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

Packages that use CloneableSerializable

Package	Description
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.hash	Provides hash function implementations.
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	Provides confidence-weighted categorization algorithms.
gov.sandia.cognition.learning.algorithm.delta	Provides an abstract class for helping to implement variants of the Burrows' Delta algorithm.
gov.sandia.cognition.learning.algorithm.ensemble	Provides ensemble methods.
gov.sandia.cognition.learning.algorithm.factor.machine	Provides factorization machine algorithms.
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.minimization.matrix	Provides matrix solving algorithms.
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.custom	Provides a custom linear algebra package implementation for both dense and sparse classes.
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

Modifier and Type	Interface and Description
interface	ParallelAlgorithm Interface for algorithms that are parallelized using multithreading.

Classes in gov.sandia.cognition.algorithm that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
class	AbstractLogNumberMap<KeyType> A partial implementation of a ScalarMap with a LogNumber value
class	AbstractMutableDoubleMap<KeyType> A partial implementation of a ScalarMap with a MutableDouble value
class	AbstractScalarMap<KeyType,NumberType extends java.lang.Number> Partial implementation of ScalarMap
class	DefaultComparator<T extends java.lang.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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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 java.lang.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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Field and Description
protected CreatedType	PrototypeFactory.prototype The prototype to create clones from.

Methods in gov.sandia.cognition.factory with type parameters of type CloneableSerializable

Modifier and Type	Method and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Method and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Method and Description
CloneableSerializable	CognitiveModuleStateWrapper.getInternalState() Gets the internal state object.

Methods in gov.sandia.cognition.framework.lite with parameters of type CloneableSerializable

Modifier and Type	Method and Description
void	CognitiveModuleStateWrapper.setInternalState(CloneableSerializable internalState) Sets the internal state object.

Constructors in gov.sandia.cognition.framework.lite with parameters of type CloneableSerializable

Constructor and Description
CognitiveModuleStateWrapper(CloneableSerializable internalState) Creates a new instance of CognitiveModuleStateWrapper.

Uses of CloneableSerializable in gov.sandia.cognition.hash

Subinterfaces of CloneableSerializable in gov.sandia.cognition.hash

Modifier and Type	Interface and Description
interface	HashFunction A hash function maps an arbitrarily large input and maps it onto a finite-length output.

Classes in gov.sandia.cognition.hash that implement CloneableSerializable

Modifier and Type	Class and Description
class	AbstractHashFunction Partial implementation of HashFunction
class	Eva32Hash This implements the 32-bit "evahash" due to Robert Jenkins.
class	Eva64Hash This implements the 64-bit "evahash" due to Robert Jenkins.
class	FNV1a32Hash Implementation of the 32-bit (4-byte) Fowler-Noll-Vo (FNV-1a) hash function.
class	FNV1a64Hash Implementation of the 32-bit (4-byte) Fowler-Noll-Vo (FNV-1a) hash function.
class	MD5Hash Implementation of the MD5 128-bit (16-byte) cryptographic hash function.
class	Murmur32Hash Implementation of the MurmurHash2 32-bit (4-byte) non-cryptographic hash function.
class	Prime32Hash Implementation of the prime-hash function using 32-bit (4-byte) precision.
class	Prime64Hash Implementation of the prime-hash function using 64-bit (8-byte) precision.
class	SHA1Hash A Java implementation of the Secure Hash Algorithm, SHA-1, as defined in FIPS PUB 180-1.
class	SHA256Hash The SHA-2, 256-bit (32 byte) hash function.
class	SHA512Hash The SHA-2 512-bit (64-byte) hash function.

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

Classes in gov.sandia.cognition.io.serialization that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Classes in gov.sandia.cognition.learning.algorithm.annealing that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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	DBSCANClusterer<DataType extends Vectorizable,ClusterType extends Cluster<DataType>> The DBSCAN algorithm requires three parameters: a distance metric, a value for neighborhood radius, and a value for the minimum number of surrounding neighbors for a point to be considered non-noise.
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	MiniBatchKMeansClusterer<DataType extends Vector> Approximates k-means clustering by working on random subsets of the data.
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 PartitionalClusterer 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

Modifier and Type	Interface and Description
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	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

Modifier and Type	Class and Description
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	MiniBatchCentroidCluster
class	NormalizedCentroidCluster<ClusterType> Add the ability to store the centroid of the normalized vectors belonging to a centroid cluster.
class	NormalizedCentroidClusterCreator A cluster creator for NormalizedCentroidClusters which are clusters that have a normalized centroid in addition to the usual centroid.
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.
class	VectorMeanMiniBatchCentroidClusterCreator Implementation of VectorMeanCentroidClusterCreator for mini-batch clustering.

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

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

Modifier and Type	Interface and Description
interface	WithinClusterDivergence<ClusterType extends Cluster<DataType>,DataType> Defines a function that computes the divergence of the elements in a cluster.

Classes in gov.sandia.cognition.learning.algorithm.clustering.divergence that implement CloneableSerializable

Modifier and Type	Class and Description
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.
class	WithinClusterDivergenceWrapper<ClusterType extends Cluster<DataType>,DataType> Accumulates the results of a ClusterDivergenceFunction by summing the divergence of each point to its cluster.
class	WithinNormalizedCentroidClusterCosineDivergence<V extends Vectorizable> This class calculates the total cosine divergence between all members of a cluster and the cluster's centroid

Methods in gov.sandia.cognition.learning.algorithm.clustering.divergence that return CloneableSerializable

Modifier and Type	Method and Description
CloneableSerializable	WithinClusterDivergenceWrapper.clone()
CloneableSerializable	WithinNormalizedCentroidClusterCosineDivergence.clone()

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

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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Classes in gov.sandia.cognition.learning.algorithm.clustering.initializer that implement CloneableSerializable

Modifier and Type	Class and Description
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.
class	RandomClusterInitializer<ClusterType extends Cluster<DataType>,DataType> Creates initial clusters by selecting random data points as singleton clusters.

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

Classes in gov.sandia.cognition.learning.algorithm.confidence that implement CloneableSerializable

Modifier and Type	Class and Description
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.delta

Classes in gov.sandia.cognition.learning.algorithm.delta that implement CloneableSerializable

Modifier and Type	Class and Description
class	AbstractDeltaCategorizer<CategoryType> The Burrows Delta algorithm is primarily used for authorship attribution, but can be used for other applications.
static class	AbstractDeltaCategorizer.AbstractLearner<CategoryType> Abstract learner for delta algorithms.
class	BurrowsDeltaCategorizer<CategoryType> The regular Burrows' Delta algorithm implementation.
static class	BurrowsDeltaCategorizer.Learner<CategoryType> Learner for a BurrowsDeltaCategorizer.
class	CosineDeltaCategorizer<CategoryType> The Cosine Delta algorithm implementation.
static class	CosineDeltaCategorizer.Learner<CategoryType> Learner for a CosineDeltaCategorizer.

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

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

Modifier and Type	Interface and Description
interface	BagBasedCategorizerEnsembleLearner<InputType,CategoryType> Interface for a bag-based ensemble learner.

Classes in gov.sandia.cognition.learning.algorithm.ensemble that implement CloneableSerializable

Modifier and Type	Class and Description
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	AbstractCategorizerOutOfBagStoppingCriteria<InputType,CategoryType> Abstract class for implementing a out-of-bag stopping criteria for a bagging-based ensemble.
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 java.lang.Number>> An ensemble of regression functions that determine the result by adding their outputs together.
class	AveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.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.
static class	BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType> Implements a stopping criteria for bagging that uses the out-of-bag error to determine when to stop learning the ensemble.
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 java.lang.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 java.lang.Number>> An implementation of an ensemble that takes the weighted average of its members.
class	WeightedBinaryEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.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.factor.machine

Classes in gov.sandia.cognition.learning.algorithm.factor.machine that implement CloneableSerializable

Modifier and Type	Class and Description
class	AbstractFactorizationMachineLearner An abstract class for learning FactorizationMachines.
class	FactorizationMachine Implements a Factorization Machine.
class	FactorizationMachineAlternatingLeastSquares Implements an Alternating Least Squares (ALS) algorithm for learning a Factorization Machine.
class	FactorizationMachineStochasticGradient Implements a Stochastic Gradient Descent (SGD) algorithm for learning a Factorization Machine.

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

Classes in gov.sandia.cognition.learning.algorithm.genetic that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
interface	GradientDescendable Defines the functionality of an object that is required in order to apply the gradient descent algorithm to it.
interface	ParameterGradientEvaluator<InputType,OutputType,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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
interface	LineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.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

Modifier and Type	Class and Description
class	AbstractAnytimeLineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.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

Modifier and Type	Interface and Description
interface	LineBracketInterpolator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> Definition of an interpolator/extrapolator for a LineBracket.

Classes in gov.sandia.cognition.learning.algorithm.minimization.line.interpolator that implement CloneableSerializable

Modifier and Type	Class and Description
class	AbstractLineBracketInterpolator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>> Partial implementation of LinearBracketInterpolator
class	AbstractLineBracketInterpolatorPolynomial<EvaluatorType extends Evaluator<java.lang.Double,java.lang.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.minimization.matrix

Classes in gov.sandia.cognition.learning.algorithm.minimization.matrix that implement CloneableSerializable

Modifier and Type	Class and Description
class	ConjugateGradientMatrixSolver Implements a matrix solver using Conjugate Gradient.
class	ConjugateGradientWithPreconditionerMatrixSolver Implements a matrix solver using Conjugate Gradient with a preconditioner.
class	IterativeMatrixSolver<Operator extends MatrixVectorMultiplier> Base class for all iterative matrix solvers that takes care of most of the basic iterative logic and the function minimizer interface.
class	OverconstrainedConjugateGradientMatrixMinimizer Implements a overconstrained conjugate gradient matrix optimizer.
class	SteepestDescentMatrixSolver Implements a basic Steepest Descent iterative solver for linear systems of equations.

Methods in gov.sandia.cognition.learning.algorithm.minimization.matrix that return CloneableSerializable

Modifier and Type	Method and Description
CloneableSerializable	ConjugateGradientMatrixSolver.clone()
CloneableSerializable	ConjugateGradientWithPreconditionerMatrixSolver.clone()
abstract CloneableSerializable	IterativeMatrixSolver.clone()
CloneableSerializable	OverconstrainedConjugateGradientMatrixMinimizer.clone()
CloneableSerializable	SteepestDescentMatrixSolver.clone()

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

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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
interface	MultivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends Vectorizable>> A regression algorithm that maps one or more independent (input) variables onto multiple output variables.
interface	ParameterCostMinimizer<ResultType extends VectorizableVectorFunction> A anytime algorithm that is used to estimate the locally minimum-cost parameters of an object.
interface	Regression<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>> A supervised learning algorithm that attempts to interpolate/extrapolate inputs given a training set of input/output pairs.
interface	UnivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends java.lang.Double>> A type of Regression algorithm that has a single dependent (output) variable that we are trying to predict.

Classes in gov.sandia.cognition.learning.algorithm.regression that implement CloneableSerializable

Modifier and Type	Class and Description
class	AbstractLogisticRegression<InputType,OutputType,FunctionType extends Evaluator<? super InputType,OutputType>> Abstract partial implementation for logistic regression classes.
class	AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends java.lang.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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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	VectorThresholdLearner<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

Modifier and Type	Class and Description
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	RandomForestFactory A factory class for creating Random Forest learners.
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 regression 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

Modifier and Type	Class and Description
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 java.lang.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 java.lang.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

Modifier and Type	Class and Description
class	DelayFunction<DataType> Delays the input and returns the input from the parameterized number of samples previous.
class	FeatureHashing Implements a function that applies vector feature hashing.
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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 java.lang.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

Modifier and Type	Class and Description
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 java.lang.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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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 java.lang.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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
interface	KernelContainer<InputType> Defines an object that contains a Kernel.

Classes in gov.sandia.cognition.learning.function.kernel that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
class	AbstractRegressor<InputType> An abstract implementation of the Regressor interface.

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

Classes in gov.sandia.cognition.learning.function.scalar that implement CloneableSerializable

Modifier and Type	Class and Description
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	HardSigmoidFunction A hard sigmoid function, which is an approximation of a logistic sigmoid whose output is between 0 and 1.
class	HardTanHFunction A hard sigmoid function, which is an approximation of a tanh sigmoid whose output is between -1 and 1.
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	LeakyRectifiedLinearFunction A leaky rectified linear unit.
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	RectifiedLinearFunction A rectified linear unit, which is the maximum of its input or 0.
class	SigmoidFunction An implementation of a sigmoid squashing function.
class	SoftPlusFunction A smoothed approximation for rectified linear unit.
class	TanHFunction The hyperbolic tangent (tanh) 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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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[Vector] 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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Classes in gov.sandia.cognition.learning.performance that implement CloneableSerializable

Modifier and Type	Class and Description
class	AbstractSupervisedPerformanceEvaluator<InputType,TargetType,EstimateType,ResultType> The AbstractSupervisedPerformanceEvaluator class contains an abstract implementation of the SupervisedPerformanceEvaluator class.
class	AnytimeBatchLearnerValidationPerformanceReporter<DataType,ObjectType> A performance reporter for a validation set.
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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	Ring<RingType extends Ring<RingType>> Defines something similar to a mathematical ring.

Classes in gov.sandia.cognition.math that implement CloneableSerializable

Modifier and Type	Class and Description
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	Combinations Enumerates all the combinations on a given number of items sampled from a larger set without considering order, that is, (1,2) is the same as (2,1).
protected static class	Combinations.AbstractCombinationsIterator<IteratorType extends Combinations.AbstractCombinationsIterator<IteratorType,ClassType>,ClassType> Partial implementation of a CombinationsIterator.
class	Combinations.IndexIterator Iterator that returns the index into a set: 0, 1, 2, ...
static class	Combinations.SubsetIterator<ClassType> Creates a new instance of SubsetIterator, one that returns the elements from a given set
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
interface	DiagonalMatrix Interface describing a diagonal matrix.
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

Modifier and Type	Class and Description
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.custom

Classes in gov.sandia.cognition.math.matrix.custom that implement CloneableSerializable

Modifier and Type	Class and Description
class	DenseMatrix A dense matrix implementation.
class	DenseVector Our dense vector implementation.
class	DiagonalMatrix Diagonal matrices are a special case, but a rather common one with very quick and simple solutions to multiplications, inverses, etc.
class	ParallelSparseMatrix A sparse matrix implementation.
class	SparseMatrix A sparse matrix implementation.
class	SparseVector Our sparse vector implementation.

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

Classes in gov.sandia.cognition.math.matrix.decomposition that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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	DiagonalMatrixMTJ A diagonal matrix that wraps MTJ's BandMatrix class.
class	SparseColumnMatrix A sparse matrix, represented as a collection of sparse column vectors.
class	SparseRowMatrix A sparse matrix, represented as a collection of sparse row vectors.
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
interface	DiscreteTimeFilter<StateType extends CloneableSerializable> A discrete-time filter.

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

Modifier and Type	Interface and Description
interface	DiscreteTimeFilter<StateType extends CloneableSerializable> A discrete-time filter.

Classes in gov.sandia.cognition.math.signals that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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 java.lang.Number> Functionality of a cumulative distribution function that's defined with closed-form parameters.
interface	ClosedFormDiscreteUnivariateDistribution<DomainType extends java.lang.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 java.lang.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 java.lang.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	EstimableWeightedDistribution<ObservationType,DistributionType extends EstimableWeightedDistribution<ObservationType,? extends DistributionType>> A Distribution that has an estimator associated with it, typically a closed-form estimator, that can estimate the distribution from weighted data.
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	IntegerDistribution Defines a distribution over natural numbers.
interface	InvertibleCumulativeDistributionFunction<NumberType extends java.lang.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 java.lang.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

Modifier and Type	Class and Description
class	AbstractClosedFormIntegerDistribution An abstract class for closed-form integer distributions.
class	AbstractClosedFormSmoothUnivariateDistribution Partial implementation of SmoothUnivariateDistribution
class	AbstractClosedFormUnivariateDistribution<NumberType extends java.lang.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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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<java.lang.Double,java.lang.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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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-binomial 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 java.lang.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 uniform distribution.
static class	UniformDistribution.PDF Probability density function of a Uniform Distribution
class	UniformIntegerDistribution Contains the (very simple) definition of a continuous Uniform distribution, parameterized between the minimum and maximum bounds.
static class	UniformIntegerDistribution.CDF Implements the cumulative distribution function for the discrete uniform distribution.
static class	UniformIntegerDistribution.MaximumLikelihoodEstimator Implements a maximum likelihood estimator for the discrete uniform distribution.
static class	UniformIntegerDistribution.PMF Probability mass function of a discrete 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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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 java.lang.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

Modifier and Type	Interface and Description
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,java.lang.Double>> A sampling technique based on the Monte Carlo method.

Classes in gov.sandia.cognition.statistics.montecarlo that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
interface	LocalTermWeighter Defines the functionality of a local term weighting scheme.

Classes in gov.sandia.cognition.text.term.vector.weighter.local that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
interface	TermWeightNormalizer Interface for a tem weight normalization scheme.

Classes in gov.sandia.cognition.text.term.vector.weighter.normalize that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Interface and Description
interface	Tokenizer Interface for a class that converts strings into tokens.

Classes in gov.sandia.cognition.text.token that implement CloneableSerializable

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Class and Description
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

Modifier and Type	Method and Description
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

Modifier and Type	Method and Description
CloneableSerializable	AbstractCloneableSerializable.clone() This makes public the clone method on the Object class and removes the exception that it throws.
CloneableSerializable	CloneableSerializable.clone() Creates a new clone (shallow copy) of this object.