CExactInferenceMethod Class Reference

Detailed Description

The Gaussian exact form inference method class.

This inference method computes the Gaussian Method exactly using matrix equations.

\[ L = cholesky(K + \sigma^{2}I) \]

\(L\) is the cholesky decomposition of \(K\), the covariance matrix, plus a diagonal matrix with entries \(\sigma^{2}\), the observation noise.

\[ \boldsymbol{\alpha} = L^{T} \backslash(L \backslash \boldsymbol{y}}) \]

where \(L\) is the matrix mentioned above, \(\boldsymbol{y}\) are the labels, and \(\backslash\) is an operator ( \(x = A \backslash B\) means \(Ax=B\).)

NOTE: The Gaussian Likelihood Function must be used for this inference method.

Definition at line 67 of file ExactInferenceMethod.h.

Inheritance diagram for CExactInferenceMethod:

Public Member Functions
	CExactInferenceMethod ()
	CExactInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
virtual	~CExactInferenceMethod ()
virtual EInferenceType	get_inference_type () const
virtual const char *	get_name () const
virtual float64_t	get_negative_log_marginal_likelihood ()
virtual SGVector< float64_t >	get_alpha ()
virtual SGMatrix< float64_t >	get_cholesky ()
virtual SGVector< float64_t >	get_diagonal_vector ()
virtual SGVector< float64_t >	get_posterior_mean ()
virtual SGMatrix< float64_t >	get_posterior_covariance ()
virtual bool	supports_regression () const
virtual void	update ()
float64_t	get_marginal_likelihood_estimate (int32_t num_importance_samples=1, float64_t ridge_size=1e-15)
virtual CMap< TParameter *, SGVector< float64_t > > *	get_negative_log_marginal_likelihood_derivatives (CMap< TParameter *, CSGObject * > *parameters)
virtual CMap< TParameter *, SGVector< float64_t > > *	get_gradient (CMap< TParameter *, CSGObject * > *parameters)
virtual SGVector< float64_t >	get_value ()
virtual CFeatures *	get_features ()
virtual void	set_features (CFeatures *feat)
virtual CKernel *	get_kernel ()
virtual void	set_kernel (CKernel *kern)
virtual CMeanFunction *	get_mean ()
virtual void	set_mean (CMeanFunction *m)
virtual CLabels *	get_labels ()
virtual void	set_labels (CLabels *lab)
CLikelihoodModel *	get_model ()
virtual void	set_model (CLikelihoodModel *mod)
virtual float64_t	get_scale () const
virtual void	set_scale (float64_t scale)
virtual bool	supports_binary () const
virtual bool	supports_multiclass () const
virtual SGMatrix< float64_t >	get_multiclass_E ()
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 ()

Static Public Member Functions
static CExactInferenceMethod *	obtain_from_generic (CInferenceMethod *inference)

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	check_members () const
virtual void	update_alpha ()
virtual void	update_chol ()
virtual void	update_mean ()
virtual void	update_cov ()
virtual void	update_deriv ()
virtual SGVector< float64_t >	get_derivative_wrt_inference_method (const TParameter *param)
virtual SGVector< float64_t >	get_derivative_wrt_likelihood_model (const TParameter *param)
virtual SGVector< float64_t >	get_derivative_wrt_kernel (const TParameter *param)
virtual SGVector< float64_t >	get_derivative_wrt_mean (const TParameter *param)
virtual void	compute_gradient ()
virtual void	update_train_kernel ()
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)

Static Protected Member Functions
static void *	get_derivative_helper (void *p)

Protected Attributes
CKernel *	m_kernel
CMeanFunction *	m_mean
CLikelihoodModel *	m_model
CFeatures *	m_features
CLabels *	m_labels
SGVector< float64_t >	m_alpha
SGMatrix< float64_t >	m_L
float64_t	m_log_scale
SGMatrix< float64_t >	m_ktrtr
SGMatrix< float64_t >	m_E
bool	m_gradient_update

Constructor & Destructor Documentation

CExactInferenceMethod

(

)

default constructor

Definition at line 46 of file ExactInferenceMethod.cpp.

CExactInferenceMethod	(	CKernel *	kernel,
		CFeatures *	features,
		CMeanFunction *	mean,
		CLabels *	labels,
		CLikelihoodModel *	model
	)

constructor

Parameters

kernel	covariance function
features	features to use in inference
mean	mean function to use
labels	labels of the features
model	likelihood model to use

Definition at line 50 of file ExactInferenceMethod.cpp.

~CExactInferenceMethod ( )

virtual

Definition at line 56 of file ExactInferenceMethod.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.

void check_members ( ) const

protectedvirtual

check if members of object are valid for inference

Reimplemented from CInferenceMethod.

Definition at line 87 of file ExactInferenceMethod.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.

void compute_gradient ( )

protectedvirtual

update gradients

Reimplemented from CInferenceMethod.

Definition at line 60 of file ExactInferenceMethod.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.

SGVector< float64_t > get_alpha ( )

virtual

get alpha vector

Returns

vector to compute posterior mean of Gaussian Process:

\[ \mu = K\alpha \]

where \(\mu\) is the mean and \(K\) is the prior covariance matrix.

Definition at line 143 of file ExactInferenceMethod.cpp.

SGMatrix< float64_t > get_cholesky ( )

virtual

get Cholesky decomposition matrix

Returns

Cholesky decomposition of matrix:

\[ L = Cholesky(sW*K*sW+I) \]

where \(K\) is the prior covariance matrix, \(sW\) is the vector returned by get_diagonal_vector(), and \(I\) is the identity matrix.

Definition at line 151 of file ExactInferenceMethod.cpp.

void * get_derivative_helper ( void *  p )

staticprotectedinherited

pthread helper method to compute negative log marginal likelihood derivatives wrt hyperparameter

Definition at line 255 of file InferenceMethod.cpp.

SGVector< float64_t > get_derivative_wrt_inference_method ( const TParameter *  param )

protectedvirtual

returns derivative of negative log marginal likelihood wrt parameter of CInferenceMethod class

Parameters

param

parameter of CInferenceMethod class

Returns

derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 284 of file ExactInferenceMethod.cpp.

SGVector< float64_t > get_derivative_wrt_kernel ( const TParameter *  param )

protectedvirtual

returns derivative of negative log marginal likelihood wrt kernel's parameter

Parameters

param

parameter of given kernel

Returns

derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 340 of file ExactInferenceMethod.cpp.

SGVector< float64_t > get_derivative_wrt_likelihood_model ( const TParameter *  param )

protectedvirtual

returns derivative of negative log marginal likelihood wrt parameter of likelihood model

Parameters

param

parameter of given likelihood model

Returns

derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 316 of file ExactInferenceMethod.cpp.

SGVector< float64_t > get_derivative_wrt_mean ( const TParameter *  param )

protectedvirtual

returns derivative of negative log marginal likelihood wrt mean function's parameter

Parameters

param

parameter of given mean function

Returns

derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 370 of file ExactInferenceMethod.cpp.

SGVector< float64_t > get_diagonal_vector ( )

virtual

get diagonal vector

Returns

diagonal of matrix used to calculate posterior covariance matrix

\[ Cov = (K^{-1}+sW^{2})^{-1} \]

where \(Cov\) is the posterior covariance matrix, \(K\) is the prior covariance matrix, and \(sW\) is the diagonal vector.

Definition at line 97 of file ExactInferenceMethod.cpp.

virtual CFeatures* get_features ( )

virtualinherited

get features

Returns

features

Definition at line 266 of file InferenceMethod.h.

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.

virtual CMap<TParameter*, SGVector<float64_t> >* get_gradient ( CMap< TParameter *, CSGObject * > *  parameters )

virtualinherited

get the gradient

Parameters

parameters

parameter's dictionary

Returns

map of gradient. Keys are names of parameters, values are values of derivative with respect to that parameter.

Implements CDifferentiableFunction.

Definition at line 245 of file InferenceMethod.h.

virtual EInferenceType get_inference_type ( ) const

virtual

return what type of inference we are

Returns

inference type EXACT

Reimplemented from CInferenceMethod.

Definition at line 90 of file ExactInferenceMethod.h.

virtual CKernel* get_kernel ( )

virtualinherited

get kernel

Returns

kernel

Definition at line 283 of file InferenceMethod.h.

virtual CLabels* get_labels ( )

virtualinherited

get labels

Returns

labels

Definition at line 317 of file InferenceMethod.h.

float64_t get_marginal_likelihood_estimate ( int32_t  num_importance_samples = 1, float64_t  ridge_size = 1e-15  )

inherited

Computes an unbiased estimate of the marginal-likelihood (in log-domain),

\[ p(y|X,\theta), \]

where \(y\) are the labels, \(X\) are the features (omitted from in the following expressions), and \(\theta\) represent hyperparameters.

This is done via a Gaussian approximation to the posterior \(q(f|y, \theta)\approx p(f|y, \theta)\), which is computed by the underlying CInferenceMethod instance (if implemented, otherwise error), and then using an importance sample estimator

\[ p(y|\theta)=\int p(y|f)p(f|\theta)df =\int p(y|f)\frac{p(f|\theta)}{q(f|y, \theta)}q(f|y, \theta)df \approx\frac{1}{n}\sum_{i=1}^n p(y|f^{(i)})\frac{p(f^{(i)}|\theta)} {q(f^{(i)}|y, \theta)}, \]

where \( f^{(i)} \) are samples from the posterior approximation \( q(f|y, \theta) \). The resulting estimator has a low variance if \( q(f|y, \theta) \) is a good approximation. It has large variance otherwise (while still being consistent). Storing all number of log-domain ensures numerical stability.

Parameters

num_importance_samples	the number of importance samples \(n\) from \( q(f|y, \theta) \).
ridge_size	scalar that is added to the diagonal of the involved Gaussian distribution's covariance of GP prior and posterior approximation to stabilise things. Increase if covariance matrix is not numerically positive semi-definite.

Returns

unbiased estimate of the marginal likelihood function \( p(y|\theta),\) in log-domain.

Definition at line 126 of file InferenceMethod.cpp.

virtual CMeanFunction* get_mean ( )

virtualinherited

get mean

Returns

mean

Definition at line 300 of file InferenceMethod.h.

CLikelihoodModel* get_model ( )

inherited

get likelihood model

Returns

likelihood

Definition at line 334 of file InferenceMethod.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.

SGMatrix< float64_t > get_multiclass_E ( )

virtualinherited

get the E matrix used for multi classification

Returns

the matrix for multi classification

Definition at line 72 of file InferenceMethod.cpp.

virtual const char* get_name ( ) const

virtual

returns the name of the inference method

Returns

name Exact

Implements CSGObject.

Definition at line 96 of file ExactInferenceMethod.h.

float64_t get_negative_log_marginal_likelihood ( )

virtual

get negative log marginal likelihood

Returns

the negative log of the marginal likelihood function:

\[ -log(p(y|X, \theta)) \]

where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.

Implements CInferenceMethod.

Definition at line 114 of file ExactInferenceMethod.cpp.

CMap< TParameter *, SGVector< float64_t > > * get_negative_log_marginal_likelihood_derivatives ( CMap< TParameter *, CSGObject * > *  parameters )

virtualinherited

get log marginal likelihood gradient

Returns

vector of the marginal likelihood function gradient with respect to hyperparameters (under the current approximation to the posterior \(q(f|y)\approx p(f|y)\):

\[ -\frac{\partial log(p(y|X, \theta))}{\partial \theta} \]

where \(y\) are the labels, \(X\) are the features, and \(\theta\) represent hyperparameters.

Definition at line 185 of file InferenceMethod.cpp.

SGMatrix< float64_t > get_posterior_covariance ( )

virtual

returns covariance matrix \(\Sigma\) of the posterior Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\)

\[ p(f|y) = \mathcal{N}(\mu,\Sigma) \]

Returns

covariance matrix

Implements CInferenceMethod.

Definition at line 166 of file ExactInferenceMethod.cpp.

SGVector< float64_t > get_posterior_mean ( )

virtual

returns mean vector \(\mu\) of the posterior Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\)

\[ p(f|y) = \mathcal{N}(\mu,\Sigma) \]

Returns

mean vector

Implements CInferenceMethod.

Definition at line 159 of file ExactInferenceMethod.cpp.

float64_t get_scale ( ) const

virtualinherited

get kernel scale

Returns

kernel scale

Definition at line 61 of file InferenceMethod.cpp.

virtual SGVector<float64_t> get_value ( )

virtualinherited

get the function value

Returns

vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 255 of file InferenceMethod.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.

CExactInferenceMethod * obtain_from_generic ( CInferenceMethod *  inference )

static

helper method used to specialize a base class instance

Parameters

inference

inference method

Returns

casted CExactInferenceMethod object

Definition at line 303 of file ExactInferenceMethod.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.

virtual void set_features ( CFeatures *  feat )

virtualinherited

set features

Parameters

feat	features to set

Definition at line 272 of file InferenceMethod.h.

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.

virtual void set_kernel ( CKernel *  kern )

virtualinherited

set kernel

Parameters

kern	kernel to set

Reimplemented in CSingleSparseInferenceBase.

Definition at line 289 of file InferenceMethod.h.

virtual void set_labels ( CLabels *  lab )

virtualinherited

set labels

Parameters

lab	label to set

Definition at line 323 of file InferenceMethod.h.

virtual void set_mean ( CMeanFunction *  m )

virtualinherited

set mean

Parameters

m	mean function to set

Definition at line 306 of file InferenceMethod.h.

virtual void set_model ( CLikelihoodModel *  mod )

virtualinherited

set likelihood model

Parameters

mod	model to set

Reimplemented in CKLInferenceMethod, and CKLDualInferenceMethod.

Definition at line 340 of file InferenceMethod.h.

void set_scale ( float64_t  scale )

virtualinherited

set kernel scale

Parameters

scale

scale to be set

Definition at line 66 of file InferenceMethod.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

virtualinherited

whether combination of inference method and given likelihood function supports binary classification

Returns

false

Reimplemented in CEPInferenceMethod, CKLInferenceMethod, CSingleFITCLaplacianInferenceMethod, and CSingleLaplacianInferenceMethod.

Definition at line 371 of file InferenceMethod.h.

virtual bool supports_multiclass ( ) const

virtualinherited

whether combination of inference method and given likelihood function supports multiclass classification

Returns

false

Definition at line 378 of file InferenceMethod.h.

virtual bool supports_regression ( ) const

virtual

Returns

whether combination of exact inference method and given likelihood function supports regression

Reimplemented from CInferenceMethod.

Definition at line 182 of file ExactInferenceMethod.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 ( )

virtual

update matrices except gradients

Reimplemented from CInferenceMethod.

Definition at line 74 of file ExactInferenceMethod.cpp.

void update_alpha ( )

protectedvirtual

update alpha matrix

Implements CInferenceMethod.

Definition at line 192 of file ExactInferenceMethod.cpp.

void update_chol ( )

protectedvirtual

update Cholesky matrix

Implements CInferenceMethod.

Definition at line 173 of file ExactInferenceMethod.cpp.

void update_cov ( )

protectedvirtual

update covariance matrix of the posterior Gaussian

Definition at line 234 of file ExactInferenceMethod.cpp.

void update_deriv ( )

protectedvirtual

update matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter

Implements CInferenceMethod.

Definition at line 258 of file ExactInferenceMethod.cpp.

void update_mean ( )

protectedvirtual

update mean vector of the posterior Gaussian

Definition at line 218 of file ExactInferenceMethod.cpp.

void update_parameter_hash ( )

virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

void update_train_kernel ( )

protectedvirtualinherited

update train kernel matrix

Reimplemented in CSparseInferenceBase.

Definition at line 324 of file InferenceMethod.cpp.

Member Data Documentation

SGIO* io

inherited

io

Definition at line 369 of file SGObject.h.

SGVector<float64_t> m_alpha

protectedinherited

alpha vector used in process mean calculation

Definition at line 475 of file InferenceMethod.h.

SGMatrix<float64_t> m_E

protectedinherited

the matrix used for multi classification

Definition at line 487 of file InferenceMethod.h.

CFeatures* m_features

protectedinherited

features to use

Definition at line 469 of file InferenceMethod.h.

Parameter* m_gradient_parameters

inherited

parameters wrt which we can compute gradients

Definition at line 384 of file SGObject.h.

bool m_gradient_update

protectedinherited

Whether gradients are updated

Definition at line 490 of file InferenceMethod.h.

uint32_t m_hash

inherited

Hash of parameter values

Definition at line 387 of file SGObject.h.

CKernel* m_kernel

protectedinherited

covariance function

Definition at line 460 of file InferenceMethod.h.

SGMatrix<float64_t> m_ktrtr

protectedinherited

kernel matrix from features (non-scalled by inference scalling)

Definition at line 484 of file InferenceMethod.h.

SGMatrix<float64_t> m_L

protectedinherited

upper triangular factor of Cholesky decomposition

Definition at line 478 of file InferenceMethod.h.

CLabels* m_labels

protectedinherited

labels of features

Definition at line 472 of file InferenceMethod.h.

float64_t m_log_scale

protectedinherited

kernel scale

Definition at line 481 of file InferenceMethod.h.

CMeanFunction* m_mean

protectedinherited

mean function

Definition at line 463 of file InferenceMethod.h.

CLikelihoodModel* m_model

protectedinherited

likelihood function to use

Definition at line 466 of file InferenceMethod.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.

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

• ExactInferenceMethod.h
• ExactInferenceMethod.cpp