SHOGUN  6.0.0
CLogitDVGLikelihood Class Reference

## Detailed Description

Class that models dual variational logit 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 mathematically definition (equation 19 in the paper) is as below

$\text{Fenchel}_i(\alpha_i,\lambda_i) = \max_{h_i,\rho_i}{\alpha_i h_i+\lambda_i \rho_i /2 - E_{q(f_i|h_i,\rho_i)}(-log(p(y_i|f_i)))}$

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.

Note that $$p(y_i)$$ is Logistic distribution and a local variational bound defined as below is used to approximate $$-\text{E}_{q(f_i|h_i,\rho_i)}(-\log(p(y_i|f_i)))$$

The local variational bound used here is

$log(x) \leq t^{-1}x+log(t)-1$

, where t is a local variable and the inequality holds for every t>0. See Bernoulli-logit in Table 2 of the paper for detailed information

Definition at line 74 of file LogitDVGLikelihood.h.

Inheritance diagram for CLogitDVGLikelihood:
[legend]

## Public Member Functions

CLogitDVGLikelihood ()

virtual ~CLogitDVGLikelihood ()

virtual const char * get_name () const

virtual SGVector< float64_tget_dual_objective_value ()

virtual SGVector< float64_tget_dual_first_derivative (const TParameter *param) const

virtual float64_t get_dual_upper_bound () const

virtual float64_t get_dual_lower_bound () const

virtual bool dual_upper_bound_strict () const

virtual bool dual_lower_bound_strict () const

virtual SGVector< float64_tget_mu_dual_parameter () const

virtual SGVector< float64_tget_variance_dual_parameter () const

virtual SGVector< float64_tget_variational_expection ()

virtual SGVector< float64_tget_variational_first_derivative (const TParameter *param) const

virtual bool supports_derivative_wrt_hyperparameter () const

virtual SGVector< float64_tget_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 void set_strict_scale (float64_t strict_scale)

virtual void set_noise_factor (float64_t noise_factor)

virtual SGVector< float64_tget_predictive_means (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const

virtual SGVector< float64_tget_predictive_variances (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const

virtual ELikelihoodModelType get_model_type () const

virtual SGVector< float64_tget_log_probability_f (const CLabels *lab, SGVector< float64_t > func) const

virtual SGVector< float64_tget_log_probability_derivative_f (const CLabels *lab, SGVector< float64_t > func, index_t i) const

virtual SGVector< float64_tget_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_tget_first_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const

virtual SGVector< float64_tget_second_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const

virtual SGVector< float64_tget_third_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const

virtual SGVector< float64_tget_predictive_log_probabilities (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL)

virtual SGVector< float64_tget_log_probability_fmatrix (const CLabels *lab, SGMatrix< float64_t > F) const

virtual SGVector< float64_tget_first_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const

virtual SGVector< float64_tget_second_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const

virtual CSGObjectshallow_copy () const

virtual CSGObjectdeep_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)

SGIOget_global_io ()

void set_global_parallel (Parallel *parallel)

Parallelget_global_parallel ()

void set_global_version (Version *version)

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

template<typename T , typename U = void>
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 CSGObjectclone ()

## Public Attributes

SGIOio

Parallelparallel

Versionversion

Parameterm_parameters

Parameterm_model_selection_parameters

Parameterm_gradient_parameters

uint32_t m_hash

## Protected Member Functions

virtual void init_likelihood ()

virtual void precompute ()

virtual
CVariationalGaussianLikelihood
get_variational_likelihood () const

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)

bool clone_parameters (CSGObject *other)

## Protected Attributes

SGVector< float64_tm_lambda

float64_t m_strict_scale

bool m_is_valid

SGVector< float64_tm_mu

SGVector< float64_tm_s2

SGVector< float64_tm_lab

CLikelihoodModelm_likelihood

## Constructor & Destructor Documentation

 CLogitDVGLikelihood ( )

default constructor

Definition at line 46 of file LogitDVGLikelihood.cpp.

 ~CLogitDVGLikelihood ( )
virtual

Definition at line 52 of file LogitDVGLikelihood.cpp.

## Member Function Documentation

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

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 729 of file SGObject.cpp.

 bool clone_parameters ( CSGObject * other )
protectedinherited

Definition at line 754 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
virtual

whether the lower bound is strict

Returns
true if the lower bound is strict

Implements CDualVariationalGaussianLikelihood.

Definition at line 124 of file LogitDVGLikelihood.h.

 bool dual_parameters_valid ( ) const
virtualinherited

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
virtual

whether the upper bound is strict

Returns
true if the upper bound is strict

Implements CDualVariationalGaussianLikelihood.

Definition at line 118 of file LogitDVGLikelihood.h.

 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 376 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 399 of file SGObject.h.

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

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

Parameters
 param parameter
Returns
the value of of the derivative

Implements CDualVariationalGaussianLikelihood.

Definition at line 92 of file LogitDVGLikelihood.cpp.

 virtual float64_t get_dual_lower_bound ( ) const
virtual

get the lower bound for dual parameter (lambda)

Returns
the lower bound

Implements CDualVariationalGaussianLikelihood.

Definition at line 112 of file LogitDVGLikelihood.h.

 SGVector< float64_t > get_dual_objective_value ( )
virtual

evaluate the dual objective function

Returns
the value of Fenchel conjugates given m_lambda

Implements CDualVariationalGaussianLikelihood.

Definition at line 73 of file LogitDVGLikelihood.cpp.

 virtual float64_t get_dual_upper_bound ( ) const
virtual

get the upper bound for dual parameter (lambda)

Returns
the upper bound

Implements CDualVariationalGaussianLikelihood.

Definition at line 106 of file LogitDVGLikelihood.h.

 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
virtualinherited

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.

 SGVector< float64_t > get_mu_dual_parameter ( ) const
virtual

get the dual parameter (alpha) for variational mu

Note that alpha = m_lambda - label For detailed information, please refer to the paper.

Returns
the dual parameter (alpha)

Implements CDualVariationalGaussianLikelihood.

Definition at line 63 of file LogitDVGLikelihood.cpp.

 virtual const char* get_name ( ) const
virtual

returns the name of the likelihood model

Returns
name LogitDVGLikelihood

Reimplemented from CDualVariationalGaussianLikelihood.

Definition at line 86 of file LogitDVGLikelihood.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.

 SGVector< float64_t > get_variance_dual_parameter ( ) const
virtual

get the dual parameter (lambda) for variational s2

Returns
the dual parameter (lambda)

Implements CDualVariationalGaussianLikelihood.

Definition at line 56 of file LogitDVGLikelihood.cpp.

 SGVector< float64_t > get_variational_expection ( )
virtualinherited

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
virtualinherited

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
protectedvirtualinherited

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 299 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 310 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 321 of file SGObject.h.

 void init_likelihood ( )
protectedvirtual

this method is called to initialize m_likelihood in init()

Implements CVariationalGaussianLikelihood.

Definition at line 120 of file LogitDVGLikelihood.cpp.

 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.

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.

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 ( )
protectedvirtualinherited

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 450 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 463 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.

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 337 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 363 of file SGObject.h.

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

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 )
virtualinherited

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 )
virtualinherited

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 )
virtualinherited

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
virtualinherited

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 558 of file SGObject.h.

 Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 573 of file SGObject.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 576 of file SGObject.h.

 bool m_is_valid
protectedinherited

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

Definition at line 237 of file DualVariationalGaussianLikelihood.h.

 SGVector m_lab
protectedinherited

the label of data

Definition at line 277 of file VariationalLikelihood.h.

 SGVector m_lambda
protectedinherited

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 570 of file SGObject.h.

 SGVector m_mu
protectedinherited

The mean of variational Gaussian distribution

Definition at line 79 of file VariationalGaussianLikelihood.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 567 of file SGObject.h.

 SGVector m_s2
protectedinherited

The variance of variational Gaussian distribution

Definition at line 82 of file VariationalGaussianLikelihood.h.

 float64_t m_strict_scale
protectedinherited

The value used to ensure strict bound(s) for m_lambda in adjust_step_wrt_dual_parameter()

Note that the value should be between 0 and 1 exclusively.

The default value is 1e-5.

Definition at line 234 of file DualVariationalGaussianLikelihood.h.

 Parallel* parallel
inherited

parallel

Definition at line 561 of file SGObject.h.

 Version* version
inherited

version

Definition at line 564 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation