Uses of Interface
gov.sandia.cognition.learning.algorithm.BatchLearner

Packages that use BatchLearner

Package	Description
gov.sandia.cognition.framework.learning	Provides a mechanism for putting learned objects into the Cognitive Framework.
gov.sandia.cognition.learning.algorithm	Provides general interfaces for learning algorithms.
gov.sandia.cognition.learning.algorithm.annealing	Provides the Simulated Annealing algorithm.
gov.sandia.cognition.learning.algorithm.baseline	Provides baseline (dummy) learning algorithms.
gov.sandia.cognition.learning.algorithm.bayes	Provides algorithms for computing Bayesian categorizers.
gov.sandia.cognition.learning.algorithm.clustering	Provides clustering algorithms.
gov.sandia.cognition.learning.algorithm.confidence	Provides confidence-weighted categorization algorithms.
gov.sandia.cognition.learning.algorithm.delta	Provides an abstract class for helping to implement variants of the Burrows' Delta algorithm.
gov.sandia.cognition.learning.algorithm.ensemble	Provides ensemble methods.
gov.sandia.cognition.learning.algorithm.factor.machine	Provides factorization machine algorithms.
gov.sandia.cognition.learning.algorithm.genetic	Provides a genetic algorithm implementation.
gov.sandia.cognition.learning.algorithm.hmm	Provides hidden Markov model (HMM) algorithms.
gov.sandia.cognition.learning.algorithm.minimization	Provides minimization algorithms.
gov.sandia.cognition.learning.algorithm.minimization.line	Provides line (scalar) minimization algorithms.
gov.sandia.cognition.learning.algorithm.minimization.matrix	Provides matrix solving algorithms.
gov.sandia.cognition.learning.algorithm.nearest	Provides algorithms for Nearest-Neighbor memory-based functions.
gov.sandia.cognition.learning.algorithm.pca	Provides implementations of Principle Components Analysis (PCA).
gov.sandia.cognition.learning.algorithm.perceptron	Provides the Perceptron algorithm and some of its variations.
gov.sandia.cognition.learning.algorithm.perceptron.kernel
gov.sandia.cognition.learning.algorithm.regression	Provides regression algorithms, such as Linear Regression.
gov.sandia.cognition.learning.algorithm.root	Provides algorithms for finding the roots, or zero crossings, of scalar functions.
gov.sandia.cognition.learning.algorithm.svm	Provides implementations of Support Vector Machine (SVM) learning algorithms.
gov.sandia.cognition.learning.algorithm.tree	Provides decision tree learning algorithms.
gov.sandia.cognition.learning.data.feature	Provides data feature extractors.
gov.sandia.cognition.learning.experiment	Provides experiments for validating the performance of learning algorithms.
gov.sandia.cognition.learning.function.categorization	Provides functions that output a discrete set of categories.
gov.sandia.cognition.learning.function.distance	Provides distance functions.
gov.sandia.cognition.learning.function.scalar	Provides functions that output real numbers.
gov.sandia.cognition.learning.function.vector	Provides functions that output vectors.
gov.sandia.cognition.learning.parameter	Provides utility classes for handling learning algorithm parameters.
gov.sandia.cognition.statistics	Provides the inheritance hierarchy for general statistical methods and distributions.
gov.sandia.cognition.statistics.bayesian	Provides algorithms for computing Bayesian estimates of parameters.
gov.sandia.cognition.statistics.bayesian.conjugate	Provides Bayesian estimation routines based on conjugate prior distribution of parameters of specific conditional distributions.
gov.sandia.cognition.statistics.distribution	Provides statistical distributions.
gov.sandia.cognition.statistics.method	Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods.
gov.sandia.cognition.text.spelling	Provides classes for spelling.
gov.sandia.cognition.text.topic	Provides topic modeling algorithms.

Uses of BatchLearner in gov.sandia.cognition.framework.learning

Methods in gov.sandia.cognition.framework.learning that return BatchLearner

Modifier and Type	Method and Description
BatchLearner<? super java.util.Collection<LearningDataType>,? extends Evaluator<? super InputType,? extends OutputType>>	EvaluatorBasedCognitiveModuleFactoryLearner.getLearner() Gets the learner used to create the Evaluator for the module.

Methods in gov.sandia.cognition.framework.learning with parameters of type BatchLearner

Modifier and Type	Method and Description
void	EvaluatorBasedCognitiveModuleFactoryLearner.setLearner(BatchLearner<? super java.util.Collection<LearningDataType>,? extends Evaluator<? super InputType,? extends OutputType>> learner) Sets the learner used to create the Evaluator for the module.

Constructors in gov.sandia.cognition.framework.learning with parameters of type BatchLearner

Constructor and Description
EvaluatorBasedCognitiveModuleFactoryLearner(BatchLearner<? super java.util.Collection<LearningDataType>,? extends Evaluator<? super InputType,? extends OutputType>> learner, java.lang.String name, CogxelConverter<InputType> inputConverter, CogxelConverter<OutputType> outputConverter, CogxelConverter<LearningDataType> learningDataConverter) Creates a new instance of CognitiveModuleFactoryEvaluatorLearner.
EvaluatorBasedCognitiveModuleFactoryLearner(BatchLearner<? super java.util.Collection<LearningDataType>,? extends Evaluator<InputType,OutputType>> learner, java.lang.String name) Creates a new instance of CognitiveModuleFactoryEvaluatorLearner.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm

Classes in gov.sandia.cognition.learning.algorithm with type parameters of type BatchLearner

Modifier and Type	Class and Description
class	AbstractBatchLearnerContainer<LearnerType extends BatchLearner<?,?>> An abstract class for objects that contain a batch learning algorithm.
interface	BatchLearnerContainer<LearnerType extends BatchLearner<?,?>> An interface for an object that contains a batch learner.

Subinterfaces of BatchLearner in gov.sandia.cognition.learning.algorithm

Modifier and Type	Interface and Description
interface	AnytimeBatchLearner<DataType,ResultType> A batch learner that is also and Anytime algorithm.
interface	BatchAndIncrementalLearner<DataType,ResultType> Interface for an algorithm that is both a batch and incremental learner.
interface	BatchCostMinimizationLearner<CostParametersType,ResultType> The BatchCostMinimizationLearner interface defines the functionality of a cost-minimization learning algorithm should follow.
interface	SupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> Interface for a class that is a supervised learning algorithm that can be used both batch and incremental contexts.
interface	SupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> The BatchSupervisedLearner interface is an extension of the BatchLearner interface that contains the typical generic definition conventions for a batch, supervised learning algorithm.

Classes in gov.sandia.cognition.learning.algorithm that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractAnytimeBatchLearner<DataType,ResultType> The AbstractAnytimeBatchLearner abstract class implements a standard method for conforming to the BatchLearner and AnytimeLearner (IterativeAlgorithm and StoppableAlgorithm) interfaces.
class	AbstractAnytimeSupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> The AbstractAnytimeSupervisedBatchLearner abstract class extends the AbstractAnytimeBatchLearner to implement the SupervisedBatchLearner interface.
class	AbstractBatchAndIncrementalLearner<DataType,ResultType> An abstract class that has both batch learning ability as well as online learning ability by taking a Collection of input data.
class	AbstractSupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>> An abstract implementation of the batch and incremental learning for an incremental supervised learner.
class	CompositeBatchLearnerPair<InputType,IntermediateType,OutputType> Composes together a pair of batch (typically unsupervised) learners.
class	InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType> An adapter class for performing supervised learning from data where both the input and output have to be transformed before they are passed to the learning algorithm.
class	SequencePredictionLearner<DataType,LearnedType> A wrapper learner that converts an unlabeled sequence of data into a sequence of prediction data using a fixed prediction horizon.
class	TimeSeriesPredictionLearner<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> A learner used to predict the future of a sequence of data by wrapping another learner and created a future-aligned data set.

Fields in gov.sandia.cognition.learning.algorithm declared as BatchLearner

Modifier and Type	Field and Description
protected BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>>	CompositeBatchLearnerPair.firstLearner The first learner that is trained on the input data.
protected BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>>	InputOutputTransformedBatchLearner.inputLearner The unsupervised learning algorithm for creating the input transformation.
protected LearnerType	AbstractBatchLearnerContainer.learner The wrapped learner.
protected BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>>	InputOutputTransformedBatchLearner.outputLearner The unsupervised learning algorithm for creating the output transformation, which must be reversible for evaluation.
protected BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>>	CompositeBatchLearnerPair.secondLearner The second learner that is trained on the output of the evaluator created by the first learner.

Methods in gov.sandia.cognition.learning.algorithm that return BatchLearner

Modifier and Type	Method and Description
BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>>	CompositeBatchLearnerPair.getFirst()
BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>>	CompositeBatchLearnerPair.getFirstLearner() Gets the first learner that is applied to the input data.
BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>>	InputOutputTransformedBatchLearner.getInputLearner() Gets the unsupervised learning algorithm for creating the input transformation.
BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>>	InputOutputTransformedBatchLearner.getOutputLearner() Gets the unsupervised learning algorithm for creating the output transformation, which must be reversible for evaluation.
BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>>	CompositeBatchLearnerPair.getSecond()
BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>>	CompositeBatchLearnerPair.getSecondLearner() Gets the second learner that is applied to the output of the first learner.

Methods in gov.sandia.cognition.learning.algorithm with parameters of type BatchLearner

Modifier and Type	Method and Description
static <InputType,IntermediateType,OutputType> CompositeBatchLearnerPair<InputType,IntermediateType,OutputType>	CompositeBatchLearnerPair.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> firstLearner, BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> secondLearner) Creates a new CompositeBatchLearnerPair from the given learners.
static <InputType,IntermediateType,OutputType> CompositeBatchLearnerPair<InputType,IntermediateType,OutputType>	CompositeBatchLearnerPair.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> firstLearner, BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> secondLearner) Creates a new CompositeBatchLearnerPair from the given learners.
static <InputType,TransformedInputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Creates a new InputOutputTransformedBatchLearner from the three learners.
static <InputType,TransformedInputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Creates a new InputOutputTransformedBatchLearner from the three learners.
static <InputType,TransformedInputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Creates a new InputOutputTransformedBatchLearner from the three learners.
static <InputType,TransformedInputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, ReversibleEvaluator<OutputType,TransformedOutputType,?> outputTransform) Creates a new InputOutputTransformedBatchLearner from the unsupervised input transform learner, supervised learners, and output transform.
static <InputType,TransformedInputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, ReversibleEvaluator<OutputType,TransformedOutputType,?> outputTransform) Creates a new InputOutputTransformedBatchLearner from the unsupervised input transform learner, supervised learners, and output transform.
static <InputType,TransformedInputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.create(Evaluator<? super InputType,? extends TransformedInputType> inputTransform, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Creates a new InputOutputTransformedBatchLearner from the input transform, supervised learner, and unsupervised output transform learner.
static <InputType,TransformedInputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.create(Evaluator<? super InputType,? extends TransformedInputType> inputTransform, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Creates a new InputOutputTransformedBatchLearner from the input transform, supervised learner, and unsupervised output transform learner.
static <InputType,TransformedInputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.create(Evaluator<? super InputType,? extends TransformedInputType> inputTransform, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, ReversibleEvaluator<OutputType,TransformedOutputType,?> outputTransform) Creates a new InputOutputTransformedBatchLearner from the predefined input and output transforms and the supervised learner.
static <InputType,TransformedInputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,OutputType,OutputType>	InputOutputTransformedBatchLearner.createInputTransformed(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,OutputType>>,? extends Evaluator<? super TransformedInputType,? extends OutputType>> learner) Creates a new InputOutputTransformedBatchLearner from the input and supervised learners, performing no transformation on the output type.
static <InputType,TransformedInputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,OutputType,OutputType>	InputOutputTransformedBatchLearner.createInputTransformed(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,OutputType>>,? extends Evaluator<? super TransformedInputType,? extends OutputType>> learner) Creates a new InputOutputTransformedBatchLearner from the input and supervised learners, performing no transformation on the output type.
static <InputType,IntermediateType,OutputType> CompositeBatchLearnerPair<InputType,IntermediateType,OutputType>	CompositeBatchLearnerPair.createInputTransformed(Evaluator<? super InputType,? extends IntermediateType> inputTransform, BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> learner) Creates a new CompositeBatchLearnerPair from the given input transform and learner.
static <InputType,TransformedInputType,OutputType> InputOutputTransformedBatchLearner<InputType,TransformedInputType,OutputType,OutputType>	InputOutputTransformedBatchLearner.createInputTransformed(Evaluator<? super InputType,? extends TransformedInputType> inputTransform, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,OutputType>>,? extends Evaluator<? super TransformedInputType,? extends OutputType>> learner) Creates a new InputOutputTransformedBatchLearner from the predefined input transform and the supervised learner.
static <InputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,InputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.createOutputTransformed(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,TransformedOutputType>>,? extends Evaluator<? super InputType,? extends TransformedOutputType>> learner, BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Creates a new InputOutputTransformedBatchLearner from the supervised and output learners, performing no transformation on the input type.
static <InputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,InputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.createOutputTransformed(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,TransformedOutputType>>,? extends Evaluator<? super InputType,? extends TransformedOutputType>> learner, BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Creates a new InputOutputTransformedBatchLearner from the supervised and output learners, performing no transformation on the input type.
static <InputType,TransformedOutputType,OutputType> InputOutputTransformedBatchLearner<InputType,InputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.createOutputTransformed(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,TransformedOutputType>>,? extends Evaluator<? super InputType,? extends TransformedOutputType>> learner, ReversibleEvaluator<OutputType,TransformedOutputType,?> outputTransform) Creates a new InputOutputTransformedBatchLearner from the predefined output transforms and the supervised learner.
static <InputType,IntermediateType,OutputType> CompositeBatchLearnerPair<InputType,IntermediateType,OutputType>	CompositeBatchLearnerPair.createOutputTransformed(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> learner, Evaluator<? super IntermediateType,? extends OutputType> outputTransform) Creates a new CompositeBatchLearnerPair from the given learner and output transform..
void	CompositeBatchLearnerPair.setFirstLearner(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> firstLearner) Sets the first learner that is applied to the input data.
void	InputOutputTransformedBatchLearner.setInputLearner(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner) Sets the unsupervised learning algorithm for creating the input transformation.
void	InputOutputTransformedBatchLearner.setOutputLearner(BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Gets the unsupervised learning algorithm for creating the output transformation, which must be reversible for evaluation.
void	CompositeBatchLearnerPair.setSecondLearner(BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> secondLearner) Sets the second learner that is applied to the output of the first learner.

Constructors in gov.sandia.cognition.learning.algorithm with parameters of type BatchLearner

Constructor and Description
CompositeBatchLearnerPair(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> firstLearner, BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> secondLearner) Creates a new CompositeBatchLearnerPair with the given two learner.
CompositeBatchLearnerPair(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> firstLearner, BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> secondLearner) Creates a new CompositeBatchLearnerPair with the given two learner.
InputOutputTransformedBatchLearner(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Creates a new InputOutputTransformedBatchLearner with the given learners.
InputOutputTransformedBatchLearner(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Creates a new InputOutputTransformedBatchLearner with the given learners.
InputOutputTransformedBatchLearner(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner, BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner) Creates a new InputOutputTransformedBatchLearner with the given learners.
SequencePredictionLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends DataType,DataType>>,? extends LearnedType> learner, int predictionHorizon) Creates a new SequencePredictionLearner with the given learner and prediction horizon.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.annealing

Classes in gov.sandia.cognition.learning.algorithm.annealing that implement BatchLearner

Modifier and Type	Class and Description
class	SimulatedAnnealer<CostParametersType,AnnealedType> The SimulatedAnnealer class implements the simulated annealing algorithm using the provided cost function and perturbation function.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.baseline

Classes in gov.sandia.cognition.learning.algorithm.baseline that implement BatchLearner

Modifier and Type	Class and Description
class	ConstantLearner<ValueType> A learner that always returns the same value as the result.
class	IdentityLearner<ValueType> A batch learner implementation that just returns its inputs, creating an identity function.
class	MeanLearner The MeanLearner class implements a baseline learner that computes the mean of a given set of values.
class	MostFrequentLearner<OutputType> The MostFrequentLearner class implements a baseline learner that computes the most frequent output value.
class	WeightedMeanLearner The WeightedMeanLearner class implements a baseline learner that computes the weighted mean output value.
class	WeightedMostFrequentLearner<OutputType> The WeightedMostFrequentLearner class implements a baseline learning algorithm that finds the most frequent output of a given dataset based on the weights of the examples.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.bayes

Classes in gov.sandia.cognition.learning.algorithm.bayes that implement BatchLearner

Modifier and Type	Class and Description
static class	DiscreteNaiveBayesCategorizer.Learner<InputType,CategoryType> Learner for a DiscreteNaiveBayesCategorizer.
static class	VectorNaiveBayesCategorizer.BatchGaussianLearner<CategoryType> A supervised batch distributionLearner for a vector Naive Bayes categorizer that fits a Gaussian.
static class	VectorNaiveBayesCategorizer.Learner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction> A supervised batch distributionLearner for a vector Naive Bayes categorizer.
static class	VectorNaiveBayesCategorizer.OnlineLearner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction> An online (incremental) distributionLearner for the Naive Bayes categorizer that uses an incremental distribution learner for the distribution representing each dimension for each category.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.clustering

Subinterfaces of BatchLearner in gov.sandia.cognition.learning.algorithm.clustering

Modifier and Type	Interface and Description
interface	BatchClusterer<DataType,ClusterType extends Cluster<DataType>> The BatchClusterer interface defines the functionality of a batch clustering algorithm.

Classes in gov.sandia.cognition.learning.algorithm.clustering that implement BatchLearner

Modifier and Type	Class and Description
class	AffinityPropagation<DataType> The AffinityPropagation algorithm requires three parameters: a divergence function, a value to use for self-divergence, and a damping factor (called lambda in the paper; 0.5 is the default).
class	AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>> The AgglomerativeClusterer implements an agglomerative clustering algorithm, which is a type of hierarchical clustering algorithm.
class	DBSCANClusterer<DataType extends Vectorizable,ClusterType extends Cluster<DataType>> The DBSCAN algorithm requires three parameters: a distance metric, a value for neighborhood radius, and a value for the minimum number of surrounding neighbors for a point to be considered non-noise.
class	DirichletProcessClustering Clustering algorithm that wraps Dirichlet Process Mixture Model.
class	KMeansClusterer<DataType,ClusterType extends Cluster<DataType>> The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.
class	KMeansClustererWithRemoval<DataType,ClusterType extends Cluster<DataType>> Creates a k-means clustering algorithm that removes clusters that do not have sufficient membership to pass a simple statistical significance test.
class	MiniBatchKMeansClusterer<DataType extends Vector> Approximates k-means clustering by working on random subsets of the data.
class	OptimizedKMeansClusterer<DataType> This class implements an optimized version of the k-means algorithm that makes use of the triangle inequality to compute the same answer as k-means while using less distance calculations.
class	ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>> This is a parallel implementation of the k-means clustering algorithm.
class	PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>> The PartitionalClusterer implements a partitional clustering algorithm, which is a type of hierarchical clustering algorithm.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.confidence

Classes in gov.sandia.cognition.learning.algorithm.confidence that implement BatchLearner

Modifier and Type	Class and Description
class	AdaptiveRegularizationOfWeights An implementation of the Adaptive Regularization of Weights (AROW) algorithm for online learning of a linear binary categorizer.
class	ConfidenceWeightedDiagonalDeviation An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer.
class	ConfidenceWeightedDiagonalDeviationProject An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer.
class	ConfidenceWeightedDiagonalVariance An implementation of the Variance algorithm for learning a confidence-weighted linear categorizer.
class	ConfidenceWeightedDiagonalVarianceProject An implementation of the Variance algorithm for learning a confidence-weighted linear categorizer.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.delta

Classes in gov.sandia.cognition.learning.algorithm.delta that implement BatchLearner

Modifier and Type	Class and Description
static class	AbstractDeltaCategorizer.AbstractLearner<CategoryType> Abstract learner for delta algorithms.
static class	BurrowsDeltaCategorizer.Learner<CategoryType> Learner for a BurrowsDeltaCategorizer.
static class	CosineDeltaCategorizer.Learner<CategoryType> Learner for a CosineDeltaCategorizer.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.ensemble

Subinterfaces of BatchLearner in gov.sandia.cognition.learning.algorithm.ensemble

Modifier and Type	Interface and Description
interface	BagBasedCategorizerEnsembleLearner<InputType,CategoryType> Interface for a bag-based ensemble learner.

Classes in gov.sandia.cognition.learning.algorithm.ensemble that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractBaggingLearner<InputType,OutputType,MemberType,EnsembleType extends Evaluator<? super InputType,? extends OutputType>> Learns an ensemble by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
class	AdaBoost<InputType> The AdaBoost class implements the Adaptive Boosting (AdaBoost) algorithm formulated by Yoav Freund and Robert Shapire.
class	BaggingCategorizerLearner<InputType,CategoryType> Learns an categorization ensemble by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
class	BaggingRegressionLearner<InputType> Learns an ensemble for regression by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
class	BinaryBaggingLearner<InputType> The BinaryBaggingLearner implements the Bagging learning algorithm.
class	BinaryCategorizerSelector<InputType> The BinaryCategorizerSelector class implements a "weak learner" meant for use in boosting algorithms that selects the best BinaryCategorizer from a pre-set list by picking the one with the best weighted error.
class	CategoryBalancedBaggingLearner<InputType,CategoryType> An extension of the basic bagging learner that attempts to sample bags that have equal numbers of examples from every category.
class	CategoryBalancedIVotingLearner<InputType,CategoryType> An extension of IVoting for dealing with skew problems that makes sure that there are an equal number of examples from each category in each sample that an ensemble member is trained on.
class	IVotingCategorizerLearner<InputType,CategoryType> Learns an ensemble in a method similar to bagging except that on each iteration the bag is built from two parts, each sampled from elements from disjoint sets.
class	MultiCategoryAdaBoost<InputType,CategoryType> An implementation of a multi-class version of the Adaptive Boosting (AdaBoost) algorithm, known as AdaBoost.M1.
class	OnlineBaggingCategorizerLearner<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>> An implementation of an online version of the Bagging algorithm for learning an ensemble of categorizers.

Fields in gov.sandia.cognition.learning.algorithm.ensemble declared as BatchLearner

Modifier and Type	Field and Description
protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,? extends MemberType>	AbstractBaggingLearner.learner The learner to use to create the categorizer for each iteration.
protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>	IVotingCategorizerLearner.learner The learner used to produce each ensemble member.
protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>>	AdaBoost.weakLearner The "weak learner" that must learn from the weighted input-output pairs on each iteration.
protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>	MultiCategoryAdaBoost.weakLearner The "weak learner" that must learn from the weighted input-output pairs on each iteration.

Methods in gov.sandia.cognition.learning.algorithm.ensemble that return BatchLearner

Modifier and Type	Method and Description
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,? extends MemberType>	AbstractBaggingLearner.getLearner() Gets the learner used to learn each ensemble member.
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>>	AdaBoost.getLearner()
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>	IVotingCategorizerLearner.getLearner() Gets the learner used to learn each ensemble member.
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>	MultiCategoryAdaBoost.getLearner()
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>>	AdaBoost.getWeakLearner() Gets the weak learner that is passed the weighted training data on each step of the algorithm.
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>	MultiCategoryAdaBoost.getWeakLearner() Gets the weak learner that is passed the weighted training data on each step of the algorithm.

Methods in gov.sandia.cognition.learning.algorithm.ensemble with parameters of type BatchLearner

Modifier and Type	Method and Description
void	IVotingCategorizerLearner.setLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner) Sets the learner used to learn each ensemble member.
void	AbstractBaggingLearner.setLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,? extends MemberType> learner) Sets the learner used to learn each ensemble member.
void	AdaBoost.setWeakLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> weakLearner) Sets the weak learner that is passed the weighted training data on each step of the algorithm.
void	MultiCategoryAdaBoost.setWeakLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> weakLearner) Sets the weak learner that is passed the weighted training data on each step of the algorithm.

Constructors in gov.sandia.cognition.learning.algorithm.ensemble with parameters of type BatchLearner

Constructor and Description
AbstractBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,? extends MemberType> learner) Creates a new instance of AbstractBaggingLearner.
AbstractBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,? extends MemberType> learner, int maxIterations, double percentToSample, java.util.Random random) Creates a new instance of AbstractBaggingLearner.
AdaBoost(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> weakLearner) Creates a new instance of AdaBoost.
AdaBoost(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> weakLearner, int maxIterations) Creates a new instance of AdaBoost.
BaggingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner) Creates a new instance of BaggingCategorizerLearner.
BaggingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner, int maxIterations, double percentToSample, java.util.Random random) Creates a new instance of BaggingCategorizerLearner.
BaggingRegressionLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,? extends java.lang.Number>> learner) Creates a new instance of BaggingRegressionLearner.
BaggingRegressionLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,? extends java.lang.Number>> learner, int maxIterations, double percentToSample, java.util.Random random) Creates a new instance of BaggingRegressionLearner.
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner) Creates a new instance of BinaryBaggingLearner.
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner, int maxIterations) Creates a new instance of BinaryBaggingLearner.
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner, int maxIterations, double percentToSample, java.util.Random random) Creates a new instance of BinaryBaggingLearner.
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner, int maxIterations, java.util.Random random) Creates a new instance of BinaryBaggingLearner.
CategoryBalancedBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner) Creates a new instance of CategoryBalancedBaggingLearner.
CategoryBalancedBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner, int maxIterations, double percentToSample, java.util.Random random) Creates a new instance of CategoryBalancedBaggingLearner.
CategoryBalancedIVotingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner, int maxIterations, double percentToSample, double proportionIncorrectInSample, boolean voteOutOfBagOnly, Factory<? extends DataDistribution<CategoryType>> counterFactory, java.util.Random random) Creates a new CategoryBalancedIVotingLearner.
CategoryBalancedIVotingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner, int maxIterations, double percentToSample, java.util.Random random) Creates a new CategoryBalancedIVotingLearner.
IVotingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner, int maxIterations, double percentToSample, double proportionIncorrectInSample, boolean voteOutOfBagOnly, Factory<? extends DataDistribution<CategoryType>> counterFactory, java.util.Random random) Creates a new IVotingCategorizerLearner.
IVotingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner, int maxIterations, double percentToSample, java.util.Random random) Creates a new IVotingCategorizerLearner.
MultiCategoryAdaBoost(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> weakLearner, int maxIterations) Creates a new MultiCategoryAdaBoost with the given parameters.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.factor.machine

Classes in gov.sandia.cognition.learning.algorithm.factor.machine that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractFactorizationMachineLearner An abstract class for learning FactorizationMachines.
class	FactorizationMachineAlternatingLeastSquares Implements an Alternating Least Squares (ALS) algorithm for learning a Factorization Machine.
class	FactorizationMachineStochasticGradient Implements a Stochastic Gradient Descent (SGD) algorithm for learning a Factorization Machine.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.genetic

Classes in gov.sandia.cognition.learning.algorithm.genetic that implement BatchLearner

Modifier and Type	Class and Description
class	GeneticAlgorithm<CostParametersType,GenomeType> The GeneticAlgorithm class implements a generic genetic algorithm that uses a given cost function to minimize and a given reproduction function for generating the population.
class	ParallelizedGeneticAlgorithm<CostParametersType,GenomeType> This is a parallel implementation of the genetic algorithm.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.hmm

Classes in gov.sandia.cognition.learning.algorithm.hmm that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractBaumWelchAlgorithm<ObservationType,DataType> Partial implementation of the Baum-Welch algorithm.
class	BaumWelchAlgorithm<ObservationType> Implements the Baum-Welch algorithm, also known as the "forward-backward algorithm", the expectation-maximization algorithm, etc for Hidden Markov Models (HMMs).
class	ParallelBaumWelchAlgorithm<ObservationType> A Parallelized implementation of some of the methods of the Baum-Welch Algorithm.

Fields in gov.sandia.cognition.learning.algorithm.hmm declared as BatchLearner

Modifier and Type	Field and Description
protected BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>>	AbstractBaumWelchAlgorithm.distributionLearner Learner for the Distribution Functions of the HMM.
protected BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>>	ParallelBaumWelchAlgorithm.DistributionEstimatorTask.distributionLearner My copy of the PDF estimator.

Methods in gov.sandia.cognition.learning.algorithm.hmm that return BatchLearner

Modifier and Type	Method and Description
BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>>	AbstractBaumWelchAlgorithm.getDistributionLearner() Getter for distributionLearner

Methods in gov.sandia.cognition.learning.algorithm.hmm with parameters of type BatchLearner

Modifier and Type	Method and Description
static <ObservationType> HiddenMarkovModel<ObservationType>	HiddenMarkovModel.createRandom(int numStates, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> learner, java.util.Collection<? extends ObservationType> data, java.util.Random random) Creates a Hidden Markov Model with the same PMF/PDF for each state, but sampling the columns of the transition matrix and the initial probability distributions from a diffuse Dirichlet.
void	AbstractBaumWelchAlgorithm.setDistributionLearner(BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner) Setter for distributionLearner

Constructors in gov.sandia.cognition.learning.algorithm.hmm with parameters of type BatchLearner

Constructor and Description
AbstractBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities) Creates a new instance of AbstractBaumWelchAlgorithm
BaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities) Creates a new instance of BaumWelchAlgorithm
DistributionEstimatorTask(java.util.Collection<? extends ObservationType> data, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, int index) Creates an instance of DistributionEstimatorTask
ParallelBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities) Creates a new instance of ParallelBaumWelchAlgorithm

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.minimization

Subinterfaces of BatchLearner in gov.sandia.cognition.learning.algorithm.minimization

Modifier and Type	Interface and Description
interface	FunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> Interface for unconstrained minimization of nonlinear functions.

Classes in gov.sandia.cognition.learning.algorithm.minimization that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractAnytimeFunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> A partial implementation of a minimization algorithm that is iterative, stoppable, and approximate.
class	FunctionMinimizerBFGS Implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton nonlinear minimization algorithm.
class	FunctionMinimizerConjugateGradient Conjugate gradient method is a class of algorithms for finding the unconstrained local minimum of a nonlinear function.
class	FunctionMinimizerDFP Implementation of the Davidon-Fletcher-Powell (DFP) formula for a Quasi-Newton minimization update.
class	FunctionMinimizerDirectionSetPowell Implementation of the derivative-free unconstrained nonlinear direction-set minimization algorithm called "Powell's Method" by Numerical Recipes.
class	FunctionMinimizerFletcherReeves This is an implementation of the Fletcher-Reeves conjugate gradient minimization procedure.
class	FunctionMinimizerGradientDescent This is an implementation of the classic Gradient Descent algorithm, also known as Steepest Descent, Backpropagation (for neural nets), or Hill Climbing.
class	FunctionMinimizerLiuStorey This is an implementation of the Liu-Storey conjugate gradient minimization procedure.
class	FunctionMinimizerNelderMead Implementation of the Downhill Simplex minimization algorithm, also known as the Nelder-Mead method.
class	FunctionMinimizerPolakRibiere This is an implementation of the Polack-Ribiere conjugate gradient minimization procedure.
class	FunctionMinimizerQuasiNewton This is an abstract implementation of the Quasi-Newton minimization method, sometimes called "Variable-Metric methods." This family of minimization algorithms uses first-order gradient information to find a locally minimum to a scalar function.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.minimization.line

Subinterfaces of BatchLearner in gov.sandia.cognition.learning.algorithm.minimization.line

Modifier and Type	Interface and Description
interface	LineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> Defines the functionality of a line-minimization algorithm, often called a "line search" algorithm.

Classes in gov.sandia.cognition.learning.algorithm.minimization.line that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractAnytimeLineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> Partial AnytimeAlgorithm implementation of a LineMinimizer.
class	LineMinimizerBacktracking Implementation of the backtracking line-minimization algorithm.
class	LineMinimizerDerivativeBased This is an implementation of a line-minimization algorithm proposed by Fletcher that makes extensive use of first-order derivative information.
class	LineMinimizerDerivativeFree This is an implementation of a LineMinimizer that does not require derivative information.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.minimization.matrix

Classes in gov.sandia.cognition.learning.algorithm.minimization.matrix that implement BatchLearner

Modifier and Type	Class and Description
class	ConjugateGradientMatrixSolver Implements a matrix solver using Conjugate Gradient.
class	ConjugateGradientWithPreconditionerMatrixSolver Implements a matrix solver using Conjugate Gradient with a preconditioner.
class	IterativeMatrixSolver<Operator extends MatrixVectorMultiplier> Base class for all iterative matrix solvers that takes care of most of the basic iterative logic and the function minimizer interface.
class	OverconstrainedConjugateGradientMatrixMinimizer Implements a overconstrained conjugate gradient matrix optimizer.
class	SteepestDescentMatrixSolver Implements a basic Steepest Descent iterative solver for linear systems of equations.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.nearest

Classes in gov.sandia.cognition.learning.algorithm.nearest that implement BatchLearner

Modifier and Type	Class and Description
static class	KNearestNeighborExhaustive.Learner<InputType,OutputType> This is a BatchLearner interface for creating a new KNearestNeighborExhaustive from a given dataset, simply a pass-through to the constructor of KNearestNeighborExhaustive
static class	KNearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType> This is a BatchLearner interface for creating a new KNearestNeighbor from a given dataset, simply a pass-through to the constructor of KNearestNeighbor
static class	NearestNeighborExhaustive.Learner<InputType,OutputType> The NearestNeighborExhaustive.Learner class implements a batch learner for the NearestNeighborExhaustive class.
static class	NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType> This is a BatchLearner interface for creating a new NearestNeighbor from a given dataset, simply a pass-through to the constructor of NearestNeighbor

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.pca

Subinterfaces of BatchLearner in gov.sandia.cognition.learning.algorithm.pca

Modifier and Type	Interface and Description
interface	PrincipalComponentsAnalysis Principal Components Analysis is a family of algorithms that map from a high-dimensional input space to a low-dimensional output space.

Classes in gov.sandia.cognition.learning.algorithm.pca that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractPrincipalComponentsAnalysis Abstract implementation of PCA.
class	GeneralizedHebbianAlgorithm Implementation of the Generalized Hebbian Algorithm, also known as Sanger's Rule, which is a generalization of Oja's Rule.
class	KernelPrincipalComponentsAnalysis<DataType> An implementation of the Kernel Principal Components Analysis (KPCA) algorithm.
class	ThinSingularValueDecomposition Computes the "thin" singular value decomposition of a dataset.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.perceptron

Subinterfaces of BatchLearner in gov.sandia.cognition.learning.algorithm.perceptron

Modifier and Type	Interface and Description
interface	KernelizableBinaryCategorizerOnlineLearner Interface for an online learner of a linear binary categorizer that can also be used with a kernel function.
interface	LinearizableBinaryCategorizerOnlineLearner<InputType> Interface for an online learner of a kernel binary categorizer that can also be used for learning a linear categorizer.

Classes in gov.sandia.cognition.learning.algorithm.perceptron that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractKernelizableBinaryCategorizerOnlineLearner An abstract implementation of the KernelizableBinaryCategorizerOnlineLearner interface.
class	AbstractLinearCombinationOnlineLearner An abstract class for online learning of linear binary categorizers that take the form of a weighted sum of inputs.
class	AbstractOnlineLinearBinaryCategorizerLearner An abstract class for online (incremental) learning algorithms that produce an LinearBinaryCategorizer.
class	AggressiveRelaxedOnlineMaximumMarginAlgorithm An implementation of the Aggressive Relaxed Online Maximum Margin Algorithm (AROMMA).
class	Ballseptron An implementation of the Ballseptron algorithm.
class	BatchMultiPerceptron<CategoryType> Implements a multi-class version of the standard batch Perceptron learning algorithm.
class	OnlineBinaryMarginInfusedRelaxedAlgorithm An implementation of the binary MIRA algorithm.
class	OnlineMultiPerceptron<CategoryType> An online, multiple category version of the Perceptron algorithm.
static class	OnlineMultiPerceptron.ProportionalUpdate<CategoryType> Variant of a multi-category Perceptron that performs a proportional weight update on all categories that are scored higher than the true category such that the weights sum to 1.0 and are proportional how much larger the score was for each incorrect category than the true category.
static class	OnlineMultiPerceptron.UniformUpdate<CategoryType> Variant of a multi-category Perceptron that performs a uniform weight update on all categories that are scored higher than the true category such that the weights are equal and sum to -1.
class	OnlinePassiveAggressivePerceptron An implementation of the Passive-Aggressive algorithm for learning a linear binary categorizer.
static class	OnlinePassiveAggressivePerceptron.AbstractSoftMargin An abstract class for soft-margin versions of the Passive-Aggressive algorithm.
static class	OnlinePassiveAggressivePerceptron.LinearSoftMargin An implementation of the linear soft-margin variant of the Passive- Aggressive algorithm (PA-I).
static class	OnlinePassiveAggressivePerceptron.QuadraticSoftMargin An implementation of the quadratic soft-margin variant of the Passive- Aggressive algorithm (PA-II).
class	OnlinePerceptron An online version of the classic Perceptron algorithm.
class	OnlineRampPassiveAggressivePerceptron An implementation of the Ramp Loss Passive Aggressive Perceptron (PA^R) from the referenced paper.
class	OnlineShiftingPerceptron An implementation of the Shifting Perceptron algorithm.
class	OnlineVotedPerceptron An online version of the Voted-Perceptron algorithm.
class	Perceptron The Perceptron class implements the standard Perceptron learning algorithm that learns a binary classifier based on vector input.
class	RelaxedOnlineMaximumMarginAlgorithm An implementation of the Relaxed Online Maximum Margin Algorithm (ROMMA).
class	Winnow An implementation of the Winnow incremental learning algorithm.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.perceptron.kernel

Classes in gov.sandia.cognition.learning.algorithm.perceptron.kernel that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractOnlineBudgetedKernelBinaryCategorizerLearner<InputType> An abstract implementation of the BudgetedKernelBinaryCategorizerLearner for online learners.
class	AbstractOnlineKernelBinaryCategorizerLearner<InputType> An abstract class for an online kernel binary categorizer learner.
class	Forgetron<InputType> An implementation of the "self-tuned" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
static class	Forgetron.Basic<InputType> An implementation of the "basic" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
static class	Forgetron.Greedy<InputType> An implementation of the "greedy" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
class	KernelAdatron<InputType> The KernelAdatron class implements an online version of the Support Vector Machine learning algorithm.
class	KernelBinaryCategorizerOnlineLearnerAdapter<InputType> A wrapper class for a KernelizableBinaryCategorizerOnlineLearner that allows it to be used as a batch or incremental learner over the input type directly, rather than using utility methods.
class	KernelPerceptron<InputType> The KernelPerceptron class implements the kernel version of the Perceptron algorithm.
class	OnlineKernelPerceptron<InputType> An implementation of the online version of the Perceptron algorithm.
class	OnlineKernelRandomizedBudgetPerceptron<InputType> An implementation of a fixed-memory kernel Perceptron algorithm.
class	Projectron<InputType> An implementation of the Projectron algorithm, which is an online kernel binary categorizer learner that has a budget parameter tuned by the eta parameter.
static class	Projectron.LinearSoftMargin<InputType> An implementation of the Projectron++ algorithm, which is an online kernel binary categorizer learner that has a budget parameter tuned by the eta parameter.
class	RemoveOldestKernelPerceptron<InputType> A budget kernel Perceptron that always removes the oldest item.
class	Stoptron<InputType> An online, budgeted, kernel version of the Perceptron algorithm that stops learning once it has reached its budget.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.regression

Subinterfaces of BatchLearner in gov.sandia.cognition.learning.algorithm.regression

Modifier and Type	Interface and Description
interface	MultivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends Vectorizable>> A regression algorithm that maps one or more independent (input) variables onto multiple output variables.
interface	ParameterCostMinimizer<ResultType extends VectorizableVectorFunction> A anytime algorithm that is used to estimate the locally minimum-cost parameters of an object.
interface	Regression<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> A supervised learning algorithm that attempts to interpolate/extrapolate inputs given a training set of input/output pairs.
interface	UnivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends java.lang.Double>> A type of Regression algorithm that has a single dependent (output) variable that we are trying to predict.

Classes in gov.sandia.cognition.learning.algorithm.regression that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractLogisticRegression<InputType,OutputType,FunctionType extends Evaluator<? super InputType,OutputType>> Abstract partial implementation for logistic regression classes.
class	AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends java.lang.Double>> Partial implementation of ParameterCostMinimizer, based on the algorithms from the minimization package.
class	AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>> Partial implementation of ParameterCostMinimizer.
class	FletcherXuHybridEstimation The Fletcher-Xu hybrid estimation for solving the nonlinear least-squares parameters.
class	GaussNewtonAlgorithm Implementation of the Gauss-Newton parameter-estimation procedure.
class	KernelBasedIterativeRegression<InputType> The KernelBasedIterativeRegression class implements an online version of the Support Vector Regression algorithm.
class	KernelWeightedRobustRegression<InputType,OutputType> KernelWeightedRobustRegression takes a supervised learning algorithm that operates on a weighted collection of InputOutputPairs and modifies the weight of a sample based on the dataset output and its corresponding estimate from the Evaluator from the supervised learning algorithm at each iteration.
class	LeastSquaresEstimator Abstract implementation of iterative least-squares estimators.
class	LevenbergMarquardtEstimation Implementation of the nonlinear regression algorithm, known as Levenberg-Marquardt Estimation (or LMA).
class	LinearBasisRegression<InputType> Computes the least-squares regression for a LinearCombinationFunction given a dataset.
class	LinearRegression Computes the least-squares regression for a LinearCombinationFunction given a dataset.
static class	LocallyWeightedFunction.Learner<InputType,OutputType> Learning algorithm for creating LocallyWeightedFunctions.
class	LogisticRegression Performs Logistic Regression by means of the iterative reweighted least squares (IRLS) algorithm, where the logistic function has an explicit bias term, and a diagonal L2 regularization term.
class	MultivariateLinearRegression Performs multivariate regression with an explicit bias term, with optional L2 regularization.
class	ParameterDerivativeFreeCostMinimizer Implementation of a class of objects that uses a derivative-free minimization algorithm.
class	ParameterDifferentiableCostMinimizer This class adapts the unconstrained nonlinear minimization algorithms in the "minimization" package to the task of estimating locally optimal (minimum-cost) parameter sets.
class	UnivariateLinearRegression An implementation of simple univariate linear regression.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.root

Subinterfaces of BatchLearner in gov.sandia.cognition.learning.algorithm.root

Modifier and Type	Interface and Description
interface	RootBracketer Defines the functionality of a algorithm that finds a bracket of a root from an initial guess.
interface	RootFinder Defines the functionality of a root-finding algorithm.

Classes in gov.sandia.cognition.learning.algorithm.root that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractBracketedRootFinder Partial implementation of RootFinder that maintains a bracket on the root.
class	AbstractRootFinder Partial implementation of RootFinder.
class	MinimizerBasedRootFinder A root finder that uses minimization techniques to find the roots (zero-crossings).
class	RootBracketExpander The root-bracketing expansion algorithm.
class	RootFinderBisectionMethod Bisection algorithm for root finding.
class	RootFinderFalsePositionMethod The false-position algorithm for root finding.
class	RootFinderNewtonsMethod Newton's method, sometimes called Newton-Raphson method, uses first-order derivative information to iteratively locate a root.
class	RootFinderRiddersMethod The root-finding algorithm due to Ridders.
class	RootFinderSecantMethod The secant algorithm for root finding.

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.svm

Classes in gov.sandia.cognition.learning.algorithm.svm that implement BatchLearner

Modifier and Type	Class and Description
class	PrimalEstimatedSubGradient An implementation of the Primal Estimated Sub-Gradient Solver (PEGASOS) algorithm for learning a linear support vector machine (SVM).
class	SequentialMinimalOptimization<InputType> An implementation of the Sequential Minimal Optimization (SMO) algorithm for training a Support Vector Machine (SVM), which is a kernel-based binary categorizer.
class	SuccessiveOverrelaxation<InputType> The SuccessiveOverrelaxation class implements the Successive Overrelaxation (SOR) algorithm for learning a Support Vector Machine (SVM).

Uses of BatchLearner in gov.sandia.cognition.learning.algorithm.tree

Subinterfaces of BatchLearner in gov.sandia.cognition.learning.algorithm.tree

Modifier and Type	Interface and Description
interface	DeciderLearner<InputType,OutputType,CategoryType,DeciderType extends Categorizer<? super InputType,? extends CategoryType>> The DeciderLearner interface defines the functionality of a learner that can be used to learn a decision function inside a decision tree.
interface	VectorThresholdLearner<OutputType> An interface class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.

Classes in gov.sandia.cognition.learning.algorithm.tree that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractVectorThresholdMaximumGainLearner<OutputType> An abstract class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.
class	CategorizationTreeLearner<InputType,OutputType> The CategorizationTreeLearner class implements a supervised learning algorithm for learning a categorization tree.
class	RandomSubVectorThresholdLearner<OutputType> Learns a decision function by taking a randomly sampling a subspace from a given set of input vectors and then learning a threshold function by passing the subspace vectors to a sublearner.
class	RegressionTreeLearner<InputType> The RegressionTreeLearner class implements a learning algorithm for a regression tree that makes use of a decider learner and a regression learner.
class	VectorThresholdGiniImpurityLearner<OutputType> Learns vector thresholds based on the Gini impurity measure.
class	VectorThresholdHellingerDistanceLearner<OutputType> A categorization tree decision function learner on vector data that learns a vector value threshold function using the Hellinger distance.
class	VectorThresholdInformationGainLearner<OutputType> The VectorThresholdInformationGainLearner computes the best threshold over a dataset of vectors using information gain to determine the optimal index and threshold.
class	VectorThresholdVarianceLearner The VectorThresholdVarianceLearner computes the best threshold over a dataset of vectors using the reduction in variance to determine the optimal index and threshold.

Fields in gov.sandia.cognition.learning.algorithm.tree declared as BatchLearner

Modifier and Type	Field and Description
protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>>	RegressionTreeLearner.regressionLearner The learning algorithm for the regression function.

Methods in gov.sandia.cognition.learning.algorithm.tree that return BatchLearner

Modifier and Type	Method and Description
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>>	RegressionTreeLearner.getRegressionLearner() Gets the regression learner that is to be used to fit a function approximator to the values in the tree.

Methods in gov.sandia.cognition.learning.algorithm.tree with parameters of type BatchLearner

Modifier and Type	Method and Description
void	RegressionTreeLearner.setRegressionLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>> regressionLearner) Sets the regression learner that is to be used to fit a function approximator to the values in the tree.

Constructors in gov.sandia.cognition.learning.algorithm.tree with parameters of type BatchLearner

Constructor and Description
RegressionTreeLearner(DeciderLearner<? super InputType,java.lang.Double,?,?> deciderLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>> regressionLearner) Creates a new instance of CategorizationTreeLearner.
RegressionTreeLearner(DeciderLearner<? super InputType,java.lang.Double,?,?> deciderLearner, BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>> regressionLearner, int leafCountThreshold, int maxDepth) Creates a new instance of CategorizationTreeLearner.

Uses of BatchLearner in gov.sandia.cognition.learning.data.feature

Classes in gov.sandia.cognition.learning.data.feature that implement BatchLearner

Modifier and Type	Class and Description
static class	MultivariateDecorrelator.DiagonalCovarianceLearner The DiagonalCovarianceLearner class implements a BatchLearner object for a MultivariateDecorrelator.
static class	MultivariateDecorrelator.FullCovarianceLearner The FullCovarianceLearner class implements a BatchLearner object for a MultivariateDecorrelator.
class	RandomSubspace Selects a random subspace from the given vector, which is a random set of indices.
static class	StandardDistributionNormalizer.Learner The Learner class implements a BatchLearner object for a StandardDistributionNormalizer.

Uses of BatchLearner in gov.sandia.cognition.learning.experiment

Fields in gov.sandia.cognition.learning.experiment with type parameters of type BatchLearner

Modifier and Type	Field and Description
protected Pair<BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>,BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>>	LearnerComparisonExperiment.learners The learners that the experiment is being performed on.
protected Pair<BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>,BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>>	LearnerComparisonExperiment.learners The learners that the experiment is being performed on.

Methods in gov.sandia.cognition.learning.experiment that return BatchLearner

Modifier and Type	Method and Description
BatchLearner<? super java.util.Collection<? extends InputDataType>,? extends LearnedType>	LearnerRepeatExperiment.getLearner() Gets the learner the experiment is being run on.
BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>	LearnerValidationExperiment.getLearner() Gets the learner the experiment is being run on.

Methods in gov.sandia.cognition.learning.experiment that return types with arguments of type BatchLearner

Modifier and Type	Method and Description
Pair<BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>,BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>>	LearnerComparisonExperiment.getLearners() Gets the learners the experiment is being run on.
Pair<BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>,BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>>	LearnerComparisonExperiment.getLearners() Gets the learners the experiment is being run on.

Methods in gov.sandia.cognition.learning.experiment with parameters of type BatchLearner

Modifier and Type	Method and Description
LearnerComparisonExperiment.Result<SummaryType>	LearnerComparisonExperiment.evaluate(BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType> learner1, BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType> learner2, java.util.Collection<? extends InputDataType> data) Evaluates the two batch learners using the given data on the same set of validation folds and returns the resulting information including the confidence statistic that the two are different along with the summary of their performance.
LearnerComparisonExperiment.Result<SummaryType>	LearnerComparisonExperiment.evaluate(BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType> learner1, BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType> learner2, java.util.Collection<? extends InputDataType> data) Evaluates the two batch learners using the given data on the same set of validation folds and returns the resulting information including the confidence statistic that the two are different along with the summary of their performance.
SummaryType	LearnerValidationExperiment.evaluate(BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType> learner, java.util.Collection<? extends InputDataType> data) Deprecated. Use evaluatePerformance instead. Performs the experiment.
SummaryType	LearnerValidationExperiment.evaluatePerformance(BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType> learner, java.util.Collection<? extends InputDataType> data)
SummaryType	LearnerRepeatExperiment.evaluatePerformance(BatchLearner<? super java.util.Collection<? extends InputDataType>,? extends LearnedType> learner, PartitionedDataset<? extends InputDataType> data) Performs the experiment.
protected void	LearnerValidationExperiment.setLearner(BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType> learner) Sets the learner the experiment is being run on.
protected void	LearnerRepeatExperiment.setLearner(BatchLearner<? super java.util.Collection<? extends InputDataType>,? extends LearnedType> learner) Sets the learner the experiment is being run on.

Method parameters in gov.sandia.cognition.learning.experiment with type arguments of type BatchLearner

Modifier and Type	Method and Description
LearnerComparisonExperiment.Result<SummaryType>	LearnerComparisonExperiment.evaluate(Pair<BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>,BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>> learners, java.util.Collection<? extends InputDataType> data) Evaluates the two batch learners using the given data on the same set of validation folds and returns the resulting information including the confidence statistic that the two are different along with the summary of their performance.
LearnerComparisonExperiment.Result<SummaryType>	LearnerComparisonExperiment.evaluate(Pair<BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>,BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>> learners, java.util.Collection<? extends InputDataType> data) Evaluates the two batch learners using the given data on the same set of validation folds and returns the resulting information including the confidence statistic that the two are different along with the summary of their performance.
protected void	LearnerComparisonExperiment.setLearners(Pair<BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>,BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>> learners) Sets the learners the experiment is being run on.
protected void	LearnerComparisonExperiment.setLearners(Pair<BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>,BatchLearner<? super java.util.Collection<? extends FoldDataType>,? extends LearnedType>> learners) Sets the learners the experiment is being run on.

Uses of BatchLearner in gov.sandia.cognition.learning.function.categorization

Classes in gov.sandia.cognition.learning.function.categorization that implement BatchLearner

Modifier and Type	Class and Description
static class	BinaryVersusCategorizer.Learner<InputType,CategoryType> A learner for the BinaryVersusCategorizer.
static class	EvaluatorToCategorizerAdapter.Learner<InputType,CategoryType> The EvaluatorToCategorizerAdapter.Learner class implements a simple supervised learner for a EvaluatorToCategorizerAdapter.
static class	FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver This class implements a closed form solver for the Fisher linear discriminant binary categorizer.
static class	MaximumAPosterioriCategorizer.Learner<ObservationType,CategoryType> Learner for the MAP categorizer
static class	WinnerTakeAllCategorizer.Learner<InputType,CategoryType> A learner for the adapter.

Methods in gov.sandia.cognition.learning.function.categorization that return BatchLearner

Modifier and Type	Method and Description
BatchLearner<java.util.Collection<? extends ObservationType>,? extends ComputableDistribution<ObservationType>>	MaximumAPosterioriCategorizer.Learner.getConditionalLearner() Getter for conditionalLearner
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>>	EvaluatorToCategorizerAdapter.Learner.getLearner() Gets the learner whose output is to be adapted to be a categorizer.

Methods in gov.sandia.cognition.learning.function.categorization with parameters of type BatchLearner

Modifier and Type	Method and Description
void	MaximumAPosterioriCategorizer.Learner.setConditionalLearner(BatchLearner<java.util.Collection<? extends ObservationType>,? extends ComputableDistribution<ObservationType>> conditionalLearner) Setter for conditionalLearner
void	EvaluatorToCategorizerAdapter.Learner.setLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner) Sets the learner whose output is to be adapted to be a categorizer.

Constructors in gov.sandia.cognition.learning.function.categorization with parameters of type BatchLearner

Constructor and Description
Learner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,java.lang.Boolean>> learner) Creates a new BinaryVersusCategorizer.Learner with an binary categorizer learner to learn category versus category.
Learner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner) Creates a new EvaluatorToCategorizerAdapter.
Learner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,Evaluator<? super InputType,? extends Vectorizable>> learner) Creates a new learner adapter with the given internal learner.
Learner(BatchLearner<java.util.Collection<? extends ObservationType>,? extends ComputableDistribution<ObservationType>> conditionalLearner) Creates a new instance of Learner

Uses of BatchLearner in gov.sandia.cognition.learning.function.distance

Classes in gov.sandia.cognition.learning.function.distance that implement BatchLearner

Modifier and Type	Class and Description
static class	DivergencesEvaluator.Learner<DataType,InputType,ValueType> A learner adapter for the DivergencesEvaluator.

Methods in gov.sandia.cognition.learning.function.distance with parameters of type BatchLearner

Modifier and Type	Method and Description
static <DataType,InputType,ValueType> DivergencesEvaluator.Learner<DataType,InputType,ValueType>	DivergencesEvaluator.Learner.create(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner, DivergenceFunction<? super ValueType,? super InputType> divergenceFunction) Convenience method for creating a DivergencesEvaluator.Learner.

Constructors in gov.sandia.cognition.learning.function.distance with parameters of type BatchLearner

Constructor and Description
Learner(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner, DivergenceFunction<? super ValueType,? super InputType> divergenceFunction) Creates a new DivergenceFunction.Learner with the given properties.
Learner(BatchLearner<DataType,? extends java.util.Collection<ValueType>> learner, DivergenceFunction<? super ValueType,? super InputType> divergenceFunction, VectorFactory<?> vectorFactory) Creates a new DivergenceFunction.Learner with the given properties.

Uses of BatchLearner in gov.sandia.cognition.learning.function.scalar

Classes in gov.sandia.cognition.learning.function.scalar that implement BatchLearner

Modifier and Type	Class and Description
static class	PolynomialFunction.Regression Performs Linear Regression using an arbitrary set of PolynomialFunction basis functions
static class	VectorFunctionToScalarFunction.Learner<InputType> The VectorFunctionToScalarFunction.Learner class implements a simple learner for a VectorFunctionToScalarFunction that allows a learning algorithm that outputs a vector function to be adapted to learn on data whose output are doubles.

Fields in gov.sandia.cognition.learning.function.scalar declared as BatchLearner

Modifier and Type	Field and Description
protected BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,? extends Evaluator<? super InputType,? extends Vectorizable>>	VectorFunctionToScalarFunction.Learner.vectorLearner The supervised learner that learns on vectors as outputs.

Constructors in gov.sandia.cognition.learning.function.scalar with parameters of type BatchLearner

Constructor and Description
Learner(BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,? extends Evaluator<? super InputType,? extends Vectorizable>> vectorLearner) Creates a new VectorFunctionToScalarFunction.Learner.

Uses of BatchLearner in gov.sandia.cognition.learning.function.vector

Classes in gov.sandia.cognition.learning.function.vector that implement BatchLearner

Modifier and Type	Class and Description
static class	GaussianContextRecognizer.Learner Creates a GaussianContextRecognizer from a Dataset[Vector] using a BatchClusterer

Uses of BatchLearner in gov.sandia.cognition.learning.parameter

Classes in gov.sandia.cognition.learning.parameter with type parameters of type BatchLearner

Modifier and Type	Class and Description
class	ParameterAdaptableBatchLearnerWrapper<DataType,ResultType,LearnerType extends BatchLearner<? super DataType,? extends ResultType>> A wrapper for adding parameter adapters to a batch learner.

Classes in gov.sandia.cognition.learning.parameter that implement BatchLearner

Modifier and Type	Class and Description
class	ParameterAdaptableBatchLearnerWrapper<DataType,ResultType,LearnerType extends BatchLearner<? super DataType,? extends ResultType>> A wrapper for adding parameter adapters to a batch learner.

Methods in gov.sandia.cognition.learning.parameter with type parameters of type BatchLearner

Modifier and Type	Method and Description
static <DataType,ResultType,LearnerType extends BatchLearner<? super DataType,? extends ResultType>> ParameterAdaptableBatchLearnerWrapper<DataType,ResultType,LearnerType>	ParameterAdaptableBatchLearnerWrapper.create(LearnerType learner) A convenience method for creating the wrapper for a learner.

Uses of BatchLearner in gov.sandia.cognition.statistics

Subinterfaces of BatchLearner in gov.sandia.cognition.statistics

Modifier and Type	Interface and Description
interface	DistributionEstimator<ObservationType,DistributionType extends Distribution<? extends ObservationType>> A BatchLearner that estimates a Distribution.
interface	DistributionWeightedEstimator<ObservationType,DistributionType extends Distribution<? extends ObservationType>> A BatchLearner that estimates a Distribution from a Collection of weighted data.
interface	IncrementalEstimator<DataType,DistributionType extends Distribution<? extends DataType>,SufficientStatisticsType extends SufficientStatistic<? super DataType,? extends DistributionType>> An estimator of a Distribution that uses SufficientStatistic to arrive at its result.

Classes in gov.sandia.cognition.statistics that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractIncrementalEstimator<DataType,DistributionType extends Distribution<? extends DataType>,SufficientStatisticsType extends SufficientStatistic<DataType,DistributionType>> Partial implementation of IncrementalEstimator.

Uses of BatchLearner in gov.sandia.cognition.statistics.bayesian

Subinterfaces of BatchLearner in gov.sandia.cognition.statistics.bayesian

Modifier and Type	Interface and Description
interface	BayesianEstimator<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>> A type of estimation procedure based on Bayes's rule, which allows us to estimate the uncertainty of parameters given a set of observations that we are given.
interface	BayesianEstimatorPredictor<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>> A BayesianEstimator that can also compute the predictive distribution of new data given the posterior.
interface	BayesianRegression<OutputType,PosteriorType extends Distribution<? extends Vector>> A type of regression algorithm maps a Vector space, and the weights of this Vector space are represented as a posterior distribution given the observed InputOutputPairs.
interface	MarkovChainMonteCarlo<ObservationType,ParameterType> Defines the functionality of a Markov chain Monte Carlo algorithm.
interface	ParticleFilter<ObservationType,ParameterType> A particle filter aims to estimate a sequence of hidden parameters based on observed data using point-mass estimates of the posterior distribution.
interface	RecursiveBayesianEstimator<ObservationType,ParameterType,BeliefType extends Distribution<ParameterType>> A recursive Bayesian estimator is an estimation method that uses the previous belief of the system parameter and a single observation to refine the estimate of the system parameter.

Classes in gov.sandia.cognition.statistics.bayesian that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractKalmanFilter Contains fields useful to both Kalman filters and extended Kalman filters.
class	AbstractMarkovChainMonteCarlo<ObservationType,ParameterType> Partial abstract implementation of MarkovChainMonteCarlo.
class	AbstractParticleFilter<ObservationType,ParameterType> Partial abstract implementation of ParticleFilter.
class	BayesianLinearRegression Computes a Bayesian linear estimator for a given feature function and a set of observed data.
static class	BayesianLinearRegression.IncrementalEstimator Incremental estimator for BayesianLinearRegression
class	BayesianRobustLinearRegression Computes a Bayesian linear estimator for a given feature function given a set of InputOutputPair observed values.
static class	BayesianRobustLinearRegression.IncrementalEstimator Incremental estimator for BayesianRobustLinearRegression
class	DirichletProcessMixtureModel<ObservationType> An implementation of Dirichlet Process clustering, which estimates the number of clusters and the centroids of the clusters from a set of data.
class	ExtendedKalmanFilter Implements the Extended Kalman Filter (EKF), which is an extension of the Kalman filter that allows nonlinear motion and observation models.
class	GaussianProcessRegression<InputType> Gaussian Process Regression, is also known as Kriging, is a nonparametric method to interpolate and extrapolate using Bayesian regression, where the expressiveness of the estimator can grow with the data.
class	ImportanceSampling<ObservationType,ParameterType> Importance sampling is a Monte Carlo inference technique where we sample from an easy distribution over the hidden variables (parameters) and then weight the result by the ratio of the likelihood of the parameters given the evidence and the likelihood of generating the parameters.
class	KalmanFilter A Kalman filter estimates the state of a dynamical system corrupted with white Gaussian noise with observations that are corrupted with white Gaussian noise.
class	MetropolisHastingsAlgorithm<ObservationType,ParameterType> An implementation of the Metropolis-Hastings MCMC algorithm, which is the most general formulation of MCMC but can be slow.
class	ParallelDirichletProcessMixtureModel<ObservationType> A Parallelized version of vanilla Dirichlet Process Mixture Model learning.
class	RejectionSampling<ObservationType,ParameterType> Rejection sampling is a method of inferring hidden parameters by using an easy-to-sample-from distribution (times a scale factor) that envelopes another distribution that is difficult to sample from.
class	SamplingImportanceResamplingParticleFilter<ObservationType,ParameterType> An implementation of the standard Sampling Importance Resampling particle filter.

Uses of BatchLearner in gov.sandia.cognition.statistics.bayesian.conjugate

Subinterfaces of BatchLearner in gov.sandia.cognition.statistics.bayesian.conjugate

Modifier and Type	Interface and Description
interface	ConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>> A Bayesian Estimator that makes use of conjugate priors, which is a mathematical trick when the conditional and the prior result a posterior that is the same type as the prior.
interface	ConjugatePriorBayesianEstimatorPredictor<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>> A conjugate prior estimator that also has a closed-form predictive posterior.

Classes in gov.sandia.cognition.statistics.bayesian.conjugate that implement BatchLearner

Modifier and Type	Class and Description
class	AbstractConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>> Partial implementation of ConjugatePriorBayesianEstimator that contains a initial belief (prior) distribution function.
class	BernoulliBayesianEstimator A Bayesian estimator for the parameter of a BernoulliDistribution using the conjugate prior BetaDistribution.
class	BinomialBayesianEstimator A Bayesian estimator for the parameter of a Bernoulli parameter, p, of a BinomialDistribution using the conjugate prior BetaDistribution.
class	ExponentialBayesianEstimator Conjugate prior Bayesian estimator of the "rate" parameter of an Exponential distribution using the conjugate prior Gamma distribution.
class	GammaInverseScaleBayesianEstimator A Bayesian estimator for the scale parameter of a Gamma distribution using the conjugate prior Gamma distribution for the inverse-scale (rate) of the Gamma.
class	MultinomialBayesianEstimator A Bayesian estimator for the parameters of a MultinomialDistribution using its conjugate prior distribution, the DirichletDistribution.
class	MultivariateGaussianMeanBayesianEstimator Bayesian estimator for the mean of a MultivariateGaussian using its conjugate prior, which is also a MultivariateGaussian.
class	MultivariateGaussianMeanCovarianceBayesianEstimator Performs robust estimation of both the mean and covariance of a MultivariateGaussian conditional distribution using the conjugate prior Normal-Inverse-Wishart distribution.
class	PoissonBayesianEstimator A Bayesian estimator for the parameter of a PoissonDistribution using the conjugate prior GammaDistribution.
class	UniformDistributionBayesianEstimator A Bayesian estimator for a conditional Uniform(0,theta) distribution using its conjugate prior Pareto distribution.
class	UnivariateGaussianMeanBayesianEstimator Bayesian estimator for the mean of a UnivariateGaussian using its conjugate prior, which is also a UnivariateGaussian.
class	UnivariateGaussianMeanVarianceBayesianEstimator Computes the mean and variance of a univariate Gaussian using the conjugate prior NormalInverseGammaDistribution

Uses of BatchLearner in gov.sandia.cognition.statistics.distribution

Classes in gov.sandia.cognition.statistics.distribution that implement BatchLearner

Modifier and Type	Class and Description
static class	BetaBinomialDistribution.MomentMatchingEstimator Estimates the parameters of a Beta-binomial distribution using the matching of moments, not maximum likelihood.
static class	BetaDistribution.MomentMatchingEstimator Estimates the parameters of a Beta distribution using the matching of moments, not maximum likelihood.
static class	BetaDistribution.WeightedMomentMatchingEstimator Estimates the parameters of a Beta distribution using the matching of moments, not maximum likelihood.
static class	BinomialDistribution.MaximumLikelihoodEstimator Maximum likelihood estimator of the distribution
static class	DefaultDataDistribution.Estimator<KeyType> Estimator for a DefaultDataDistribution
static class	DefaultDataDistribution.WeightedEstimator<KeyType> A weighted estimator for a DefaultDataDistribution
static class	ExponentialDistribution.MaximumLikelihoodEstimator Creates a ExponentialDistribution from data
static class	ExponentialDistribution.WeightedMaximumLikelihoodEstimator Creates a ExponentialDistribution from weighted data
static class	GammaDistribution.MomentMatchingEstimator Computes the parameters of a Gamma distribution by the Method of Moments
static class	GammaDistribution.WeightedMomentMatchingEstimator Estimates the parameters of a Gamma distribution using the matching of moments, not maximum likelihood.
static class	GeometricDistribution.MaximumLikelihoodEstimator Maximum likelihood estimator of the distribution
static class	LaplaceDistribution.MaximumLikelihoodEstimator Estimates the ML parameters of a Laplace distribution from a Collection of Numbers.
static class	LaplaceDistribution.WeightedMaximumLikelihoodEstimator Creates a UnivariateGaussian from weighted data
static class	LogNormalDistribution.MaximumLikelihoodEstimator Maximum Likelihood Estimator of a log-normal distribution.
static class	LogNormalDistribution.WeightedMaximumLikelihoodEstimator Maximum Likelihood Estimator from weighted data
static class	MixtureOfGaussians.EMLearner An Expectation-Maximization based "soft" assignment learner.
static class	MixtureOfGaussians.Learner A hard-assignment learner for a MixtureOfGaussians
static class	MultivariateGaussian.IncrementalEstimator The estimator that creates a MultivariateGaussian from a stream of values.
static class	MultivariateGaussian.IncrementalEstimatorCovarianceInverse The estimator that creates a MultivariateGaussian from a stream of values by estimating the mean and covariance inverse (as opposed to the covariance directly), without ever performing a matrix inversion.
static class	MultivariateGaussian.MaximumLikelihoodEstimator Computes the Maximum Likelihood Estimate of the MultivariateGaussian given a set of Vectors
static class	MultivariateGaussian.WeightedMaximumLikelihoodEstimator Computes the Weighted Maximum Likelihood Estimate of the MultivariateGaussian given a weighted set of Vectors
static class	NegativeBinomialDistribution.MaximumLikelihoodEstimator Maximum likelihood estimator of the distribution
static class	NegativeBinomialDistribution.WeightedMaximumLikelihoodEstimator Weighted maximum likelihood estimator of the distribution
static class	PoissonDistribution.MaximumLikelihoodEstimator Creates a PoissonDistribution from data
static class	PoissonDistribution.WeightedMaximumLikelihoodEstimator Creates a PoissonDistribution from weighted data.
static class	ScalarDataDistribution.Estimator Estimator for a ScalarDataDistribution
static class	ScalarMixtureDensityModel.EMLearner An EM learner that estimates a mixture model from data
static class	StudentTDistribution.MaximumLikelihoodEstimator Estimates the parameters of the Student-t distribution from the given data, where the degrees of freedom are estimated from the Kurtosis of the sample data.
static class	StudentTDistribution.WeightedMaximumLikelihoodEstimator Creates a UnivariateGaussian from weighted data
static class	UniformDistribution.MaximumLikelihoodEstimator Maximum Likelihood Estimator of a uniform distribution.
static class	UniformIntegerDistribution.MaximumLikelihoodEstimator Implements a maximum likelihood estimator for the discrete uniform distribution.
static class	UnivariateGaussian.IncrementalEstimator Implements an incremental estimator for the sufficient statistics for a UnivariateGaussian.
static class	UnivariateGaussian.MaximumLikelihoodEstimator Creates a UnivariateGaussian from data
static class	UnivariateGaussian.WeightedMaximumLikelihoodEstimator Creates a UnivariateGaussian from weighted data

Uses of BatchLearner in gov.sandia.cognition.statistics.method

Classes in gov.sandia.cognition.statistics.method that implement BatchLearner

Modifier and Type	Class and Description
class	DistributionParameterEstimator<DataType,DistributionType extends ClosedFormDistribution<? extends DataType>> A method of estimating the parameters of a distribution using an arbitrary CostFunction and FunctionMinimizer algorithm.
class	MaximumLikelihoodDistributionEstimator<DataType> Estimates the most-likely distribution, and corresponding parameters, of that generated the given data from a pre-determined collection of candidate parameteric distributions.

Uses of BatchLearner in gov.sandia.cognition.text.spelling

Classes in gov.sandia.cognition.text.spelling that implement BatchLearner

Modifier and Type	Class and Description
static class	SimpleStatisticalSpellingCorrector.Learner A learner for the SimpleStatisticalSpellingCorrector.

Uses of BatchLearner in gov.sandia.cognition.text.topic

Classes in gov.sandia.cognition.text.topic that implement BatchLearner

Modifier and Type	Class and Description
class	LatentDirichletAllocationVectorGibbsSampler A Gibbs sampler for performing Latent Dirichlet Allocation (LDA).
class	LatentSemanticAnalysis Implements the Latent Semantic Analysis (LSA) algorithm using Singular Value Decomposition (SVD).
class	ParallelLatentDirichletAllocationVectorGibbsSampler A parallel implementation of LatentDirichletAllocationVectorGibbsSampler.
class	ProbabilisticLatentSemanticAnalysis An implementation of the Probabilistic Latent Semantic Analysis (PLSA) algorithm.