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

Packages that use DefaultWeightedValue
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
protected  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
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
 KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>> KernelAdatron.getResult()
           
protected  LinkedHashMap<InputOutputPair<? extends InputType,Boolean>,DefaultWeightedValue<InputType>> KernelAdatron.getSupportsMap()
          Gets the mapping of examples to weight objects (support vectors).
protected  LinkedHashMap<InputOutputPair<? extends InputType,? extends 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
protected  void KernelAdatron.setLearned(KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>> result)
          Sets the object currently being result.
protected  void KernelPerceptron.setSupportsMap(LinkedHashMap<InputOutputPair<? extends InputType,? extends Boolean>,DefaultWeightedValue<InputType>> supportsMap)
          Gets the mapping of examples to weight objects (support vectors).
protected  void KernelAdatron.setSupportsMap(LinkedHashMap<InputOutputPair<? extends InputType,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
protected  LinkedHashMap<InputOutputPair<? extends InputType,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
protected  void KernelBasedIterativeRegression.setSupportsMap(LinkedHashMap<InputOutputPair<? extends InputType,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
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
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
 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
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
protected  DefaultPair<Double,Double> AbstractVectorThresholdMaximumGainLearner.computeBestGainAndThreshold(Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data, int dimension, DefaultDataDistribution<OutputType> baseCounts, 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
 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
 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
DefaultKernelBinaryCategorizer(Kernel<? super InputType> kernel, 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
static
<ValueType>
ArrayList<DefaultWeightedValue<ValueType>>
ImportanceSampling.sample(ProbabilityDensityFunction<ValueType> importanceDistribution, Evaluator<ValueType,Double> targetDistribution, 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
 ArrayList<DefaultWeightedValue<DataType>> ImportanceSampler.sample(Evaluator<? super DataType,Double> targetFunction, Random random, int numSamples)
           
 

Uses of DefaultWeightedValue in gov.sandia.cognition.util
 

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