Package gov.sandia.cognition.learning.function.categorization

Provides functions that output a discrete set of categories.

See:
Description

Interface Summary

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.
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.
ConfidenceWeightedBinaryCategorizer	Interface for a confidence-weighted binary categorizer, which defines a distribution over linear binary categorizers.
DiscriminantBinaryCategorizer<InputType>	Interface for a linear discriminant categorizer in the binary categorization domain.
DiscriminantCategorizer<InputType,CategoryType,DiscriminantType extends Comparable<? super DiscriminantType>>	Interface for a Categorizer that can produce a value to discriminate between how well different instances fit a given category.
ThresholdBinaryCategorizer<InputType>	Interface for a binary categorizer that uses a threshold to determine the categorization.

Class Summary

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

Package gov.sandia.cognition.learning.function.categorization Description

Provides functions that output a discrete set of categories.

Since:

2.0

Author:

Justin Basilico