gov.sandia.cognition.learning.function.vector

Class GaussianContextRecognizer

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.function.vector.GaussianContextRecognizer

All Implemented Interfaces:

Evaluator<Vector,Vector>, VectorFunction, VectorInputEvaluator<Vector,Vector>, VectorOutputEvaluator<Vector,Vector>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

public class GaussianContextRecognizer
extends AbstractCloneableSerializable
implements VectorFunction, VectorInputEvaluator<Vector,Vector>, VectorOutputEvaluator<Vector,Vector>

Uses a MixtureOfGaussians to compute the probability of the different constituent MultivariateGaussians (that is, the contexts)

Since:

1.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	GaussianContextRecognizer.Learner Creates a GaussianContextRecognizer from a Dataset[Vector] using a BatchClusterer

Constructor Summary

Constructors

Constructor and Description
GaussianContextRecognizer() Creates a new instance of GaussianContextRecognizer
GaussianContextRecognizer(java.util.Collection<GaussianCluster> clusters) Creates a new instance of GaussianContextRecognizer
GaussianContextRecognizer(GaussianContextRecognizer other) Copy constructor
GaussianContextRecognizer(MixtureOfGaussians.PDF gaussianMixture) Creates a new instance of GaussianContextRecognizer

Method Summary

Modifier and Type	Method and Description
GaussianContextRecognizer	clone() This makes public the clone method on the Object class and removes the exception that it throws.
void	consumeClusters(java.util.Collection<GaussianCluster> clusters) Uses the given clusters to populate the internal clusters of this
Vector	evaluate(Vector input) Evaluates the function on the given input and returns the output.
MixtureOfGaussians.PDF	getGaussianMixture() Getter for gaussianMixture
int	getInputDimensionality() Gets the expected dimensionality of the input vector to the evaluator, if it is known.
int	getOutputDimensionality() Gets the expected dimensionality of the output vector of the evaluator, if it is known.
void	setGaussianMixture(MixtureOfGaussians.PDF gaussianMixture) Setter for gaussianMixture

Methods inherited from class java.lang.Object

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

Constructor Detail

GaussianContextRecognizer

public GaussianContextRecognizer()

Creates a new instance of GaussianContextRecognizer

GaussianContextRecognizer

public GaussianContextRecognizer(MixtureOfGaussians.PDF gaussianMixture)

Creates a new instance of GaussianContextRecognizer

Parameters:

gaussianMixture - Underlying MixtureOfGaussians that computes context probabilities

GaussianContextRecognizer

public GaussianContextRecognizer(java.util.Collection<GaussianCluster> clusters)

Creates a new instance of GaussianContextRecognizer

Parameters:

clusters -

GaussianContextRecognizer

public GaussianContextRecognizer(GaussianContextRecognizer other)

Copy constructor

Parameters:

other - GaussianContextRecognizer to clone

Method Detail

clone

public GaussianContextRecognizer clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractCloneableSerializable

Returns:

A clone of this object.

evaluate

public Vector evaluate(Vector input)

Description copied from interface: Evaluator

Evaluates the function on the given input and returns the output.

Specified by:

evaluate in interface Evaluator<Vector,Vector>

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.

consumeClusters

public void consumeClusters(java.util.Collection<GaussianCluster> clusters)

Uses the given clusters to populate the internal clusters of this

Parameters:

clusters - Clusters from which to create the data for this

getGaussianMixture

public MixtureOfGaussians.PDF getGaussianMixture()

Getter for gaussianMixture

Returns:

Underlying MixtureOfGaussians that computes context probabilities

setGaussianMixture

public void setGaussianMixture(MixtureOfGaussians.PDF gaussianMixture)

Setter for gaussianMixture

Parameters:

gaussianMixture - Underlying MixtureOfGaussians that computes context probabilities

getInputDimensionality

public int getInputDimensionality()

Description copied from interface: VectorInputEvaluator

Gets the expected dimensionality of the input vector to the evaluator, if it is known. If it is not known, -1 is returned.

Specified by:

getInputDimensionality in interface VectorInputEvaluator<Vector,Vector>

Returns:

The expected dimensionality of the input vector to the evaluator, or -1 if it is not known.

getOutputDimensionality

public int getOutputDimensionality()

Description copied from interface: VectorOutputEvaluator

Gets the expected dimensionality of the output vector of the evaluator, if it is known. If it is not known, -1 is returned.

Specified by:

getOutputDimensionality in interface VectorOutputEvaluator<Vector,Vector>

Returns:

The expected dimensionality of the output vector of the evaluator, or -1 if it is not known.