Uses of Class
gov.sandia.cognition.util.DefaultWeightedValue

Packages that use DefaultWeightedValue

Package	Description
gov.sandia.cognition.learning.algorithm.hmm	Provides hidden Markov model (HMM) algorithms.
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.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.function.categorization	Provides functions that output a discrete set of categories.
gov.sandia.cognition.statistics.method	Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods.
gov.sandia.cognition.statistics.montecarlo	Provides Monte Carlo procedures for numerical integration and sampling.
gov.sandia.cognition.util	Provides general utility classes.

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

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

Modifier and Type	Field and Description
protected java.util.ArrayList<DefaultWeightedValue<ObservationType>>	ParallelBaumWelchAlgorithm.DistributionEstimatorTask.weightedValues Weighted values for the PDF estimator.

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

Methods in gov.sandia.cognition.learning.algorithm.perceptron that return DefaultWeightedValue

Modifier and Type	Method and Description
static DefaultWeightedValue<LinearBinaryCategorizer>	OnlineVotedPerceptron.getLastMember(WeightedBinaryEnsemble<Vectorizable,LinearBinaryCategorizer> ensemble) Gets the last member in the ensemble.

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

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

Modifier and Type	Method and Description
KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>	KernelAdatron.getResult()
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).

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

Modifier and Type	Method and Description
protected void	KernelAdatron.setLearned(KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>> result) Sets the object currently being result.
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 DefaultWeightedValue in gov.sandia.cognition.learning.algorithm.regression

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

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 DefaultWeightedValue

Modifier and Type	Method and Description
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).

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

Subclasses of DefaultWeightedValue in gov.sandia.cognition.learning.algorithm.svm

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

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

Modifier and Type	Field and Description
protected KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>	SuccessiveOverrelaxation.result The result categorizer.

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

Modifier and Type	Method and Description
KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>	SequentialMinimalOptimization.getResult()
KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>	SuccessiveOverrelaxation.getResult()

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

Modifier and Type	Method and Description
protected void	SuccessiveOverrelaxation.setResult(KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>> result) Sets the object currently being result.

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

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

Modifier and Type

Method and Description

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.

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

Subclasses of DefaultWeightedValue in gov.sandia.cognition.learning.data

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

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

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

Modifier and Type	Method and Description
DefaultWeightedValue<InputType>	DefaultKernelBinaryCategorizer.get(int i) Gets the i-th example.
DefaultWeightedValue<InputType>	DefaultKernelBinaryCategorizer.remove(int i) Removes the i-th example.

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

Constructor and Description
DefaultKernelBinaryCategorizer(Kernel<? super InputType> kernel, java.util.Collection<DefaultWeightedValue<InputType>> examples, double bias) Creates a new DefaultKernelBinaryCategorizer with the given parameters.

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

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

Modifier and Type	Method and Description
static <ValueType> java.util.ArrayList<DefaultWeightedValue<ValueType>>	ImportanceSampling.sample(ProbabilityDensityFunction<ValueType> importanceDistribution, Evaluator<ValueType,java.lang.Double> targetDistribution, java.util.Random random, int numSamples) Importance sampling is a technique for estimating properties of a target distribution, while only having samples generated from an "importance" distribution rather than the target distribution.

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

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

Modifier and Type	Method and Description
java.util.ArrayList<DefaultWeightedValue<DataType>>	ImportanceSampler.sample(Evaluator<? super DataType,java.lang.Double> targetFunction, java.util.Random random, int numSamples)

Uses of DefaultWeightedValue in gov.sandia.cognition.util

Methods in gov.sandia.cognition.util that return DefaultWeightedValue

Modifier and Type	Method and Description
DefaultWeightedValue<ValueType>	DefaultWeightedValue.clone() Creates a shallow copy of the WeightedValue.
static <ValueType> DefaultWeightedValue<ValueType>	DefaultWeightedValue.create(ValueType value, double weight) Convenience method to create a new WeightedValue.