gov.sandia.cognition.text.topic

Class ProbabilisticLatentSemanticAnalysis.Result

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.text.topic.ProbabilisticLatentSemanticAnalysis.Result

All Implemented Interfaces:

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

Enclosing class:

ProbabilisticLatentSemanticAnalysis

public static class ProbabilisticLatentSemanticAnalysis.Result
extends AbstractCloneableSerializable
implements Evaluator<Vectorizable,Vector>, VectorInputEvaluator<Vectorizable,Vector>, VectorOutputEvaluator<Vectorizable,Vector>

The dimensionality transform created by probabilistic latent semantic analysis.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected int	latentCount The number of latent variables.
protected ProbabilisticLatentSemanticAnalysis.LatentData[]	latents The latent variable data.
protected int	maxIterations The maximum number of iterations for the E-M evaluation.
protected double	minimumChange The minimum change in log-likelihood for the E-M evaluation.
protected int	termCount The number of terms.

Constructor Summary

Constructors

Constructor and Description
Result(int termCount, ProbabilisticLatentSemanticAnalysis.LatentData[] latents) Creates a new probabilistic latent semantic analysis transform.

Method Summary

Modifier and Type	Method and Description
Vector	evaluate(Vectorizable input) Evaluates the function on the given input and returns the output.
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.
protected double	step(Vector query, Matrix pLatentGivenQueryTerm, Vector pQueryGivenLatent) Take a step of the expectation-maximization algorithm for computing the probability of the query given each latent variable.

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

Field Detail

termCount

protected int termCount

The number of terms.

latentCount

protected int latentCount

The number of latent variables.

latents

protected ProbabilisticLatentSemanticAnalysis.LatentData[] latents

The latent variable data.

maxIterations

protected int maxIterations

The maximum number of iterations for the E-M evaluation.

minimumChange

protected double minimumChange

The minimum change in log-likelihood for the E-M evaluation.

Constructor Detail

Result

public Result(int termCount,
              ProbabilisticLatentSemanticAnalysis.LatentData[] latents)

Creates a new probabilistic latent semantic analysis transform.

Parameters:

termCount - The number of terms.

latents - The latent variable data.

Method Detail

evaluate

public Vector evaluate(Vectorizable input)

Description copied from interface: Evaluator

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

Specified by:

evaluate in interface Evaluator<Vectorizable,Vector>

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.

step

protected double step(Vector query,
                      Matrix pLatentGivenQueryTerm,
                      Vector pQueryGivenLatent)

Take a step of the expectation-maximization algorithm for computing the probability of the query given each latent variable.

Parameters:

query - The query to evaluate.

pLatentGivenQueryTerm - The probability of each latent variable given each term in the query.

pQueryGivenLatent - The probability of the query given each latent variable.

Returns:

The log-likelihood of the query.

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<Vectorizable,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<Vectorizable,Vector>

Returns:

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