gov.sandia.cognition.learning.algorithm.bayes

Class VectorNaiveBayesCategorizer.Learner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer.Learner<CategoryType,DistributionType>

Type Parameters:

CategoryType - The output category type for the categorizer. Must implement equals and hash code.

DistributionType - The type of distribution that the distributionLearner produces.

All Implemented Interfaces:

BatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>>,VectorNaiveBayesCategorizer<CategoryType,DistributionType>>, SupervisedBatchLearner<Vectorizable,CategoryType,VectorNaiveBayesCategorizer<CategoryType,DistributionType>>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

VectorNaiveBayesCategorizer<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>

public static class VectorNaiveBayesCategorizer.Learner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>
extends AbstractCloneableSerializable
implements SupervisedBatchLearner<Vectorizable,CategoryType,VectorNaiveBayesCategorizer<CategoryType,DistributionType>>

A supervised batch distributionLearner for a vector Naive Bayes categorizer.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected DistributionEstimator<? super java.lang.Double,? extends DistributionType>	distributionEstimator The distributionLearner for the distribution of each dimension of each category.

Constructor Summary

Constructors

Constructor and Description
Learner() Creates a new BatchLearner with a null estimator.
Learner(DistributionEstimator<? super java.lang.Double,? extends DistributionType> distributionEstimator) Creates a new BatchLearner with the given distribution estimator.

Method Summary

Modifier and Type	Method and Description
DistributionEstimator<? super java.lang.Double,? extends DistributionType>	getDistributionEstimator() Gets the estimation method for the distribution of each dimension of each category.
VectorNaiveBayesCategorizer<CategoryType,DistributionType>	learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>> data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
void	setDistributionEstimator(DistributionEstimator<? super java.lang.Double,? extends DistributionType> distributionEstimator) Sets the estimation method for the distribution of each dimension of each category.

Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable

clone

Methods inherited from class java.lang.Object

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

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

clone

Field Detail

distributionEstimator

protected DistributionEstimator<? super java.lang.Double,? extends DistributionType extends UnivariateProbabilityDensityFunction> distributionEstimator

The distributionLearner for the distribution of each dimension of each category.

Constructor Detail

Learner

public Learner()

Creates a new BatchLearner with a null estimator.

Learner

public Learner(DistributionEstimator<? super java.lang.Double,? extends DistributionType> distributionEstimator)

Creates a new BatchLearner with the given distribution estimator.

Parameters:

distributionEstimator - The estimator for the distribution of each dimension of each category.

Method Detail

learn

public VectorNaiveBayesCategorizer<CategoryType,DistributionType> learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>> data)

Description copied from interface: BatchLearner

The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Specified by:

learn in interface BatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>>,VectorNaiveBayesCategorizer<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>>

Parameters:

data - The data that the learning algorithm will use to create an object of ResultType.

Returns:

The object that is created based on the given data using the learning algorithm.

getDistributionEstimator

public DistributionEstimator<? super java.lang.Double,? extends DistributionType> getDistributionEstimator()

Gets the estimation method for the distribution of each dimension of each category.

Returns:

The estimator for the distribution of each dimension of each category.

setDistributionEstimator

public void setDistributionEstimator(DistributionEstimator<? super java.lang.Double,? extends DistributionType> distributionEstimator)

Sets the estimation method for the distribution of each dimension of each category.

Parameters:

distributionEstimator - The estimator for the distribution of each dimension of each category.