Uses of Interface
gov.sandia.cognition.util.WeightedValue

Packages that use WeightedValue

Package	Description
gov.sandia.cognition.learning.algorithm.ensemble	Provides ensemble methods.
gov.sandia.cognition.learning.algorithm.hmm	Provides hidden Markov model (HMM) algorithms.
gov.sandia.cognition.learning.algorithm.svm	Provides implementations of Support Vector Machine (SVM) learning algorithms.
gov.sandia.cognition.learning.data	Provides data set utilities for learning.
gov.sandia.cognition.learning.function.categorization	Provides functions that output a discrete set of categories.
gov.sandia.cognition.learning.function.scalar	Provides functions that output real numbers.
gov.sandia.cognition.math	Provides classes for mathematical computation.
gov.sandia.cognition.statistics.bayesian	Provides algorithms for computing Bayesian estimates of parameters.
gov.sandia.cognition.statistics.distribution	Provides statistical distributions.
gov.sandia.cognition.statistics.montecarlo	Provides Monte Carlo procedures for numerical integration and sampling.
gov.sandia.cognition.util	Provides general utility classes.

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

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

Modifier and Type	Field and Description
protected java.util.List<WeightedValue<MemberType>>	AbstractWeightedEnsemble.members The members of the ensemble.
protected java.util.List<WeightedValue<MemberType>>	WeightedBinaryEnsemble.members The members of the ensemble.
protected java.util.List<WeightedValue<MemberType>>	WeightedVotingCategorizerEnsemble.members The members of the ensemble.

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

Modifier and Type	Method and Description
java.util.List<WeightedValue<MemberType>>	AbstractWeightedEnsemble.getMembers() Gets the members of the ensemble.
java.util.List<WeightedValue<MemberType>>	WeightedBinaryEnsemble.getMembers() Gets the members of the ensemble.
java.util.List<WeightedValue<MemberType>>	WeightedVotingCategorizerEnsemble.getMembers() Gets the members of the ensemble.

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

Modifier and Type	Method and Description
void	AbstractWeightedEnsemble.setMembers(java.util.List<WeightedValue<MemberType>> members) Sets the members of the ensemble.
void	WeightedBinaryEnsemble.setMembers(java.util.List<WeightedValue<MemberType>> members) Sets the members of the ensemble.
void	WeightedVotingCategorizerEnsemble.setMembers(java.util.List<WeightedValue<MemberType>> members) Sets the members of the ensemble.

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

Constructor and Description
AbstractWeightedEnsemble(java.util.List<WeightedValue<MemberType>> members) Creates a new instance of AbstractWeightedEnsemble.
WeightedAdditiveEnsemble(java.util.List<WeightedValue<MemberType>> members) Creates a new instance of WeightedAdditiveEnsemble.
WeightedAdditiveEnsemble(java.util.List<WeightedValue<MemberType>> members, double bias) Creates a new instance of WeightedAdditiveEnsemble.
WeightedAveragingEnsemble(java.util.List<WeightedValue<MemberType>> members) Creates a new instance of WeightedAveragingEnsemble.
WeightedBinaryEnsemble(java.util.List<WeightedValue<MemberType>> members) Creates a new instance of WeightedBinaryEnsemble.
WeightedVotingCategorizerEnsemble(java.util.Set<CategoryType> categories, java.util.List<WeightedValue<MemberType>> members) Creates a new instance of WeightedVotingCategorizerEnsemble.

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

Fields in gov.sandia.cognition.learning.algorithm.hmm with type parameters of type WeightedValue

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 WeightedValue

Modifier and Type	Method and Description
WeightedValue<java.lang.Integer>	ParallelHiddenMarkovModel.ViterbiTask.call()
protected WeightedValue<Vector>	HiddenMarkovModel.computeBackwardProbabilities(Vector beta, Vector b, double weight) Computes the backward probability recursion.
protected WeightedValue<Vector>	HiddenMarkovModel.computeForwardProbabilities(Vector alpha, Vector b, boolean normalize) Computes the recursive solution to the forward probabilities of the HMM.
protected WeightedValue<java.lang.Integer>	HiddenMarkovModel.findMostLikelyState(int destinationState, Vector delta) Finds the most-likely next state given the previous "delta" in the Viterbi algorithm.

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

Modifier and Type	Method and Description
protected java.util.ArrayList<WeightedValue<Vector>>	HiddenMarkovModel.computeBackwardProbabilities(java.util.ArrayList<Vector> b, java.util.ArrayList<WeightedValue<Vector>> alphas) Computes the backward-probabilities for the given observation likelihoods and the weights from the alphas.
protected java.util.ArrayList<WeightedValue<Vector>>	HiddenMarkovModel.computeForwardProbabilities(java.util.ArrayList<Vector> b, boolean normalize) Computes the forward probabilities for the given observation likelihood sequence.
BatchLearner<java.util.Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>>	AbstractBaumWelchAlgorithm.getDistributionLearner() Getter for distributionLearner

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

Modifier and Type	Method and Description
protected java.util.ArrayList<WeightedValue<Vector>>	HiddenMarkovModel.computeBackwardProbabilities(java.util.ArrayList<Vector> b, java.util.ArrayList<WeightedValue<Vector>> alphas) Computes the backward-probabilities for the given observation likelihoods and the weights from the alphas.
protected java.util.ArrayList<Vector>	HiddenMarkovModel.computeStateObservationLikelihood(java.util.ArrayList<WeightedValue<Vector>> alphas, java.util.ArrayList<WeightedValue<Vector>> betas, double scaleFactor) Computes the probabilities of the various states over time given the observation sequence.
protected java.util.ArrayList<Vector>	HiddenMarkovModel.computeStateObservationLikelihood(java.util.ArrayList<WeightedValue<Vector>> alphas, java.util.ArrayList<WeightedValue<Vector>> betas, double scaleFactor) Computes the probabilities of the various states over time given the observation sequence.
protected java.util.ArrayList<Vector>	ParallelHiddenMarkovModel.computeStateObservationLikelihood(java.util.ArrayList<WeightedValue<Vector>> alphas, java.util.ArrayList<WeightedValue<Vector>> betas, double scaleFactor)
protected java.util.ArrayList<Vector>	ParallelHiddenMarkovModel.computeStateObservationLikelihood(java.util.ArrayList<WeightedValue<Vector>> alphas, java.util.ArrayList<WeightedValue<Vector>> betas, double scaleFactor)
protected Matrix	HiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas, java.util.ArrayList<WeightedValue<Vector>> betas, java.util.ArrayList<Vector> b) Computes the stochastic transition-probability matrix from the given probabilities.
protected Matrix	HiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas, java.util.ArrayList<WeightedValue<Vector>> betas, java.util.ArrayList<Vector> b) Computes the stochastic transition-probability matrix from the given probabilities.
protected Matrix	ParallelHiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas, java.util.ArrayList<WeightedValue<Vector>> betas, java.util.ArrayList<Vector> b)
protected Matrix	ParallelHiddenMarkovModel.computeTransitions(java.util.ArrayList<WeightedValue<Vector>> alphas, java.util.ArrayList<WeightedValue<Vector>> betas, java.util.ArrayList<Vector> b)
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

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

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 WeightedValue in gov.sandia.cognition.learning.algorithm.svm

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

Modifier and Type	Class and Description
protected class	SuccessiveOverrelaxation.Entry The Entry class represents the data that the algorithm keeps about each training example.

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

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

Modifier and Type	Class and Description
class	DefaultWeightedValueDiscriminant<ValueType> An implementation of ValueDiscriminantPair that stores a double as the discriminant.

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

Modifier and Type	Method and Description
static <ValueType> DefaultWeightedValueDiscriminant<ValueType>	DefaultWeightedValueDiscriminant.create(WeightedValue<? extends ValueType> other) Convenience method for creating a new DefaultWeightedValueDiscriminant with a shallow copy of the given the given value and weight.

Constructors in gov.sandia.cognition.learning.data with parameters of type WeightedValue

Constructor and Description
DefaultWeightedValueDiscriminant(WeightedValue<? extends ValueType> other) Creates a new DefaultWeightedValueDiscriminant whose weight and value are taken from the given weighted value.

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

Classes in gov.sandia.cognition.learning.function.categorization with type parameters of type WeightedValue

Modifier and Type	Class and Description
class	KernelBinaryCategorizer<InputType,EntryType extends WeightedValue<? extends InputType>> The KernelBinaryCategorizer class implements a binary categorizer that uses a kernel to do its categorization.

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

Modifier and Type	Method and Description
WeightedValue<ProbabilityFunction<ObservationType>>	MaximumAPosterioriCategorizer.getCategory(CategoryType category) Gets the prior probability weight and conditional distribution for the given category.

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

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

Modifier and Type	Field and Description
protected java.util.Collection<? extends WeightedValue<? extends InputType>>	KernelScalarFunction.examples The list of weighted examples that are used for categorization.

Methods in gov.sandia.cognition.learning.function.scalar that return types with arguments of type WeightedValue

Modifier and Type	Method and Description
java.util.Collection<? extends WeightedValue<? extends InputType>>	KernelScalarFunction.getExamples() Gets the list of weighted examples that categorizer is using.

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

Modifier and Type	Method and Description
void	KernelScalarFunction.setExamples(java.util.Collection<? extends WeightedValue<? extends InputType>> examples) Sets the list of weighted examples that categorizer is using.

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

Constructor and Description
KernelScalarFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends WeightedValue<? extends InputType>> examples, double bias) Creates a new instance of KernelScalarFunction with the given kernel, weighted examples, and bias.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends WeightedValue<? extends InputType>> examples) Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel and weighted examples.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends WeightedValue<? extends InputType>> examples, double bias) Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel, weighted examples, and bias.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType> kernel, java.util.Collection<? extends WeightedValue<? extends InputType>> examples, double bias, double constantWeight, double constantValue) Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel, weighted examples, and bias.

Uses of WeightedValue in gov.sandia.cognition.math

Method parameters in gov.sandia.cognition.math with type arguments of type WeightedValue

Modifier and Type	Method and Description
static double	UnivariateStatisticsUtil.computeWeightedCentralMoment(java.lang.Iterable<? extends WeightedValue<? extends java.lang.Number>> data, double mean, int moment) Computes the desired biased estimate central moment of the given dataset.
static double	UnivariateStatisticsUtil.computeWeightedKurtosis(java.util.Collection<? extends WeightedValue<? extends java.lang.Number>> data) Computes the biased excess kurtosis of the given dataset.
static double	UnivariateStatisticsUtil.computeWeightedMean(java.lang.Iterable<? extends WeightedValue<? extends java.lang.Number>> data) Computes the arithmetic mean (average, expectation, first central moment) of a dataset.
static Pair<Vector,Matrix>	MultivariateStatisticsUtil.computeWeightedMeanAndCovariance(java.lang.Iterable<? extends WeightedValue<? extends Vectorizable>> data) Computes the mean and biased covariance Matrix of a multivariate weighted data set.
static Pair<java.lang.Double,java.lang.Double>	UnivariateStatisticsUtil.computeWeightedMeanAndVariance(java.lang.Iterable<? extends WeightedValue<? extends java.lang.Number>> data) Computes the mean and unbiased variance of a Collection of data using the one-pass approach.
java.lang.Double	WeightedNumberAverager.summarize(java.util.Collection<? extends WeightedValue<? extends java.lang.Number>> data)
RingType	WeightedRingAverager.summarize(java.util.Collection<? extends WeightedValue<RingType>> data)
static double	WeightedNumberAverager.weightedAverage(java.lang.Iterable<? extends WeightedValue<? extends java.lang.Number>> data) Computes the weighted average of the given data.

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

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

Modifier and Type	Method and Description
WeightedValue<ParameterType>	MetropolisHastingsAlgorithm.Updater.makeProposal(ParameterType location) Makes a proposal update given the current parameter set

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

Methods in gov.sandia.cognition.statistics.distribution with parameters of type WeightedValue

Modifier and Type	Method and Description
void	DefaultDataDistribution.WeightedEstimator.update(DefaultDataDistribution.PMF<KeyType> target, WeightedValue<? extends KeyType> data)

Method parameters in gov.sandia.cognition.statistics.distribution with type arguments of type WeightedValue

Modifier and Type	Method and Description
BetaDistribution	BetaDistribution.WeightedMomentMatchingEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data)
ExponentialDistribution	ExponentialDistribution.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data)
GammaDistribution	GammaDistribution.WeightedMomentMatchingEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data)
LaplaceDistribution	LaplaceDistribution.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data) Creates a new instance of LaplaceDistribution using a weighted Maximum Likelihood estimate based on the given data
LogNormalDistribution.PDF	LogNormalDistribution.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data)
StudentTDistribution.PDF	StudentTDistribution.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data) Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
UnivariateGaussian.PDF	UnivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data) Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
static StudentTDistribution.PDF	StudentTDistribution.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> data, double defaultVariance) Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
NegativeBinomialDistribution	NegativeBinomialDistribution.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Number>> data)
PoissonDistribution.PMF	PoissonDistribution.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Number>> data) Creates a new instance of PoissonDistribution using a weighted Maximum Likelihood estimate based on the given data.
static UnivariateGaussian.PDF	UnivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Number>> data, double defaultVariance) Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
MultivariateGaussian.PDF	MultivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends Vector>> data) Computes the Gaussian that estimates the maximum likelihood of generating the given set of weighted samples.
static MultivariateGaussian.PDF	MultivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(java.util.Collection<? extends WeightedValue<? extends Vector>> data, double defaultCovariance) Computes the Gaussian that estimates the maximum likelihood of generating the given set of weighted samples.

Uses of WeightedValue in gov.sandia.cognition.statistics.montecarlo

Method parameters in gov.sandia.cognition.statistics.montecarlo with type arguments of type WeightedValue

Modifier and Type	Method and Description
UnivariateGaussian.PDF	UnivariateMonteCarloIntegrator.getMean(java.util.List<? extends WeightedValue<? extends java.lang.Double>> samples)
Distribution<? extends OutputType>	MonteCarloIntegrator.getMean(java.util.List<? extends WeightedValue<? extends OutputType>> samples) Computes the Monte Carlo distribution of the given weighted samples.
MultivariateGaussian.PDF	MultivariateMonteCarloIntegrator.getMean(java.util.List<? extends WeightedValue<? extends Vector>> samples)
<SampleType> UnivariateGaussian.PDF	UnivariateMonteCarloIntegrator.integrate(java.util.List<? extends WeightedValue<? extends SampleType>> samples, Evaluator<? super SampleType,? extends java.lang.Double> expectationFunction)
<SampleType> Distribution<? extends OutputType>	MonteCarloIntegrator.integrate(java.util.List<? extends WeightedValue<? extends SampleType>> samples, Evaluator<? super SampleType,? extends OutputType> expectationFunction) Integrates the given function given weighted samples from another function.
<SampleType> MultivariateGaussian.PDF	MultivariateMonteCarloIntegrator.integrate(java.util.List<? extends WeightedValue<? extends SampleType>> samples, Evaluator<? super SampleType,? extends Vector> expectationFunction)

Uses of WeightedValue in gov.sandia.cognition.util

Classes in gov.sandia.cognition.util that implement WeightedValue

Modifier and Type	Class and Description
class	DefaultWeightedValue<ValueType> The WeightedValue class implements a simple generic container that holds a value and a weight assigned to the value.

Methods in gov.sandia.cognition.util with parameters of type WeightedValue

Modifier and Type	Method and Description
int	DefaultWeightedValue.WeightComparator.compare(WeightedValue<?> first, WeightedValue<?> second)
int	DefaultWeightedValue.WeightComparator.compare(WeightedValue<?> first, WeightedValue<?> second)

Constructors in gov.sandia.cognition.util with parameters of type WeightedValue

Constructor and Description
DefaultWeightedValue(WeightedValue<? extends ValueType> other) Creates a new shallow copy of a WeightedValue.