SHOGUN  3.2.1
CLaplacianInferenceMethodWithLBFGS Class Reference

## Detailed Description

The Laplace approximation inference method with LBFGS class.

This inference method approximates the posterior likelihood function by using Laplace's method. Here, we compute a Gaussian approximation to the posterior via a Taylor expansion around the maximum of the posterior likelihood function. We use the Limited-memory BFGS method to obtain the maximum of likelihood. Note that due to the Laplace approximation, the time complexity of the class still is O(n^3), where n is the number of training data points. However, in the optimization step we use L-BFGS method, which of the time complexity is O(n*m) to replace the Newton method, which of the time complexity is O(n^3). Here L-BFGS only uses the last m (m<<n) function/gradient pairs to find the optimal pointer

For more details, see Nocedal, Jorge, and Stephen J. Wright. "Numerical Optimization 2nd." (2006), Pages 177-180.

This specific implementation was based on the idea from Murphy, Kevin P. "Machine learning: a probabilistic perspective." (2012), Pages 251-252.

Definition at line 66 of file LaplacianInferenceMethodWithLBFGS.h.

Inheritance diagram for CLaplacianInferenceMethodWithLBFGS:
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## Public Member Functions

CLaplacianInferenceMethodWithLBFGS ()
CLaplacianInferenceMethodWithLBFGS (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
virtual ~CLaplacianInferenceMethodWithLBFGS ()
virtual const char * get_name () const
virtual void set_lbfgs_parameters (int m=100, int max_linesearch=1000, int linesearch=LBFGS_LINESEARCH_DEFAULT, int max_iterations=1000, float64_t delta=0.0, int past=0, float64_t epsilon=1e-5, bool enable_newton_if_fail=true, float64_t min_step=1e-20, float64_t max_step=1e+20, float64_t ftol=1e-4, float64_t wolfe=0.9, float64_t gtol=0.9, float64_t xtol=1e-16, float64_t orthantwise_c=0.0, int orthantwise_start=0, int orthantwise_end=1)
virtual EInferenceType get_inference_type () 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_newton_tolerance ()
virtual void set_newton_tolerance (float64_t tol)
virtual int32_t get_newton_iterations ()
virtual void set_newton_iterations (int32_t iter)
virtual float64_t get_minimization_tolerance ()
virtual void set_minimization_tolerance (float64_t tol)
virtual float64_t get_minimization_max ()
virtual void set_minimization_max (float64_t max)
virtual bool supports_regression () const
virtual bool supports_binary () 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_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 void update_parameter_hash ()
virtual bool parameter_hash_changed ()
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
virtual CSGObjectclone ()

## Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
ParameterMapm_parameter_map
uint32_t m_hash

## Protected Member Functions

virtual void update_alpha ()
virtual void update_chol ()
virtual void update_approx_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 check_members () const
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

SGVector< float64_tm_mu
SGMatrix< float64_tm_Sigma
SGVector< float64_tW
SGVector< float64_tsW
SGVector< float64_tdlp
SGVector< float64_td2lp
SGVector< float64_td3lp
SGVector< float64_tm_dfhat
SGMatrix< float64_tm_Z
SGVector< float64_tm_g
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

 CLaplacianInferenceMethodWithLBFGS ( )

Definition at line 42 of file LaplacianInferenceMethodWithLBFGS.cpp.

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

Definition at line 48 of file LaplacianInferenceMethodWithLBFGS.cpp.

 ~CLaplacianInferenceMethodWithLBFGS ( )
virtual

Definition at line 154 of file LaplacianInferenceMethodWithLBFGS.cpp.

## Member Function Documentation

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

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters
 dict dictionary of parameters to be built.

Definition at line 1185 of file SGObject.cpp.

 void check_members ( ) const
protectedvirtualinherited

check if members of object are valid for inference

Reimplemented in CFITCInferenceMethod, and CExactInferenceMethod.

Definition at line 263 of file InferenceMethod.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 1302 of file SGObject.cpp.

 CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 146 of file SGObject.cpp.

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

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters
 other object to compare with accuracy accuracy to use for comparison (optional) tolerant allows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 1206 of file SGObject.cpp.

 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 CInferenceMethod.

Definition at line 161 of file LaplacianInferenceMethod.cpp.

 SGMatrix< float64_t > get_cholesky ( )
virtualinherited

get Cholesky decomposition matrix

Returns
Cholesky decomposition of matrix:

$L = Cholesky(W^{\frac{1}{2}}*K*W^{\frac{1}{2}}+I)$

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 170 of file LaplacianInferenceMethod.cpp.

 void * get_derivative_helper ( void * p )
staticprotectedinherited

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 )
protectedvirtualinherited

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

Parameters
 param parameter of CInferenceMethod class
Returns
derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 453 of file LaplacianInferenceMethod.cpp.

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

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

Parameters
 param parameter of given kernel
Returns
derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 519 of file LaplacianInferenceMethod.cpp.

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

returns derivative of negative log marginal likelihood wrt parameter of likelihood model

Parameters
 param parameter of given likelihood model
Returns
derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 485 of file LaplacianInferenceMethod.cpp.

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

returns derivative of negative log marginal likelihood wrt mean function's parameter

Parameters
 param parameter of given mean function
Returns
derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 569 of file LaplacianInferenceMethod.cpp.

 SGVector< float64_t > get_diagonal_vector ( )
virtualinherited

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 112 of file LaplacianInferenceMethod.cpp.

 virtual CFeatures* get_features ( )
virtualinherited

get features

Returns
features

Definition at line 245 of file InferenceMethod.h.

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 183 of file SGObject.cpp.

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 224 of file SGObject.cpp.

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 237 of file SGObject.cpp.

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

Parameters
 parameters parameter's dictionary
Returns
map of gradient. Keys are names of parameters, values are values of derivative with respect to that parameter.

Implements CDifferentiableFunction.

Definition at line 224 of file InferenceMethod.h.

 virtual EInferenceType get_inference_type ( ) const
virtualinherited

return what type of inference we are

Returns
inference type LAPLACIAN

Reimplemented from CInferenceMethod.

Definition at line 65 of file LaplacianInferenceMethod.h.

 virtual CKernel* get_kernel ( )
virtualinherited

get kernel

Returns
kernel

Definition at line 262 of file InferenceMethod.h.

 virtual CLabels* get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 296 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 marginal-likelihood,

$p(y|X,\theta),$

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 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_samples the number of importance samples $$n$$ from $$q(f|y, \theta)$$. ridge_size scalar that is added to the diagonal of the involved Gaussian distribution's covariance of GP prior and posterior approximation to stabilise things. Increase if Cholesky factorization fails.
Returns
Unbiased estimate of the the marginal likelihood $$p(y|\theta)$$. Note this is not an estimate for the log-marginal likelihood but the marginal likelihood itself (using log-sum-exp).

Definition at line 79 of file InferenceMethod.cpp.

 virtual CMeanFunction* get_mean ( )
virtualinherited

get mean

Returns
mean

Definition at line 279 of file InferenceMethod.h.

 virtual float64_t get_minimization_max ( )
virtualinherited

get maximum for Brent's minimization method

Returns
maximum for Brent's minimization method

Definition at line 198 of file LaplacianInferenceMethod.h.

 virtual float64_t get_minimization_tolerance ( )
virtualinherited

get tolerance for Brent's minimization method

Returns
tolerance for Brent's minimization method

Definition at line 186 of file LaplacianInferenceMethod.h.

 CLikelihoodModel* get_model ( )
inherited

get likelihood model

Returns
likelihood

Definition at line 313 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 1077 of file SGObject.cpp.

 char * get_modsel_param_descr ( const char * param_name )
inherited

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

Parameters
 param_name name of the parameter
Returns
description of the parameter

Definition at line 1101 of file SGObject.cpp.

 index_t get_modsel_param_index ( const char * param_name )
inherited

Returns index of model selection parameter with provided index

Parameters
 param_name name of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 1114 of file SGObject.cpp.

 virtual const char* get_name ( ) const
virtual

returns the name of the inference method

Returns
name Laplacian

Reimplemented from CLaplacianInferenceMethod.

Definition at line 92 of file LaplacianInferenceMethodWithLBFGS.h.

 float64_t get_negative_log_marginal_likelihood ( )
virtualinherited

get negative log marginal likelihood

Returns
the negative log of the marginal likelihood function:

$-log(p(y|X, \theta))$

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

Implements CInferenceMethod.

Definition at line 120 of file LaplacianInferenceMethod.cpp.

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

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

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

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

Definition at line 138 of file InferenceMethod.cpp.

 virtual int32_t get_newton_iterations ( )
virtualinherited

get max Newton iterations

Returns
max Newton iterations

Definition at line 174 of file LaplacianInferenceMethod.h.

 virtual float64_t get_newton_tolerance ( )
virtualinherited

get tolerance for newton iterations

Returns
tolerance for newton iterations

Definition at line 162 of file LaplacianInferenceMethod.h.

 SGMatrix< float64_t > get_posterior_covariance ( )
virtualinherited

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

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

Covariance matrix is evaluated using matrix inversion lemma:

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

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

Returns
covariance matrix

Implements CInferenceMethod.

Definition at line 186 of file LaplacianInferenceMethod.cpp.

 SGVector< float64_t > get_posterior_mean ( )
virtualinherited

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

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

Mean vector $$\mu$$ is evaluated using Newton's method.

Returns
mean vector

Implements CInferenceMethod.

Definition at line 178 of file LaplacianInferenceMethod.cpp.

 virtual float64_t get_scale ( ) const
virtualinherited

get kernel scale

Returns
kernel scale

Definition at line 330 of file InferenceMethod.h.

 virtual SGVector get_value ( )
virtualinherited

get the function value

Returns
vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 234 of file InferenceMethod.h.

 bool is_generic ( EPrimitiveType * generic ) const
virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters
 generic set to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 243 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_version parameter version of the file current_version version from which mapping begins (you want to use Version::get_version_parameter() for this in most cases) file file to load from prefix prefix for members
Returns
(sorted) array of created TParameter instances with file data

Definition at line 648 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_info information of parameter file_version parameter version of the file, must be <= provided parameter version file file to load from prefix prefix for members
Returns
new array with TParameter instances with the attached data

Definition at line 489 of file SGObject.cpp.

 bool load_serializable ( CSerializableFile * file, const char * prefix = "", int32_t param_version = Version::get_version_parameter() )
virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters
 file where to load from prefix prefix for members 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 320 of file SGObject.cpp.

 void load_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 1004 of file SGObject.cpp.

 void load_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 999 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_base set of TParameter instances that are mapped to the provided target parameter infos base_version version of the parameter base target_param_infos set of SGParamInfo instances that specify the target parameter base

Definition at line 686 of file SGObject.cpp.

 TParameter * migrate ( DynArray< TParameter * > * param_base, const SGParamInfo * target )
protectedvirtualinherited

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_base set of TParameter instances to use for migration target parameter 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 893 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 )
protectedvirtualinherited

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_base set of TParameter instances to use for migration target parameter info for the resulting TParameter replacement (used as output) here the TParameter instance which is returned by migration is created into to_migrate the only source that is used for migration old_name with this parameter, a name change may be specified

Definition at line 833 of file SGObject.cpp.

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

Definition at line 209 of file SGObject.cpp.

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1053 of file SGObject.cpp.

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

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 255 of file SGObject.cpp.

 bool save_serializable ( CSerializableFile * file, const char * prefix = "", int32_t param_version = Version::get_version_parameter() )
virtualinherited

Save this object to file.

Parameters
 file where to save the object; will be closed during returning if PREFIX is an empty string. prefix prefix for members 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 261 of file SGObject.cpp.

 void save_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
 ShogunException Will be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 1014 of file SGObject.cpp.

 void save_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
 ShogunException Will be thrown if an error occurres.

Definition at line 1009 of file SGObject.cpp.

 virtual void set_features ( CFeatures * feat )
virtualinherited

set features

Parameters
 feat features to set

Definition at line 251 of file InferenceMethod.h.

 void set_generic< complex128_t > ( )
inherited

set generic type to T

Definition at line 38 of file SGObject.cpp.

 void set_global_io ( SGIO * io )
inherited

set the io object

Parameters
 io io object to use

Definition at line 176 of file SGObject.cpp.

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 189 of file SGObject.cpp.

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 230 of file SGObject.cpp.

 virtual void set_kernel ( CKernel * kern )
virtualinherited

set kernel

Parameters
 kern kernel to set

Definition at line 268 of file InferenceMethod.h.

 virtual void set_labels ( CLabels * lab )
virtualinherited

set labels

Parameters
 lab label to set

Definition at line 302 of file InferenceMethod.h.

 void set_lbfgs_parameters ( int m = 100, int max_linesearch = 1000, int linesearch = LBFGS_LINESEARCH_DEFAULT, int max_iterations = 1000, float64_t delta = 0.0, int past = 0, float64_t epsilon = 1e-5, bool enable_newton_if_fail = true, float64_t min_step = 1e-20, float64_t max_step = 1e+20, float64_t ftol = 1e-4, float64_t wolfe = 0.9, float64_t gtol = 0.9, float64_t xtol = 1e-16, float64_t orthantwise_c = 0.0, int orthantwise_start = 0, int orthantwise_end = 1 )
virtual

Definition at line 59 of file LaplacianInferenceMethodWithLBFGS.cpp.

 virtual void set_mean ( CMeanFunction * m )
virtualinherited

set mean

Parameters
 m mean function to set

Definition at line 285 of file InferenceMethod.h.

 virtual void set_minimization_max ( float64_t max )
virtualinherited

set maximum for Brent's minimization method

Parameters
 max maximum for Brent's minimization method

Definition at line 204 of file LaplacianInferenceMethod.h.

 virtual void set_minimization_tolerance ( float64_t tol )
virtualinherited

set tolerance for Brent's minimization method

Parameters
 tol tolerance for Brent's minimization method

Definition at line 192 of file LaplacianInferenceMethod.h.

 virtual void set_model ( CLikelihoodModel * mod )
virtualinherited

set likelihood model

Parameters
 mod model to set

Reimplemented in CKLInferenceMethod, and CKLDualInferenceMethod.

Definition at line 319 of file InferenceMethod.h.

 virtual void set_newton_iterations ( int32_t iter )
virtualinherited

set max Newton iterations

Parameters
 iter max Newton iterations

Definition at line 180 of file LaplacianInferenceMethod.h.

 virtual void set_newton_tolerance ( float64_t tol )
virtualinherited

set tolerance for newton iterations

Parameters
 tol tolerance for newton iterations to set

Definition at line 168 of file LaplacianInferenceMethod.h.

 virtual void set_scale ( float64_t scale )
virtualinherited

set kernel scale

Parameters
 scale scale to be set

Definition at line 336 of file InferenceMethod.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 140 of file SGObject.cpp.

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

Reimplemented from CInferenceMethod.

Definition at line 220 of file LaplacianInferenceMethod.h.

 virtual bool supports_multiclass ( ) const
virtualinherited

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

Returns
false

Definition at line 357 of file InferenceMethod.h.

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

Reimplemented from CInferenceMethod.

Definition at line 210 of file LaplacianInferenceMethod.h.

 void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 250 of file SGObject.cpp.

 void update ( )
virtualinherited

update data all matrices

Reimplemented from CInferenceMethod.

Definition at line 98 of file LaplacianInferenceMethod.cpp.

 void update_alpha ( )
protectedvirtual

update alpha matrix

Reimplemented from CLaplacianInferenceMethod.

Definition at line 181 of file LaplacianInferenceMethodWithLBFGS.cpp.

 void update_approx_cov ( )
protectedvirtualinherited

update covariance matrix of the approximation to the posterior

Definition at line 194 of file LaplacianInferenceMethod.cpp.

 void update_chol ( )
protectedvirtualinherited

update cholesky matrix

Implements CInferenceMethod.

Definition at line 214 of file LaplacianInferenceMethod.cpp.

 void update_deriv ( )
protectedvirtualinherited

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

Implements CInferenceMethod.

Definition at line 397 of file LaplacianInferenceMethod.cpp.

 void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 196 of file SGObject.cpp.

 void update_train_kernel ( )
protectedvirtualinherited

update train kernel matrix

Reimplemented in CFITCInferenceMethod.

Definition at line 279 of file InferenceMethod.cpp.

## Member Data Documentation

 SGVector d2lp
protectedinherited

second derivative of log likelihood with respect to function location

Definition at line 315 of file LaplacianInferenceMethod.h.

 SGVector d3lp
protectedinherited

third derivative of log likelihood with respect to function location

Definition at line 318 of file LaplacianInferenceMethod.h.

 SGVector dlp
protectedinherited

derivative of log likelihood with respect to function location

Definition at line 312 of file LaplacianInferenceMethod.h.

 SGIO* io
inherited

io

Definition at line 461 of file SGObject.h.

 SGVector m_alpha
protectedinherited

alpha vector used in process mean calculation

Definition at line 445 of file InferenceMethod.h.

 SGVector m_dfhat
protectedinherited

Definition at line 320 of file LaplacianInferenceMethod.h.

 CFeatures* m_features
protectedinherited

features to use

Definition at line 439 of file InferenceMethod.h.

 SGVector m_g
protectedinherited

Definition at line 324 of file LaplacianInferenceMethod.h.

inherited

parameters wrt which we can compute gradients

Definition at line 476 of file SGObject.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 482 of file SGObject.h.

 CKernel* m_kernel
protectedinherited

covariance function

Definition at line 430 of file InferenceMethod.h.

 SGMatrix m_ktrtr
protectedinherited

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

Definition at line 454 of file InferenceMethod.h.

 SGMatrix m_L
protectedinherited

upper triangular factor of Cholesky decomposition

Definition at line 448 of file InferenceMethod.h.

 CLabels* m_labels
protectedinherited

labels of features

Definition at line 442 of file InferenceMethod.h.

 CMeanFunction* m_mean
protectedinherited

mean function

Definition at line 433 of file InferenceMethod.h.

 CLikelihoodModel* m_model
protectedinherited

likelihood function to use

Definition at line 436 of file InferenceMethod.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 473 of file SGObject.h.

 SGVector m_mu
protectedinherited

mean vector of the approximation to the posterior

Definition at line 300 of file LaplacianInferenceMethod.h.

 ParameterMap* m_parameter_map
inherited

map for different parameter versions

Definition at line 479 of file SGObject.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 470 of file SGObject.h.

 float64_t m_scale
protectedinherited

kernel scale

Definition at line 451 of file InferenceMethod.h.

 SGMatrix m_Sigma
protectedinherited

covariance matrix of the approximation to the posterior

Definition at line 303 of file LaplacianInferenceMethod.h.

 SGMatrix m_Z
protectedinherited

Definition at line 322 of file LaplacianInferenceMethod.h.

 Parallel* parallel
inherited

parallel

Definition at line 464 of file SGObject.h.

 SGVector sW
protectedinherited

square root of W

Definition at line 309 of file LaplacianInferenceMethod.h.

 Version* version
inherited

version

Definition at line 467 of file SGObject.h.

 protectedinherited

noise matrix

Definition at line 306 of file LaplacianInferenceMethod.h.

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

SHOGUN Machine Learning Toolbox - Documentation