Uses of Class
gov.sandia.cognition.statistics.distribution.DefaultDataDistribution

Packages that use DefaultDataDistribution

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.algorithm.tree	Provides decision tree learning algorithms.
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.
gov.sandia.cognition.text.spelling	Provides classes for spelling.

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

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

Constructor and Description
DiscreteNaiveBayesCategorizer(int inputDimensionality, DefaultDataDistribution<CategoryType> priorProbabilities, java.util.Map<CategoryType,java.util.List<DefaultDataDistribution<InputType>>> conditionalProbabilities) Creates a new instance of DiscreteNaiveBayesCategorizer.

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

Constructor and Description
DiscreteNaiveBayesCategorizer(int inputDimensionality, DefaultDataDistribution<CategoryType> priorProbabilities, java.util.Map<CategoryType,java.util.List<DefaultDataDistribution<InputType>>> conditionalProbabilities) Creates a new instance of DiscreteNaiveBayesCategorizer.

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

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

Modifier and Type	Method and Description
DefaultDataDistribution<CategoryType>	VotingCategorizerEnsemble.evaluateAsVotes(InputType input) Evaluates the ensemble as votes.
DefaultDataDistribution<CategoryType>	WeightedVotingCategorizerEnsemble.evaluateAsVotes(InputType input) Evaluates the ensemble on the given input and returns the distribution of votes over the output categories.

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

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

Modifier and Type	Method and Description
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.

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

Modifier and Type	Method and Description
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.
abstract double	AbstractVectorThresholdMaximumGainLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts) Computes the gain of a given split.
abstract double	AbstractVectorThresholdMaximumGainLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts) Computes the gain of a given split.
abstract double	AbstractVectorThresholdMaximumGainLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts) Computes the gain of a given split.
double	VectorThresholdGiniImpurityLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts) Computes the split gain by computing the Gini impurity for the given split.
double	VectorThresholdGiniImpurityLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts) Computes the split gain by computing the Gini impurity for the given split.
double	VectorThresholdGiniImpurityLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts) Computes the split gain by computing the Gini impurity for the given split.
double	VectorThresholdHellingerDistanceLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts) Computes the split gain by computing the mean Hellinger distance for the given split.
double	VectorThresholdHellingerDistanceLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts) Computes the split gain by computing the mean Hellinger distance for the given split.
double	VectorThresholdHellingerDistanceLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts) Computes the split gain by computing the mean Hellinger distance for the given split.
double	VectorThresholdInformationGainLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts)
double	VectorThresholdInformationGainLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts)
double	VectorThresholdInformationGainLearner.computeSplitGain(DefaultDataDistribution<OutputType> baseCounts, DefaultDataDistribution<OutputType> positiveCounts, DefaultDataDistribution<OutputType> negativeCounts)
static <DataType> double	VectorThresholdGiniImpurityLearner.giniImpurity(DefaultDataDistribution<DataType> counts) Computes the Gini impurity of a histogram.

Uses of DefaultDataDistribution in gov.sandia.cognition.statistics

Constructors in gov.sandia.cognition.statistics with parameters of type DefaultDataDistribution

Constructor and Description
TransferEntropy(DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> oneTermPriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> oneTermPosteriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> twoTermPriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> twoTermPosteriorDistribution) Creates a new transfer entropy object using distributions.
TransferEntropy(DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> oneTermPriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> oneTermPosteriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> twoTermPriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> twoTermPosteriorDistribution) Creates a new transfer entropy object using distributions.
TransferEntropy(DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> oneTermPriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> oneTermPosteriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> twoTermPriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> twoTermPosteriorDistribution) Creates a new transfer entropy object using distributions.
TransferEntropy(DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> oneTermPriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> oneTermPosteriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> twoTermPriorDistribution, DefaultDataDistribution<TransferEntropy.TransferEntropyDistributionObject> twoTermPosteriorDistribution) Creates a new transfer entropy object using distributions.

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

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

Modifier and Type	Method and Description
DefaultDataDistribution<ParameterType>	AbstractMarkovChainMonteCarlo.getResult()

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

Modifier and Type	Method and Description
protected void	AbstractMarkovChainMonteCarlo.setResult(DefaultDataDistribution<ParameterType> result) Setter for result

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

Subclasses of DefaultDataDistribution in gov.sandia.cognition.statistics.distribution

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.
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 DefaultDataDistribution

Modifier and Type	Method and Description
DefaultDataDistribution<KeyType>	DefaultDataDistribution.clone()
DefaultDataDistribution<DataType>	DefaultDataDistribution.DefaultFactory.create()

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

Fields in gov.sandia.cognition.text.spelling declared as DefaultDataDistribution

Modifier and Type	Field and Description
protected DefaultDataDistribution<java.lang.String>	SimpleStatisticalSpellingCorrector.wordCounts Maps known words to the number of times they've been seen.

Methods in gov.sandia.cognition.text.spelling that return DefaultDataDistribution

Modifier and Type	Method and Description
DefaultDataDistribution<java.lang.String>	SimpleStatisticalSpellingCorrector.getWordCounts() Gets the dictionary of word counts.

Methods in gov.sandia.cognition.text.spelling with parameters of type DefaultDataDistribution

Modifier and Type	Method and Description
void	SimpleStatisticalSpellingCorrector.setWordCounts(DefaultDataDistribution<java.lang.String> wordCounts) Sets the dictionary of words counts.

Constructors in gov.sandia.cognition.text.spelling with parameters of type DefaultDataDistribution

Constructor and Description
SimpleStatisticalSpellingCorrector(DefaultDataDistribution<java.lang.String> wordCounts, char[] alphabet) Creates a new SimpleStatisticalSpellingCorrector.