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CKLLowerTriangularInferenceMethod Class Referenceabstract

Detailed Description

The KL approximation inference method class.

The class is implemented based on the KL method in the Challis's paper, which uses lower triangular represention.

Code adapted from http://hannes.nickisch.org/code/approxXX.tar.gz and Gaussian Process Machine Learning Toolbox http://www.gaussianprocess.org/gpml/code/matlab/doc/ and the reference paper is Challis, Edward, and David Barber. "Concave Gaussian variational approximations for inference in large-scale Bayesian linear models." International conference on Artificial Intelligence and Statistics. 2011.

Note that "lowerTriangular" means lowerTriangular represention of the variational co-variance matrix is explicitly used in inference

Definition at line 69 of file KLLowerTriangularInferenceMethod.h.

Inheritance diagram for CKLLowerTriangularInferenceMethod:
Inheritance graph
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Public Member Functions

 CKLLowerTriangularInferenceMethod ()
 
 CKLLowerTriangularInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
 
virtual ~CKLLowerTriangularInferenceMethod ()
 
virtual const char * get_name () const
 
virtual SGVector< float64_t > get_diagonal_vector ()
 
virtual EInferenceType get_inference_type () const
 
virtual float64_t get_negative_log_marginal_likelihood ()
 
virtual SGVector< float64_t > get_posterior_mean ()
 
virtual SGMatrix< float64_t > get_posterior_covariance ()
 
virtual bool supports_regression () const
 
virtual bool supports_binary () const
 
virtual void set_model (CLikelihoodModel *mod)
 
virtual void update ()
 
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 SGMatrix< float64_t > get_cholesky ()
 
virtual void set_noise_factor (float64_t noise_factor)
 
virtual void set_max_attempt (index_t max_attempt)
 
virtual void set_exp_factor (float64_t exp_factor)
 
virtual void set_min_coeff_kernel (float64_t min_coeff_kernel)
 
float64_t get_marginal_likelihood_estimate (int32_t num_importance_samples=1, float64_t ridge_size=1e-15)
 
virtual CMap< TParameter
*, SGVector< float64_t > > * 		get_negative_log_marginal_likelihood_derivatives (CMap< TParameter *, CSGObject * > *parameters)
 
virtual CMap< TParameter
*, SGVector< float64_t > > * 		get_gradient (CMap< TParameter *, CSGObject * > *parameters)
 
virtual SGVector< float64_t > get_value ()
 
virtual CFeatures * get_features ()
 
virtual void set_features (CFeatures *feat)
 
virtual CKernel * get_kernel ()
 
virtual void set_kernel (CKernel *kern)
 
virtual CMeanFunction * get_mean ()
 
virtual void set_mean (CMeanFunction *m)
 
virtual CLabels * get_labels ()
 
virtual void set_labels (CLabels *lab)
 
CLikelihoodModel * get_model ()
 
virtual float64_t get_scale () const
 
virtual void set_scale (float64_t scale)
 
virtual bool supports_multiclass () const
 
virtual SGMatrix< float64_t > get_multiclass_E ()
 
virtual CSGObject * shallow_copy () const
 
virtual CSGObject * deep_copy () const
 
virtual bool is_generic (EPrimitiveType *generic) const
 
template<class T >
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
void unset_generic ()
 
virtual void print_serializable (const char *prefix="")
 
virtual bool save_serializable (CSerializableFile *file, const char *prefix="")
 
virtual bool load_serializable (CSerializableFile *file, const char *prefix="")
 
void set_global_io (SGIO *io)
 
SGIO * get_global_io ()
 
void set_global_parallel (Parallel *parallel)
 
Parallel * get_global_parallel ()
 
void set_global_version (Version *version)
 
Version * get_global_version ()
 
SGStringList< char > get_modelsel_names ()
 
void print_modsel_params ()
 
char * get_modsel_param_descr (const char *param_name)
 
index_t get_modsel_param_index (const char *param_name)
 
void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
 
virtual void update_parameter_hash ()
 
virtual bool parameter_hash_changed ()
 
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
 
virtual CSGObject * clone ()
 

Public Attributes

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

Protected Member Functions

virtual void update_chol ()
 
virtual void update_deriv ()
 
virtual float64_t get_derivative_related_cov (SGMatrix< float64_t > dK)
 
virtual void update_approx_cov ()
 
Eigen::MatrixXd solve_inverse (Eigen::MatrixXd A)
 
virtual void update_init ()
 
virtual void update_Sigma ()=0
 
virtual void update_InvK_Sigma ()=0
 
virtual void compute_gradient ()
 
virtual Eigen::LDLT
< Eigen::MatrixXd, 0x1 > 		update_init_helper ()
 
virtual
CVariationalGaussianLikelihood * 		get_variational_likelihood () const
 
virtual void check_variational_likelihood (CLikelihoodModel *mod) const
 
virtual float64_t lbfgs_optimization ()
 
virtual SGVector< float64_t > get_derivative_wrt_inference_method (const TParameter *param)
 
virtual SGVector< float64_t > get_derivative_wrt_likelihood_model (const TParameter *param)
 
virtual SGVector< float64_t > get_derivative_wrt_kernel (const TParameter *param)
 
virtual SGVector< float64_t > get_derivative_wrt_mean (const TParameter *param)
 
virtual float64_t get_negative_log_marginal_likelihood_helper ()=0
 
virtual float64_t get_nlml_wrt_parameters ()
 
virtual void get_gradient_of_nlml_wrt_parameters (SGVector< float64_t > gradient)=0
 
virtual bool lbfgs_precompute ()=0
 
virtual void check_members () const
 
virtual void update_alpha ()=0
 
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

SGMatrix< float64_t > m_InvK_Sigma
 
SGVector< float64_t > m_mean_vec
 
float64_t m_log_det_Kernel
 
SGMatrix< float64_t > m_Kernel_LsD
 
SGVector< index_t > m_Kernel_P
 
float64_t m_min_coeff_kernel
 
float64_t m_noise_factor
 
float64_t m_exp_factor
 
index_t m_max_attempt
 
SGVector< float64_t > m_mu
 
SGMatrix< float64_t > m_Sigma
 
SGVector< float64_t > m_s2
 
int m_m
 
int m_max_linesearch
 
int m_linesearch
 
int m_max_iterations
 
float64_t m_delta
 
int m_past
 
float64_t m_epsilon
 
float64_t m_min_step
 
float64_t m_max_step
 
float64_t m_ftol
 
float64_t m_wolfe
 
float64_t m_gtol
 
float64_t m_xtol
 
float64_t m_orthantwise_c
 
int m_orthantwise_start
 
int m_orthantwise_end
 
CKernel * m_kernel
 
CMeanFunction * m_mean
 
CLikelihoodModel * m_model
 
CFeatures * m_features
 
CLabels * m_labels
 
SGVector< float64_t > m_alpha
 
SGMatrix< float64_t > m_L
 
float64_t m_log_scale
 
SGMatrix< float64_t > m_ktrtr
 
SGMatrix< float64_t > m_E
 
bool m_gradient_update
 

Constructor & Destructor Documentation

CKLLowerTriangularInferenceMethod ( )

default constructor

Definition at line 54 of file KLLowerTriangularInferenceMethod.cpp.

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

constructor

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

Definition at line 59 of file KLLowerTriangularInferenceMethod.cpp.

~CKLLowerTriangularInferenceMethod ( )
virtual

Definition at line 87 of file KLLowerTriangularInferenceMethod.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 597 of file SGObject.cpp.

void check_members ( ) const
protectedvirtualinherited

check if members of object are valid for inference

Reimplemented in CSparseInferenceBase, CExactInferenceMethod, CFITCInferenceMethod, CSparseVGInferenceMethod, and CMultiLaplacianInferenceMethod.

Definition at line 309 of file InferenceMethod.cpp.

void check_variational_likelihood ( CLikelihoodModel *  mod) const
protectedvirtualinherited

check the provided likelihood model supports variational inference

Parameters
modthe provided likelihood model
Returns
whether the provided likelihood model supports variational inference or not

Definition at line 57 of file KLInferenceMethod.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.

void compute_gradient ( )
protectedvirtualinherited

update gradients

Reimplemented from CInferenceMethod.

Definition at line 156 of file KLInferenceMethod.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
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 618 of file SGObject.cpp.

SGMatrix< float64_t > get_cholesky ( )
virtualinherited

get Cholesky decomposition matrix

Returns
Cholesky decomposition of matrix:

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

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

Note that in some sub class L is not the Cholesky decomposition In this case, L will still be used to compute required matrix for prediction see CGaussianProcessMachine::get_posterior_variances()

Definition at line 451 of file KLInferenceMethod.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.

float64_t get_derivative_related_cov ( SGMatrix< float64_t >  dK)
protectedvirtual

compute matrices which are required to compute negative log marginal likelihood derivatives wrt hyperparameter in cov function Note that get_derivative_wrt_inference_method(const TParameter* param) and get_derivative_wrt_kernel(const TParameter* param) will call this function

Parameters
thegradient wrt hyperparameter related to cov

Implements CKLInferenceMethod.

Definition at line 153 of file KLLowerTriangularInferenceMethod.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
paramparameter of CInferenceMethod class
Returns
derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 411 of file KLInferenceMethod.cpp.

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

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

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

Implements CInferenceMethod.

Definition at line 427 of file KLInferenceMethod.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
paramparameter of given likelihood model
Returns
derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 337 of file KLInferenceMethod.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
paramparameter of given mean function
Returns
derivative of negative log marginal likelihood

Implements CInferenceMethod.

Definition at line 353 of file KLInferenceMethod.cpp.

SGVector< float64_t > get_diagonal_vector ( )
virtual

get diagonal vector

Returns
diagonal of matrix used to calculate posterior covariance matrix:

Note that this vector is not avaliable for the KL method

The diagonal vector W is NOT used in this KL method Therefore, return empty vector

Definition at line 91 of file KLLowerTriangularInferenceMethod.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<TParameter*, SGVector<float64_t> >* get_gradient ( CMap< TParameter *, CSGObject * > *  parameters)
virtualinherited

get the gradient

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

Implements CDifferentiableFunction.

Definition at line 245 of file InferenceMethod.h.

virtual void get_gradient_of_nlml_wrt_parameters ( SGVector< float64_t >  gradient)
protectedpure virtualinherited

compute the gradient wrt variational parameters given the current variational parameters (mu and s2)

Returns
gradient of negative log marginal likelihood

Implemented in CKLCovarianceInferenceMethod, CKLDualInferenceMethod, CKLApproxDiagonalInferenceMethod, and CKLCholeskyInferenceMethod.

virtual EInferenceType get_inference_type ( ) const
virtualinherited

return what type of inference we are

Reimplemented from CInferenceMethod.

Reimplemented in CKLApproxDiagonalInferenceMethod, CKLCholeskyInferenceMethod, CKLCovarianceInferenceMethod, and CKLDualInferenceMethod.

Definition at line 99 of file KLInferenceMethod.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_samplesthe number of importance samples \(n\) from \( q(f|y, \theta) \).
ridge_sizescalar that is added to the diagonal of the involved Gaussian distribution's covariance of GP prior and posterior approximation to stabilise things. Increase if covariance matrix is not numerically positive semi-definite.
Returns
unbiased estimate of the marginal likelihood function \( p(y|\theta),\) in log-domain.

Definition at line 126 of file InferenceMethod.cpp.

virtual CMeanFunction* get_mean ( )
virtualinherited

get mean

Returns
mean

Definition at line 300 of file InferenceMethod.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_namename 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_namename 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 KLLowerTriangularInferenceMethod

Reimplemented from CKLInferenceMethod.

Reimplemented in CKLApproxDiagonalInferenceMethod, and CKLCholeskyInferenceMethod.

Definition at line 92 of file KLLowerTriangularInferenceMethod.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 329 of file KLInferenceMethod.cpp.

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

get log marginal likelihood gradient

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

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

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

Definition at line 185 of file InferenceMethod.cpp.

virtual float64_t get_negative_log_marginal_likelihood_helper ( )
protectedpure virtualinherited

the helper function to compute the negative log marginal likelihood

Returns
negative log marginal likelihood

Implemented in CKLDualInferenceMethod, CKLCovarianceInferenceMethod, CKLApproxDiagonalInferenceMethod, and CKLCholeskyInferenceMethod.

float64_t get_nlml_wrt_parameters ( )
protectedvirtualinherited

compute the negative log marginal likelihood given the current variational parameters (mu and s2)

Returns
negative log marginal likelihood

Definition at line 286 of file KLInferenceMethod.cpp.

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 251 of file KLInferenceMethod.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:

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

Returns
mean vector

Implements CInferenceMethod.

Definition at line 244 of file KLInferenceMethod.cpp.

float64_t get_scale ( ) const
virtualinherited

get kernel scale

Returns
kernel scale

Definition at line 61 of file InferenceMethod.cpp.

virtual SGVector<float64_t> get_value ( )
virtualinherited

get the function value

Returns
vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 255 of file InferenceMethod.h.

CVariationalGaussianLikelihood * get_variational_likelihood ( ) const
protectedvirtualinherited

this method is used to dynamic-cast the likelihood model, m_model, to variational likelihood model.

Definition at line 279 of file KLInferenceMethod.cpp.

bool is_generic ( EPrimitiveType *  generic) const
virtualinherited

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

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

Definition at line 296 of file SGObject.cpp.

float64_t lbfgs_optimization ( )
protectedvirtualinherited

Using L-BFGS to estimate posterior parameters

Reimplemented in CKLDualInferenceMethod.

Definition at line 382 of file KLInferenceMethod.cpp.

virtual bool lbfgs_precompute ( )
protectedpure virtualinherited

pre-compute the information for lbfgs optimization. This function needs to be called before calling get_negative_log_marginal_likelihood_wrt_parameters() and/or get_gradient_of_nlml_wrt_parameters(SGVector<float64_t> gradient)

Returns
true if precomputed parameters are valid

Implemented in CKLDualInferenceMethod, CKLCovarianceInferenceMethod, CKLApproxDiagonalInferenceMethod, and CKLCholeskyInferenceMethod.

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

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

Parameters
filewhere to load from
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 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
ShogunExceptionwill be thrown if an error occurs.

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

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
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 421 of file SGObject.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
prefixprefix 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
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 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
ShogunExceptionwill 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
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 431 of file SGObject.cpp.

void set_exp_factor ( float64_t  exp_factor)
virtualinherited

set exp factor to exponentially increase noise factor

Parameters
exp_factorshould be greater than 1.0 default value is 2

Definition at line 201 of file KLInferenceMethod.cpp.

virtual void set_features ( CFeatures *  feat)
virtualinherited

set features

Parameters
featfeatures 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

set the io object

Parameters
ioio object to use

Definition at line 228 of file SGObject.cpp.

void set_global_parallel ( Parallel *  parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 241 of file SGObject.cpp.

void set_global_version ( Version *  version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 283 of file SGObject.cpp.

virtual void set_kernel ( CKernel *  kern)
virtualinherited

set kernel

Parameters
kernkernel to set

Reimplemented in CSingleSparseInferenceBase.

Definition at line 289 of file InferenceMethod.h.

virtual void set_labels ( CLabels *  lab)
virtualinherited

set labels

Parameters
lablabel 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 
)
virtualinherited

Definition at line 293 of file KLInferenceMethod.cpp.

void set_max_attempt ( index_t  max_attempt)
virtualinherited

set max attempt to ensure Kernel matrix to be positive definite

Parameters
max_attemptshould be non-negative. 0 means infinity attempts default value is 0

Definition at line 195 of file KLInferenceMethod.cpp.

virtual void set_mean ( CMeanFunction *  m)
virtualinherited

set mean

Parameters
mmean function to set

Definition at line 306 of file InferenceMethod.h.

void set_min_coeff_kernel ( float64_t  min_coeff_kernel)
virtualinherited

set minimum coeefficient of kernel matrix used in LDLT factorization

Parameters
min_coeff_kernelshould be non-negative default value is 1e-5

Definition at line 189 of file KLInferenceMethod.cpp.

void set_model ( CLikelihoodModel *  mod)
virtualinherited

set variational likelihood model

Parameters
modmodel to set

Reimplemented from CInferenceMethod.

Reimplemented in CKLDualInferenceMethod.

Definition at line 67 of file KLInferenceMethod.cpp.

void set_noise_factor ( float64_t  noise_factor)
virtualinherited

set noise factor to ensure Kernel matrix to be positive definite by adding non-negative noise to diagonal elements of Kernel matrix

Parameters
noise_factorshould be non-negative default value is 1e-10

Definition at line 183 of file KLInferenceMethod.cpp.

void set_scale ( float64_t  scale)
virtualinherited

set kernel scale

Parameters
scalescale 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.

MatrixXd solve_inverse ( Eigen::MatrixXd  A)
protected

compute the inv(corrected_Kernel*sq(m_scale))*A

Parameters
Ainput matrix
Returns
inv(corrected_Kernel*sq(m_scale))*A:

Definition at line 126 of file KLLowerTriangularInferenceMethod.cpp.

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

Reimplemented from CInferenceMethod.

Definition at line 167 of file KLInferenceMethod.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 KL approximation inference method and given likelihood function supports regression

Reimplemented from CInferenceMethod.

Definition at line 157 of file KLInferenceMethod.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

update all matrices except gradients

Reimplemented from CInferenceMethod.

Definition at line 169 of file KLInferenceMethod.cpp.

virtual void update_alpha ( )
protectedpure virtualinherited

update alpha vector

Implemented in CEPInferenceMethod, CSingleFITCLaplacianInferenceMethod, CExactInferenceMethod, CSingleFITCLaplacianBase, CFITCInferenceMethod, CSparseVGInferenceMethod, CMultiLaplacianInferenceMethod, CKLDualInferenceMethod, CSingleLaplacianInferenceMethodWithLBFGS, CSingleFITCLaplacianInferenceMethodWithLBFGS, CSingleLaplacianInferenceMethod, CKLCovarianceInferenceMethod, CKLApproxDiagonalInferenceMethod, and CKLCholeskyInferenceMethod.

void update_approx_cov ( )
protectedvirtual

update covariance matrix of the approximation to the posterior

update_Sigma() does the similar job Therefore, this function body is empty

Implements CKLInferenceMethod.

Definition at line 167 of file KLLowerTriangularInferenceMethod.cpp.

void update_chol ( )
protectedvirtual

update cholesky matrix

Implements CInferenceMethod.

Definition at line 174 of file KLLowerTriangularInferenceMethod.cpp.

void update_deriv ( )
protectedvirtual

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

get_derivative_related_cov() does the similar job Therefore, this function body is empty

Implements CInferenceMethod.

Definition at line 99 of file KLLowerTriangularInferenceMethod.cpp.

void update_init ( )
protectedvirtual

correct the kernel matrix and factorizated the corrected Kernel matrix for update

Reimplemented from CKLInferenceMethod.

Definition at line 106 of file KLLowerTriangularInferenceMethod.cpp.

Eigen::LDLT< Eigen::MatrixXd > update_init_helper ( )
protectedvirtualinherited

a helper function used to correct the kernel matrix using LDLT factorization

Returns
the LDLT factorization of the corrected kernel matrix

Definition at line 212 of file KLInferenceMethod.cpp.

virtual void update_InvK_Sigma ( )
protectedpure virtual

compute inv(corrected_Kernel)*Sigma matrix

Implemented in CKLApproxDiagonalInferenceMethod, and CKLCholeskyInferenceMethod.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

virtual void update_Sigma ( )
protectedpure virtual

compute posterior Sigma matrix

Implemented in CKLApproxDiagonalInferenceMethod, and CKLCholeskyInferenceMethod.

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 369 of file SGObject.h.

SGVector<float64_t> m_alpha
protectedinherited

alpha vector used in process mean calculation

Definition at line 475 of file InferenceMethod.h.

float64_t m_delta
protectedinherited

Definition at line 440 of file KLInferenceMethod.h.

SGMatrix<float64_t> m_E
protectedinherited

the matrix used for multi classification

Definition at line 487 of file InferenceMethod.h.

float64_t m_epsilon
protectedinherited

Definition at line 446 of file KLInferenceMethod.h.

float64_t m_exp_factor
protectedinherited

The factor used to exponentially increase noise_factor

Definition at line 297 of file KLInferenceMethod.h.

CFeatures* m_features
protectedinherited

features to use

Definition at line 469 of file InferenceMethod.h.

float64_t m_ftol
protectedinherited

Definition at line 455 of file KLInferenceMethod.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 384 of file SGObject.h.

bool m_gradient_update
protectedinherited

Whether gradients are updated

Definition at line 490 of file InferenceMethod.h.

float64_t m_gtol
protectedinherited

Definition at line 461 of file KLInferenceMethod.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 387 of file SGObject.h.

SGMatrix<float64_t> m_InvK_Sigma
protected

The K^{-1}Sigma matrix

Definition at line 128 of file KLLowerTriangularInferenceMethod.h.

CKernel* m_kernel
protectedinherited

covariance function

Definition at line 460 of file InferenceMethod.h.

SGMatrix<float64_t> m_Kernel_LsD
protected

The L*sqrt(D) matrix, where L and D are defined in LDLT factorization on Kernel*sq(m_scale)

Definition at line 137 of file KLLowerTriangularInferenceMethod.h.

SGVector<index_t> m_Kernel_P
protected

The permutation sequence of P, where P are defined in LDLT factorization on Kernel*sq(m_scale)

Definition at line 140 of file KLLowerTriangularInferenceMethod.h.

SGMatrix<float64_t> m_ktrtr
protectedinherited

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

Definition at line 484 of file InferenceMethod.h.

SGMatrix<float64_t> 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.

int m_linesearch
protectedinherited

Definition at line 434 of file KLInferenceMethod.h.

float64_t m_log_det_Kernel
protected

The Log-determinant of Kernel

Definition at line 134 of file KLLowerTriangularInferenceMethod.h.

float64_t m_log_scale
protectedinherited

kernel scale

Definition at line 481 of file InferenceMethod.h.

int m_m
protectedinherited

Definition at line 428 of file KLInferenceMethod.h.

index_t m_max_attempt
protectedinherited

Max number of attempt to correct kernel matrix to be positive definite

Definition at line 300 of file KLInferenceMethod.h.

int m_max_iterations
protectedinherited

Definition at line 437 of file KLInferenceMethod.h.

int m_max_linesearch
protectedinherited

Definition at line 431 of file KLInferenceMethod.h.

float64_t m_max_step
protectedinherited

Definition at line 452 of file KLInferenceMethod.h.

CMeanFunction* m_mean
protectedinherited

mean function

Definition at line 463 of file InferenceMethod.h.

SGVector<float64_t> m_mean_vec
protected

The mean vector generated from mean function

Definition at line 131 of file KLLowerTriangularInferenceMethod.h.

float64_t m_min_coeff_kernel
protectedinherited

The minimum coeefficient of kernel matrix in LDLT factorization used to check whether the kernel matrix is positive definite or not

Definition at line 291 of file KLInferenceMethod.h.

float64_t m_min_step
protectedinherited

Definition at line 449 of file KLInferenceMethod.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 381 of file SGObject.h.

SGVector<float64_t> m_mu
protectedinherited

mean vector of the approximation to the posterior Note that m_mu is also a variational parameter

Definition at line 417 of file KLInferenceMethod.h.

float64_t m_noise_factor
protectedinherited

The factor used to ensure kernel matrix to be positive definite

Definition at line 294 of file KLInferenceMethod.h.

float64_t m_orthantwise_c
protectedinherited

Definition at line 467 of file KLInferenceMethod.h.

int m_orthantwise_end
protectedinherited

Definition at line 473 of file KLInferenceMethod.h.

int m_orthantwise_start
protectedinherited

Definition at line 470 of file KLInferenceMethod.h.

Parameter* m_parameters
inherited

parameters

Definition at line 378 of file SGObject.h.

int m_past
protectedinherited

Definition at line 443 of file KLInferenceMethod.h.

SGVector<float64_t> m_s2
protectedinherited

variational parameter sigma2 Note that sigma2 = diag(m_Sigma)

Definition at line 425 of file KLInferenceMethod.h.

SGMatrix<float64_t> m_Sigma
protectedinherited

covariance matrix of the approximation to the posterior

Definition at line 420 of file KLInferenceMethod.h.

float64_t m_wolfe
protectedinherited

Definition at line 458 of file KLInferenceMethod.h.

float64_t m_xtol
protectedinherited

Definition at line 464 of file KLInferenceMethod.h.

Parallel* parallel
inherited

parallel

Definition at line 372 of file SGObject.h.

Version* version
inherited

version

Definition at line 375 of file SGObject.h.

The documentation for this class was generated from the following files:
SHOGUN Machine Learning Toolbox - Documentation