CProbitLikelihood Class Reference

Detailed Description

Class that models Probit likelihood.

\[ p(y|f) = \prod_{i=1}^n \Phi(y_i \cdot f_i) \]

where \(\Phi: \mathbb{R} \mapsto [0, 1]\) is the cumulative distribution function (CDF) of the normal distribution \(\mathcal{N}(0, 1)\).

Definition at line 52 of file ProbitLikelihood.h.

Inheritance diagram for CProbitLikelihood:

Public Member Functions
	CProbitLikelihood ()
virtual	~CProbitLikelihood ()
virtual const char *	get_name () const
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_binary () 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_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_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 bool	supports_regression () const
virtual bool	supports_multiclass () 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)
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	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)

Constructor & Destructor Documentation

CProbitLikelihood

(

)

default constructor

Definition at line 42 of file ProbitLikelihood.cpp.

~CProbitLikelihood ( )

virtual

Definition at line 46 of file ProbitLikelihood.cpp.

Member Function Documentation

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 597 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 714 of file SGObject.cpp.

CSGObject * deep_copy ( ) const

virtualinherited

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

Definition at line 198 of file SGObject.cpp.

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 618 of file SGObject.cpp.

virtual 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 in CVariationalLikelihood, CStudentsTLikelihood, and CGaussianLikelihood.

Definition at line 192 of file LikelihoodModel.h.

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

virtual

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 213 of file ProbitLikelihood.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 235 of file SGObject.cpp.

Parallel * get_global_parallel ( )

inherited

get the parallel object

Returns

parallel object

Definition at line 277 of file SGObject.cpp.

Version * get_global_version ( )

inherited

get the version object

Returns

version object

Definition at line 290 of file SGObject.cpp.

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

virtual

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

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 107 of file ProbitLikelihood.cpp.

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

virtual

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 80 of file ProbitLikelihood.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

virtual

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 moments \(log(Z_i)\)

Implements CLikelihoodModel.

Definition at line 171 of file ProbitLikelihood.cpp.

virtual ELikelihoodModelType get_model_type ( ) const

virtual

get model type

Returns

model type PROBIT

Reimplemented from CLikelihoodModel.

Definition at line 104 of file ProbitLikelihood.h.

SGStringList< char > get_modelsel_names ( )

inherited

Returns

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

Definition at line 498 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 522 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 535 of file SGObject.cpp.

virtual const char* get_name ( ) const

virtual

returns the name of the likelihood model

Returns

name ProbitLikelihood

Implements CSGObject.

Definition at line 64 of file ProbitLikelihood.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

virtual

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 50 of file ProbitLikelihood.cpp.

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

virtual

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 65 of file ProbitLikelihood.cpp.

virtual 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 in CVariationalLikelihood, CStudentsTLikelihood, and CGaussianLikelihood.

Definition at line 210 of file LikelihoodModel.h.

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

virtual

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 242 of file ProbitLikelihood.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.

virtual 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 in CVariationalLikelihood, CStudentsTLikelihood, and CGaussianLikelihood.

Definition at line 227 of file LikelihoodModel.h.

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 296 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 369 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 426 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 421 of file SGObject.cpp.

bool parameter_hash_changed ( )

virtualinherited

Returns

whether parameter combination has changed since last update

Definition at line 262 of file SGObject.cpp.

void print_modsel_params ( )

inherited

prints all parameter registered for model selection and their type

Definition at line 474 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "" )

virtualinherited

prints registered parameters out

Parameters

prefix

prefix for members

Definition at line 308 of file SGObject.cpp.

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 314 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 436 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 431 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 41 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 46 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 51 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 56 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 61 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 66 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 71 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 76 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 81 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 86 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 91 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 96 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 101 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 106 of file SGObject.cpp.

void set_generic ( )

inherited

Definition at line 111 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 228 of file SGObject.cpp.

void set_global_parallel ( Parallel *  parallel )

inherited

set the parallel object

Parameters

parallel

parallel object to use

Definition at line 241 of file SGObject.cpp.

void set_global_version ( Version *  version )

inherited

set the version object

Parameters

version

version object to use

Definition at line 283 of file SGObject.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 192 of file SGObject.cpp.

virtual bool supports_binary ( ) const

virtual

return whether logit likelihood function supports binary classification

Returns

true

Reimplemented from CLikelihoodModel.

Definition at line 190 of file ProbitLikelihood.h.

virtual bool supports_multiclass ( ) const

virtualinherited

return whether likelihood function supports multiclass classification

Returns

false

Reimplemented in CSoftMaxLikelihood, and CVariationalLikelihood.

Definition at line 335 of file LikelihoodModel.h.

virtual bool supports_regression ( ) const

virtualinherited

return whether likelihood function supports regression

Returns

false

Reimplemented in CStudentsTLikelihood, CGaussianLikelihood, and CVariationalLikelihood.

Definition at line 323 of file LikelihoodModel.h.

void unset_generic ( )

inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 303 of file SGObject.cpp.

void update_parameter_hash ( )

virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

Member Data Documentation

SGIO* io

inherited

io

Definition at line 369 of file SGObject.h.

Parameter* m_gradient_parameters

inherited

parameters wrt which we can compute gradients

Definition at line 384 of file SGObject.h.

uint32_t m_hash

inherited

Hash of parameter values

Definition at line 387 of file SGObject.h.

Parameter* m_model_selection_parameters

inherited

model selection parameters

Definition at line 381 of file SGObject.h.

Parameter* m_parameters

inherited

parameters

Definition at line 378 of file SGObject.h.

Parallel* parallel

inherited

parallel

Definition at line 372 of file SGObject.h.

Version* version

inherited

version

Definition at line 375 of file SGObject.h.

---

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

• ProbitLikelihood.h
• ProbitLikelihood.cpp