SHOGUN  6.1.3
CKLInference Class Referenceabstract

## Detailed Description

The KL approximation inference method class.

This inference method approximates the posterior likelihood function by using KL method. Here, we compute a Gaussian approximation to the posterior via minimizing the KL divergence between variational Gaussian distribution and posterior distribution.

Code adapted from http://hannes.nickisch.org/code/approxXX.tar.gz and Gaussian Process Machine Learning Toolbox http://www.gaussianprocess.org/gpml/code/matlab/doc/ and the reference paper is Nickisch, Hannes, and Carl Edward Rasmussen. "Approximations for Binary Gaussian Process Classification." Journal of Machine Learning Research 9.10 (2008).

Definition at line 75 of file KLInference.h.

Inheritance diagram for CKLInference:
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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

CKLInference ()

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

virtual ~CKLInference ()

virtual EInferenceType get_inference_type () const

virtual const char * get_name () const

virtual float64_t get_negative_log_marginal_likelihood ()

virtual SGVector< float64_tget_posterior_mean ()

virtual SGMatrix< float64_tget_posterior_covariance ()

virtual bool supports_regression () const

virtual bool supports_binary () const

virtual void set_model (CLikelihoodModel *mod)

virtual void update ()

virtual SGMatrix< float64_tget_cholesky ()

virtual void set_noise_factor (float64_t noise_factor)

virtual void set_max_attempt (index_t max_attempt)

virtual void set_exp_factor (float64_t exp_factor)

virtual void set_min_coeff_kernel (float64_t min_coeff_kernel)

virtual void register_minimizer (Minimizer *minimizer)

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_alpha ()=0

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 float64_t get_scale () const

virtual void set_scale (float64_t scale)

virtual bool supports_multiclass () const

virtual SGMatrix< float64_tget_multiclass_E ()

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 update_init ()

virtual Eigen::LDLT< Eigen::MatrixXd, 0x1 > update_init_helper ()

virtual CVariationalGaussianLikelihoodget_variational_likelihood () const

virtual void check_variational_likelihood (CLikelihoodModel *mod) const

virtual void update_approx_cov ()=0

virtual float64_t get_derivative_related_cov (SGMatrix< float64_t > dK)=0

virtual float64_t optimization ()

virtual SGVector< float64_tget_derivative_wrt_inference_method (const TParameter *param)

virtual SGVector< float64_tget_derivative_wrt_likelihood_model (const TParameter *param)

virtual SGVector< float64_tget_derivative_wrt_kernel (const TParameter *param)

virtual SGVector< float64_tget_derivative_wrt_mean (const TParameter *param)

virtual float64_t get_negative_log_marginal_likelihood_helper ()=0

virtual float64_t get_nlml_wrt_parameters ()

virtual bool precompute ()=0

virtual void check_members () const

virtual void update_alpha ()=0

virtual void update_chol ()=0

virtual void update_deriv ()=0

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)

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

float64_t m_min_coeff_kernel

float64_t m_noise_factor

float64_t m_exp_factor

index_t m_max_attempt

SGVector< float64_tm_mu

SGMatrix< float64_tm_Sigma

SGVector< float64_tm_s2

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

## Friends

class KLInferenceCostFunction

## Member Typedef Documentation

 inherited

Definition at line 130 of file SGObject.h.

 inherited

Definition at line 127 of file SGObject.h.

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

Definition at line 133 of file SGObject.h.

## Constructor & Destructor Documentation

 CKLInference ( )

default constructor

Definition at line 110 of file KLInference.cpp.

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

constructor

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

Definition at line 115 of file KLInference.cpp.

 ~CKLInference ( )
virtual

Definition at line 169 of file KLInference.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.

 void check_members ( ) const
protectedvirtualinherited

check if members of object are valid for inference

Definition at line 249 of file Inference.cpp.

 void check_variational_likelihood ( CLikelihoodModel * mod ) const
protectedvirtual

check the provided likelihood model supports variational inference

Parameters
 mod the provided likelihood model
Returns
whether the provided likelihood model supports variational inference or not

Definition at line 123 of file KLInference.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 734 of file SGObject.cpp.

 bool clone_parameters ( CSGObject * other )
protectedinherited

Definition at line 759 of file SGObject.cpp.

protectedvirtual

Reimplemented from CInference.

Definition at line 173 of file KLInference.cpp.

 CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 232 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 656 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 381 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 404 of file SGObject.h.

 virtual SGVector get_alpha ( )
pure virtualinherited

get alpha vector

Returns
vector to compute posterior mean of Gaussian Process:

$\mu = K\alpha+meanf$

where $$\mu$$ is the mean, $$K$$ is the prior covariance matrix, and $$meanf$$ is the mean prior fomr MeanFunction

 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.

Note that in some sub class L is not the Cholesky decomposition In this case, L will still be used to compute required matrix for prediction see CGaussianProcessMachine::get_posterior_variances()

Implements CInference.

Definition at line 412 of file KLInference.cpp.

 virtual float64_t get_derivative_related_cov ( SGMatrix< float64_t > dK )
protectedpure virtual

compute matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter in cov function Note that get_derivative_wrt_inference_method(const TParameter* param) and get_derivative_wrt_kernel(const TParameter* param) will call this function

Parameters
 dK the gradient wrt hyperparameter related to cov

Implemented in CKLDualInferenceMethod, CKLCovarianceInferenceMethod, and CKLLowerTriangularInference.

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

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.

Definition at line 372 of file KLInference.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 CInference.

Definition at line 388 of file KLInference.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 CInference.

Definition at line 297 of file KLInference.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 CInference.

Definition at line 313 of file KLInference.cpp.

 virtual SGVector get_diagonal_vector ( )
pure virtualinherited

get diagonal vector

Returns
diagonal of matrix used to calculate posterior covariance matrix
 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 269 of file SGObject.cpp.

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 311 of file SGObject.cpp.

 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.

protectedpure virtual

compute the gradient wrt variational parameters given the current variational parameters (mu and s2)

Returns
gradient of negative log marginal likelihood
 virtual EInferenceType get_inference_type ( ) const
virtual

return what type of inference we are

Reimplemented from CInference.

Definition at line 97 of file KLInference.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 127 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 536 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 560 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 573 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 59 of file Inference.cpp.

 virtual const char* get_name ( ) const
virtual

returns the name of the inference method

Returns
name KLInference

Implements CSGObject.

Definition at line 103 of file KLInference.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 CInference.

Definition at line 289 of file KLInference.cpp.

 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.

 virtual float64_t get_negative_log_marginal_likelihood_helper ( )
protectedpure virtual

the helper function to compute the negative log marginal likelihood

Returns
negative log marginal likelihood
 float64_t get_nlml_wrt_parameters ( )
protectedvirtual

compute the negative log marginal likelihood given the current variational parameters (mu and s2)

Returns
negative log marginal likelihood

Definition at line 282 of file KLInference.cpp.

 SGObservable* get_parameters_observable ( )
inherited

Get parameters observable

Returns
RxCpp observable

Definition at line 415 of file SGObject.h.

 SGMatrix< float64_t > get_posterior_covariance ( )
virtual

returns covariance matrix $$\Sigma=(K^{-1}+W)^{-1}$$ of the Gaussian distribution $$\mathcal{N}(\mu,\Sigma)$$, which is an approximation to the posterior:

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

Covariance matrix is evaluated using matrix inversion lemma:

$(K^{-1}+W)^{-1} = K - KW^{\frac{1}{2}}B^{-1}W^{\frac{1}{2}}K$

where $$B=(W^{frac{1}{2}}*K*W^{frac{1}{2}}+I)$$.

Returns
covariance matrix

Implements CInference.

Definition at line 268 of file KLInference.cpp.

 SGVector< float64_t > get_posterior_mean ( )
virtual

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

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

Returns
mean vector

Implements CInference.

Definition at line 261 of file KLInference.cpp.

 float64_t get_scale ( ) const
virtualinherited

get kernel scale

Returns
kernel scale

Definition at line 48 of file Inference.cpp.

 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.

 CVariationalGaussianLikelihood * get_variational_likelihood ( ) const
protectedvirtual

this method is used to dynamic-cast the likelihood model, m_model, to variational likelihood model.

Definition at line 275 of file KLInference.cpp.

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

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

Parameters
 name name of the parameter
Returns
true if the parameter exists with the input name

Definition at line 304 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 315 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 326 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 330 of file SGObject.cpp.

 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.

 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.

 float64_t optimization ( )
protectedvirtual

Using an optimizer to estimate posterior parameters

Reimplemented in CKLDualInferenceMethod.

Definition at line 342 of file KLInference.cpp.

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

Definition at line 296 of file SGObject.cpp.

 virtual bool precompute ( )
protectedpure virtual

pre-compute the information for optimization. This function needs to be called before calling get_negative_log_marginal_likelihood_wrt_parameters() and/or get_gradient_of_nlml_wrt_parameters(SGVector<float64_t> gradient)

Returns
true if precomputed parameters are valid
 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 512 of file SGObject.cpp.

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

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 342 of file SGObject.cpp.

 int32_t ref ( )
inherited

increase reference counter

Returns
reference count

Definition at line 186 of file SGObject.cpp.

 int32_t ref_count ( )
inherited

display reference counter

Returns
reference count

Definition at line 193 of file SGObject.cpp.

 void register_minimizer ( Minimizer * minimizer )
virtual

Set a minimizer

Parameters
 minimizer minimizer used in inference method

Reimplemented from CInference.

Reimplemented in CKLDualInferenceMethod.

Definition at line 363 of file KLInference.cpp.

 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.

 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.

 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.

 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.

 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.

 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.

 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.

 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.

 void set_exp_factor ( float64_t exp_factor )
virtual

set exp factor to exponentially increase noise factor

Parameters
 exp_factor should be greater than 1.0 default value is 2

Definition at line 218 of file KLInference.cpp.

 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 73 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 78 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 83 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 88 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 93 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 98 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 103 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 108 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 113 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 118 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 123 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 128 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 133 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 138 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 143 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 262 of file SGObject.cpp.

 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.

 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.

 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.

 void set_max_attempt ( index_t max_attempt )
virtual

set max attempt to ensure Kernel matrix to be positive definite

Parameters
 max_attempt should be non-negative. 0 means infinity attempts default value is 0

Definition at line 212 of file KLInference.cpp.

 virtual void set_mean ( CMeanFunction * m )
virtualinherited

set mean

Parameters
 m mean function to set

Definition at line 306 of file Inference.h.

 void set_min_coeff_kernel ( float64_t min_coeff_kernel )
virtual

set minimum coeefficient of kernel matrix used in LDLT factorization

Parameters
 min_coeff_kernel should be non-negative default value is 1e-5

Definition at line 206 of file KLInference.cpp.

 void set_model ( CLikelihoodModel * mod )
virtual

set variational likelihood model

Parameters
 mod model to set

Reimplemented from CInference.

Reimplemented in CKLDualInferenceMethod.

Definition at line 133 of file KLInference.cpp.

 void set_noise_factor ( float64_t noise_factor )
virtual

set noise factor to ensure Kernel matrix to be positive definite by adding non-negative noise to diagonal elements of Kernel matrix

Parameters
 noise_factor should be non-negative default value is 1e-10

Definition at line 200 of file KLInference.cpp.

 void set_scale ( float64_t scale )
virtualinherited

set kernel scale

Parameters
 scale scale to be set

Definition at line 53 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 226 of file SGObject.cpp.

 void subscribe_to_parameters ( ParameterObserverInterface * obs )
inherited

Subscribe a parameter observer to watch over params

Definition at line 811 of file SGObject.cpp.

 virtual bool supports_binary ( ) const
virtual
Returns
whether combination of KL approximation inference method and given likelihood function supports binary classification

Reimplemented from CInference.

Definition at line 165 of file KLInference.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
virtual
Returns
whether combination of KL approximation inference method and given likelihood function supports regression

Reimplemented from CInference.

Definition at line 155 of file KLInference.h.

 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.

 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.

 void update ( )
virtual

Reimplemented from CInference.

Definition at line 186 of file KLInference.cpp.

 virtual void update_alpha ( )
protectedpure virtualinherited
 virtual void update_approx_cov ( )
protectedpure virtual

update covariance matrix of the approximation to the posterior

Implemented in CKLDualInferenceMethod, CKLCovarianceInferenceMethod, and CKLLowerTriangularInference.

 virtual void update_chol ( )
protectedpure virtualinherited
 virtual void update_deriv ( )
protectedpure virtualinherited

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

 void update_init ( )
protectedvirtual

correct the kernel matrix and factorizated the corrected Kernel matrix for update

Reimplemented in CKLLowerTriangularInference.

Definition at line 224 of file KLInference.cpp.

 Eigen::LDLT< Eigen::MatrixXd > update_init_helper ( )
protectedvirtual

a helper function used to correct the kernel matrix using LDLT factorization

Returns
the LDLT factorization of the corrected kernel matrix

Definition at line 229 of file KLInference.cpp.

 void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 282 of file SGObject.cpp.

 void update_train_kernel ( )
protectedvirtualinherited

update train kernel matrix

Reimplemented in CSparseInference.

Definition at line 264 of file Inference.cpp.

## Friends And Related Function Documentation

 friend class KLInferenceCostFunction
friend

Definition at line 77 of file KLInference.h.

## Member Data Documentation

 SGIO* io
inherited

io

Definition at line 600 of file SGObject.h.

 SGVector m_alpha
protectedinherited

alpha vector used in process mean calculation

Definition at line 479 of file Inference.h.

 SGMatrix m_E
protectedinherited

the matrix used for multi classification

Definition at line 491 of file Inference.h.

 float64_t m_exp_factor
protected

The factor used to exponentially increase noise_factor

Definition at line 247 of file KLInference.h.

 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.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 618 of file SGObject.h.

 CKernel* m_kernel
protectedinherited

covariance function

Definition at line 464 of file Inference.h.

 SGMatrix m_ktrtr
protectedinherited

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

Definition at line 488 of file Inference.h.

 SGMatrix m_L
protectedinherited

upper triangular factor of Cholesky decomposition

Definition at line 482 of file Inference.h.

 CLabels* m_labels
protectedinherited

labels of features

Definition at line 476 of file Inference.h.

 float64_t m_log_scale
protectedinherited

kernel scale

Definition at line 485 of file Inference.h.

 index_t m_max_attempt
protected

Max number of attempt to correct kernel matrix to be positive definite

Definition at line 250 of file KLInference.h.

 CMeanFunction* m_mean
protectedinherited

mean function

Definition at line 467 of file Inference.h.

 float64_t m_min_coeff_kernel
protected

The minimum coeefficient of kernel matrix in LDLT factorization used to check whether the kernel matrix is positive definite or not

Definition at line 241 of file KLInference.h.

 Minimizer* m_minimizer
protectedinherited

minimizer

Definition at line 461 of file Inference.h.

 CLikelihoodModel* m_model
protectedinherited

likelihood function to use

Definition at line 470 of file Inference.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 612 of file SGObject.h.

 SGVector m_mu
protected

mean vector of the approximation to the posterior Note that m_mu is also a variational parameter

Definition at line 367 of file KLInference.h.

 float64_t m_noise_factor
protected

The factor used to ensure kernel matrix to be positive definite

Definition at line 244 of file KLInference.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 609 of file SGObject.h.

 SGVector m_s2
protected

variational parameter sigma2 Note that sigma2 = diag(m_Sigma)

Definition at line 375 of file KLInference.h.

 SGMatrix m_Sigma
protected

covariance matrix of the approximation to the posterior

Definition at line 370 of file KLInference.h.

 Parallel* parallel
inherited

parallel

Definition at line 603 of file SGObject.h.

 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