SHOGUN  4.1.0
CSingleLaplacianInferenceMethodWithLBFGS Class Reference

## Detailed Description

The Laplace approximation inference method with LBFGS class for regression and binary classification.

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 65 of file SingleLaplacianInferenceMethodWithLBFGS.h.

Inheritance diagram for CSingleLaplacianInferenceMethodWithLBFGS:
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## Public Member Functions

CSingleLaplacianInferenceMethodWithLBFGS ()

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

virtual ~CSingleLaplacianInferenceMethodWithLBFGS ()

virtual const char * get_name () const

virtual void set_lbfgs_parameters (int m=100, int max_linesearch=1000, int linesearch=LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE, int max_iterations=1000, float64_t delta=0.0, int past=0, float64_t epsilon=1e-5, 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 void set_newton_method (bool enable_newton_if_fail)

virtual EInferenceType get_inference_type () const

virtual float64_t get_negative_log_marginal_likelihood ()

virtual bool supports_regression () const

virtual bool supports_binary () const

virtual SGVector< float64_tget_diagonal_vector ()

virtual void update ()

virtual SGVector< float64_tget_posterior_mean ()

virtual SGMatrix< float64_tget_cholesky ()

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)

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 SGMatrix< float64_tget_multiclass_E ()

virtual CSGObjectshallow_copy () const

virtual CSGObjectdeep_copy () const

virtual bool is_generic (EPrimitiveType *generic) const

template<class T >
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

void unset_generic ()

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

virtual bool save_serializable (CSerializableFile *file, const char *prefix="")

virtual bool load_serializable (CSerializableFile *file, const char *prefix="")

void set_global_io (SGIO *io)

SGIOget_global_io ()

void set_global_parallel (Parallel *parallel)

Parallelget_global_parallel ()

void set_global_version (Version *version)

Versionget_global_version ()

SGStringList< char > get_modelsel_names ()

void print_modsel_params ()

char * get_modsel_param_descr (const char *param_name)

index_t get_modsel_param_index (const char *param_name)

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

virtual void update_parameter_hash ()

virtual bool parameter_hash_changed ()

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

virtual CSGObjectclone ()

## Static Public Member Functions

static
CSingleLaplacianInferenceMethod
obtain_from_generic (CInferenceMethod *inference)

## Public Attributes

SGIOio

Parallelparallel

Versionversion

Parameterm_parameters

Parameterm_model_selection_parameters

uint32_t m_hash

## Protected Member Functions

virtual void update_alpha ()

virtual void update_init ()

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 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_sW

SGVector< float64_tm_d2lp

SGVector< float64_tm_d3lp

SGVector< float64_tm_dfhat

SGMatrix< float64_tm_Z

SGVector< float64_tm_g

float64_t m_Psi

SGVector< float64_tm_dlp

SGVector< float64_tm_W

SGVector< float64_tm_mu

SGMatrix< float64_tm_Sigma

float64_t m_tolerance

index_t m_iter

float64_t m_opt_tolerance

float64_t m_opt_max

CKernelm_kernel

CMeanFunctionm_mean

CLikelihoodModelm_model

CFeaturesm_features

CLabelsm_labels

SGVector< float64_tm_alpha

SGMatrix< float64_tm_L

float64_t m_log_scale

SGMatrix< float64_tm_ktrtr

SGMatrix< float64_tm_E

## Constructor & Destructor Documentation

 CSingleLaplacianInferenceMethodWithLBFGS ( )

Definition at line 43 of file SingleLaplacianInferenceMethodWithLBFGS.cpp.

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

Definition at line 49 of file SingleLaplacianInferenceMethodWithLBFGS.cpp.

 ~CSingleLaplacianInferenceMethodWithLBFGS ( )
virtual

Definition at line 160 of file SingleLaplacianInferenceMethodWithLBFGS.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 597 of file SGObject.cpp.

 void check_members ( ) const
protectedvirtualinherited

check if members of object are valid for inference

Definition at line 309 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 714 of file SGObject.cpp.

protectedvirtualinherited

Reimplemented from CInferenceMethod.

Definition at line 80 of file LaplacianInferenceBase.cpp.

 CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 198 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 618 of file SGObject.cpp.

 SGMatrix< float64_t > get_cholesky ( )
virtualinherited

get alpha vector

Returns
vector to compute posterior mean of Gaussian Process:

$\mu = K\alpha+meanf$

where $$\mu$$ is the mean, $$K$$ is the prior covariance matrix, and $$meanf\f is the mean prior fomr MeanFunction */ virtual SGVector<float64_t> get_alpha(); /** get Cholesky decomposition matrix @return Cholesky decomposition of matrix: for binary and regression case \f[ L = Cholesky(W^{\frac{1}{2}}*K*W^{\frac{1}{2}}+I) \f] where$$ $$is the prior covariance matrix,$$sW $$is the vector returned by get_diagonal_vector(), and$$ $$is the identity matrix. for multiclass case \f[ M = Cholesky(\sum_\text{c}{E_\text{c}) \f] where$${c}

Definition at line 115 of file LaplacianInferenceBase.cpp.

 void * get_derivative_helper ( void * p )
staticprotectedinherited

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

Definition at line 255 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 450 of file SingleLaplacianInferenceMethod.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 515 of file SingleLaplacianInferenceMethod.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 481 of file SingleLaplacianInferenceMethod.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 557 of file SingleLaplacianInferenceMethod.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.

Definition at line 95 of file SingleLaplacianInferenceMethod.cpp.

 virtual CFeatures* get_features ( )
virtualinherited

get features

Returns
features

Definition at line 266 of file InferenceMethod.h.

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 235 of file SGObject.cpp.

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 277 of file SGObject.cpp.

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 290 of file SGObject.cpp.

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

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

Implements CDifferentiableFunction.

Definition at line 245 of file InferenceMethod.h.

 virtual EInferenceType get_inference_type ( ) const
virtualinherited

return what type of inference we are

Returns
inference type Laplacian_Single

Reimplemented from CLaplacianInferenceBase.

Definition at line 72 of file SingleLaplacianInferenceMethod.h.

 virtual CKernel* get_kernel ( )
virtualinherited

get kernel

Returns
kernel

Definition at line 283 of file InferenceMethod.h.

 virtual CLabels* get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 317 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 (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 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). Storing all number of log-domain ensures numerical stability.

Parameters
 num_importance_samples the number of importance samples $$n$$ from $$q(f|y, \theta)$$. ridge_size scalar that is added to the diagonal of the involved Gaussian distribution's covariance of GP prior and posterior approximation to stabilise things. Increase if covariance matrix is not numerically positive semi-definite.
Returns
unbiased estimate of the marginal likelihood function $$p(y|\theta),$$ in log-domain.

Definition at line 126 of file InferenceMethod.cpp.

 virtual CMeanFunction* get_mean ( )
virtualinherited

get mean

Returns
mean

Definition at line 300 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 177 of file LaplacianInferenceBase.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 165 of file LaplacianInferenceBase.h.

 CLikelihoodModel* get_model ( )
inherited

get likelihood model

Returns
likelihood

Definition at line 334 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 498 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 522 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 535 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 72 of file InferenceMethod.cpp.

 virtual const char* get_name ( ) const
virtual

returns the name of the inference method

Returns
name SingleLaplacian

Reimplemented from CSingleLaplacianInferenceMethod.

Definition at line 91 of file SingleLaplacianInferenceMethodWithLBFGS.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 121 of file SingleLaplacianInferenceMethod.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 185 of file InferenceMethod.cpp.

 virtual int32_t get_newton_iterations ( )
virtualinherited

get max Newton iterations

Returns
max Newton iterations

Definition at line 153 of file LaplacianInferenceBase.h.

 virtual float64_t get_newton_tolerance ( )
virtualinherited

get tolerance for newton iterations

Returns
tolerance for newton iterations

Definition at line 141 of file LaplacianInferenceBase.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:

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

Returns
covariance matrix

Implements CInferenceMethod.

Definition at line 124 of file LaplacianInferenceBase.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 590 of file SingleLaplacianInferenceMethod.cpp.

 float64_t get_scale ( ) const
virtualinherited

get kernel scale

Returns
kernel scale

Definition at line 61 of file InferenceMethod.cpp.

 virtual SGVector get_value ( )
virtualinherited

get the function value

Returns
vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 255 of file 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 296 of file SGObject.cpp.

 bool load_serializable ( CSerializableFile * file, const char * prefix = "" )
virtualinherited

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

Parameters
 file where to load from prefix prefix for members 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 369 of file SGObject.cpp.

 void load_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

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

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 426 of file SGObject.cpp.

 void load_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

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

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 421 of file SGObject.cpp.

 CSingleLaplacianInferenceMethod * obtain_from_generic ( CInferenceMethod * inference )
staticinherited

helper method used to specialize a base class instance

Parameters
 inference inference method
Returns
casted CSingleLaplacianInferenceMethod object

Definition at line 104 of file SingleLaplacianInferenceMethod.cpp.

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

Definition at line 262 of file SGObject.cpp.

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 474 of file SGObject.cpp.

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

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 308 of file SGObject.cpp.

 bool save_serializable ( CSerializableFile * file, const char * prefix = "" )
virtualinherited

Save this object to file.

Parameters
 file where to save the object; will be closed during returning if PREFIX is an empty string. prefix prefix for members 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 314 of file SGObject.cpp.

 void save_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

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

Exceptions
 ShogunException will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 436 of file SGObject.cpp.

 void save_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

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

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 431 of file SGObject.cpp.

 virtual void set_features ( CFeatures * feat )
virtualinherited

set features

Parameters
 feat features to set

Definition at line 272 of file InferenceMethod.h.

 void set_generic ( )
inherited

Definition at line 41 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 46 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 51 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 56 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 61 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 66 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 71 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 76 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 81 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 86 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 91 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 96 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 101 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 106 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 111 of file SGObject.cpp.

 void set_generic ( )
inherited

set generic type to T

 void set_global_io ( SGIO * io )
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 set the io object
Parameters
 io io object to use

Definition at line 228 of file SGObject.cpp.

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 241 of file SGObject.cpp.

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 283 of file SGObject.cpp.

 virtual void set_kernel ( CKernel * kern )
virtualinherited

set kernel

Parameters
 kern kernel to set

Reimplemented in CSingleSparseInferenceBase.

Definition at line 289 of file InferenceMethod.h.

 virtual void set_labels ( CLabels * lab )
virtualinherited

set labels

Parameters
 lab label to set

Definition at line 323 of file InferenceMethod.h.

 void set_lbfgs_parameters ( int m = 100, int max_linesearch = 1000, int linesearch = LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE, int max_iterations = 1000, float64_t delta = 0.0, int past = 0, float64_t epsilon = 1e-5, 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 66 of file SingleLaplacianInferenceMethodWithLBFGS.cpp.

 virtual void set_mean ( CMeanFunction * m )
virtualinherited

set mean

Parameters
 m mean function to set

Definition at line 306 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 183 of file LaplacianInferenceBase.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 171 of file LaplacianInferenceBase.h.

 virtual void set_model ( CLikelihoodModel * mod )
virtualinherited

set likelihood model

Parameters
 mod model to set

Reimplemented in CKLInferenceMethod, and CKLDualInferenceMethod.

Definition at line 340 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 159 of file LaplacianInferenceBase.h.

 void set_newton_method ( bool enable_newton_if_fail )
virtual

Definition at line 60 of file SingleLaplacianInferenceMethodWithLBFGS.cpp.

 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 147 of file LaplacianInferenceBase.h.

 void set_scale ( float64_t scale )
virtualinherited

set kernel scale

Parameters
 scale scale to be set

Definition at line 66 of file InferenceMethod.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 192 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 108 of file SingleLaplacianInferenceMethod.h.

 virtual bool supports_multiclass ( ) const
virtualinherited

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

Returns
false

Definition at line 378 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 98 of file SingleLaplacianInferenceMethod.h.

 void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 303 of file SGObject.cpp.

 void update ( )
virtualinherited

Reimplemented from CLaplacianInferenceBase.

Definition at line 234 of file SingleLaplacianInferenceMethod.cpp.

 void update_alpha ( )
protectedvirtual

update alpha matrix

Reimplemented from CSingleLaplacianInferenceMethod.

Definition at line 185 of file SingleLaplacianInferenceMethodWithLBFGS.cpp.

 void update_approx_cov ( )
protectedvirtualinherited

update covariance matrix of the approximation to the posterior

Implements CLaplacianInferenceBase.

Definition at line 162 of file SingleLaplacianInferenceMethod.cpp.

 void update_chol ( )
protectedvirtualinherited

update cholesky matrix

Implements CInferenceMethod.

Definition at line 182 of file SingleLaplacianInferenceMethod.cpp.

 void update_deriv ( )
protectedvirtualinherited

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

Implements CInferenceMethod.

Definition at line 394 of file SingleLaplacianInferenceMethod.cpp.

 void update_init ( )
protectedvirtualinherited

Definition at line 249 of file SingleLaplacianInferenceMethod.cpp.

 void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

 void update_train_kernel ( )
protectedvirtualinherited

update train kernel matrix

Reimplemented in CSparseInferenceBase.

Definition at line 324 of file InferenceMethod.cpp.

## Member Data Documentation

 SGIO* io
inherited

io

Definition at line 387 of file SGObject.h.

 SGVector m_alpha
protectedinherited

alpha vector used in process mean calculation

Definition at line 475 of file InferenceMethod.h.

 SGVector m_d2lp
protectedinherited

second derivative of log likelihood with respect to function location

Definition at line 208 of file SingleLaplacianInferenceMethod.h.

 SGVector m_d3lp
protectedinherited

third derivative of log likelihood with respect to function location

Definition at line 211 of file SingleLaplacianInferenceMethod.h.

 SGVector m_dfhat
protectedinherited

Definition at line 213 of file SingleLaplacianInferenceMethod.h.

 SGVector m_dlp
protectedinherited

derivative of log likelihood with respect to function location

Definition at line 197 of file LaplacianInferenceBase.h.

 SGMatrix m_E
protectedinherited

the matrix used for multi classification

Definition at line 487 of file InferenceMethod.h.

 CFeatures* m_features
protectedinherited

features to use

Definition at line 469 of file InferenceMethod.h.

 SGVector m_g
protectedinherited

Definition at line 217 of file SingleLaplacianInferenceMethod.h.

inherited

parameters wrt which we can compute gradients

Definition at line 402 of file SGObject.h.

protectedinherited

Definition at line 490 of file InferenceMethod.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 405 of file SGObject.h.

 index_t m_iter
protectedinherited

max Newton's iterations

Definition at line 212 of file LaplacianInferenceBase.h.

 CKernel* m_kernel
protectedinherited

covariance function

Definition at line 460 of file InferenceMethod.h.

 SGMatrix m_ktrtr
protectedinherited

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

Definition at line 484 of file InferenceMethod.h.

 SGMatrix m_L
protectedinherited

upper triangular factor of Cholesky decomposition

Definition at line 478 of file InferenceMethod.h.

 CLabels* m_labels
protectedinherited

labels of features

Definition at line 472 of file InferenceMethod.h.

 float64_t m_log_scale
protectedinherited

kernel scale

Definition at line 481 of file InferenceMethod.h.

 CMeanFunction* m_mean
protectedinherited

mean function

Definition at line 463 of file InferenceMethod.h.

 CLikelihoodModel* m_model
protectedinherited

likelihood function to use

Definition at line 466 of file InferenceMethod.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 399 of file SGObject.h.

 SGVector m_mu
protectedinherited

mean vector of the approximation to the posterior

Definition at line 203 of file LaplacianInferenceBase.h.

 float64_t m_opt_max
protectedinherited

max iterations for Brent's minimization method

Definition at line 218 of file LaplacianInferenceBase.h.

 float64_t m_opt_tolerance
protectedinherited

amount of tolerance for Brent's minimization method

Definition at line 215 of file LaplacianInferenceBase.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 396 of file SGObject.h.

 float64_t m_Psi
protectedinherited

Definition at line 219 of file SingleLaplacianInferenceMethod.h.

 SGMatrix m_Sigma
protectedinherited

covariance matrix of the approximation to the posterior

Definition at line 206 of file LaplacianInferenceBase.h.

 SGVector m_sW
protectedinherited

square root of W

Definition at line 205 of file SingleLaplacianInferenceMethod.h.

 float64_t m_tolerance
protectedinherited

amount of tolerance for Newton's iterations

Definition at line 209 of file LaplacianInferenceBase.h.

 SGVector m_W
protectedinherited

noise matrix

Definition at line 200 of file LaplacianInferenceBase.h.

 SGMatrix m_Z
protectedinherited

Definition at line 215 of file SingleLaplacianInferenceMethod.h.

 Parallel* parallel
inherited

parallel

Definition at line 390 of file SGObject.h.

 Version* version
inherited

version

Definition at line 393 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation