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CVarDTCInferenceMethod Class Reference

Detailed Description

The inference method class based on the Titsias' variational bound. For more details, see Titsias, Michalis K. "Variational learning of inducing variables in sparse Gaussian processes." International Conference on Artificial Intelligence and Statistics. 2009.

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

Definition at line 52 of file VarDTCInferenceMethod.h.

Inheritance diagram for CVarDTCInferenceMethod:
[legend]

Public Member Functions

 CVarDTCInferenceMethod ()
 
 CVarDTCInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model, CFeatures *inducing_features)
 
virtual ~CVarDTCInferenceMethod ()
 
virtual const char * get_name () const
 
virtual EInferenceType get_inference_type () const
 
virtual float64_t get_negative_log_marginal_likelihood ()
 
virtual SGVector< float64_t > get_diagonal_vector ()
 
virtual bool supports_regression () const
 
virtual SGVector< float64_t > get_posterior_mean ()
 
virtual SGMatrix< float64_t > get_posterior_covariance ()
 
virtual void update ()
 
virtual void register_minimizer (Minimizer *minimizer)
 
virtual void set_kernel (CKernel *kern)
 
virtual void optimize_inducing_features ()
 
virtual void set_lower_bound_of_inducing_features (SGVector< float64_t > bound)
 
virtual void set_upper_bound_of_inducing_features (SGVector< float64_t > bound)
 
virtual void set_tolearance_for_inducing_features (float64_t tol)
 
virtual void set_max_iterations_for_inducing_features (int32_t it)
 
virtual void enable_optimizing_inducing_features (bool is_optmization, FirstOrderMinimizer *minimizer=NULL)
 
virtual void set_inducing_features (CFeatures *feat)
 
virtual CFeatures * get_inducing_features ()
 
virtual SGVector< float64_t > get_alpha ()
 
virtual SGMatrix< float64_t > get_cholesky ()
 
virtual void set_inducing_noise (float64_t noise)
 
virtual float64_t get_inducing_noise ()
 
float64_t get_marginal_likelihood_estimate (int32_t num_importance_samples=1, float64_t ridge_size=1e-15)
 
virtual CMap< TParameter
*, SGVector< float64_t > > * 		get_negative_log_marginal_likelihood_derivatives (CMap< TParameter *, CSGObject * > *parameters)
 
virtual CMap< TParameter
*, SGVector< float64_t > > * 		get_gradient (CMap< TParameter *, CSGObject * > *parameters)
 
virtual SGVector< float64_t > get_value ()
 
virtual CFeatures * get_features ()
 
virtual void set_features (CFeatures *feat)
 
virtual CKernel * get_kernel ()
 
virtual CMeanFunction * get_mean ()
 
virtual void set_mean (CMeanFunction *m)
 
virtual CLabels * get_labels ()
 
virtual void set_labels (CLabels *lab)
 
CLikelihoodModel * get_model ()
 
virtual void set_model (CLikelihoodModel *mod)
 
virtual float64_t get_scale () const
 
virtual void set_scale (float64_t scale)
 
virtual bool supports_binary () const
 
virtual bool supports_multiclass () const
 
virtual SGMatrix< float64_t > get_multiclass_E ()
 
virtual CSGObject * shallow_copy () const
 
virtual CSGObject * deep_copy () const
 
virtual bool is_generic (EPrimitiveType *generic) const
 
template<class T >
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
void unset_generic ()
 
virtual void print_serializable (const char *prefix="")
 
virtual bool save_serializable (CSerializableFile *file, const char *prefix="")
 
virtual bool load_serializable (CSerializableFile *file, const char *prefix="")
 
void set_global_io (SGIO *io)
 
SGIO * get_global_io ()
 
void set_global_parallel (Parallel *parallel)
 
Parallel * get_global_parallel ()
 
void set_global_version (Version *version)
 
Version * get_global_version ()
 
SGStringList< char > get_modelsel_names ()
 
void print_modsel_params ()
 
char * get_modsel_param_descr (const char *param_name)
 
index_t get_modsel_param_index (const char *param_name)
 
void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
 
bool has (const std::string &name) const
 
template<typename T >
bool has (const Tag< T > &tag) const
 
template<typename T , typename U = void>
bool has (const std::string &name) const
 
template<typename T >
void set (const Tag< T > &_tag, const T &value)
 
template<typename T , typename U = void>
void set (const std::string &name, const T &value)
 
template<typename T >
T get (const Tag< T > &_tag) const
 
template<typename T , typename U = void>
T get (const std::string &name) const
 
virtual void update_parameter_hash ()
 
virtual bool parameter_hash_changed ()
 
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
 
virtual CSGObject * clone ()
 

Static Public Member Functions

static CVarDTCInferenceMethod * obtain_from_generic (CInference *inference)
 

Public Attributes

SGIO * io
 
Parallel * parallel
 
Version * version
 
Parameter * m_parameters
 
Parameter * m_model_selection_parameters
 
Parameter * m_gradient_parameters
 
uint32_t m_hash
 

Protected Member Functions

virtual void check_members () const
 
virtual void update_alpha ()
 
virtual void update_chol ()
 
virtual void update_deriv ()
 
virtual SGVector< float64_t > get_derivative_wrt_likelihood_model (const TParameter *param)
 
virtual SGVector< float64_t > get_derivative_wrt_inducing_features (const TParameter *param)
 
virtual SGVector< float64_t > get_derivative_wrt_inducing_noise (const TParameter *param)
 
virtual SGVector< float64_t > get_derivative_wrt_mean (const TParameter *param)
 
virtual float64_t get_derivative_related_cov (SGVector< float64_t > ddiagKi, SGMatrix< float64_t > dKuui, SGMatrix< float64_t > dKui)
 
virtual void compute_gradient ()
 
virtual SGVector< float64_t > get_derivative_wrt_inference_method (const TParameter *param)
 
virtual SGVector< float64_t > get_derivative_wrt_kernel (const TParameter *param)
 
virtual void check_bound (SGVector< float64_t > bound, const char *name)
 
virtual void check_fully_sparse ()
 
virtual void convert_features ()
 
virtual void check_features ()
 
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

SGMatrix< float64_t > m_inv_Lm
 
SGMatrix< float64_t > m_Knm_inv_Lm
 
SGMatrix< float64_t > m_inv_La
 
float64_t m_yy
 
float64_t m_f3
 
float64_t m_sigma2
 
float64_t m_trk
 
SGMatrix< float64_t > m_Tmm
 
SGMatrix< float64_t > m_Tnm
 
SGVector< float64_t > m_lower_bound
 
SGVector< float64_t > m_upper_bound
 
float64_t m_max_ind_iterations
 
float64_t m_ind_tolerance
 
bool m_opt_inducing_features
 
bool m_fully_sparse
 
CLock * m_lock
 
FirstOrderMinimizer * m_inducing_minimizer
 
SGMatrix< float64_t > m_inducing_features
 
float64_t m_log_ind_noise
 
SGMatrix< float64_t > m_kuu
 
SGMatrix< float64_t > m_ktru
 
SGMatrix< float64_t > m_Sigma
 
SGVector< float64_t > m_mu
 
SGVector< float64_t > m_ktrtr_diag
 
Minimizer * m_minimizer
 
CKernel * m_kernel
 
CMeanFunction * m_mean
 
CLikelihoodModel * m_model
 
CFeatures * m_features
 
CLabels * m_labels
 
SGVector< float64_t > m_alpha
 
SGMatrix< float64_t > m_L
 
float64_t m_log_scale
 
SGMatrix< float64_t > m_ktrtr
 
SGMatrix< float64_t > m_E
 
bool m_gradient_update
 

Constructor & Destructor Documentation

CVarDTCInferenceMethod ( )

default constructor

Definition at line 48 of file VarDTCInferenceMethod.cpp.

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

constructor

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

Definition at line 53 of file VarDTCInferenceMethod.cpp.

~CVarDTCInferenceMethod ( )
virtual

Definition at line 83 of file VarDTCInferenceMethod.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_bound ( SGVector< float64_t >  bound,
const char *  name 
)
protectedvirtualinherited

check the bound constraint is vailid or not

Parameters
boundbound constrains of inducing features
namethe name of the bound

Definition at line 282 of file SingleSparseInference.cpp.

void check_features ( )
protectedvirtualinherited

check whether features and inducing features are set

Definition at line 48 of file SparseInference.cpp.

void check_fully_sparse ( )
protectedvirtualinherited

check whether the provided kernel can compute the gradient wrt inducing features

Note that currently we check the name of the provided kernel to determine whether the kernel can compute the derivatives wrt inducing_features

The name of a supported Kernel must end with "SparseKernel"

Definition at line 176 of file SingleSparseInference.cpp.

void check_members ( ) const
protectedvirtual

check if members of object are valid for inference

Reimplemented from CSparseInference.

Definition at line 125 of file VarDTCInferenceMethod.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 87 of file VarDTCInferenceMethod.cpp.

void convert_features ( )
protectedvirtualinherited

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.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 231 of file SGObject.cpp.

void enable_optimizing_inducing_features ( bool  is_optmization,
FirstOrderMinimizer *  minimizer = NULL 
)
virtualinherited

whether enable to opitmize inducing features

Parameters
is_optmizationenable optimization
minimizerminimizer used in optimization

Definition at line 321 of file SingleSparseInference.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 ( )
virtualinherited

get alpha vector

Returns
vector to compute posterior mean of Gaussian Process:

\[ \mu = K\alpha \]

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

Implements CInference.

Definition at line 135 of file SparseInference.cpp.

SGMatrix< float64_t > get_cholesky ( )
virtualinherited

get Cholesky decomposition matrix

Returns
Cholesky decomposition of matrix:

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

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

Implements CInference.

Definition at line 144 of file SparseInference.cpp.

void * get_derivative_helper ( void *  p)
staticprotectedinherited

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

Definition at line 268 of file Inference.cpp.

float64_t get_derivative_related_cov ( SGVector< float64_t >  ddiagKi,
SGMatrix< float64_t >  dKuui,
SGMatrix< float64_t >  dKui 
)
protectedvirtual

compute variables which are required to compute negative log marginal likelihood full derivatives wrt cov-like hyperparameter \(\theta\)

Note that scale, which is a hyperparameter in inference_method, is a cov-like hyperparameter hyperparameters in cov function are cov-like hyperparameters

Parameters
ddiagKi\(\textbf{diag}(\frac{\partial {\Sigma_{n}}}{\partial {\theta}})\)
dKuui\(\frac{\partial {\Sigma_{m}}}{\partial {\theta}}\)
dKui\(\frac{\partial {\Sigma_{m,n}}}{\partial {\theta}}\)
Returns
derivative of negative log marginal likelihood

Implements CSingleSparseInference.

Definition at line 389 of file VarDTCInferenceMethod.cpp.

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

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

Note that the kernel must support to compute the derivatives wrt inducing features

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

Implements CSingleSparseInference.

Definition at line 331 of file VarDTCInferenceMethod.cpp.

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

returns derivative of negative log marginal likelihood wrt inducing noise

Parameters
paramparameter of given inference class
Returns
derivative of negative log marginal likelihood

Implements CSingleSparseInference.

Definition at line 375 of file VarDTCInferenceMethod.cpp.

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

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 CSparseInference.

Reimplemented in CSingleFITCLaplaceInferenceMethod.

Definition at line 188 of file SingleSparseInference.cpp.

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

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

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

Implements CSparseInference.

Reimplemented in CSingleFITCLaplaceInferenceMethod.

Definition at line 240 of file SingleSparseInference.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 CSparseInference.

Definition at line 306 of file VarDTCInferenceMethod.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 CSparseInference.

Definition at line 407 of file VarDTCInferenceMethod.cpp.

SGVector< float64_t > get_diagonal_vector ( )
virtual

get diagonal vector

Returns
diagonal of matrix used to calculate posterior covariance matrix:

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

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

Implements CInference.

Definition at line 135 of file VarDTCInferenceMethod.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 CFeatures* get_inducing_features ( )
virtualinherited

get inducing features

Returns
features

Definition at line 121 of file SparseInference.h.

float64_t get_inducing_noise ( )
virtualinherited

get the noise for inducing points

Returns
noise noise for inducing points

Definition at line 118 of file SparseInference.cpp.

virtual EInferenceType get_inference_type ( ) const
virtual

return what type of inference we are

Returns
inference type KL_SPARSE_REGRESSION

Reimplemented from CSparseInference.

Definition at line 83 of file VarDTCInferenceMethod.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 CMeanFunction* get_mean ( )
virtualinherited

get mean

Returns
mean

Definition at line 300 of file Inference.h.

CLikelihoodModel* get_model ( )
inherited

get likelihood model

Returns
likelihood

Definition at line 334 of file Inference.h.

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

Definition at line 531 of file SGObject.cpp.

char * get_modsel_param_descr ( const char *  param_name)
inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters
param_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 VarDTC

Reimplemented from CSingleSparseInference.

Definition at line 77 of file VarDTCInferenceMethod.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 142 of file VarDTCInferenceMethod.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 \(\Sigma\) 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 CSparseInference.

Definition at line 299 of file VarDTCInferenceMethod.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:

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

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 CSparseInference.

Definition at line 292 of file VarDTCInferenceMethod.cpp.

float64_t get_scale ( ) const
virtualinherited

get kernel scale

Returns
kernel scale

Definition at line 60 of file Inference.cpp.

virtual SGVector<float64_t> get_value ( )
virtualinherited

get the function value

Returns
vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 255 of file Inference.h.

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

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

Parameters
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.

CVarDTCInferenceMethod * obtain_from_generic ( CInference *  inference)
static

helper method used to specialize a base class instance

Parameters
inferenceinference method
Returns
casted CVarDTCInferenceMethod object

Definition at line 112 of file VarDTCInferenceMethod.cpp.

void optimize_inducing_features ( )
virtualinherited

opitmize inducing features

Definition at line 357 of file SingleSparseInference.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 435 of file VarDTCInferenceMethod.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.

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_inducing_features ( CFeatures *  feat)
virtualinherited

set inducing features

Parameters
featfeatures to set

Definition at line 108 of file SparseInference.h.

void set_inducing_noise ( float64_t  noise)
virtualinherited

set the noise for inducing points

Parameters
noisenoise 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.

void set_kernel ( CKernel *  kern)
virtualinherited

set kernel

Parameters
kernkernel to set

Reimplemented from CInference.

Definition at line 164 of file SingleSparseInference.cpp.

virtual void set_labels ( CLabels *  lab)
virtualinherited

set labels

Parameters
lablabel to set

Definition at line 323 of file Inference.h.

void set_lower_bound_of_inducing_features ( SGVector< float64_t >  bound)
virtualinherited

set the lower bound of inducing features

Parameters
boundlower bound constrains of inducing features

Note that if the length of the bound is 1, it means the bound constraint applies to each dimension of all inducing features

Note that if the length of the bound is greater than 1, it means each dimension of the bound constraint applies to the corresponding dimension of inducing features

Definition at line 299 of file SingleSparseInference.cpp.

void set_max_iterations_for_inducing_features ( int32_t  it)
virtualinherited

set the max number of iterations used in optimization of inducing features

Parameters
itmax number of iterations

Definition at line 310 of file SingleSparseInference.cpp.

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_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.

void set_tolearance_for_inducing_features ( float64_t  tol)
virtualinherited

set the tolearance used in optimization of inducing features

Parameters
toltolearance

Definition at line 315 of file SingleSparseInference.cpp.

void set_upper_bound_of_inducing_features ( SGVector< float64_t >  bound)
virtualinherited

set the upper bound of inducing features

Parameters
boundupper bound constrains of inducing features

Note that if the length of the bound is 1, it means the bound constraint applies to each dimension of all inducing features

Note that if the length of the bound is greater than 1, it means each dimension of the bound constraint applies to the corresponding dimension of inducing features

Definition at line 304 of file SingleSparseInference.cpp.

CSGObject * shallow_copy ( ) const
virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 225 of file SGObject.cpp.

virtual bool supports_binary ( ) const
virtualinherited

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

Returns
false

Reimplemented in CEPInferenceMethod, CKLInference, CSingleFITCLaplaceInferenceMethod, and CSingleLaplaceInferenceMethod.

Definition at line 371 of file Inference.h.

virtual bool supports_multiclass ( ) const
virtualinherited

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

Returns
false

Reimplemented in CMultiLaplaceInferenceMethod.

Definition at line 378 of file Inference.h.

virtual bool supports_regression ( ) const
virtual
Returns
whether combination of sparse inference method and given likelihood function supports regression

Reimplemented from CInference.

Definition at line 123 of file VarDTCInferenceMethod.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

Implements CSparseInference.

Definition at line 99 of file VarDTCInferenceMethod.cpp.

void update_alpha ( )
protectedvirtual

update alpha matrix

Implements CInference.

Definition at line 222 of file VarDTCInferenceMethod.cpp.

void update_chol ( )
protectedvirtual

update cholesky Matrix.

Implements CInference.

Definition at line 167 of file VarDTCInferenceMethod.cpp.

void update_deriv ( )
protectedvirtual

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

Implements CInference.

Definition at line 248 of file VarDTCInferenceMethod.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 from CInference.

Definition at line 153 of file SparseInference.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

SGVector<float64_t> m_alpha
protectedinherited

alpha vector used in process mean calculation

Definition at line 484 of file Inference.h.

SGMatrix<float64_t> m_E
protectedinherited

the matrix used for multi classification

Definition at line 496 of file Inference.h.

float64_t m_f3
protected

the term used to compute gradient wrt likelihood and marginal likelihood

Definition at line 256 of file VarDTCInferenceMethod.h.

CFeatures* m_features
protectedinherited

features to use

Definition at line 478 of file Inference.h.

bool m_fully_sparse
protectedinherited

whether the kernel supports to get the gradient wrt inducing points or not

Definition at line 224 of file SingleSparseInference.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.

float64_t m_ind_tolerance
protectedinherited

tolearance used in optimizing inducing_features

Definition at line 197 of file SingleSparseInference.h.

SGMatrix<float64_t> m_inducing_features
protectedinherited

inducing features for approximation

Definition at line 304 of file SparseInference.h.

FirstOrderMinimizer* m_inducing_minimizer
protectedinherited

minimizer used in finding optimal inducing features

Definition at line 230 of file SingleSparseInference.h.

SGMatrix<float64_t> m_inv_La
protected

invLa=inv(La) where La*La'=sigma2*eye(m)+inv_Lm*Kmn*Knm*inv_Lm'

Definition at line 252 of file VarDTCInferenceMethod.h.

SGMatrix<float64_t> m_inv_Lm
protected

inv_Lm=inv(Lm) where Lm*Lm'=Kmm

Definition at line 248 of file VarDTCInferenceMethod.h.

CKernel* m_kernel
protectedinherited

covariance function

Definition at line 469 of file Inference.h.

SGMatrix<float64_t> m_Knm_inv_Lm
protected

Knm*inv_Lm

Definition at line 250 of file VarDTCInferenceMethod.h.

SGMatrix<float64_t> m_ktrtr
protectedinherited

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

Definition at line 493 of file Inference.h.

SGVector<float64_t> m_ktrtr_diag
protectedinherited

diagonal elements of kernel matrix m_ktrtr

Definition at line 322 of file SparseInference.h.

SGMatrix<float64_t> m_ktru
protectedinherited

covariance matrix of inducing features and training features

Definition at line 313 of file SparseInference.h.

SGMatrix<float64_t> m_kuu
protectedinherited

covariance matrix of inducing features

Definition at line 310 of file SparseInference.h.

SGMatrix<float64_t> m_L
protectedinherited

upper triangular factor of Cholesky decomposition

Definition at line 487 of file Inference.h.

CLabels* m_labels
protectedinherited

labels of features

Definition at line 481 of file Inference.h.

CLock* m_lock
protectedinherited

a lock used to parallelly compute derivatives wrt hyperparameters

Definition at line 227 of file SingleSparseInference.h.

float64_t m_log_ind_noise
protectedinherited

noise of the inducing variables

Definition at line 307 of file SparseInference.h.

float64_t m_log_scale
protectedinherited

kernel scale

Definition at line 490 of file Inference.h.

SGVector<float64_t> m_lower_bound
protectedinherited

lower bound of inducing features

Definition at line 188 of file SingleSparseInference.h.

float64_t m_max_ind_iterations
protectedinherited

max number of iterations

Definition at line 194 of file SingleSparseInference.h.

CMeanFunction* m_mean
protectedinherited

mean function

Definition at line 472 of file Inference.h.

Minimizer* m_minimizer
protectedinherited

minimizer

Definition at line 466 of file Inference.h.

CLikelihoodModel* m_model
protectedinherited

likelihood function to use

Definition at line 475 of file Inference.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 549 of file SGObject.h.

SGVector<float64_t> m_mu
protectedinherited

mean vector of the the posterior Gaussian distribution

Definition at line 319 of file SparseInference.h.

bool m_opt_inducing_features
protectedinherited

whether optimize inducing features

Definition at line 200 of file SingleSparseInference.h.

Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.h.

SGMatrix<float64_t> m_Sigma
protectedinherited

covariance matrix of the the posterior Gaussian distribution

Definition at line 316 of file SparseInference.h.

float64_t m_sigma2
protected

square of sigma from Gaussian likelihood

Definition at line 258 of file VarDTCInferenceMethod.h.

SGMatrix<float64_t> m_Tmm
protected

a matrix used to compute gradients wrt kernel (Kmm)

Definition at line 262 of file VarDTCInferenceMethod.h.

SGMatrix<float64_t> m_Tnm
protected

a matrix used to compute gradients wrt kernel (Knm)

Definition at line 264 of file VarDTCInferenceMethod.h.

float64_t m_trk
protected

the trace term to compute marginal likelihood

Definition at line 260 of file VarDTCInferenceMethod.h.

SGVector<float64_t> m_upper_bound
protectedinherited

upper bound of inducing features

Definition at line 191 of file SingleSparseInference.h.

float64_t m_yy
protected

yy=(y-meanfun)'*(y-meanfun)

Definition at line 254 of file VarDTCInferenceMethod.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