Index (Classification-SGD 4.3.2 API)

A B C D E F G H I K L M N O P R S T U V W
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

alphas - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults: The alpha values array from forward propagation.

B

begin - Variable in class org.tribuo.classification.sgd.crf.Chunk: The starting point of this chunk.
beliefPropagation(ChainHelper.ChainCliqueValues) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper: Runs belief propagation on a linear chain CRF.
betas - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults: The beta values array from backward propagation.

C

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.
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.
CONSTRAINED_BP - Enum constant in enum org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType: Constrained Belief Propagation from "Confidence Estimation for Information Extraction" Culotta and McCallum 2004.
constrainedBeliefPropagation(ChainHelper.ChainCliqueValues, int[]) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper: Runs constrained belief propagation on a linear chain CRF.
convert(SequenceExample<Label>, ImmutableFeatureMap, ImmutableOutputInfo<Label>) - Static method in class org.tribuo.classification.sgd.crf.CRFModel: Deprecated.
As it's replaced with CRFModel.convertToVector(org.tribuo.sequence.SequenceExample<T>, org.tribuo.ImmutableFeatureMap) which is more flexible.
convert(SequenceExample<T>, ImmutableFeatureMap) - Static method in class org.tribuo.classification.sgd.crf.CRFModel: Deprecated.
As it's replaced with CRFModel.convertToVector(org.tribuo.sequence.SequenceExample<T>, org.tribuo.ImmutableFeatureMap) which is more flexible.
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.
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
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
CRFModel - Class in org.tribuo.classification.sgd.crf: An inference time model for a linear chain CRF trained using SGD.
CRFModel.ConfidenceType - Enum in org.tribuo.classification.sgd.crf: The type of subsequence level confidence to predict.
CRFOptions - Class in org.tribuo.classification.sgd.crf: CLI options for training a linear chain CRF model.
CRFOptions() - Constructor for class org.tribuo.classification.sgd.crf.CRFOptions
CRFOptions() - Constructor for class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
CRFParameters - Class in org.tribuo.classification.sgd.crf: A Parameters for training a CRF using SGD.
CRFTrainer - Class in org.tribuo.classification.sgd.crf: A trainer for CRFs using SGD.
CRFTrainer(StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer: Creates a CRFTrainer which uses SGD to learn the parameters.
CRFTrainer(StochasticGradientOptimiser, int, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer: Sets the minibatch size to 1.
CRFTrainer(StochasticGradientOptimiser, int, long) - Constructor for class org.tribuo.classification.sgd.crf.CRFTrainer: Sets the minibatch size to 1 and the logging interval to 100.
CURRENT_VERSION - Static variable in class org.tribuo.classification.sgd.crf.CRFModel: Protobuf serialization version.
CURRENT_VERSION - Static variable in class org.tribuo.classification.sgd.crf.CRFParameters: Protobuf serialization version.
CURRENT_VERSION - Static variable in class org.tribuo.classification.sgd.fm.FMClassificationModel: Protobuf serialization version.
CURRENT_VERSION - Static variable in class org.tribuo.classification.sgd.kernel.KernelSVMModel: Protobuf serialization version.
CURRENT_VERSION - Static variable in class org.tribuo.classification.sgd.linear.LinearSGDModel: Protobuf serialization version.

D

datasetName - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Name of the example dataset, options are {gorilla}.
deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sgd.crf.CRFModel: Deserialization factory.
deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sgd.crf.CRFParameters: Deserialization factory.
deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sgd.fm.FMClassificationModel: Deserialization factory.
deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sgd.kernel.KernelSVMModel: Deserialization factory.
deserializeFromProto(int, String, Any) - Static method in class org.tribuo.classification.sgd.linear.LinearSGDModel: Deserialization factory.

E

ensembleOptions - Variable in class org.tribuo.classification.sgd.fm.TrainTest.TrainTestOptions: The ensemble options.
ensembleOptions - Variable in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions: The ensemble options.
epochs - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Number of SGD epochs.
ExampleArray(SparseVector[], int[], double[]) - Constructor for class org.tribuo.classification.sgd.Util.ExampleArray: Constructs an example array.

F

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.
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 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.
fmFactorSize - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions: Factor size.
fmLoggingInterval - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions: Log the objective after n examples.
fmMinibatchSize - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions: Minibatch size.
fmObjective - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions: Loss function.
fmVariance - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions: Variance of the initialization gaussian.

G

general - Variable in class org.tribuo.classification.sgd.fm.TrainTest.TrainTestOptions: The data loading options.
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.
generateWeightsString() - Method in class org.tribuo.classification.sgd.crf.CRFModel: Generates a human readable string containing all the weights in this model.
get() - Method in class org.tribuo.classification.sgd.crf.CRFParameters
getBias(int) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Returns the bias for the specified label id.
getCliqueValues(SGDVector[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Generates the local scores and tuples them with the label - label transition weights.
getDimensionName(int) - Method in class org.tribuo.classification.sgd.fm.FMClassificationModel
getDimensionName(int) - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel
getEmptyCopy() - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Returns a 3 element Tensor array.
getExcuse(Example<Label>) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel
getFeatureWeights(int) - Method in class org.tribuo.classification.sgd.crf.CRFModel: Get a copy of the weights for feature featureID.
getFeatureWeights(int) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Gets a copy of the weights for the specified label id.
getFeatureWeights(String) - Method in class org.tribuo.classification.sgd.crf.CRFModel: Get a copy of the weights for feature named featureName.
getInvocationCount() - Method in class org.tribuo.classification.sgd.crf.CRFTrainer
getInvocationCount() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
getLocalScores(SGDVector[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Generate the local scores (i.e., the linear classifier for each token).
getLoss() - Method in class org.tribuo.classification.sgd.fm.FMClassificationOptions: Returns the loss function specified in the arguments.
getLoss() - Method in class org.tribuo.classification.sgd.linear.LinearSGDOptions: Returns the loss function specified in the arguments.
getModelClassName() - Method in class org.tribuo.classification.sgd.fm.FMClassificationTrainer
getModelClassName() - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
getNormalizer() - Method in interface org.tribuo.classification.sgd.LabelObjective: Generates a new VectorNormalizer which normalizes the predictions into [0,1].
getNormalizer() - Method in class org.tribuo.classification.sgd.objectives.Hinge: Returns a new NoopNormalizer.
getNormalizer() - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass
getNumberOfSupportVectors() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel: Returns the number of support vectors used.
getObjective() - Method in class org.tribuo.classification.sgd.fm.FMClassificationTrainer
getObjective() - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
getOptionsDescription() - Method in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions
getOptionsDescription() - Method in class org.tribuo.classification.sgd.fm.TrainTest.TrainTestOptions
getOptionsDescription() - Method in class org.tribuo.classification.sgd.kernel.TrainTest.TrainTestOptions
getOptionsDescription() - Method in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
getProvenance() - Method in class org.tribuo.classification.sgd.crf.CRFTrainer
getProvenance() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
getProvenance() - Method in class org.tribuo.classification.sgd.objectives.Hinge
getProvenance() - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass
getSequenceTrainer() - Method in class org.tribuo.classification.sgd.crf.CRFOptions: Returns the configured CRF trainer.
getTarget(ImmutableOutputInfo<Label>, Label) - Method in class org.tribuo.classification.sgd.fm.FMClassificationTrainer
getTarget(ImmutableOutputInfo<Label>, Label) - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
getTopFeatures(int) - Method in class org.tribuo.classification.sgd.crf.CRFModel
getTopFeatures(int) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel
getTrainer() - Method in class org.tribuo.classification.sgd.fm.FMClassificationOptions
getTrainer() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMOptions
getTrainer() - Method in class org.tribuo.classification.sgd.linear.LinearSGDOptions
getWeight(int, int) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Returns the feature/label weight for the specified feature and label id.
gradientOptions - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Gradient descent options.

H

Hinge - Class in org.tribuo.classification.sgd.objectives: Hinge loss, scores the correct value margin and any incorrect predictions -margin.
Hinge() - Constructor for class org.tribuo.classification.sgd.objectives.Hinge: Construct a hinge objective with a margin of 1.0.
Hinge(double) - Constructor for class org.tribuo.classification.sgd.objectives.Hinge: Construct a hinge objective with the supplied margin.
HINGE - Enum constant in enum org.tribuo.classification.sgd.fm.FMClassificationOptions.LossEnum: Hinge loss (like an SVM).
HINGE - Enum constant in enum org.tribuo.classification.sgd.linear.LinearSGDOptions.LossEnum: Hinge loss.

I

isProbabilistic() - Method in interface org.tribuo.classification.sgd.LabelObjective: Does the objective function score probabilities or not?
isProbabilistic() - Method in class org.tribuo.classification.sgd.objectives.Hinge: Returns false.
isProbabilistic() - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass: Returns true.

K

kernelDegree - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions: Degree in polynomial kernel function.
kernelEpochs - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions: Number of SGD epochs.
kernelGamma - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions: Gamma value in kernel function.
kernelIntercept - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions: Intercept in kernel function.
kernelKernel - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions: Kernel function.
kernelLambda - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions: Lambda value in gradient optimisation.
kernelLoggingInterval - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions: Log the objective after n examples.
kernelSeed - Variable in class org.tribuo.classification.sgd.kernel.KernelSVMOptions: Sets the random seed for the Kernel SVM.
KernelSVMModel - Class in org.tribuo.classification.sgd.kernel: The inference time version of a kernel model trained using Pegasos.
KernelSVMOptions - Class in org.tribuo.classification.sgd.kernel: Options for using the KernelSVMTrainer.
KernelSVMOptions() - Constructor for class org.tribuo.classification.sgd.kernel.KernelSVMOptions
KernelSVMOptions.KernelEnum - Enum in org.tribuo.classification.sgd.kernel: The kernel types.
KernelSVMTrainer - Class in org.tribuo.classification.sgd.kernel: A trainer for a kernelised model using the Pegasos optimiser.
KernelSVMTrainer(Kernel, double, int, int, long) - Constructor for class org.tribuo.classification.sgd.kernel.KernelSVMTrainer: Constructs a trainer for a kernel SVM model.
KernelSVMTrainer(Kernel, double, int, long) - Constructor for class org.tribuo.classification.sgd.kernel.KernelSVMTrainer: Constructs a trainer for a kernel SVM model.

L

LabelObjective - Interface in org.tribuo.classification.sgd: An interface for single label prediction objectives.
labels - Variable in class org.tribuo.classification.sgd.crf.Chunk: The labels for the next labels.length values.
labels - Variable in class org.tribuo.classification.sgd.Util.ExampleArray: The label indices.
labels - Variable in class org.tribuo.classification.sgd.Util.SequenceExampleArray: The sequence example label indices.
LINEAR - Enum constant in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum: Uses a Linear kernel.
LinearSGDModel - Class in org.tribuo.classification.sgd.linear: The inference time version of a linear model trained using SGD.
LinearSGDOptions - Class in org.tribuo.classification.sgd.linear: CLI options for training a linear classifier.
LinearSGDOptions() - Constructor for class org.tribuo.classification.sgd.linear.LinearSGDOptions
LinearSGDOptions.LossEnum - Enum in org.tribuo.classification.sgd.linear: Available loss types.
LinearSGDTrainer - Class in org.tribuo.classification.sgd.linear: A trainer for a linear classifier using SGD.
LinearSGDTrainer(LabelObjective, StochasticGradientOptimiser, int, int, int, long) - Constructor for class org.tribuo.classification.sgd.linear.LinearSGDTrainer: Constructs an SGD trainer for a linear model.
LinearSGDTrainer(LabelObjective, StochasticGradientOptimiser, int, int, long) - Constructor for class org.tribuo.classification.sgd.linear.LinearSGDTrainer: Constructs an SGD trainer for a linear model.
LinearSGDTrainer(LabelObjective, StochasticGradientOptimiser, int, long) - Constructor for class org.tribuo.classification.sgd.linear.LinearSGDTrainer: Constructs an SGD trainer for a linear model.
localValues - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainCliqueValues: The per element values.
LOG - Enum constant in enum org.tribuo.classification.sgd.fm.FMClassificationOptions.LossEnum: Log loss (i.e., a logistic regression).
LOG - Enum constant in enum org.tribuo.classification.sgd.linear.LinearSGDOptions.LossEnum: Log loss, i.e., cross-entropy.
loggingInterval - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Log the objective after n examples.
LogisticRegressionTrainer - Class in org.tribuo.classification.sgd.linear: A logistic regression trainer that uses a reasonable objective, optimiser, number of epochs and minibatch size.
LogisticRegressionTrainer() - Constructor for class org.tribuo.classification.sgd.linear.LogisticRegressionTrainer: Constructs a simple logistic regression, using AdaGrad with a learning rate of 1.0 as the gradient optimizer, training for 5 epochs.
logModel - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Print out feature, label and other model details.
LogMulticlass - Class in org.tribuo.classification.sgd.objectives: A multiclass version of the log loss.
LogMulticlass() - Constructor for class org.tribuo.classification.sgd.objectives.LogMulticlass: Constructs a multiclass log loss.
logZ - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults: The normalization constant.
lossAndGradient(Integer, SGDVector) - Method in interface org.tribuo.classification.sgd.LabelObjective
lossAndGradient(Integer, SGDVector) - Method in class org.tribuo.classification.sgd.objectives.Hinge: Returns a Pair of Double and SGDVector representing the loss and per label gradients respectively.
lossAndGradient(Integer, SGDVector) - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass: Returns a Pair of Double and SGDVector representing the loss and per label gradients respectively.

M

main(String[]) - Static method in class org.tribuo.classification.sgd.crf.SeqTest
main(String[]) - Static method in class org.tribuo.classification.sgd.fm.TrainTest: Runs a TrainTest CLI.
main(String[]) - Static method in class org.tribuo.classification.sgd.kernel.TrainTest: Runs a TrainTest CLI.
main(String[]) - Static method in class org.tribuo.classification.sgd.TrainTest: Runs a TrainTest CLI.
mapScore - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainViterbiResults: The score for this result.
mapValues - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainViterbiResults: The viterbi states.
merge(Tensor[][], int) - Method in class org.tribuo.classification.sgd.crf.CRFParameters
modelHashingAlgorithm - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Hash the model during training.
modelHashingSalt - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Salt for hashing the model.
MULTIPLY - Enum constant in enum org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType: Belief Propagation

N

NONE - Enum constant in enum org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType: No confidence predction.

O

onnxModelName() - Method in class org.tribuo.classification.sgd.fm.FMClassificationModel
onnxModelName() - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel
onnxOutput(ONNXNode) - Method in class org.tribuo.classification.sgd.fm.FMClassificationModel
onnxOutput(ONNXNode) - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel
org.tribuo.classification.sgd - package org.tribuo.classification.sgd: Provides infrastructure for Stochastic Gradient Descent for classification problems.
org.tribuo.classification.sgd.crf - package org.tribuo.classification.sgd.crf: Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
org.tribuo.classification.sgd.fm - package org.tribuo.classification.sgd.fm: Provides an implementation of a classification factorization machine using Stochastic Gradient Descent.
org.tribuo.classification.sgd.kernel - package org.tribuo.classification.sgd.kernel: Provides a SGD implementation of a Kernel SVM using the Pegasos algorithm.
org.tribuo.classification.sgd.linear - package org.tribuo.classification.sgd.linear: Provides an implementation of a classification linear model using Stochastic Gradient Descent.
org.tribuo.classification.sgd.objectives - package org.tribuo.classification.sgd.objectives: Provides classification loss functions for Stochastic Gradient Descent.
outputPath - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Path to serialize model to.

P

POLYNOMIAL - Enum constant in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum: Uses a Polynomial kernel.
postConfig() - Method in class org.tribuo.classification.sgd.crf.CRFTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer: Used by the OLCUT configuration system, and should not be called by external code.
predict(Example<Label>) - Method in class org.tribuo.classification.sgd.fm.FMClassificationModel
predict(Example<Label>) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel
predict(Example<Label>) - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel
predict(SGDVector[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Generate a prediction using Viterbi.
predict(SequenceExample<Label>) - Method in class org.tribuo.classification.sgd.crf.CRFModel
predictConfidenceUsingCBP(SGDVector[], List<Chunk>) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: This predicts per chunk confidence using the constrained forward backward algorithm from Culotta and McCallum 2004.
predictMarginals(SGDVector[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Generate a prediction using Belief Propagation.
protobufFormat - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Load in the model in protobuf format.

R

RBF - Enum constant in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum: Uses an RBF kernel.

S

scoreChunks(SequenceExample<Label>, List<Chunk>) - Method in class org.tribuo.classification.sgd.crf.CRFModel: Scores the chunks using constrained belief propagation.
scores - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainBPResults: The local clique scores (i.e., the linear model scores).
scores - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainViterbiResults: The pre-viterbi scores.
scoreSubsequences(SequenceExample<Label>, List<Prediction<Label>>, List<SUB>) - Method in class org.tribuo.classification.sgd.crf.CRFModel
seed - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: RNG seed.
SeqTest - Class in org.tribuo.classification.sgd.crf: Build and run a sequence classifier on a generated dataset.
SeqTest() - Constructor for class org.tribuo.classification.sgd.crf.SeqTest
SeqTest.CRFOptions - Class in org.tribuo.classification.sgd.crf: Command line options.
serialize() - Method in class org.tribuo.classification.sgd.crf.CRFModel
serialize() - Method in class org.tribuo.classification.sgd.crf.CRFParameters
serialize() - Method in class org.tribuo.classification.sgd.fm.FMClassificationModel
serialize() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMModel
serialize() - Method in class org.tribuo.classification.sgd.linear.LinearSGDModel
set(Tensor[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters
setConfidenceType(CRFModel.ConfidenceType) - Method in class org.tribuo.classification.sgd.crf.CRFModel: Sets the inference method used for confidence prediction.
setInvocationCount(int) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
setShuffle(boolean) - Method in class org.tribuo.classification.sgd.crf.CRFTrainer: Turn on or off shuffling of examples.
setShuffle(boolean) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer: Turn on or off shuffling of examples.
sgdEpochs - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions: Number of SGD epochs.
sgdLoggingInterval - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions: Log the objective after n examples.
sgdMinibatchSize - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions: Minibatch size.
sgdObjective - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions: Loss function.
sgdSeed - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions: Sets the random seed for the LinearSGDTrainer.
sgoOptions - Variable in class org.tribuo.classification.sgd.crf.CRFOptions: Options for the gradient optimiser.
sgoOptions - Variable in class org.tribuo.classification.sgd.fm.FMClassificationOptions: The gradient descent algorithm options.
sgoOptions - Variable in class org.tribuo.classification.sgd.linear.LinearSGDOptions: The gradient descent optimiser options.
shuffle - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Shuffle the data each epoch (default: true).
shuffle(SGDVector[][], int[][], double[], SplittableRandom) - Static method in class org.tribuo.classification.sgd.Util: Shuffles a sequence of features, labels and weights, returning a tuple of the shuffled values.
shuffle(SparseVector[], int[], double[], SplittableRandom) - Static method in class org.tribuo.classification.sgd.Util: Shuffles the features, labels and weights returning a tuple of the shuffled inputs.
shuffleInPlace(SGDVector[][], int[][], double[], SplittableRandom) - Static method in class org.tribuo.classification.sgd.Util: In place shuffle used for sequence problems.
shuffleInPlace(SparseVector[], int[], double[], int[], SplittableRandom) - Static method in class org.tribuo.classification.sgd.Util: In place shuffle of the features, labels, weights and indices.
shuffleInPlace(SparseVector[], int[], double[], SplittableRandom) - Static method in class org.tribuo.classification.sgd.Util: Deprecated.
In favour of AbstractSGDTrainer.shuffleInPlace(org.tribuo.math.la.SGDVector[], T[], double[], java.util.SplittableRandom).
SIGMOID - Enum constant in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum: Uses a Sigmoid kernel.
sumLogProbs(double[]) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper: Sums the log probabilities.
sumLogProbs(DenseVector) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper: Sums the log probabilities.

T

testDataset - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Path to a serialised SequenceDataset used for testing.
toString() - Method in class org.tribuo.classification.sgd.crf.CRFTrainer
toString() - Method in class org.tribuo.classification.sgd.fm.FMClassificationTrainer
toString() - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
toString() - Method in class org.tribuo.classification.sgd.linear.LinearSGDTrainer
toString() - Method in class org.tribuo.classification.sgd.objectives.Hinge
toString() - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass
train(Dataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
train(Dataset<Label>, Map<String, Provenance>, int) - Method in class org.tribuo.classification.sgd.kernel.KernelSVMTrainer
train(SequenceDataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.sgd.crf.CRFTrainer
trainDataset - Variable in class org.tribuo.classification.sgd.crf.SeqTest.CRFOptions: Path to a serialised SequenceDataset used for training.
trainerOptions - Variable in class org.tribuo.classification.sgd.fm.TrainTest.TrainTestOptions: The factorization machine trainer options.
trainerOptions - Variable in class org.tribuo.classification.sgd.kernel.TrainTest.TrainTestOptions: The Kernel SVM SGD options.
trainerOptions - Variable in class org.tribuo.classification.sgd.TrainTest.TrainTestOptions: The linear SGD trainer options.
TrainTest - Class in org.tribuo.classification.sgd.fm: Build and run a classifier for a standard dataset using FMClassificationTrainer.
TrainTest - Class in org.tribuo.classification.sgd.kernel: Build and run a kernel SVM classifier for a standard dataset.
TrainTest - Class in org.tribuo.classification.sgd: Build and run a classifier for a standard dataset using LinearSGDTrainer.
TrainTest() - Constructor for class org.tribuo.classification.sgd.fm.TrainTest
TrainTest() - Constructor for class org.tribuo.classification.sgd.kernel.TrainTest
TrainTest() - Constructor for class org.tribuo.classification.sgd.TrainTest
TrainTest.TrainTestOptions - Class in org.tribuo.classification.sgd.fm: Command line options.
TrainTest.TrainTestOptions - Class in org.tribuo.classification.sgd.kernel: Command line options.
TrainTest.TrainTestOptions - Class in org.tribuo.classification.sgd: Command line options.
TrainTestOptions() - Constructor for class org.tribuo.classification.sgd.fm.TrainTest.TrainTestOptions
TrainTestOptions() - Constructor for class org.tribuo.classification.sgd.kernel.TrainTest.TrainTestOptions
TrainTestOptions() - Constructor for class org.tribuo.classification.sgd.TrainTest.TrainTestOptions
transitionValues - Variable in class org.tribuo.classification.sgd.crf.ChainHelper.ChainCliqueValues: The label-label transition matrix.

U

unpack(int[]) - Method in class org.tribuo.classification.sgd.crf.Chunk: Copies the labels into the constraints array starting at Chunk.begin.
update(Tensor[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters
Util - Class in org.tribuo.classification.sgd: SGD utilities.
Util() - Constructor for class org.tribuo.classification.sgd.Util
Util.ExampleArray - Class in org.tribuo.classification.sgd: A nominal tuple.
Util.SequenceExampleArray - Class in org.tribuo.classification.sgd: A nominal tuple.

V

valueAndGradient(int, SGDVector) - Method in interface org.tribuo.classification.sgd.LabelObjective: Deprecated.
In 4.1, to migrate to the new name LabelObjective.lossAndGradient(java.lang.Integer, org.tribuo.math.la.SGDVector).
valueAndGradient(int, SGDVector) - Method in class org.tribuo.classification.sgd.objectives.Hinge: Deprecated.
valueAndGradient(int, SGDVector) - Method in class org.tribuo.classification.sgd.objectives.LogMulticlass: Deprecated.
valueAndGradient(SGDVector[], int[]) - Method in class org.tribuo.classification.sgd.crf.CRFParameters: Generates predictions based on the input features and labels, then scores those predictions to produce a loss for the example and a gradient update.
valueOf(String) - Static method in enum org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.sgd.fm.FMClassificationOptions.LossEnum: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.sgd.linear.LinearSGDOptions.LossEnum: Returns the enum constant of this type with the specified name.
values() - Static method in enum org.tribuo.classification.sgd.crf.CRFModel.ConfidenceType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.sgd.fm.FMClassificationOptions.LossEnum: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.sgd.kernel.KernelSVMOptions.KernelEnum: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.sgd.linear.LinearSGDOptions.LossEnum: Returns an array containing the constants of this enum type, in the order they are declared.
viterbi(ChainHelper.ChainCliqueValues) - Static method in class org.tribuo.classification.sgd.crf.ChainHelper: Runs Viterbi on a linear chain CRF.

W

weights - Variable in class org.tribuo.classification.sgd.Util.ExampleArray: The example weights.
weights - Variable in class org.tribuo.classification.sgd.Util.SequenceExampleArray: The sequence example weights.

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