gov.sandia.cognition.statistics.bayesian

Class BayesianLinearRegression.IncrementalEstimator

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression

gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression.IncrementalEstimator

All Implemented Interfaces:

BatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>>,MultivariateGaussian>, IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Double>,BayesianLinearRegression.IncrementalEstimator.SufficientStatistic>, BayesianEstimator<InputOutputPair<? extends Vectorizable,java.lang.Double>,Vector,MultivariateGaussian>, BayesianRegression<java.lang.Double,MultivariateGaussian>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

BayesianLinearRegression

public static class BayesianLinearRegression.IncrementalEstimator
extends BayesianLinearRegression
implements IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Double>,BayesianLinearRegression.IncrementalEstimator.SufficientStatistic>

Incremental estimator for BayesianLinearRegression

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	BayesianLinearRegression.IncrementalEstimator.SufficientStatistic SufficientStatistic for incremental Bayesian linear regression

Nested classes/interfaces inherited from class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression

BayesianLinearRegression.IncrementalEstimator, BayesianLinearRegression.PredictiveDistribution

Field Summary

Fields inherited from class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression

DEFAULT_OUTPUT_VARIANCE, DEFAULT_WEIGHT_VARIANCE, outputVariance, weightPrior

Constructor Summary

Constructors

Constructor and Description
IncrementalEstimator(double outputVariance, MultivariateGaussian weightPrior) Creates a new instance of IncrementalEstimator
IncrementalEstimator(int dimensionality) Creates a new instance of IncrementalEstimator

Method Summary

Modifier and Type	Method and Description
BayesianLinearRegression.IncrementalEstimator.SufficientStatistic	createInitialLearnedObject() Creates a new initial learned object, before any data is given.
MultivariateGaussian.PDF	learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
void	update(BayesianLinearRegression.IncrementalEstimator.SufficientStatistic target, InputOutputPair<? extends Vectorizable,java.lang.Double> data) The update method updates an object of ResultType using the given new data of type DataType, using some form of "learning" algorithm.
void	update(BayesianLinearRegression.IncrementalEstimator.SufficientStatistic target, java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data) The update method updates an object of ResultType using the given new Iterable containing some number of type DataType, using some form of "learning" algorithm.

Methods inherited from class gov.sandia.cognition.statistics.bayesian.BayesianLinearRegression

clone, createConditionalDistribution, createPredictiveDistribution, getOutputVariance, getWeightPrior, setOutputVariance, setWeightPrior

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

Constructor Detail

IncrementalEstimator

public IncrementalEstimator(int dimensionality)

Creates a new instance of IncrementalEstimator

Parameters:

dimensionality - Sets up the parameters (except featureMap) for the given dimensionality of objects in feature space.

IncrementalEstimator

public IncrementalEstimator(double outputVariance,
                            MultivariateGaussian weightPrior)

Creates a new instance of IncrementalEstimator

Parameters:

outputVariance - Assumed known variance of the outputs (measurements), must be greater than zero.

weightPrior - Prior distribution of the weights, typically a zero-mean, diagonal-variance distribution.

Method Detail

createInitialLearnedObject

public BayesianLinearRegression.IncrementalEstimator.SufficientStatistic createInitialLearnedObject()

Description copied from interface: IncrementalLearner

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

Specified by:

createInitialLearnedObject in interface IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Double>,BayesianLinearRegression.IncrementalEstimator.SufficientStatistic>

Returns:

The initial learned object.

learn

public MultivariateGaussian.PDF learn(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> 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,java.lang.Double>>,MultivariateGaussian>

Overrides:

learn in class BayesianLinearRegression

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.

update

public void update(BayesianLinearRegression.IncrementalEstimator.SufficientStatistic target,
                   InputOutputPair<? extends Vectorizable,java.lang.Double> 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.

Specified by:

update in interface IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Double>,BayesianLinearRegression.IncrementalEstimator.SufficientStatistic>

Parameters:

target - The object to update.

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

update

public void update(BayesianLinearRegression.IncrementalEstimator.SufficientStatistic target,
                   java.lang.Iterable<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data)

Description copied from interface: IncrementalLearner

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

Specified by:

update in interface IncrementalLearner<InputOutputPair<? extends Vectorizable,java.lang.Double>,BayesianLinearRegression.IncrementalEstimator.SufficientStatistic>

Parameters:

target - The object to update.

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