Uses of Interface
gov.sandia.cognition.statistics.DataDistribution

Packages that use DataDistribution

Package	Description
gov.sandia.cognition.learning.algorithm.bayes	Provides algorithms for computing Bayesian categorizers.
gov.sandia.cognition.learning.algorithm.ensemble	Provides ensemble methods.
gov.sandia.cognition.learning.data	Provides data set utilities for learning.
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.distribution	Provides statistical distributions.

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

Fields in gov.sandia.cognition.learning.algorithm.bayes declared as DataDistribution

Modifier and Type	Field and Description
protected DataDistribution<CategoryType>	VectorNaiveBayesCategorizer.priors The prior distribution for the categorizer.

Methods in gov.sandia.cognition.learning.algorithm.bayes that return DataDistribution

Modifier and Type	Method and Description
DataDistribution<CategoryType>	VectorNaiveBayesCategorizer.getPriors() Gets the prior distribution over the categories.

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

Modifier and Type	Method and Description
void	VectorNaiveBayesCategorizer.setPriors(DataDistribution<CategoryType> priors) Sets the prior distribution over the categories.

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

Constructor and Description
VectorNaiveBayesCategorizer(DataDistribution<CategoryType> priors, java.util.Map<CategoryType,java.util.List<DistributionType>> conditionals) Creates a new VectorNaiveBayesCategorizer with the given prior and conditionals.

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

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

Modifier and Type	Field and Description
protected Factory<? extends DataDistribution<CategoryType>>	IVotingCategorizerLearner.counterFactory Factory for counting votes.
protected java.util.ArrayList<DataDistribution<CategoryType>>	IVotingCategorizerLearner.dataFullEstimates The running estimate of the ensemble for each example.
protected java.util.ArrayList<DataDistribution<CategoryType>>	IVotingCategorizerLearner.dataOutOfBagEstimates The running estimate of the ensemble for each example where an ensemble member can only vote on elements that were not in the bag used to train it.
protected java.util.ArrayList<DataDistribution<CategoryType>>	BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria.outOfBagEstimates The running estimate of the ensemble for each example where an ensemble member can only vote on elements that were not in the bag used to train it.

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

Modifier and Type	Method and Description
abstract DataDistribution<CategoryType>	AbstractCategorizerOutOfBagStoppingCriteria.getOutOfBagEstimate(int index) Gets the out-of-bag estimate distribution across categories for the training example with the given index.
DataDistribution<CategoryType>	BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria.getOutOfBagEstimate(int index)
DataDistribution<CategoryType>	IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria.getOutOfBagEstimate(int index)

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

Modifier and Type	Method and Description
Factory<? extends DataDistribution<CategoryType>>	IVotingCategorizerLearner.getCounterFactory() Gets the factory used for creating the object for counting the votes of the learned ensemble members.
java.util.List<DataDistribution<CategoryType>>	IVotingCategorizerLearner.getDataFullEstimates() Gets the current estimates for each data point.
java.util.List<DataDistribution<CategoryType>>	IVotingCategorizerLearner.getDataOutOfBagEstimates() Gets the current out-of-bag estimates for each data point.

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

Modifier and Type	Method and Description
void	IVotingCategorizerLearner.setCounterFactory(Factory<? extends DataDistribution<CategoryType>> counterFactory) Sets the factory used for creating the object for counting the votes of the learned ensemble members.

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

Constructor and Description
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.
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.

Uses of DataDistribution in gov.sandia.cognition.learning.data

Methods in gov.sandia.cognition.learning.data that return DataDistribution

Modifier and Type	Method and Description
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.

Uses of DataDistribution in gov.sandia.cognition.statistics

Subinterfaces of DataDistribution in gov.sandia.cognition.statistics

Modifier and Type	Interface and Description
static interface	DataDistribution.PMF<KeyType> Interface for the probability mass function (PMF) of a data distribution.

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

Modifier and Type	Class and Description
class	AbstractDataDistribution<KeyType> An abstract implementation of the DataDistribution interface.

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

Modifier and Type	Method and Description
DataDistribution<DataType>	DataDistribution.clone()

Methods in gov.sandia.cognition.statistics that return types with arguments of type DataDistribution

Modifier and Type	Method and Description
DistributionEstimator<DataType,? extends DataDistribution<DataType>>	DataDistribution.getEstimator()

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

Methods in gov.sandia.cognition.statistics.bayesian that return DataDistribution

Modifier and Type	Method and Description
DataDistribution<ParameterType>	AbstractParticleFilter.createInitialLearnedObject()
DataDistribution<ParameterType>	ParticleFilter.Updater.createInitialParticles(int numParticles) Creates the initial particles.
DataDistribution<ParameterType>	ImportanceSampling.learn(java.util.Collection<? extends ObservationType> data)
DataDistribution<ParameterType>	RejectionSampling.learn(java.util.Collection<? extends ObservationType> data)

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

Modifier and Type	Method and Description
double	AbstractParticleFilter.computeEffectiveParticles(DataDistribution<ParameterType> particles)
double	ParticleFilter.computeEffectiveParticles(DataDistribution<ParameterType> particles) Computes the effective number of particles.
void	SamplingImportanceResamplingParticleFilter.update(DataDistribution<ParameterType> particles, ObservationType value)

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

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

Modifier and Type	Class and Description
class	DataCountTreeSetBinnedMapHistogram<ValueType extends java.lang.Comparable<? super ValueType>> The DataCountTreeSetBinnedMapHistogram class extends a DefaultDataDistribution by mapping values to user defined bins using a TreeSetBinner.
class	DefaultDataDistribution<KeyType> A default implementation of ScalarDataDistribution that uses a backing map.
static class	DefaultDataDistribution.PMF<KeyType> PMF of the DefaultDataDistribution
class	ScalarDataDistribution A Data Distribution that uses Doubles as its keys, making it a univariate distribution
static class	ScalarDataDistribution.CDF CDF of the ScalarDataDistribution, maintains the keys/domain in sorted order (TreeMap), so it's slower than it's peers.
static class	ScalarDataDistribution.PMF PMF of the ScalarDataDistribution

Methods in gov.sandia.cognition.statistics.distribution that return types with arguments of type DataDistribution

Modifier and Type	Method and Description
DistributionEstimator<KeyType,? extends DataDistribution<KeyType>>	DefaultDataDistribution.getEstimator()

Constructors in gov.sandia.cognition.statistics.distribution with parameters of type DataDistribution

Constructor and Description
DefaultDataDistribution(DataDistribution<? extends KeyType> other) Creates a new instance of DefaultDataDistribution
PMF(DataDistribution<KeyType> other) Copy constructor