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CEPInferenceMethod Class Reference

Detailed Description

Class of the Expectation Propagation (EP) posterior approximation inference method.

For more details, see: Minka, T. P. (2001). A Family of Algorithms for Approximate Bayesian Inference. PhD thesis, Massachusetts Institute of Technology

Definition at line 52 of file EPInferenceMethod.h.

Inheritance diagram for CEPInferenceMethod:
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Public Member Functions

 CEPInferenceMethod ()
 
 CEPInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
 
virtual ~CEPInferenceMethod ()
 
virtual EInferenceType get_inference_type () const
 
virtual const char * get_name () const
 
virtual float64_t get_negative_log_marginal_likelihood ()
 
virtual SGVector< float64_tget_alpha ()
 
virtual SGMatrix< float64_tget_cholesky ()
 
virtual SGVector< float64_tget_diagonal_vector ()
 
virtual SGVector< float64_tget_posterior_mean ()
 
virtual SGMatrix< float64_tget_posterior_covariance ()
 
virtual float64_t get_tolerance () const
 
virtual void set_tolerance (const float64_t tol)
 
virtual uint32_t get_min_sweep () const
 
virtual void set_min_sweep (const uint32_t min_sweep)
 
virtual uint32_t get_max_sweep () const
 
virtual void set_max_sweep (const uint32_t max_sweep)
 
virtual bool supports_binary () const
 
virtual void update ()
 
virtual void register_minimizer (Minimizer *minimizer)
 
void set_fail_on_non_convergence (bool fail_on_non_convergence)
 
float64_t get_marginal_likelihood_estimate (int32_t num_importance_samples=1, float64_t ridge_size=1e-15)
 
virtual CMap< TParameter
*, SGVector< float64_t > > * 
get_negative_log_marginal_likelihood_derivatives (CMap< TParameter *, CSGObject * > *parameters)
 
virtual CMap< TParameter
*, SGVector< float64_t > > * 
get_gradient (CMap< TParameter *, CSGObject * > *parameters)
 
virtual SGVector< float64_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_multiclass () const
 
virtual SGMatrix< float64_tget_multiclass_E ()
 
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
 
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 ()
 

Static Public Member Functions

static CEPInferenceMethodobtain_from_generic (CInference *inference)
 

Public Attributes

SGIOio
 
Parallelparallel
 
Versionversion
 
Parameterm_parameters
 
Parameterm_model_selection_parameters
 
Parameterm_gradient_parameters
 
uint32_t m_hash
 

Protected Member Functions

virtual void compute_gradient ()
 
virtual void update_alpha ()
 
virtual void update_chol ()
 
virtual void update_approx_cov ()
 
virtual void update_approx_mean ()
 
virtual void update_negative_ml ()
 
virtual void update_deriv ()
 
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 void check_members () const
 
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)
 

Static Protected Member Functions

static void * get_derivative_helper (void *p)
 

Protected Attributes

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
 
bool m_gradient_update
 

Constructor & Destructor Documentation

default constructor

Definition at line 62 of file EPInferenceMethod.cpp.

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

constructor

Parameters
kernelcovariance function
featuresfeatures to use in inference
meanmean function
labelslabels of the features
modellikelihood model to use

Definition at line 67 of file EPInferenceMethod.cpp.

~CEPInferenceMethod ( )
virtual

Definition at line 74 of file EPInferenceMethod.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
dictdictionary of parameters to be built.

Definition at line 630 of file SGObject.cpp.

void check_members ( ) const
protectedvirtualinherited

check if members of object are valid for inference

Reimplemented in CSparseInference, CMultiLaplaceInferenceMethod, CExactInferenceMethod, CFITCInferenceMethod, and CVarDTCInferenceMethod.

Definition at line 322 of file Inference.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 ( )
protectedvirtual

update gradients

Reimplemented from CInference.

Definition at line 150 of file EPInferenceMethod.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
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows 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
_tagname 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
namename 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

returns vector to compute posterior mean of Gaussian Process under EP approximation:

\[ \mathbb{E}_q[f_*|X,y,x_*] = k^T_*\alpha \]

where \(k^T_*\) - covariance between training points \(X\) and test point \(x_*\), and for EP approximation:

\[ \alpha = (K + \tilde{S}^{-1})^{-1}\tilde{S}^{-1}\tilde{\nu} = (I-\tilde{S}^{\frac{1}{2}}B^{-1}\tilde{S}^{\frac{1}{2}}K)\tilde{\nu} \]

where \(K\) is the prior covariance matrix, \(\tilde{S}^{\frac{1}{2}}\) is the diagonal matrix (see description of get_diagonal_vector() method) and \(\tilde{\nu}\) - natural parameter ( \(\tilde{\nu} = \tilde{S}\tilde{\mu}\)).

Returns
vector \(\alpha\)

Implements CInference.

Definition at line 112 of file EPInferenceMethod.cpp.

SGMatrix< float64_t > get_cholesky ( )
virtual

returns upper triangular factor \(L^T\) of the Cholesky decomposition ( \(LL^T\)) of the matrix:

\[ B = (\tilde{S}^{\frac{1}{2}}K\tilde{S}^{\frac{1}{2}}+I) \]

where \(\tilde{S}^{\frac{1}{2}}\) is the diagonal matrix (see description of get_diagonal_vector() method) and \(K\) is the prior covariance matrix.

Returns
upper triangular factor of the Cholesky decomposition of the matrix \(B\)

Implements CInference.

Definition at line 120 of file EPInferenceMethod.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.

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
paramparameter of CInference class
Returns
derivative of negative log marginal likelihood

Implements CInference.

Definition at line 474 of file EPInferenceMethod.cpp.

SGVector< float64_t > get_derivative_wrt_kernel ( const TParameter param)
protectedvirtual

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

Parameters
paramparameter of given kernel
Returns
derivative of negative log marginal likelihood

Implements CInference.

Definition at line 500 of file EPInferenceMethod.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
paramparameter of given likelihood model
Returns
derivative of negative log marginal likelihood

Implements CInference.

Definition at line 493 of file EPInferenceMethod.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
paramparameter of given mean function
Returns
derivative of negative log marginal likelihood

Implements CInference.

Definition at line 530 of file EPInferenceMethod.cpp.

SGVector< float64_t > get_diagonal_vector ( )
virtual

returns diagonal vector of the diagonal matrix:

\[ \tilde{S}^{\frac{1}{2}} = \sqrt{\tilde{S}} \]

where \(\tilde{S} = \text{diag}(\tilde{\tau})\), and \(\tilde{\tau}\)

  • natural parameter ( \(\tilde{\tau}_i = \tilde{\sigma}_i^{-2}\)).
Returns
diagonal vector of the matrix \(\tilde{S}^{\frac{1}{2}}\)

Implements CInference.

Definition at line 128 of file EPInferenceMethod.cpp.

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
parametersparameter'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 EInferenceType get_inference_type ( ) const
virtual

return what type of inference we are

Returns
inference type EP

Reimplemented from CInference.

Definition at line 75 of file EPInferenceMethod.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_samplesthe number of importance samples \(n\) from \( q(f|y, \theta) \).
ridge_sizescalar 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 uint32_t get_max_sweep ( ) const
virtual

returns maximum number of sweeps over all variables

Returns
maximum number of sweeps

Definition at line 227 of file EPInferenceMethod.h.

virtual CMeanFunction* get_mean ( )
virtualinherited

get mean

Returns
mean

Definition at line 300 of file Inference.h.

virtual uint32_t get_min_sweep ( ) const
virtual

returns minimum number of sweeps over all variables

Returns
minimum number of sweeps

Definition at line 215 of file EPInferenceMethod.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_namename 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_namename 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 EP

Implements CSGObject.

Definition at line 81 of file EPInferenceMethod.h.

float64_t get_negative_log_marginal_likelihood ( )
virtual

returns the negative logarithm of the marginal likelihood function:

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

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

Returns
negative log marginal likelihood

Implements CInference.

Definition at line 104 of file EPInferenceMethod.cpp.

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

get log marginal likelihood gradient

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

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

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

Definition at line 198 of file Inference.cpp.

SGMatrix< float64_t > get_posterior_covariance ( )
virtual

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

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

Covariance matrix \(\Sigma\) is evaluated using matrix inversion lemma:

\[ \Sigma = (K^{-1}+\tilde{S})^{-1} = K - K\tilde{S}^{\frac{1}{2}}B^{-1}\tilde{S}^{\frac{1}{2}}K \]

where \(B=(\tilde{S}^{\frac{1}{2}}K\tilde{S}^{\frac{1}{2}}+I)\).

Returns
covariance matrix \(\Sigma\)

Implements CInference.

Definition at line 143 of file EPInferenceMethod.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:

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

Mean vector \(\mu\) is evaluated like:

\[ \mu = \Sigma\tilde{\nu} \]

where \(\Sigma\) - covariance matrix of the posterior approximation and \(\tilde{\nu}\) - natural parameter ( \(\tilde{\nu} = \tilde{S}\tilde{\mu}\)).

Returns
mean vector \(\mu\)

Implements CInference.

Definition at line 136 of file EPInferenceMethod.cpp.

float64_t get_scale ( ) const
virtualinherited

get kernel scale

Returns
kernel scale

Definition at line 60 of file Inference.cpp.

virtual float64_t get_tolerance ( ) const
virtual

returns tolerance of the EP approximation

Returns
tolerance

Definition at line 203 of file EPInferenceMethod.h.

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
namename 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
tagtag 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
namename 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
genericset 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
filewhere to load from
prefixprefix 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
ShogunExceptionwill 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
ShogunExceptionwill 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.

CEPInferenceMethod * obtain_from_generic ( CInference inference)
static

helper method used to specialize a base class instance

Parameters
inferenceinference method
Returns
casted CEPInferenceMethod object

Definition at line 91 of file EPInferenceMethod.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
prefixprefix for members

Definition at line 341 of file SGObject.cpp.

void register_minimizer ( Minimizer minimizer)
virtual

Set a minimizer

Parameters
minimizerminimizer used in inference method

Reimplemented from CInference.

Definition at line 78 of file EPInferenceMethod.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
_tagname and type information of parameter
valuevalue 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
namename of the parameter
valuevalue 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
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix 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
ShogunExceptionwill 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
ShogunExceptionwill 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
_tagname and type information of parameter
valuevalue 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
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 354 of file SGObject.h.

void set_fail_on_non_convergence ( bool  fail_on_non_convergence)

Specify behavious when EP does not converge: failure or warning

Parameters
fail_on_non_convergenceIf True, throws error, otherwise prints warning

Definition at line 257 of file EPInferenceMethod.h.

virtual void set_features ( CFeatures feat)
virtualinherited

set features

Parameters
featfeatures 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
ioio object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 316 of file SGObject.cpp.

virtual void set_kernel ( CKernel kern)
virtualinherited

set kernel

Parameters
kernkernel to set

Reimplemented in CSingleSparseInference.

Definition at line 289 of file Inference.h.

virtual void set_labels ( CLabels lab)
virtualinherited

set labels

Parameters
lablabel to set

Definition at line 323 of file Inference.h.

virtual void set_max_sweep ( const uint32_t  max_sweep)
virtual

sets maximum number of sweeps over all variables

Parameters
max_sweepmaximum number of sweeps to set

Definition at line 233 of file EPInferenceMethod.h.

virtual void set_mean ( CMeanFunction m)
virtualinherited

set mean

Parameters
mmean function to set

Definition at line 306 of file Inference.h.

virtual void set_min_sweep ( const uint32_t  min_sweep)
virtual

sets minimum number of sweeps over all variables

Parameters
min_sweepminimum number of sweeps to set

Definition at line 221 of file EPInferenceMethod.h.

virtual void set_model ( CLikelihoodModel mod)
virtualinherited

set likelihood model

Parameters
modmodel 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
scalescale to be set

Definition at line 65 of file Inference.cpp.

virtual void set_tolerance ( const float64_t  tol)
virtual

sets tolerance of the EP approximation

Parameters
toltolerance to set

Definition at line 209 of file EPInferenceMethod.h.

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
virtual
Returns
whether combination of Laplace approximation inference method and given likelihood function supports binary classification

Reimplemented from CInference.

Definition at line 239 of file EPInferenceMethod.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.

void update ( )
virtual

update all matrices Expect gradients

Reimplemented from CInference.

Definition at line 163 of file EPInferenceMethod.cpp.

void update_alpha ( )
protectedvirtual

update alpha matrix

Implements CInference.

Definition at line 318 of file EPInferenceMethod.cpp.

void update_approx_cov ( )
protectedvirtual

update covariance matrix of the approximation to the posterior

Definition at line 361 of file EPInferenceMethod.cpp.

void update_approx_mean ( )
protectedvirtual

update mean vector of the approximation to the posterior

Definition at line 384 of file EPInferenceMethod.cpp.

void update_chol ( )
protectedvirtual

update Cholesky matrix

Implements CInference.

Definition at line 341 of file EPInferenceMethod.cpp.

void update_deriv ( )
protectedvirtual

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

Implements CInference.

Definition at line 454 of file EPInferenceMethod.cpp.

void update_negative_ml ( )
protectedvirtual

update negative marginal likelihood

Definition at line 398 of file EPInferenceMethod.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

void update_train_kernel ( )
protectedvirtualinherited

update train kernel matrix

Reimplemented in CSparseInference.

Definition at line 337 of file Inference.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.

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.

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_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.

Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.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:

SHOGUN Machine Learning Toolbox - Documentation