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

Packages that use CloneableSerializable
gov.sandia.cognition.algorithm Provides general interfaces and implementations for algorithms. 
gov.sandia.cognition.algorithm.event Provides useful components for handling algorithm events. 
gov.sandia.cognition.collection Provides commonly useful collection implementations. 
gov.sandia.cognition.data.convert Provides utilities for doing data type conversion. 
gov.sandia.cognition.data.convert.number Provides utilities for doing data type conversion with numbers. 
gov.sandia.cognition.data.convert.vector Provides utilities for doing data type conversion with vectors. 
gov.sandia.cognition.evaluator Provides interfaces and classes to do with the Evaluator interface. 
gov.sandia.cognition.factory Provides interfaces and implementations of general factory objects. 
gov.sandia.cognition.framework Provides the interfaces for the Cognitive Framework. 
gov.sandia.cognition.framework.learning Provides a mechanism for putting learned objects into the Cognitive Framework. 
gov.sandia.cognition.framework.learning.converter Provides implementations of CogxelConverters. 
gov.sandia.cognition.framework.lite Provides a lightweight implementation of the Cognitive Framework. 
gov.sandia.cognition.io.serialization Provides general classes for object serialization. 
gov.sandia.cognition.learning.algorithm Provides general interfaces for learning algorithms. 
gov.sandia.cognition.learning.algorithm.annealing Provides the Simulated Annealing algorithm. 
gov.sandia.cognition.learning.algorithm.baseline Provides baseline (dummy) learning algorithms. 
gov.sandia.cognition.learning.algorithm.bayes Provides algorithms for computing Bayesian categorizers. 
gov.sandia.cognition.learning.algorithm.clustering Provides clustering algorithms. 
gov.sandia.cognition.learning.algorithm.clustering.cluster Provides implementations of different types of clusters. 
gov.sandia.cognition.learning.algorithm.clustering.divergence Provides divergence functions for use in clustering. 
gov.sandia.cognition.learning.algorithm.clustering.hierarchy Provides a hierarchy for clusters. 
gov.sandia.cognition.learning.algorithm.clustering.initializer Provides implementations of methods for selecting initial clusters. 
gov.sandia.cognition.learning.algorithm.confidence   
gov.sandia.cognition.learning.algorithm.ensemble Provides ensmble methods. 
gov.sandia.cognition.learning.algorithm.genetic Provides a genetic algorithm implementation. 
gov.sandia.cognition.learning.algorithm.genetic.reproducer Provides reproduction functions for use with a Genetic Algorithm. 
gov.sandia.cognition.learning.algorithm.gradient Provides gradient based learning algorithms. 
gov.sandia.cognition.learning.algorithm.hmm Provides hidden Markov model (HMM) algorithms. 
gov.sandia.cognition.learning.algorithm.minimization Provides minimization algorithms. 
gov.sandia.cognition.learning.algorithm.minimization.line Provides line (scalar) minimization algorithms. 
gov.sandia.cognition.learning.algorithm.minimization.line.interpolator Provides line (scalar) interpolation/extrapolation algorithms that fit an algebraic function to a (small) collection of data points. 
gov.sandia.cognition.learning.algorithm.nearest Provides algorithms for Nearest-Neighbor memory-based functions. 
gov.sandia.cognition.learning.algorithm.pca Provides implementations of Principle Components Analysis (PCA). 
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.root Provides algorithms for finding the roots, or zero crossings, of scalar functions. 
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.data.feature Provides data feature extractors. 
gov.sandia.cognition.learning.experiment Provides experiments for validating the performance of learning algorithms. 
gov.sandia.cognition.learning.function Provides function objects for learning algorithms. 
gov.sandia.cognition.learning.function.categorization Provides functions that output a discrete set of categories. 
gov.sandia.cognition.learning.function.cost Provides cost functions. 
gov.sandia.cognition.learning.function.distance Provides distance functions. 
gov.sandia.cognition.learning.function.kernel Provides kernel functions. 
gov.sandia.cognition.learning.function.regression Provides functions that output real numbers from some input data structure. 
gov.sandia.cognition.learning.function.scalar Provides functions that output real numbers. 
gov.sandia.cognition.learning.function.summarizer Provides classes for summarizing data. 
gov.sandia.cognition.learning.function.vector Provides functions that output vectors. 
gov.sandia.cognition.learning.parameter Provides utility classes for handling learning algorithm parameters. 
gov.sandia.cognition.learning.performance Provides performance measures. 
gov.sandia.cognition.learning.performance.categorization Provides performance measures for categorizers. 
gov.sandia.cognition.math Provides classes for mathematical computation. 
gov.sandia.cognition.math.geometry Provides classes and interfaces for computational geometry. 
gov.sandia.cognition.math.matrix Provides interfaces and classes for linear algebra. 
gov.sandia.cognition.math.matrix.decomposition Provides matrix decompositions. 
gov.sandia.cognition.math.matrix.mtj Provides a linear algebra package implementation wrapper using the Matrix Toolkits for Java (MTJ) library. 
gov.sandia.cognition.math.matrix.mtj.decomposition Provides matrix decomposition implementations using the Matrix Toolkits for Java (MTJ) library. 
gov.sandia.cognition.math.signals Provides mathematical signal processing methods. 
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.bayesian.conjugate Provides Bayesian estimation routines based on conjugate prior distribution of parameters of specific conditional distributions. 
gov.sandia.cognition.statistics.distribution Provides statistical distributions. 
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.text Provides classes for processing text. 
gov.sandia.cognition.text.convert Provides classes for converting objects to a textual representation. 
gov.sandia.cognition.text.document Provides representations for textual documents. 
gov.sandia.cognition.text.document.extractor Provides extractors for pulling textual documents out of files. 
gov.sandia.cognition.text.evaluation Provides methods for evaluating text processing algorithms. 
gov.sandia.cognition.text.relation Provides classes for relationships involving text. 
gov.sandia.cognition.text.spelling Provides classes for spelling. 
gov.sandia.cognition.text.term Provides term representing text content in documents. 
gov.sandia.cognition.text.term.filter Provides classes for filtering and transforming terms. 
gov.sandia.cognition.text.term.filter.stem Provides stemming algorithms for terms. 
gov.sandia.cognition.text.term.relation Provides relationships between terms. 
gov.sandia.cognition.text.term.vector Provides methods for handling documents represented as term vectors. 
gov.sandia.cognition.text.term.vector.weighter Provides term weighting algorithms. 
gov.sandia.cognition.text.term.vector.weighter.global Provides global term weighting algorithms. 
gov.sandia.cognition.text.term.vector.weighter.local Provides local term weighting algorithms. 
gov.sandia.cognition.text.term.vector.weighter.normalize Provides term weight normalization algorithms. 
gov.sandia.cognition.text.token Provides text tokenization algorithms. 
gov.sandia.cognition.text.topic Provides topic modeling algorithms. 
gov.sandia.cognition.time Provides classes for dealing with temporal data. 
gov.sandia.cognition.util Provides general utility classes. 
 

Uses of CloneableSerializable in gov.sandia.cognition.algorithm
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.algorithm
 interface ParallelAlgorithm
          Interface for algorithms that are parallelized using multithreading.
 

Classes in gov.sandia.cognition.algorithm that implement CloneableSerializable
 class AbstractAnytimeAlgorithm<ResultType>
          A partial implementation of the common functionality of an AnytimeAlgorithm.
 class AbstractIterativeAlgorithm
          The AbstractIterativeAlgorithm class implements a simple part of the IterativeAlgorithm interface that manages the listeners for the algorithm.
 class AbstractParallelAlgorithm
          Partial implementation of ParallelAlgorithm.
 class AnytimeAlgorithmWrapper<ResultType,InternalAlgorithm extends AnytimeAlgorithm<?>>
          Wraps an AnytimeAlgorithm.
 

Uses of CloneableSerializable in gov.sandia.cognition.algorithm.event
 

Classes in gov.sandia.cognition.algorithm.event that implement CloneableSerializable
 class AbstractIterativeAlgorithmListener
          An abstract implementation of the IterativeAlgorithmListener interface that provides default implementations of the event methods that do nothing.
 class IterationMeasurablePerformanceReporter
          An iterative algorithm listeners for MeasurablePerformanceAlgorithm objects that reports the performance of the algorithm at the end of each iteration.
 class IterationStartReporter
          An iterative algorithm listener that reports the start of each iteration to the given print stream.
 

Uses of CloneableSerializable in gov.sandia.cognition.collection
 

Classes in gov.sandia.cognition.collection that implement CloneableSerializable
 class AbstractMutableDoubleMap<KeyType>
          A partial implementation of a ScalarMap with a MutableDouble value
 class AbstractScalarMap<KeyType>
          Partial implementation of ScalarMap
protected  class AbstractScalarMap.MapWrapper
          Wrapper when using the asMap method
 class DefaultComparator<T extends Comparable<? super T>>
          A default comparator that just calls compare on the comparable generic it uses.
 class DefaultIndexer<ValueType>
          A default implementation of the Indexer interface that simply maps objects to a range from 0 to n-1 in the order they are given.
 class DynamicArrayMap<ValueType>
          A DynamicArrayList is a class that implements a map from an integer to an Object type on top of an expanding array.
 class FiniteCapacityBuffer<DataType>
          A finite capacity buffer backed by a fixed array.
 class IntegerSpan
          An Iterable that starts at a given Integer and goes until another, inclusive.
 class NumberComparator
          Compares two Numbers (base class of Double, Integer, etc.) for sorting.
 

Uses of CloneableSerializable in gov.sandia.cognition.data.convert
 

Classes in gov.sandia.cognition.data.convert that implement CloneableSerializable
 class AbstractDataConverter<InputType,OutputType>
          Abstract implementation of DataConverter interface.
 class AbstractReverseCachedDataConverter<InputType,OutputType,ReverseConverterType extends DataConverter<? super OutputType,? extends InputType>>
          Abstract implementation of ReversibleDataConverter that caches the reverse converter.
 class AbstractReversibleDataConverter<InputType,OutputType>
          Abstract implementation of sthe ReversibleDataConverter interface.
 class IdentityDataConverter<DataType>
          A pass-through converter that just returns the given value.
 class ObjectToStringConverter
          Converts an Object to a String using the toString method.
 

Uses of CloneableSerializable in gov.sandia.cognition.data.convert.number
 

Classes in gov.sandia.cognition.data.convert.number that implement CloneableSerializable
 class DefaultBooleanToNumberConverter
          Converts a Boolean to a Number by using predefined values for true, false, and (optionally) null.
 class DefaultBooleanToNumberConverter.Reverse
          The reverse converter for the DefaultBooleanToNumberConverter.
 class StringToDoubleConverter
          Converts a String to a Double using the Double.valueOf method.
 class StringToIntegerConverter
          Converts a String to a Integer using the Integer.valueOf method.
 

Uses of CloneableSerializable in gov.sandia.cognition.data.convert.vector
 

Classes in gov.sandia.cognition.data.convert.vector that implement CloneableSerializable
 class AbstractToVectorEncoder<InputType>
          An abstract implementation of the DataToVectorEncoder interface.
 class NumberConverterToVectorAdapter<InputType>
          Adapts a DataConverter that outputs a number to be a VectorEncoder.
 class NumberToVectorEncoder
          An encoder that encodes a number as an element of a Vector.
 class UniqueBooleanVectorEncoder<InputType>
          An encoder for arbitrary objects that encodes an equality comparison between a given input and a set of unique values.
 

Uses of CloneableSerializable in gov.sandia.cognition.evaluator
 

Classes in gov.sandia.cognition.evaluator with type parameters of type CloneableSerializable
 class AbstractStatefulEvaluator<InputType,OutputType,StateType extends CloneableSerializable>
          The AbstractStatefulEvalutor class is an abstract implementation of the StatefulEvalutor interface.
 interface StatefulEvaluator<InputType,OutputType,StateType extends CloneableSerializable>
          The StatefulEvaluator interface defines the functionality of an Evaluator that maintains an internal state.
 

Classes in gov.sandia.cognition.evaluator that implement CloneableSerializable
 class AbstractStatefulEvaluator<InputType,OutputType,StateType extends CloneableSerializable>
          The AbstractStatefulEvalutor class is an abstract implementation of the StatefulEvalutor interface.
 class CompositeEvaluatorList<InputType,OutputType>
          Implements the composition of a list of evaluators.
 class CompositeEvaluatorPair<InputType,IntermediateType,OutputType>
          Implements a composition of two evaluators.
 class CompositeEvaluatorTriple<InputType,FirstIntermediateType,SecondIntermediateType,OutputType>
          Implements a composition of three evaluators.
 class ForwardReverseEvaluatorPair<InputType,OutputType,ForwardType extends Evaluator<? super InputType,? extends OutputType>,ReverseType extends Evaluator<? super OutputType,? extends InputType>>
          Represents a both a (normal) forward evaluator and its reverse as a pair.
 class IdentityEvaluator<DataType>
          An identity function that returns its input as its output.
 class ValueClamper<DataType extends Comparable<DataType>>
          An evaluator that clamps a number between minimum and maximum values.
 class ValueMapper<InputType,OutputType>
          An evaluator that uses a map to map input values to their appropriate output values.
 

Uses of CloneableSerializable in gov.sandia.cognition.factory
 

Classes in gov.sandia.cognition.factory with type parameters of type CloneableSerializable
 class PrototypeFactory<CreatedType extends CloneableSerializable>
          The PrototypeFactory class implements a Factory that uses a prototype object to create new objects from by cloning it.
 

Classes in gov.sandia.cognition.factory that implement CloneableSerializable
 class DefaultFactory<CreatedType>
          The DefaultFactory class is a default implementation of the Factory interface that takes a class as its parameter and uses the default constructor of the class, called through newInstance(), to create new objects of that class.
 class PrototypeFactory<CreatedType extends CloneableSerializable>
          The PrototypeFactory class implements a Factory that uses a prototype object to create new objects from by cloning it.
 

Fields in gov.sandia.cognition.factory declared as CloneableSerializable
protected  CreatedType PrototypeFactory.prototype
          The prototype to create clones from.
 

Methods in gov.sandia.cognition.factory with type parameters of type CloneableSerializable
static
<T extends CloneableSerializable>
PrototypeFactory<T>
PrototypeFactory.createFactory(T prototype)
          A convenience method for creating prototype factories.
 

Uses of CloneableSerializable in gov.sandia.cognition.framework
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.framework
 interface ActivatableCogxel
          ActivatableCogxel is an interface which defines a cogxel which is activatable.
 interface CognitiveModelState
          The CognitiveModelState interface defines the general functionality required of an object that represents the state of a CognitiveModel.
 interface CognitiveModuleSettings
          The CogntiviteModuleSettings class defines the functionality required for the settings of a CognitiveModule.
 interface CognitiveModuleState
          The CognitiveModuleState defines the interface for the state of a CognitiveModule.
 interface Cogxel
          The interface for the fundamental unit of operation inside a CognitiveModel.
 interface CogxelState
          Keeps a collection of Cogxels and some accessor methods.
 interface ShareableCognitiveModuleSettings
          The ShareableCognitiveModuleSettings is an interface for module settings that can be shared between two instances of a CognitiveModule.
 

Classes in gov.sandia.cognition.framework that implement CloneableSerializable
 class DefaultCogxel
          The DefaultCogxel provides a default implementation of the Cogxel interface that just stores the necessary peices of information: the SemanticIdentifier and its activation.
 class DefaultSemanticIdentifierMap
          The DefaultSemanticIdentifierMap is an implementation of SemanticIdentifierMap that is backed by a HashMap (a hashtable).
 

Uses of CloneableSerializable in gov.sandia.cognition.framework.learning
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.framework.learning
 interface CognitiveModuleFactoryLearner
          The CognitiveModuleFactoryLearner is an interface defining the functionality of an Object that can learn a CognitiveModuleFactory from a collection of input data.
 

Classes in gov.sandia.cognition.framework.learning that implement CloneableSerializable
 class EvaluatorBasedCognitiveModuleFactory<InputType,OutputType>
          The EvaluatorBasedCognitiveModuleFactory class implements a factory for the EvaluatorBasedCognitiveModule.
 class EvaluatorBasedCognitiveModuleFactoryLearner<InputType,OutputType,LearningDataType>
          The EvaluatorBasedCognitiveModuleFactoryLearner class implements a CognitiveModuleFactoryLearner for the EvaluatorBasedCognitiveModuleFactory.
 class EvaluatorBasedCognitiveModuleSettings<InputType,OutputType>
          The EvaluatorBasedCognitiveModuleSettings class implements the settings for the EvaluatorBasedCognitiveModule.
 

Methods in gov.sandia.cognition.framework.learning that return types with arguments of type CloneableSerializable
 StatefulEvaluator<InputType,OutputType,CloneableSerializable> StatefulEvaluatorBasedCognitiveModule.getStatefulEvaluator()
          Gets the StatefulEvaluator used by the module.
 

Uses of CloneableSerializable in gov.sandia.cognition.framework.learning.converter
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.framework.learning.converter
 interface CogxelConverter<DataType>
          The CogxelConverter interface defines the functionality required for an object to act as a converter from some DataType to and from a CogxelState object.
 

Classes in gov.sandia.cognition.framework.learning.converter that implement CloneableSerializable
 class AbstractCogxelConverter<DataType>
          Partial implementation of CogxelConverter
 class AbstractCogxelPairConverter<FirstType,SecondType,PairType extends Pair<FirstType,SecondType>>
          Partial implementation of CogxelConverters based on a Pair
 class CogxelBooleanConverter
          Implements a CogxelConverter that encodes booleans as positive and negative values (+1/-1).
 class CogxelDoubleConverter
          The CogxelDoubleConverter class converts a Cogxel to and from a double value by using its activation.
 class CogxelInputOutputPairConverter<InputType,OutputType>
          The InputOutputPairCogxelConverter class implements a converter to and from Cogxels to InputOutputPair objects.
 class CogxelMatrixConverter
          The CogxelVectorConverter implements a converter to convert Cogxels to and from Matrix objects.
 class CogxelTargetEstimatePairConverter<TargetType,EstimateType>
          CogxelConverter based on a TargetEstimatePair.
 class CogxelVectorCollectionConverter
          Converts a Collection of Vectors to and from a CogxelState
 class CogxelVectorConverter
          The CogxelVectorConverter implements a converter to convert Cogxels to and from Vector objects.
 class CogxelWeightedInputOutputPairConverter<InputType,OutputType>
          A CogxelConverter for creating WeightedInputOutputPairs
 

Uses of CloneableSerializable in gov.sandia.cognition.framework.lite
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.framework.lite
 interface MutablePatternRecognizerLite
          The MutablePatternRecognizerLite interface extends the PatternRecognizerLite interface to add methods for changing the recognizer dynamically.
 interface PatternRecognizerLite
          The PatternRecognizerLite interface defines the functionality needed by a pattern recognizer that is to be used by a SemanticMemoryLite.
 

Classes in gov.sandia.cognition.framework.lite that implement CloneableSerializable
 class BooleanActivatableCogxel
          BooleanActivatableCogxel extends the DefaultCogxel class to add an "activated" flag.
 class CognitiveModelLiteState
          The CognitiveModelLiteState class implements a CognitiveModelState object for the CognitiveModelLite.
 class CognitiveModuleStateWrapper
          The CognitiveModuleStateWrapper wraps some other object as a CognitiveModuleState object.
 class CogxelStateLite
          The CogxelStateLite class implements a CogxelState to be used with the CognitiveModelLite.
 class SharedSemanticMemoryLiteFactory
          The SharedSemanticMemoryLiteFactory implements a CognitiveModuleFactory for SharedSemanticMemoryLite modules.
 class SharedSemanticMemoryLiteSettings
          The SharedSemanticMemoryLiteSettings class implements the settings for the SharedSemanticMemoryLite module.
 class SimplePatternRecognizer
          The SimplePatternRecognizer class implements a simple version of the PatternRecognizerLite interface.
 class SimplePatternRecognizerState
          The SimplePatternRecognizerState class implements a CognitiveModuleState for the SimplePatternRecognizer.
 class VectorBasedCognitiveModelInput
          Vector-based cognitive model input used by VectorBasedPerceptionModule.
 

Methods in gov.sandia.cognition.framework.lite that return CloneableSerializable
 CloneableSerializable CognitiveModuleStateWrapper.getInternalState()
          Gets the internal state object.
 

Methods in gov.sandia.cognition.framework.lite with parameters of type CloneableSerializable
 void CognitiveModuleStateWrapper.setInternalState(CloneableSerializable internalState)
          Sets the internal state object.
 

Constructors in gov.sandia.cognition.framework.lite with parameters of type CloneableSerializable
CognitiveModuleStateWrapper(CloneableSerializable internalState)
          Creates a new instance of CognitiveModuleStateWrapper.
 

Uses of CloneableSerializable in gov.sandia.cognition.io.serialization
 

Classes in gov.sandia.cognition.io.serialization that implement CloneableSerializable
 class AbstractFileSerializationHandler<SerializedType>
          An abstract implementation of FileSerializationHandler.
 class AbstractStreamSerializationHandler<SerializedType>
          An abstract implementation of StreamSerializationHandler.
 class AbstractTextSerializationHandler<SerializedType>
          An abstract implementation of the TextSerializationHandler interface.
 class GZIPSerializationHandler<SerializedType>
          Implements a serialization handler that uses the GZip compression algorithm on the output.
 class JavaDefaultBinarySerializationHandler
          A serialization handler based on basic Java binary serialization.
 class XStreamSerializationHandler
          A serialization
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm
 interface AnytimeBatchLearner<DataType,ResultType>
          A batch learner that is also and Anytime algorithm.
 interface BatchAndIncrementalLearner<DataType,ResultType>
          Interface for an algorithm that is both a batch and incremental learner.
 interface BatchCostMinimizationLearner<CostParametersType,ResultType>
          The BatchCostMinimizationLearner interface defines the functionality of a cost-minimization learning algorithm should follow.
 interface BatchLearner<DataType,ResultType>
          The BatchLearner interface defines the general functionality of an object that is the implementation of a data-driven, batch machine learning algorithm.
 interface IncrementalLearner<DataType,ResultType>
          The IncrementalLearner interface defines the general functionality of an object that is the implementation of a data-driven, incremental machine learning algorithm.
 interface SupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
          Interface for a class that is a supervised learning algorithm that can be used both batch and incremental contexts.
 interface SupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
          The BatchSupervisedLearner interface is an extension of the BatchLearner interface that contains the typical generic definition conventions for a batch, supervised learning algorithm.
 interface SupervisedIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
          Interface for supervised incremental learning algorithms.
 

Classes in gov.sandia.cognition.learning.algorithm that implement CloneableSerializable
 class AbstractAnytimeBatchLearner<DataType,ResultType>
          The AbstractAnytimeBatchLearner abstract class implements a standard method for conforming to the BatchLearner and AnytimeLearner (IterativeAlgorithm and StoppableAlgorithm) interfaces.
 class AbstractAnytimeSupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
          The AbstractAnytimeSupervisedBatchLearner abstract class extends the AbstractAnytimeBatchLearner to implement the SupervisedBatchLearner interface.
 class AbstractBatchAndIncrementalLearner<DataType,ResultType>
          An abstract class that has both batch learning ability as well as online learning ability by taking a Collection of input data.
 class AbstractBatchLearnerContainer<LearnerType extends BatchLearner<?,?>>
          An abstract class for objects that contain a batch learning algorithm.
 class AbstractSupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
          An abstract implementation of the batch and incremental learning for an incremental supervised learner.
 class CompositeBatchLearnerPair<InputType,IntermediateType,OutputType>
          Composes together a pair of batch (typically unsupervised) learners.
 class InputOutputTransformedBatchLearner<InputType,TransformedInputType,TransformedOutputType,OutputType>
          An adapter class for performing supervised learning from data where both the input and output have to be transformed before they are passed to the learning algorithm.
 class SequencePredictionLearner<DataType,LearnedType>
          A wrapper learner that converts an unlabeled sequence of data into a sequence of prediction data using a fixed prediction horizon.
 class TimeSeriesPredictionLearner<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>
          A learner used to predict the future of a sequence of data by wrapping another learner and created a future-aligned data set.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.annealing
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.annealing
 interface Perturber<PerturbedType>
          The Perturber interface defines the functionality of an object that can take an object and perturb it, returning the perturbed value.
 

Classes in gov.sandia.cognition.learning.algorithm.annealing that implement CloneableSerializable
 class SimulatedAnnealer<CostParametersType,AnnealedType>
          The SimulatedAnnealer class implements the simulated annealing algorithm using the provided cost function and perturbation function.
 class VectorizablePerturber
          The VectorizablePerturber implements a Perturber for Vectorizable objects.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.baseline
 

Classes in gov.sandia.cognition.learning.algorithm.baseline that implement CloneableSerializable
 class ConstantLearner<ValueType>
          A learner that always returns the same value as the result.
 class IdentityLearner<ValueType>
          A batch learner implementation that just returns its inputs, creating an identity function.
 class MeanLearner
          The MeanLearner class implements a baseline learner that computes the mean of a given set of values.
 class MostFrequentLearner<OutputType>
          The MostFrequentLearner class implements a baseline learner that computes the most frequent output value.
 class WeightedMeanLearner
          The WeightedMeanLearner class implements a baseline learner that computes the weighted mean output value.
 class WeightedMostFrequentLearner<OutputType>
          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.
 

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

Classes in gov.sandia.cognition.learning.algorithm.bayes that implement CloneableSerializable
 class DiscreteNaiveBayesCategorizer<InputType,CategoryType>
          Implementation of a Naive Bayes Classifier for Discrete Data.
static class DiscreteNaiveBayesCategorizer.Learner<InputType,CategoryType>
          Learner for a DiscreteNaiveBayesCategorizer.
 class VectorNaiveBayesCategorizer<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>
          A naive Bayesian categorizer that takes an input vector and applies an independent scalar probability density function to each one.
static class VectorNaiveBayesCategorizer.BatchGaussianLearner<CategoryType>
          A supervised batch distributionLearner for a vector Naive Bayes categorizer that fits a Gaussian.
static class VectorNaiveBayesCategorizer.Learner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>
          A supervised batch distributionLearner for a vector Naive Bayes categorizer.
static class VectorNaiveBayesCategorizer.OnlineLearner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>
          An online (incremental) distributionLearner for the Naive Bayes categorizer that uses an incremental distribution learner for the distribution representing each dimension for each category.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.clustering
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.clustering
 interface BatchClusterer<DataType,ClusterType extends Cluster<DataType>>
          The BatchClusterer interface defines the functionality of a batch clustering algorithm.
 

Classes in gov.sandia.cognition.learning.algorithm.clustering that implement CloneableSerializable
 class AffinityPropagation<DataType>
          The AffinityPropagation algorithm requires three parameters: a divergence function, a value to use for self-divergence, and a damping factor (called lambda in the paper; 0.5 is the default).
 class AgglomerativeClusterer<DataType,ClusterType extends Cluster<DataType>>
          The AgglomerativeClusterer implements an agglomerative clustering algorithm, which is a type of hierarchical clustering algorithm.
static class AgglomerativeClusterer.HierarchyNode<DataType,ClusterType extends Cluster<DataType>>
          Holds the hierarchy information for the agglomerative clusterer.
 class DirichletProcessClustering
          Clustering algorithm that wraps Dirichlet Process Mixture Model.
 class KMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
          The KMeansClusterer class implements the standard k-means (k-centroids) clustering algorithm.
 class KMeansClustererWithRemoval<DataType,ClusterType extends Cluster<DataType>>
          Creates a k-means clustering algorithm that removes clusters that do not have sufficient membership to pass a simple statistical significance test.
 class KMeansFactory
          Creates a parallelized version of the k-means clustering algorithm for the typical use: clustering vector data with a Euclidean distance metric.
 class OptimizedKMeansClusterer<DataType>
          This class implements an optimized version of the k-means algorithm that makes use of the triangle inequality to compute the same answer as k-means while using less distance calculations.
 class ParallelizedKMeansClusterer<DataType,ClusterType extends Cluster<DataType>>
          This is a parallel implementation of the k-means clustering algorithm.
 class PartitionalClusterer<DataType,ClusterType extends Cluster<DataType>>
          The PartitionClusterer implements a partitional clustering algorithm, which is a type of hierarchical clustering algorithm.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.clustering.cluster
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.clustering.cluster
 interface Cluster<ClusterType>
          The Cluster interface defines the general functionality of a cluster, which is just the ability to get the members of the cluster.
 interface ClusterCreator<ClusterType extends Cluster<DataType>,DataType>
          The ClusterCreator defines the functionality of a class that can create a new cluster from a given collection of members of that cluster.
 interface IncrementalClusterCreator<ClusterType extends Cluster<DataType>,DataType>
          An interface for a ClusterCreator that can incrementally add and remove members from a cluster.
 

Classes in gov.sandia.cognition.learning.algorithm.clustering.cluster that implement CloneableSerializable
 class CentroidCluster<ClusterType>
          The CentroidCluster class extends the default cluster to contain a central element.
 class DefaultCluster<ClusterType>
          The DefaultCluster class implements a default cluster which contains a list of members in an ArrayList along with an index that identifies the cluster.
 class DefaultClusterCreator<DataType>
          The DefaultClusterCreator class implements a default ClusterCreator that just creates a DefaultCluster from the given list of members.
 class DefaultIncrementalClusterCreator<DataType>
          A default implementation of the IncrementalClusterCreator interface that just creates a cluster as having a collection of members.
 class GaussianCluster
          The GaussianCluster class implements a cluster of Vector objects that has a MultivariateGaussian object representing the cluster.
 class GaussianClusterCreator
          The GaussianClusterCreator class implements a ClusterCreator for creating GaussianClusters by fitting a MultivariateGaussian to the given set of example vectors.
 class MedoidClusterCreator<DataType>
          The MedoidClusterCreator class creates a CentroidCluster at the sample that minimizes the sum of the divergence to the objects assigned to the cluster.
 class VectorMeanCentroidClusterCreator
          The VectorMeanCentroidClusterCreator class implements a cluster creator for centroid clusters where the centroid is the mean of the vectors that are members of the cluster.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.clustering.divergence
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.clustering.divergence
 interface ClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
          The ClusterDivergenceFunction interface defines a function that computes the divergence between a cluster and some other object.
 interface ClusterToClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
          The ClusterToClusterDivergenceFunction defines a DivergenceFunction between two clusters of the same data type.
 

Classes in gov.sandia.cognition.learning.algorithm.clustering.divergence that implement CloneableSerializable
 class AbstractClusterToClusterDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
          The AbstractClusterToClusterDivergenceFunction class is an abstract class that helps out implementations of ClusterToClusterDivergenceFunction implementations by holding a DivergenceFunction between elements of a cluster.
 class CentroidClusterDivergenceFunction<DataType>
          The CentroidClusterDivergenceFunction class implements a divergence function between a cluster and an object by computing the divergence between the center of the cluster and the object.
 class ClusterCentroidDivergenceFunction<DataType>
          The ClusterCentroidDivergenceFunction class implements the distance between two clusters by computing the distance between the cluster's centroid.
 class ClusterCompleteLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
          The ClusterCompleteLinkDivergenceFunction class implements the complete linkage distance metric between two clusters.
 class ClusterMeanLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
          The ClusterMeanLinkDivergenceFunction class implements the mean linkage distance metric between two clusters.
 class ClusterSingleLinkDivergenceFunction<ClusterType extends Cluster<DataType>,DataType>
          The ClusterSingleLinkDivergenceFunction class implements the complete linkage distance metric between two clusters.
 class GaussianClusterDivergenceFunction
          The GaussianClusterDivergenceFunction class implements a divergence function between a Gaussian cluster and a vector, which is calculated by finding the likelihood that the vector was generated from that Gaussian and then returning the negative of the likelihood since it is a divergence measure, not a similarity measure.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.clustering.hierarchy
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.clustering.hierarchy
 interface ClusterHierarchyNode<DataType,ClusterType extends Cluster<DataType>>
          Defines a node in a hierarchy of clusters.
 

Classes in gov.sandia.cognition.learning.algorithm.clustering.hierarchy that implement CloneableSerializable
 class AbstractClusterHierarchyNode<DataType,ClusterType extends Cluster<DataType>>
          An abstract implementation of the ClusterHierarchyNode class.
 class BinaryClusterHierarchyNode<DataType,ClusterType extends Cluster<DataType>>
          Implements a binary cluster hierarchy node.
 class DefaultClusterHierarchyNode<DataType,ClusterType extends Cluster<DataType>>
          A default implementation of the cluster hierarchy node.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.clustering.initializer
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.clustering.initializer
 interface FixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
          The FixedClusterInitializer interface defines the functionality of a class that can initialize a given number of clusters from a set of elements.
 

Classes in gov.sandia.cognition.learning.algorithm.clustering.initializer that implement CloneableSerializable
 class AbstractMinDistanceFixedClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
          Implements an abstract FixedClusterInitializer that works by using the minimum distance from a point to the cluster.
 class DistanceSamplingClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
          Implements FixedClusterInitializer that initializes clusters by first selecting a random point for the first cluster and then randomly sampling each successive cluster based on the squared minimum distance from the point to the existing selected clusters.
 class GreedyClusterInitializer<ClusterType extends Cluster<DataType>,DataType>
          Implements a FixedClusterInitializer that greedily attempts to create the initial clusters.
 class NeighborhoodGaussianClusterInitializer
          Creates GaussianClusters near existing, but not on top of, data points.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.confidence
 

Classes in gov.sandia.cognition.learning.algorithm.confidence that implement CloneableSerializable
 class AdaptiveRegularizationOfWeights
          An implementation of the Adaptive Regularization of Weights (AROW) algorithm for online learning of a linear binary categorizer.
 class ConfidenceWeightedDiagonalDeviation
          An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer.
 class ConfidenceWeightedDiagonalDeviationProject
          An implementation of the Standard Deviation (Stdev) algorithm for learning a confidence-weighted categorizer.
 class ConfidenceWeightedDiagonalVariance
          An implementation of the Variance algorithm for learning a confidence-weighted linear categorizer.
 class ConfidenceWeightedDiagonalVarianceProject
          An implementation of the Variance algorithm for learning a confidence-weighted linear categorizer.
 

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

Classes in gov.sandia.cognition.learning.algorithm.ensemble that implement CloneableSerializable
 class AbstractBaggingLearner<InputType,OutputType,MemberType,EnsembleType extends Evaluator<? super InputType,? extends OutputType>>
          Learns an ensemble by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
 class AbstractUnweightedEnsemble<MemberType>
          An abstract implementation of the Ensemble interface for unweighted ensembles.
 class AbstractWeightedEnsemble<MemberType>
          An abstract implementation of the Ensemble interface for ensembles that have a weight associated with each member.
 class AdaBoost<InputType>
          The AdaBoost class implements the Adaptive Boosting (AdaBoost) algorithm formulated by Yoav Freund and Robert Shapire.
 class AdditiveEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends Number>>
          An ensemble of regression functions that determine the result by adding their outputs together.
 class AveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends Number>>
          An ensemble for regression functions that averages together the output value of each ensemble member to get the final output.
 class BaggingCategorizerLearner<InputType,CategoryType>
          Learns an categorization ensemble by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
 class BaggingRegressionLearner<InputType>
          Learns an ensemble for regression by randomly sampling with replacement (duplicates allowed) some percentage of the size of the data (defaults to 100%) on each iteration to train a new ensemble member.
 class BinaryBaggingLearner<InputType>
          The BinaryBaggingLearner implements the Bagging learning algorithm.
 class BinaryCategorizerSelector<InputType>
          The BinaryCategorizerSelector class implements a "weak learner" meant for use in boosting algorithms that selects the best BinaryCategorizer from a pre-set list by picking the one with the best weighted error.
 class CategoryBalancedBaggingLearner<InputType,CategoryType>
          An extension of the basic bagging learner that attempts to sample bags that have equal numbers of examples from every category.
 class CategoryBalancedIVotingLearner<InputType,CategoryType>
          An extension of IVoting for dealing with skew problems that makes sure that there are an equal number of examples from each category in each sample that an ensemble member is trained on.
 class IVotingCategorizerLearner<InputType,CategoryType>
          Learns an ensemble in a method similar to bagging except that on each iteration the bag is built from two parts, each sampled from elements from disjoint sets.
static class IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType>
          Implements a stopping criteria for IVoting that uses the out-of-bag error to determine when to stop learning the ensemble.
 class MultiCategoryAdaBoost<InputType,CategoryType>
          An implementation of a multi-class version of the Adaptive Boosting (AdaBoost) algorithm, known as AdaBoost.M1.
 class OnlineBaggingCategorizerLearner<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
          An implementation of an online version of the Bagging algorithm for learning an ensemble of categorizers.
 class VotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
          An ensemble of categorizers that determine the result based on an equal-weight vote.
 class WeightedAdditiveEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends Number>>
          An implementation of an ensemble that takes a weighted sum of the values returned by its members.
 class WeightedAveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends Number>>
          An implementation of an ensemble that takes the weighted average of its members.
 class WeightedBinaryEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends Boolean>>
          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.
 class WeightedVotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
          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.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.genetic
 

Classes in gov.sandia.cognition.learning.algorithm.genetic that implement CloneableSerializable
 class GeneticAlgorithm<CostParametersType,GenomeType>
          The GeneticAlgorithm class implements a generic genetic algorithm that uses a given cost function to minimize and a given reproduction function for generating the population.
 class ParallelizedGeneticAlgorithm<CostParametersType,GenomeType>
          This is a parallel implementation of the genetic algorithm.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.genetic.reproducer
 

Classes in gov.sandia.cognition.learning.algorithm.genetic.reproducer that implement CloneableSerializable
 class VectorizableCrossoverFunction
          The VectorizableCrossoverFunction class is a CrossoverFunction that takes two Vectorizable.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.gradient
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.gradient
 interface GradientDescendable
          The GradientDescendable interface defines the functionality of an object that is required in order to apply the gradient descent algorithm to it.
 interface ParameterGradientEvaluator<InputOutputType,GradientType>
          Interface for computing the derivative of the output with respect to the parameters for a given input.
 

Classes in gov.sandia.cognition.learning.algorithm.gradient that implement CloneableSerializable
 class GradientDescendableApproximator
          Creates a radientDescendable from a VectorizableVectorFunction by estimating the parameter gradient using a forward-difference approximation of the parameter Jacobian.
 

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

Classes in gov.sandia.cognition.learning.algorithm.hmm that implement CloneableSerializable
 class AbstractBaumWelchAlgorithm<ObservationType,DataType>
          Partial implementation of the Baum-Welch algorithm.
 class BaumWelchAlgorithm<ObservationType>
          Implements the Baum-Welch algorithm, also known as the "forward-backward algorithm", the expectation-maximization algorithm, etc for Hidden Markov Models (HMMs).
 class HiddenMarkovModel<ObservationType>
          A discrete-state Hidden Markov Model (HMM) with either continuous or discrete observations.
 class MarkovChain
          A Markov chain is a random process that has a finite number of states with random transition probabilities between states at discrete time steps.
 class ParallelBaumWelchAlgorithm<ObservationType>
          A Parallelized implementation of some of the methods of the Baum-Welch Algorithm.
protected static class ParallelBaumWelchAlgorithm.DistributionEstimatorTask<ObservationType>
          Re-estimates the PDF from the gammas.
 class ParallelHiddenMarkovModel<ObservationType>
          A Hidden Markov Model with parallelized processing.
protected static class ParallelHiddenMarkovModel.ComputeTransitionsTask
          Calls the computeTransitions method.
protected  class ParallelHiddenMarkovModel.LogLikelihoodTask
          Computes the log-likelihood of a particular data sequence
protected static class ParallelHiddenMarkovModel.NormalizeTransitionTask
          Calls the normalizeTransitionMatrix method.
protected static class ParallelHiddenMarkovModel.ObservationLikelihoodTask<ObservationType>
          Calls the computeObservationLikelihoods() method.
protected static class ParallelHiddenMarkovModel.StateObservationLikelihoodTask
          Calls the computeStateObservationLikelihood() method.
protected  class ParallelHiddenMarkovModel.ViterbiTask
          Computes the most-likely "from state" for the given "destination state" and the given deltas.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.minimization
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.minimization
 interface FunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>
          Interface for unconstrained minimization of nonlinear functions.
 

Classes in gov.sandia.cognition.learning.algorithm.minimization that implement CloneableSerializable
 class AbstractAnytimeFunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>
          A partial implementation of a minimization algorithm that is iterative, stoppable, and approximate.
 class FunctionMinimizerBFGS
          Implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton nonlinear minimization algorithm.
 class FunctionMinimizerConjugateGradient
          Conjugate gradient method is a class of algorithms for finding the unconstrained local minimum of a nonlinear function.
 class FunctionMinimizerDFP
          Implementation of the Davidon-Fletcher-Powell (DFP) formula for a Quasi-Newton minimization update.
 class FunctionMinimizerDirectionSetPowell
          Implementation of the derivative-free unconstrained nonlinear direction-set minimization algorithm called "Powell's Method" by Numerical Recipes.
 class FunctionMinimizerFletcherReeves
          This is an implementation of the Fletcher-Reeves conjugate gradient minimization procedure.
 class FunctionMinimizerGradientDescent
          This is an implementation of the classic Gradient Descent algorithm, also known as Steepest Descent, Backpropagation (for neural nets), or Hill Climbing.
 class FunctionMinimizerLiuStorey
          This is an implementation of the Liu-Storey conjugate gradient minimization procedure.
 class FunctionMinimizerNelderMead
          Implementation of the Downhill Simplex minimization algorithm, also known as the Nelder-Mead method.
 class FunctionMinimizerPolakRibiere
          This is an implementation of the Polack-Ribiere conjugate gradient minimization procedure.
 class FunctionMinimizerQuasiNewton
          This is an abstract implementation of the Quasi-Newton minimization method, sometimes called "Variable-Metric methods." This family of minimization algorithms uses first-order gradient information to find a locally minimum to a scalar function.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.minimization.line
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.minimization.line
 interface LineMinimizer<EvaluatorType extends Evaluator<Double,Double>>
          Defines the functionality of a line-minimization algorithm, often called a "line search" algorithm.
 

Classes in gov.sandia.cognition.learning.algorithm.minimization.line that implement CloneableSerializable
 class AbstractAnytimeLineMinimizer<EvaluatorType extends Evaluator<Double,Double>>
          Partial AnytimeAlgorithm implementation of a LineMinimizer.
 class DirectionalVectorToDifferentiableScalarFunction
          Creates a truly differentiable scalar function from a differentiable Vector function, instead of using a forward-differences approximation to the derivative like DirectionalVectorToScalarFunction does.
 class DirectionalVectorToScalarFunction
          Maps a vector function onto a scalar one by using a directional vector and vector offset, and the parameter to the function is a scalar value along the direction from the start-point offset.
 class InputOutputSlopeTriplet
          Stores an InputOutputPair with corresponding slope (gradient) information
 class LineBracket
          Class that defines a bracket for a scalar function.
 class LineMinimizerBacktracking
          Implementation of the backtracking line-minimization algorithm.
 class LineMinimizerDerivativeBased
          This is an implementation of a line-minimization algorithm proposed by Fletcher that makes extensive use of first-order derivative information.
 class LineMinimizerDerivativeBased.InternalFunction
          Internal function used to map/remap/unmap the search direction.
 class LineMinimizerDerivativeFree
          This is an implementation of a LineMinimizer that does not require derivative information.
 class WolfeConditions
          The Wolfe conditions define a set of sufficient conditions for "sufficient decrease" in inexact line search.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator
 interface LineBracketInterpolator<EvaluatorType extends Evaluator<Double,Double>>
          Definition of an interpolator/extrapolator for a LineBracket.
 

Classes in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator that implement CloneableSerializable
 class AbstractLineBracketInterpolator<EvaluatorType extends Evaluator<Double,Double>>
          Partial implementation of LinearBracketInterpolator
 class AbstractLineBracketInterpolatorPolynomial<EvaluatorType extends Evaluator<Double,Double>>
          Partial implementation of a LineBracketInterpolator based on a closed-form polynomial function.
 class LineBracketInterpolatorBrent
          Implements Brent's method of function interpolation to find a minimum.
 class LineBracketInterpolatorGoldenSection
          Interpolates between the two bound points of a LineBracket using the golden-section step rule, if that step fails, then the interpolator uses a linear (secant) interpolation.
 class LineBracketInterpolatorHermiteCubic
          Interpolates using a cubic with two points, both of which must have slope information.
 class LineBracketInterpolatorHermiteParabola
          Interpolates using a parabola with two points, at least one of which must have slope information.
 class LineBracketInterpolatorLinear
          Interpolates using a linear (stright-line) curve between two points, neither of which need slope information.
 class LineBracketInterpolatorParabola
          Interpolates using a parabola based on three points without slope information.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.nearest
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.nearest
 interface KNearestNeighbor<InputType,OutputType>
          A generic k-nearest-neighbor classifier.
 interface NearestNeighbor<InputType,OutputType>
          The NearestNeighborExhaustive class implements a simple evaluator that looks up a given input object in a collection of input-output pair examples and returns the output associated with the most similar input.
 

Classes in gov.sandia.cognition.learning.algorithm.nearest that implement CloneableSerializable
 class AbstractKNearestNeighbor<InputType,OutputType>
          Partial implementation of KNearestNeighbor.
 class AbstractNearestNeighbor<InputType,OutputType>
          Partial implementation of KNearestNeighbor.
 class KNearestNeighborExhaustive<InputType,OutputType>
          A generic k-nearest-neighbor classifier.
static class KNearestNeighborExhaustive.Learner<InputType,OutputType>
          This is a BatchLearner interface for creating a new KNearestNeighborExhaustive from a given dataset, simply a pass-through to the constructor of KNearestNeighborExhaustive
protected  class KNearestNeighborExhaustive.Neighbor
          Holds neighbor information used during the evaluate method and is put into a priority queue.
 class KNearestNeighborKDTree<InputType extends Vectorizable,OutputType>
          A KDTree-based implementation of the k-nearest neighbor algorithm.
static class KNearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
          This is a BatchLearner interface for creating a new KNearestNeighbor from a given dataset, simply a pass-through to the constructor of KNearestNeighbor
 class NearestNeighborExhaustive<InputType,OutputType>
          The NearestNeighborExhaustive class implements a simple evaluator that looks up a given input object in a collection of input-output pair examples and returns the output associated with the most similar input.
static class NearestNeighborExhaustive.Learner<InputType,OutputType>
          The NearestNeighborExhaustive.Learner class implements a batch learner for the NearestNeighborExhaustive class.
 class NearestNeighborKDTree<InputType extends Vectorizable,OutputType>
          A KDTree-based implementation of the nearest neighbor algorithm.
static class NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
          This is a BatchLearner interface for creating a new NearestNeighbor from a given dataset, simply a pass-through to the constructor of NearestNeighbor
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.pca
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.pca
 interface PrincipalComponentsAnalysis
          Principal Components Analysis is a family of algorithms that map from a high-dimensional input space to a low-dimensional output space.
 

Classes in gov.sandia.cognition.learning.algorithm.pca that implement CloneableSerializable
 class AbstractPrincipalComponentsAnalysis
          Abstract implementation of PCA.
 class GeneralizedHebbianAlgorithm
          Implementation of the Generalized Hebbian Algorithm, also known as Sanger's Rule, which is a generalization of Oja's Rule.
 class KernelPrincipalComponentsAnalysis<DataType>
          An implementation of the Kernel Principal Components Analysis (KPCA) algorithm.
static class KernelPrincipalComponentsAnalysis.Function<DataType>
          The resulting transformation function learned by Kernel Principal Components Analysis.
 class PrincipalComponentsAnalysisFunction
          This VectorFunction maps a high-dimension input space onto a (hopefully) simple low-dimensional output space by subtracting the mean of the input data, and passing the zero-mean input through a dimension-reducing matrix multiplication function.
 class ThinSingularValueDecomposition
          Computes the "thin" singular value decomposition of a dataset.
 

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

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.perceptron
 interface KernelizableBinaryCategorizerOnlineLearner
          Interface for an online learner of a linear binary categorizer that can also be used with a kernel function.
 interface LinearizableBinaryCategorizerOnlineLearner<InputType>
          Interface for an online learner of a kernel binary categorizer that can also be used for learning a linear categorizer.
 

Classes in gov.sandia.cognition.learning.algorithm.perceptron that implement CloneableSerializable
 class AbstractKernelizableBinaryCategorizerOnlineLearner
          An abstract implementation of the KernelizableBinaryCategorizerOnlineLearner interface.
 class AbstractLinearCombinationOnlineLearner
          An abstract class for online learning of linear binary categorizers that take the form of a weighted sum of inputs.
 class AbstractOnlineLinearBinaryCategorizerLearner
          An abstract class for online (incremental) learning algorithms that produce an LinearBinaryCategorizer.
 class AggressiveRelaxedOnlineMaximumMarginAlgorithm
          An implementation of the Aggressive Relaxed Online Maximum Margin Algorithm (AROMMA).
 class Ballseptron
          An implementation of the Ballseptron algorithm.
 class BatchMultiPerceptron<CategoryType>
          Implements a multi-class version of the standard batch Perceptron learning algorithm.
 class OnlineBinaryMarginInfusedRelaxedAlgorithm
          An implementation of the binary MIRA algorithm.
 class OnlineMultiPerceptron<CategoryType>
          An online, multiple category version of the Perceptron algorithm.
static class OnlineMultiPerceptron.ProportionalUpdate<CategoryType>
          Variant of a multi-category Perceptron that performs a proportional weight update on all categories that are scored higher than the true category such that the weights sum to 1.0 and are proportional how much larger the score was for each incorrect category than the true category.
static class OnlineMultiPerceptron.UniformUpdate<CategoryType>
          Variant of a multi-category Perceptron that performs a uniform weight update on all categories that are scored higher than the true category such that the weights are equal and sum to -1.
 class OnlinePassiveAggressivePerceptron
          An implementation of the Passive-Aggressive algorithm for learning a linear binary categorizer.
static class OnlinePassiveAggressivePerceptron.AbstractSoftMargin
          An abstract class for soft-margin versions of the Passive-Aggressive algorithm.
static class OnlinePassiveAggressivePerceptron.LinearSoftMargin
          An implementation of the linear soft-margin variant of the Passive- Aggressive algorithm (PA-I).
static class OnlinePassiveAggressivePerceptron.QuadraticSoftMargin
          An implementation of the quadratic soft-margin variant of the Passive- Aggressive algorithm (PA-II).
 class OnlinePerceptron
          An online version of the classic Perceptron algorithm.
 class OnlineRampPassiveAggressivePerceptron
          An implementation of the Ramp Loss Passive Aggressive Perceptron (PA^R) from the referenced paper.
 class OnlineShiftingPerceptron
          An implementation of the Shifting Perceptron algorithm.
static class OnlineShiftingPerceptron.LinearResult
          This is the result learned by the shifting perceptron.
 class OnlineVotedPerceptron
          An online version of the Voted-Perceptron algorithm.
 class Perceptron
          The Perceptron class implements the standard Perceptron learning algorithm that learns a binary classifier based on vector input.
 class RelaxedOnlineMaximumMarginAlgorithm
          An implementation of the Relaxed Online Maximum Margin Algorithm (ROMMA).
 class Winnow
          An implementation of the Winnow incremental learning algorithm.
 

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

Classes in gov.sandia.cognition.learning.algorithm.perceptron.kernel that implement CloneableSerializable
 class AbstractOnlineBudgetedKernelBinaryCategorizerLearner<InputType>
          An abstract implementation of the BudgetedKernelBinaryCategorizerLearner for online learners.
 class AbstractOnlineKernelBinaryCategorizerLearner<InputType>
          An abstract class for an online kernel binary categorizer learner.
 class Forgetron<InputType>
          An implementation of the "self-tuned" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
static class Forgetron.Basic<InputType>
          An implementation of the "basic" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
static class Forgetron.Greedy<InputType>
          An implementation of the "greedy" Forgetron algorithm, which is an online budgeted kernel binary categorizer learner.
static class Forgetron.Result<InputType>
          The result object learned by the Forgetron, which extends the DefaultKernelBinaryCategorizer with some additional state information needed in the update step.
 class KernelAdatron<InputType>
          The KernelAdatron class implements an online version of the Support Vector Machine learning algorithm.
 class KernelBinaryCategorizerOnlineLearnerAdapter<InputType>
          A wrapper class for a KernelizableBinaryCategorizerOnlineLearner that allows it to be used as a batch or incremental learner over the input type directly, rather than using utility methods.
 class KernelPerceptron<InputType>
          The KernelPerceptron class implements the kernel version of the Perceptron algorithm.
 class OnlineKernelPerceptron<InputType>
          An implementation of the online version of the Perceptron algorithm.
 class OnlineKernelRandomizedBudgetPerceptron<InputType>
          An implementation of a fixed-memory kernel Perceptron algorithm.
 class Projectron<InputType>
          An implementation of the Projectron algorithm, which is an online kernel binary categorizer learner that has a budget parameter tuned by the eta parameter.
static class Projectron.LinearSoftMargin<InputType>
          An implementation of the Projectron++ algorithm, which is an online kernel binary categorizer learner that has a budget parameter tuned by the eta parameter.
 class RemoveOldestKernelPerceptron<InputType>
          A budget kernel Perceptron that always removes the oldest item.
 class Stoptron<InputType>
          An online, budgeted, kernel version of the Perceptron algorithm that stops learning once it has reached its budget.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.regression
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.regression
 interface ParameterCostMinimizer<ResultType extends VectorizableVectorFunction>
          A anytime algorithm that is used to estimate the locally minimum-cost parameters of an object.
 

Classes in gov.sandia.cognition.learning.algorithm.regression that implement CloneableSerializable
 class AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends Double>>
          Partial implementation of ParameterCostMinimizer, based on the algorithms from the minimization package.
 class AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>>
          Partial implementation of ParameterCostMinimizer.
 class FletcherXuHybridEstimation
          The Fletcher-Xu hybrid estimation for solving the nonlinear least-squares parameters.
 class GaussNewtonAlgorithm
          Implementation of the Gauss-Newton parameter-estimation procedure.
 class KernelBasedIterativeRegression<InputType>
          The KernelBasedIterativeRegression class implements an online version of the Support Vector Regression algorithm.
 class KernelWeightedRobustRegression<InputType,OutputType>
          KernelWeightedRobustRegression takes a supervised learning algorithm that operates on a weighted collection of InputOutputPairs and modifies the weight of a sample based on the dataset output and its corresponding estimate from the Evaluator from the supervised learning algorithm at each iteration.
 class LeastSquaresEstimator
          Abstract implementation of iterative least-squares estimators.
 class LevenbergMarquardtEstimation
          Implementation of the nonlinear regression algorithm, known as Levenberg-Marquardt Estimation (or LMA).
 class LinearBasisRegression<InputType>
          Computes the least-squares regression for a LinearCombinationFunction given a dataset.
 class LinearRegression
          Computes the least-squares regression for a LinearCombinationFunction given a dataset.
static class LinearRegression.Statistic
          Computes regression statistics using a chi-square measure of the statistical significance of the learned approximator
static class LocallyWeightedFunction.Learner<InputType,OutputType>
          Learning algorithm for creating LocallyWeightedFunctions.
 class LogisticRegression
          Performs Logistic Regression by means of the iterative reweighted least squares (IRLS) algorithm, where the logistic function has an explicit bias term, and a diagonal L2 regularization term.
static class LogisticRegression.Function
          Class that is a linear discriminant, followed by a sigmoid function.
 class MultivariateLinearRegression
          Performs multivariate regression with an explicit bias term, with optional L2 regularization.
 class ParameterDerivativeFreeCostMinimizer
          Implementation of a class of objects that uses a derivative-free minimization algorithm.
static class ParameterDerivativeFreeCostMinimizer.ParameterCostEvaluatorDerivativeFree
          Function that maps the parameters of an object to its inputs, so that minimization algorithms can tune the parameters of an object against a cost function.
 class ParameterDifferentiableCostMinimizer
          This class adapts the unconstrained nonlinear minimization algorithms in the "minimization" package to the task of estimating locally optimal (minimum-cost) parameter sets.
static class ParameterDifferentiableCostMinimizer.ParameterCostEvaluatorDerivativeBased
          Function that maps the parameters of an object to its inputs, so that minimization algorithms can tune the parameters of an object against a cost function.
 class UnivariateLinearRegression
          An implementation of simple univariate linear regression.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.algorithm.root
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.root
 interface RootBracketer
          Defines the functionality of a algorithm that finds a bracket of a root from an initial guess.
 interface RootFinder
          Defines the functionality of a root-finding algorithm.
 

Classes in gov.sandia.cognition.learning.algorithm.root that implement CloneableSerializable
 class AbstractBracketedRootFinder
          Partial implementation of RootFinder that maintains a bracket on the root.
 class AbstractRootFinder
          Partial implementation of RootFinder.
 class MinimizerBasedRootFinder
          A root finder that uses minimization techniques to find the roots (zero-crossings).
 class RootBracketExpander
          The root-bracketing expansion algorithm.
 class RootFinderBisectionMethod
          Bisection algorithm for root finding.
 class RootFinderFalsePositionMethod
          The false-position algorithm for root finding.
 class RootFinderNewtonsMethod
          Newton's method, sometimes called Newton-Raphson method, uses first-order derivative information to iteratively locate a root.
 class RootFinderRiddersMethod
          The root-finding algorithm due to Ridders.
 class RootFinderSecantMethod
          The secant algorithm for root finding.
 class SolverFunction
          Evaluator that allows RootFinders to solve for nonzero values by setting a "target" parameter.
 

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

Classes in gov.sandia.cognition.learning.algorithm.svm that implement CloneableSerializable
 class PrimalEstimatedSubGradient
          An implementation of the Primal Estimated Sub-Gradient Solver (PEGASOS) algorithm for learning a linear support vector machine (SVM).
 class SequentialMinimalOptimization<InputType>
          An implementation of the Sequential Minimal Optimization (SMO) algorithm for training a Support Vector Machine (SVM), which is a kernel-based binary categorizer.
 class SuccessiveOverrelaxation<InputType>
          The SuccessiveOverrelaxation class implements the Successive Overrelaxation (SOR) algorithm for learning a Support Vector Machine (SVM).
protected  class SuccessiveOverrelaxation.Entry
          The Entry class represents the data that the algorithm keeps about each training example.
 

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

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.algorithm.tree
 interface DeciderLearner<InputType,OutputType,CategoryType,DeciderType extends Categorizer<? super InputType,? extends CategoryType>>
          The DeciderLearner interface defines the functionality of a learner that can be used to learn a decision function inside a decision tree.
 interface DecisionTreeNode<InputType,OutputType>
          The DecisionTreeNode interface defines the functionality of a node in a decision tree.
 interface VectorThresholdMaximumGainLearner<OutputType>
          An interface class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.
 

Classes in gov.sandia.cognition.learning.algorithm.tree that implement CloneableSerializable
 class AbstractDecisionTreeLearner<InputType,OutputType>
          The AbstractDecisionTreeLearner implements common functionality for learning algorithms that learn a decision tree.
 class AbstractDecisionTreeNode<InputType,OutputType,InteriorType>
          The AbstractDecisionTreeNode class implements common functionality for a decision tree node.
 class AbstractVectorThresholdMaximumGainLearner<OutputType>
          An abstract class for decider learners that produce a threshold function on a vector element based on maximizing some gain value.
 class CategorizationTree<InputType,OutputType>
          The CategorizationTree class extends the DecisionTree class to implement a decision tree that does categorization.
 class CategorizationTreeLearner<InputType,OutputType>
          The CategorizationTreeLearner class implements a supervised learning algorithm for learning a categorization tree.
 class CategorizationTreeNode<InputType,OutputType,InteriorType>
          The CategorizationTreeNode implements a DecisionTreeNode for a tree that does categorization.
 class DecisionTree<InputType,OutputType>
          The DecisionTree class implements a standard decision tree that is made up of DecisionTreeNode objects.
 class RandomSubVectorThresholdLearner<OutputType>
          Learns a decision function by taking a randomly sampling a subspace from a given set of input vectors and then learning a threshold function by passing the subspace vectors to a sublearner.
 class RegressionTree<InputType>
          The RegressionTree class extends the DecisionTree class to implement a decision tree that does regression.
 class RegressionTreeLearner<InputType>
          The RegressionTreeLearner class implements a learning algorithm for a regression tree that makes use of a decider learner and a regresion learner.
 class RegressionTreeNode<InputType,InteriorType>
          The RegressionTreeNode implements a DecisionTreeNode for a tree that does regression.
 class VectorThresholdGiniImpurityLearner<OutputType>
          Learns vector thresholds based on the Gini impurity measure.
 class VectorThresholdHellingerDistanceLearner<OutputType>
          A categorization tree decision function learner on vector data that learns a vector value threshold function using the Hellinger distance.
 class VectorThresholdInformationGainLearner<OutputType>
          The VectorThresholdInformationGainLearner computes the best threshold over a dataset of vectors using information gain to determine the optimal index and threshold.
 class VectorThresholdVarianceLearner
          The VectorThresholdVarianceLearner computes the best threshold over a dataset of vectors using the reduction in variance to determine the optimal index and threshold.
 

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

Classes in gov.sandia.cognition.learning.data that implement CloneableSerializable
 class AbstractInputOutputPair<InputType,OutputType>
          An abstract implementation of the InputOutputPair interface.
 class AbstractTargetEstimatePair<TargetType,EstimateType>
          An abstract implementation of the TargetEstimatePair.
 class AbstractValueDiscriminantPair<ValueType,DiscriminantType extends Comparable<? super DiscriminantType>>
          An abstract implementation of the ValueDiscriminantPair interface.
 class DefaultInputOutputPair<InputType,OutputType>
          A default implementation of the InputOutputPair interface.
 class DefaultTargetEstimatePair<TargetType,EstimateType>
          A default implementation of the TargetEstimatePair.
 class DefaultValueDiscriminantPair<ValueType,DiscriminantType extends Comparable<? super DiscriminantType>>
          A default implementation of the ValueDiscriminantPair interface.
 class DefaultWeightedInputOutputPair<InputType,OutputType>
          A default implementation of the WeightedInputOutputPair interface.
 class DefaultWeightedTargetEstimatePair<TargetType,EstimateType>
          Extends TargetEstimatePair with an additional weight field.
 class DefaultWeightedValueDiscriminant<ValueType>
          An implementation of ValueDiscriminantPair that stores a double as the discriminant.
 class RandomDataPartitioner<DataType>
          The RandomDataPartitioner class implements a randomized data partitioner that takes a collection of data and randomly splits it into training and testing sets based on a fixed percentage of training data.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.data.feature
 

Classes in gov.sandia.cognition.learning.data.feature that implement CloneableSerializable
 class DelayFunction<DataType>
          Delays the input and returns the input from the parameterized number of samples previous.
 class LinearRegressionCoefficientExtractor
          Takes a sampled sequence of equal-dimension vectors as input and computes the linear regression coefficients for each dimension in the vectors.
 class MultivariateDecorrelator
          Decorrelates a data using a mean and full or diagonal covariance matrix.
static class MultivariateDecorrelator.DiagonalCovarianceLearner
          The DiagonalCovarianceLearner class implements a BatchLearner object for a MultivariateDecorrelator.
static class MultivariateDecorrelator.FullCovarianceLearner
          The FullCovarianceLearner class implements a BatchLearner object for a MultivariateDecorrelator.
 class RandomSubspace
          Selects a random subspace from the given vector, which is a random set of indices.
 class StandardDistributionNormalizer
          The StandardDistributionNormalizer class implements a normalization method where a real value is converted onto a standard distribution.
static class StandardDistributionNormalizer.Learner
          The Learner class implements a BatchLearner object for a StandardDistributionNormalizer.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.experiment
 

Classes in gov.sandia.cognition.learning.experiment that implement CloneableSerializable
 class AbstractLearningExperiment
          The AbstractLearningExperiment class implements the general functionality of the LearningExperiment interface, which is mainly the handling of listeners and firing of events.
 class AbstractValidationFoldExperiment<InputDataType,FoldDataType>
          The AbstractValidationFoldExperiment class implements a common way of structuring an experiment around a ValidationFoldCreator object where the fold creator is used to create each of the individual trials of the experiment.
 class CrossFoldCreator<DataType>
          The CrossFoldCreator implements a validation fold creator that creates folds for a typical k-fold cross-validation experiment.
 class LearnerComparisonExperiment<InputDataType,FoldDataType,LearnedType,StatisticType,SummaryType>
          The LearnerComparisonExperiment compares the performance of two machine learning algorithms to determine (using a statistical test) if the two algorithms have significantly different performance.
 class LearnerRepeatExperiment<InputDataType,LearnedType,StatisticType,SummaryType>
          Runs an experiment where the same learner is evaluated multiple times on the same data.
 class LearnerValidationExperiment<InputDataType,FoldDataType,LearnedType,StatisticType,SummaryType>
          The LearnerValidationExperiment class implements an experiment where a supervised machine learning algorithm is evaluated by applying it to a set of folds created from a given set of data.
 class OnlineLearnerValidationExperiment<DataType,LearnedType,StatisticType,SummaryType>
          Implements an experiment where an incremental supervised machine learning algorithm is evaluated by applying it to a set of data by successively testing on each item and then training on it.
 class ParallelLearnerValidationExperiment<InputDataType,FoldDataType,LearnedType,StatisticType,SummaryType>
          Parallel version of the LearnerValidationExperiment class that executes the validations experiments across available cores and hyperthreads.
 class RandomByTwoFoldCreator<DataType>
          A validation fold creator that takes a given collection of data and randomly splits it in half a given number of times, returning two folds for each split, using one half as training and the other half as testing.
 class SupervisedLearnerComparisonExperiment<InputType,OutputType,StatisticType,SummaryType>
          A comparison experiment for supervised learners.
 class SupervisedLearnerValidationExperiment<InputType,OutputType,StatisticType,SummaryType>
          The SupervisedLearnerValidationExperiment class extends the LearnerValidationExperiment class to provide a easy way to create a learner validation experiment for supervised learning.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.function
 

Classes in gov.sandia.cognition.learning.function that implement CloneableSerializable
 class ConstantEvaluator<OutputType>
          The ConstantEvaluator class implements an Evaluator that always returns the same output value.
 class LinearCombinationFunction<InputType,OutputType>
          A function whose output is a weighted linear combination of (potentially) nonlinear basis function.
 

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

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.function.categorization
 interface BinaryCategorizer<InputType>
          The BinaryCategorizer extends the Categorizer interface by enforcing that the output categories are boolean values, which means that the categorizer is performing binary categorization.
 interface Categorizer<InputType,CategoryType>
          The Categorizer interface defines the functionality of an object that can take an input and evaluate what category out of a fixed set of categories it belongs to.
 interface ConfidenceWeightedBinaryCategorizer
          Interface for a confidence-weighted binary categorizer, which defines a distribution over linear binary categorizers.
 interface DiscriminantBinaryCategorizer<InputType>
          Interface for a linear discriminant categorizer in the binary categorization domain.
 interface DiscriminantCategorizer<InputType,CategoryType,DiscriminantType extends Comparable<? super DiscriminantType>>
          Interface for a Categorizer that can produce a value to discriminate between how well different instances fit a given category.
 interface ThresholdBinaryCategorizer<InputType>
          Interface for a binary categorizer that uses a threshold to determine the categorization.
 

Classes in gov.sandia.cognition.learning.function.categorization that implement CloneableSerializable
 class AbstractBinaryCategorizer<InputType>
          The AbstractBinaryCategorizer implements the commonality of the BinaryCategorizer, holding the collection of possible values.
 class AbstractCategorizer<InputType,CategoryType>
          An abstract implementation of the Categorizer interface.
 class AbstractConfidenceWeightedBinaryCategorizer
          Unit tests for class AbstractConfidenceWeightedBinaryCategorizer.
 class AbstractDiscriminantBinaryCategorizer<InputType>
          An abstract implementation of the DiscriminantBinaryCategorizer interface.
 class AbstractDiscriminantCategorizer<InputType,CategoryType,DiscriminantType extends Comparable<? super DiscriminantType>>
          An abstract implementation of the DiscriminantCategorizer interface.
 class AbstractThresholdBinaryCategorizer<InputType>
          Categorizer that first maps the input space onto a real value, then uses a threshold to map the result onto lowValue (for strictly less than the threshold) or highValue (for greater than or equal to the threshold).
 class BinaryVersusCategorizer<InputType,CategoryType>
          An adapter that allows binary categorizers to be adapted for multi-category problems by applying a binary categorizer to each pair of categories.
static class BinaryVersusCategorizer.Learner<InputType,CategoryType>
          A learner for the BinaryVersusCategorizer.
 class CompositeCategorizer<InputType,IntermediateType,CategoryType>
          Composes a preprocessor function with a categorizer.
 class DefaultConfidenceWeightedBinaryCategorizer
          A default implementation of the ConfidenceWeightedBinaryCategorizer that stores a full mean and covariance matrix.
 class DefaultKernelBinaryCategorizer<InputType>
          A default implementation of the KernelBinaryCategorizer that uses the standard way of representing the examples (supports) using a DefaultWeightedValue.
 class DiagonalConfidenceWeightedBinaryCategorizer
          A confidence-weighted linear predictor with a diagonal covariance, which is stored as a vector.
 class EvaluatorToCategorizerAdapter<InputType,CategoryType>
          The EvaluatorToCategorizerAdapter class implements an adapter from a general Evaluator to be a Categorizer.
static class EvaluatorToCategorizerAdapter.Learner<InputType,CategoryType>
          The EvaluatorToCategorizerAdapter.Learner class implements a simple supervised learner for a EvaluatorToCategorizerAdapter.
 class FisherLinearDiscriminantBinaryCategorizer
          A Fisher Linear Discriminant classifier, which creates an optimal linear separating plane between two Gaussian classes of different covariances.
static class FisherLinearDiscriminantBinaryCategorizer.ClosedFormSolver
          This class implements a closed form solver for the Fisher linear discriminant binary categorizer.
 class KernelBinaryCategorizer<InputType,EntryType extends WeightedValue<? extends InputType>>
          The KernelBinaryCategorizer class implements a binary categorizer that uses a kernel to do its categorization.
 class LinearBinaryCategorizer
          The LinearBinaryCategorizer class implements a binary categorizer that is implemented by a linear function.
 class LinearMultiCategorizer<CategoryType>
          A multi-category version of the LinearBinaryCategorizer that keeps a separate LinearBinaryCategorizer for each category.
 class MaximumAPosterioriCategorizer<ObservationType,CategoryType>
          Categorizer that returns the category with the highest posterior likelihood for a given observation.
static class MaximumAPosterioriCategorizer.Learner<ObservationType,CategoryType>
          Learner for the MAP categorizer
 class ScalarFunctionToBinaryCategorizerAdapter<InputType>
          Adapts a scalar function to be a categorizer using a threshold.
 class ScalarThresholdBinaryCategorizer
          The ScalarThresholdBinaryCategorizer class implements a binary categorizer that uses a threshold to categorize a given double.
 class VectorElementThresholdCategorizer
          The VectorElementThresholdCategorizer class implements a BinaryCategorizer that categorizes an input vector by applying a threshold to an element in a the vector.
 class WinnerTakeAllCategorizer<InputType,CategoryType>
          Adapts an evaluator that outputs a vector to be used as a categorizer.
static class WinnerTakeAllCategorizer.Learner<InputType,CategoryType>
          A learner for the adapter.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.function.cost
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.function.cost
 interface CostFunction<EvaluatedType,CostParametersType>
          The CostFunction interface defines the interface to evaluate some object to determine its cost.
 interface DifferentiableCostFunction
          The DifferentiableCostFunction is a cost function that can be differentiated.
 interface ParallelizableCostFunction
          Interface describing a cost function that can (largely) be computed in parallel.
 interface SupervisedCostFunction<InputType,TargetType>
          A type of CostFunction normally used in supervised-learning applications.
 

Classes in gov.sandia.cognition.learning.function.cost that implement CloneableSerializable
 class AbstractCostFunction<EvaluatedType,CostParametersType>
          Partial implementation of CostFunction.
 class AbstractParallelizableCostFunction
          Partial implementation of the ParallelizableCostFunction
 class AbstractSupervisedCostFunction<InputType,TargetType>
          Partial implementation of SupervisedCostFunction
 class ClusterDistortionMeasure<DataType,ClusterType extends Cluster<DataType>>
          Computes the objective measure for a clustering algorithm, based on the internal "distortion" of each cluster.
 class EuclideanDistanceCostFunction
          The EuclideanDistanceCostFunction class implements a CostFunction that calculates the Euclidean distance the given Vectorizable and the goal vector.
 class KolmogorovSmirnovDivergence<DataType extends Number>
          CostFunction that induces a CDF that most-closely resembles the target distribution according to the Kolmogorov-Smirnov (K-S) test.
 class MeanL1CostFunction
          Cost function that evaluates the mean 1-norm error (absolute value of difference) weighted by a sample "weight" that is embedded in each sample.
 class MeanSquaredErrorCostFunction
          The MeanSquaredErrorCostFunction implements a cost function for functions that take as input a vector and return a vector.
 class NegativeLogLikelihood<DataType>
          CostFunction for computing the maximum likelihood (because we are minimizing the negative of the log likelihood)
 class ParallelClusterDistortionMeasure<DataType,ClusterType extends Cluster<DataType>>
          A parallel implementation of ClusterDistortionMeasure.
 class ParallelizedCostFunctionContainer
          A cost function that automatically splits a ParallelizableCostFunction across multiple cores/processors to speed up computation.
 class ParallelNegativeLogLikelihood<DataType>
          Parallel implementation of the NegativeLogLikleihood cost function
 class SumSquaredErrorCostFunction
          This is the sum-squared error cost function
static class SumSquaredErrorCostFunction.Cache
          Caches often-used values for the Cost Function
static class SumSquaredErrorCostFunction.GradientPartialSSE
          Partial result from the SSE gradient computation
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.function.distance
 

Classes in gov.sandia.cognition.learning.function.distance that implement CloneableSerializable
 class ChebyshevDistanceMetric
          An implementation of the Chebyshev distance, which is the absolute value of the largest difference between two vectors in a single dimension.
 class CosineDistanceMetric
          The CosineDistanceMetric class implements a semimetric between two vectors based on the cosine between the vectors.
 class DefaultDivergenceFunctionContainer<FirstType,SecondType>
          The DefaultDivergenceFunctionContainer class implements an object that holds a divergence function.
 class DivergencesEvaluator<InputType,ValueType>
          Evaluates the divergence (distance) between an input and a list of values, storing the resulting divergence values in a vector.
static class DivergencesEvaluator.Learner<DataType,InputType,ValueType>
          A learner adapter for the DivergencesEvaluator.
 class EuclideanDistanceMetric
          The EuclideanDistanceMetric implements a distance metric that computes the Euclidean distance between two points.
 class EuclideanDistanceSquaredMetric
          The EuclideanDistanceSquaredMetric implements a distance metric that computes the squared Euclidean distance between two points.
 class IdentityDistanceMetric
          A distance metric that is 0 if two objects are equal and 1 if they are not.
 class ManhattanDistanceMetric
          The ManhattanDistanceMetric class implements a distance metric between two vectors that is implemented as the sum of the absolute value of the difference between the elements in the vectors.
 class MinkowskiDistanceMetric
          An implementation of the Minkowski distance metric.
 class WeightedEuclideanDistanceMetric
          A distance metric that weights each dimension of a vector differently before computing Euclidean distance.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.function.kernel
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.function.kernel
 interface Kernel<InputType>
          The Kernel interface the functionality required from an object that implements a kernel function.
 interface KernelContainer<InputType>
          Defines an object that contains a Kernel.
 

Classes in gov.sandia.cognition.learning.function.kernel that implement CloneableSerializable
 class DefaultKernelContainer<InputType>
          The DefaultKernelContainer class implements an object that contains a kernel inside.
 class DefaultKernelsContainer<InputType>
          The DefaultKernelsContainer class implements a container of kernels.
 class ExponentialKernel<InputType>
          The ExponentialKernel class implements a kernel that applies the exponential function to the result of another kernel.
 class KernelDistanceMetric<InputType>
          The KernelDistanceMetric class implements a distance metric that utilizes an underlying Kernel for computing the distance.
 class LinearKernel
          The LinearKernel class implements the most basic kernel: it just does the actual inner product between two vectors.
 class NormalizedKernel<InputType>
          The NormalizedKernel class implements an Kernel that returns a normalized value between 0.0 and 1.0 by normalizing the results of a given kernel.
 class PolynomialKernel
          The PolynomialKernel class implements a kernel for two given vectors that is the polynomial function:
(x dot y + c)^d
d is the degree of the polynomial, which must be a positive integer.
 class ProductKernel<InputType>
          The ProductKernel class implements a kernel that takes the product of applying multiple kernels to the same pair of inputs.
 class RadialBasisKernel
          The RadialBasisKernel implements the standard radial basis kernel, which is:
exp( -||x - y||^2 / (2 * sigma^2) )
where sigma is the parameter that controls the bandwidth of the kernel.
 class ScalarFunctionKernel<InputType>
          The ScalarFunctionKernel class implements a kernel that applies a scalar function two the two inputs to the kernel and then returns their product.
 class SigmoidKernel
          The SigmoidKernel class implements a sigmoid kernel based on the hyperbolic tangent.
 class SumKernel<InputType>
          The SumKernel class implements a kernel that adds together the result of applying multiple kernels to the same pair of inputs.
 class VectorFunctionKernel
          The VectorFunctionKernel implements a kernel that makes use of a vector function plus a kernel that operates on vectors.
 class WeightedKernel<InputType>
          The WeightedKernel class implements a kernel that takes another kernel, evaluates it, and then the result is rescaled by a given weight.
 class ZeroKernel
          The ZeroKernel always returns zero.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.function.regression
 

Classes in gov.sandia.cognition.learning.function.regression that implement CloneableSerializable
 class AbstractRegressor<InputType>
          An abstract implementation of the Regressor interface.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.function.scalar
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.function.scalar
static interface PolynomialFunction.ClosedForm
          Describes functionality of a closed-form algebraic polynomial function
 

Classes in gov.sandia.cognition.learning.function.scalar that implement CloneableSerializable
 class AtanFunction
          Returns the element-wise arctangent of the input vector, compressed between -maxMagnitude and maxMagnitude (instead of just -PI/2 and PI/2)
 class CosineFunction
          A closed-form cosine function.
 class IdentityScalarFunction
          A univariate scalar identity function: f(x) = x.
 class KernelScalarFunction<InputType>
          The KernelScalarFunction class implements a scalar function that uses a kernel to compute its output value.
 class KolmogorovSmirnovEvaluator
          You can specify a particular CDF.
 class LinearCombinationScalarFunction<InputType>
          A weighted linear combination of scalar functions.
 class LinearDiscriminant
          LinearDiscriminant takes the dot product between the weight Vector and the input Vector.
 class LinearDiscriminantWithBias
          A LinearDiscriminant with an additional bias term that gets added to the output of the dot product.
 class LinearFunction
          This function acts as a simple linear function of the form f(x) = m*x + b.
 class LinearVectorScalarFunction
          The LinearVectorScalarFunction class implements a scalar function that is implemented by a linear function.
 class LocallyWeightedKernelScalarFunction<InputType>
          The LocallyWeightedKernelScalarFunction class implements a scalar function that uses kernels and does local weighting on them to get the result value.
 class PolynomialFunction
          A single polynomial term specified by a real-valued exponent.
static class PolynomialFunction.Cubic
          Algebraic treatment for a polynomial of the form y(x) = q0 + q1*x + q2*x^2 + q3*x^3
static class PolynomialFunction.Linear
          Utilities for algebraic treatment of a linear polynomial of the form y(x) = q0 + q1*x
static class PolynomialFunction.Quadratic
          Utilities for algebraic treatment of a quadratic polynomial of the form y(x) = q0 + q1*x + q2*x^2.
static class PolynomialFunction.Regression
          Performs Linear Regression using an arbitrary set of PolynomialFunction basis functions
 class SigmoidFunction
          An implementation of a sigmoid squashing function.
 class ThresholdFunction
          Maps the input space onto the set {LOW_VALUE,HIGH_VALUE}.
 class VectorEntryFunction
          An evaluator that returns the value of an input vector at a specified index.
 class VectorFunctionLinearDiscriminant<InputType>
          This class takes a function that maps a generic InputType to a Vector.
 class VectorFunctionToScalarFunction<InputType>
          The VectorFunctionToScalarFunction class implements an adapter for using a vector function that outputs a single-dimensional vector as a scalar function.
static class VectorFunctionToScalarFunction.Learner<InputType>
          The VectorFunctionToScalarFunction.Learner class implements a simple learner for a VectorFunctionToScalarFunction that allows a learning algorithm that outputs a vector function to be adapted to learn on data whose output are doubles.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.function.summarizer
 

Classes in gov.sandia.cognition.learning.function.summarizer that implement CloneableSerializable
 class MostFrequentSummarizer<DataType>
          Summarizes a set of values by returning the most frequent value.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.function.vector
 

Classes in gov.sandia.cognition.learning.function.vector that implement CloneableSerializable
 class DifferentiableFeedforwardNeuralNetwork
          A feedforward neural network that can have an arbitrary number of layers, and an arbitrary differentiable squashing (activation) function assigned to each layer.
 class DifferentiableGeneralizedLinearModel
          A GradientDescenable version of a GeneralizedLinearModel, in other words, a GeneralizedLinearModel where the squashing function is differentiable
 class ElementWiseDifferentiableVectorFunction
          An ElementWiseVectorFunction that is also a DifferentiableVectorFunction
 class ElementWiseVectorFunction
          A VectorFunction that operates on each element of the Vector indepenently of all others.
 class EntropyEvaluator
          Takes a vector of inputs and computes the log base 2 entropy of the input.
 class FeedforwardNeuralNetwork
          A feedforward neural network that can have an arbitrary number of layers, and an arbitrary squashing (activation) function assigned to each layer.
 class GaussianContextRecognizer
          Uses a MixtureOfGaussians to compute the probability of the different constituent MultivariateGaussians (that is, the contexts)
static class GaussianContextRecognizer.Learner
          Creates a GaussianContextRecognizer from a Dataset using a BatchClusterer
 class GeneralizedLinearModel
          A VectorizableVectorFunction that is a matrix multiply followed by a VectorFunction...
 class LinearCombinationVectorFunction
          A weighted linear combination of scalar functions.
 class LinearVectorFunction
          The LinearFunction class is a simple VectorFunction that just scales the given input vector by a scalar value.
 class MultivariateDiscriminant
          Allows learning algorithms (vectorizing, differentiating) on a matrix*vector multiply.
 class MultivariateDiscriminantWithBias
          A multivariate discriminant (matrix multiply) plus a constant vector that gets added to the output of the discriminant.
 class ScalarBasisSet<InputType>
          Collection of scalar basis functions, where the ith function operates on the ith element of the output Vector
 class SubVectorEvaluator
          Extracts the given set of indices from an input vector to create a new vector containing the input vector's elements at those indices.
 class ThreeLayerFeedforwardNeuralNetwork
          This is a "standard" feedforward neural network with a single hidden layer.
 class VectorizableVectorConverter
          The VectorizableVectorConverter class implements a conversion between a Vectorizable and an Vector by calling the proper conversion method on the Vectorizable.
 class VectorizableVectorConverterWithBias
          The VectorizableVectorConverterWithBias class extends the VectorizableVectorConverter class to append a constant bias value of 1.0 to the vector returned by the converter.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.parameter
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.parameter
 interface ParameterAdapter<ObjectType,DataType>
          Interface for an object that can adapt the parameters of another object based on some given data.
 

Classes in gov.sandia.cognition.learning.parameter that implement CloneableSerializable
 class ParameterAdaptableBatchLearnerWrapper<DataType,ResultType,LearnerType extends BatchLearner<? super DataType,? extends ResultType>>
          A wrapper for adding parameter adapters to a batch learner.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.performance
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.performance
 interface PerformanceEvaluator<ObjectType,DataType,ResultType>
          The PerformanceEvaluator class defines the functionality of some object with regards to some data.
 interface SupervisedPerformanceEvaluator<InputType,TargetType,EstimateType,ResultType>
          The SupervisedPerformanceEvaluator interface extends the PerformanceEvaluator interface for performance evaluations of supervised machine learning algorithms where the target type is evaluated against the estimated type produced by the evaluator.
 

Classes in gov.sandia.cognition.learning.performance that implement CloneableSerializable
 class AbstractSupervisedPerformanceEvaluator<InputType,TargetType,EstimateType,ResultType>
          The AbstractSupervisedPerformanceEvaluator class contains an abstract implementation of the SupervisedPerformanceEvaluator class.
 class MeanAbsoluteErrorEvaluator<InputType>
          The MeanAbsoluteError class implements a method for computing the performance of a supervised learner for a scalar function by the mean absolute value between the target and estimated outputs.
 class MeanSquaredErrorEvaluator<InputType>
          The MeanSquaredError class implements the method for computing the performance of a supervised learner for a scalar function by the mean squared between the target and estimated outputs.
 class MeanZeroOneErrorEvaluator<InputType,DataType>
          The MeanZeroOneErrorEvaluator class implements a method for computing the performance of a supervised learner by the mean number of incorrect values between the target and estimated outputs.
 class RootMeanSquaredErrorEvaluator<InputType>
          The RootMeanSquaredErrorEvaluator class implements a method for computing the performance of a supervised learner for a scalar function by the root mean squared error (RMSE or RSE) between the target and estimated outputs.
 

Uses of CloneableSerializable in gov.sandia.cognition.learning.performance.categorization
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.learning.performance.categorization
 interface BinaryConfusionMatrix
          An interface for a binary confusion matrix.
 interface ConfusionMatrix<CategoryType>
          An interface for a general confusion matrix, which is used to tabulate a set of actual category values against the values predicted for those categories.
 

Classes in gov.sandia.cognition.learning.performance.categorization that implement CloneableSerializable
 class AbstractBinaryConfusionMatrix
          An abstract implementation of the BinaryConfusionMatrix interface.
 class AbstractConfusionMatrix<CategoryType>
          An abstract implementation of the ConfusionMatrix interface.
 class ConfusionMatrixPerformanceEvaluator<InputType,CategoryType>
          A performance evaluator that builds a confusion matrix.
 class DefaultBinaryConfusionMatrix
          A default implementation of the BinaryConfusionMatrix.
static class DefaultBinaryConfusionMatrix.ActualPredictedPairSummarizer
          A confusion matrix summarizer that summarizes actual-predicted pairs.
static class DefaultBinaryConfusionMatrix.CombineSummarizer
          A confusion matrix summarizer that adds together confusion matrices.
static class DefaultBinaryConfusionMatrix.PerformanceEvaluator<InputType>
          An implementation of the SupervisedPerformanceEvaluator interface for creating a DefaultBinaryConfusionMatrix.
static class DefaultBinaryConfusionMatrixConfidenceInterval.Summary
          An implementation of the Summarizer interface for creating a ConfusionMatrixInterval
 class DefaultConfusionMatrix<CategoryType>
          A default implementation of the ConfusionMatrix interface.
static class DefaultConfusionMatrix.ActualPredictedPairSummarizer<CategoryType>
          A confusion matrix summarizer that summarizes actual-predicted pairs.
static class DefaultConfusionMatrix.CombineSummarizer<CategoryType>
          A confusion matrix summarizer that adds together confusion matrices.
static class DefaultConfusionMatrix.Factory<CategoryType>
          A factory for default confusion matrices.
 

Uses of CloneableSerializable in gov.sandia.cognition.math
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.math
 interface ClosedFormDifferentiableEvaluator<InputType,OutputType,DerivativeType>
          A differentiable function that has a closed-form derivative.
 interface DifferentiableEvaluator<InputType,OutputType,DerivativeType>
          Interface that indicates that the Evaluator can be differentiated about the given input.
 interface DifferentiableUnivariateScalarFunction
          A differentiable univariate scalar function
 interface DivergenceFunction<FirstType,SecondType>
          The DivergenceFunction class defines the functionality of something that computes the divergence between two objects.
 interface EuclideanRing<RingType extends EuclideanRing<RingType>>
          Defines something similar to a Euclidean ring from abstract algebra.
 interface Field<FieldType extends Field<FieldType>>
          Defines something similar to a mathematical field.
 interface Metric<EvaluatedType>
          A metric is a non-negative function that satisfies the following properties g(x, y) + g(y, z) >= g(x, z) g(x, y) == g(y, x) g(x, x) == 0.
 interface Ring<RingType extends Ring<RingType>>
          Defines something similar to a mathematical ring.
 interface Semimetric<InputType>
          A semimetric is a divergence function that takes inputs from the same set (domain) and is positive definite and symmetric.
 interface UnivariateScalarFunction
          Simple interface that describes a function that maps the reals to the reals, has a Double to Double and double to double
 

Classes in gov.sandia.cognition.math that implement CloneableSerializable
 class AbstractDifferentiableUnivariateScalarFunction
          Partial implementation of DifferentiableUnivariateScalarFunction that implements the differentiate(Double) method with a callback to the differentiate(double) method, so that a concrete class only to implement the differentiate(double) method
 class AbstractEuclideanRing<RingType extends EuclideanRing<RingType>>
          An abstract implementation of the EuclideanRing interface.
 class AbstractField<FieldType extends Field<FieldType>>
          An abstract implementation of the Field interface.
 class AbstractRing<RingType extends Ring<RingType>>
          Implements the non-inline versions of the various Ring functions.
 class AbstractScalarFunction<InputType>
          An abstract implementation of the ScalarFunction interface.
 class AbstractUnivariateScalarFunction
          Abstract implementation of ScalarFunction where the evaluate(Double) method calls back into the evaluate(double) method.
 class ComplexNumber
          Represents a complex number in a rectangular manner, explicitly storing the real and imaginary portions: real + j*imaginary
 class LentzMethod
          This class implements Lentz's method for evaluating continued fractions.
 class LogNumber
          Represents a number in log-space, storing the log of the absolute value log(|value|) and the sign of the value sign(value).
 class MutableDouble
          A mutable object containing a double.
 class MutableInteger
          A mutable object containing an integer.
 class MutableLong
          A mutable object containing a long.
 class NumberAverager
          Returns an average (arithmetic mean) of a collection of Numbers
 class RingAverager<RingType extends Ring<RingType>>
          A type of Averager for Rings (Matrices, Vectors, ComplexNumbers).
 class UnivariateSummaryStatistics
          A Bayesian-style synopsis of a Collection of scalar data.
 class UnsignedLogNumber
          Represents an unsigned number in log space, storing log(value) and operating directly on it.
 class WeightedNumberAverager
          Averages together given set of weighted values by adding up the weight times the value and then dividing by the total weight.
 class WeightedRingAverager<RingType extends Ring<RingType>>
          A type of Summarizer for Rings (Matrices, Vectors, ComplexNumbers).
 

Uses of CloneableSerializable in gov.sandia.cognition.math.geometry
 

Classes in gov.sandia.cognition.math.geometry that implement CloneableSerializable
 class KDTree<VectorType extends Vectorizable,DataType,PairType extends Pair<? extends VectorType,DataType>>
          Implementation of a kd-tree.
protected  class KDTree.Neighborhood.Neighbor<VectorType extends Vectorizable,DataType,PairType extends Pair<? extends VectorType,DataType>>
          Holds neighbor information used during the evaluate method and is put into a priority queue.
protected static class KDTree.PairFirstVectorizableIndexComparator
          Comparator for Pairs that have a Vectorizable as its first parameter.
 class Quadtree<DataType extends Vectorizable>
          Implements the quadtree region-partitioning algorithm and data structure.
 class Quadtree.Node
          Represents a node in the quadtree.
 

Uses of CloneableSerializable in gov.sandia.cognition.math.matrix
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.math.matrix
 interface DiagonalMatrix
          Interface describing a diagonal matrix.
 interface DifferentiableVectorFunction
          A VectorFunction that can is also differentiable
 interface InfiniteVector<KeyType>
          A Vector that has a potentially infinite number of indices (keys), but will only contain a countable number in any instance.
 interface Matrix
          Defines the base functionality for all implementations of a Matrix
 interface Quaternion
          Interface for a mathematical quaternion, which represents rotations using four dimensions.
 interface Vector
          The Vector interface defines the operations that are expected on a mathematical vector.
 interface Vector1D
          An interface for a 1-dimensional vector.
 interface Vector2D
          An interface for a 2-dimensional vector.
 interface Vector3D
          An interface for a 3-dimensional vector.
 interface Vectorizable
          The Vectorizable interface is an interface for an object that can be converted to and from a Vector.
 interface VectorizableDifferentiableVectorFunction
          A VectorizableVectorFunction that also define a derivative (this is needed for GradientDescendable).
 interface VectorizableVectorFunction
          The VectorizableVectorFunction interface defines a useful interface for doing machine learning, which is a function that takes and returns vectors and also is parameterizable as a vector.
 interface VectorSpace<VectorType extends VectorSpace<VectorType,?>,EntryType extends VectorSpace.Entry>
          In the Foundry, a VectorSpace is a type of Ring that we can perform Vector-like operations on: norm, distances between Vectors, etc.
 

Classes in gov.sandia.cognition.math.matrix that implement CloneableSerializable
 class AbstractMatrix
          Abstract implementation of some low-hanging functions in the Matrix interface.
 class AbstractVector
          Abstract implementation of some of the Vector interface, in a storage-free manner
 class AbstractVectorSpace<VectorType extends VectorSpace<VectorType,? extends EntryType>,EntryType extends VectorSpace.Entry>
          Partial implementation of VectorSpace
 class DefaultInfiniteVector<KeyType>
          An implementation of an InfiniteVector backed by a LinkedHashMap.
 class DefaultVectorFactoryContainer
          A default implementation of the VectorFactoryContainer interface.
 class NumericalDifferentiator<InputType,OutputType,DerivativeType>
          Automatically differentiates a function by the method of forward differences.
static class NumericalDifferentiator.DoubleJacobian
          Numerical differentiator based on a Vector Jacobian.
static class NumericalDifferentiator.MatrixJacobian
          Numerical differentiator based on a Matrix Jacobian.
static class NumericalDifferentiator.VectorJacobian
          Numerical differentiator based on a Vector Jacobian.
 class VectorizableIndexComparator
          Compares the given index of two Vectorizables.
 

Uses of CloneableSerializable in gov.sandia.cognition.math.matrix.decomposition
 

Classes in gov.sandia.cognition.math.matrix.decomposition that implement CloneableSerializable
 class EigenvectorPowerIteration
          Implementation of the Eigenvector Power Iteration algorithm.
 

Uses of CloneableSerializable in gov.sandia.cognition.math.matrix.mtj
 

Classes in gov.sandia.cognition.math.matrix.mtj that implement CloneableSerializable
 class AbstractMTJMatrix
          Relies on internal MTJ matrix to do some of the heavy lifting, without assuming that the underlying matrix is Dense or Sparse
 class AbstractMTJVector
          Implementation of the Vector interface that relies on MTJ Vectors, but does not specify sparse or dense storage.
 class AbstractSparseMatrix
          Implements some generic operations that any sparse-matrix representation must do.
 class DenseMatrix
          Matrix that represents all its entries using a fixed-size storage scheme, based on MTJ's DenseMatrix storage class.
 class DenseVector
          A generally useful vector representation that allocates a fixed-size underlying vector, based on MTJ's DenseVector
 class DiagonalMatrixMTJ
          A diagonal matrix that wraps MTJ's BandMatrix class.
 class SparseColumnMatrix
          A sparse matrix, represented as a collection of sparse column vectors.
 class SparseMatrix
          A sparse matrix, represented as a collection of sparse row vectors.
 class SparseRowMatrix
          A sparse matrix, represented as a collection of sparse row vectors.
 class SparseVector
          A vector that only stores the nonzero elements, relies on MTJ's SparseVector.
 class Vector1
          Implements a one-dimensional MTJ DenseVector.
 class Vector2
          Implements a two-dimensional MTJ DenseVector.
 class Vector3
          Implements a three-dimensional DenseVector.
 

Uses of CloneableSerializable in gov.sandia.cognition.math.matrix.mtj.decomposition
 

Classes in gov.sandia.cognition.math.matrix.mtj.decomposition that implement CloneableSerializable
 class CholeskyDecompositionMTJ
          Computes the Cholesky decomposition of the symmetric positive definite matrix.
 

Uses of CloneableSerializable in gov.sandia.cognition.math.signals
 

Classes in gov.sandia.cognition.math.signals with type parameters of type CloneableSerializable
 interface DiscreteTimeFilter<StateType extends CloneableSerializable>
          A discrete-time filter.
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.math.signals
 interface DiscreteTimeFilter<StateType extends CloneableSerializable>
          A discrete-time filter.
 

Classes in gov.sandia.cognition.math.signals that implement CloneableSerializable
 class AutoRegressiveMovingAverageFilter
          A type of filter using a moving-average calculation.
 class FourierTransform
          Computes the Fast Fourier Transform, or brute-force discrete Fourier transform, of a discrete input sequence.
static class FourierTransform.Inverse
          Evaluator that inverts a Fourier transform.
 class LinearDynamicalSystem
          A generic Linear Dynamical System of the form
x_n = A*x_(n-1) + B*u_n
y_n = C*x_n,
where x_(n-1) is the previous state, x_n is the current state, u_n is the current input, y_n is the current output, A is the system matrix, B is the input-gain matrix, and C is the output-selector matrix
 class MovingAverageFilter
          A type of filter using a moving-average calculation.
 class PIDController
          This class defines a Proportional-plus-Integral-plus-Derivative set-point controller.
static class PIDController.State
          State of a PIDController
 

Uses of CloneableSerializable in gov.sandia.cognition.statistics
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.statistics
 interface ClosedFormComputableDiscreteDistribution<DataType>
          A discrete, closed-form Distribution with a PMF.
 interface ClosedFormComputableDistribution<DataType>
          A closed-form Distribution that also has an associated distribution function.
 interface ClosedFormCumulativeDistributionFunction<DomainType extends Number>
          Functionality of a cumulative distribution function that's defined with closed-form parameters.
 interface ClosedFormDiscreteUnivariateDistribution<DomainType extends Number>
          A ClosedFormUnivariateDistribution that is also a DiscreteDistribution
 interface ClosedFormDistribution<DataType>
          Defines a distribution that is described a parameterized mathematical equation.
 interface ClosedFormUnivariateDistribution<NumberType extends Number>
          Defines the functionality associated with a closed-form scalar distribution.
 interface ComputableDistribution<DomainType>
          A type of Distribution that has an associated distribution function, either a PDF or PMF.
 interface CumulativeDistributionFunction<NumberType extends Number>
          Functionality of a cumulative distribution function.
 interface DataDistribution<DataType>
          A distribution of data from which we can sample and perform Ring operations.
static interface DataDistribution.PMF<KeyType>
          Interface for the probability mass function (PMF) of a data distribution.
 interface DiscreteDistribution<DataType>
          A Distribution with a countable domain (input) set.
 interface Distribution<DataType>
          Describes a very high-level distribution of data.
 interface DistributionEstimator<ObservationType,DistributionType extends Distribution<? extends ObservationType>>
          A BatchLearner that estimates a Distribution.
 interface DistributionParameter<ParameterType,ConditionalType extends Distribution<?>>
          Allows access to a parameter within a closed-form distribution, given by the high-level String value.
 interface DistributionWeightedEstimator<ObservationType,DistributionType extends Distribution<? extends ObservationType>>
          A BatchLearner that estimates a Distribution from a Collection of weighted data.
 interface DistributionWithMean<DataType>
          A Distribution that has a well-defined mean, or first central moment.
 interface EstimableDistribution<ObservationType,DistributionType extends EstimableDistribution<ObservationType,? extends DistributionType>>
          A Distribution that has an estimator associated with it, typically a closed-form estimator.
 interface IncrementalEstimator<DataType,DistributionType extends Distribution<? extends DataType>,SufficientStatisticsType extends SufficientStatistic<? super DataType,? extends DistributionType>>
          An estimator of a Distribution that uses SufficientStatistic to arrive at its result.
 interface InvertibleCumulativeDistributionFunction<NumberType extends Number>
          A cumulative distribution function that is empirically invertible.
 interface ProbabilityDensityFunction<DataType>
          Defines a probability density function.
 interface ProbabilityFunction<DataType>
          A Distribution that has an evaluate method that indicates p(x), such as a probability density function or a probability mass function (but NOT a cumulative distribution function).
 interface ProbabilityMassFunction<DataType>
          The ProbabilityMassFunction interface defines the functionality of a probability mass function.
 interface RandomVariable<DataType>
          Describes the functionality of a random variable.
 interface SmoothCumulativeDistributionFunction
          This defines a CDF that has an associated derivative, which is its PDF.
 interface SmoothUnivariateDistribution
          A closed-form scalar distribution that is also smooth.
 interface SufficientStatistic<DataType,DistributionType>
          Sufficient statistics are the data which are sufficient to store all information to create an underlying parameter, such as a Distribution.
 interface UnivariateDistribution<NumberType extends Number>
          A Distribution that takes Doubles as inputs and can compute its variance.
 interface UnivariateProbabilityDensityFunction
          A PDF that takes doubles as input.
 

Classes in gov.sandia.cognition.statistics that implement CloneableSerializable
 class AbstractClosedFormSmoothUnivariateDistribution
          Partial implementation of SmoothUnivariateDistribution
 class AbstractClosedFormUnivariateDistribution<NumberType extends Number>
          Partial implementation of a ClosedFormUnivariateDistribution.
 class AbstractDataDistribution<KeyType>
          An abstract implementation of the DataDistribution interface.
 class AbstractDistribution<DataType>
          Partial implementation of Distribution.
 class AbstractIncrementalEstimator<DataType,DistributionType extends Distribution<? extends DataType>,SufficientStatisticsType extends SufficientStatistic<DataType,DistributionType>>
          Partial implementation of IncrementalEstimator.
 class AbstractRandomVariable<DataType>
          Partial implementation of RandomVariable.
 class AbstractSufficientStatistic<DataType,DistributionType>
          Partial implementation of SufficientStatistic
 class DefaultDistributionParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>>
          Default implementation of DistributionParameter using introspection.
 class UnivariateRandomVariable
          This is an implementation of a RandomVariable for scalar distributions.
 

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

Subinterfaces of CloneableSerializable in gov.sandia.cognition.statistics.bayesian
 interface BayesianEstimator<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>>
          A type of estimation procedure based on Bayes's rule, which allows us to estimate the uncertainty of parameters given a set of observations that we are given.
 interface BayesianEstimatorPredictor<ObservationType,ParameterType,PosteriorType extends Distribution<? extends ParameterType>>
          A BayesianEstimator that can also compute the predictive distribution of new data given the posterior.
 interface BayesianParameter<ParameterType,ConditionalType extends Distribution<?>,PriorType extends Distribution<ParameterType>>
          A parameter from a Distribution that has an assumed Distribution of values.
 interface BayesianRegression<OutputType,PosteriorType extends Distribution<? extends Vector>>
          A type of regression algorithm maps a Vector space, and the weights of this Vector space are represented as a posterior distribution given the observed InputOutputPairs.
static interface DirichletProcessMixtureModel.Updater<ObservationType>
          Updater for the DPMM
static interface ImportanceSampling.Updater<ObservationType,ParameterType>
          Updater for ImportanceSampling
 interface MarkovChainMonteCarlo<ObservationType,ParameterType>
          Defines the functionality of a Markov chain Monte Carlo algorithm.
static interface MetropolisHastingsAlgorithm.Updater<ObservationType,ParameterType>
          Creates proposals for the MCMC steps.
 interface ParticleFilter<ObservationType,ParameterType>
          A particle filter aims to estimate a sequence of hidden parameters based on observed data using point-mass estimates of the posterior distribution.
static interface ParticleFilter.Updater<ObservationType,ParameterType>
          Updates the particles.
 interface RecursiveBayesianEstimator<ObservationType,ParameterType,BeliefType extends Distribution<ParameterType>>
          A recursive Bayesian estimator is an estimation method that uses the previous belief of the system parameter and a single observation to refine the estimate of the system parameter.
static interface RejectionSampling.Updater<ObservationType,ParameterType>
          Updater for ImportanceSampling
 

Classes in gov.sandia.cognition.statistics.bayesian that implement CloneableSerializable
 class AbstractBayesianParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>,PriorType extends Distribution<ParameterType>>
          Partial implementation of BayesianParameter
 class AbstractKalmanFilter
          Contains fields useful to both Kalman filters and extended Kalman filters.
 class AbstractMarkovChainMonteCarlo<ObservationType,ParameterType>
          Partial abstract implementation of MarkovChainMonteCarlo.
 class AbstractParticleFilter<ObservationType,ParameterType>
          Partial abstract implementation of ParticleFilter.
 class AdaptiveRejectionSampling
          Samples form a univariate distribution using the method of adaptive rejection sampling, which is a very efficient method that iteratively improves the rejection and acceptance envelopes in response to additional points.
 class AdaptiveRejectionSampling.AbstractEnvelope
          Describes an enveloping function comprised of a sorted sequence of lines
static class AdaptiveRejectionSampling.LineSegment
          A line that has a minimum and maximum support (x-axis) value.
static class AdaptiveRejectionSampling.LogEvaluator<EvaluatorType extends Evaluator<Double,Double>>
          Wraps an Evaluator and takes the natural logarithm of the evaluate method
 class AdaptiveRejectionSampling.LowerEnvelope
          Define the lower envelope for Adaptive Rejection Sampling
static class AdaptiveRejectionSampling.PDFLogEvaluator
          Wraps a PDF so that it returns the logEvaluate method.
static class AdaptiveRejectionSampling.Point
          An InputOutputPair that has a natural ordering according to their input (x-axis) values.
 class AdaptiveRejectionSampling.UpperEnvelope
          Constructs the upper envelope for sampling.
 class BayesianCredibleInterval
          A Bayesian credible interval defines a bound that a scalar parameter is within the given interval.
 class BayesianLinearRegression
          Computes a Bayesian linear estimator for a given feature function and a set of observed data.
static class BayesianLinearRegression.IncrementalEstimator
          Incremental estimator for BayesianLinearRegression
 class BayesianLinearRegression.IncrementalEstimator.SufficientStatistic
          SufficientStatistic for incremental Bayesian linear regression
 class BayesianLinearRegression.PredictiveDistribution
          Creates the predictive distribution for the likelihood of a given point.
 class BayesianRobustLinearRegression
          Computes a Bayesian linear estimator for a given feature function given a set of InputOutputPair observed values.
static class BayesianRobustLinearRegression.IncrementalEstimator
          Incremental estimator for BayesianRobustLinearRegression
 class BayesianRobustLinearRegression.IncrementalEstimator.SufficientStatistic
          SufficientStatistic for incremental Bayesian linear regression
 class BayesianRobustLinearRegression.PredictiveDistribution
          Predictive distribution of future data given the posterior of the weights given the data.
 class DefaultBayesianParameter<ParameterType,ConditionalType extends ClosedFormDistribution<?>,PriorType extends Distribution<ParameterType>>
          Default implementation of BayesianParameter using reflection.
 class DirichletProcessMixtureModel<ObservationType>
          An implementation of Dirichlet Process clustering, which estimates the number of clusters and the centroids of the clusters from a set of data.
static class DirichletProcessMixtureModel.DPMMCluster<ObservationType>
          Cluster for a step in the DPMM
protected static class DirichletProcessMixtureModel.DPMMLogConditional
          Container for the log conditional likelihood
static class DirichletProcessMixtureModel.MultivariateMeanCovarianceUpdater
          Updater that creates specified clusters with distinct means and covariances
static class DirichletProcessMixtureModel.MultivariateMeanUpdater
          Updater that creates specified clusters with identical covariances
static class DirichletProcessMixtureModel.Sample<ObservationType>
          A sample from the Dirichlet Process Mixture Model.
 class ExtendedKalmanFilter
          Implements the Extended Kalman Filter (EKF), which is an extension of the Kalman filter that allows nonlinear motion and observation models.
 class GaussianProcessRegression<InputType>
          Gaussian Process Regression, is also known as Kriging, is a nonparametric method to interpolate and extrapolate using Bayesian regression, where the expressiveness of the estimator can grow with the data.
 class GaussianProcessRegression.PredictiveDistribution
          Predictive distribution for Gaussian Process Regression.
 class ImportanceSampling<ObservationType,ParameterType>
          Importance sampling is a Monte Carlo inference technique where we sample from an easy distribution over the hidden variables (parameters) and then weight the result by the ratio of the likelihood of the parameters given the evidence and the likelihood of generating the parameters.
static class ImportanceSampling.DefaultUpdater<ObservationType,ParameterType>
          Default ImportanceSampling Updater that uses a BayesianParameter to compute the quantities of interest.
 class KalmanFilter
          A Kalman filter estimates the state of a dynamical system corrupted with white Gaussian noise with observations that are corrupted with white Gaussian noise.
 class MetropolisHastingsAlgorithm<ObservationType,ParameterType>
          An implementation of the Metropolis-Hastings MCMC algorithm, which is the most general formulation of MCMC but can be slow.
 class ParallelDirichletProcessMixtureModel<ObservationType>
          A Parallelized version of vanilla Dirichlet Process Mixture Model learning.
protected  class ParallelDirichletProcessMixtureModel.ClusterUpdaterTask
          Tasks that update the values of the clusters for Gibbs sampling
protected  class ParallelDirichletProcessMixtureModel.ObservationAssignmentTask
          Task that assign observations to cluster indices
 class RejectionSampling<ObservationType,ParameterType>
          Rejection sampling is a method of inferring hidden parameters by using an easy-to-sample-from distribution (times a scale factor) that envelopes another distribution that is difficult to sample from.
static class RejectionSampling.DefaultUpdater<ObservationType,ParameterType>
          Default ImportanceSampling Updater that uses a BayesianParameter to compute the quantities of interest.
 class SamplingImportanceResamplingParticleFilter<ObservationType,ParameterType>
          An implementation of the standard Sampling Importance Resampling particle filter.
 

Uses of CloneableSerializable in gov.sandia.cognition.statistics.bayesian.conjugate
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.statistics.bayesian.conjugate
 interface ConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>>
          A Bayesian Estimator that makes use of conjugate priors, which is a mathematical trick when the conditional and the prior result a posterior that is the same type as the prior.
 interface ConjugatePriorBayesianEstimatorPredictor<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>>
          A conjugate prior estimator that also has a closed-form predictive posterior.
 

Classes in gov.sandia.cognition.statistics.bayesian.conjugate that implement CloneableSerializable
 class AbstractConjugatePriorBayesianEstimator<ObservationType,ParameterType,ConditionalType extends ClosedFormDistribution<ObservationType>,BeliefType extends ClosedFormDistribution<ParameterType>>
          Partial implementation of ConjugatePriorBayesianEstimator that contains a initial belief (prior) distribution function.
 class BernoulliBayesianEstimator
          A Bayesian estimator for the parameter of a BernoulliDistribution using the conjugate prior BetaDistribution.
static class BernoulliBayesianEstimator.Parameter
          Parameter of this conjugate prior relationship.
 class BinomialBayesianEstimator
          A Bayesian estimator for the parameter of a Bernoulli parameter, p, of a BinomialDistribution using the conjugate prior BetaDistribution.
static class BinomialBayesianEstimator.Parameter
          Parameter of this relationship
 class ExponentialBayesianEstimator
          Conjugate prior Bayesian estimator of the "rate" parameter of an Exponential distribution using the conjugate prior Gamma distribution.
static class ExponentialBayesianEstimator.Parameter
          Bayesian parameter describing this conjugate relationship.
 class GammaInverseScaleBayesianEstimator
          A Bayesian estimator for the scale parameter of a Gamma distribution using the conjugate prior Gamma distribution for the inverse-scale (rate) of the Gamma.
static class GammaInverseScaleBayesianEstimator.Parameter
          Bayesian parameter describing this conjugate relationship.
 class MultinomialBayesianEstimator
          A Bayesian estimator for the parameters of a MultinomialDistribution using its conjugate prior distribution, the DirichletDistribution.
static class MultinomialBayesianEstimator.Parameter
          Parameter of this conjugate prior relationship.
 class MultivariateGaussianMeanBayesianEstimator
          Bayesian estimator for the mean of a MultivariateGaussian using its conjugate prior, which is also a MultivariateGaussian.
static class MultivariateGaussianMeanBayesianEstimator.Parameter
          Parameter of this conjugate prior relationship.
 class MultivariateGaussianMeanCovarianceBayesianEstimator
          Performs robust estimation of both the mean and covariance of a MultivariateGaussian conditional distribution using the conjugate prior Normal-Inverse-Wishart distribution.
static class MultivariateGaussianMeanCovarianceBayesianEstimator.Parameter
          Parameter for this conjugate prior estimator.
 class PoissonBayesianEstimator
          A Bayesian estimator for the parameter of a PoissonDistribution using the conjugate prior GammaDistribution.
static class PoissonBayesianEstimator.Parameter
          Parameter of this conjugate prior relationship.
 class UniformDistributionBayesianEstimator
          A Bayesian estimator for a conditional Uniform(0,theta) distribution using its conjugate prior Pareto distribution.
static class UniformDistributionBayesianEstimator.Parameter
          Parameter of this conjugate prior relationship.
 class UnivariateGaussianMeanBayesianEstimator
          Bayesian estimator for the mean of a UnivariateGaussian using its conjugate prior, which is also a UnivariateGaussian.
static class UnivariateGaussianMeanBayesianEstimator.Parameter
          Parameter of this conjugate prior relationship.
 class UnivariateGaussianMeanVarianceBayesianEstimator
          Computes the mean and variance of a univariate Gaussian using the conjugate prior NormalInverseGammaDistribution
static class UnivariateGaussianMeanVarianceBayesianEstimator.Parameter
          Parameter for this conjugate prior estimator.
 

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

Classes in gov.sandia.cognition.statistics.distribution that implement CloneableSerializable
 class BernoulliDistribution
          A Bernoulli distribution, which takes a value of "1" with probability "p" and value of "0" with probability "1-p".
static class BernoulliDistribution.CDF
          CDF of a Bernoulli distribution.
static class BernoulliDistribution.PMF
          PMF of the Bernoulli distribution.
 class BetaBinomialDistribution
          A Binomial distribution where the binomial parameter, p, is set according to a Beta distribution instead of a single value.
static class BetaBinomialDistribution.CDF
          CDF of BetaBinomialDistribution
static class BetaBinomialDistribution.MomentMatchingEstimator
          Estimates the parameters of a Beta distribution using the matching of moments, not maximum likelihood.
static class BetaBinomialDistribution.PMF
          PMF of the BetaBinomialDistribution
 class BetaDistribution
          Computes the Beta-family of probability distributions.
static class BetaDistribution.CDF
          CDF of the Beta-family distribution
static class BetaDistribution.MomentMatchingEstimator
          Estimates the parameters of a Beta distribution using the matching of moments, not maximum likelihood.
static class BetaDistribution.PDF
          Beta distribution probability density function
static class BetaDistribution.WeightedMomentMatchingEstimator
          Estimates the parameters of a Beta distribution using the matching of moments, not maximum likelihood.
 class BinomialDistribution
          Binomial distribution, which is a collection of Bernoulli trials
static class BinomialDistribution.CDF
          CDF of the Binomial distribution, which is the probability of getting up to "x" successes in "N" trials with a Bernoulli probability of "p"
static class BinomialDistribution.MaximumLikelihoodEstimator
          Maximum likelihood estimator of the distribution
static class BinomialDistribution.PMF
          The Probability Mass Function of a binomial distribution.
 class CategoricalDistribution
          The Categorical Distribution is the multivariate generalization of the Bernoulli distribution, where the outcome of an experiment is a one-of-N output, where the output is a selector Vector.
static class CategoricalDistribution.PMF
          PMF of the Categorical Distribution
 class CauchyDistribution
          A Cauchy Distribution is the ratio of two Gaussian Distributions, sometimes known as the Lorentz distribution.
static class CauchyDistribution.CDF
          CDF of the CauchyDistribution.
static class CauchyDistribution.PDF
          PDF of the CauchyDistribution.
 class ChineseRestaurantProcess
          A Chinese Restaurant Process is a discrete stochastic processes that partitions data points to clusters.
static class ChineseRestaurantProcess.PMF
          PMF of the Chinese Restaurant Process
 class ChiSquareDistribution
          Describes a Chi-Square Distribution.
static class ChiSquareDistribution.CDF
          Cumulative Distribution Function (CDF) of a Chi-Square Distribution
static class ChiSquareDistribution.PDF
          PDF of the Chi-Square distribution
 class DataCountTreeSetBinnedMapHistogram<ValueType extends Comparable<? super ValueType>>
          The DataCountTreeSetBinnedMapHistogram class extends a DefaultDataDistribution by mapping values to user defined bins using a TreeSetBinner.
 class DefaultDataDistribution<KeyType>
          A default implementation of ScalarDataDistribution that uses a backing map.
static class DefaultDataDistribution.DefaultFactory<DataType>
          A factory for DefaultDataDistribution objects using some given initial capacity for them.
static class DefaultDataDistribution.Estimator<KeyType>
          Estimator for a DefaultDataDistribution
static class DefaultDataDistribution.PMF<KeyType>
          PMF of the DefaultDataDistribution
static class DefaultDataDistribution.WeightedEstimator<KeyType>
          A weighted estimator for a DefaultDataDistribution
 class DeterministicDistribution
          A deterministic distribution that returns samples at a single point.
static class DeterministicDistribution.CDF
          CDF of the deterministic distribution.
static class DeterministicDistribution.PMF
          PMF of the deterministic distribution.
 class DirichletDistribution
          The Dirichlet distribution is the multivariate generalization of the beta distribution.
static class DirichletDistribution.PDF
          PDF of the Dirichlet distribution.
 class ExponentialDistribution
          An Exponential distribution describes the time between events in a poisson process, resulting in a memoryless distribution.
static class ExponentialDistribution.CDF
          CDF of the ExponentialDistribution.
static class ExponentialDistribution.MaximumLikelihoodEstimator
          Creates a ExponentialDistribution from data
static class ExponentialDistribution.PDF
          PDF of the ExponentialDistribution.
static class ExponentialDistribution.WeightedMaximumLikelihoodEstimator
          Creates a ExponentialDistribution from weighted data
 class GammaDistribution
          Class representing the Gamma distribution.
static class GammaDistribution.CDF
          CDF of the Gamma distribution
static class GammaDistribution.MomentMatchingEstimator
          Computes the parameters of a Gamma distribution by the Method of Moments
static class GammaDistribution.PDF
          Closed-form PDF of the Gamma distribution
static class GammaDistribution.WeightedMomentMatchingEstimator
          Estimates the parameters of a Gamma distribution using the matching of moments, not maximum likelihood.
 class GeometricDistribution
          The geometric distribution models the number of successes before the first failure occurs under an independent succession of Bernoulli tests.
static class GeometricDistribution.CDF
          CDF of the Geometric distribution
static class GeometricDistribution.MaximumLikelihoodEstimator
          Maximum likelihood estimator of the distribution
static class GeometricDistribution.PMF
          PMF of the Geometric distribution
 class InverseGammaDistribution
          Defines an inverse-gamma distribution.
static class InverseGammaDistribution.CDF
          CDF of the inverseRootFinder-gamma distribution.
static class InverseGammaDistribution.PDF
          PDF of the inverseRootFinder-Gamma distribution.
 class InverseWishartDistribution
          The Inverse-Wishart distribution is the multivariate generalization of the inverse-gamma distribution.
static class InverseWishartDistribution.PDF
          PDF of the Inverse-Wishart distribution, though I have absolutely no idea why anybody would evaluate the PDF of an Inverse-Wishart...
 class KolmogorovDistribution
          Contains the Cumulative Distribution Function description for the "D" statistic used within the Kolmogorov-Smirnov test.
static class KolmogorovDistribution.CDF
          Contains the Cumulative Distribution Function description for the "D" statistic used within the Kolmogorov-Smirnov test.
 class LaplaceDistribution
          A Laplace distribution, sometimes called a double exponential distribution.
static class LaplaceDistribution.CDF
          CDF of the Laplace distribution.
static class LaplaceDistribution.MaximumLikelihoodEstimator
          Estimates the ML parameters of a Laplace distribution from a Collection of Numbers.
static class LaplaceDistribution.PDF
          The PDF of a Laplace Distribution.
static class LaplaceDistribution.WeightedMaximumLikelihoodEstimator
          Creates a UnivariateGaussian from weighted data
 class LinearMixtureModel<DataType,DistributionType extends Distribution<DataType>>
          A linear mixture of RandomVariables, with a prior probability distribution.
 class LogisticDistribution
          A implementation of the scalar logistic distribution, which measures the log-odds of a binary event.
static class LogisticDistribution.CDF
          CDF of the LogisticDistribution
static class LogisticDistribution.PDF
          PDF of the LogisticDistribution
 class LogNormalDistribution
          Log-Normal distribution PDF and CDF implementations.
static class LogNormalDistribution.CDF
          CDF of the Log-Normal Distribution
static class LogNormalDistribution.MaximumLikelihoodEstimator
          Maximum Likelihood Estimator of a log-normal distribution.
static class LogNormalDistribution.PDF
          PDF of a Log-normal distribution
static class LogNormalDistribution.WeightedMaximumLikelihoodEstimator
          Maximum Likelihood Estimator from weighted data
static class MixtureOfGaussians.EMLearner
          An Expectation-Maximization based "soft" assignment learner.
static class MixtureOfGaussians.Learner
          A hard-assignment learner for a MixtureOfGaussians
static class MixtureOfGaussians.PDF
          PDF of the MixtureOfGaussians
 class MultinomialDistribution
          A multinomial distribution is the multivariate/multiclass generalization of the Binomial distribution.
protected static class MultinomialDistribution.Domain.MultinomialIterator
          An Iterator over a Domain
static class MultinomialDistribution.PMF
          Probability Mass Function of the Multinomial Distribution.
 class MultivariateGaussian
          The MultivariateGaussian class implements a multidimensional Gaussian distribution that contains a mean vector and a covariance matrix.
static class MultivariateGaussian.IncrementalEstimator
          The estimator that creates a MultivariateGaussian from a stream of values.
static class MultivariateGaussian.IncrementalEstimatorCovarianceInverse
          The estimator that creates a MultivariateGaussian from a stream of values by estimating the mean and covariance inverse (as opposed to the covariance directly), without ever performing a matrix inversion.
static class MultivariateGaussian.MaximumLikelihoodEstimator
          Computes the Maximum Likelihood Estimate of the MultivariateGaussian given a set of Vectors
static class MultivariateGaussian.PDF
          PDF of a multivariate Gaussian
static class MultivariateGaussian.SufficientStatistic
          Implements the sufficient statistics of the MultivariateGaussian.
static class MultivariateGaussian.SufficientStatisticCovarianceInverse
          Implements the sufficient statistics of the MultivariateGaussian while estimating the inverse of the covariance matrix.
static class MultivariateGaussian.WeightedMaximumLikelihoodEstimator
          Computes the Weighted Maximum Likelihood Estimate of the MultivariateGaussian given a weighted set of Vectors
 class MultivariateGaussianInverseGammaDistribution
          A distribution where the mean is selected by a multivariate Gaussian and a variance parameter (either for a univariate Gaussian or isotropic Gaussian) is determined by an Inverse-Gamma distribution.
 class MultivariateMixtureDensityModel<DistributionType extends ClosedFormComputableDistribution<Vector>>
          A LinearMixtureModel of multivariate distributions with associated PDFs.
static class MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>>
          PDF of the MultivariateMixtureDensityModel
 class MultivariatePolyaDistribution
          A multivariate Polya Distribution, also known as a Dirichlet-Multinomial model, is a compound distribution where the parameters of a multinomial are drawn from a Dirichlet distribution with fixed parameters and a constant number of trials and then the observations are generated by this multinomial.
static class MultivariatePolyaDistribution.PMF
          PMF of the MultivariatePolyaDistribution
 class MultivariateStudentTDistribution
          Multivariate generalization of the noncentral Student's t-distribution.
static class MultivariateStudentTDistribution.PDF
          PDF of the MultivariateStudentTDistribution
 class NegativeBinomialDistribution
          Negative binomial distribution, also known as the Polya distribution, gives the number of successes of a series of Bernoulli trials before recording a given number of failures.
static class NegativeBinomialDistribution.CDF
          CDF of the NegativeBinomialDistribution
static class NegativeBinomialDistribution.MaximumLikelihoodEstimator
          Maximum likelihood estimator of the distribution
static class NegativeBinomialDistribution.PMF
          PMF of the NegativeBinomialDistribution.
static class NegativeBinomialDistribution.WeightedMaximumLikelihoodEstimator
          Weighted maximum likelihood estimator of the distribution
 class NormalInverseGammaDistribution
          The normal inverse-gamma distribution is the product of a univariate Gaussian distribution with an inverse-gamma distribution.
static class NormalInverseGammaDistribution.PDF
          PDF of the NormalInverseGammaDistribution
 class NormalInverseWishartDistribution
          The normal inverse Wishart distribution
static class NormalInverseWishartDistribution.PDF
          PDF of the normal inverse-Wishart distribution.
 class ParetoDistribution
          This class describes the Pareto distribution, sometimes called the Bradford Distribution.
static class ParetoDistribution.CDF
          CDF of the Pareto Distribution.
static class ParetoDistribution.PDF
          PDF of the ParetoDistribution
 class PoissonDistribution
          A Poisson distribution is the limits of what happens when a Bernoulli trial with "rare" events are sampled on a continuous basis and then binned into discrete time intervals.
static class PoissonDistribution.CDF
          CDF of the PoissonDistribution
static class PoissonDistribution.MaximumLikelihoodEstimator
          Creates a PoissonDistribution from data
static class PoissonDistribution.PMF
          PMF of the PoissonDistribution.
static class PoissonDistribution.WeightedMaximumLikelihoodEstimator
          Creates a PoissonDistribution from weighted data.
 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.Estimator
          Estimator for a ScalarDataDistribution
static class ScalarDataDistribution.PMF
          PMF of the ScalarDataDistribution
 class ScalarMixtureDensityModel
          ScalarMixtureDensityModel (SMDM) implements just that: a scalar mixture density model.
static class ScalarMixtureDensityModel.CDF
          CDFof the SMDM
static class ScalarMixtureDensityModel.EMLearner
          An EM learner that estimates a mixture model from data
static class ScalarMixtureDensityModel.PDF
          PDF of the SMDM
 class SnedecorFDistribution
          CDF of the Snedecor F-distribution (also known as Fisher F-distribution, Fisher-Snedecor F-distribution, or just plain old F-distribution).
static class SnedecorFDistribution.CDF
          CDF of the F-distribution.
 class StudentizedRangeDistribution
          Implementation of the Studentized Range distribution, which defines the population correction factor when performing multiple comparisons.
static class StudentizedRangeDistribution.CDF
          CDF of the StudentizedRangeDistribution
 class StudentTDistribution
          Defines a noncentral Student-t Distribution.
static class StudentTDistribution.CDF
          Evaluator that computes the Cumulative Distribution Function (CDF) of a Student-t distribution with a fixed number of degrees of freedom
static class StudentTDistribution.MaximumLikelihoodEstimator
          Estimates the parameters of the Student-t distribution from the given data, where the degrees of freedom are estimated from the Kurtosis of the sample data.
static class StudentTDistribution.PDF
          Evaluator that computes the Probability Density Function (CDF) of a Student-t distribution with a fixed number of degrees of freedom
static class StudentTDistribution.WeightedMaximumLikelihoodEstimator
          Creates a UnivariateGaussian from weighted data
 class UniformDistribution
          Contains the (very simple) definition of a continuous Uniform distribution, parameterized between the minimum and maximum bounds.
static class UniformDistribution.CDF
          Cumulative Distribution Function of a uniform
static class UniformDistribution.MaximumLikelihoodEstimator
          Maximum Likelihood Estimator of a log-normal distribution.
static class UniformDistribution.PDF
          Probability density function of a Uniform Distribution
 class UnivariateGaussian
          This class contains internal classes that implement useful functions based on the Gaussian distribution.
static class UnivariateGaussian.CDF
          CDF of the underlying Gaussian.
static class UnivariateGaussian.CDF.Inverse
          Inverts the CumulativeDistribution function.
static class UnivariateGaussian.ErrorFunction
          Gaussian Error Function, useful for computing the cumulative distribution function for a Gaussian.
static class UnivariateGaussian.ErrorFunction.Inverse
          Inverse of the ErrorFunction
static class UnivariateGaussian.IncrementalEstimator
          Implements an incremental estimator for the sufficient statistics for a UnivariateGaussian.
static class UnivariateGaussian.MaximumLikelihoodEstimator
          Creates a UnivariateGaussian from data
static class UnivariateGaussian.PDF
          PDF of the underlying Gaussian.
static class UnivariateGaussian.SufficientStatistic
          Captures the sufficient statistics of a UnivariateGaussian, which are the values to estimate the mean and variance.
static class UnivariateGaussian.WeightedMaximumLikelihoodEstimator
          Creates a UnivariateGaussian from weighted data
 class WeibullDistribution
          Describes a Weibull distribution, which is often used to describe the mortality, lifespan, or size distribution of objects.
static class WeibullDistribution.CDF
          CDF of the Weibull distribution
static class WeibullDistribution.PDF
          PDF of the Weibull distribution
 class YuleSimonDistribution
          The Yule-Simon distribution is a model of preferential attachment, such as a model of the number of groups follows a power-law distribution (Zipf's Law).
static class YuleSimonDistribution.CDF
          CDF of the Yule-Simon Distribution
static class YuleSimonDistribution.PMF
          PMF of the Yule-Simon Distribution
 

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

Subinterfaces of CloneableSerializable in gov.sandia.cognition.statistics.method
 interface BlockExperimentComparison<DataType>
          Implements a null-hypothesis multiple-comparison test from a block-design experiment.
 interface ConfidenceStatistic
          An interface that describes the result of a statistical confidence test.
 interface MultipleHypothesisComparison<TreatmentData>
          Describes the functionality of an algorithm for accepting or rejecting multiple null hypothesis at the same time.
static interface MultipleHypothesisComparison.Statistic
          Statistic associated with the multiple hypothesis comparison
 interface NullHypothesisEvaluator<DataType>
          Evaluates the probability that the null-hypothesis is correct.
 

Classes in gov.sandia.cognition.statistics.method that implement CloneableSerializable
 class AbstractConfidenceStatistic
          Abstract implementation of ConfidenceStatistic.
 class AbstractMultipleHypothesisComparison<TreatmentData,StatisticType extends MultipleHypothesisComparison.Statistic>
          Partial implementation of MultipleHypothesisComparison
static class AbstractMultipleHypothesisComparison.Statistic
          Partial implementation of MultipleHypothesisComparison.Statistic
 class AbstractPairwiseMultipleHypothesisComparison<StatisticType extends AbstractPairwiseMultipleHypothesisComparison.Statistic>
          A multiple-hypothesis comparison algorithm based on making multiple pair-wise null-hypothesis comparisons.
static class AbstractPairwiseMultipleHypothesisComparison.Statistic
          Result from a pairwise multiple-comparison statistic.
 class AdjustedPValueStatistic
          A multiple-comparison statistic derived from a single adjusted p-value.
 class AnalysisOfVarianceOneWay
          Analysis of Variance single-factor null-hypothesis testing procedure, usually called "1-way ANOVA".
static class AnalysisOfVarianceOneWay.Statistic
          Returns the confidence statistic for an ANOVA test
 class BernoulliConfidence
          Computes the Bernoulli confidence interval.
 class BonferroniCorrection
          The Bonferroni correction takes a pair-wise null-hypothesis test and generalizes it to multiple comparisons by adjusting the requisite p-value to find significance as alpha / NumComparisons.
 class ChebyshevInequality
          Computes the Chebyshev Inequality for the given level of confidence.
 class ChiSquareConfidence
          This is the chi-square goodness-of-fit test.
static class ChiSquareConfidence.Statistic
          Confidence Statistic for a chi-square test
 class ConfidenceInterval
          Contains a specification for a confidence interval, that is, the solution of Pr{ lowerBound <= x(centralValue) <= upperBound } >= confidence
 class ConvexReceiverOperatingCharacteristic
          Computes the convex hull of the Receiver Operating Characteristic (ROC), which a mathematician might call a "concave down" function.
 class DistributionParameterEstimator<DataType,DistributionType extends ClosedFormDistribution<? extends DataType>>
          A method of estimating the parameters of a distribution using an arbitrary CostFunction and FunctionMinimizer algorithm.
protected  class DistributionParameterEstimator.DistributionWrapper
          Maps the parameters of a Distribution and a CostFunction into a Vector/Double Evaluator.
 class FieldConfidenceInterval
          This class has methods that automatically compute confidence intervals for Double/double Fields in dataclasses.
 class FisherSignConfidence
          This is an implementation of the Fisher Sign Test, which is a robust nonparameteric test to determine if two groups have a different mean.
static class FisherSignConfidence.Statistic
          Contains the parameters from the Sign Test null-hypothesis evaluation
 class FriedmanConfidence
          The Friedman test determines if the rankings associated with various treatments are equal.
static class FriedmanConfidence.Statistic
          Confidence statistic associated with the Friedman test using the tighter F-statistic.
 class GaussianConfidence
          This test is sometimes called the "Z test" Defines a range of values that the statistic can take, as well as the confidence that the statistic is between the lower and upper bounds.
static class GaussianConfidence.Statistic
          Confidence statistics for a Gaussian distribution
 class HolmCorrection
          The Holm correction is a uniformly tighter bound than the Bonferroni/Sidak correction by first sorting the pair-wide p-values and then adjusting the p-values by the number of remaining hypotheses.
static class HolmCorrection.Statistic
          Test statistic from the Shaffer static multiple-comparison test
 class KolmogorovSmirnovConfidence
          Performs a Kolmogorov-Smirnov Confidence Test.
static class KolmogorovSmirnovConfidence.Statistic
          Computes the ConfidenceStatistic associated with a K-S test
 class MannWhitneyUConfidence
          Performs a Mann-Whitney U-test on the given data (usually simply called a "U-test", sometimes called a Wilcoxon-Mann-Whitney U-test, or Wilcoxon rank-sum test).
static class MannWhitneyUConfidence.Statistic
          Statistics from the Mann-Whitney U-test
 class MarkovInequality
          Implementation of the Markov Inequality hypothesis test.
 class MaximumLikelihoodDistributionEstimator<DataType>
          Estimates the most-likely distribution, and corresponding parameters, of that generated the given data from a pre-determined collection of candidate parameteric distributions.
static class MaximumLikelihoodDistributionEstimator.DistributionEstimationTask<DataType>
          Estimates the optimal parameters of a single distribution
 class MultipleComparisonExperiment
          A multiple comparisons experiment that does a block comparison and then a post-hoc test.
static class MultipleComparisonExperiment.Statistic
          Result of running the MultipleHypothesisComparison hypothesis test
 class NemenyiConfidence
          The Nemenyi test is the rank-based analogue of the Tukey multiple-comparison test.
static class NemenyiConfidence.Statistic
          Statistic from Nemenyi's multiple comparison test
 class ReceiverOperatingCharacteristic
          Class that describes a Receiver Operating Characteristic (usually called an "ROC Curve").
static class ReceiverOperatingCharacteristic.DataPoint
          Contains information about a datapoint on an ROC curve
static class ReceiverOperatingCharacteristic.DataPoint.Sorter
          Sorts DataPoints in ascending order according to their falsePositiveRate (x-axis)
static class ReceiverOperatingCharacteristic.Statistic
          Contains useful statistics derived from a ROC curve
 class ShafferStaticCorrection
          The Shaffer Static Correction uses logical relationships to tighten up the Bonferroni/Sidak corrections when performing pairwise multiple hypothesis comparisons.
static class ShafferStaticCorrection.Statistic
          Test statistic from the Shaffer static multiple-comparison test
 class SidakCorrection
          The Sidak correction takes a pair-wise null-hypothesis test and generalizes it to multiple comparisons by adjusting the requisite p-value to find significance as alpha / NumComparisons.
 class StudentTConfidence
          This class implements Student's t-tests for different uses.
static class StudentTConfidence.Statistic
          Confidence statistics for a Student-t test
static class StudentTConfidence.Summary
          An implementation of the Summarizer interface for creating a ConfidenceInterval
 class TreeSetBinner<ValueType extends Comparable<? super ValueType>>
          Implements a Binner that employs a TreeSet to define the boundaries of a contiguous set of bins.
 class TukeyKramerConfidence
          Tukey-Kramer test is the multiple-comparison generalization of the unpaired Student's t-test when conducting multiple comparisons.
static class TukeyKramerConfidence.Statistic
          Statistic from Tukey-Kramer's multiple comparison test
 class WilcoxonSignedRankConfidence
          This is a Wilcoxon Signed-Rank Sum test, which performs a pair-wise test to determine if two datasets are different.
static class WilcoxonSignedRankConfidence.Statistic
          ConfidenceStatistics associated with a Wilcoxon test
 

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

Subinterfaces of CloneableSerializable in gov.sandia.cognition.statistics.montecarlo
 interface MonteCarloIntegrator<OutputType>
          Monte Carlo integration is a way of compute the integral of a function using samples from another.
 interface MonteCarloSampler<DataType,SampleType,FunctionType extends Evaluator<? super DataType,Double>>
          A sampling technique based on the Monte Carlo method.
 

Classes in gov.sandia.cognition.statistics.montecarlo that implement CloneableSerializable
 class DirectSampler<DataType>
          Sampler that generates samples directly from a target distribution.
 class ImportanceSampler<DataType>
          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.
 class MultivariateMonteCarloIntegrator
          A Monte Carlo integrator for multivariate (vector) outputs.
 class UnivariateMonteCarloIntegrator
          A Monte Carlo integrator for univariate (scalar) outputs.
 

Uses of CloneableSerializable in gov.sandia.cognition.text
 

Classes in gov.sandia.cognition.text that implement CloneableSerializable
 class AbstractOccurrenceInText<DataType>
          An abstract implementation of the OccurrenceInText interface.
 class AbstractTextual
          A default implementation of the Textual interface.
 class DefaultTextual
          A default implementation of the Textual interface that just stores a string value.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.convert
 

Classes in gov.sandia.cognition.text.convert that implement CloneableSerializable
 class AbstractMultiTextualConverter<InputType,OutputType extends Textual>
          An abstract implementation of the MultiTextualConverter interface.
 class AbstractSingleTextualConverter<InputType,OutputType extends Textual>
          An abstract implementation of the SingleTextualConverter interface.
 class AbstractTextualConverter<InputType,OutputType extends Textual>
          An abstract implementation of the TextualConverter interface.
 class DocumentFieldConcatenator
          A document-text converter that concatenates multiple text fields from a document together for further processing.
 class DocumentSingleFieldConverter
          Extracts a single field from a document.
 class ObjectToStringTextualConverter
          A text converter that can take in any type of object and then returns a new DefaultTextual that wraps that object's toString().
 class SingleToMultiTextualConverterAdapter<InputType,OutputType extends Textual>
          Adapts a SingleTextualConverter to work within the interface of an MultiTextualConverter.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.document
 

Classes in gov.sandia.cognition.text.document that implement CloneableSerializable
 class AbstractDocument
          An abstract implementation of the Document interface.
 class DefaultDateField
          A field for storing a date.
 class DefaultDocument
          A default implementation of the Document interface.
 class DefaultTextField
          A default implementation of the Field interface.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.document.extractor
 

Classes in gov.sandia.cognition.text.document.extractor that implement CloneableSerializable
 class AbstractDocumentExtractor
          An abstract implementation of the DocumentExtractor interface.
 class AbstractSingleDocumentExtractor
          An abstract implementation of the SingleDocumentExtractor interface.
 class TextDocumentExtractor
          Extracts text from plain text documents.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.evaluation
 

Classes in gov.sandia.cognition.text.evaluation that implement CloneableSerializable
 class DefaultPrecisionRecallPair
          A default implementation of the PrecisionRecallPair interface.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.relation
 

Classes in gov.sandia.cognition.text.relation that implement CloneableSerializable
 class AbstractRelation<SourceType,TargetType>
          An abstract implementation of a relation between two objects.
 

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

Classes in gov.sandia.cognition.text.spelling that implement CloneableSerializable
 class SimpleStatisticalSpellingCorrector
          A simple statistical spelling corrector based on word counts that looks at possible one and two-character edits.
static class SimpleStatisticalSpellingCorrector.Learner
          A learner for the SimpleStatisticalSpellingCorrector.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.term
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.text.term
 interface Term
          Interface for a term, which is a basic unit of data in information retrieval.
 interface TermNGram
          Interface for a term that is some type of n-gram.
 

Classes in gov.sandia.cognition.text.term that implement CloneableSerializable
 class AbstractTerm
          Creates a new AbstractTerm.
 class AbstractTermIndex
          An abstract implementation of the TermIndex class that handles a lot of the convenience method implementations.
 class DefaultIndexedTerm
          Default implementation of the IndexedTerm interface.
 class DefaultTerm
          A default implementation of the Term interface.
 class DefaultTermCounts
          A default implementation of the TermCounts interface.
 class DefaultTermIndex
          A default implementation of the TermIndex interface.
 class DefaultTermNGram
          A default implementation of the TermNGram interface.
 class DefaultTermOccurrence
          A default implementation of the TermOccurrence interface.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.term.filter
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.text.term.filter
 interface SingleTermFilter
          Interface for a term filter that looks at each term individually.
 interface TermFilter
          Interface for an object that can filter a list of terms to create a new list of terms.
 

Classes in gov.sandia.cognition.text.term.filter that implement CloneableSerializable
 class AbstractSingleTermFilter
          An abstract implementation of the SingleTermFilter interface.
 class DefaultStopList
          A default, case-insensitive stop-list.
 class DictionaryFilter
          A term filter that only allows terms in its dictionary.
 class LowerCaseTermFilter
          A term filter that converts all terms to lower case.
 class NGramFilter
          A term filter that creates an n-gram of terms.
 class StopListFilter
          A term filter that rejects any term that appears in a given stop list.
 class StringEvaluatorSingleTermFilter
          Adapts an evaluator from string to string to be a term filter on individual terms.
 class SynonymFilter
          A term filter that uses a mapping of synonyms to replace a word with its synonym.
 class TermLengthFilter
          Implements a filter based on the length of a term.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.term.filter.stem
 

Classes in gov.sandia.cognition.text.term.filter.stem that implement CloneableSerializable
 class PorterEnglishStemmingFilter
          A term filter that uses the Porter Stemming algorithm.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.term.relation
 

Classes in gov.sandia.cognition.text.term.relation that implement CloneableSerializable
 class IndexedTermSimilarityRelation
          A relationship between two indexed terms describing their term similarity.
 class TermVectorSimilarityNetworkCreator
          Creates term similarity networks by comparing vectors representing the terms.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.term.vector
 

Classes in gov.sandia.cognition.text.term.vector that implement CloneableSerializable
 class AbstractVectorSpaceModel
          An abstract implementation of the VectorSpaceModel class.
 class BagOfWordsTransform
          Transforms a list of term occurrences into a vector of counts.
 class CosineSimilarityFunction
          A vector cosine similarity function.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.term.vector.weighter
 

Classes in gov.sandia.cognition.text.term.vector.weighter that implement CloneableSerializable
 class CompositeLocalGlobalTermWeighter
          Composes together local and global term weighters along with a normalizer.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.term.vector.weighter.global
 

Classes in gov.sandia.cognition.text.term.vector.weighter.global that implement CloneableSerializable
 class AbstractEntropyBasedGlobalTermWeighter
          An abstract implementation of a global term weighting scheme that keeps track of the sum of the entropy term (f_ij * log(f_ij)) over all documents.
 class AbstractFrequencyBasedGlobalTermWeighter
          An abstract GlobalTermWeighter that keeps track of term frequencies in documents.
 class AbstractGlobalTermWeighter
          An abstract implementation of the GlobalTermWeighter interface.
 class DominanceGlobalTermWeighter
          Implements the dominance term gloal weighting scheme.
 class EntropyGlobalTermWeighter
          Implements the entropy global term weighting scheme.
 class InverseDocumentFrequencyGlobalTermWeighter
          Implements the inverse-document-frequency (IDF) term global weighting scheme.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.term.vector.weighter.local
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.text.term.vector.weighter.local
 interface LocalTermWeighter
          Defines the functionality of a local term weighting scheme.
 

Classes in gov.sandia.cognition.text.term.vector.weighter.local that implement CloneableSerializable
 class AbstractLocalTermWeighter
          Abstract implementation of the LocalTermWeighter interface.
 class BinaryLocalTermWeighter
          Makes the given term weights binary, by creating a vector that contains a 1.0 for all non-zero entries in the given vector and a 0.0 for the all the zeros.
 class LogLocalTermWeighter
          Implements the log-based local term weighting scheme.
 class NormalizedLogLocalTermWeighter
          Implements a normalized version of the log local weighter.
 class TermFrequencyLocalTermWeighter
          Local weighting for term frequency.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.term.vector.weighter.normalize
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.text.term.vector.weighter.normalize
 interface TermWeightNormalizer
          Interface for a tem weight normalization scheme.
 

Classes in gov.sandia.cognition.text.term.vector.weighter.normalize that implement CloneableSerializable
 class AbstractTermWeightNormalizer
          An abstract implementation of the TermWeightNormalizer interface.
 class UnitTermWeightNormalizer
          Normalizes term weights to be a unit vector.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.token
 

Subinterfaces of CloneableSerializable in gov.sandia.cognition.text.token
 interface Tokenizer
          Interface for a class that converts strings into tokens.
 

Classes in gov.sandia.cognition.text.token that implement CloneableSerializable
 class AbstractCharacterBasedTokenizer
          An abstract implementation of a tokenizer that considers each character individually.
 class AbstractTokenizer
          Abstract implementation of the Tokenizer interface.
 class DefaultToken
          A default implementation of the Token interface.
 class LetterNumberTokenizer
          A tokenizer that creates tokens from sequences of letters and numbers, treating everything else as a delimiter.
 

Uses of CloneableSerializable in gov.sandia.cognition.text.topic
 

Classes in gov.sandia.cognition.text.topic that implement CloneableSerializable
 class LatentDirichletAllocationVectorGibbsSampler
          A Gibbs sampler for performing Latent Dirichlet Allocation (LDA).
static class LatentDirichletAllocationVectorGibbsSampler.Result
          Represents the result of performing Latent Dirichlet Allocation.
 class LatentSemanticAnalysis
          Implements the Latent Semantic Analysis (LSA) algorithm using Singular Value Decomposition (SVD).
static class LatentSemanticAnalysis.Transform
          The result from doing latent semantic analysis (LSA).
 class ParallelLatentDirichletAllocationVectorGibbsSampler
          A parallel implementation of LatentDirichletAllocationVectorGibbsSampler.
protected  class ParallelLatentDirichletAllocationVectorGibbsSampler.DocumentSampleTask
          A document sampling task
 class ProbabilisticLatentSemanticAnalysis
          An implementation of the Probabilistic Latent Semantic Analysis (PLSA) algorithm.
static class ProbabilisticLatentSemanticAnalysis.Result
          The dimensionality transform created by probabilistic latent semantic analysis.
static class ProbabilisticLatentSemanticAnalysis.StatusPrinter
          Prints out the status of the probabilistic latent semantic analysis algorithm.
 

Uses of CloneableSerializable in gov.sandia.cognition.time
 

Classes in gov.sandia.cognition.time that implement CloneableSerializable
 class DefaultDuration
          A default implementation of the Duration interface.
 

Uses of CloneableSerializable in gov.sandia.cognition.util
 

Classes in gov.sandia.cognition.util that implement CloneableSerializable
 class AbstractCloneableSerializable
          The AbstractCloneableserializable abstract class implements a default version of the clone method that calls the Object clone method, but traps the exception that can be thrown.
 class AbstractNamed
          The AbstractNamed class implements the Named interface in a standard way by having a name field inside the object.
 class AbstractRandomized
          The AbstractRandomized abstract class implements the Randomized interface by containing the random object in a protected field.
 class AbstractTemporal
          Partial implementation of Temporal
 class AbstractWeighted
          Container class for a Weighted object
 class DefaultIdentifiedValue<IdentifierType,ValueType>
          A default implementation of the IdentifiedValue interface that stores a value along with its identifier.
 class DefaultKeyValuePair<KeyType,ValueType>
          A default implementation of the KeyValuePair interface.
 class DefaultNamedValue<ValueType>
          The DefaultNamedValue class implements a container of a name-value pair.
 class DefaultPair<FirstType,SecondType>
          The DefaultPair class implements a simple structure for a pair of two objects, potentially of different types.
 class DefaultTemporalValue<ValueType>
          The DefaultTemporalValue class is a default implementation of the TemporalValue interface.
 class DefaultTriple<FirstType,SecondType,ThirdType>
          The DefaultTriple class implements a simple structure for a triple of three objects, potentially of different types.
 class DefaultWeightedPair<FirstType,SecondType>
          The DefaultWeightedPair class extends the DefaultPair class to add a weight to the pair.
 class DefaultWeightedValue<ValueType>
          The WeightedValue class implements a simple generic container that holds a value and a weight assigned to the value.
static class DefaultWeightedValue.WeightComparator
          A comparator for weighted values based on the weight.
 

Methods in gov.sandia.cognition.util with type parameters of type CloneableSerializable
static
<T extends CloneableSerializable>
T
ObjectUtil.cloneSafe(T object)
          Calls the Clone method on the given object of some type that extends CloneableSerializable.
 

Methods in gov.sandia.cognition.util that return CloneableSerializable
 CloneableSerializable AbstractCloneableSerializable.clone()
          This makes public the clone method on the Object class and removes the exception that it throws.
 CloneableSerializable AbstractRandomized.clone()
           
 CloneableSerializable CloneableSerializable.clone()
          Creates a new clone (shallow copy) of this object.