Uses of Interface
gov.sandia.cognition.learning.data.InputOutputPair

Packages that use InputOutputPair

Package	Description
gov.sandia.cognition.framework.learning.converter	Provides implementations of CogxelConverters.
gov.sandia.cognition.learning.algorithm	Provides general interfaces for learning algorithms.
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.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.minimization	Provides minimization algorithms.
gov.sandia.cognition.learning.algorithm.minimization.line	Provides line (scalar) minimization algorithms.
gov.sandia.cognition.learning.algorithm.minimization.line.interpolator	Provides line (scalar) interpolation/extrapolation algorithms that fit an algebraic function to a (small) collection of data points.
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.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	Provides data set utilities for learning.
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.cost	Provides cost functions.
gov.sandia.cognition.learning.function.scalar	Provides functions that output real numbers.
gov.sandia.cognition.learning.performance	Provides performance measures.
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.method	Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods.

Uses of InputOutputPair in gov.sandia.cognition.framework.learning.converter

Methods in gov.sandia.cognition.framework.learning.converter that return InputOutputPair

Modifier and Type	Method and Description
InputOutputPair<InputType,OutputType>	CogxelInputOutputPairConverter.createPair(InputType first, OutputType second)

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

Methods in gov.sandia.cognition.learning.algorithm that return types with arguments of type InputOutputPair

Modifier and Type	Method and Description
static <DataType> MultiCollection<InputOutputPair<DataType,DataType>>	SequencePredictionLearner.createPredictionDataset(java.util.Collection<? extends DataType> data, int predictionHorizon) Takes a collection and creates a multi-collection of sequences of input-output pairs that are from the given sequence with the given prediction horizon.
static <InputType,OutputType> java.util.ArrayList<InputOutputPair<InputType,OutputType>>	TimeSeriesPredictionLearner.createPredictionDataset(int predictionHorizon, java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Creates a dataset that can be used to predict the future by "predictionHorizon" samples.
static <DataType> MultiCollection<InputOutputPair<DataType,DataType>>	SequencePredictionLearner.createPredictionDataset(MultiCollection<? extends DataType> data, int predictionHorizon) Takes a multi-collection and creates a multi-collection of sequences of input-output pairs that are from the given sequence with the given prediction horizon.

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

Modifier and Type	Method and Description
void	AbstractSupervisedBatchAndIncrementalLearner.update(ResultType target, InputOutputPair<? extends InputType,OutputType> data)

Method parameters in gov.sandia.cognition.learning.algorithm with type arguments of type InputOutputPair

Modifier and Type	Method and Description
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(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(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, ReversibleEvaluator<OutputType,TransformedOutputType,?> outputTransform) Creates a new InputOutputTransformedBatchLearner from the predefined output transforms and the supervised learner.
static <InputType,OutputType> java.util.ArrayList<InputOutputPair<InputType,OutputType>>	TimeSeriesPredictionLearner.createPredictionDataset(int predictionHorizon, java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Creates a dataset that can be used to predict the future by "predictionHorizon" samples.
CompositeEvaluatorTriple<InputType,TransformedInputType,TransformedOutputType,OutputType>	InputOutputTransformedBatchLearner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Learn by first calling the input transformation learner on all the input values and the output transformation on the output values.
EvaluatorType	TimeSeriesPredictionLearner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data)

Constructor parameters in gov.sandia.cognition.learning.algorithm with type arguments of type InputOutputPair

Constructor and Description
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 InputOutputPair in gov.sandia.cognition.learning.algorithm.baseline

Method parameters in gov.sandia.cognition.learning.algorithm.baseline with type arguments of type InputOutputPair

Modifier and Type	Method and Description
ConstantEvaluator<java.lang.Double>	MeanLearner.learn(java.util.Collection<? extends InputOutputPair<?,java.lang.Double>> data) Creates a constant evaluator that returns the mean output value of the given dataset.
ConstantEvaluator<java.lang.Double>	WeightedMeanLearner.learn(java.util.Collection<? extends InputOutputPair<?,java.lang.Double>> data) Creates a constant evaluator for the weighted mean output value of the given dataset.
ConstantEvaluator<OutputType>	MostFrequentLearner.learn(java.util.Collection<? extends InputOutputPair<? extends java.lang.Object,OutputType>> data) Creates a constant evaluator that for the most frequent output value of the given dataset.
ConstantEvaluator<OutputType>	WeightedMostFrequentLearner.learn(java.util.Collection<? extends InputOutputPair<? extends java.lang.Object,OutputType>> data) Creates a constant evaluator based on the most frequent output in a given collection of input-output pairs, taking the weight into account.

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

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

Modifier and Type	Method and Description
void	VectorNaiveBayesCategorizer.OnlineLearner.update(VectorNaiveBayesCategorizer<CategoryType,DistributionType> target, InputOutputPair<? extends Vectorizable,CategoryType> data)

Method parameters in gov.sandia.cognition.learning.algorithm.bayes with type arguments of type InputOutputPair

Modifier and Type	Method and Description
DiscreteNaiveBayesCategorizer<InputType,CategoryType>	DiscreteNaiveBayesCategorizer.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends java.util.Collection<InputType>,CategoryType>> data)
VectorNaiveBayesCategorizer<CategoryType,DistributionType>	VectorNaiveBayesCategorizer.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>> data)
VectorNaiveBayesCategorizer<CategoryType,UnivariateGaussian.PDF>	VectorNaiveBayesCategorizer.BatchGaussianLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>> data)

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

Methods in gov.sandia.cognition.learning.algorithm.clustering that return types with arguments of type InputOutputPair

Modifier and Type	Method and Description
KDTree<DataType,java.lang.Double,InputOutputPair<DataType,java.lang.Double>>	DBSCANClusterer.getSpatialIndex() Gets the spatial index.

Method parameters in gov.sandia.cognition.learning.algorithm.clustering with type arguments of type InputOutputPair

Modifier and Type	Method and Description
void	DBSCANClusterer.setCreator(KDTree<DataType,java.lang.Double,InputOutputPair<DataType,java.lang.Double>> spatialIndex) Sets the spatial index.

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

Fields in gov.sandia.cognition.learning.algorithm.delta with type parameters of type InputOutputPair

Modifier and Type	Field and Description
protected java.util.Collection<? extends InputOutputPair<? extends Vector,CategoryType>>	AbstractDeltaCategorizer.AbstractLearner.trainingSet The training set.

Method parameters in gov.sandia.cognition.learning.algorithm.delta with type arguments of type InputOutputPair

Modifier and Type	Method and Description
abstract AbstractDeltaCategorizer<CategoryType>	AbstractDeltaCategorizer.AbstractLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,CategoryType>> trainingSet) Method that does the training.
BurrowsDeltaCategorizer<CategoryType>	BurrowsDeltaCategorizer.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,CategoryType>> trainingSet)
CosineDeltaCategorizer<CategoryType>	CosineDeltaCategorizer.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,CategoryType>> trainingSet)

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

Fields in gov.sandia.cognition.learning.algorithm.ensemble with type parameters of type InputOutputPair

Modifier and Type	Field and Description
protected java.util.ArrayList<InputOutputPair<? extends InputType,OutputType>>	AbstractBaggingLearner.bag The current bag of data.
protected java.util.ArrayList<InputOutputPair<? extends InputType,CategoryType>>	IVotingCategorizerLearner.currentBag The current bag used to train the current ensemble member.
protected java.util.ArrayList<? extends InputOutputPair<? extends InputType,OutputType>>	AbstractBaggingLearner.dataList The data stored for efficient random access.
protected java.util.ArrayList<? extends InputOutputPair<? extends InputType,CategoryType>>	IVotingCategorizerLearner.dataList The data represented as an array list.
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 IncrementalLearner<? super InputOutputPair<? extends InputType,CategoryType>,MemberType>	OnlineBaggingCategorizerLearner.learner The base learner used for 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 InputOutputPair

Modifier and Type	Method and Description
InputOutputPair<? extends InputType,CategoryType>	BagBasedCategorizerEnsembleLearner.getExample(int index) Gets the training example at the given index.
InputOutputPair<? extends InputType,CategoryType>	BaggingCategorizerLearner.getExample(int index)
InputOutputPair<? extends InputType,CategoryType>	IVotingCategorizerLearner.getExample(int index)

Methods in gov.sandia.cognition.learning.algorithm.ensemble that return types with arguments of type InputOutputPair

Modifier and Type	Method and Description
java.util.ArrayList<InputOutputPair<? extends InputType,OutputType>>	AbstractBaggingLearner.getBag() Gets the most recently created bag.
java.util.ArrayList<? extends InputOutputPair<? extends InputType,OutputType>>	AbstractBaggingLearner.getDataList() Gets the data the learner is using as an array list.
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()
IncrementalLearner<? super InputOutputPair<? extends InputType,CategoryType>,MemberType>	OnlineBaggingCategorizerLearner.getLearner() Gets the incremental (online) learning algorithm to use to learn all of the ensemble members.
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 InputOutputPair

Modifier and Type	Method and Description
void	OnlineBaggingCategorizerLearner.update(VotingCategorizerEnsemble<InputType,CategoryType,MemberType> target, InputOutputPair<? extends InputType,CategoryType> data)

Method parameters in gov.sandia.cognition.learning.algorithm.ensemble with type arguments of type InputOutputPair

Modifier and Type	Method and Description
static <InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>> OnlineBaggingCategorizerLearner<InputType,CategoryType,MemberType>	OnlineBaggingCategorizerLearner.create(IncrementalLearner<? super InputOutputPair<? extends InputType,CategoryType>,MemberType> learner, int ensembleSize, double percentToSample, java.util.Random random) Convenience method for creating an OnlineBaggingCategorizerLearner.
BinaryCategorizer<? super InputType>	BinaryCategorizerSelector.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>> data) Selects the BinaryCategorizer from its list of categorizers that minimizes the weighted error on the given set of weighted input-output pairs.
protected void	AbstractBaggingLearner.setBag(java.util.ArrayList<InputOutputPair<? extends InputType,OutputType>> bag) Sets the most recently created bag.
protected void	AbstractBaggingLearner.setDataList(java.util.ArrayList<? extends InputOutputPair<? extends InputType,OutputType>> dataList) Sets the data the learner is using as an array list.
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	OnlineBaggingCategorizerLearner.setLearner(IncrementalLearner<? super InputOutputPair<? extends InputType,CategoryType>,MemberType> learner) Sets the incremental (online) learning algorithm to use to learn all of the ensemble members.
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.

Constructor parameters in gov.sandia.cognition.learning.algorithm.ensemble with type arguments of type InputOutputPair

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.
OnlineBaggingCategorizerLearner(IncrementalLearner<? super InputOutputPair<? extends InputType,CategoryType>,MemberType> learner) Creates a new OnlineBaggingCategorizerLearner with the given base learner and default parameters.
OnlineBaggingCategorizerLearner(IncrementalLearner<? super InputOutputPair<? extends InputType,CategoryType>,MemberType> learner, int ensembleSize, double percentToSample, java.util.Random random) Creates a new OnlineBaggingCategorizerLearner with the given parameters.

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

Fields in gov.sandia.cognition.learning.algorithm.factor.machine with type parameters of type InputOutputPair

Modifier and Type	Field and Description
protected java.util.ArrayList<? extends InputOutputPair<? extends Vector,java.lang.Double>>	FactorizationMachineAlternatingLeastSquares.dataList The data in the form that it can be accessed in O(1) as a list.
protected java.util.ArrayList<? extends InputOutputPair<? extends Vector,java.lang.Double>>	FactorizationMachineStochasticGradient.dataList The input data represented as a list for fast access.

Methods in gov.sandia.cognition.learning.algorithm.factor.machine with parameters of type InputOutputPair

Modifier and Type	Method and Description
protected void	FactorizationMachineStochasticGradient.update(InputOutputPair<? extends Vector,java.lang.Double> example) Performs a single update of step of the stochastic gradient descent by updating according to the given example.

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

Fields in gov.sandia.cognition.learning.algorithm.minimization declared as InputOutputPair

Modifier and Type	Field and Description
protected InputOutputPair<InputType,OutputType>	AbstractAnytimeFunctionMinimizer.result Resulting minimum input-output pair

Methods in gov.sandia.cognition.learning.algorithm.minimization that return InputOutputPair

Modifier and Type	Method and Description
InputOutputPair<InputType,OutputType>	AbstractAnytimeFunctionMinimizer.getResult() Getter for result
InputOutputPair<InputType,OutputType>	FunctionMinimizer.learn(EvaluatorType function) Finds the (local) minimum of the given function

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

Modifier and Type	Method and Description
java.util.ArrayList<DefaultInputOutputPair<Vector,java.lang.Double>>	FunctionMinimizerNelderMead.initializeSimplex(InputOutputPair<Vector,java.lang.Double> initialPoint, double offsetValue) Initializes the simplex about the initial point
protected void	AbstractAnytimeFunctionMinimizer.setResult(InputOutputPair<InputType,OutputType> result) Setter for result

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

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

Modifier and Type	Class and Description
class	InputOutputSlopeTriplet Stores an InputOutputPair with corresponding slope (gradient) information

Methods in gov.sandia.cognition.learning.algorithm.minimization.line that return InputOutputPair

Modifier and Type	Method and Description
InputOutputPair<Vector,Vector>	DirectionalVectorToDifferentiableScalarFunction.getLastGradient() Getter for lastGradient

Methods in gov.sandia.cognition.learning.algorithm.minimization.line with parameters of type InputOutputPair

Modifier and Type	Method and Description
boolean	WolfeConditions.evaluateGoldsteinCondition(InputOutputPair<java.lang.Double,java.lang.Double> trialPoint) Evaluates the Goldstein (Armijo) conditions, which is purely a sufficient decrease condition.
static boolean	WolfeConditions.evaluateGoldsteinCondition(InputOutputSlopeTriplet originalPoint, InputOutputPair<java.lang.Double,java.lang.Double> trialPoint, double slopeCondition) Evaluates the Goldstein (Armijo) conditions, which is purely a sufficient decrease condition.
void	DirectionalVectorToDifferentiableScalarFunction.setLastGradient(InputOutputPair<Vector,Vector> lastGradient) Setter for lastGradient

Uses of InputOutputPair in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator

Methods in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator with parameters of type InputOutputPair

Modifier and Type	Method and Description
static double	LineBracketInterpolatorGoldenSection.step(InputOutputPair<java.lang.Double,java.lang.Double> a, InputOutputPair<java.lang.Double,java.lang.Double> b, double tolerance) Takes a Golden Section step between the two points
static double	LineBracketInterpolatorGoldenSection.step(InputOutputPair<java.lang.Double,java.lang.Double> a, InputOutputPair<java.lang.Double,java.lang.Double> b, double tolerance) Takes a Golden Section step between the two points

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

Methods in gov.sandia.cognition.learning.algorithm.minimization.matrix that return InputOutputPair

Modifier and Type	Method and Description
protected InputOutputPair<Vector,Vector>	ConjugateGradientMatrixSolver.completeSolver()
protected InputOutputPair<Vector,Vector>	ConjugateGradientWithPreconditionerMatrixSolver.completeSolver()
protected abstract InputOutputPair<Vector,Vector>	IterativeMatrixSolver.completeSolver() Called after the final iteration.
protected InputOutputPair<Vector,Vector>	OverconstrainedConjugateGradientMatrixMinimizer.completeSolver()
protected InputOutputPair<Vector,Vector>	SteepestDescentMatrixSolver.completeSolver()
InputOutputPair<Vector,Vector>	IterativeMatrixSolver.getResult()
InputOutputPair<Vector,Vector>	IterativeMatrixSolver.learn(Operator function) Shell that solves for Ax = b (x0 and rhs passed in on initialization, A is contained in function).

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

Fields in gov.sandia.cognition.learning.algorithm.nearest with type parameters of type InputOutputPair

Modifier and Type	Field and Description
protected java.util.LinkedList<InputOutputPair<? extends InputType,OutputType>>	NearestNeighborExhaustive.data The data that nearest-neighbor is performed over.

Methods in gov.sandia.cognition.learning.algorithm.nearest that return types with arguments of type InputOutputPair

Modifier and Type	Method and Description
java.util.Collection<InputOutputPair<? extends InputType,OutputType>>	KNearestNeighborExhaustive.getData()
KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>>	KNearestNeighborKDTree.getData() Getter for data
java.util.Collection<InputOutputPair<? extends InputType,OutputType>>	NearestNeighbor.getData() Gets the data from which this computes the nearest neighbors.
java.util.LinkedList<InputOutputPair<? extends InputType,OutputType>>	NearestNeighborExhaustive.getData()
KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>>	NearestNeighborKDTree.getData() Getter for data

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

Modifier and Type	Method and Description
void	AbstractNearestNeighbor.add(InputOutputPair<? extends InputType,OutputType> value)
void	NearestNeighbor.add(InputOutputPair<? extends InputType,OutputType> value) Adds the Pair to the data.

Method parameters in gov.sandia.cognition.learning.algorithm.nearest with type arguments of type InputOutputPair

Modifier and Type	Method and Description
KNearestNeighborExhaustive<InputType,OutputType>	KNearestNeighborExhaustive.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Creates a new KNearestNeighborExhaustive from a Collection of InputType.
KNearestNeighborKDTree<InputType,OutputType>	KNearestNeighborKDTree.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Creates a new KNearestNeighbor from a Collection of InputType.
NearestNeighborExhaustive<InputType,OutputType>	NearestNeighborExhaustive.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data)
NearestNeighborKDTree<InputType,OutputType>	NearestNeighborKDTree.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Creates a new NearestNeighbor from a Collection of InputType.
void	KNearestNeighborExhaustive.setData(java.util.Collection<InputOutputPair<? extends InputType,OutputType>> data) Setter for data
void	KNearestNeighborKDTree.setData(KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> data) Setter for data
void	NearestNeighborKDTree.setData(KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> data) Setter for data
void	NearestNeighborExhaustive.setData(java.util.LinkedList<InputOutputPair<? extends InputType,OutputType>> data) Sets the data that the object performs nearest-neighbor lookup on.

Constructor parameters in gov.sandia.cognition.learning.algorithm.nearest with type arguments of type InputOutputPair

Constructor and Description
KNearestNeighborExhaustive(int k, java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data, DivergenceFunction<? super InputType,? super InputType> divergenceFunction, Summarizer<? super OutputType,? extends OutputType> averager) Creates a new instance of KNearestNeighborExhaustive
KNearestNeighborKDTree(int k, KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> data, Metric<? super InputType> distanceFunction, Summarizer<? super OutputType,? extends OutputType> averager) Creates a new instance of KNearestNeighborKDTree
NearestNeighborExhaustive(DivergenceFunction<? super InputType,? super InputType> divergenceFunction, java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Creates a new instance of NearestNeighborExhaustive.
NearestNeighborKDTree(KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> data, DivergenceFunction<? super InputType,? super InputType> divergenceFunction) Creates a new instance of NearestNeighborKDTree

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

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

Modifier and Type	Method and Description
<InputType> void	AbstractKernelizableBinaryCategorizerOnlineLearner.update(DefaultKernelBinaryCategorizer<InputType> target, InputOutputPair<? extends InputType,java.lang.Boolean> data)
<InputType> void	KernelizableBinaryCategorizerOnlineLearner.update(DefaultKernelBinaryCategorizer<InputType> target, InputOutputPair<? extends InputType,java.lang.Boolean> data) Performs a kernel-based incremental update step on the given object using the given supervised data.
void	LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target, InputOutputPair<? extends Vectorizable,java.lang.Boolean> data, VectorFactory<?> vectorFactory) Performs a linear incremental update step on the given object using the given supervised data.
void	OnlineMultiPerceptron.update(LinearMultiCategorizer<CategoryType> target, InputOutputPair<? extends Vectorizable,CategoryType> example)
void	OnlineMultiPerceptron.UniformUpdate.update(LinearMultiCategorizer<CategoryType> target, InputOutputPair<? extends Vectorizable,CategoryType> example)
void	OnlineMultiPerceptron.ProportionalUpdate.update(LinearMultiCategorizer<CategoryType> target, InputOutputPair<? extends Vectorizable,CategoryType> example)

Method parameters in gov.sandia.cognition.learning.algorithm.perceptron with type arguments of type InputOutputPair

Modifier and Type	Method and Description
<InputType> DefaultKernelBinaryCategorizer<InputType>	AbstractKernelizableBinaryCategorizerOnlineLearner.learn(Kernel<? super InputType> kernel, java.lang.Iterable<? extends InputOutputPair<? extends InputType,java.lang.Boolean>> data)
<InputType> DefaultKernelBinaryCategorizer<InputType>	KernelizableBinaryCategorizerOnlineLearner.learn(Kernel<? super InputType> kernel, java.lang.Iterable<? extends InputOutputPair<? extends InputType,java.lang.Boolean>> data) Run this algorithm on a batch of data using the given kernel function.
<InputType> void	AbstractKernelizableBinaryCategorizerOnlineLearner.update(DefaultKernelBinaryCategorizer<InputType> target, java.lang.Iterable<? extends InputOutputPair<? extends InputType,java.lang.Boolean>> data)
<InputType> void	KernelizableBinaryCategorizerOnlineLearner.update(DefaultKernelBinaryCategorizer<InputType> target, java.lang.Iterable<? extends InputOutputPair<? extends InputType,java.lang.Boolean>> data) Performs a kernel-based incremental update step on the given object using the given supervised data.
void	LinearizableBinaryCategorizerOnlineLearner.update(LinearBinaryCategorizer target, java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Boolean>> data, VectorFactory<?> vectorFactory) Performs a linear incremental update step on the given object using the given supervised data.

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

Methods in gov.sandia.cognition.learning.algorithm.perceptron.kernel that return types with arguments of type InputOutputPair

Modifier and Type	Method and Description
protected java.util.LinkedHashMap<InputOutputPair<? extends InputType,java.lang.Boolean>,DefaultWeightedValue<InputType>>	KernelAdatron.getSupportsMap() Gets the mapping of examples to weight objects (support vectors).
protected java.util.LinkedHashMap<InputOutputPair<? extends InputType,? extends java.lang.Boolean>,DefaultWeightedValue<InputType>>	KernelPerceptron.getSupportsMap() Gets the mapping of examples to weight objects (support vectors).

Methods in gov.sandia.cognition.learning.algorithm.perceptron.kernel with parameters of type InputOutputPair

Modifier and Type	Method and Description
void	KernelBinaryCategorizerOnlineLearnerAdapter.update(DefaultKernelBinaryCategorizer<InputType> target, InputOutputPair<? extends InputType,java.lang.Boolean> data)

Method parameters in gov.sandia.cognition.learning.algorithm.perceptron.kernel with type arguments of type InputOutputPair

Modifier and Type	Method and Description
protected void	KernelPerceptron.setSupportsMap(java.util.LinkedHashMap<InputOutputPair<? extends InputType,? extends java.lang.Boolean>,DefaultWeightedValue<InputType>> supportsMap) Gets the mapping of examples to weight objects (support vectors).
protected void	KernelAdatron.setSupportsMap(java.util.LinkedHashMap<InputOutputPair<? extends InputType,java.lang.Boolean>,DefaultWeightedValue<InputType>> supportsMap) Gets the mapping of examples to weight objects (support vectors).

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

Methods in gov.sandia.cognition.learning.algorithm.regression that return types with arguments of type InputOutputPair

Modifier and Type	Method and Description
protected java.util.LinkedHashMap<InputOutputPair<? extends InputType,java.lang.Double>,DefaultWeightedValue<InputType>>	KernelBasedIterativeRegression.getSupportsMap() Gets the mapping of examples to weight objects (support vectors).

Method parameters in gov.sandia.cognition.learning.algorithm.regression with type arguments of type InputOutputPair

Modifier and Type	Method and Description
LinearFunction	UnivariateLinearRegression.learn(java.util.Collection<? extends InputOutputPair<? extends java.lang.Double,java.lang.Double>> data)
VectorFunctionLinearDiscriminant<InputType>	LinearBasisRegression.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>> data) Computes the linear regression for the given Collection of InputOutputPairs.
LocallyWeightedFunction<InputType,OutputType>	LocallyWeightedFunction.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data)
LinearDiscriminantWithBias	LinearRegression.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data) Computes the linear regression for the given Collection of InputOutputPairs.
ResultType	AbstractMinimizerBasedParameterCostMinimizer.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> data)
MultivariateDiscriminantWithBias	MultivariateLinearRegression.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> data)
protected void	KernelBasedIterativeRegression.setSupportsMap(java.util.LinkedHashMap<InputOutputPair<? extends InputType,java.lang.Double>,DefaultWeightedValue<InputType>> supportsMap) Gets the mapping of examples to weight objects (support vectors).

Constructor parameters in gov.sandia.cognition.learning.algorithm.regression with type arguments of type InputOutputPair

Constructor and Description
LocallyWeightedFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> rawData, SupervisedBatchLearner<InputType,OutputType,?> learner) Evaluator that implements the concept of LocallyWeightedLearning.

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

Methods in gov.sandia.cognition.learning.algorithm.root that return InputOutputPair

Modifier and Type	Method and Description
InputOutputPair<java.lang.Double,java.lang.Double>	AbstractBracketedRootFinder.getResult()
InputOutputPair<java.lang.Double,java.lang.Double>	MinimizerBasedRootFinder.getResult()
InputOutputPair<java.lang.Double,java.lang.Double>	RootFinderSecantMethod.getResult()
InputOutputPair<java.lang.Double,java.lang.Double>	MinimizerBasedRootFinder.learn(Evaluator<java.lang.Double,java.lang.Double> data)

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

Fields in gov.sandia.cognition.learning.algorithm.svm declared as InputOutputPair

Modifier and Type	Field and Description
protected InputOutputPair<? extends InputType,? extends java.lang.Boolean>	SuccessiveOverrelaxation.Entry.example The example the data pertains to.

Fields in gov.sandia.cognition.learning.algorithm.svm with type parameters of type InputOutputPair

Modifier and Type	Field and Description
protected java.util.ArrayList<? extends InputOutputPair<? extends Vectorizable,java.lang.Boolean>>	PrimalEstimatedSubGradient.dataList The data represented as a list.
protected java.util.LinkedHashMap<InputOutputPair<? extends InputType,? extends java.lang.Boolean>,SuccessiveOverrelaxation.Entry>	SuccessiveOverrelaxation.supportsMap The mapping of weight objects to non-zero weighted examples (support vectors).

Methods in gov.sandia.cognition.learning.algorithm.svm that return types with arguments of type InputOutputPair

Modifier and Type	Method and Description
protected java.util.LinkedHashMap<InputOutputPair<? extends InputType,? extends java.lang.Boolean>,SuccessiveOverrelaxation.Entry>	SuccessiveOverrelaxation.getSupportsMap() Gets the mapping of examples to weight objects (support vectors).

Method parameters in gov.sandia.cognition.learning.algorithm.svm with type arguments of type InputOutputPair

Modifier and Type	Method and Description
protected void	SuccessiveOverrelaxation.setSupportsMap(java.util.LinkedHashMap<InputOutputPair<? extends InputType,? extends java.lang.Boolean>,SuccessiveOverrelaxation.Entry> supportsMap) Gets the mapping of examples to weight objects (support vectors).

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

Constructor and Description
Entry(InputOutputPair<? extends InputType,? extends java.lang.Boolean> example) Creates a new Entry for the given example.

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

Fields in gov.sandia.cognition.learning.algorithm.tree with type parameters of type InputOutputPair

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 types with arguments of type InputOutputPair

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.
<DecisionType> java.util.Map<DecisionType,java.util.LinkedList<InputOutputPair<? extends InputType,OutputType>>>	AbstractDecisionTreeLearner.splitData(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data, Categorizer<? super InputType,? extends DecisionType> decider) Splits the data into new lists based on the given decision function.

Method parameters in gov.sandia.cognition.learning.algorithm.tree with type arguments of type InputOutputPair

Modifier and Type	Method and Description
boolean	AbstractDecisionTreeLearner.areAllOutputsEqual(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Determines if all of the output values in the collection are equal.
DefaultPair<java.lang.Double,java.lang.Double>	AbstractVectorThresholdMaximumGainLearner.computeBestGainAndThreshold(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data, int dimension, DefaultDataDistribution<OutputType> baseCounts) Computes the best gain and threshold for a given dimension using the computeSplitGain method for each potential split point of values for the given dimension.
protected DefaultPair<java.lang.Double,java.lang.Double>	AbstractVectorThresholdMaximumGainLearner.computeBestGainAndThreshold(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data, int dimension, DefaultDataDistribution<OutputType> baseCounts, java.util.ArrayList<DefaultWeightedValue<OutputType>> values) Computes the best gain and threshold for a given dimension using the computeSplitGain method for each potential split point of values for the given dimension.
DefaultPair<java.lang.Double,java.lang.Double>	VectorThresholdVarianceLearner.computeBestGainThreshold(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data, int dimension, double baseVariance) Computes the best information gain-threshold pair for the given dimension on the given data.
static <OutputType> DefaultDataDistribution<OutputType>	CategorizationTreeLearner.getOutputCounts(java.util.Collection<? extends InputOutputPair<?,OutputType>> data) Creates a histogram of values based on the output values in the given collection of pairs.
RegressionTree<InputType>	RegressionTreeLearner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>> data)
CategorizationTree<InputType,OutputType>	CategorizationTreeLearner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data)
VectorElementThresholdCategorizer	VectorThresholdVarianceLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data) Learns a VectorElementThresholdCategorizer from the given data by picking the vector element and threshold that best maximizes information gain.
VectorElementThresholdCategorizer	AbstractVectorThresholdMaximumGainLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data)
VectorElementThresholdCategorizer	RandomSubVectorThresholdLearner.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data)
protected <DecisionType> void	AbstractDecisionTreeLearner.learnChildNodes(AbstractDecisionTreeNode<InputType,OutputType,DecisionType> node, java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data, Categorizer<? super InputType,? extends DecisionType> decider) Learns the child nodes for a node using the given data at the node plus the decision function for the node.
protected RegressionTreeNode<InputType,?>	RegressionTreeLearner.learnNode(java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>> data, AbstractDecisionTreeNode<InputType,java.lang.Double,?> parent) Recursively learns the regression tree using the given collection of data, returning the created node.
protected abstract AbstractDecisionTreeNode<InputType,OutputType,?>	AbstractDecisionTreeLearner.learnNode(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data, AbstractDecisionTreeNode<InputType,OutputType,?> parent) Recursively learns the decision tree using the given collection of data, returning the created node.
protected CategorizationTreeNode<InputType,OutputType,?>	CategorizationTreeLearner.learnNode(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data, AbstractDecisionTreeNode<InputType,OutputType,?> parent) Recursively learns the categorization tree using the given collection of data, returning the created node.
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.
<DecisionType> java.util.Map<DecisionType,java.util.LinkedList<InputOutputPair<? extends InputType,OutputType>>>	AbstractDecisionTreeLearner.splitData(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data, Categorizer<? super InputType,? extends DecisionType> decider) Splits the data into new lists based on the given decision function.

Constructor parameters in gov.sandia.cognition.learning.algorithm.tree with type arguments of type InputOutputPair

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 InputOutputPair in gov.sandia.cognition.learning.data

Subinterfaces of InputOutputPair in gov.sandia.cognition.learning.data

Modifier and Type	Interface and Description
interface	WeightedInputOutputPair<InputType,OutputType> The WeightedInputOutputPair class implements an additional weighting term on an InputOutputPair, typically used to inform learning algorithms of the relative weight between examples.

Classes in gov.sandia.cognition.learning.data that implement InputOutputPair

Modifier and Type	Class and Description
class	AbstractInputOutputPair<InputType,OutputType> An abstract implementation of the InputOutputPair interface.
class	DefaultInputOutputPair<InputType,OutputType> A default implementation of the InputOutputPair interface.
class	DefaultWeightedInputOutputPair<InputType,OutputType> A default implementation of the WeightedInputOutputPair interface.

Methods in gov.sandia.cognition.learning.data that return types with arguments of type InputOutputPair

Modifier and Type	Method and Description
static java.util.ArrayList<java.util.ArrayList<InputOutputPair<java.lang.Double,java.lang.Double>>>	DatasetUtil.decoupleVectorPairDataset(java.util.Collection<? extends InputOutputPair<? extends Vector,? extends Vector>> dataset) Takes a set of equal-dimension Vector-Vector InputOutputPairs and turns them into a collection of Double-Double InputOutputPairs.

Methods in gov.sandia.cognition.learning.data with parameters of type InputOutputPair

Modifier and Type	Method and Description
static double	DatasetUtil.getWeight(InputOutputPair<?,?> pair) Gets the weight of a given input-output pair.

Method parameters in gov.sandia.cognition.learning.data with type arguments of type InputOutputPair

Modifier and Type	Method and Description
static void	DatasetUtil.assertInputDimensionalitiesAllEqual(java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,?>> data) Asserts that all of the dimensionalities of the input vectors in the given set of input-output pairs are the same.
static void	DatasetUtil.assertInputDimensionalitiesAllEqual(java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,?>> data, int dimensionality) Asserts that all of the dimensionalities of the input vectors in the given set of input-output pairs equal the given dimensionality.
static double	DatasetUtil.computeOutputMean(java.util.Collection<? extends InputOutputPair<?,? extends java.lang.Number>> data) Computes the mean of the output data.
static double	DatasetUtil.computeOutputVariance(java.util.Collection<? extends InputOutputPair<?,? extends java.lang.Number>> data) Computes the variance of the output of a given set of input-output pairs.
static double	DatasetUtil.computeWeightedOutputMean(java.util.Collection<? extends InputOutputPair<?,? extends java.lang.Number>> data) Computes the mean of the output data.
static <OutputType> DataDistribution<OutputType>	DatasetUtil.countOutputValues(java.lang.Iterable<? extends InputOutputPair<?,? extends OutputType>> data) Creates a data histogram over the output values from the given data.
static java.util.ArrayList<java.util.ArrayList<InputOutputPair<java.lang.Double,java.lang.Double>>>	DatasetUtil.decoupleVectorPairDataset(java.util.Collection<? extends InputOutputPair<? extends Vector,? extends Vector>> dataset) Takes a set of equal-dimension Vector-Vector InputOutputPairs and turns them into a collection of Double-Double InputOutputPairs.
static <OutputType> java.util.Set<OutputType>	DatasetUtil.findUniqueOutputs(java.lang.Iterable<? extends InputOutputPair<?,? extends OutputType>> data) Creates a set containing the unique output values from the given data.
static int	DatasetUtil.getInputDimensionality(java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,?>> data) Gets the dimensionality of the input vectors in given set of input-output pairs.
static <InputType> java.util.List<InputType>	DatasetUtil.inputsList(java.lang.Iterable<? extends InputOutputPair<? extends InputType,?>> data) Creates a list containing all of the input values from the given data.
static <OutputType> java.util.List<OutputType>	DatasetUtil.outputsList(java.lang.Iterable<? extends InputOutputPair<?,? extends OutputType>> data) Creates a list containing all of the output values from the given data.
static <DataType> DefaultPair<java.util.LinkedList<DataType>,java.util.LinkedList<DataType>>	DatasetUtil.splitDatasets(java.util.Collection<? extends InputOutputPair<? extends DataType,java.lang.Boolean>> data) Splits a dataset of input-output pair into two datasets, one for the inputs that have a "true" output and another for the inputs that have a "false" output
static <InputType,CategoryType> java.util.Map<CategoryType,java.util.List<InputType>>	DatasetUtil.splitOnOutput(java.lang.Iterable<? extends InputOutputPair<? extends InputType,? extends CategoryType>> data) Splits a dataset according to its output value (usually a category) so that all the inputs for that category are given in a list.
static double	DatasetUtil.sumWeights(java.util.Collection<? extends InputOutputPair<?,?>> data) Gets the sum of the weights of the weights of the elements of the dataset.

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

Constructor parameters in gov.sandia.cognition.learning.experiment with type arguments of type InputOutputPair

Constructor and Description
SupervisedLearnerComparisonExperiment(ValidationFoldCreator<InputOutputPair<InputType,OutputType>,InputOutputPair<InputType,OutputType>> foldCreator, PerformanceEvaluator<? super Evaluator<? super InputType,OutputType>,? super java.util.Collection<? extends InputOutputPair<InputType,OutputType>>,? extends StatisticType> performanceEvaluator, NullHypothesisEvaluator<java.util.Collection<? extends StatisticType>> statisticalTest, Summarizer<? super StatisticType,? extends SummaryType> summarizer) Creates a new instance of SupervisedLearnerComparisonExperiment.
SupervisedLearnerComparisonExperiment(ValidationFoldCreator<InputOutputPair<InputType,OutputType>,InputOutputPair<InputType,OutputType>> foldCreator, PerformanceEvaluator<? super Evaluator<? super InputType,OutputType>,? super java.util.Collection<? extends InputOutputPair<InputType,OutputType>>,? extends StatisticType> performanceEvaluator, NullHypothesisEvaluator<java.util.Collection<? extends StatisticType>> statisticalTest, Summarizer<? super StatisticType,? extends SummaryType> summarizer) Creates a new instance of SupervisedLearnerComparisonExperiment.
SupervisedLearnerComparisonExperiment(ValidationFoldCreator<InputOutputPair<InputType,OutputType>,InputOutputPair<InputType,OutputType>> foldCreator, PerformanceEvaluator<? super Evaluator<? super InputType,OutputType>,? super java.util.Collection<? extends InputOutputPair<InputType,OutputType>>,? extends StatisticType> performanceEvaluator, NullHypothesisEvaluator<java.util.Collection<? extends StatisticType>> statisticalTest, Summarizer<? super StatisticType,? extends SummaryType> summarizer) Creates a new instance of SupervisedLearnerComparisonExperiment.
SupervisedLearnerValidationExperiment(ValidationFoldCreator<InputOutputPair<InputType,OutputType>,InputOutputPair<InputType,OutputType>> foldCreator, PerformanceEvaluator<? super Evaluator<? super InputType,? extends OutputType>,? super java.util.Collection<? extends InputOutputPair<InputType,OutputType>>,? extends StatisticType> performanceEvaluator, Summarizer<? super StatisticType,? extends SummaryType> summarizer) Creates a new instance of SupervisedLearnerValidationExperiment.
SupervisedLearnerValidationExperiment(ValidationFoldCreator<InputOutputPair<InputType,OutputType>,InputOutputPair<InputType,OutputType>> foldCreator, PerformanceEvaluator<? super Evaluator<? super InputType,? extends OutputType>,? super java.util.Collection<? extends InputOutputPair<InputType,OutputType>>,? extends StatisticType> performanceEvaluator, Summarizer<? super StatisticType,? extends SummaryType> summarizer) Creates a new instance of SupervisedLearnerValidationExperiment.
SupervisedLearnerValidationExperiment(ValidationFoldCreator<InputOutputPair<InputType,OutputType>,InputOutputPair<InputType,OutputType>> foldCreator, PerformanceEvaluator<? super Evaluator<? super InputType,? extends OutputType>,? super java.util.Collection<? extends InputOutputPair<InputType,OutputType>>,? extends StatisticType> performanceEvaluator, Summarizer<? super StatisticType,? extends SummaryType> summarizer) Creates a new instance of SupervisedLearnerValidationExperiment.

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

Methods in gov.sandia.cognition.learning.function.categorization that return types with arguments of type InputOutputPair

Modifier and Type	Method and Description
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.

Method parameters in gov.sandia.cognition.learning.function.categorization with type arguments of type InputOutputPair

Modifier and Type	Method and Description
BinaryVersusCategorizer<InputType,CategoryType>	BinaryVersusCategorizer.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>> data)
EvaluatorToCategorizerAdapter<InputType,CategoryType>	EvaluatorToCategorizerAdapter.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>> data)
WinnerTakeAllCategorizer<InputType,CategoryType>	WinnerTakeAllCategorizer.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>> data)
MaximumAPosterioriCategorizer<ObservationType,CategoryType>	MaximumAPosterioriCategorizer.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends ObservationType,CategoryType>> data)
FisherLinearDiscriminantBinaryCategorizer	FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,java.lang.Boolean>> data)
static FisherLinearDiscriminantBinaryCategorizer	FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver.learn(java.util.Collection<? extends InputOutputPair<? extends Vector,java.lang.Boolean>> data, double defaultCovariance) Closed-form learning algorithm for a Fisher Linear Discriminant.
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.

Constructor parameters in gov.sandia.cognition.learning.function.categorization with type arguments of type InputOutputPair

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.

Uses of InputOutputPair in gov.sandia.cognition.learning.function.cost

Methods in gov.sandia.cognition.learning.function.cost that return types with arguments of type InputOutputPair

Modifier and Type	Method and Description
java.util.Collection<? extends InputOutputPair<? extends InputType,TargetType>>	AbstractSupervisedCostFunction.getCostParameters()

Method parameters in gov.sandia.cognition.learning.function.cost with type arguments of type InputOutputPair

Modifier and Type	Method and Description
static SumSquaredErrorCostFunction.Cache	SumSquaredErrorCostFunction.Cache.compute(GradientDescendable objectToOptimize, java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> data) Computes often-used parameters of a sum-squared error term
void	AbstractSupervisedCostFunction.setCostParameters(java.util.Collection<? extends InputOutputPair<? extends InputType,TargetType>> costParameters)
void	ParallelizedCostFunctionContainer.setCostParameters(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> costParameters)

Constructor parameters in gov.sandia.cognition.learning.function.cost with type arguments of type InputOutputPair

Constructor and Description
AbstractParallelizableCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> costParameters) Creates a new instance of AbstractParallelizableCostFunction
AbstractSupervisedCostFunction(java.util.Collection<? extends InputOutputPair<? extends InputType,TargetType>> costParameters) Creates a new instance of AbstractSupervisedCostFunction
MeanL1CostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset) Creates a new instance of MeanL1CostFunction
MeanSquaredErrorCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset) Creates a new instance of MeanSquaredErrorCostFunction
SumSquaredErrorCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset) Creates a new instance of MeanSquaredErrorCostFunction

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

Fields in gov.sandia.cognition.learning.function.scalar with type parameters of type InputOutputPair

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.

Methods in gov.sandia.cognition.learning.function.scalar with parameters of type InputOutputPair

Modifier and Type	Method and Description
static PolynomialFunction.Linear	PolynomialFunction.Linear.fit(InputOutputPair<java.lang.Double,java.lang.Double> p0, InputOutputPair<java.lang.Double,java.lang.Double> p1) Fits a linear (straight-line) curve to the given data points
static PolynomialFunction.Linear	PolynomialFunction.Linear.fit(InputOutputPair<java.lang.Double,java.lang.Double> p0, InputOutputPair<java.lang.Double,java.lang.Double> p1) Fits a linear (straight-line) curve to the given data points
static PolynomialFunction.Quadratic	PolynomialFunction.Quadratic.fit(InputOutputPair<java.lang.Double,java.lang.Double> p0, InputOutputPair<java.lang.Double,java.lang.Double> p1, InputOutputPair<java.lang.Double,java.lang.Double> p2) Fits a quadratic to three points
static PolynomialFunction.Quadratic	PolynomialFunction.Quadratic.fit(InputOutputPair<java.lang.Double,java.lang.Double> p0, InputOutputPair<java.lang.Double,java.lang.Double> p1, InputOutputPair<java.lang.Double,java.lang.Double> p2) Fits a quadratic to three points
static PolynomialFunction.Quadratic	PolynomialFunction.Quadratic.fit(InputOutputPair<java.lang.Double,java.lang.Double> p0, InputOutputPair<java.lang.Double,java.lang.Double> p1, InputOutputPair<java.lang.Double,java.lang.Double> p2) Fits a quadratic to three points
static PolynomialFunction.Quadratic	PolynomialFunction.Quadratic.fit(InputOutputSlopeTriplet p0, InputOutputPair<java.lang.Double,java.lang.Double> p1) Fits a quadratic to two points, one of which has slope information.

Method parameters in gov.sandia.cognition.learning.function.scalar with type arguments of type InputOutputPair

Modifier and Type	Method and Description
VectorFunctionLinearDiscriminant<java.lang.Double>	PolynomialFunction.Regression.learn(java.util.Collection<? extends InputOutputPair<? extends java.lang.Double,java.lang.Double>> data)
VectorFunctionToScalarFunction<InputType>	VectorFunctionToScalarFunction.Learner.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>> data)
static VectorFunctionLinearDiscriminant<java.lang.Double>	PolynomialFunction.Regression.learn(int maxOrder, java.util.Collection<? extends InputOutputPair<java.lang.Double,java.lang.Double>> data) Performs LinearRegression using all integer-exponent polynomials less than or equal to the maxOrder

Constructor parameters in gov.sandia.cognition.learning.function.scalar with type arguments of type InputOutputPair

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 InputOutputPair in gov.sandia.cognition.learning.performance

Method parameters in gov.sandia.cognition.learning.performance with type arguments of type InputOutputPair

Modifier and Type	Method and Description
ResultType	AbstractSupervisedPerformanceEvaluator.evaluatePerformance(Evaluator<? super InputType,? extends EstimateType> evaluator, java.util.Collection<? extends InputOutputPair<? extends InputType,TargetType>> data) Evaluates the performance accuracy of the given estimates against the given targets.

Uses of InputOutputPair in gov.sandia.cognition.statistics

Methods in gov.sandia.cognition.statistics that return InputOutputPair

Modifier and Type	Method and Description
static <DataType extends java.lang.Number> InputOutputPair<DataType,java.lang.Double>	ProbabilityMassFunctionUtil.inverse(CumulativeDistributionFunction<DataType> cdf, double p) Inverts the discrete CDF, that is p=Pr{x<=X}.

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

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

Modifier and Type	Class and Description
static class	AdaptiveRejectionSampling.Point An InputOutputPair that has a natural ordering according to their input (x-axis) values.

Methods in gov.sandia.cognition.statistics.bayesian with parameters of type InputOutputPair

Modifier and Type	Method and Description
void	BayesianLinearRegression.IncrementalEstimator.update(BayesianLinearRegression.IncrementalEstimator.SufficientStatistic target, InputOutputPair<? extends Vectorizable,java.lang.Double> data)
void	BayesianRobustLinearRegression.IncrementalEstimator.update(BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic target, InputOutputPair<? extends Vectorizable,java.lang.Double> data)
void	BayesianLinearRegression.IncrementalEstimator.SufficientStatistic.update(InputOutputPair<? extends Vectorizable,java.lang.Double> value)
void	BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic.update(InputOutputPair<? extends Vectorizable,java.lang.Double> value)

Method parameters in gov.sandia.cognition.statistics.bayesian with type arguments of type InputOutputPair

Modifier and Type	Method and Description
MultivariateGaussian	GaussianProcessRegression.learn(java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>> data)
MultivariateGaussian.PDF	BayesianLinearRegression.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
MultivariateGaussian.PDF	BayesianLinearRegression.IncrementalEstimator.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
MultivariateGaussianInverseGammaDistribution	BayesianRobustLinearRegression.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
MultivariateGaussianInverseGammaDistribution	BayesianRobustLinearRegression.IncrementalEstimator.learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
void	BayesianLinearRegression.IncrementalEstimator.update(BayesianLinearRegression.IncrementalEstimator.SufficientStatistic target, java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)
void	BayesianRobustLinearRegression.IncrementalEstimator.update(BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic target, java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)

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

Methods in gov.sandia.cognition.statistics.method that return InputOutputPair

Modifier and Type	Method and Description
protected static InputOutputPair<java.lang.Double,java.lang.Double>	InverseTransformSampling.initializeNewtonsMethod(SmoothCumulativeDistributionFunction cdf, double p, double tolerance) Initializes Newton's method for inverse transform sampling.
static <NumberType extends java.lang.Number> InputOutputPair<NumberType,java.lang.Double>	InverseTransformSampling.inverse(CumulativeDistributionFunction<NumberType> cdf, double p) Inverts the given CDF, finding the value of "x" so that CDF(x)=p using a root-finding algorithm.
static InputOutputPair<java.lang.Double,java.lang.Double>	InverseTransformSampling.inverseNewtonsMethod(SmoothUnivariateDistribution distribution, double p, double tolerance) Inverts the given CDF, finding the value of "x" so that CDF(x)=p using a root-finding algorithm.
static InputOutputPair<java.lang.Double,java.lang.Double>	InverseTransformSampling.inverseRootFinder(RootFinder rootFinder, CumulativeDistributionFunction<java.lang.Double> cdf, double p) Inverts the given CDF, finding the value of "x" so that CDF(x)=p using a root-finding algorithm.

Method parameters in gov.sandia.cognition.statistics.method with type arguments of type InputOutputPair

Modifier and Type	Method and Description
static ReceiverOperatingCharacteristic	ReceiverOperatingCharacteristic.create(java.util.Collection<? extends InputOutputPair<java.lang.Double,java.lang.Boolean>> data) Creates an ROC curve based on the scored data with target information
MannWhitneyUConfidence.Statistic	MannWhitneyUConfidence.evaluateNullHypothesis(java.util.Collection<? extends InputOutputPair<? extends java.lang.Number,java.lang.Boolean>> scoreClassPairs) Performs a U-test on the score-class pairs.