gov.sandia.cognition.learning.algorithm.bayes

Class VectorNaiveBayesCategorizer.OnlineLearner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner<InputOutputPair<? extends Vectorizable,CategoryType>,VectorNaiveBayesCategorizer<CategoryType,DistributionType>>

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

Type Parameters:

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

DistributionType - The type of the distributions used to compute the conditionals for each dimension.

All Implemented Interfaces:

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

Enclosing class:

VectorNaiveBayesCategorizer<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>

public static class VectorNaiveBayesCategorizer.OnlineLearner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>
extends AbstractBatchAndIncrementalLearner<InputOutputPair<? extends Vectorizable,CategoryType>,VectorNaiveBayesCategorizer<CategoryType,DistributionType>>

An online (incremental) distributionLearner for the Naive Bayes categorizer that uses an incremental distribution learner for the distribution representing each dimension for each category.

Since:

3.3.0

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected IncrementalLearner<? super java.lang.Double,DistributionType>	distributionLearner The incremental learner for the distribution used to represent each dimension.

Constructor Summary

Constructors

Constructor and Description
OnlineLearner() Creates a new learner with a null distribution learner.
OnlineLearner(IncrementalLearner<? super java.lang.Double,DistributionType> distributionLearner) Creates a new learner with a given distribution learner.

Method Summary

Modifier and Type	Method and Description
VectorNaiveBayesCategorizer<CategoryType,DistributionType>	createInitialLearnedObject() Creates a new initial learned object, before any data is given.
IncrementalLearner<? super java.lang.Double,DistributionType>	getDistributionLearner() Gets the learner used for the distribution representing each dimension.
void	setDistributionLearner(IncrementalLearner<? super java.lang.Double,DistributionType> distributionLearner) Sets the learner used for the distribution representing each dimension.
void	update(VectorNaiveBayesCategorizer<CategoryType,DistributionType> target, InputOutputPair<? extends Vectorizable,CategoryType> data) The update method updates an object of ResultType using the given new data of type DataType, using some form of "learning" algorithm.

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner

clone, learn, learn, update

Methods inherited from class java.lang.Object

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

Field Detail

distributionLearner

protected IncrementalLearner<? super java.lang.Double,DistributionType extends UnivariateProbabilityDensityFunction> distributionLearner

The incremental learner for the distribution used to represent each dimension. By the generic, it must learn a univariate probability density function.

Constructor Detail

OnlineLearner

public OnlineLearner()

Creates a new learner with a null distribution learner.

OnlineLearner

public OnlineLearner(IncrementalLearner<? super java.lang.Double,DistributionType> distributionLearner)

Creates a new learner with a given distribution learner.

Parameters:

distributionLearner - The learner for the distribution representing each dimension.

Method Detail

createInitialLearnedObject

public VectorNaiveBayesCategorizer<CategoryType,DistributionType> createInitialLearnedObject()

Description copied from interface: IncrementalLearner

Creates a new initial learned object, before any data is given.

Returns:

The initial learned object.

update

public void update(VectorNaiveBayesCategorizer<CategoryType,DistributionType> target,
                   InputOutputPair<? extends Vectorizable,CategoryType> data)

Description copied from interface: IncrementalLearner

The update method updates an object of ResultType using the given new data of type DataType, using some form of "learning" algorithm.

Parameters:

target - The object to update.

data - The new data for the learning algorithm to use to update the object.

getDistributionLearner

public IncrementalLearner<? super java.lang.Double,DistributionType> getDistributionLearner()

Gets the learner used for the distribution representing each dimension.

Returns:

The distribution learner.

setDistributionLearner

public void setDistributionLearner(IncrementalLearner<? super java.lang.Double,DistributionType> distributionLearner)

Sets the learner used for the distribution representing each dimension.

Parameters:

distributionLearner - The distribution learner.