#include <trf-sa-train.h>

Inheritance diagram for trf::SAfunc:

Public Member Functions
	SAfunc ()

	SAfunc (Model pModel, CorpusBase pTrain, CorpusBase pValid=NULL, CorpusBase pTest=NULL, int nMinibatch=100)

	~SAfunc ()

virtual void	Reset (Model pModel, CorpusBase pTrain, CorpusBase pValid=NULL, CorpusBase pTest=NULL, int nMinibatch=100)
	reset More...

void	PrintInfo ()
	print information More...

int	GetFeatNum () const
	get the ngram feature number More...

int	GetZetaNum () const
	get the zeta parameter number More...

void	RandSeq (Seq &seq, int nLen=-1)
	get a random sequence More...

void	GetParam (double *pdParams)
	get the parameters More...

void	GetEmpVar (CorpusBase *pCorpus, Vec< double > &vVar)
	calculate the empirical expectation More...

virtual void	GetSampleExp (VecShell< double > &vExp, VecShell< double > &vExp2, VecShell< double > &vLen)
	calcualte the expectation of SA samples More...

void	IterEnd (double *pFinalParams)
	do something at the end of the SA iteration More...

void	WriteModel (int nEpoch)
	Write Model. More...

virtual void	SetParam (double *pdParams)
	set the parameter. More...

virtual void	GetGradient (double *pdGradient)
	calculate the gradient g(x) More...

virtual double	GetValue ()
	calculate the function value f(x) More...

virtual int	GetExtraValues (int t, double *pdValues)
	calculate extra values which will be print at each iteration More...

Public Member Functions inherited from trf::MLfunc
	MLfunc ()

	MLfunc (Model pModel, CorpusBase pTrain, CorpusBase pValid=NULL, CorpusBase pTest=NULL)

void	Reset (Model pModel, CorpusBase pTrain, CorpusBase pValid=NULL, CorpusBase pTest=NULL)

void	GetParam (double *pdParams)

virtual double	GetLL (CorpusBase pCorpus, int nCalNum=-1, Vec< double > pLL=NULL)
	calculate the log-likelihood on corpus More...

void	GetEmpExp (CorpusBase *pCorpus, Vec< double > &vExp)
	get the empirical expectation More...

Public Member Functions inherited from wb::Func
	Func (int nParamNum=0)

void	SetParamNum (int n)
	setting the parameter number More...

int	GetParamNum () const
	get the paremeter number More...

Public Attributes
double	m_fRegL2
	l2 regularization More...

double	m_var_gap
	a varicance gap used in gradient sacling More...

AISConfig	m_AISConfigForZ
	the AIS configuration for normalization More...

AISConfig	m_AISConfigForP
	the AIS configuration for calculating the LL. More...

int	m_nCDSampleTimes
	the CD-n: the sample number. More...

int	m_nSASampleTimes
	the SA sample times More...

File	m_fdbg
	output the sample pi/zete information More...

File	m_fparm
	output the parameters of each iteration More...

File	m_fgrad
	output the gradient of each iteration More...

File	m_fmean
	output the p[f] on training set More...

File	m_fvar
	output the variance at each iteration More...

File	m_fexp
	output the expectation of each iteartion More...

File	m_fsamp
	output all the samples More...

File	m_ftrain
	output all the training sequences More...

File	m_ftrainLL
	output loglikelihood on training set More...

File	m_fvallidLL
	output loglikelihood on valid set More...

File	m_ftestLL
	output loglikelihood on test set More...

Public Attributes inherited from trf::MLfunc
const char *	m_pathOutputModel
	Write to model during iteration. More...

Protected Attributes
int	m_nMiniBatchSample
	mini-batch for samples More...

Vec< Prob >	m_samplePi
	the length distribution used for sample More...

Vec< double >	m_vAllSampleLenCount
	the count of each length in all samples More...

Vec< double >	m_vCurSampleLenCount
	the count of length in samples of current iteration More...

int	m_nTotalSample
	the total sample number More...

Vec< double >	m_vSampleExp
	the sample expectation More...

Vec< double >	m_vSampleExp2
	the sample expectation^2 More...

Vec< double >	m_vSampleLen
	the sample length expectation More...

Array< Seq * >	m_threadSeq
	save the last sequence of each threads More...

Mat< double >	m_matSampleExp
	the sample expectation of each thread More...

Mat< double >	m_matSampleExp2
	the sample expectation^2 of each thread More...

Mat< double >	m_matSampleLen
	the length count of sample of each thread More...

Vec< double >	m_vEmpiricalVar
	empirical variance More...

Protected Attributes inherited from trf::MLfunc
Model *	m_pModel
	HRF model. More...

Vec< PValue >	m_value
	save the temp value of type PValue. More...

CorpusBase *	m_pCorpusTrain
	training corpus More...

CorpusBase *	m_pCorpusValid
	valid corpus More...

CorpusBase *	m_pCorpusTest
	test corpus More...

Vec< Prob >	m_trainPi
	the length distribution in training corpus More...

Vec< double >	m_vEmpiricalExp
	the empirical expectation More...

Protected Attributes inherited from wb::Func
Solve *	m_pSolve
	Save the solve pointor. More...

int	m_nParamNum
	the parameter number More...

Friends
class	SAtrain

Additional Inherited Members
Static Public Attributes inherited from wb::Func
static const int	cn_exvalue_max_num = 100

Detailed Description

Definition at line 66 of file trf-sa-train.h.

Constructor & Destructor Documentation

§ SAfunc() [1/2]

trf::SAfunc::SAfunc ( )

inline

Definition at line 120 of file trf-sa-train.h.

§ SAfunc() [2/2]

trf::SAfunc::SAfunc	(	Model *	pModel,
		CorpusBase *	pTrain,
		CorpusBase *	pValid = `NULL`,
		CorpusBase *	pTest = `NULL`,
		int	nMinibatch = `100`
	)

inline

Definition at line 124 of file trf-sa-train.h.

§ ~SAfunc()

trf::SAfunc::~SAfunc ( )

inline

Definition at line 130 of file trf-sa-train.h.

Member Function Documentation

§ GetEmpVar()

void trf::SAfunc::GetEmpVar	(	CorpusBase *	pCorpus,
		Vec< double > &	vVar
	)

calculate the empirical expectation

Count p[f^2]

Count p_l[f] As save p_l[f] for all the length cost too much memory. So we calculate each p_l[f] separately.

find all the sequence with length nLen

calcualte p[f^2] - * p_l[f]^2

output the zero number

save

Definition at line 144 of file trf-sa-train.cpp.

§ GetExtraValues()

int trf::SAfunc::GetExtraValues	(	int	k,
		double *	pdValues
	)

virtual

calculate extra values which will be print at each iteration

Parameters

[in]	k	iteration number form 1 to ...
[out]	pdValues	Return the values needed to be outputed. The memory is allocated outside and the maximum size = cn_exvalue_max_num

Returns: return the pdValues number

Reimplemented from trf::MLfunc.

Definition at line 460 of file trf-sa-train.cpp.

§ GetFeatNum()

int trf::SAfunc::GetFeatNum ( ) const

inline

get the ngram feature number

Definition at line 143 of file trf-sa-train.h.

§ GetGradient()

void trf::SAfunc::GetGradient ( double * pdGradient )

virtual

calculate the gradient g(x)

( m_fEmpiricalVarGap + m_fRegL2 )

Reimplemented from trf::MLfunc.

Definition at line 400 of file trf-sa-train.cpp.

§ GetParam()

void trf::SAfunc::GetParam ( double * pdParams )

get the parameters

Definition at line 120 of file trf-sa-train.cpp.

§ GetSampleExp()

void trf::SAfunc::GetSampleExp	(	VecShell< double > &	vExp,
		VecShell< double > &	vExp2,
		VecShell< double > &	vLen
	)

virtual

calcualte the expectation of SA samples

save the length count

save current length count

Definition at line 301 of file trf-sa-train.cpp.

§ GetValue()

virtual double trf::SAfunc::GetValue ( )

inlinevirtual

calculate the function value f(x)

Reimplemented from trf::MLfunc.

Definition at line 163 of file trf-sa-train.h.

§ GetZetaNum()

int trf::SAfunc::GetZetaNum ( ) const

inline

get the zeta parameter number

Definition at line 145 of file trf-sa-train.h.

§ IterEnd()

void trf::SAfunc::IterEnd ( double * pFinalParams )

do something at the end of the SA iteration

Definition at line 380 of file trf-sa-train.cpp.

§ PrintInfo()

void trf::SAfunc::PrintInfo ( )

print information

Definition at line 85 of file trf-sa-train.cpp.

§ RandSeq()

void trf::SAfunc::RandSeq	(	Seq &	seq,
		int	nLen = `-1`
	)

get a random sequence

[1, maxlen]

Definition at line 93 of file trf-sa-train.cpp.

§ Reset()

void trf::SAfunc::Reset	(	Model *	pModel,
		CorpusBase *	pTrain,
		CorpusBase *	pValid = `NULL`,
		CorpusBase *	pTest = `NULL`,
		int	nMinibatch = `100`
	)

virtual

reset

Definition at line 39 of file trf-sa-train.cpp.

§ SetParam()

void trf::SAfunc::SetParam ( double * pdParams )

virtual

set the parameter.

Reimplemented from trf::MLfunc.

Definition at line 102 of file trf-sa-train.cpp.

§ WriteModel()

void trf::SAfunc::WriteModel ( int nEpoch )

Write Model.

Definition at line 386 of file trf-sa-train.cpp.

Friends And Related Function Documentation

§ SAtrain

friend class SAtrain

friend

Definition at line 68 of file trf-sa-train.h.

Member Data Documentation

§ m_AISConfigForP

AISConfig trf::SAfunc::m_AISConfigForP

the AIS configuration for calculating the LL.

Definition at line 101 of file trf-sa-train.h.

§ m_AISConfigForZ

AISConfig trf::SAfunc::m_AISConfigForZ

the AIS configuration for normalization

Definition at line 100 of file trf-sa-train.h.

§ m_fdbg

File trf::SAfunc::m_fdbg

output the sample pi/zete information

Definition at line 106 of file trf-sa-train.h.

§ m_fexp

File trf::SAfunc::m_fexp

output the expectation of each iteartion

Definition at line 111 of file trf-sa-train.h.

§ m_fgrad

File trf::SAfunc::m_fgrad

output the gradient of each iteration

Definition at line 108 of file trf-sa-train.h.

§ m_fmean

File trf::SAfunc::m_fmean

output the p[f] on training set

Definition at line 109 of file trf-sa-train.h.

§ m_fparm

File trf::SAfunc::m_fparm

output the parameters of each iteration

Definition at line 107 of file trf-sa-train.h.

§ m_fRegL2

double trf::SAfunc::m_fRegL2

l2 regularization

Definition at line 97 of file trf-sa-train.h.

§ m_fsamp

File trf::SAfunc::m_fsamp

output all the samples

Definition at line 112 of file trf-sa-train.h.

§ m_ftestLL

File trf::SAfunc::m_ftestLL

output loglikelihood on test set

Definition at line 116 of file trf-sa-train.h.

§ m_ftrain

File trf::SAfunc::m_ftrain

output all the training sequences

Definition at line 113 of file trf-sa-train.h.

§ m_ftrainLL

File trf::SAfunc::m_ftrainLL

output loglikelihood on training set

Definition at line 114 of file trf-sa-train.h.

§ m_fvallidLL

File trf::SAfunc::m_fvallidLL

output loglikelihood on valid set

Definition at line 115 of file trf-sa-train.h.

§ m_fvar

File trf::SAfunc::m_fvar

output the variance at each iteration

Definition at line 110 of file trf-sa-train.h.

§ m_matSampleExp

Mat<double> trf::SAfunc::m_matSampleExp

protected

the sample expectation of each thread

Definition at line 90 of file trf-sa-train.h.

§ m_matSampleExp2

Mat<double> trf::SAfunc::m_matSampleExp2

protected

the sample expectation^2 of each thread

Definition at line 91 of file trf-sa-train.h.

§ m_matSampleLen

Mat<double> trf::SAfunc::m_matSampleLen

protected

the length count of sample of each thread

Definition at line 92 of file trf-sa-train.h.

§ m_nCDSampleTimes

int trf::SAfunc::m_nCDSampleTimes

the CD-n: the sample number.

Definition at line 102 of file trf-sa-train.h.

§ m_nMiniBatchSample

int trf::SAfunc::m_nMiniBatchSample

protected

mini-batch for samples

Definition at line 70 of file trf-sa-train.h.

§ m_nSASampleTimes

int trf::SAfunc::m_nSASampleTimes

the SA sample times

Definition at line 103 of file trf-sa-train.h.

§ m_nTotalSample

int trf::SAfunc::m_nTotalSample

protected

the total sample number

Definition at line 76 of file trf-sa-train.h.

§ m_samplePi

Vec<Prob> trf::SAfunc::m_samplePi

protected

the length distribution used for sample

Definition at line 72 of file trf-sa-train.h.

§ m_threadSeq

Array<Seq*> trf::SAfunc::m_threadSeq

protected

save the last sequence of each threads

Definition at line 86 of file trf-sa-train.h.

§ m_vAllSampleLenCount

Vec<double> trf::SAfunc::m_vAllSampleLenCount

protected

the count of each length in all samples

Definition at line 74 of file trf-sa-train.h.

§ m_var_gap

double trf::SAfunc::m_var_gap

a varicance gap used in gradient sacling

Definition at line 98 of file trf-sa-train.h.

§ m_vCurSampleLenCount

Vec<double> trf::SAfunc::m_vCurSampleLenCount

protected

the count of length in samples of current iteration

Definition at line 75 of file trf-sa-train.h.

§ m_vEmpiricalVar

Vec<double> trf::SAfunc::m_vEmpiricalVar

protected

empirical variance

Definition at line 94 of file trf-sa-train.h.

§ m_vSampleExp

Vec<double> trf::SAfunc::m_vSampleExp

protected

the sample expectation

Definition at line 80 of file trf-sa-train.h.

§ m_vSampleExp2

Vec<double> trf::SAfunc::m_vSampleExp2

protected

the sample expectation^2

Definition at line 81 of file trf-sa-train.h.

§ m_vSampleLen

Vec<double> trf::SAfunc::m_vSampleLen

protected

the sample length expectation

Definition at line 82 of file trf-sa-train.h.

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

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

Public Member Functions

Public Attributes

Protected Attributes

Friends

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

§ SAfunc() [1/2]

§ SAfunc() [2/2]

§ ~SAfunc()

Member Function Documentation

§ GetEmpVar()

§ GetExtraValues()

§ GetFeatNum()

§ GetGradient()

§ GetParam()

§ GetSampleExp()

§ GetValue()

§ GetZetaNum()

§ IterEnd()

§ PrintInfo()

§ RandSeq()

§ Reset()

§ SetParam()

§ WriteModel()

Friends And Related Function Documentation

§ SAtrain

Member Data Documentation

§ m_AISConfigForP

§ m_AISConfigForZ

§ m_fdbg

§ m_fexp

§ m_fgrad

§ m_fmean

§ m_fparm

§ m_fRegL2

§ m_fsamp

§ m_ftestLL

§ m_ftrain

§ m_ftrainLL

§ m_fvallidLL

§ m_fvar

§ m_matSampleExp

§ m_matSampleExp2

§ m_matSampleLen

§ m_nCDSampleTimes

§ m_nMiniBatchSample

§ m_nSASampleTimes

§ m_nTotalSample

§ m_samplePi

§ m_threadSeq

§ m_vAllSampleLenCount

§ m_var_gap

§ m_vCurSampleLenCount

§ m_vEmpiricalVar

§ m_vSampleExp

§ m_vSampleExp2

§ m_vSampleLen