gov.sandia.cognition.learning.function.categorization

Class AbstractConfidenceWeightedBinaryCategorizer

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.function.categorization.AbstractBinaryCategorizer<InputType>

gov.sandia.cognition.learning.function.categorization.AbstractDiscriminantBinaryCategorizer<Vectorizable>

gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer

gov.sandia.cognition.learning.function.categorization.AbstractConfidenceWeightedBinaryCategorizer

All Implemented Interfaces:

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

Direct Known Subclasses:

DefaultConfidenceWeightedBinaryCategorizer, DiagonalConfidenceWeightedBinaryCategorizer

public abstract class AbstractConfidenceWeightedBinaryCategorizer
extends LinearBinaryCategorizer
implements ConfidenceWeightedBinaryCategorizer

Unit tests for class AbstractConfidenceWeightedBinaryCategorizer.

Since:

3.3.0

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields inherited from class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer

bias, DEFAULT_BIAS, weights

Fields inherited from class gov.sandia.cognition.learning.function.categorization.AbstractBinaryCategorizer

BINARY_CATEGORIES

Constructor Summary

Constructors

Constructor and Description
AbstractConfidenceWeightedBinaryCategorizer() Creates a new, uninitialized AbstractConfidenceWeightedBinaryCategorizer.
AbstractConfidenceWeightedBinaryCategorizer(Vector mean) Creates a new AbstractConfidenceWeightedBinaryCategorizer with the given mean vector.

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.
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.
void	setMean(Vector mean) Sets the mean of the categorizer, which is the weight vector.

Methods inherited from class gov.sandia.cognition.learning.function.categorization.LinearBinaryCategorizer

clone, evaluateAsDouble, evaluateAsDouble, getBias, getInputDimensionality, getThreshold, getWeights, setBias, setThreshold, setWeights, toString

Methods inherited from class gov.sandia.cognition.learning.function.categorization.AbstractDiscriminantBinaryCategorizer

evaluate, evaluateWithDiscriminant

Methods inherited from class gov.sandia.cognition.learning.function.categorization.AbstractBinaryCategorizer

getCategories

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

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

evaluateAsGaussian, getCovariance

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

Constructor Detail

AbstractConfidenceWeightedBinaryCategorizer

public AbstractConfidenceWeightedBinaryCategorizer()

Creates a new, uninitialized AbstractConfidenceWeightedBinaryCategorizer.

AbstractConfidenceWeightedBinaryCategorizer

public AbstractConfidenceWeightedBinaryCategorizer(Vector mean)

Creates a new AbstractConfidenceWeightedBinaryCategorizer with the given mean vector.

Parameters:

mean - The mean vector.

Method Detail

evaluateAsBernoulli

public BernoulliDistribution evaluateAsBernoulli(Vectorizable input)

Description copied from interface: ConfidenceWeightedBinaryCategorizer

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.

Specified by:

evaluateAsBernoulli in interface ConfidenceWeightedBinaryCategorizer

Parameters:

input - The input to evaluate.

Returns:

The distribution over outputs as a Bernoulli.

isInitialized

public boolean isInitialized()

Description copied from interface: ConfidenceWeightedBinaryCategorizer

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

Specified by:

isInitialized in interface ConfidenceWeightedBinaryCategorizer

Returns:

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

createWeightDistribution

public MultivariateGaussian createWeightDistribution()

Description copied from interface: ConfidenceWeightedBinaryCategorizer

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

Specified by:

createWeightDistribution in interface ConfidenceWeightedBinaryCategorizer

Returns:

The distribution of weight vectors.

getMean

public Vector getMean()

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

Specified by:

getMean in interface ConfidenceWeightedBinaryCategorizer

Returns:

The mean of the categorizer.

setMean

public void setMean(Vector mean)

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

Parameters:

mean - The mean of the categorizer.