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

Packages that use WeightedValue
gov.sandia.cognition.learning.algorithm.ensemble Provides ensmble 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
protected  List<WeightedValue<MemberType>> AbstractWeightedEnsemble.members
          The members of the ensemble.
protected  List<WeightedValue<MemberType>> WeightedBinaryEnsemble.members
          The members of the ensemble.
protected  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
 List<WeightedValue<MemberType>> AbstractWeightedEnsemble.getMembers()
          Gets the members of the ensemble.
 List<WeightedValue<MemberType>> WeightedBinaryEnsemble.getMembers()
          Gets the members of the ensemble.
 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
 void AbstractWeightedEnsemble.setMembers(List<WeightedValue<MemberType>> members)
          Sets the members of the ensemble.
 void WeightedBinaryEnsemble.setMembers(List<WeightedValue<MemberType>> members)
          Sets the members of the ensemble.
 void WeightedVotingCategorizerEnsemble.setMembers(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
AbstractWeightedEnsemble(List<WeightedValue<MemberType>> members)
          Creates a new instance of AbstractWeightedEnsemble.
WeightedAdditiveEnsemble(List<WeightedValue<MemberType>> members)
          Creates a new instance of WeightedAdditiveEnsemble.
WeightedAdditiveEnsemble(List<WeightedValue<MemberType>> members, double bias)
          Creates a new instance of WeightedAdditiveEnsemble.
WeightedAveragingEnsemble(List<WeightedValue<MemberType>> members)
          Creates a new instance of WeightedAveragingEnsemble.
WeightedBinaryEnsemble(List<WeightedValue<MemberType>> members)
          Creates a new instance of WeightedBinaryEnsemble.
WeightedVotingCategorizerEnsemble(Set<CategoryType> categories, 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
protected  BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> AbstractBaumWelchAlgorithm.distributionLearner
          Learner for the Distribution Functions of the HMM.
protected  BatchLearner<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
 WeightedValue<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<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
protected  ArrayList<WeightedValue<Vector>> HiddenMarkovModel.computeBackwardProbabilities(ArrayList<Vector> b, ArrayList<WeightedValue<Vector>> alphas)
          Computes the backward-probabilities for the given observation likelihoods and the weights from the alphas.
protected  ArrayList<WeightedValue<Vector>> HiddenMarkovModel.computeForwardProbabilities(ArrayList<Vector> b, boolean normalize)
          Computes the forward probabilities for the given observation likelihood sequence.
 BatchLearner<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
protected  ArrayList<WeightedValue<Vector>> HiddenMarkovModel.computeBackwardProbabilities(ArrayList<Vector> b, ArrayList<WeightedValue<Vector>> alphas)
          Computes the backward-probabilities for the given observation likelihoods and the weights from the alphas.
protected  ArrayList<Vector> HiddenMarkovModel.computeStateObservationLikelihood(ArrayList<WeightedValue<Vector>> alphas, ArrayList<WeightedValue<Vector>> betas, double scaleFactor)
          Computes the probabilities of the various states over time given the observation sequence.
protected  ArrayList<Vector> HiddenMarkovModel.computeStateObservationLikelihood(ArrayList<WeightedValue<Vector>> alphas, ArrayList<WeightedValue<Vector>> betas, double scaleFactor)
          Computes the probabilities of the various states over time given the observation sequence.
protected  ArrayList<Vector> ParallelHiddenMarkovModel.computeStateObservationLikelihood(ArrayList<WeightedValue<Vector>> alphas, ArrayList<WeightedValue<Vector>> betas, double scaleFactor)
           
protected  ArrayList<Vector> ParallelHiddenMarkovModel.computeStateObservationLikelihood(ArrayList<WeightedValue<Vector>> alphas, ArrayList<WeightedValue<Vector>> betas, double scaleFactor)
           
protected  Matrix HiddenMarkovModel.computeTransitions(ArrayList<WeightedValue<Vector>> alphas, ArrayList<WeightedValue<Vector>> betas, ArrayList<Vector> b)
          Computes the stochastic transition-probability matrix from the given probabilities.
protected  Matrix HiddenMarkovModel.computeTransitions(ArrayList<WeightedValue<Vector>> alphas, ArrayList<WeightedValue<Vector>> betas, ArrayList<Vector> b)
          Computes the stochastic transition-probability matrix from the given probabilities.
protected  Matrix ParallelHiddenMarkovModel.computeTransitions(ArrayList<WeightedValue<Vector>> alphas, ArrayList<WeightedValue<Vector>> betas, ArrayList<Vector> b)
           
protected  Matrix ParallelHiddenMarkovModel.computeTransitions(ArrayList<WeightedValue<Vector>> alphas, ArrayList<WeightedValue<Vector>> betas, ArrayList<Vector> b)
           
static
<ObservationType>
HiddenMarkovModel<ObservationType>
HiddenMarkovModel.createRandom(int numStates, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> learner, Collection<? extends ObservationType> data, 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<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
AbstractBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities)
          Creates a new instance of AbstractBaumWelchAlgorithm
BaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, boolean reestimateInitialProbabilities)
          Creates a new instance of BaumWelchAlgorithm
ParallelBaumWelchAlgorithm.DistributionEstimatorTask(Collection<? extends ObservationType> data, BatchLearner<Collection<? extends WeightedValue<? extends ObservationType>>,? extends ComputableDistribution<ObservationType>> distributionLearner, int index)
          Creates an instance of DistributionEstimatorTask
ParallelBaumWelchAlgorithm(HiddenMarkovModel<ObservationType> initialGuess, BatchLearner<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
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
 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
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
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
 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
 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
protected  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
 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
 void KernelScalarFunction.setExamples(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
KernelScalarFunction(Kernel<? super InputType> kernel, 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, Collection<? extends WeightedValue<? extends InputType>> examples)
          Creates a new instance of LocallyWeightedKernelScalarFunction with the given kernel and weighted examples.
LocallyWeightedKernelScalarFunction(Kernel<? super InputType> kernel, 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, 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
static double UnivariateStatisticsUtil.computeWeightedCentralMoment(Iterable<? extends WeightedValue<? extends Number>> data, double mean, int moment)
          Computes the desired biased estimate central moment of the given dataset.
static double UnivariateStatisticsUtil.computeWeightedKurtosis(Collection<? extends WeightedValue<? extends Number>> data)
          Computes the biased excess kurtosis of the given dataset.
static double UnivariateStatisticsUtil.computeWeightedMean(Iterable<? extends WeightedValue<? extends Number>> data)
          Computes the arithmetic mean (average, expectation, first central moment) of a dataset.
static Pair<Vector,Matrix> MultivariateStatisticsUtil.computeWeightedMeanAndCovariance(Iterable<? extends WeightedValue<? extends Vectorizable>> data)
          Computes the mean and biased covariance Matrix of a multivariate weighted data set.
static Pair<Double,Double> UnivariateStatisticsUtil.computeWeightedMeanAndVariance(Iterable<? extends WeightedValue<? extends Number>> data)
          Computes the mean and unbiased variance of a Collection of data using the one-pass approach.
 Double WeightedNumberAverager.summarize(Collection<? extends WeightedValue<? extends Number>> data)
           
 RingType WeightedRingAverager.summarize(Collection<? extends WeightedValue<RingType>> data)
           
static double WeightedNumberAverager.weightedAverage(Iterable<? extends WeightedValue<? extends 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
 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
 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
 BetaDistribution BetaDistribution.WeightedMomentMatchingEstimator.learn(Collection<? extends WeightedValue<? extends Double>> data)
           
 ExponentialDistribution ExponentialDistribution.WeightedMaximumLikelihoodEstimator.learn(Collection<? extends WeightedValue<? extends Double>> data)
           
 GammaDistribution GammaDistribution.WeightedMomentMatchingEstimator.learn(Collection<? extends WeightedValue<? extends Double>> data)
           
 LaplaceDistribution LaplaceDistribution.WeightedMaximumLikelihoodEstimator.learn(Collection<? extends WeightedValue<? extends Double>> data)
          Creates a new instance of LaplaceDistribution using a weighted Maximum Likelihood estimate based on the given data
 LogNormalDistribution.PDF LogNormalDistribution.WeightedMaximumLikelihoodEstimator.learn(Collection<? extends WeightedValue<? extends Double>> data)
           
 StudentTDistribution.PDF StudentTDistribution.WeightedMaximumLikelihoodEstimator.learn(Collection<? extends WeightedValue<? extends Double>> data)
          Creates a new instance of UnivariateGaussian using a weighted Maximum Likelihood estimate based on the given data
 UnivariateGaussian.PDF UnivariateGaussian.WeightedMaximumLikelihoodEstimator.learn(Collection<? extends WeightedValue<? extends 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(Collection<? extends WeightedValue<? extends 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(Collection<? extends WeightedValue<? extends Number>> data)
           
 PoissonDistribution.PMF PoissonDistribution.WeightedMaximumLikelihoodEstimator.learn(Collection<? extends WeightedValue<? extends 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(Collection<? extends WeightedValue<? extends 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(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(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
 UnivariateGaussian.PDF UnivariateMonteCarloIntegrator.getMean(List<? extends WeightedValue<? extends Double>> samples)
           
 Distribution<? extends OutputType> MonteCarloIntegrator.getMean(List<? extends WeightedValue<? extends OutputType>> samples)
          Computes the Monte Carlo distribution of the given weighted samples.
 MultivariateGaussian.PDF MultivariateMonteCarloIntegrator.getMean(List<? extends WeightedValue<? extends Vector>> samples)
           
<SampleType>
UnivariateGaussian.PDF
UnivariateMonteCarloIntegrator.integrate(List<? extends WeightedValue<? extends SampleType>> samples, Evaluator<? super SampleType,? extends Double> expectationFunction)
           
<SampleType>
Distribution<? extends OutputType>
MonteCarloIntegrator.integrate(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(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
 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
 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
DefaultWeightedValue(WeightedValue<? extends ValueType> other)
          Creates a new shallow copy of a WeightedValue.