cat.lump.ir.sim.cl.len

Class LengthModelEstimate

java.lang.Object

cat.lump.ir.sim.cl.len.LengthModelEstimate

public class LengthModelEstimate
extends java.lang.Object

A class to estimate the length factor between two texts according to previously learnt parameters.

Version:

0.3

Author:

albarron

See Also:

cat.talp.lump.sim.lenMod.LengthModel

Constructor Summary

Constructors

Constructor and Description
LengthModelEstimate()

Method Summary

Methods

Modifier and Type	Method and Description
void	display() Display only estimation
void	displayVerbose() Display estimation, source and target sentences
double[]	estimate()
double[][]	estimateMatrix() Estimates the length factors of the sentences among them.
void	setFiles(java.io.File srcFile, java.io.File trgFile) Set the input files to estimate the model from
void	setMuSigma(double mu, double sigma) Set values for mu and sigma
void	setVerbose(java.lang.Boolean verb) Deprecated.

Methods inherited from class java.lang.Object

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

Constructor Detail

LengthModelEstimate

public LengthModelEstimate()

Method Detail

estimate

public double[] estimate()

displayVerbose

public void displayVerbose()

Display estimation, source and target sentences

display

public void display()

Display only estimation

estimateMatrix

public double[][] estimateMatrix()

Estimates the length factors of the sentences among them.

Returns:

A matrix of length factors. Each row is a source sentence and each column a target one.

setVerbose

@Deprecated
public void setVerbose(java.lang.Boolean verb)

Deprecated.

Whether the output should include source and target texts or only the length factor.
Use either displayVerbose() or display() instead

setFiles

public void setFiles(java.io.File srcFile,
            java.io.File trgFile)

Set the input files to estimate the model from

Parameters:

srcFile -

trgFile -

setMuSigma

public void setMuSigma(double mu,
              double sigma)

Set values for mu and sigma

Parameters:

mu - mean for the distribution

sigma - for the distribution