#include <hrf-sa-train.h>

Inheritance diagram for hrf::SAfunc:

Public Member Functions
	SAfunc ()

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

	~SAfunc ()

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

void	PrintInfo ()
	print information More...

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

int	GetVHmatSize () const
	get the VH mat number More...

int	GetCHmatSize () const
	get the CH mat number More...

int	GetHHmatSize () const
	get the HH mat number More...

int	GetWeightNum () const
	get the bias mat 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	GetEmpiricalFeatExp (Vec< double > &vExp)
	get the empirical variance of features More...

void	GetEmpiricalFeatVar (Vec< double > &vVar)
	claculate the empirical variance of features More...

int	GetEmpiricalExp (VecShell< double > &vExp, VecShell< double > &vExp2, Array< int > &aRandIdx)
	calculate the empirical expectation of given sequence More...

int	GetEmpiricalExp (VecShell< double > &vExp, VecShell< double > &vExp2)
	calculate the empirical expectation More...

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

void	PerfromCD (VecShell< double > &vEmpExp, VecShell< double > &vSamExp, VecShell< double > &vEmpExp2, VecShell< double > &vLen)
	perform CD process and get the expectation More...

void	PerfromSA (VecShell< double > &vEmpExp, VecShell< double > &vSamExp, VecShell< double > &vEmpExp2, VecShell< double > &vLen)
	perform SA process and get the expectation More...

double	GetSampleLL (CorpusBase *pCorpus, int nCalNum=-1, int method=0)
	perform SAMS, and then select the training sequences of the same length. 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 hrf::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)

Model *	GetModel () const

void	GetParam (double *pdParams)

virtual double	GetLL (CorpusBase *pCorpus, int nCalNum=-1)
	calculate the log-likelihood on corpus 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
AISConfig	m_AISConfigForZ
	the AIS configuration for normalization More...

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

int	m_nTrainHiddenSampleTimes
	the sample times for training sequence More...

int	m_nSampleHiddenSampleTimes
	the sample times for the hidden of samples More...

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

int	m_nSASampleTimes
	the SA sample times More...

bool	m_bSAMSSample
	if using the sams sampling method 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_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_feat_mean
	output the empirical mean More...

File	m_feat_var
	output the empirical variance More...

bool	m_bPrintTrain
	output the LL on training set More...

bool	m_bPrintValie
	output the LL on valid set More...

bool	m_bPrintTest
	output the LL on test set More...

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

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

int	m_nMiniBatchTraining
	mini-batch for training set More...

trf::CorpusRandSelect	m_TrainSelect
	random select the sequence from corpus More...

CorpusCache	m_TrainCache
	cache all the h of training sequences. More...

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

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

CorpusBase *	m_pCorpusTrain
	training corpus More...

CorpusBase *	m_pCorpusValid
	valid corpus More...

CorpusBase *	m_pCorpusTest
	test corpus More...

Vec< PValue >	m_values

Vec< Prob >	m_trainPi
	the length distribution in training corpus 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 42 of file hrf-sa-train.h.

Constructor & Destructor Documentation

§ SAfunc() [1/2]

hrf::SAfunc::SAfunc ( )

inline

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

§ SAfunc() [2/2]

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

inline

Definition at line 122 of file hrf-sa-train.h.

§ ~SAfunc()

hrf::SAfunc::~SAfunc ( )

inline

Definition at line 134 of file hrf-sa-train.h.

Member Function Documentation

§ GetCHmatSize()

int hrf::SAfunc::GetCHmatSize ( ) const

inline

get the CH mat number

Definition at line 157 of file hrf-sa-train.h.

§ GetEmpiricalExp() [1/2]

int hrf::SAfunc::GetEmpiricalExp	(	VecShell< double > &	vExp,
		VecShell< double > &	vExp2,
		Array< int > &	aRandIdx
	)

calculate the empirical expectation of given sequence

several times sampling

Definition at line 332 of file hrf-sa-train.cpp.

§ GetEmpiricalExp() [2/2]

int hrf::SAfunc::GetEmpiricalExp	(	VecShell< double > &	vExp,
		VecShell< double > &	vExp2
	)

calculate the empirical expectation

Definition at line 396 of file hrf-sa-train.cpp.

§ GetEmpiricalFeatExp()

void hrf::SAfunc::GetEmpiricalFeatExp ( Vec< double > & vExp )

get the empirical variance of features

claculate the empirical expectation of features

Definition at line 165 of file hrf-sa-train.cpp.

§ GetEmpiricalFeatVar()

void hrf::SAfunc::GetEmpiricalFeatVar ( Vec< double > & vVar )

claculate the empirical variance of features

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 199 of file hrf-sa-train.cpp.

§ GetExtraValues()

int hrf::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 hrf::MLfunc.

Definition at line 937 of file hrf-sa-train.cpp.

§ GetGradient()

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

virtual

calculate the gradient g(x)

Reimplemented from hrf::MLfunc.

Definition at line 816 of file hrf-sa-train.cpp.

§ GetHHmatSize()

int hrf::SAfunc::GetHHmatSize ( ) const

inline

get the HH mat number

Definition at line 159 of file hrf-sa-train.h.

§ GetNgramFeatNum()

int hrf::SAfunc::GetNgramFeatNum ( ) const

inline

get the ngram feature number

Definition at line 153 of file hrf-sa-train.h.

§ GetParam()

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

get the parameters

Definition at line 134 of file hrf-sa-train.cpp.

§ GetSampleExp()

int hrf::SAfunc::GetSampleExp	(	VecShell< double > &	vExp,
		VecShell< double > &	vLen
	)

calcualte the expectation of SA samples

< sample hidden

save the length count

save current length count

Definition at line 405 of file hrf-sa-train.cpp.

§ GetSampleLL()

double hrf::SAfunc::GetSampleLL	(	CorpusBase *	pCorpus,
		int	nCalNum = `-1`,
		int	method = `0`
	)

perform SAMS, and then select the training sequences of the same length.

Sample the most possible hidden and calculate the LL

Definition at line 756 of file hrf-sa-train.cpp.

§ GetValue()

virtual double hrf::SAfunc::GetValue ( )

inlinevirtual

calculate the function value f(x)

Reimplemented from hrf::MLfunc.

Definition at line 200 of file hrf-sa-train.h.

§ GetVHmatSize()

int hrf::SAfunc::GetVHmatSize ( ) const

inline

get the VH mat number

Definition at line 155 of file hrf-sa-train.h.

§ GetWeightNum()

int hrf::SAfunc::GetWeightNum ( ) const

inline

get the bias mat number

get the nunber of all the weight up the exp

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

§ GetZetaNum()

int hrf::SAfunc::GetZetaNum ( ) const

inline

get the zeta parameter number

Definition at line 165 of file hrf-sa-train.h.

§ IterEnd()

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

do something at the end of the SA iteration

Definition at line 796 of file hrf-sa-train.cpp.

§ PerfromCD()

void hrf::SAfunc::PerfromCD	(	VecShell< double > &	vEmpExp,
		VecShell< double > &	vSamExp,
		VecShell< double > &	vEmpExp2,
		VecShell< double > &	vLen
	)

perform CD process and get the expectation

save the length count

save current length count

Definition at line 485 of file hrf-sa-train.cpp.

§ PerfromSA()

void hrf::SAfunc::PerfromSA	(	VecShell< double > &	vEmpExp,
		VecShell< double > &	vSamExp,
		VecShell< double > &	vEmpExp2,
		VecShell< double > &	vLen
	)

perform SA process and get the expectation

record the length of the training sequence

several times sampling

count

< sample hidden

save the length count

save current length count

Definition at line 584 of file hrf-sa-train.cpp.

§ PrintInfo()

void hrf::SAfunc::PrintInfo ( )

print information

Definition at line 87 of file hrf-sa-train.cpp.

§ RandSeq()

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

get a random sequence

Definition at line 104 of file hrf-sa-train.cpp.

§ Reset()

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

reset

Definition at line 22 of file hrf-sa-train.cpp.

§ SetParam()

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

virtual

set the parameter.

Reimplemented from hrf::MLfunc.

Definition at line 108 of file hrf-sa-train.cpp.

§ WriteModel()

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

Write Model.

Definition at line 802 of file hrf-sa-train.cpp.

Friends And Related Function Documentation

§ SAtrain

friend class SAtrain

friend

Definition at line 44 of file hrf-sa-train.h.

Member Data Documentation

§ m_AISConfigForP

AISConfig hrf::SAfunc::m_AISConfigForP

the AIS configuration for calculating the LL.

Definition at line 89 of file hrf-sa-train.h.

§ m_AISConfigForZ

AISConfig hrf::SAfunc::m_AISConfigForZ

the AIS configuration for normalization

Definition at line 88 of file hrf-sa-train.h.

§ m_bPrintTest

bool hrf::SAfunc::m_bPrintTest

output the LL on test set

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

§ m_bPrintTrain

bool hrf::SAfunc::m_bPrintTrain

output the LL on training set

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

§ m_bPrintValie

bool hrf::SAfunc::m_bPrintValie

output the LL on valid set

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

§ m_bSAMSSample

bool hrf::SAfunc::m_bSAMSSample

if using the sams sampling method

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

§ m_fdbg

File hrf::SAfunc::m_fdbg

output the sample pi/zete information

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

§ m_feat_mean

File hrf::SAfunc::m_feat_mean

output the empirical mean

Definition at line 104 of file hrf-sa-train.h.

§ m_feat_var

File hrf::SAfunc::m_feat_var

output the empirical variance

Definition at line 105 of file hrf-sa-train.h.

§ m_fexp

File hrf::SAfunc::m_fexp

output the expectation of each iteartion

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

§ m_fgrad

File hrf::SAfunc::m_fgrad

output the gradient of each iteration

Definition at line 99 of file hrf-sa-train.h.

§ m_fparm

File hrf::SAfunc::m_fparm

output the parameters of each iteration

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

§ m_fsamp

File hrf::SAfunc::m_fsamp

output all the samples

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

§ m_ftrain

File hrf::SAfunc::m_ftrain

output all the training sequences

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

§ m_fvar

File hrf::SAfunc::m_fvar

output the variance at each iteration

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

§ m_nCDSampleTimes

int hrf::SAfunc::m_nCDSampleTimes

the CD-n: the sample number.

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

§ m_nMiniBatchSample

int hrf::SAfunc::m_nMiniBatchSample

protected

mini-batch for samples

Definition at line 46 of file hrf-sa-train.h.

§ m_nMiniBatchTraining

int hrf::SAfunc::m_nMiniBatchTraining

protected

mini-batch for training set

Definition at line 47 of file hrf-sa-train.h.

§ m_nSampleHiddenSampleTimes

int hrf::SAfunc::m_nSampleHiddenSampleTimes

the sample times for the hidden of samples

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

§ m_nSASampleTimes

int hrf::SAfunc::m_nSASampleTimes

the SA sample times

Definition at line 93 of file hrf-sa-train.h.

§ m_nTrainHiddenSampleTimes

int hrf::SAfunc::m_nTrainHiddenSampleTimes

the sample times for training sequence

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

§ m_samplePi

Vec<Prob> hrf::SAfunc::m_samplePi

protected

the length distribution used for sample

Definition at line 51 of file hrf-sa-train.h.

§ m_TrainCache

CorpusCache hrf::SAfunc::m_TrainCache

protected

cache all the h of training sequences.

Definition at line 49 of file hrf-sa-train.h.

§ m_TrainSelect

trf::CorpusRandSelect hrf::SAfunc::m_TrainSelect

protected

random select the sequence from corpus

Definition at line 48 of file hrf-sa-train.h.

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

C:/Users/zhang/spmi/SPMILM/tools/trf/src/HRF/hrf-sa-train.h
C:/Users/zhang/spmi/SPMILM/tools/trf/src/HRF/hrf-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

§ GetCHmatSize()

§ GetEmpiricalExp() [1/2]

§ GetEmpiricalExp() [2/2]

§ GetEmpiricalFeatExp()

§ GetEmpiricalFeatVar()

§ GetExtraValues()

§ GetGradient()

§ GetHHmatSize()

§ GetNgramFeatNum()

§ GetParam()

§ GetSampleExp()

§ GetSampleLL()

§ GetValue()

§ GetVHmatSize()

§ GetWeightNum()

§ GetZetaNum()

§ IterEnd()

§ PerfromCD()

§ PerfromSA()

§ PrintInfo()

§ RandSeq()

§ Reset()

§ SetParam()

§ WriteModel()

Friends And Related Function Documentation

§ SAtrain

Member Data Documentation

§ m_AISConfigForP

§ m_AISConfigForZ

§ m_bPrintTest

§ m_bPrintTrain

§ m_bPrintValie

§ m_bSAMSSample

§ m_fdbg

§ m_feat_mean

§ m_feat_var

§ m_fexp

§ m_fgrad

§ m_fparm

§ m_fsamp

§ m_ftrain

§ m_fvar

§ m_nCDSampleTimes

§ m_nMiniBatchSample

§ m_nMiniBatchTraining

§ m_nSampleHiddenSampleTimes

§ m_nSASampleTimes

§ m_nTrainHiddenSampleTimes

§ m_samplePi

§ m_TrainCache

§ m_TrainSelect