SHOGUN  6.1.3
CSparseInference Class Referenceabstract

## Detailed Description

The Fully Independent Conditional Training inference base class.

For more details, see QuiĆ±onero-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:
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## Public Types

typedef rxcpp::subjects::subject< ObservedValueSGSubject

typedef rxcpp::observable< ObservedValue, rxcpp::dynamic_observable< ObservedValue > > SGObservable

typedef rxcpp::subscriber< ObservedValue, rxcpp::observer< ObservedValue, void, void, void, void > > SGSubscriber

## 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 CFeaturesget_inducing_features ()

virtual SGVector< float64_tget_alpha ()

virtual SGMatrix< float64_tget_cholesky ()

virtual void update ()=0

virtual void set_inducing_noise (float64_t noise)

virtual float64_t get_inducing_noise ()

virtual SGVector< float64_tget_derivative_wrt_inducing_features (const TParameter *param)=0

virtual SGVector< float64_tget_posterior_mean ()=0

virtual SGMatrix< float64_tget_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_tget_diagonal_vector ()=0

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

virtual SGVector< float64_tget_value ()

virtual CFeaturesget_features ()

virtual void set_features (CFeatures *feat)

virtual CKernelget_kernel ()

virtual void set_kernel (CKernel *kern)

virtual CMeanFunctionget_mean ()

virtual void set_mean (CMeanFunction *m)

virtual CLabelsget_labels ()

virtual void set_labels (CLabels *lab)

CLikelihoodModelget_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_tget_multiclass_E ()

virtual void register_minimizer (Minimizer *minimizer)

int32_t ref ()

int32_t ref_count ()

int32_t unref ()

virtual CSGObjectshallow_copy () const

virtual CSGObjectdeep_copy () const

virtual bool is_generic (EPrimitiveType *generic) const

template<class T >
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

void unset_generic ()

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

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

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

void set_global_io (SGIO *io)

SGIOget_global_io ()

void set_global_parallel (Parallel *parallel)

Parallelget_global_parallel ()

void set_global_version (Version *version)

Versionget_global_version ()

SGStringList< char > get_modelsel_names ()

void print_modsel_params ()

char * get_modsel_param_descr (const char *param_name)

index_t get_modsel_param_index (const char *param_name)

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

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

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

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

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

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

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

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

SGObservableget_parameters_observable ()

void subscribe_to_parameters (ParameterObserverInterface *obs)

void list_observable_parameters ()

virtual void update_parameter_hash ()

virtual bool parameter_hash_changed ()

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

virtual CSGObjectclone ()

## Public Attributes

SGIOio

Parallelparallel

Versionversion

Parameterm_parameters

Parameterm_model_selection_parameters

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_tget_derivative_wrt_inference_method (const TParameter *param)=0

virtual SGVector< float64_tget_derivative_wrt_likelihood_model (const TParameter *param)=0

virtual SGVector< float64_tget_derivative_wrt_kernel (const TParameter *param)=0

virtual SGVector< float64_tget_derivative_wrt_mean (const TParameter *param)=0

virtual SGVector< float64_tget_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 load_serializable_pre () throw (ShogunException)

virtual void load_serializable_post () throw (ShogunException)

virtual void save_serializable_pre () throw (ShogunException)

virtual void save_serializable_post () throw (ShogunException)

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

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

bool clone_parameters (CSGObject *other)

void observe (const ObservedValue value)

void register_observable_param (const std::string &name, const SG_OBS_VALUE_TYPE type, const std::string &description)

## Protected Attributes

SGMatrix< float64_tm_inducing_features

float64_t m_log_ind_noise

SGMatrix< float64_tm_kuu

SGMatrix< float64_tm_ktru

SGMatrix< float64_tm_Sigma

SGVector< float64_tm_mu

SGVector< float64_tm_ktrtr_diag

Minimizerm_minimizer

CKernelm_kernel

CMeanFunctionm_mean

CLikelihoodModelm_model

CFeaturesm_features

CLabelsm_labels

SGVector< float64_tm_alpha

SGMatrix< float64_tm_L

float64_t m_log_scale

SGMatrix< float64_tm_ktrtr

SGMatrix< float64_tm_E

## ◆ SGObservable

 inherited

Definition at line 130 of file SGObject.h.

## ◆ SGSubject

 inherited

Definition at line 127 of file SGObject.h.

## ◆ SGSubscriber

 typedef rxcpp::subscriber< ObservedValue, rxcpp::observer > SGSubscriber
inherited

Definition at line 133 of file SGObject.h.

## ◆ CSparseInference() [1/2]

 CSparseInference ( )

default constructor

Definition at line 43 of file SparseInference.cpp.

## ◆ CSparseInference() [2/2]

 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()

 ~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 635 of file SGObject.cpp.

## ◆ check_features()

 void check_features ( )
protectedvirtual

check whether features and inducing features are set

Definition at line 48 of file SparseInference.cpp.

## ◆ check_members()

 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.

## ◆ clone()

 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 734 of file SGObject.cpp.

## ◆ clone_parameters()

 bool clone_parameters ( CSGObject * other )
protectedinherited

Definition at line 759 of file SGObject.cpp.

protectedvirtualinherited

Definition at line 270 of file Inference.cpp.

## ◆ convert_features()

 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. 1. The type of the gradient wrt inducing features is float64_t, which is used to update inducing features 2. Reason 1 implies that the type of inducing features can be float64_t while the type of features does not required as float64_t 3. 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.

## ◆ deep_copy()

 CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 232 of file SGObject.cpp.

## ◆ equals()

 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 656 of file SGObject.cpp.

## ◆ get() [1/2]

 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 381 of file SGObject.h.

## ◆ get() [2/2]

 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 404 of file SGObject.h.

## ◆ get_alpha()

 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.

## ◆ get_cholesky()

 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.

## ◆ get_derivative_wrt_inducing_features()

 virtual SGVector 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

## ◆ get_derivative_wrt_inducing_noise()

 virtual SGVector 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

## ◆ get_derivative_wrt_inference_method()

 virtual SGVector 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.

## ◆ get_derivative_wrt_kernel()

 virtual SGVector 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.

## ◆ get_derivative_wrt_likelihood_model()

 virtual SGVector 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.

## ◆ get_derivative_wrt_mean()

 virtual SGVector 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.

## ◆ get_diagonal_vector()

 virtual SGVector get_diagonal_vector ( )
pure virtualinherited

get diagonal vector

Returns
diagonal of matrix used to calculate posterior covariance matrix

## ◆ get_features()

 virtual CFeatures* get_features ( )
virtualinherited

get features

Returns
features

Definition at line 266 of file Inference.h.

## ◆ get_global_io()

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 269 of file SGObject.cpp.

## ◆ get_global_parallel()

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 311 of file SGObject.cpp.

## ◆ get_global_version()

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 324 of file SGObject.cpp.

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

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.

## ◆ get_inducing_features()

 virtual CFeatures* get_inducing_features ( )
virtual

get inducing features

Returns
features

Definition at line 121 of file SparseInference.h.

## ◆ get_inducing_noise()

 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.

## ◆ get_inference_type()

 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.

## ◆ get_kernel()

 virtual CKernel* get_kernel ( )
virtualinherited

get kernel

Returns
kernel

Definition at line 283 of file Inference.h.

## ◆ get_labels()

 virtual CLabels* get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 317 of file Inference.h.

## ◆ get_marginal_likelihood_estimate()

 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 127 of file Inference.cpp.

## ◆ get_mean()

 virtual CMeanFunction* get_mean ( )
virtualinherited

get mean

Returns
mean

Definition at line 300 of file Inference.h.

## ◆ get_model()

 CLikelihoodModel* get_model ( )
inherited

get likelihood model

Returns
likelihood

Definition at line 334 of file Inference.h.

## ◆ get_modelsel_names()

 SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 536 of file SGObject.cpp.

## ◆ get_modsel_param_descr()

 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 560 of file SGObject.cpp.

## ◆ get_modsel_param_index()

 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 573 of file SGObject.cpp.

## ◆ get_multiclass_E()

 SGMatrix< float64_t > get_multiclass_E ( )
virtualinherited

get the E matrix used for multi classification

Returns
the matrix for multi classification

Definition at line 59 of file Inference.cpp.

## ◆ get_name()

 virtual const char* get_name ( ) const
virtual

returns the name of the inference method

Returns
name SparseBase

Implements CSGObject.

Definition at line 102 of file SparseInference.h.

## ◆ get_negative_log_marginal_likelihood()

 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.

## ◆ get_negative_log_marginal_likelihood_derivatives()

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

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 186 of file Inference.cpp.

## ◆ get_parameters_observable()

 SGObservable* get_parameters_observable ( )
inherited

Get parameters observable

Returns
RxCpp observable

Definition at line 415 of file SGObject.h.

## ◆ get_posterior_covariance()

 virtual SGMatrix 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.

## ◆ get_posterior_mean()

 virtual SGVector 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.

## ◆ get_scale()

 float64_t get_scale ( ) const
virtualinherited

get kernel scale

Returns
kernel scale

Definition at line 48 of file Inference.cpp.

## ◆ get_value()

 virtual SGVector get_value ( )
virtualinherited

get the function value

Returns
vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 255 of file Inference.h.

## ◆ has() [1/3]

 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 304 of file SGObject.h.

## ◆ has() [2/3]

 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 315 of file SGObject.h.

## ◆ has() [3/3]

 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 326 of file SGObject.h.

## ◆ is_generic()

 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 330 of file SGObject.cpp.

## ◆ list_observable_parameters()

 void list_observable_parameters ( )
inherited

Print to stdout a list of observable parameters

Definition at line 878 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 403 of file SGObject.cpp.

 void load_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

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

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 460 of file SGObject.cpp.

 void load_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

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

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 455 of file SGObject.cpp.

## ◆ observe()

 void observe ( const ObservedValue value )
protectedinherited

Observe a parameter value and emit them to observer.

Parameters
 value Observed parameter's value

Definition at line 828 of file SGObject.cpp.

## ◆ parameter_hash_changed()

 bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 296 of file SGObject.cpp.

## ◆ print_modsel_params()

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 512 of file SGObject.cpp.

## ◆ print_serializable()

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

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 342 of file SGObject.cpp.

## ◆ ref()

 int32_t ref ( )
inherited

increase reference counter

Returns
reference count

Definition at line 186 of file SGObject.cpp.

## ◆ ref_count()

 int32_t ref_count ( )
inherited

display reference counter

Returns
reference count

Definition at line 193 of file SGObject.cpp.

## ◆ register_minimizer()

 void register_minimizer ( Minimizer * minimizer )
virtualinherited

Set a minimizer

Parameters
 minimizer minimizer used in inference method

Definition at line 116 of file Inference.cpp.

## ◆ register_observable_param()

 void register_observable_param ( const std::string & name, const SG_OBS_VALUE_TYPE type, const std::string & description )
protectedinherited

Register which params this object can emit.

Parameters
 name the param name type the param type description a user oriented description

Definition at line 871 of file SGObject.cpp.

## ◆ register_param() [1/2]

 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 472 of file SGObject.h.

## ◆ register_param() [2/2]

 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 485 of file SGObject.h.

## ◆ save_serializable()

 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 348 of file SGObject.cpp.

## ◆ save_serializable_post()

 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 470 of file SGObject.cpp.

## ◆ save_serializable_pre()

 void save_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

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

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 465 of file SGObject.cpp.

## ◆ set() [1/2]

 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 342 of file SGObject.h.

## ◆ set() [2/2]

 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 368 of file SGObject.h.

## ◆ set_features()

 virtual void set_features ( CFeatures * feat )
virtualinherited

set features

Parameters
 feat features to set

Definition at line 272 of file Inference.h.

## ◆ set_generic() [1/16]

 void set_generic ( )
inherited

Definition at line 73 of file SGObject.cpp.

## ◆ set_generic() [2/16]

 void set_generic ( )
inherited

Definition at line 78 of file SGObject.cpp.

## ◆ set_generic() [3/16]

 void set_generic ( )
inherited

Definition at line 83 of file SGObject.cpp.

## ◆ set_generic() [4/16]

 void set_generic ( )
inherited

Definition at line 88 of file SGObject.cpp.

## ◆ set_generic() [5/16]

 void set_generic ( )
inherited

Definition at line 93 of file SGObject.cpp.

## ◆ set_generic() [6/16]

 void set_generic ( )
inherited

Definition at line 98 of file SGObject.cpp.

## ◆ set_generic() [7/16]

 void set_generic ( )
inherited

Definition at line 103 of file SGObject.cpp.

## ◆ set_generic() [8/16]

 void set_generic ( )
inherited

Definition at line 108 of file SGObject.cpp.

## ◆ set_generic() [9/16]

 void set_generic ( )
inherited

Definition at line 113 of file SGObject.cpp.

## ◆ set_generic() [10/16]

 void set_generic ( )
inherited

Definition at line 118 of file SGObject.cpp.

## ◆ set_generic() [11/16]

 void set_generic ( )
inherited

Definition at line 123 of file SGObject.cpp.

## ◆ set_generic() [12/16]

 void set_generic ( )
inherited

Definition at line 128 of file SGObject.cpp.

## ◆ set_generic() [13/16]

 void set_generic ( )
inherited

Definition at line 133 of file SGObject.cpp.

## ◆ set_generic() [14/16]

 void set_generic ( )
inherited

Definition at line 138 of file SGObject.cpp.

## ◆ set_generic() [15/16]

 void set_generic ( )
inherited

Definition at line 143 of file SGObject.cpp.

## ◆ set_generic() [16/16]

 void set_generic ( )
inherited

set generic type to T

## ◆ set_global_io()

 void set_global_io ( SGIO * io )
inherited

set the io object

Parameters
 io io object to use

Definition at line 262 of file SGObject.cpp.

## ◆ set_global_parallel()

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 275 of file SGObject.cpp.

## ◆ set_global_version()

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 317 of file SGObject.cpp.

## ◆ set_inducing_features()

 virtual void set_inducing_features ( CFeatures * feat )
virtual

set inducing features

Parameters
 feat features to set

Definition at line 108 of file SparseInference.h.

## ◆ set_inducing_noise()

 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.

## ◆ set_kernel()

 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.

## ◆ set_labels()

 virtual void set_labels ( CLabels * lab )
virtualinherited

set labels

Parameters
 lab label to set

Definition at line 323 of file Inference.h.

## ◆ set_mean()

 virtual void set_mean ( CMeanFunction * m )
virtualinherited

set mean

Parameters
 m mean function to set

Definition at line 306 of file Inference.h.

## ◆ set_model()

 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.

## ◆ set_scale()

 void set_scale ( float64_t scale )
virtualinherited

set kernel scale

Parameters
 scale scale to be set

Definition at line 53 of file Inference.cpp.

## ◆ shallow_copy()

 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 226 of file SGObject.cpp.

## ◆ subscribe_to_parameters()

 void subscribe_to_parameters ( ParameterObserverInterface * obs )
inherited

Subscribe a parameter observer to watch over params

Definition at line 811 of file SGObject.cpp.

## ◆ supports_binary()

 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.

## ◆ supports_multiclass()

 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.

## ◆ supports_regression()

 virtual bool supports_regression ( ) const
virtualinherited

whether combination of inference method and given likelihood function supports regression

Returns
false

Definition at line 364 of file Inference.h.

## ◆ unref()

 int32_t unref ( )
inherited

decrement reference counter and deallocate object if refcount is zero before or after decrementing it

Returns
reference count

Definition at line 200 of file SGObject.cpp.

## ◆ unset_generic()

 void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 337 of file SGObject.cpp.

## ◆ update()

 virtual void update ( )
pure virtual

update all matrices

Reimplemented from CInference.

Implemented in CFITCInferenceMethod, CVarDTCInferenceMethod, and CSingleFITCLaplaceInferenceMethod.

## ◆ update_alpha()

 virtual void update_alpha ( )
protectedpure virtualinherited

## ◆ update_chol()

 virtual void update_chol ( )
protectedpure virtualinherited

## ◆ update_deriv()

 virtual void update_deriv ( )
protectedpure virtualinherited

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

## ◆ update_parameter_hash()

 void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 282 of file SGObject.cpp.

## ◆ update_train_kernel()

 void update_train_kernel ( )
protectedvirtual

update train kernel matrix

Reimplemented from CInference.

Definition at line 153 of file SparseInference.cpp.

## ◆ io

 SGIO* io
inherited

io

Definition at line 600 of file SGObject.h.

## ◆ m_alpha

 SGVector m_alpha
protectedinherited

alpha vector used in process mean calculation

Definition at line 479 of file Inference.h.

## ◆ m_E

 SGMatrix m_E
protectedinherited

the matrix used for multi classification

Definition at line 491 of file Inference.h.

## ◆ m_features

 CFeatures* m_features
protectedinherited

features to use

Definition at line 473 of file Inference.h.

inherited

parameters wrt which we can compute gradients

Definition at line 615 of file SGObject.h.

protectedinherited

Definition at line 494 of file Inference.h.

## ◆ m_hash

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 618 of file SGObject.h.

## ◆ m_inducing_features

 SGMatrix m_inducing_features
protected

inducing features for approximation

Definition at line 304 of file SparseInference.h.

## ◆ m_kernel

 CKernel* m_kernel
protectedinherited

covariance function

Definition at line 464 of file Inference.h.

## ◆ m_ktrtr

 SGMatrix m_ktrtr
protectedinherited

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

Definition at line 488 of file Inference.h.

## ◆ m_ktrtr_diag

 SGVector m_ktrtr_diag
protected

diagonal elements of kernel matrix m_ktrtr

Definition at line 322 of file SparseInference.h.

## ◆ m_ktru

 SGMatrix m_ktru
protected

covariance matrix of inducing features and training features

Definition at line 313 of file SparseInference.h.

## ◆ m_kuu

 SGMatrix m_kuu
protected

covariance matrix of inducing features

Definition at line 310 of file SparseInference.h.

## ◆ m_L

 SGMatrix m_L
protectedinherited

upper triangular factor of Cholesky decomposition

Definition at line 482 of file Inference.h.

## ◆ m_labels

 CLabels* m_labels
protectedinherited

labels of features

Definition at line 476 of file Inference.h.

## ◆ m_log_ind_noise

 float64_t m_log_ind_noise
protected

noise of the inducing variables

Definition at line 307 of file SparseInference.h.

## ◆ m_log_scale

 float64_t m_log_scale
protectedinherited

kernel scale

Definition at line 485 of file Inference.h.

## ◆ m_mean

 CMeanFunction* m_mean
protectedinherited

mean function

Definition at line 467 of file Inference.h.

## ◆ m_minimizer

 Minimizer* m_minimizer
protectedinherited

minimizer

Definition at line 461 of file Inference.h.

## ◆ m_model

 CLikelihoodModel* m_model
protectedinherited

likelihood function to use

Definition at line 470 of file Inference.h.

## ◆ m_model_selection_parameters

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 612 of file SGObject.h.

## ◆ m_mu

 SGVector m_mu
protected

mean vector of the the posterior Gaussian distribution

Definition at line 319 of file SparseInference.h.

## ◆ m_parameters

 Parameter* m_parameters
inherited

parameters

Definition at line 609 of file SGObject.h.

## ◆ m_Sigma

 SGMatrix m_Sigma
protected

covariance matrix of the the posterior Gaussian distribution

Definition at line 316 of file SparseInference.h.

## ◆ parallel

 Parallel* parallel
inherited

parallel

Definition at line 603 of file SGObject.h.

## ◆ version

 Version* version
inherited

version

Definition at line 606 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation