edu.stanford.nlp.mt
Class ATableHMM

java.lang.Object
  |
  +--edu.stanford.nlp.mt.ATable
        |
        +--edu.stanford.nlp.mt.ATableHMM

public class ATableHMM

extends ATable

Constructor Summary

ATableHMM()

ATableHMM(int maxsize)

Method Summary

boolean	checkOK()
boolean	checkOK(int len) Check OK for a specifi length len
double	DKL(ATable a1)
float	getEmpty()
float	getInitialProb(int index, int l) Get the initial probability p(i|l) i is in 0..l , l is the length of the english sentence
float	getProb(int i, int i_prev, int l) Smooth the basic probability if smoothUniform is on
float	getProbHMM(int i, int i_prev, int l) Get the probability p(i|i_prev,l) i is from 1 to 2L and i_prev is in the same set as well
float	getProbJump(int distance) Get the prior probability of jumping a distance distance
void	incCount(int distance, float cnt) Increment the count for a jump of distance distance
void	incCount(int i, int i_prev, int l, double val1) Increment the corresponding counts
void	incCountInitPos(int i, float cnt) Increment the count for an initial jump to position I
void	incEmpty(float cnt) Increment the count for a zero jump with cnt
void	initialize(ATable a1)
void	initializeUniform() Initialize the probabilities in a brain dead manner uniformly
boolean	isPopulated()
float	Mabs(float x)
static void	main(String[] args) Some code to test the class
void	normalize() This does the normalization of the component distributions
void	normalizeInitialProbs() Calculate normalized initial parameters from the counts in initialCounts This assumes we already have pEmpty calculated from params First, normalizes initialCounts so that p(1)+ ..p(MAX_LENGTH)+p0 is 1
void	normalizeProbArr() Normalize the transition table prob_arr and put the appropriate probabilities there
void	printBasicProbs()
void	printProbs()
void	read(String filename) reading the jump probabilities and initializing
void	save(String filename) Saving just the jump probabilities
void	zeroCounts() Before starting a new iteration the counts should be zero-ed

Methods inherited from class edu.stanford.nlp.mt.ATable

getCount, getProb, incCount, incCount

Methods inherited from class java.lang.Object

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

Constructor Detail

ATableHMM

public ATableHMM(int maxsize)

ATableHMM

public ATableHMM()

Method Detail

getInitialProb

public float getInitialProb(int index,
                            int l)

Get the initial probability p(i|l) i is in 0..l , l is the length of the english sentence

isPopulated

public boolean isPopulated()

Overrides:

isPopulated in class ATable

getProbHMM

public float getProbHMM(int i,
                        int i_prev,
                        int l)

Get the probability p(i|i_prev,l) i is from 1 to 2L and i_prev is in the same set as well

getProb

public float getProb(int i,
                     int i_prev,
                     int l)

Smooth the basic probability if smoothUniform is on

Overrides:

getProb in class ATable

incCount

public void incCount(int i,
                     int i_prev,
                     int l,
                     double val1)

Increment the corresponding counts

Overrides:

incCount in class ATable

incCount

public void incCount(int distance,
                     float cnt)

Increment the count for a jump of distance distance

getProbJump

public float getProbJump(int distance)

Get the prior probability of jumping a distance distance

incEmpty

public void incEmpty(float cnt)

Increment the count for a zero jump with cnt

incCountInitPos

public void incCountInitPos(int i,
                            float cnt)

Increment the count for an initial jump to position I

normalizeInitialProbs

public void normalizeInitialProbs()

Calculate normalized initial parameters from the counts in initialCounts This assumes we already have pEmpty calculated from params First, normalizes initialCounts so that p(1)+ ..p(MAX_LENGTH)+p0 is 1

zeroCounts

public void zeroCounts()

Before starting a new iteration the counts should be zero-ed

normalizeProbArr

public void normalizeProbArr()

Normalize the transition table prob_arr and put the appropriate probabilities there

normalize

public void normalize()

This does the normalization of the component distributions

Overrides:

normalize in class ATable

initializeUniform

public void initializeUniform()

Initialize the probabilities in a brain dead manner uniformly

Overrides:

initializeUniform in class ATable

initialize

public void initialize(ATable a1)

Overrides:

initialize in class ATable

getEmpty

public float getEmpty()

Overrides:

getEmpty in class ATable

checkOK

public boolean checkOK()

Overrides:

checkOK in class ATable

checkOK

public boolean checkOK(int len)

Check OK for a specifi length len

printProbs

public void printProbs()

Overrides:

printProbs in class ATable

printBasicProbs

public void printBasicProbs()

Mabs

public float Mabs(float x)

main

public static void main(String[] args)

Some code to test the class

save

public void save(String filename)

Saving just the jump probabilities

Overrides:

save in class ATable

read

public void read(String filename)

reading the jump probabilities and initializing

DKL

public double DKL(ATable a1)

Overrides:

DKL in class ATable