gov.sandia.cognition.learning.function.categorization

Interface ConfidenceWeightedBinaryCategorizer

All Superinterfaces:

BinaryCategorizer<Vectorizable>, Categorizer<Vectorizable,java.lang.Boolean>, java.lang.Cloneable, CloneableSerializable, DiscriminantBinaryCategorizer<Vectorizable>, DiscriminantCategorizer<Vectorizable,java.lang.Boolean,java.lang.Double>, Evaluator<Vectorizable,java.lang.Boolean>, java.io.Serializable, ThresholdBinaryCategorizer<Vectorizable>, VectorInputEvaluator<Vectorizable,java.lang.Boolean>

All Known Implementing Classes:

AbstractConfidenceWeightedBinaryCategorizer, DefaultConfidenceWeightedBinaryCategorizer, DiagonalConfidenceWeightedBinaryCategorizer

public interface ConfidenceWeightedBinaryCategorizer
extends VectorInputEvaluator<Vectorizable,java.lang.Boolean>, ThresholdBinaryCategorizer<Vectorizable>

Interface for a confidence-weighted binary categorizer, which defines a distribution over linear binary categorizers. It extends the vector input evaluator and threshold binary categorizer like a LinearBinaryCategorizer so that it can behave as a binary categorizer, it but also has methods for accessing the distribution of binary categorizers that it represents. It is typically represented using a mean vector and a covariance matrix.

Since:

3.3.0

Author:

Justin Basilico

Method Summary

Modifier and Type	Method and Description
MultivariateGaussian	createWeightDistribution() Creates a multivariate Gaussian distribution that represents the distribution of weight vectors that the algorithm has learned.
BernoulliDistribution	evaluateAsBernoulli(Vectorizable input) Returns a Bernoulli distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.
UnivariateGaussian	evaluateAsGaussian(Vectorizable input) Returns the univariate Gaussian distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.
Matrix	getCovariance() Gets the covariance matrix of the categorizer.
Vector	getMean() Gets the mean of the categorizer, which is the weight vector.
boolean	isInitialized() Determines if this category has been initialized with a mean and covariance.

Methods inherited from interface gov.sandia.cognition.math.matrix.VectorInputEvaluator

getInputDimensionality

Methods inherited from interface gov.sandia.cognition.learning.function.categorization.ThresholdBinaryCategorizer

getThreshold, setThreshold

Methods inherited from interface gov.sandia.cognition.learning.function.categorization.DiscriminantBinaryCategorizer

evaluateAsDouble

Methods inherited from interface gov.sandia.cognition.learning.function.categorization.DiscriminantCategorizer

evaluateWithDiscriminant

Methods inherited from interface gov.sandia.cognition.learning.function.categorization.Categorizer

getCategories

Methods inherited from interface gov.sandia.cognition.evaluator.Evaluator

evaluate

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Method Detail

evaluateAsGaussian

UnivariateGaussian evaluateAsGaussian(Vectorizable input)

Returns the univariate Gaussian distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.

Parameters:

input - The input to evaluate.

Returns:

The distribution of outputs as a Gaussian.

evaluateAsBernoulli

BernoulliDistribution evaluateAsBernoulli(Vectorizable input)

Returns a Bernoulli distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.

Parameters:

input - The input to evaluate.

Returns:

The distribution over outputs as a Bernoulli.

createWeightDistribution

MultivariateGaussian createWeightDistribution()

Creates a multivariate Gaussian distribution that represents the distribution of weight vectors that the algorithm has learned.

Returns:

The distribution of weight vectors.

isInitialized

boolean isInitialized()

Determines if this category has been initialized with a mean and covariance.

Returns:

True if this categorizer has been initialized. Otherwise, false.

getMean

Vector getMean()

Gets the mean of the categorizer, which is the weight vector.

Returns:

The mean of the categorizer.

getCovariance

Matrix getCovariance()

Gets the covariance matrix of the categorizer.

Returns:

The covariance matrix.