Detailed Description

The Fully Independent Conditional Training inference base class.

For more details, see Quiñonero-Candela, Joaquin, and Carl Edward Rasmussen. "A unifying view of sparse approximate Gaussian process regression." The Journal of Machine Learning Research 6 (2005): 1939-1959.

The key idea of Sparse inference is to use the following kernel matrix \(\Sigma_{fitc}\) to approximate a kernel matrix, \(\Sigma_{N}\) derived from a GP prior.

\[ *\Sigma_{Sparse}=\textbf{diag}(\Sigma_{N}-\Phi)+\Phi *\]

where \(\Phi=\Sigma_{NM}\Sigma_{M}^{-1}\Sigma_{MN}\) \(\Sigma_{N}\) is the kernel matrix on features \(\Sigma_{M}\) is the kernel matrix on inducing points \(\Sigma_{NM}=\Sigma_{MN}^{T}\) is the kernel matrix between features and inducing features

Note that the number of inducing points (m) is usually far less than the number of input points (n). (the time complexity is computed based on the assumption m < n) The idea of Sparse approximation is to use a lower-ranked matrix plus a diagonal matrix to approximate the full kernel matrix. The time complexity of the main inference process can be reduced from O(n^3) to O(m^2*n).

Since we use \(\Sigma_{Sparse}\) to approximate \(\Sigma_{N}\), the (approximated) negative log marginal likelihood are computed based on \(\Sigma_{Sparse}\).

Definition at line 71 of file SparseInference.h.

Inheritance diagram for CSparseInference:

[legend]

Public Member Functions
	CSparseInference ()

	CSparseInference (CKernel kernel, CFeatures features, CMeanFunction mean, CLabels labels, CLikelihoodModel model, CFeatures inducing_features)

virtual	~CSparseInference ()

virtual EInferenceType	get_inference_type () const

virtual const char *	get_name () const

virtual void	set_inducing_features (CFeatures *feat)

virtual CFeatures *	get_inducing_features ()

virtual SGVector< float64_t >	get_alpha ()

virtual SGMatrix< float64_t >	get_cholesky ()

virtual void	update ()=0

virtual void	set_inducing_noise (float64_t noise)

virtual float64_t	get_inducing_noise ()

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

virtual SGVector< float64_t >	get_posterior_mean ()=0

virtual SGMatrix< float64_t >	get_posterior_covariance ()=0

virtual float64_t	get_negative_log_marginal_likelihood ()=0

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 SGVector< float64_t >	get_diagonal_vector ()=0

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_regression () const

virtual bool	supports_binary () const

virtual bool	supports_multiclass () const

virtual SGMatrix< float64_t >	get_multiclass_E ()

virtual void	register_minimizer (Minimizer *minimizer)

virtual CSGObject *	shallow_copy () const

virtual CSGObject *	deep_copy () const

virtual bool	is_generic (EPrimitiveType *generic) const

template<class T >
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

template<>
void	set_generic ()

void	unset_generic ()

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

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

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

void	set_global_io (SGIO *io)

SGIO *	get_global_io ()

void	set_global_parallel (Parallel *parallel)

Parallel *	get_global_parallel ()

void	set_global_version (Version *version)

Version *	get_global_version ()

SGStringList< char >	get_modelsel_names ()

void	print_modsel_params ()

char *	get_modsel_param_descr (const char *param_name)

index_t	get_modsel_param_index (const char *param_name)

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

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

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

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

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

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

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

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

virtual void	update_parameter_hash ()

virtual bool	parameter_hash_changed ()

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

virtual CSGObject *	clone ()

Public Attributes
SGIO *	io

Parallel *	parallel

Version *	version

Parameter *	m_parameters

Parameter *	m_model_selection_parameters

Parameter *	m_gradient_parameters

uint32_t	m_hash

Protected Member Functions
virtual void	convert_features ()

virtual void	check_features ()

virtual void	check_members () const

virtual void	update_train_kernel ()

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

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

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

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

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

virtual void	update_alpha ()=0

virtual void	update_chol ()=0

virtual void	update_deriv ()=0

virtual void	compute_gradient ()

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)

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

Protected Attributes
SGMatrix< float64_t >	m_inducing_features

float64_t	m_log_ind_noise

SGMatrix< float64_t >	m_kuu

SGMatrix< float64_t >	m_ktru

SGMatrix< float64_t >	m_Sigma

SGVector< float64_t >	m_mu

SGVector< float64_t >	m_ktrtr_diag

Minimizer *	m_minimizer

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

CSparseInference ( )

default constructor

Definition at line 43 of file SparseInference.cpp.

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

constructor

Parameters

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

Definition at line 90 of file SparseInference.cpp.

~CSparseInference ( )

virtual

Definition at line 123 of file SparseInference.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 630 of file SGObject.cpp.

void check_features ( )

protectedvirtual

check whether features and inducing features are set

Definition at line 48 of file SparseInference.cpp.

void check_members ( ) const

protectedvirtual

check if members of object are valid for inference

Reimplemented from CInference.

Reimplemented in CFITCInferenceMethod, and CVarDTCInferenceMethod.

Definition at line 127 of file SparseInference.cpp.

CSGObject * clone ( )

virtualinherited

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

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

Definition at line 747 of file SGObject.cpp.

void compute_gradient ( )

protectedvirtualinherited

update gradients

Reimplemented in CExactInferenceMethod, CEPInferenceMethod, CVarDTCInferenceMethod, CKLInference, CFITCInferenceMethod, CSingleFITCLaplaceInferenceMethod, and CLaplaceInference.

Definition at line 343 of file Inference.cpp.

void convert_features ( )

protectedvirtual

convert inducing features and features to the same represention

Note that these two kinds of features can be different types. The reasons are listed below.

The type of the gradient wrt inducing features is float64_t, which is used to update inducing features
Reason 1 implies that the type of inducing features can be float64_t while the type of features does not required as float64_t
Reason 2 implies that the type of features must be a subclass of CDotFeatures, which can represent features as float64_t

Definition at line 53 of file SparseInference.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.

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

virtualinherited

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

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

Parameters

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

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

Definition at line 651 of file SGObject.cpp.

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

inherited

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

Parameters

_tag	name and type information of parameter

Returns: value of the parameter identified by the input tag

Definition at line 367 of file SGObject.h.

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

inherited

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

Parameters

name	name of the parameter

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

Definition at line 388 of file SGObject.h.

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.

Implements CInference.

Definition at line 135 of file SparseInference.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.

Implements CInference.

Definition at line 144 of file SparseInference.cpp.

void * get_derivative_helper ( void * p )

staticprotectedinherited

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

Definition at line 268 of file Inference.cpp.

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

pure virtual

returns derivative of negative log marginal likelihood wrt inducing features (input) Note that in order to call this method, kernel must support Sparse inference

Returns: derivative of negative log marginal likelihood

Implemented in CSingleFITCLaplaceInferenceMethod, CSingleFITCInference, CSingleSparseInference, and CVarDTCInferenceMethod.

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

protectedpure virtual

returns derivative of negative log marginal likelihood wrt inducing noise (noise from inducing features) parameter

Parameters

param parameter of given SparseInference class

In order to enforce symmetrc positive definiteness of the kernel matrix on inducing points, \(\Sigma_{M}\), the following ridge trick is used since the matrix is learned from data.

\[ \Sigma_{M'}=\Sigma_{M}+\lambda*I \]

where \(\lambda \ge 0\) is the inducing noise.

In practice, we use the corrected matrix, \(\Sigma_{M'}\) in the following approximation.

\[ *\Sigma_{Sparse}=\textbf{diag}(\Sigma_{N}-\Phi)+\Phi *\]

where \(\Phi=\Sigma_{NM}\Sigma_{M'}^{-1}\Sigma_{MN}\)

Returns: derivative of negative log marginal likelihood

Implemented in CSingleFITCLaplaceInferenceMethod, CVarDTCInferenceMethod, CSingleFITCInference, and CSingleSparseInference.

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

protectedpure virtual

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

Parameters

param parameter of CInference class

Returns: derivative of negative log marginal likelihood

Implements CInference.

Implemented in CSingleFITCLaplaceInferenceMethod, and CSingleSparseInference.

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

protectedpure virtual

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 CInference.

Implemented in CSingleFITCLaplaceInferenceMethod, and CSingleSparseInference.

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

protectedpure virtual

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 CInference.

Implemented in CSingleFITCLaplaceInferenceMethod, CSingleFITCInference, CFITCInferenceMethod, and CVarDTCInferenceMethod.

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

protectedpure virtual

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 CInference.

Implemented in CSingleFITCLaplaceInferenceMethod, CVarDTCInferenceMethod, and CSingleFITCInference.

virtual SGVector<float64_t> get_diagonal_vector ( )

pure virtualinherited

get diagonal vector

Returns: diagonal of matrix used to calculate posterior covariance matrix

Implemented in CKLDualInferenceMethod, CEPInferenceMethod, CExactInferenceMethod, CKLCovarianceInferenceMethod, CMultiLaplaceInferenceMethod, CSingleFITCLaplaceInferenceMethod, CSingleLaplaceInferenceMethod, CFITCInferenceMethod, CVarDTCInferenceMethod, and CKLLowerTriangularInference.

virtual CFeatures* get_features ( )

virtualinherited

get features

Returns: features

Definition at line 266 of file Inference.h.

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.

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 Inference.h.

virtual CFeatures* get_inducing_features ( )

virtual

get inducing features

Returns: features

Definition at line 121 of file SparseInference.h.

float64_t get_inducing_noise ( )

virtual

get the noise for inducing points

Returns: noise noise for inducing points

Definition at line 118 of file SparseInference.cpp.

virtual EInferenceType get_inference_type ( ) const

virtual

return what type of inference we are

Returns: inference type Sparse

Reimplemented from CInference.

Reimplemented in CSingleFITCLaplaceInferenceMethod, CFITCInferenceMethod, and CVarDTCInferenceMethod.

Definition at line 96 of file SparseInference.h.

virtual CKernel* get_kernel ( )

virtualinherited

get kernel

Returns: kernel

Definition at line 283 of file Inference.h.

virtual CLabels* get_labels ( )

virtualinherited

get labels

Returns: labels

Definition at line 317 of file Inference.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 CInference 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 139 of file Inference.cpp.

virtual CMeanFunction* get_mean ( )

virtualinherited

get mean

Returns: mean

Definition at line 300 of file Inference.h.

CLikelihoodModel* get_model ( )

inherited

get likelihood model

Returns: likelihood

Definition at line 334 of file Inference.h.

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.

SGMatrix< float64_t > get_multiclass_E ( )

virtualinherited

get the E matrix used for multi classification

Returns: the matrix for multi classification

Definition at line 71 of file Inference.cpp.

virtual const char* get_name ( ) const

virtual

returns the name of the inference method

Returns: name SparseBase

Implements CSGObject.

Reimplemented in CSingleFITCInference, CSingleFITCLaplaceInferenceMethod, CFITCInferenceMethod, CVarDTCInferenceMethod, and CSingleSparseInference.

Definition at line 102 of file SparseInference.h.

virtual float64_t get_negative_log_marginal_likelihood ( )

pure virtualinherited

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.

Implemented in CSingleFITCLaplaceInferenceMethod, CMultiLaplaceInferenceMethod, CKLInference, CExactInferenceMethod, CFITCInferenceMethod, CVarDTCInferenceMethod, CEPInferenceMethod, and CSingleLaplaceInferenceMethod.

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 198 of file Inference.cpp.

virtual SGMatrix<float64_t> get_posterior_covariance ( )

pure virtual

returns covariance matrix \(\Sigma\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:

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

in case if particular inference method doesn't compute posterior \(p(f|y)\) exactly, and it returns covariance matrix \(\Sigma\) of the posterior Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\) otherwise.

Returns: covariance matrix

Implements CInference.

Implemented in CFITCInferenceMethod, CVarDTCInferenceMethod, and CSingleFITCLaplaceInferenceMethod.

virtual SGVector<float64_t> get_posterior_mean ( )

pure virtual

returns mean vector \(\mu\) of the Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\), which is an approximation to the posterior:

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

in case if particular inference method doesn't compute posterior \(p(f|y)\) exactly, and it returns covariance matrix \(\Sigma\) of the posterior Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\) otherwise.

Returns: mean vector

Implements CInference.

Implemented in CFITCInferenceMethod, CVarDTCInferenceMethod, and CSingleFITCLaplaceInferenceMethod.

float64_t get_scale ( ) const

virtualinherited

get kernel scale

Returns: kernel scale

Definition at line 60 of file Inference.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 Inference.h.

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

inherited

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

Parameters

name	name of the parameter

Returns: true if the parameter exists with the input name

Definition at line 289 of file SGObject.h.

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

inherited

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

Parameters

tag	tag of the parameter containing name and type information

Returns: true if the parameter exists with the input tag

Definition at line 301 of file SGObject.h.

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

inherited

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

Parameters

name	name of the parameter

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

Definition at line 312 of file SGObject.h.

bool is_generic ( EPrimitiveType * generic ) const

virtualinherited

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

Parameters

generic set to the type of the generic if returning TRUE

Returns: TRUE if a class template.

Definition at line 329 of file SGObject.cpp.

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

virtualinherited

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

Parameters

file	where to load from
prefix	prefix for members

Returns: TRUE if done, otherwise FALSE

Definition at line 402 of file SGObject.cpp.

void load_serializable_post	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

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

Exceptions

ShogunException will be thrown if an error occurs.

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

Definition at line 459 of file SGObject.cpp.

void load_serializable_pre	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

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

Exceptions

ShogunException will be thrown if an error occurs.

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

Definition at line 454 of file SGObject.cpp.

bool parameter_hash_changed ( )

virtualinherited

Returns: whether parameter combination has changed since last update

Definition at line 295 of file SGObject.cpp.

void 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_minimizer ( Minimizer * minimizer )

virtualinherited

Set a minimizer

Parameters

minimizer minimizer used in inference method

Reimplemented in CEPInferenceMethod, CKLInference, CExactInferenceMethod, CKLDualInferenceMethod, CSingleFITCLaplaceInferenceMethod, CFITCInferenceMethod, CVarDTCInferenceMethod, and CSingleLaplaceInferenceMethod.

Definition at line 128 of file Inference.cpp.

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

protectedinherited

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

Parameters

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

Definition at line 439 of file SGObject.h.

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

protectedinherited

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

Parameters

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

Definition at line 452 of file SGObject.h.

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

virtualinherited

Save this object to file.

Parameters

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

Returns: TRUE if done, otherwise FALSE

Definition at line 347 of file SGObject.cpp.

void save_serializable_post	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

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

Exceptions

ShogunException will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 469 of file SGObject.cpp.

void save_serializable_pre	(		)
throw	(	ShogunException
	)

protectedvirtualinherited

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

Exceptions

ShogunException will be thrown if an error occurs.

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

Definition at line 464 of file SGObject.cpp.

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

inherited

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

Parameters

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

Definition at line 328 of file SGObject.h.

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

inherited

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

Parameters

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

Definition at line 354 of file SGObject.h.

virtual void set_features ( CFeatures * feat )

virtualinherited

set features

Parameters

feat	features to set

Definition at line 272 of file Inference.h.

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.

virtual void set_inducing_features ( CFeatures * feat )

virtual

set inducing features

Parameters

feat	features to set

Definition at line 108 of file SparseInference.h.

void set_inducing_noise ( float64_t noise )

virtual

set the noise for inducing points

Parameters

noise noise for inducing points

The noise is used to enfore the kernel matrix about the inducing points are positive definite

Definition at line 112 of file SparseInference.cpp.

virtual void set_kernel ( CKernel * kern )

virtualinherited

set kernel

Parameters

kern	kernel to set

Reimplemented in CSingleSparseInference.

Definition at line 289 of file Inference.h.

virtual void set_labels ( CLabels * lab )

virtualinherited

set labels

Parameters

lab	label to set

Definition at line 323 of file Inference.h.

virtual void set_mean ( CMeanFunction * m )

virtualinherited

set mean

Parameters

m	mean function to set

Definition at line 306 of file Inference.h.

virtual void set_model ( CLikelihoodModel * mod )

virtualinherited

set likelihood model

Parameters

mod	model to set

Reimplemented in CKLDualInferenceMethod, and CKLInference.

Definition at line 340 of file Inference.h.

void set_scale ( float64_t scale )

virtualinherited

set kernel scale

Parameters

scale scale to be set

Definition at line 65 of file Inference.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.

virtual bool supports_binary ( ) const

virtualinherited

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

Returns: false

Reimplemented in CEPInferenceMethod, CKLInference, CSingleFITCLaplaceInferenceMethod, and CSingleLaplaceInferenceMethod.

Definition at line 371 of file Inference.h.

virtual bool supports_multiclass ( ) const

virtualinherited

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

Returns: false

Reimplemented in CMultiLaplaceInferenceMethod.

Definition at line 378 of file Inference.h.

virtual bool supports_regression ( ) const

virtualinherited

whether combination of inference method and given likelihood function supports regression

Returns: false

Reimplemented in CExactInferenceMethod, CKLInference, CFITCInferenceMethod, CVarDTCInferenceMethod, CSingleFITCLaplaceInferenceMethod, and CSingleLaplaceInferenceMethod.

Definition at line 364 of file Inference.h.

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.

virtual void update ( )

pure virtual

update all matrices

Reimplemented from CInference.

Implemented in CFITCInferenceMethod, CVarDTCInferenceMethod, and CSingleFITCLaplaceInferenceMethod.

virtual void update_alpha ( )

protectedpure virtualinherited

update alpha vector

Implemented in CEPInferenceMethod, CKLDualInferenceMethod, CMultiLaplaceInferenceMethod, CExactInferenceMethod, CSingleFITCLaplaceInferenceMethod, CSingleFITCInference, CFITCInferenceMethod, CVarDTCInferenceMethod, CSingleLaplaceInferenceMethod, CKLCovarianceInferenceMethod, CKLDiagonalInferenceMethod, and CKLCholeskyInferenceMethod.

virtual void update_chol ( )

protectedpure virtualinherited

update cholesky matrix

Implemented in CEPInferenceMethod, CKLDualInferenceMethod, CMultiLaplaceInferenceMethod, CExactInferenceMethod, CSingleFITCLaplaceInferenceMethod, CSingleFITCInference, CFITCInferenceMethod, CVarDTCInferenceMethod, CSingleLaplaceInferenceMethod, CKLCovarianceInferenceMethod, and CKLLowerTriangularInference.

virtual void update_deriv ( )

protectedpure virtualinherited

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

Implemented in CEPInferenceMethod, CMultiLaplaceInferenceMethod, CKLDualInferenceMethod, CExactInferenceMethod, CSingleFITCLaplaceInferenceMethod, CSingleFITCInference, CFITCInferenceMethod, CVarDTCInferenceMethod, CSingleLaplaceInferenceMethod, CKLCovarianceInferenceMethod, and CKLLowerTriangularInference.

void update_parameter_hash ( )

virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

void update_train_kernel ( )

protectedvirtual

update train kernel matrix

Reimplemented from CInference.

Definition at line 153 of file SparseInference.cpp.

Member Data Documentation

SGIO* io

inherited

io

Definition at line 537 of file SGObject.h.

SGVector<float64_t> m_alpha

protectedinherited

alpha vector used in process mean calculation

Definition at line 484 of file Inference.h.

SGMatrix<float64_t> m_E

protectedinherited

the matrix used for multi classification

Definition at line 496 of file Inference.h.

CFeatures* m_features

protectedinherited

features to use

Definition at line 478 of file Inference.h.

Parameter* m_gradient_parameters

inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

bool m_gradient_update

protectedinherited

Whether gradients are updated

Definition at line 499 of file Inference.h.

uint32_t m_hash

inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

SGMatrix<float64_t> m_inducing_features

protected

inducing features for approximation

Definition at line 304 of file SparseInference.h.

CKernel* m_kernel

protectedinherited

covariance function

Definition at line 469 of file Inference.h.

SGMatrix<float64_t> m_ktrtr

protectedinherited

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

Definition at line 493 of file Inference.h.

SGVector<float64_t> m_ktrtr_diag

protected

diagonal elements of kernel matrix m_ktrtr

Definition at line 322 of file SparseInference.h.

SGMatrix<float64_t> m_ktru

protected

covariance matrix of inducing features and training features

Definition at line 313 of file SparseInference.h.

SGMatrix<float64_t> m_kuu

protected

covariance matrix of inducing features

Definition at line 310 of file SparseInference.h.

SGMatrix<float64_t> m_L

protectedinherited

upper triangular factor of Cholesky decomposition

Definition at line 487 of file Inference.h.

CLabels* m_labels

protectedinherited

labels of features

Definition at line 481 of file Inference.h.

float64_t m_log_ind_noise

protected

noise of the inducing variables

Definition at line 307 of file SparseInference.h.

float64_t m_log_scale

protectedinherited

kernel scale

Definition at line 490 of file Inference.h.

CMeanFunction* m_mean

protectedinherited

mean function

Definition at line 472 of file Inference.h.

Minimizer* m_minimizer

protectedinherited

minimizer

Definition at line 466 of file Inference.h.

CLikelihoodModel* m_model

protectedinherited

likelihood function to use

Definition at line 475 of file Inference.h.

Parameter* m_model_selection_parameters

inherited

model selection parameters

Definition at line 549 of file SGObject.h.

SGVector<float64_t> m_mu

protected

mean vector of the the posterior Gaussian distribution

Definition at line 319 of file SparseInference.h.

Parameter* m_parameters

inherited

parameters

Definition at line 546 of file SGObject.h.

SGMatrix<float64_t> m_Sigma

protected

covariance matrix of the the posterior Gaussian distribution

Definition at line 316 of file SparseInference.h.

Parallel* parallel

inherited

parallel

Definition at line 540 of file SGObject.h.

Version* version

inherited

version

Definition at line 543 of file SGObject.h.

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

Detailed Description

Public Member Functions

Public Attributes

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation