Detailed Description

Class that models dual variational likelihood.

This likelihood model is described in the reference paper Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias Seeger Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. ICML2013

The mathematical definition (equation 19 in the paper) is as below

\[ Fenchel_i(\alpha_i,\lambda_i) = max_{h_i,\rho_i}{\alpha_i h_i+\lambda_i \rho_i /2 - E_{q(f_i|h_i,\rho_i)}(-log(p(y_i|f_i)))} \]

where \(\alpha_i\), \(\lambda_i\) are Lagrange multipliers with respective to constraints \(h_i=\mu_i\) and \(\rho_i=\sigma_i^2\) respectively, \(\mu\) and \(\sigma_i\) are variational Gaussian parameters, y_i is data label, \(q(f_i)\) is the variational Gaussian distribution, and p(y_i) is the data distribution to be specified. In this setting, \(\alpha\) and \(\lambda\) are called dual parameters for \(\mu\) and \(\sigma^2\) respectively.

Definition at line 62 of file DualVariationalGaussianLikelihood.h.

Inheritance diagram for CDualVariationalGaussianLikelihood:

[legend]

Public Member Functions
	CDualVariationalGaussianLikelihood ()

virtual	~CDualVariationalGaussianLikelihood ()

virtual const char *	get_name () const

virtual SGVector< float64_t >	get_variational_expection ()

virtual SGVector< float64_t >	get_variational_first_derivative (const TParameter *param) const

virtual bool	supports_derivative_wrt_hyperparameter () const

virtual SGVector< float64_t >	get_first_derivative_wrt_hyperparameter (const TParameter *param) const

virtual bool	set_variational_distribution (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab)

virtual bool	dual_parameters_valid () const

virtual float64_t	adjust_step_wrt_dual_parameter (SGVector< float64_t > direction, const float64_t step) const

virtual void	set_dual_parameters (SGVector< float64_t > the_lambda, const CLabels *lab)

virtual SGVector< float64_t >	get_mu_dual_parameter () const =0

virtual SGVector< float64_t >	get_variance_dual_parameter () const =0

virtual float64_t	get_dual_upper_bound () const =0

virtual float64_t	get_dual_lower_bound () const =0

virtual bool	dual_upper_bound_strict () const =0

virtual bool	dual_lower_bound_strict () const =0

virtual SGVector< float64_t >	get_dual_objective_value ()=0

virtual SGVector< float64_t >	get_dual_first_derivative (const TParameter *param) const =0

virtual void	set_strict_scale (float64_t strict_scale)

virtual void	set_noise_factor (float64_t noise_factor)

virtual SGVector< float64_t >	get_predictive_means (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const

virtual SGVector< float64_t >	get_predictive_variances (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const

virtual ELikelihoodModelType	get_model_type () const

virtual SGVector< float64_t >	get_log_probability_f (const CLabels *lab, SGVector< float64_t > func) const

virtual SGVector< float64_t >	get_log_probability_derivative_f (const CLabels *lab, SGVector< float64_t > func, index_t i) const

virtual SGVector< float64_t >	get_log_zeroth_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const

virtual float64_t	get_first_moment (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const

virtual float64_t	get_second_moment (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const

virtual bool	supports_regression () const

virtual bool	supports_binary () const

virtual bool	supports_multiclass () const

virtual SGVector< float64_t >	get_first_derivative (const CLabels lab, SGVector< float64_t > func, const TParameter param) const

virtual SGVector< float64_t >	get_second_derivative (const CLabels lab, SGVector< float64_t > func, const TParameter param) const

virtual SGVector< float64_t >	get_third_derivative (const CLabels lab, SGVector< float64_t > func, const TParameter param) const

virtual SGVector< float64_t >	get_predictive_log_probabilities (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL)

virtual SGVector< float64_t >	get_log_probability_fmatrix (const CLabels *lab, SGMatrix< float64_t > F) const

virtual SGVector< float64_t >	get_first_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const

virtual SGVector< float64_t >	get_second_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const

virtual CSGObject *	shallow_copy () const

virtual CSGObject *	deep_copy () const

virtual bool	is_generic (EPrimitiveType *generic) const

template<class T >
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

void	unset_generic ()

virtual void	print_serializable (const char *prefix="")

virtual bool	save_serializable (CSerializableFile file, const char prefix="")

virtual bool	load_serializable (CSerializableFile file, const char prefix="")

void	set_global_io (SGIO *io)

SGIO *	get_global_io ()

void	set_global_parallel (Parallel *parallel)

Parallel *	get_global_parallel ()

void	set_global_version (Version *version)

Version *	get_global_version ()

SGStringList< char >	get_modelsel_names ()

void	print_modsel_params ()

char *	get_modsel_param_descr (const char *param_name)

index_t	get_modsel_param_index (const char *param_name)

void	build_gradient_parameter_dictionary (CMap< TParameter , CSGObject > *dict)

bool	has (const std::string &name) const

template<typename T >
bool	has (const Tag< T > &tag) const

template<typename T , typename U = void>
bool	has (const std::string &name) const

template<typename T >
void	set (const Tag< T > &_tag, const T &value)

template<typename T , typename U = void>
void	set (const std::string &name, const T &value)

template<typename T >
T	get (const Tag< T > &_tag) const

template<typename T , typename U = void>
T	get (const std::string &name) const

virtual void	update_parameter_hash ()

virtual bool	parameter_hash_changed ()

virtual bool	equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)

virtual CSGObject *	clone ()

Public Attributes
SGIO *	io

Parallel *	parallel

Version *	version

Parameter *	m_parameters

Parameter *	m_model_selection_parameters

Parameter *	m_gradient_parameters

uint32_t	m_hash

Protected Member Functions
virtual void	precompute ()

virtual CVariationalGaussianLikelihood *	get_variational_likelihood () const

virtual void	init_likelihood ()=0

virtual void	set_likelihood (CLikelihoodModel *lik)

virtual void	load_serializable_pre () throw (ShogunException)

virtual void	load_serializable_post () throw (ShogunException)

virtual void	save_serializable_pre () throw (ShogunException)

virtual void	save_serializable_post () throw (ShogunException)

template<typename T >
void	register_param (Tag< T > &_tag, const T &value)

template<typename T >
void	register_param (const std::string &name, const T &value)

Protected Attributes
SGVector< float64_t >	m_lambda

float64_t	m_strict_scale

bool	m_is_valid

SGVector< float64_t >	m_mu

SGVector< float64_t >	m_s2

SGVector< float64_t >	m_lab

CLikelihoodModel *	m_likelihood

Constructor & Destructor Documentation

CDualVariationalGaussianLikelihood ( )

default constructor

Definition at line 44 of file DualVariationalGaussianLikelihood.cpp.

~CDualVariationalGaussianLikelihood ( )

virtual

Definition at line 50 of file DualVariationalGaussianLikelihood.cpp.

Member Function Documentation

float64_t adjust_step_wrt_dual_parameter	(	SGVector< float64_t >	direction,
		const float64_t	step
	)		const

virtual

this method is used for adjusting step size to ensure the updated value satisfied lower/upper bound constrain

The updated value is defined as below. lambda_new = m_lambda + direction * step

Parameters

direction	direction for m_lambda update
step	original step size (non-negative)

Returns: adjusted step size

Definition at line 110 of file DualVariationalGaussianLikelihood.cpp.

void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject * > * dict )

inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters

dict	dictionary of parameters to be built.

Definition at line 630 of file SGObject.cpp.

CSGObject * clone ( )

virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns: an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 747 of file SGObject.cpp.

CSGObject * deep_copy ( ) const

virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 231 of file SGObject.cpp.

virtual bool dual_lower_bound_strict ( ) const

pure virtual

whether the lower bound is strict

Returns: true if the lower bound is strict

Implemented in CLogitDVGLikelihood.

bool dual_parameters_valid ( ) const

virtual

check whether the dual parameters are valid or not.

Returns: true if dual parameters are valid

Definition at line 181 of file DualVariationalGaussianLikelihood.cpp.

virtual bool dual_upper_bound_strict ( ) const

pure virtual

whether the upper bound is strict

Returns: true if the upper bound is strict

Implemented in CLogitDVGLikelihood.

bool equals	(	CSGObject *	other,
		float64_t	accuracy = `0.0`,
		bool	tolerant = `false`
	)

virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters

other	object to compare with
accuracy	accuracy to use for comparison (optional)
tolerant	allows linient check on float equality (within accuracy)

Returns: true if all parameters were equal, false if not

Definition at line 651 of file SGObject.cpp.

T get ( const Tag< T > & _tag ) const

inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters

_tag	name and type information of parameter

Returns: value of the parameter identified by the input tag

Definition at line 367 of file SGObject.h.

T get ( const std::string & name ) const

inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters

name	name of the parameter

Returns: value of the parameter corresponding to the input name and type

Definition at line 388 of file SGObject.h.

virtual SGVector<float64_t> get_dual_first_derivative ( const TParameter * param ) const

pure virtual

get the derivative of the dual objective function with respect to param

Parameters

param parameter

Returns: the value of of the derivative

Implemented in CLogitDVGLikelihood.

virtual float64_t get_dual_lower_bound ( ) const

pure virtual

get the lower bound for dual parameter (lambda)

Returns: the lower bound

Implemented in CLogitDVGLikelihood.

virtual SGVector<float64_t> get_dual_objective_value ( )

pure virtual

evaluate the dual objective function

Returns: the value of Fenchel conjugates given m_lambda

Implemented in CLogitDVGLikelihood.

virtual float64_t get_dual_upper_bound ( ) const

pure virtual

get the upper bound for dual parameter (lambda)

Returns: the upper bound

Implemented in CLogitDVGLikelihood.

SGVector< float64_t > get_first_derivative	(	const CLabels *	lab,
		SGVector< float64_t >	func,
		const TParameter *	param
	)		const

virtualinherited

get derivative of log likelihood \(log(p(y|f))\) with respect to given parameter

Parameters

lab	labels used
func	function location
param	parameter

Returns: derivative

Reimplemented from CLikelihoodModel.

Definition at line 88 of file VariationalLikelihood.cpp.

SGVector< float64_t > get_first_derivative_wrt_hyperparameter ( const TParameter * param ) const

virtual

get derivative of log likelihood \(log(p(y|f))\) with respect to given hyperparameter Note that variational parameters are NOT considered as hyperparameters

Parameters

param parameter

Returns: derivative

Implements CVariationalLikelihood.

Definition at line 89 of file DualVariationalGaussianLikelihood.cpp.

float64_t get_first_moment	(	SGVector< float64_t >	mu,
		SGVector< float64_t >	s2,
		const CLabels *	lab,
		index_t	i
	)		const

virtualinherited

returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).

This method is useful for EP local likelihood approximation.

Parameters

mu	mean of the \(\mathcal{N}(f_i\|\mu,\sigma^2)\)
s2	variance of the \(\mathcal{N}(f_i\|\mu,\sigma^2)\)
lab	labels \(y_i\)
i	index i

Returns: first moment of \(q(f_i)\)

Implements CLikelihoodModel.

Definition at line 140 of file VariationalLikelihood.cpp.

SGVector< float64_t > get_first_moments	(	SGVector< float64_t >	mu,
		SGVector< float64_t >	s2,
		const CLabels *	lab
	)		const

virtualinherited

returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) for each \(f_i\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).

Wrapper method which calls get_first_moment multiple times.

Parameters

mu	mean of the \(\mathcal{N}(f_i\|\mu,\sigma^2)\)
s2	variance of the \(\mathcal{N}(f_i\|\mu,\sigma^2)\)
lab	labels \(y_i\)

Returns: the first moment of \(q(f_i)\) for each \(f_i\)

Definition at line 72 of file LikelihoodModel.cpp.

SGIO * get_global_io ( )

inherited

get the io object

Returns: io object

Definition at line 268 of file SGObject.cpp.

Parallel * get_global_parallel ( )

inherited

get the parallel object

Returns: parallel object

Definition at line 310 of file SGObject.cpp.

Version * get_global_version ( )

inherited

get the version object

Returns: version object

Definition at line 323 of file SGObject.cpp.

SGVector< float64_t > get_log_probability_derivative_f	(	const CLabels *	lab,
		SGVector< float64_t >	func,
		index_t	i
	)		const

virtualinherited

get derivative of log likelihood \(log(p(y|f))\) with respect to location function \(f\)

Parameters

lab	labels used
func	function location
i	index, choices are 1, 2, and 3 for first, second, and third derivatives respectively

Returns: derivative

Implements CLikelihoodModel.

Definition at line 125 of file VariationalLikelihood.cpp.

SGVector< float64_t > get_log_probability_f	(	const CLabels *	lab,
		SGVector< float64_t >	func
	)		const

virtualinherited

Returns the logarithm of the point-wise likelihood \(log(p(y_i|f_i))\) for each label \(y_i\).

One can evaluate log-likelihood like: \( log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\)

Parameters

lab	labels \(y_i\)
func	values of the function \(f_i\)

Returns: logarithm of the point-wise likelihood

Implements CLikelihoodModel.

Definition at line 118 of file VariationalLikelihood.cpp.

SGVector< float64_t > get_log_probability_fmatrix	(	const CLabels *	lab,
		SGMatrix< float64_t >	F
	)		const

virtualinherited

Returns the log-likelihood \(log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\) for each of the provided functions \( f \) in the given matrix.

Wrapper method which calls get_log_probability_f multiple times.

Parameters

lab	labels \(y_i\)
F	values of the function \(f_i\) where each column of the matrix is one function \( f \).

Returns: log-likelihood for every provided function

Definition at line 51 of file LikelihoodModel.cpp.

SGVector< float64_t > get_log_zeroth_moments	(	SGVector< float64_t >	mu,
		SGVector< float64_t >	s2,
		const CLabels *	lab
	)		const

virtualinherited

returns the zeroth moment of a given (unnormalized) probability distribution:

\[ log(Z_i) = log\left(\int p(y_i|f_i) \mathcal{N}(f_i|\mu,\sigma^2) df_i\right) \]

for each \(f_i\).

Parameters

mu	mean of the \(\mathcal{N}(f_i\|\mu,\sigma^2)\)
s2	variance of the \(\mathcal{N}(f_i\|\mu,\sigma^2)\)
lab	labels \(y_i\)

Returns: log zeroth moment \(log(Z_i)\)

Implements CLikelihoodModel.

Definition at line 132 of file VariationalLikelihood.cpp.

ELikelihoodModelType get_model_type ( ) const

virtualinherited

get model type

Returns: model type NONE

Reimplemented from CLikelihoodModel.

Definition at line 112 of file VariationalLikelihood.cpp.

SGStringList< char > get_modelsel_names ( )

inherited

Returns: vector of names of all parameters which are registered for model selection

Definition at line 531 of file SGObject.cpp.

char * get_modsel_param_descr ( const char * param_name )

inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters

param_name name of the parameter

Returns: description of the parameter

Definition at line 555 of file SGObject.cpp.

index_t get_modsel_param_index ( const char * param_name )

inherited

Returns index of model selection parameter with provided index

Parameters

param_name name of model selection parameter

Returns: index of model selection parameter with provided name, -1 if there is no such

Definition at line 568 of file SGObject.cpp.

virtual SGVector<float64_t> get_mu_dual_parameter ( ) const

pure virtual

get the dual parameter (alpha) for variational mu

Returns: the dual parameter (alpha)

Implemented in CLogitDVGLikelihood.

virtual const char* get_name ( ) const

virtual

returns the name of the likelihood model

Returns: name DualVariationalGaussianLikelihood

Implements CSGObject.

Reimplemented in CLogitDVGLikelihood.

Definition at line 74 of file DualVariationalGaussianLikelihood.h.

SGVector< float64_t > get_predictive_log_probabilities	(	SGVector< float64_t >	mu,
		SGVector< float64_t >	s2,
		const CLabels *	lab = `NULL`
	)

virtualinherited

returns the logarithm of the predictive density of \(y_*\):

\[ log(p(y_*|X,y,x_*)) = log\left(\int p(y_*|f_*) p(f_*|X,y,x_*) df_*\right) \]

which approximately equals to

\[ log\left(\int p(y_*|f_*) \mathcal{N}(f_*|\mu,\sigma^2) df_*\right) \]

where normal distribution \(\mathcal{N}(\mu,\sigma^2)\) is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\).

NOTE: if lab equals to NULL, then each \(y_*\) equals to one.

Parameters

mu	posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_\|X,y,x_)\)
s2	posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_\|X,y,x_)\)
lab	labels \(y_*\)

Returns: \(log(p(y_*|X, y, x*))\) for each label \(y_*\)

Reimplemented in CSoftMaxLikelihood.

Definition at line 45 of file LikelihoodModel.cpp.

SGVector< float64_t > get_predictive_means	(	SGVector< float64_t >	mu,
		SGVector< float64_t >	s2,
		const CLabels *	lab = `NULL`
	)		const

virtualinherited

returns mean of the predictive marginal \(p(y_*|X,y,x_*)\)

NOTE: if lab equals to NULL, then each \(y_*\) equals to one.

Parameters

mu	posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_\|X,y,x_)\)
s2	posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_\|X,y,x_)\)
lab	labels \(y_*\)

Returns: final means evaluated by likelihood function

Implements CLikelihoodModel.

Definition at line 72 of file VariationalLikelihood.cpp.

SGVector< float64_t > get_predictive_variances	(	SGVector< float64_t >	mu,
		SGVector< float64_t >	s2,
		const CLabels *	lab = `NULL`
	)		const

virtualinherited

returns variance of the predictive marginal \(p(y_*|X,y,x_*)\)

NOTE: if lab equals to NULL, then each \(y_*\) equals to one.

Parameters

mu	posterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_\|X,y,x_)\)
s2	posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_\|X,y,x_)\)
lab	labels \(y_*\)

Returns: final variances evaluated by likelihood function

Implements CLikelihoodModel.

Definition at line 80 of file VariationalLikelihood.cpp.

SGVector< float64_t > get_second_derivative	(	const CLabels *	lab,
		SGVector< float64_t >	func,
		const TParameter *	param
	)		const

virtualinherited

get derivative of the first derivative of log likelihood with respect to function location, i.e. \(\frac{\partial log(p(y|f))}{\partial f}\) with respect to given parameter

Parameters

lab	labels used
func	function location
param	parameter

Returns: derivative

Reimplemented from CLikelihoodModel.

Definition at line 96 of file VariationalLikelihood.cpp.

float64_t get_second_moment	(	SGVector< float64_t >	mu,
		SGVector< float64_t >	s2,
		const CLabels *	lab,
		index_t	i
	)		const

virtualinherited

returns the second moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).

This method is useful for EP local likelihood approximation.

Parameters

mu	mean of the \(\mathcal{N}(f_i\|\mu,\sigma^2)\)
s2	variance of the \(\mathcal{N}(f_i\|\mu,\sigma^2)\)
lab	labels \(y_i\)
i	index i

Returns: the second moment of \(q(f_i)\)

Implements CLikelihoodModel.

Definition at line 148 of file VariationalLikelihood.cpp.

SGVector< float64_t > get_second_moments	(	SGVector< float64_t >	mu,
		SGVector< float64_t >	s2,
		const CLabels *	lab
	)		const

virtualinherited

returns the second moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) for each \(f_i\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).

Wrapper method which calls get_second_moment multiple times.

Parameters

mu	mean of the \(\mathcal{N}(f_i\|\mu,\sigma^2)\)
s2	variance of the \(\mathcal{N}(f_i\|\mu,\sigma^2)\)
lab	labels \(y_i\)

Returns: the second moment of \(q(f_i)\) for each \(f_i\)

Definition at line 89 of file LikelihoodModel.cpp.

SGVector< float64_t > get_third_derivative	(	const CLabels *	lab,
		SGVector< float64_t >	func,
		const TParameter *	param
	)		const

virtualinherited

get derivative of the second derivative of log likelihood with respect to function location, i.e. \(\frac{\partial^{2} log(p(y|f))}{\partial f^{2}}\) with respect to given parameter

Parameters

lab	labels used
func	function location
param	parameter

Returns: derivative

Reimplemented from CLikelihoodModel.

Definition at line 104 of file VariationalLikelihood.cpp.

virtual SGVector<float64_t> get_variance_dual_parameter ( ) const

pure virtual

get the dual parameter (lambda) for variational s2

Returns: the dual parameter (lambda)

Implemented in CLogitDVGLikelihood.

SGVector< float64_t > get_variational_expection ( )

virtual

returns the expection of the logarithm of a given probability distribution wrt the variational distribution given m_mu and m_s2

Returns: expection

Implements CVariationalLikelihood.

Definition at line 65 of file DualVariationalGaussianLikelihood.cpp.

SGVector< float64_t > get_variational_first_derivative ( const TParameter * param ) const

virtual

get derivative of the variational expection of log likelihood with respect to given parameter

Parameters

param parameter

Returns: derivative

Implements CVariationalLikelihood.

Definition at line 77 of file DualVariationalGaussianLikelihood.cpp.

CVariationalGaussianLikelihood * get_variational_likelihood ( ) const

protectedvirtual

this method is used to dynamic-cast the likelihood model, m_likelihood, to variational likelihood model.

Definition at line 54 of file DualVariationalGaussianLikelihood.cpp.

bool has ( const std::string & name ) const

inherited

Checks if object has a class parameter identified by a name.

Parameters

name	name of the parameter

Returns: true if the parameter exists with the input name

Definition at line 289 of file SGObject.h.

bool has ( const Tag< T > & tag ) const

inherited

Checks if object has a class parameter identified by a Tag.

Parameters

tag	tag of the parameter containing name and type information

Returns: true if the parameter exists with the input tag

Definition at line 301 of file SGObject.h.

bool has ( const std::string & name ) const

inherited

Checks if a type exists for a class parameter identified by a name.

Parameters

name	name of the parameter

Returns: true if the parameter exists with the input name and type

Definition at line 312 of file SGObject.h.

virtual void init_likelihood ( )

protectedpure virtualinherited

this method is called to initialize m_likelihood in init()

Implements CVariationalLikelihood.

Implemented in CLogitDVGLikelihood, CLogitVGPiecewiseBoundLikelihood, CNumericalVGLikelihood, CStudentsTVGLikelihood, CLogitVGLikelihood, and CProbitVGLikelihood.

bool is_generic ( EPrimitiveType * generic ) const

virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters

generic set to the type of the generic if returning TRUE

Returns: TRUE if a class template.

Definition at line 329 of file SGObject.cpp.

bool load_serializable	(	CSerializableFile *	file,
		const char *	prefix = `""`
	)

virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters

file	where to load from
prefix	prefix for members

Returns: TRUE if done, otherwise FALSE

Definition at line 402 of file SGObject.cpp.

void load_serializable_post	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions

ShogunException will be thrown if an error occurs.

Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.

Definition at line 459 of file SGObject.cpp.

void load_serializable_pre	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions

ShogunException will be thrown if an error occurs.

Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 454 of file SGObject.cpp.

bool parameter_hash_changed ( )

virtualinherited

Returns: whether parameter combination has changed since last update

Definition at line 295 of file SGObject.cpp.

void precompute ( )

protectedvirtual

compute common variables later used in get_variational_expection and get_variational_first_derivative. Note that this method will automatically be called when set_variational_distribution is called

Definition at line 212 of file DualVariationalGaussianLikelihood.cpp.

void print_modsel_params ( )

inherited

prints all parameter registered for model selection and their type

Definition at line 507 of file SGObject.cpp.

void print_serializable ( const char * prefix = "" )

virtualinherited

prints registered parameters out

Parameters

prefix prefix for members

Definition at line 341 of file SGObject.cpp.

void register_param	(	Tag< T > &	_tag,
		const T &	value
	)

protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters

_tag	name and type information of parameter
value	value of the parameter

Definition at line 439 of file SGObject.h.

void register_param	(	const std::string &	name,
		const T &	value
	)

protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters

name	name of the parameter
value	value of the parameter along with type information

Definition at line 452 of file SGObject.h.

bool save_serializable	(	CSerializableFile *	file,
		const char *	prefix = `""`
	)

virtualinherited

Save this object to file.

Parameters

file	where to save the object; will be closed during returning if PREFIX is an empty string.
prefix	prefix for members

Returns: TRUE if done, otherwise FALSE

Definition at line 347 of file SGObject.cpp.

void save_serializable_post	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions

ShogunException will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 469 of file SGObject.cpp.

void save_serializable_pre	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions

ShogunException will be thrown if an error occurs.

Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 464 of file SGObject.cpp.

void set	(	const Tag< T > &	_tag,
		const T &	value
	)

inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters

_tag	name and type information of parameter
value	value of the parameter

Definition at line 328 of file SGObject.h.

void set	(	const std::string &	name,
		const T &	value
	)

inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters

name	name of the parameter
value	value of the parameter along with type information

Definition at line 354 of file SGObject.h.

void set_dual_parameters	(	SGVector< float64_t >	the_lambda,
		const CLabels *	lab
	)

virtual

set dual parameters for variational parameters

Parameters

the_lambda	dual parameter for variational mean
lab	labels/data used

Note that dual parameter (alpha) for the variational variance is implicitly set based on lambda

Definition at line 156 of file DualVariationalGaussianLikelihood.cpp.

void set_generic ( )

inherited

Definition at line 74 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 79 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 84 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 89 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 94 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 99 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 104 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 109 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 114 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 119 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 124 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 129 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 134 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 139 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 144 of file SGObject.cpp.

void set_generic ( )

inherited

set generic type to T

void set_global_io ( SGIO * io )

inherited

set the io object

Parameters

io	io object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel * parallel )

inherited

set the parallel object

Parameters

parallel parallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version * version )

inherited

set the version object

Parameters

version version object to use

Definition at line 316 of file SGObject.cpp.

void set_likelihood ( CLikelihoodModel * lik )

protectedvirtualinherited

this method used to set m_likelihood

Definition at line 49 of file VariationalLikelihood.cpp.

void set_noise_factor ( float64_t noise_factor )

virtual

set a non-negative noise factor in order to correct the variance if variance is close to zero or negative setting 0 means correction is not applied

Parameters

noise_factor noise factor

The default value is 1e-6.

Reimplemented from CVariationalGaussianLikelihood.

Definition at line 71 of file DualVariationalGaussianLikelihood.cpp.

void set_strict_scale ( float64_t strict_scale )

virtual

set the m_strict_scale

Parameters

strict_scale must be between 0 and 1 exclusively

Definition at line 102 of file DualVariationalGaussianLikelihood.cpp.

bool set_variational_distribution	(	SGVector< float64_t >	mu,
		SGVector< float64_t >	s2,
		const CLabels *	lab
	)

virtual

set the variational distribution given data and parameters

Parameters

mu	mean of the variational distribution
s2	variance of the variational distribution
lab	labels/data used

Returns: true if variational parameters are valid

Note that the variational distribution is Gaussian

Reimplemented from CVariationalGaussianLikelihood.

Definition at line 95 of file DualVariationalGaussianLikelihood.cpp.

CSGObject * shallow_copy ( ) const

virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 225 of file SGObject.cpp.

bool supports_binary ( ) const

virtualinherited

return whether likelihood function supports binary classification

Returns: boolean

Reimplemented from CLikelihoodModel.

Definition at line 162 of file VariationalLikelihood.cpp.

bool supports_derivative_wrt_hyperparameter ( ) const

virtual

return whether likelihood function supports computing the derivative wrt hyperparameter Note that variational parameters are NOT considered as hyperparameters

Returns: boolean

Implements CVariationalLikelihood.

Definition at line 83 of file DualVariationalGaussianLikelihood.cpp.

bool supports_multiclass ( ) const

virtualinherited

return whether likelihood function supports multiclass classification

Returns: boolean

Reimplemented from CLikelihoodModel.

Definition at line 168 of file VariationalLikelihood.cpp.

bool supports_regression ( ) const

virtualinherited

return whether likelihood function supports regression

Returns: boolean

Reimplemented from CLikelihoodModel.

Definition at line 156 of file VariationalLikelihood.cpp.

void unset_generic ( )

inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 336 of file SGObject.cpp.

void update_parameter_hash ( )

virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

Member Data Documentation

SGIO* io

inherited

io

Definition at line 537 of file SGObject.h.

Parameter* m_gradient_parameters

inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

uint32_t m_hash

inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

bool m_is_valid

protected

whether m_lambda is satisfied lower bound and/or upper bound condition.

Definition at line 237 of file DualVariationalGaussianLikelihood.h.

SGVector<float64_t> m_lab

protectedinherited

the label of data

Definition at line 277 of file VariationalLikelihood.h.

SGVector<float64_t> m_lambda

protected

The dual variables (lambda) for the variational parameter s2.

Note that in variational Gaussian inference, there is a relationship between lambda and alpha, where alpha is the dual parameter for variational parameter mu

Therefore, the dual variables (alpha) for variational parameter mu is not explicitly saved.

Definition at line 226 of file DualVariationalGaussianLikelihood.h.

CLikelihoodModel* m_likelihood

protectedinherited

the distribution used to model data

Definition at line 280 of file VariationalLikelihood.h.

Parameter* m_model_selection_parameters

inherited

model selection parameters

Definition at line 549 of file SGObject.h.

SGVector<float64_t> m_mu

protectedinherited