- weakLearner - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost
-
The "weak learner" that must learn from the weighted input-output pairs
on each iteration.
- weakLearner - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.MultiCategoryAdaBoost
-
The "weak learner" that must learn from the weighted input-output pairs
on each iteration.
- WeibullDistribution - Class in gov.sandia.cognition.statistics.distribution
-
Describes a Weibull distribution, which is often used to describe the
mortality, lifespan, or size distribution of objects.
- WeibullDistribution() - Constructor for class gov.sandia.cognition.statistics.distribution.WeibullDistribution
-
Creates a new instance of WeibullDistribution
- WeibullDistribution(double, double) - Constructor for class gov.sandia.cognition.statistics.distribution.WeibullDistribution
-
Creates a new instance of WeibullDistribution
- WeibullDistribution(WeibullDistribution) - Constructor for class gov.sandia.cognition.statistics.distribution.WeibullDistribution
-
Copy constructor
- WeibullDistribution.CDF - Class in gov.sandia.cognition.statistics.distribution
-
CDF of the Weibull distribution
- WeibullDistribution.PDF - Class in gov.sandia.cognition.statistics.distribution
-
PDF of the Weibull distribution
- weight - Variable in class gov.sandia.cognition.learning.data.DefaultWeightedTargetEstimatePair
-
The weight.
- weight - Variable in class gov.sandia.cognition.learning.function.kernel.WeightedKernel
-
The weight on the kernel.
- weight - Variable in class gov.sandia.cognition.util.AbstractWeighted
-
The weight
- weight - Variable in class gov.sandia.cognition.util.DefaultWeightedPair
-
The weight for the pair.
- WeightComparator() - Constructor for class gov.sandia.cognition.util.DefaultWeightedValue.WeightComparator
-
Creates a new WeightComarator.
- Weighted - Interface in gov.sandia.cognition.util
-
The Weighted interface is to be used on objects that have some
weight component assigned to them.
- WeightedAdditiveEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>> - Class in gov.sandia.cognition.learning.algorithm.ensemble
-
An implementation of an ensemble that takes a weighted sum of the values
returned by its members.
- WeightedAdditiveEnsemble() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAdditiveEnsemble
-
Creates a new, empty of WeightedAdditiveEnsemble.
- WeightedAdditiveEnsemble(List<WeightedValue<MemberType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAdditiveEnsemble
-
Creates a new instance of WeightedAdditiveEnsemble.
- WeightedAdditiveEnsemble(List<WeightedValue<MemberType>>, double) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAdditiveEnsemble
-
Creates a new instance of WeightedAdditiveEnsemble.
- weightedAverage(Iterable<? extends WeightedValue<? extends Number>>) - Static method in class gov.sandia.cognition.math.WeightedNumberAverager
-
Computes the weighted average of the given data.
- WeightedAveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>> - Class in gov.sandia.cognition.learning.algorithm.ensemble
-
An implementation of an ensemble that takes the weighted average of its
members.
- WeightedAveragingEnsemble() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAveragingEnsemble
-
Creates a new, empty of WeightedAveragingEnsemble.
- WeightedAveragingEnsemble(List<WeightedValue<MemberType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.WeightedAveragingEnsemble
-
Creates a new instance of WeightedAveragingEnsemble.
- WeightedBinaryEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Boolean>> - Class in gov.sandia.cognition.learning.algorithm.ensemble
-
The WeightedBinaryEnsemble class implements an Ensemble of
BinaryCategorizer objects where each categorizer is assigned a
weight and the category is selected by choosing the one with the largest
sum of weights.
- WeightedBinaryEnsemble() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.WeightedBinaryEnsemble
-
Creates a new instance of WeightedBinaryEnsemble.
- WeightedBinaryEnsemble(List<WeightedValue<MemberType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.WeightedBinaryEnsemble
-
Creates a new instance of WeightedBinaryEnsemble.
- weightedData - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.AdaBoost
-
An array list containing the weighted version of the data.
- weightedData - Variable in class gov.sandia.cognition.learning.algorithm.ensemble.MultiCategoryAdaBoost
-
An array list containing the weighted version of the data.
- WeightedDenseMemoryGraph<NodeNameType> - Class in gov.sandia.cognition.graph
-
Implementation for a weighted graph type
- WeightedDenseMemoryGraph() - Constructor for class gov.sandia.cognition.graph.WeightedDenseMemoryGraph
-
Default constructor creates an empty graph
Execution: O(1)
- WeightedDenseMemoryGraph(int, int) - Constructor for class gov.sandia.cognition.graph.WeightedDenseMemoryGraph
-
Initialize an empty graph with a default size (for speed-ups later)
Execution: O(m + n) for reserving necessary space
- WeightedEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.DefaultDataDistribution.WeightedEstimator
-
Default constructor
- WeightedEuclideanDistanceMetric - Class in gov.sandia.cognition.learning.function.distance
-
A distance metric that weights each dimension of a vector differently before
computing Euclidean distance.
- WeightedEuclideanDistanceMetric() - Constructor for class gov.sandia.cognition.learning.function.distance.WeightedEuclideanDistanceMetric
-
Creates a new WeightedEuclideanDistanceMetric with no initial
weights.
- WeightedEuclideanDistanceMetric(Vector) - Constructor for class gov.sandia.cognition.learning.function.distance.WeightedEuclideanDistanceMetric
-
Creates a new WeightedEuclideanDistanceMetric with the given
weights.
- WeightedInputOutputPair<InputType,OutputType> - Interface in gov.sandia.cognition.learning.data
-
The WeightedInputOutputPair class implements an additional
weighting term on an InputOutputPair, typically used to inform
learning algorithms of the relative weight between examples.
- WeightedKernel<InputType> - Class in gov.sandia.cognition.learning.function.kernel
-
The WeightedKernel class implements a kernel that takes another
kernel, evaluates it, and then the result is rescaled by a given weight.
- WeightedKernel() - Constructor for class gov.sandia.cognition.learning.function.kernel.WeightedKernel
-
Creates a new instance of WeightedKernel with a default weight of 1.0
and a null kernel.
- WeightedKernel(double, Kernel<? super InputType>) - Constructor for class gov.sandia.cognition.learning.function.kernel.WeightedKernel
-
Creates a new instance of WeightedKernel from the given weight and
kernel.
- WeightedMaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.ExponentialDistribution.WeightedMaximumLikelihoodEstimator
-
Default constructor.
- WeightedMaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.LaplaceDistribution.WeightedMaximumLikelihoodEstimator
-
Default constructor
- WeightedMaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.LogNormalDistribution.WeightedMaximumLikelihoodEstimator
-
Default constructor
- WeightedMaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.WeightedMaximumLikelihoodEstimator
-
Default constructor.
- WeightedMaximumLikelihoodEstimator(double) - Constructor for class gov.sandia.cognition.statistics.distribution.MultivariateGaussian.WeightedMaximumLikelihoodEstimator
-
Creates a new instance of WeightedMaximumLikelihoodEstimator
- WeightedMaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.NegativeBinomialDistribution.WeightedMaximumLikelihoodEstimator
-
Default constructor
- WeightedMaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.PoissonDistribution.WeightedMaximumLikelihoodEstimator
-
Creates a new WeightedMaximumLikelihoodEstimator.
- WeightedMaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution.WeightedMaximumLikelihoodEstimator
-
Default constructor
- WeightedMaximumLikelihoodEstimator(double) - Constructor for class gov.sandia.cognition.statistics.distribution.StudentTDistribution.WeightedMaximumLikelihoodEstimator
-
Creates a new instance of WeightedMaximumLikelihoodEstimator
- WeightedMaximumLikelihoodEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.WeightedMaximumLikelihoodEstimator
-
Default constructor
- WeightedMaximumLikelihoodEstimator(double) - Constructor for class gov.sandia.cognition.statistics.distribution.UnivariateGaussian.WeightedMaximumLikelihoodEstimator
-
Creates a new instance of WeightedMaximumLikelihoodEstimator
- WeightedMeanLearner - Class in gov.sandia.cognition.learning.algorithm.baseline
-
The WeightedMeanLearner class implements a baseline learner that
computes the weighted mean output value.
- WeightedMeanLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.baseline.WeightedMeanLearner
-
Creates a new MeanLearner.
- WeightedMomentMatchingEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.BetaDistribution.WeightedMomentMatchingEstimator
-
Default constructor
- WeightedMomentMatchingEstimator() - Constructor for class gov.sandia.cognition.statistics.distribution.GammaDistribution.WeightedMomentMatchingEstimator
-
Default constructor
- WeightedMostFrequentLearner<OutputType> - Class in gov.sandia.cognition.learning.algorithm.baseline
-
The WeightedMostFrequentLearner class implements a baseline learning
algorithm that finds the most frequent output of a given dataset based on
the weights of the examples.
- WeightedMostFrequentLearner() - Constructor for class gov.sandia.cognition.learning.algorithm.baseline.WeightedMostFrequentLearner
-
Creates a new MostFrequentLearner.
- WeightedNumberAverager - Class in gov.sandia.cognition.math
-
Averages together given set of weighted values by adding up the weight times
the value and then dividing by the total weight.
- WeightedNumberAverager() - Constructor for class gov.sandia.cognition.math.WeightedNumberAverager
-
Creates a new WeightedNumberAverager
- WeightedPair<FirstType,SecondType> - Interface in gov.sandia.cognition.util
-
The WeightedPair interface defines an extension of a normal
Pair that includes an additional weight.
- WeightedRingAverager<RingType extends Ring<RingType>> - Class in gov.sandia.cognition.math
-
A type of Summarizer for Rings (Matrices, Vectors, ComplexNumbers).
- WeightedRingAverager() - Constructor for class gov.sandia.cognition.math.WeightedRingAverager
-
Creates a new instance of WeightedRingAverager
- WeightedTargetEstimatePair<TargetType,EstimateType> - Interface in gov.sandia.cognition.learning.data
-
Extends TargetEstimatePair with an additional weight field.
- WeightedValue<ValueType> - Interface in gov.sandia.cognition.util
-
Interface for a wrapper for a value that associates a weight with it.
- weightedValues - Variable in class gov.sandia.cognition.learning.algorithm.hmm.ParallelBaumWelchAlgorithm.DistributionEstimatorTask
-
Weighted values for the PDF estimator.
- WeightedVotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>> - Class in gov.sandia.cognition.learning.algorithm.ensemble
-
An ensemble of categorizers where each ensemble member is evaluated with the
given input to find the category to which its weighted votes are assigned.
- WeightedVotingCategorizerEnsemble() - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble
-
Creates a new instance of WeightedVotingCategorizerEnsemble.
- WeightedVotingCategorizerEnsemble(Set<CategoryType>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble
-
Creates a new instance of WeightedVotingCategorizerEnsemble.
- WeightedVotingCategorizerEnsemble(Set<CategoryType>, List<WeightedValue<MemberType>>) - Constructor for class gov.sandia.cognition.learning.algorithm.ensemble.WeightedVotingCategorizerEnsemble
-
Creates a new instance of WeightedVotingCategorizerEnsemble.
- weightPrior - Variable in class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression
-
Prior distribution of the weights, typically a zero-mean,
diagonal-variance distribution.
- weightRegularization - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
The regularization term for the linear weights.
- weights - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.FactorizationMachine
-
The weight vector (w) for each dimension.
- weights - Variable in class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer
-
The weight vector.
- weights - Variable in class gov.sandia.cognition.learning.function.distance.WeightedEuclideanDistanceMetric
-
The weights assigned to each dimension for the distance.
- weightsEnabled - Variable in class gov.sandia.cognition.learning.algorithm.factor.machine.AbstractFactorizationMachineLearner
-
True if the linear weight term is enabled.
- weightUpdate - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.Winnow
-
The amount of the weight update (alpha).
- weightUpdateInverse - Variable in class gov.sandia.cognition.learning.algorithm.perceptron.Winnow
-
The cached value of the inverse of weight update (commonly alpha or
1 + epsilon).
- weightVector - Variable in class gov.sandia.cognition.learning.function.scalar.LinearDiscriminant
-
Weight Vector to dot-product with the input
- WilcoxonSignedRankConfidence - Class in gov.sandia.cognition.statistics.method
-
This is a Wilcoxon Signed-Rank Sum test, which performs a pair-wise test
to determine if two datasets are different.
- WilcoxonSignedRankConfidence() - Constructor for class gov.sandia.cognition.statistics.method.WilcoxonSignedRankConfidence
-
Creates a new instance of WilcoxonSignedRankConfidence
- WilcoxonSignedRankConfidence.Statistic - Class in gov.sandia.cognition.statistics.method
-
ConfidenceStatistics associated with a Wilcoxon test
- WinnerTakeAllCategorizer<InputType,CategoryType> - Class in gov.sandia.cognition.learning.function.categorization
-
Adapts an evaluator that outputs a vector to be used as a categorizer.
- WinnerTakeAllCategorizer() - Constructor for class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer
-
Creates a new WinnerTakesAllCategorizer.
- WinnerTakeAllCategorizer(Evaluator<? super InputType, ? extends Vectorizable>, Set<CategoryType>) - Constructor for class gov.sandia.cognition.learning.function.categorization.WinnerTakeAllCategorizer
-
Creates a new WinnerTakesAllCategorizer.
- WinnerTakeAllCategorizer.Learner<InputType,CategoryType> - Class in gov.sandia.cognition.learning.function.categorization
-
A learner for the adapter.
- Winnow - Class in gov.sandia.cognition.learning.algorithm.perceptron
-
An implementation of the Winnow incremental learning algorithm.
- Winnow() - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.Winnow
-
Creates a new Winnow with default parameters.
- Winnow(double) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.Winnow
-
Creates a new Winnow with the given weight update and the
default demote to zero (false).
- Winnow(double, boolean) - Constructor for class gov.sandia.cognition.learning.algorithm.perceptron.Winnow
-
Creates a new Winnow with the given parameters.
- withCreator(ClusterCreator<MiniBatchCentroidCluster, Vector>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer.Builder
-
- WithinClusterDivergence<ClusterType extends Cluster<DataType>,DataType> - Interface in gov.sandia.cognition.learning.algorithm.clustering.divergence
-
Defines a function that computes the divergence of the elements in a cluster.
- WithinClusterDivergenceWrapper<ClusterType extends Cluster<DataType>,DataType> - Class in gov.sandia.cognition.learning.algorithm.clustering.divergence
-
- WithinClusterDivergenceWrapper(DivergenceFunction<? super ClusterType, ? super DataType>) - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.WithinClusterDivergenceWrapper
-
- withinInterval(double) - Method in class gov.sandia.cognition.statistics.method.ConfidenceInterval
-
Returns whether or not the value is within the specified interval
- withinInterval(Object) - Method in class gov.sandia.cognition.statistics.method.FieldConfidenceInterval
-
Determines whether the Field within the given Object is within the
specified interval
- withInitializer(FixedClusterInitializer<MiniBatchCentroidCluster, Vector>) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer.Builder
-
- WithinNormalizedCentroidClusterCosineDivergence<V extends Vectorizable> - Class in gov.sandia.cognition.learning.algorithm.clustering.divergence
-
This class calculates the total cosine divergence between all members of a
cluster and the cluster's centroid
- WithinNormalizedCentroidClusterCosineDivergence() - Constructor for class gov.sandia.cognition.learning.algorithm.clustering.divergence.WithinNormalizedCentroidClusterCosineDivergence
-
- withMaxIterations(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer.Builder
-
- withMinibatchSize(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer.Builder
-
- withNumClusters(int) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer.Builder
-
- withRandom(Random) - Method in class gov.sandia.cognition.learning.algorithm.clustering.MiniBatchKMeansClusterer.Builder
-
- WolfeConditions - Class in gov.sandia.cognition.learning.algorithm.minimization.line
-
The Wolfe conditions define a set of sufficient conditions for
"sufficient decrease" in inexact line search.
- WolfeConditions(InputOutputSlopeTriplet, double, double) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions
-
Creates a new instance of WolfeConditions
- WolfeConditions(WolfeConditions) - Constructor for class gov.sandia.cognition.learning.algorithm.minimization.line.WolfeConditions
-
Copy Constructor
- wordCounts - Variable in class gov.sandia.cognition.text.spelling.SimpleStatisticalSpellingCorrector
-
Maps known words to the number of times they've been seen.
- words - Variable in class gov.sandia.cognition.text.term.filter.DefaultStopList
-
The set of words in the stop list, all in lower-case.
- write(String, SemanticNetwork) - Static method in class gov.sandia.cognition.framework.io.CSVDefaultCognitiveModelLiteHandler
-
Writes a SemanticNetwork to the given file using the CSV format.
- write(File, SemanticNetwork) - Static method in class gov.sandia.cognition.framework.io.CSVDefaultCognitiveModelLiteHandler
-
Writes a SemanticNetwork to the given file using the CSV format.
- write(PrintWriter, SemanticNetwork) - Static method in class gov.sandia.cognition.framework.io.CSVDefaultCognitiveModelLiteHandler
-
Writes a SemanticNetwork to the given stream using the CSV format.
- write(OutputStream, Serializable) - Static method in class gov.sandia.cognition.io.XStreamSerializationHandler
-
Writes the given object to the given OutputStream
- write(Writer, Serializable) - Static method in class gov.sandia.cognition.io.XStreamSerializationHandler
-
Writes the given object to the given Writer
- write(Matrix) - Method in class gov.sandia.cognition.math.matrix.MatrixWriter
-
Writes the given matrix to the Java-based writer
- write(Vector) - Method in class gov.sandia.cognition.math.matrix.VectorWriter
-
Writes the given vector to the Java-based writer
- writeCollection(Iterable<? extends Vector>) - Method in class gov.sandia.cognition.math.matrix.VectorWriter
-
Writes the collection of vectors to the Java-based writer
- writeModelToFile(String, CognitiveModel) - Static method in class gov.sandia.cognition.framework.io.SerializedModelHandler
-
Writes the given CognitiveModel to the given file using Java object
serialization
- writeModelToFile(File, CognitiveModel) - Static method in class gov.sandia.cognition.framework.io.SerializedModelHandler
-
Writes the given CognitiveModel to the given file using Java object
serialization
- writeObject(OutputStream, SerializedType) - Method in class gov.sandia.cognition.io.serialization.AbstractTextSerializationHandler
-
- writeObject(OutputStream, SerializedType) - Method in class gov.sandia.cognition.io.serialization.GZIPSerializationHandler
-
- writeObject(OutputStream, Serializable) - Method in class gov.sandia.cognition.io.serialization.JavaDefaultBinarySerializationHandler
-
- writeObject(OutputStream, SerializedType) - Method in interface gov.sandia.cognition.io.serialization.StreamSerializationHandler
-
Writes an object to a given output stream.
- writeObject(Writer, SerializedType) - Method in interface gov.sandia.cognition.io.serialization.TextSerializationHandler
-
Writes an object to the given writer.
- writeObject(Writer, Serializable) - Method in class gov.sandia.cognition.io.serialization.XStreamSerializationHandler
-
- writeObjectToFile(File, Serializable) - Static method in class gov.sandia.cognition.io.ObjectSerializationHandler
-
Writes a Java serialized Object to the given file.
- writeState(CognitiveModelState) - Method in interface gov.sandia.cognition.framework.concurrent.ConcurrentCognitiveModule
-
Write out the model and module state changes resulting from a call to
evaluate
NOTE: output state was held temporarily for the sole purpose
of supporting concurrency of module evaluation; state is NEVER
retained locally across module update cycles
- writeState(CognitiveModelState) - Method in class gov.sandia.cognition.framework.learning.EvaluatorBasedCognitiveModule
-
Write out the model and module state changes resulting from a call to
evaluate
NOTE: output state was held temporarily for the sole purpose
of supporting concurrency of module evaluation; state is NEVER
retained locally across module update cycles
- writeState(CognitiveModelState) - Method in class gov.sandia.cognition.framework.learning.StatefulEvaluatorBasedCognitiveModule
-
Write out the model and module state changes resulting from a call to
evaluate
NOTE: output state was held temporarily for the sole purpose
of supporting concurrency of module evaluation; state is NEVER
retained locally across module update cycles
- writeState(CognitiveModelState) - Method in class gov.sandia.cognition.framework.lite.ArrayBasedPerceptionModule
-
Write out the model and module state changes resulting from a call to
evaluate
NOTE: output state was held temporarily for the sole purpose
of supporting concurrency of module evaluation; state is NEVER
retained locally across module update cycles
- writeToFile(String, SerializedType) - Method in class gov.sandia.cognition.io.serialization.AbstractFileSerializationHandler
-
- writeToFile(File, SerializedType) - Method in class gov.sandia.cognition.io.serialization.AbstractStreamSerializationHandler
-
- writeToFile(String, SerializedType) - Method in interface gov.sandia.cognition.io.serialization.FileSerializationHandler
-
Writes an object to a given file.
- writeToFile(File, SerializedType) - Method in interface gov.sandia.cognition.io.serialization.FileSerializationHandler
-
Reads an object from a given file.
- writeToFile(String, Serializable) - Static method in class gov.sandia.cognition.io.XStreamSerializationHandler
-
Writes the given object to the given file using XStream serialization.
- writeToFile(File, Serializable) - Static method in class gov.sandia.cognition.io.XStreamSerializationHandler
-
Writes the given object to the given file using XStream serialization.