgorithm() - Method in interface ai.onnx.proto.OnnxMl.TrainingInfoProtoOrBuilder
This field represents a training algorithm step.
getAlgorithmBuilder() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder
This field represents a training algorithm step.
getAlgorithmOrBuilder() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto.Builder
This field represents a training algorithm step.
getAlgorithmOrBuilder() - Method in class ai.onnx.proto.OnnxMl.TrainingInfoProto
This field represents a training algorithm step.
getAlgorithmOrBuilder() - Method in interface ai.onnx.proto.OnnxMl.TrainingInfoProtoOrBuilder
This field represents a training algorithm step.
getAnomalyCount() - Method in class org.tribuo.anomaly.AnomalyInfo
The number of anomalous events observed.
getArch() - Method in class org.tribuo.provenance.ModelProvenance
The CPU architecture used to create this model.
getAttribute(int) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder
Additional named attributes.
getAttribute(int) - Method in class ai.onnx.proto.OnnxMl.NodeProto
Additional named attributes.
getAttribute(int) - Method in interface ai.onnx.proto.OnnxMl.NodeProtoOrBuilder
Additional named attributes.
getAttributeBuilder(int) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder
Additional named attributes.
getAttributeBuilderList() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder
Additional named attributes.
getAttributeCount() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder
Additional named attributes.
getAttributeCount() - Method in class ai.onnx.proto.OnnxMl.NodeProto
Additional named attributes.
getAttributeCount() - Method in interface ai.onnx.proto.OnnxMl.NodeProtoOrBuilder
Additional named attributes.
getAttributeList() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder
Additional named attributes.
getAttributeList() - Method in class ai.onnx.proto.OnnxMl.NodeProto
Additional named attributes.
getAttributeList() - Method in interface ai.onnx.proto.OnnxMl.NodeProtoOrBuilder
Additional named attributes.
getAttributeOrBuilder(int) - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder
Additional named attributes.
getAttributeOrBuilder(int) - Method in class ai.onnx.proto.OnnxMl.NodeProto
Additional named attributes.
getAttributeOrBuilder(int) - Method in interface ai.onnx.proto.OnnxMl.NodeProtoOrBuilder
Additional named attributes.
getAttributeOrBuilderList() - Method in class ai.onnx.proto.OnnxMl.NodeProto.Builder
Additional named attributes.
getAttributeOrBuilderList() - Method in class ai.onnx.proto.OnnxMl.NodeProto
Additional named attributes.
getAttributeOrBuilderList() - Method in interface ai.onnx.proto.OnnxMl.NodeProtoOrBuilder
Additional named attributes.
getAverageTarget() - Method in class org.tribuo.evaluation.metrics.MetricTarget
Returns the average this metric computes, or Optional.empty() if it targets an output.
getB() - Method in class org.tribuo.util.infotheory.impl.CachedTriple
Gets the second element.
getBaseName() - Method in class org.tribuo.util.onnx.ONNXRef
The name of this object.
getBatchPredictions(List<OnnxValue>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.onnx.LabelOneVOneTransformer
Rationalises the output of an onnx model into a standard format suitable for downstream work in Tribuo.
getBatchPredictions(List<OnnxValue>, ImmutableOutputInfo<Label>) - Method in class org.tribuo.interop.onnx.LabelTransformer
Rationalises the output of an onnx model into a standard format suitable for downstream work in Tribuo.
getBatchSize() - Method in class org.tribuo.interop.ExternalModel
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