TRF model. More...

#include <trf-model.h>

Inheritance diagram for trf::Model:

Public Member Functions
	Model (Vocab *pv)
	constructor More...

	Model (Vocab *pv, int maxlen)
	constructor More...

	~Model ()
	destructor More...

int	GetMaxLen () const
	Get max-len. More...

Vocab *	GetVocab () const
	Get Vocab. More...

int	GetMaxOrder () const
	Get maximum order. More...

int	GetParamNum () const
	Get parameter number. More...

void	Reset (Vocab *pv, int maxlen)
	reset, the maxlen is the length excluding the beg/end symbols. More...

virtual void	SetParam (PValue *pValue)
	Set the parameters. More...

void	GetParam (PValue *pValue)
	Get the paremetre vector. More...

void	SetPi (Prob *pPi)
	Set the pi. More...

template<typename T >
void	SetZeta (T *pzeta)
	Set updated zeta. More...

template<typename T >
void	GetZeta (T *pzeta)

LogP	GetLogProb (Seq &seq, bool bNorm=true)
	calculate the probability More...

void	LoadFromCorpus (const char pcorpus, const char pfeatstyle, int nOrder)
	load ngram features from corpus More...

void	FeatCount (Seq &seq, double *pCount, double dadd=1.0)
	Count the feature number in a sequence. More...

void	ReadT (const char *pfilename)
	Read Model. More...

void	WriteT (const char *pfilename)
	Write Model. More...

LogP	ClusterSum (Seq &seq, int nPos, int nOrder)
	Read Binary. More...

virtual double	ExactNormalize (int nLen)
	[exact] Exact Normalization, return the logz of given length More...

virtual void	ExactNormalize ()
	[exact] Exact Normalization More...

void	GetNodeExp (int nLen, double *pExp)
	[exact] E_{p_l}[f]: Exactly calculate the expectation over x and h for length nLen More...

void	GetNodeExp (double pExp, Prob pLenProb=NULL)
	[exact] sum_l { n_l/n * E_{p_l}[f] }: Exactly calculate the expectation over x and h More...

void	Sample (Seq &seq)
	[sample] Perform one train-dimensional mixture sampling More...

void	LocalJump (Seq &seq)
	[sample] Local Jump - sample a new length More...

virtual void	MarkovMove (Seq &seq)
	[sample] Markov Move - perform the gibbs sampling More...

LogP	ProposeLength (int nOld, int &nNew, bool bSample)
	[sample] Propose the length, using the variable m_matLenJump More...

LogP	ProposeC0 (VocabID &ci, Seq &seq, int nPos, bool bSample)
	[sample] Propose the c_{i} at position i. Then return the propose probability R(c_i\|h_i,c_{other}) More...

void	ProposeCProbs (VecShell< LogP > &logps, Seq &seq, int nPos)
	[sample] Return the propose distribution of c_i at position nPos More...

LogP	GetReducedModelForC (Seq &seq, int nPos)
	[sample] A unnormalized reduced model to sample class c_i. More...

LogP	GetReducedModelForW (Seq &seq, int nPos)
	[sample] A unnormalized reduced model to sample word w_i. More...

LogP	GetReducedModel (Seq &seq, int nPos)
	[sample] A unnormalized reduced depending on nPos. More...

LogP	GetMarginalProbOfC (Seq &seq, int nPos)
	[sample] given c_i, summate the probabilities of x_i, i.e. P(c_i) More...

void	SampleC (Seq &seq, int nPos)
	[sample] Sample the c_i at position nPos without x_i. More...

LogP	SampleX (Seq &seq, int nPos, bool bSample=true)
	[sample] Sample the x_i at position nPos More...

LogP	AISNormalize (int nLen, int nChain, int nInter)
	perform AIS to calculate the normalization constants, return the logz of given length More...

void	AISNormalize (int nLenMin, int nLenMax, int nChain, int nInter)

Public Attributes
Feat *	m_pFeat
	hash all the features More...

Vec< PValue >	m_value
	the value for each features More...

int	m_maxlen
	the maximum length of model, excluding <s> and </s>. The min-len = 1 More...

Vec< Prob >	m_pi
	the prior length distribution More...

Vec< LogP >	m_logz
	the normalization constants log Z_l More...

Vec< LogP >	m_zeta
	the estimated normalization constants (fix = 0) More...

Vocab *	m_pVocab

Mat< Prob >	m_matLenJump
	[sample] used to propose a new length More...

int	m_maxSampleLen
	[sample] the maximum sample length, default = m_maxlen + 2 More...

int	m_nLenJumpAccTimes
	lenght jump the acceptance times More...

int	m_nLenJumpTotalTime
	total times of length jump More...

int	m_nSampleHAccTimes
	sample H the acceptance times More...

int	m_nSampleHTotalTimes

Protected Attributes
AlgNode	m_AlgNode
	the forward-backward calculation each node More...

Detailed Description

TRF model.

Definition at line 51 of file trf-model.h.

Constructor & Destructor Documentation

§ Model() [1/2]

trf::Model::Model ( Vocab * pv )

inline

constructor

Definition at line 72 of file trf-model.h.

§ Model() [2/2]

trf::Model::Model	(	Vocab *	pv,
		int	maxlen
	)

inline

constructor

Definition at line 83 of file trf-model.h.

§ ~Model()

trf::Model::~Model ( )

inline

destructor

Definition at line 95 of file trf-model.h.

Member Function Documentation

§ AISNormalize() [1/2]

LogP trf::Model::AISNormalize	(	int	nLen,
		int	nChain,
		int	nInter
	)

perform AIS to calculate the normalization constants, return the logz of given length

Definition at line 550 of file trf-model.cpp.

§ AISNormalize() [2/2]

void trf::Model::AISNormalize	(	int	nLenMin,
		int	nLenMax,
		int	nChain,
		int	nInter
	)

< record the log-prob for each word at each position

normalize, for the following sampling

Definition at line 631 of file trf-model.cpp.

§ ClusterSum()

LogP trf::Model::ClusterSum	(	Seq &	seq,
		int	nPos,
		int	nOrder
	)

Read Binary.

[exact] Calculate the logP in each cluster. Only used for forward-backword algorithms ( class AlgNode)

Definition at line 185 of file trf-model.cpp.

§ ExactNormalize() [1/2]

double trf::Model::ExactNormalize ( int nLen )

virtual

[exact] Exact Normalization, return the logz of given length

Reimplemented in hrf::Model.

Definition at line 213 of file trf-model.cpp.

§ ExactNormalize() [2/2]

void trf::Model::ExactNormalize ( )

virtual

[exact] Exact Normalization

Reimplemented in hrf::Model.

Definition at line 237 of file trf-model.cpp.

§ FeatCount()

void trf::Model::FeatCount	(	Seq &	seq,
		double *	pCount,
		double	dadd = `1.0`
	)

Count the feature number in a sequence.

Definition at line 106 of file trf-model.cpp.

§ GetLogProb()

LogP trf::Model::GetLogProb	(	Seq &	seq,
		bool	bNorm = `true`
	)

calculate the probability

Definition at line 74 of file trf-model.cpp.

§ GetMarginalProbOfC()

LogP trf::Model::GetMarginalProbOfC	(	Seq &	seq,
		int	nPos
	)

[sample] given c_i, summate the probabilities of x_i, i.e. P(c_i)

Definition at line 456 of file trf-model.cpp.

§ GetMaxLen()

int trf::Model::GetMaxLen ( ) const

inline

Get max-len.

Definition at line 100 of file trf-model.h.

§ GetMaxOrder()

int trf::Model::GetMaxOrder ( ) const

inline

Get maximum order.

Definition at line 104 of file trf-model.h.

§ GetNodeExp() [1/2]

void trf::Model::GetNodeExp	(	int	nLen,
		double *	pExp
	)

[exact] E_{p_l}[f]: Exactly calculate the expectation over x and h for length nLen

Definition at line 245 of file trf-model.cpp.

§ GetNodeExp() [2/2]

void trf::Model::GetNodeExp	(	double *	pExp,
		Prob *	pLenProb = `NULL`
	)

[exact] sum_l { n_l/n * E_{p_l}[f] }: Exactly calculate the expectation over x and h

< max-order

Definition at line 301 of file trf-model.cpp.

§ GetParam()

void trf::Model::GetParam ( PValue * pValue )

Get the paremetre vector.

Definition at line 64 of file trf-model.cpp.

§ GetParamNum()

int trf::Model::GetParamNum ( ) const

inline

Get parameter number.

Definition at line 106 of file trf-model.h.

§ GetReducedModel()

LogP trf::Model::GetReducedModel	(	Seq &	seq,
		int	nPos
	)

[sample] A unnormalized reduced depending on nPos.

Definition at line 445 of file trf-model.cpp.

§ GetReducedModelForC()

LogP trf::Model::GetReducedModelForC	(	Seq &	seq,
		int	nPos
	)

[sample] A unnormalized reduced model to sample class c_i.

Definition at line 419 of file trf-model.cpp.

§ GetReducedModelForW()

LogP trf::Model::GetReducedModelForW	(	Seq &	seq,
		int	nPos
	)

[sample] A unnormalized reduced model to sample word w_i.

< containing word

Definition at line 434 of file trf-model.cpp.

§ GetVocab()

Vocab* trf::Model::GetVocab ( ) const

inline

Get Vocab.

Definition at line 102 of file trf-model.h.

§ GetZeta()

template<typename T >

void trf::Model::GetZeta ( T * pzeta )

inline

Definition at line 126 of file trf-model.h.

§ LoadFromCorpus()

void trf::Model::LoadFromCorpus	(	const char *	pcorpus,
		const char *	pfeatstyle,
		int	nOrder
	)

load ngram features from corpus

Definition at line 95 of file trf-model.cpp.

§ LocalJump()

void trf::Model::LocalJump ( Seq & seq )

[sample] Local Jump - sample a new length

Definition at line 327 of file trf-model.cpp.

§ MarkovMove()

void trf::Model::MarkovMove ( Seq & seq )

virtual

[sample] Markov Move - perform the gibbs sampling

Reimplemented in trf::Model_FastSample.

Definition at line 372 of file trf-model.cpp.

§ ProposeC0()

LogP trf::Model::ProposeC0	(	VocabID &	ci,
		Seq &	seq,
		int	nPos,
		bool	bSample
	)

[sample] Propose the c_{i} at position i. Then return the propose probability R(c_i|h_i,c_{other})

Definition at line 389 of file trf-model.cpp.

§ ProposeCProbs()

void trf::Model::ProposeCProbs	(	VecShell< LogP > &	logps,
		Seq &	seq,
		int	nPos
	)

[sample] Return the propose distribution of c_i at position nPos

Definition at line 406 of file trf-model.cpp.

§ ProposeLength()

LogP trf::Model::ProposeLength	(	int	nOld,
		int &	nNew,
		bool	bSample
	)

[sample] Propose the length, using the variable m_matLenJump

Definition at line 381 of file trf-model.cpp.

§ ReadT()

void trf::Model::ReadT ( const char * pfilename )

Read Model.

Definition at line 114 of file trf-model.cpp.

§ Reset()

void trf::Model::Reset	(	Vocab *	pv,
		int	maxlen
	)

reset, the maxlen is the length excluding the beg/end symbols.

Definition at line 28 of file trf-model.cpp.

§ Sample()

void trf::Model::Sample ( Seq & seq )

[sample] Perform one train-dimensional mixture sampling

Definition at line 322 of file trf-model.cpp.

§ SampleC()

void trf::Model::SampleC	(	Seq &	seq,
		int	nPos
	)

[sample] Sample the c_i at position nPos without x_i.

Definition at line 474 of file trf-model.cpp.

§ SampleX()

LogP trf::Model::SampleX	(	Seq &	seq,
		int	nPos,
		bool	bSample = `true`
	)

[sample] Sample the x_i at position nPos

Definition at line 507 of file trf-model.cpp.

§ SetParam()

void trf::Model::SetParam ( PValue * pValue )

virtual

Set the parameters.

Reimplemented in hrf::Model.

Definition at line 58 of file trf-model.cpp.

§ SetPi()

void trf::Model::SetPi ( Prob * pPi )

Set the pi.

Definition at line 70 of file trf-model.cpp.

§ SetZeta()

template<typename T >

void trf::Model::SetZeta ( T * pzeta )

inline

Set updated zeta.

Definition at line 117 of file trf-model.h.

§ WriteT()

void trf::Model::WriteT ( const char * pfilename )

Write Model.

Definition at line 158 of file trf-model.cpp.

Member Data Documentation

§ m_AlgNode

AlgNode trf::Model::m_AlgNode

protected

the forward-backward calculation each node

Definition at line 68 of file trf-model.h.

§ m_logz

Vec<LogP> trf::Model::m_logz

the normalization constants log Z_l

Definition at line 59 of file trf-model.h.

§ m_matLenJump

Mat<Prob> trf::Model::m_matLenJump

[sample] used to propose a new length

Definition at line 64 of file trf-model.h.

§ m_maxlen

int trf::Model::m_maxlen

the maximum length of model, excluding <s> and </s>. The min-len = 1

Definition at line 57 of file trf-model.h.

§ m_maxSampleLen

int trf::Model::m_maxSampleLen

[sample] the maximum sample length, default = m_maxlen + 2

Definition at line 65 of file trf-model.h.

§ m_nLenJumpAccTimes

int trf::Model::m_nLenJumpAccTimes

lenght jump the acceptance times

Definition at line 167 of file trf-model.h.

§ m_nLenJumpTotalTime

int trf::Model::m_nLenJumpTotalTime

total times of length jump

Definition at line 168 of file trf-model.h.

§ m_nSampleHAccTimes

int trf::Model::m_nSampleHAccTimes

sample H the acceptance times

Definition at line 169 of file trf-model.h.

§ m_nSampleHTotalTimes

int trf::Model::m_nSampleHTotalTimes

sample H the total times

Definition at line 170 of file trf-model.h.

§ m_pFeat

Feat* trf::Model::m_pFeat

hash all the features

Definition at line 54 of file trf-model.h.

§ m_pi

Vec<Prob> trf::Model::m_pi

the prior length distribution

Definition at line 58 of file trf-model.h.

§ m_pVocab

Vocab* trf::Model::m_pVocab

Definition at line 62 of file trf-model.h.

§ m_value

Vec<PValue> trf::Model::m_value

the value for each features

Definition at line 55 of file trf-model.h.

§ m_zeta

Vec<LogP> trf::Model::m_zeta

the estimated normalization constants (fix = 0)

Definition at line 60 of file trf-model.h.

The documentation for this class was generated from the following files:

C:/Users/zhang/spmi/SPMILM/tools/trf/src/TRF/trf-model.h
C:/Users/zhang/spmi/SPMILM/tools/trf/src/TRF/trf-model.cpp

Public Member Functions

Public Attributes

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

§ Model() [1/2]

§ Model() [2/2]

§ ~Model()

Member Function Documentation

§ AISNormalize() [1/2]

§ AISNormalize() [2/2]

§ ClusterSum()

§ ExactNormalize() [1/2]

§ ExactNormalize() [2/2]

§ FeatCount()

§ GetLogProb()

§ GetMarginalProbOfC()

§ GetMaxLen()

§ GetMaxOrder()

§ GetNodeExp() [1/2]

§ GetNodeExp() [2/2]

§ GetParam()

§ GetParamNum()

§ GetReducedModel()

§ GetReducedModelForC()

§ GetReducedModelForW()

§ GetVocab()

§ GetZeta()

§ LoadFromCorpus()

§ LocalJump()

§ MarkovMove()

§ ProposeC0()

§ ProposeCProbs()

§ ProposeLength()

§ ReadT()

§ Reset()

§ Sample()

§ SampleC()

§ SampleX()

§ SetParam()

§ SetPi()

§ SetZeta()

§ WriteT()

Member Data Documentation

§ m_AlgNode

§ m_logz

§ m_matLenJump

§ m_maxlen

§ m_maxSampleLen

§ m_nLenJumpAccTimes

§ m_nLenJumpTotalTime

§ m_nSampleHAccTimes

§ m_nSampleHTotalTimes

§ m_pFeat

§ m_pi

§ m_pVocab

§ m_value

§ m_zeta