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CExactInferenceMethod Class Reference

Detailed Description

The Gaussian exact form inference method class.

This inference method computes the Gaussian Method exactly using matrix equations.

\[ L = cholesky(K + \sigma^{2}I) \]

\(L\) is the cholesky decomposition of \(K\), the covariance matrix, plus a diagonal matrix with entries \(\sigma^{2}\), the observation noise.

\[ \boldsymbol{\alpha} = L^{T} \backslash(L \backslash \boldsymbol{y}}) \]

where \(L\) is the matrix mentioned above, \(\boldsymbol{y}\) are the labels, and \(\backslash\) is an operator ( \(x = A \backslash B\) means \(Ax=B\).)

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

Definition at line 47 of file ExactInferenceMethod.h.

Inheritance diagram for CExactInferenceMethod:
Inheritance graph
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List of all members.

Public Member Functions

 CExactInferenceMethod ()
 CExactInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
virtual ~CExactInferenceMethod ()
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 bool supports_regression () const
virtual void update ()
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_binary () const
virtual bool supports_multiclass () const
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
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)
virtual bool update_parameter_hash ()
virtual bool equals (CSGObject *other, float64_t accuracy=0.0)
virtual CSGObjectclone ()

Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

Protected Member Functions

virtual void check_members () const
virtual void update_alpha ()
virtual void update_chol ()
virtual void update_mean ()
virtual void update_cov ()
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 update_train_kernel ()
virtual TParametermigrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)
virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)
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)

Static Protected Member Functions

static void * get_derivative_helper (void *p)

Protected Attributes

CKernelm_kernel
CMeanFunctionm_mean
CLikelihoodModelm_model
CFeaturesm_features
CLabelsm_labels
SGVector< float64_tm_alpha
SGMatrix< float64_tm_L
float64_t m_scale
SGMatrix< float64_tm_ktrtr

Constructor & Destructor Documentation

default constructor

Definition at line 27 of file ExactInferenceMethod.cpp.

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

constructor

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

Definition at line 31 of file ExactInferenceMethod.cpp.

~CExactInferenceMethod ( ) [virtual]

Definition at line 37 of file ExactInferenceMethod.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 1156 of file SGObject.cpp.

void check_members ( ) const [protected, virtual]

check if members of object are valid for inference

Reimplemented from CInferenceMethod.

Definition at line 51 of file ExactInferenceMethod.cpp.

CSGObject * clone ( ) [virtual, inherited]

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 1273 of file SGObject.cpp.

virtual CSGObject* deep_copy ( ) const [virtual, inherited]

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

Definition at line 126 of file SGObject.h.

bool equals ( CSGObject other,
float64_t  accuracy = 0.0 
) [virtual, inherited]

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

Definition at line 1177 of file SGObject.cpp.

SGVector< float64_t > get_alpha ( ) [virtual]

get alpha vector

Returns:
vector to compute posterior mean of Gaussian Process:

\[ \mu = K\alpha \]

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

Implements CInferenceMethod.

Definition at line 107 of file ExactInferenceMethod.cpp.

SGMatrix< float64_t > get_cholesky ( ) [virtual]

get Cholesky decomposition matrix

Returns:
Cholesky decomposition of matrix:

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

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

Implements CInferenceMethod.

Definition at line 115 of file ExactInferenceMethod.cpp.

void * get_derivative_helper ( void *  p) [static, protected, inherited]

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

Definition at line 209 of file InferenceMethod.cpp.

SGVector< float64_t > get_derivative_wrt_inference_method ( const TParameter param) [protected, virtual]

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

Parameters:
paramparameter of CInferenceMethod class
Returns:
derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 246 of file ExactInferenceMethod.cpp.

SGVector< float64_t > get_derivative_wrt_kernel ( const TParameter param) [protected, virtual]

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

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

Implements CInferenceMethod.

Definition at line 288 of file ExactInferenceMethod.cpp.

SGVector< float64_t > get_derivative_wrt_likelihood_model ( const TParameter param) [protected, virtual]

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 CInferenceMethod.

Definition at line 264 of file ExactInferenceMethod.cpp.

SGVector< float64_t > get_derivative_wrt_mean ( const TParameter param) [protected, virtual]

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 CInferenceMethod.

Definition at line 326 of file ExactInferenceMethod.cpp.

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 CInferenceMethod.

Definition at line 61 of file ExactInferenceMethod.cpp.

virtual CFeatures* get_features ( ) [virtual, inherited]

get features

Returns:
features

Definition at line 241 of file InferenceMethod.h.

SGIO * get_global_io ( ) [inherited]

get the io object

Returns:
io object

Definition at line 174 of file SGObject.cpp.

Parallel * get_global_parallel ( ) [inherited]

get the parallel object

Returns:
parallel object

Definition at line 209 of file SGObject.cpp.

Version * get_global_version ( ) [inherited]

get the version object

Returns:
version object

Definition at line 222 of file SGObject.cpp.

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

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 220 of file InferenceMethod.h.

virtual EInferenceType get_inference_type ( ) const [virtual]

return what type of inference we are

Returns:
inference type EXACT

Reimplemented from CInferenceMethod.

Definition at line 70 of file ExactInferenceMethod.h.

virtual CKernel* get_kernel ( ) [virtual, inherited]

get kernel

Returns:
kernel

Definition at line 258 of file InferenceMethod.h.

virtual CLabels* get_labels ( ) [virtual, inherited]

get labels

Returns:
labels

Definition at line 292 of file InferenceMethod.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 log-marginal-likelihood,

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

where \(y\) are the labels, \(X\) are the features (omitted from in the following expressions), and \(\theta\) represent hyperparameters.

This is done via an approximation to the posterior \(q(f|y, \theta)\approx p(f|y, \theta)\), which is computed by the underlying CInferenceMethod 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).

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 Cholesky factorization fails.
Returns:
unbiased estimate of the log of the marginal likelihood function \( log(p(y|\theta)) \)

Definition at line 79 of file InferenceMethod.cpp.

virtual CMeanFunction* get_mean ( ) [virtual, inherited]

get mean

Returns:
mean

Definition at line 275 of file InferenceMethod.h.

CLikelihoodModel* get_model ( ) [inherited]

get likelihood model

Returns:
likelihood

Definition at line 309 of file InferenceMethod.h.

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

Definition at line 1060 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 1084 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 1097 of file SGObject.cpp.

virtual const char* get_name ( ) const [virtual]

returns the name of the inference method

Returns:
name Exact

Implements CSGObject.

Definition at line 76 of file ExactInferenceMethod.h.

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 CInferenceMethod.

Definition at line 78 of file ExactInferenceMethod.cpp.

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 138 of file InferenceMethod.cpp.

returns covariance matrix \(\Sigma\) of the posterior Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\)

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

Returns:
covariance matrix

Implements CInferenceMethod.

Definition at line 131 of file ExactInferenceMethod.cpp.

returns mean vector \(\mu\) of the posterior Gaussian distribution \(\mathcal{N}(\mu,\Sigma)\)

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

Returns:
mean vector

Implements CInferenceMethod.

Definition at line 123 of file ExactInferenceMethod.cpp.

virtual float64_t get_scale ( ) const [virtual, inherited]

get kernel scale

Returns:
kernel scale

Definition at line 326 of file InferenceMethod.h.

virtual SGVector<float64_t> get_value ( ) [virtual, inherited]

get the function value

Returns:
vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 230 of file InferenceMethod.h.

bool is_generic ( EPrimitiveType *  generic) const [virtual, inherited]

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 228 of file SGObject.cpp.

DynArray< TParameter * > * load_all_file_parameters ( int32_t  file_version,
int32_t  current_version,
CSerializableFile file,
const char *  prefix = "" 
) [inherited]

maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)

Parameters:
file_versionparameter version of the file
current_versionversion from which mapping begins (you want to use Version::get_version_parameter() for this in most cases)
filefile to load from
prefixprefix for members
Returns:
(sorted) array of created TParameter instances with file data

Definition at line 633 of file SGObject.cpp.

DynArray< TParameter * > * load_file_parameters ( const SGParamInfo param_info,
int32_t  file_version,
CSerializableFile file,
const char *  prefix = "" 
) [inherited]

loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned

Parameters:
param_infoinformation of parameter
file_versionparameter version of the file, must be <= provided parameter version
filefile to load from
prefixprefix for members
Returns:
new array with TParameter instances with the attached data

Definition at line 474 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
) [virtual, inherited]

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
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns:
TRUE if done, otherwise FALSE

Definition at line 305 of file SGObject.cpp.

void load_serializable_post ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

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

Definition at line 989 of file SGObject.cpp.

void load_serializable_pre ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

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

Definition at line 984 of file SGObject.cpp.

void map_parameters ( DynArray< TParameter * > *  param_base,
int32_t &  base_version,
DynArray< const SGParamInfo * > *  target_param_infos 
) [inherited]

Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match

Parameters:
param_baseset of TParameter instances that are mapped to the provided target parameter infos
base_versionversion of the parameter base
target_param_infosset of SGParamInfo instances that specify the target parameter base

Definition at line 671 of file SGObject.cpp.

TParameter * migrate ( DynArray< TParameter * > *  param_base,
const SGParamInfo target 
) [protected, virtual, inherited]

creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.

If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass

Parameters:
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
Returns:
a new TParameter instance with migrated data from the base of the type which is specified by the target parameter

Definition at line 878 of file SGObject.cpp.

void one_to_one_migration_prepare ( DynArray< TParameter * > *  param_base,
const SGParamInfo target,
TParameter *&  replacement,
TParameter *&  to_migrate,
char *  old_name = NULL 
) [protected, virtual, inherited]

This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)

Parameters:
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
replacement(used as output) here the TParameter instance which is returned by migration is created into
to_migratethe only source that is used for migration
old_namewith this parameter, a name change may be specified

Definition at line 818 of file SGObject.cpp.

void print_modsel_params ( ) [inherited]

prints all parameter registered for model selection and their type

Definition at line 1036 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "") [virtual, inherited]

prints registered parameters out

Parameters:
prefixprefix for members

Definition at line 240 of file SGObject.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
) [virtual, inherited]

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
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns:
TRUE if done, otherwise FALSE

Definition at line 246 of file SGObject.cpp.

void save_serializable_post ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

Reimplemented in CKernel.

Definition at line 999 of file SGObject.cpp.

void save_serializable_pre ( ) throw (ShogunException) [protected, virtual, inherited]

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 occurres.

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

Definition at line 994 of file SGObject.cpp.

virtual void set_features ( CFeatures feat) [virtual, inherited]

set features

Parameters:
featfeatures to set

Definition at line 247 of file InferenceMethod.h.

void set_generic< complex128_t > ( ) [inherited]

set generic type to T

Definition at line 41 of file SGObject.cpp.

void set_global_io ( SGIO io) [inherited]

set the io object

Parameters:
ioio object to use

Definition at line 167 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel) [inherited]

set the parallel object

Parameters:
parallelparallel object to use

Definition at line 180 of file SGObject.cpp.

void set_global_version ( Version version) [inherited]

set the version object

Parameters:
versionversion object to use

Definition at line 215 of file SGObject.cpp.

virtual void set_kernel ( CKernel kern) [virtual, inherited]

set kernel

Parameters:
kernkernel to set

Definition at line 264 of file InferenceMethod.h.

virtual void set_labels ( CLabels lab) [virtual, inherited]

set labels

Parameters:
lablabel to set

Definition at line 298 of file InferenceMethod.h.

virtual void set_mean ( CMeanFunction m) [virtual, inherited]

set mean

Parameters:
mmean function to set

Definition at line 281 of file InferenceMethod.h.

virtual void set_model ( CLikelihoodModel mod) [virtual, inherited]

set likelihood model

Parameters:
modmodel to set

Definition at line 315 of file InferenceMethod.h.

virtual void set_scale ( float64_t  scale) [virtual, inherited]

set kernel scale

Parameters:
scalescale to be set

Definition at line 332 of file InferenceMethod.h.

virtual CSGObject* shallow_copy ( ) const [virtual, inherited]

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

Reimplemented in CGaussianKernel.

Definition at line 117 of file SGObject.h.

virtual bool supports_binary ( ) const [virtual, inherited]

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

Returns:
false

Reimplemented in CLaplacianInferenceMethod, and CEPInferenceMethod.

Definition at line 346 of file InferenceMethod.h.

virtual bool supports_multiclass ( ) const [virtual, inherited]

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

Returns:
false

Definition at line 353 of file InferenceMethod.h.

virtual bool supports_regression ( ) const [virtual]
Returns:
whether combination of exact inference method and given likelihood function supports regression

Reimplemented from CInferenceMethod.

Definition at line 155 of file ExactInferenceMethod.h.

void unset_generic ( ) [inherited]

unset generic type

this has to be called in classes specializing a template class

Definition at line 235 of file SGObject.cpp.

void update ( ) [virtual]

update all matrices

Reimplemented from CInferenceMethod.

Definition at line 41 of file ExactInferenceMethod.cpp.

void update_alpha ( ) [protected, virtual]

update alpha matrix

Implements CInferenceMethod.

Definition at line 158 of file ExactInferenceMethod.cpp.

void update_chol ( ) [protected, virtual]

update Cholesky matrix

Implements CInferenceMethod.

Definition at line 139 of file ExactInferenceMethod.cpp.

void update_cov ( ) [protected, virtual]

update covariance matrix of the posterior Gaussian

Definition at line 201 of file ExactInferenceMethod.cpp.

void update_deriv ( ) [protected, virtual]

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

Implements CInferenceMethod.

Definition at line 220 of file ExactInferenceMethod.cpp.

void update_mean ( ) [protected, virtual]

update mean vector of the posterior Gaussian

Definition at line 184 of file ExactInferenceMethod.cpp.

bool update_parameter_hash ( ) [virtual, inherited]

Updates the hash of current parameter combination.

Returns:
bool if parameter combination has changed since last update.

Definition at line 187 of file SGObject.cpp.

void update_train_kernel ( ) [protected, virtual, inherited]

update train kernel matrix

Reimplemented in CFITCInferenceMethod.

Definition at line 279 of file InferenceMethod.cpp.


Member Data Documentation

SGIO* io [inherited]

io

Definition at line 473 of file SGObject.h.

SGVector<float64_t> m_alpha [protected, inherited]

alpha vector used in process mean calculation

Definition at line 441 of file InferenceMethod.h.

CFeatures* m_features [protected, inherited]

features to use

Definition at line 435 of file InferenceMethod.h.

parameters wrt which we can compute gradients

Definition at line 488 of file SGObject.h.

uint32_t m_hash [inherited]

Hash of parameter values

Definition at line 494 of file SGObject.h.

CKernel* m_kernel [protected, inherited]

covariance function

Definition at line 426 of file InferenceMethod.h.

SGMatrix<float64_t> m_ktrtr [protected, inherited]

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

Definition at line 450 of file InferenceMethod.h.

SGMatrix<float64_t> m_L [protected, inherited]

upper triangular factor of Cholesky decomposition

Definition at line 444 of file InferenceMethod.h.

CLabels* m_labels [protected, inherited]

labels of features

Definition at line 438 of file InferenceMethod.h.

CMeanFunction* m_mean [protected, inherited]

mean function

Definition at line 429 of file InferenceMethod.h.

CLikelihoodModel* m_model [protected, inherited]

likelihood function to use

Definition at line 432 of file InferenceMethod.h.

model selection parameters

Definition at line 485 of file SGObject.h.

map for different parameter versions

Definition at line 491 of file SGObject.h.

Parameter* m_parameters [inherited]

parameters

Definition at line 482 of file SGObject.h.

float64_t m_scale [protected, inherited]

kernel scale

Definition at line 447 of file InferenceMethod.h.

Parallel* parallel [inherited]

parallel

Definition at line 476 of file SGObject.h.

Version* version [inherited]

version

Definition at line 479 of file SGObject.h.


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SHOGUN Machine Learning Toolbox - Documentation