hidden-random-field model More...

#include <hrf-model.h>

Inheritance diagram for hrf::Model:

Public Member Functions
	Model (Vocab *pv)

	Model (Vocab *pv, int hlayer, int hnode, int maxlen)

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

int	GetHnode () const
	get hidden node dimension More...

int	GetHiddenOrder () const
	Get HH mat order. More...

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

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

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

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

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

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

LogP	GetLogProb (VecShell< VocabID > &x, bool bNorm=true)
	[exact] calculate the probability of x More...

LogP	ClusterSum (Seq &seq, int nPos, int nOrder)
	[exact] Calculate the logP in each cluster. Only used for forward-backword algorithms ( class AlgNode) More...

LogP	HiddenClusterSum (Seq &seq, int nPos, int nOrder)
	[exact] Calculate the logp in each cluster. Only consinder the VH,CH,HH values, used in class AlgHidden More...

LogP	FeatClusterSum (trf::Seq &x, int nPos, int nOrder)
	[exact] Calculate the logp in each cluster. Only consinder the feature values More...

LogP	LayerClusterSum (Seq &seq, int nlayer, int nPos, int nOrder)
	[exact] Calculate the logp in each cluster. Only consinder the VH,CH,HH values on such layer More...

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

void	ExactNormalize ()
	[exact] Exact Normalization all the length More...

LogP	GetMarginalLogProb (int nLen, int nPos, Seq &sub, bool bNorm=true)
	[exact] Exactly calculate the marginal probability at position 'nPos' and with order 'nOrder' 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	GetNodeExp (int nLen, double *pExp)
	[exact] E_{p_l}[f]: Exactly calculate the expectation over x and h for length nLen More...

void	GetNodeExp (int nLen, VecShell< double > featexp, Mat3dShell< double > VHexp, Mat3dShell< double > CHexp, Mat3dShell< double > HHexp, MatShell< double > Bexp)
	[exact] E_{p_l}[f]: Exactly calculate the expectation over x and h for length nLen More...

void	GetHiddenExp (VecShell< int > x, double *pExp)
	[exact] E_{p_l(h\|x)}[f]: don't clean the pExp and directly add the new exp to pExp. More...

void	GetLayerExp (AlgLayer &fb, int nLayer, Mat3dShell< double > &VHexp, Mat3dShell< double > &CHexp, Mat3dShell< double > &HHexp, MatShell< double > &Bexp, LogP logz=0)
	[exact] called in GetHiddenExp. 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...

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	ProposeH0 (VecShell< HValue > &hi, Seq &seq, int nPos, bool bSample)
	[sample] Propose the h_{i} at position i. Then return the propose probability Q(h_i\|h_{other}) 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	ProposeHProbs (VecShell< LogP > &logps, Seq &seq, int nPos, bool bConsiderXandC=false)
	[sample] A reduced model only consinder HHmat(W) and VHmat(M) and CHmat(U). More...

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

LogP	GetReducedModelForH (Seq &seq, int nPos)
	[sample] A unnormalized reduced model. It only consindering the HH matrix (W) More...

LogP	GetReducedModelForC (Seq &seq, int nPos)
	[sample] A unnormalized reduced model to sample class c_i, consindering CH matrix(U) and class-ngram (lambda_c) More...

LogP	GetReducedModelForW (Seq &seq, int nPos)
	[sample] A unnormalized reduced model to sample word w_i, consindering VH matrix(M) and word-ngram (lambda_w) More...

LogP	GetConditionalProbForH (VecShell< HValue > &hi, VecShell< Prob > &probs)
	[sample] using the logprobs returned by ProposeHProb to calculate the logprob of hi. More...

LogP	GetMarginalProbOfC (Seq &seq, int nPos)
	[sample] Fixed h, 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 given h_i without x_i. More...

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

void	RandSeq (Seq &seq, int nLen=-1)
	Random init sequence, if nLen==-1, random the length also. More...

void	RandHidden (Seq &seq)
	Random init the hidden variables. More...

virtual LogP	SampleHAndCGivenX (Seq &seq, MatShell< HValue > *tagH=NULL)
	[sample] sample h given x using gibbs sampling. More...

int	EncodeNode (VocabID xi, VocabID ci, VecShell< HValue > &hi)
	encode the x_i and h_i at position i to a integer More...

void	EncodeNode (VecShell< int > &vn, Seq &seq, int nPos=0, int nDim=-1)
	encode the x and h to a integer sequence More...

void	DecodeNode (int n, VocabID &xi, VocabID &ci, VecShell< HValue > &hi)
	decode a integer to the x_i and h_i More...

void	DecodeNode (VecShell< int > &vn, Seq &seq, int nPos=0, int nDim=-1)
	decode several integer to a sequence More...

int	GetEncodeNodeLimit () const
	The encoded integer size. More...

int	EncodeHidden (VecShell< HValue > hi)
	encode the hidden vector h_i to a integer More...

void	DecodeHidden (int n, VecShell< HValue > hi)
	decode a integer to a hidden vector More...

void	DecodeHidden (VecShell< int > &vn, Mat< HValue > &h, int nPos=0, int nDim=-1)
	decoder several integer to a sequence More...

int	GetEncodeHiddenLimit () const
	The encoded integer size. More...

void	DecodeLayer (VecShell< int > &vn, Mat< HValue > &h, int layer, int nPos=0, int nDim=-1)
	decoder several integer to a sequence More...

int	GetEncodeLayerLimit () const
	The encoded integer size of one layer. More...

template<typename T >
void	BufMap (T *p, VecShell< T > &feat, Mat3dShell< T > &VH, Mat3dShell< T > &CH, Mat3dShell< T > &HH, MatShell< T > &Bias)
	Map a paremeter vector to each kinds of parameters. More...

void	FeatCount (Seq &seq, VecShell< double > featcount, Mat3dShell< double > VHcount, Mat3dShell< double > CHcount, Mat3dShell< double > HHcount, MatShell< double > Bcount, double dadd=1)
	Count the feature number in current sequence, and add to the result. More...

void	HiddenFeatCount (Seq &seq, Mat3dShell< double > VHcount, Mat3dShell< double > CHcount, Mat3dShell< double > HHcount, MatShell< double > Bcount, double dadd=1)
	Count the hidden features. More...

void	FeatCount (Seq &seq, VecShell< double > count, double dadd=1)
	Count the feature number in current sequence. More...

PValue	SumVHWeight (MatShell< PValue > m, VecShell< HValue > h)

PValue	SumHHWeight (Mat3dShell< PValue > m, VecShell< HValue > h1, VecShell< HValue > h2)

PValue	SumVHWeight (MatShell< PValue > m, VecShell< HValue > h, int layer)

PValue	SumHHWeight (Mat3dShell< PValue > m, VecShell< HValue > h1, VecShell< HValue > h2, int layer)

void	PerformSAMS (int nMinibatch, int tmax, int t0, int beta, double zgap=10)
	perform the SAMS to estimate the normalization constants zeta More...

LogP	GetLogProb_AIS (VecShell< VocabID > &x, int nChain=10, int nIntermediate=10000)
	perform AIS to esitmate the mariginal probabilities More...

LogP	GetLogProb_Gibbs (VecShell< VocabID > &x, int num=100)
	sample the best hidden and calculate the joint probability. More...

Public Member Functions inherited from trf::Model
	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...

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...

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
int	m_hlayer
	the number of hidden layer More...

int	m_hnode
	the number of hidden nodes More...

Mat3d< PValue >	m_m3dVH
	the weight between Word(V) and Hidden(H) More...

Mat3d< PValue >	m_m3dCH
	the weight between Class(C) and Hidden(H) More...

Mat3d< PValue >	m_m3dHH
	the weight between adjacent Hidden(H) More...

Mat< PValue >	m_matBias
	the bias for each value of Hidden(H) More...

int	m_nSampleHAccTimes
	sample H the acceptance times More...

int	m_nSampleHTotalTimes
	sample H the total times More...

int	m_nLenJumpAccTimes
	lenght jump the acceptance times More...

int	m_nLenJumpTotalTime

Public Attributes inherited from trf::Model
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_nodeCal
	the forward-backward calculation for node (x and h) More...

Protected Attributes inherited from trf::Model
AlgNode	m_AlgNode
	the forward-backward calculation each node More...

Detailed Description

hidden-random-field model

Definition at line 98 of file hrf-model.h.

Constructor & Destructor Documentation

§ Model() [1/2]

hrf::Model::Model ( Vocab * pv )

inline

Definition at line 112 of file hrf-model.h.

§ Model() [2/2]

hrf::Model::Model	(	Vocab *	pv,
		int	hlayer,
		int	hnode,
		int	maxlen
	)

inline

Definition at line 116 of file hrf-model.h.

Member Function Documentation

§ BufMap()

template<typename T >

void hrf::Model::BufMap	(	T *	p,
		VecShell< T > &	feat,
		Mat3dShell< T > &	VH,
		Mat3dShell< T > &	CH,
		Mat3dShell< T > &	HH,
		MatShell< T > &	Bias
	)

inline

Map a paremeter vector to each kinds of parameters.

Definition at line 256 of file hrf-model.h.

§ ClusterSum()

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

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

Definition at line 298 of file hrf-model.cpp.

§ DecodeHidden() [1/2]

void hrf::Model::DecodeHidden	(	int	n,
		VecShell< HValue >	hi
	)

decode a integer to a hidden vector

Definition at line 1152 of file hrf-model.cpp.

§ DecodeHidden() [2/2]

void hrf::Model::DecodeHidden	(	VecShell< int > &	vn,
		Mat< HValue > &	h,
		int	nPos = `0`,
		int	nDim = `-1`
	)

decoder several integer to a sequence

Definition at line 1159 of file hrf-model.cpp.

§ DecodeLayer()

void hrf::Model::DecodeLayer	(	VecShell< int > &	vn,
		Mat< HValue > &	h,
		int	layer,
		int	nPos = `0`,
		int	nDim = `-1`
	)

decoder several integer to a sequence

Definition at line 1174 of file hrf-model.cpp.

§ DecodeNode() [1/2]

void hrf::Model::DecodeNode	(	int	n,
		VocabID &	xi,
		VocabID &	ci,
		VecShell< HValue > &	hi
	)

decode a integer to the x_i and h_i

Definition at line 1124 of file hrf-model.cpp.

§ DecodeNode() [2/2]

void hrf::Model::DecodeNode	(	VecShell< int > &	vn,
		Seq &	seq,
		int	nPos = `0`,
		int	nDim = `-1`
	)

decode several integer to a sequence

Definition at line 1132 of file hrf-model.cpp.

§ EncodeHidden()

int hrf::Model::EncodeHidden ( VecShell< HValue > hi )

encode the hidden vector h_i to a integer

Definition at line 1143 of file hrf-model.cpp.

§ EncodeNode() [1/2]

int hrf::Model::EncodeNode	(	VocabID	xi,
		VocabID	ci,
		VecShell< HValue > &	hi
	)

encode the x_i and h_i at position i to a integer

Definition at line 1111 of file hrf-model.cpp.

§ EncodeNode() [2/2]

void hrf::Model::EncodeNode	(	VecShell< int > &	vn,
		Seq &	seq,
		int	nPos = `0`,
		int	nDim = `-1`
	)

encode the x and h to a integer sequence

Definition at line 1117 of file hrf-model.cpp.

§ ExactNormalize() [1/2]

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

virtual

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

< max-order

Reimplemented from trf::Model.

Definition at line 403 of file hrf-model.cpp.

§ ExactNormalize() [2/2]

void hrf::Model::ExactNormalize ( )

virtual

[exact] Exact Normalization all the length

Reimplemented from trf::Model.

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

§ FeatClusterSum()

LogP hrf::Model::FeatClusterSum	(	trf::Seq &	x,
		int	nPos,
		int	nOrder
	)

[exact] Calculate the logp in each cluster. Only consinder the feature values

Definition at line 302 of file hrf-model.cpp.

§ FeatCount() [1/2]

void hrf::Model::FeatCount	(	Seq &	seq,
		VecShell< double >	featcount,
		Mat3dShell< double >	VHcount,
		Mat3dShell< double >	CHcount,
		Mat3dShell< double >	HHcount,
		MatShell< double >	Bcount,
		double	dadd = `1`
	)

Count the feature number in current sequence, and add to the result.

Definition at line 1186 of file hrf-model.cpp.

§ FeatCount() [2/2]

void hrf::Model::FeatCount	(	Seq &	seq,
		VecShell< double >	count,
		double	dadd = `1`
	)

Count the feature number in current sequence.

Definition at line 1234 of file hrf-model.cpp.

§ GetConditionalProbForH()

LogP hrf::Model::GetConditionalProbForH	(	VecShell< HValue > &	hi,
		VecShell< Prob > &	probs
	)

[sample] using the logprobs returned by ProposeHProb to calculate the logprob of hi.

Definition at line 951 of file hrf-model.cpp.

§ GetEncodeHiddenLimit()

int hrf::Model::GetEncodeHiddenLimit ( ) const

The encoded integer size.

Definition at line 1166 of file hrf-model.cpp.

§ GetEncodeLayerLimit()

int hrf::Model::GetEncodeLayerLimit ( ) const

The encoded integer size of one layer.

Definition at line 1181 of file hrf-model.cpp.

§ GetEncodeNodeLimit()

int hrf::Model::GetEncodeNodeLimit ( ) const

The encoded integer size.

Definition at line 1139 of file hrf-model.cpp.

§ GetHiddenExp()

void hrf::Model::GetHiddenExp	(	VecShell< int >	x,
		double *	pExp
	)

[exact] E_{p_l(h|x)}[f]: don't clean the pExp and directly add the new exp to pExp.

< length

< max-order

Definition at line 622 of file hrf-model.cpp.

§ GetHiddenOrder()

int hrf::Model::GetHiddenOrder ( ) const

inline

Get HH mat order.

Definition at line 128 of file hrf-model.h.

§ GetHnode()

int hrf::Model::GetHnode ( ) const

inline

get hidden node dimension

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

§ GetLayerExp()

void hrf::Model::GetLayerExp	(	AlgLayer &	fb,
		int	nLayer,
		Mat3dShell< double > &	VHexp,
		Mat3dShell< double > &	CHexp,
		Mat3dShell< double > &	HHexp,
		MatShell< double > &	Bexp,
		LogP	logz = `0`
	)

[exact] called in GetHiddenExp.

Definition at line 650 of file hrf-model.cpp.

§ GetLogProb() [1/2]

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

calculate the probability

Definition at line 112 of file hrf-model.cpp.

§ GetLogProb() [2/2]

LogP hrf::Model::GetLogProb	(	VecShell< VocabID > &	x,
		bool	bNorm = `true`
	)

[exact] calculate the probability of x

Add all the ngram features

Definition at line 277 of file hrf-model.cpp.

§ GetLogProb_AIS()

LogP hrf::Model::GetLogProb_AIS	(	VecShell< VocabID > &	x,
		int	nChain = `10`,
		int	nIntermediate = `10000`
	)

perform AIS to esitmate the mariginal probabilities

Definition at line 1342 of file hrf-model.cpp.

§ GetLogProb_Gibbs()

LogP hrf::Model::GetLogProb_Gibbs	(	VecShell< VocabID > &	x,
		int	num = `100`
	)

sample the best hidden and calculate the joint probability.

Definition at line 1470 of file hrf-model.cpp.

§ GetMarginalLogProb()

LogP hrf::Model::GetMarginalLogProb	(	int	nLen,
		int	nPos,
		Seq &	sub,
		bool	bNorm = `true`
	)

[exact] Exactly calculate the marginal probability at position 'nPos' and with order 'nOrder'

Definition at line 453 of file hrf-model.cpp.

§ GetMarginalProbOfC()

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

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

Definition at line 961 of file hrf-model.cpp.

§ GetNodeExp() [1/3]

void hrf::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 470 of file hrf-model.cpp.

§ GetNodeExp() [2/3]

void hrf::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 499 of file hrf-model.cpp.

§ GetNodeExp() [3/3]

void hrf::Model::GetNodeExp	(	int	nLen,
		VecShell< double >	featexp,
		Mat3dShell< double >	VHexp,
		Mat3dShell< double >	CHexp,
		Mat3dShell< double >	HHexp,
		MatShell< double >	Bexp
	)

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

decoder to x and h

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

§ GetParam()

void hrf::Model::GetParam ( PValue * pParam )

Get the paremetre vector.

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

§ GetParamNum()

int hrf::Model::GetParamNum ( ) const

inline

Get the total parameter number.

Definition at line 130 of file hrf-model.h.

§ GetReducedModelForC()

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

[sample] A unnormalized reduced model to sample class c_i, consindering CH matrix(U) and class-ngram (lambda_c)

Definition at line 927 of file hrf-model.cpp.

§ GetReducedModelForH()

LogP hrf::Model::GetReducedModelForH	(	Seq &	seq,
		int	nPos
	)

[sample] A unnormalized reduced model. It only consindering the HH matrix (W)

Definition at line 913 of file hrf-model.cpp.

§ GetReducedModelForW()

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

[sample] A unnormalized reduced model to sample word w_i, consindering VH matrix(M) and word-ngram (lambda_w)

Definition at line 941 of file hrf-model.cpp.

§ HiddenClusterSum()

LogP hrf::Model::HiddenClusterSum	(	Seq &	seq,
		int	nPos,
		int	nOrder
	)

[exact] Calculate the logp in each cluster. Only consinder the VH,CH,HH values, used in class AlgHidden

Definition at line 306 of file hrf-model.cpp.

§ HiddenFeatCount()

void hrf::Model::HiddenFeatCount	(	Seq &	seq,
		Mat3dShell< double >	VHcount,
		Mat3dShell< double >	CHcount,
		Mat3dShell< double >	HHcount,
		MatShell< double >	Bcount,
		double	dadd = `1`
	)

Count the hidden features.

Definition at line 1194 of file hrf-model.cpp.

§ LayerClusterSum()

LogP hrf::Model::LayerClusterSum	(	Seq &	seq,
		int	nlayer,
		int	nPos,
		int	nOrder
	)

[exact] Calculate the logp in each cluster. Only consinder the VH,CH,HH values on such layer

Definition at line 355 of file hrf-model.cpp.

§ LocalJump()

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

[sample] Local Jump - sample a new length

Definition at line 730 of file hrf-model.cpp.

§ MarkovMove()

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

[sample] Markov Move - perform the gibbs sampling

Definition at line 777 of file hrf-model.cpp.

§ PerformSAMS()

void hrf::Model::PerformSAMS	(	int	nMinibatch,
		int	tmax,
		int	t0,
		int	beta,
		double	zgap = `10`
	)

perform the SAMS to estimate the normalization constants zeta

< the length count of sample of each thread

Definition at line 1288 of file hrf-model.cpp.

§ ProposeC0()

LogP hrf::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 824 of file hrf-model.cpp.

§ ProposeCProbs()

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

[sample] Return the distribution of c_i at position nPos

Definition at line 903 of file hrf-model.cpp.

§ ProposeH0()

LogP hrf::Model::ProposeH0	(	VecShell< HValue > &	hi,
		Seq &	seq,
		int	nPos,
		bool	bSample
	)

[sample] Propose the h_{i} at position i. Then return the propose probability Q(h_i|h_{other})

Definition at line 796 of file hrf-model.cpp.

§ ProposeHProbs()

void hrf::Model::ProposeHProbs	(	VecShell< LogP > &	logps,
		Seq &	seq,
		int	nPos,
		bool	bConsiderXandC = `false`
	)

[sample] A reduced model only consinder HHmat(W) and VHmat(M) and CHmat(U).

save the logp of 0 or 1 for each hidden vairables

Definition at line 841 of file hrf-model.cpp.

§ ProposeLength()

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

[sample] Propose the length, using the variable m_matLenJump

Definition at line 788 of file hrf-model.cpp.

§ RandHidden()

void hrf::Model::RandHidden ( Seq & seq )

Random init the hidden variables.

Definition at line 1101 of file hrf-model.cpp.

§ RandSeq()

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

Random init sequence, if nLen==-1, random the length also.

Definition at line 1083 of file hrf-model.cpp.

§ ReadT()

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

Read Model.

Definition at line 149 of file hrf-model.cpp.

§ Reset()

void hrf::Model::Reset	(	Vocab *	pv,
		int	hlayer,
		int	hnode,
		int	maxlen
	)

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

Definition at line 65 of file hrf-model.cpp.

§ Sample()

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

[sample] Perform one train-dimensional mixture sampling

Definition at line 725 of file hrf-model.cpp.

§ SampleC()

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

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

Definition at line 979 of file hrf-model.cpp.

§ SampleHAndCGivenX()

LogP hrf::Model::SampleHAndCGivenX	(	Seq &	seq,
		MatShell< HValue > *	tagH = `NULL`
	)

virtual

[sample] sample h given x using gibbs sampling.

Definition at line 1053 of file hrf-model.cpp.

§ SampleW()

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

[sample] Sample the w_i at position nPos

Definition at line 1012 of file hrf-model.cpp.

§ SetParam()

void hrf::Model::SetParam ( PValue * pParam )

virtual

Set the parameters.

Reimplemented from trf::Model.

Definition at line 78 of file hrf-model.cpp.

§ SumHHWeight() [1/2]

PValue hrf::Model::SumHHWeight	(	Mat3dShell< PValue >	m,
		VecShell< HValue >	h1,
		VecShell< HValue >	h2
	)

Definition at line 1251 of file hrf-model.cpp.

§ SumHHWeight() [2/2]

PValue hrf::Model::SumHHWeight	(	Mat3dShell< PValue >	m,
		VecShell< HValue >	h1,
		VecShell< HValue >	h2,
		int	layer
	)

Definition at line 1273 of file hrf-model.cpp.

§ SumVHWeight() [1/2]

PValue hrf::Model::SumVHWeight	(	MatShell< PValue >	m,
		VecShell< HValue >	h
	)

Definition at line 1243 of file hrf-model.cpp.

§ SumVHWeight() [2/2]

PValue hrf::Model::SumVHWeight	(	MatShell< PValue >	m,
		VecShell< HValue >	h,
		int	layer
	)

Definition at line 1265 of file hrf-model.cpp.

§ WriteT()

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

Write Model.

Definition at line 233 of file hrf-model.cpp.

Member Data Documentation

§ m_hlayer

int hrf::Model::m_hlayer

the number of hidden layer

Definition at line 101 of file hrf-model.h.

§ m_hnode

int hrf::Model::m_hnode

the number of hidden nodes

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

§ m_m3dCH

Mat3d<PValue> hrf::Model::m_m3dCH

the weight between Class(C) and Hidden(H)

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

§ m_m3dHH

Mat3d<PValue> hrf::Model::m_m3dHH

the weight between adjacent Hidden(H)

Definition at line 105 of file hrf-model.h.

§ m_m3dVH

Mat3d<PValue> hrf::Model::m_m3dVH

the weight between Word(V) and Hidden(H)

Definition at line 103 of file hrf-model.h.

§ m_matBias

Mat<PValue> hrf::Model::m_matBias

the bias for each value of Hidden(H)

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

§ m_nLenJumpAccTimes

int hrf::Model::m_nLenJumpAccTimes

lenght jump the acceptance times

Definition at line 179 of file hrf-model.h.

§ m_nLenJumpTotalTime

int hrf::Model::m_nLenJumpTotalTime

total times of length jump

Definition at line 180 of file hrf-model.h.

§ m_nodeCal

AlgNode hrf::Model::m_nodeCal

protected

the forward-backward calculation for node (x and h)

Definition at line 109 of file hrf-model.h.

§ m_nSampleHAccTimes

int hrf::Model::m_nSampleHAccTimes

sample H the acceptance times

Definition at line 177 of file hrf-model.h.

§ m_nSampleHTotalTimes

int hrf::Model::m_nSampleHTotalTimes

sample H the total times

Definition at line 178 of file hrf-model.h.

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

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

Public Member Functions

Public Attributes

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

§ Model() [1/2]

§ Model() [2/2]

Member Function Documentation

§ BufMap()

§ ClusterSum()

§ DecodeHidden() [1/2]

§ DecodeHidden() [2/2]

§ DecodeLayer()

§ DecodeNode() [1/2]

§ DecodeNode() [2/2]

§ EncodeHidden()

§ EncodeNode() [1/2]

§ EncodeNode() [2/2]

§ ExactNormalize() [1/2]

§ ExactNormalize() [2/2]

§ FeatClusterSum()

§ FeatCount() [1/2]

§ FeatCount() [2/2]

§ GetConditionalProbForH()

§ GetEncodeHiddenLimit()

§ GetEncodeLayerLimit()

§ GetEncodeNodeLimit()

§ GetHiddenExp()

§ GetHiddenOrder()

§ GetHnode()

§ GetLayerExp()

§ GetLogProb() [1/2]

§ GetLogProb() [2/2]

§ GetLogProb_AIS()

§ GetLogProb_Gibbs()

§ GetMarginalLogProb()

§ GetMarginalProbOfC()

§ GetNodeExp() [1/3]

§ GetNodeExp() [2/3]

§ GetNodeExp() [3/3]

§ GetParam()

§ GetParamNum()

§ GetReducedModelForC()

§ GetReducedModelForH()

§ GetReducedModelForW()

§ HiddenClusterSum()

§ HiddenFeatCount()

§ LayerClusterSum()

§ LocalJump()

§ MarkovMove()

§ PerformSAMS()

§ ProposeC0()

§ ProposeCProbs()

§ ProposeH0()

§ ProposeHProbs()

§ ProposeLength()

§ RandHidden()

§ RandSeq()

§ ReadT()

§ Reset()

§ Sample()

§ SampleC()

§ SampleHAndCGivenX()

§ SampleW()

§ SetParam()

§ SumHHWeight() [1/2]

§ SumHHWeight() [2/2]

§ SumVHWeight() [1/2]

§ SumVHWeight() [2/2]

§ WriteT()

Member Data Documentation

§ m_hlayer

§ m_hnode

§ m_m3dCH

§ m_m3dHH

§ m_m3dVH

§ m_matBias

§ m_nLenJumpAccTimes

§ m_nLenJumpTotalTime

§ m_nodeCal