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CKLCovarianceInferenceMethod Class Reference

Detailed Description

The KL approximation inference method class.

The class is implemented based on the KL method in the Nickisch's paper Note that lambda (m_W) is a diagonal vector defined in the paper. The implementation apply L-BFGS to finding optimal solution of negative log likelihood. Since lambda is always non-positive according to the paper, this implementation uses log(-lambda) as representation, which assumes lambda is always negative.

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 Nickisch, Hannes, and Carl Edward Rasmussen. "Approximations for Binary Gaussian Process Classification." Journal of Machine Learning Research 9.10 (2008).

The adapted Matlab code can be found at https://gist.github.com/yorkerlin/b64a015491833562d11a

Definition at line 72 of file KLCovarianceInferenceMethod.h.

Inheritance diagram for CKLCovarianceInferenceMethod:
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Public Member Functions

 CKLCovarianceInferenceMethod ()
 
 CKLCovarianceInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
 
virtual ~CKLCovarianceInferenceMethod ()
 
virtual const char * get_name () const
 
virtual EInferenceType get_inference_type () const
 
virtual SGVector< float64_t > get_alpha ()
 
virtual SGVector< float64_t > get_diagonal_vector ()
 
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 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)
 
virtual void register_minimizer (Minimizer *minimizer)
 
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)
 
bool has (const std::string &name) const
 
template<typename T >
bool has (const Tag< T > &tag) const
 
template<typename T , typename U = void>
bool has (const std::string &name) const
 
template<typename T >
void set (const Tag< T > &_tag, const T &value)
 
template<typename T , typename U = void>
void set (const std::string &name, const T &value)
 
template<typename T >
T get (const Tag< T > &_tag) const
 
template<typename T , typename U = void>
T get (const std::string &name) const
 
virtual void update_parameter_hash ()
 
virtual bool parameter_hash_changed ()
 
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
 
virtual CSGObject * clone ()
 

Static Public Member Functions

static
CKLCovarianceInferenceMethod * 		obtain_from_generic (CInference *inference)
 

Public Attributes

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

Protected Member Functions

virtual void update_approx_cov ()
 
virtual void update_alpha ()
 
virtual void update_chol ()
 
virtual void update_deriv ()
 
virtual float64_t get_negative_log_marginal_likelihood_helper ()
 
virtual void get_gradient_of_nlml_wrt_parameters (SGVector< float64_t > gradient)
 
virtual bool precompute ()
 
virtual float64_t get_derivative_related_cov (SGMatrix< float64_t > dK)
 
virtual void compute_gradient ()
 
virtual void update_init ()
 
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 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_nlml_wrt_parameters ()
 
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)
 
template<typename T >
void register_param (Tag< T > &_tag, const T &value)
 
template<typename T >
void register_param (const std::string &name, const T &value)
 

Static Protected Member Functions

static void * get_derivative_helper (void *p)
 

Protected Attributes

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
 
Minimizer * m_minimizer
 
CKernel * m_kernel
 
CMeanFunction * m_mean
 
CLikelihoodModel * m_model
 
CFeatures * m_features
 
CLabels * m_labels
 
SGVector< float64_t > m_alpha
 
SGMatrix< float64_t > m_L
 
float64_t m_log_scale
 
SGMatrix< float64_t > m_ktrtr
 
SGMatrix< float64_t > m_E
 
bool m_gradient_update
 

Constructor & Destructor Documentation

CKLCovarianceInferenceMethod ( )

default constructor

Definition at line 54 of file KLCovarianceInferenceMethod.cpp.

CKLCovarianceInferenceMethod ( 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 KLCovarianceInferenceMethod.cpp.

~CKLCovarianceInferenceMethod ( )
virtual

Definition at line 112 of file KLCovarianceInferenceMethod.cpp.

Member Function Documentation

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

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

Parameters
dictdictionary of parameters to be built.

Definition at line 630 of file SGObject.cpp.

void check_members ( ) const
protectedvirtualinherited

check if members of object are valid for inference

Reimplemented in CSparseInference, CMultiLaplaceInferenceMethod, CExactInferenceMethod, CFITCInferenceMethod, and CVarDTCInferenceMethod.

Definition at line 322 of file Inference.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 123 of file KLInference.cpp.

CSGObject * clone ( )
virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 747 of file SGObject.cpp.

void compute_gradient ( )
protectedvirtualinherited

update gradients

Reimplemented from CInference.

Definition at line 173 of file KLInference.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 231 of file SGObject.cpp.

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

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

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

Parameters
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 651 of file SGObject.cpp.

T get ( const Tag< T > &  _tag) const
inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
_tagname and type information of parameter
Returns
value of the parameter identified by the input tag

Definition at line 367 of file SGObject.h.

T get ( const std::string &  name) const
inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
namename of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 388 of file SGObject.h.

SGVector< float64_t > get_alpha ( )
virtual

get alpha vector

Returns
vector to compute posterior mean of Gaussian Process:

\[ \mu = K\alpha \]

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

Note that m_alpha contains not only the alpha vector defined in the reference but also a vector corresponding to the diagonal part of W

Note that alpha=K^{-1}(mu-mean), where mean is generated from mean function, K is generated from cov function and mu is not only the posterior mean but also the variational mean

Implements CInference.

Definition at line 89 of file KLCovarianceInferenceMethod.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()

Implements CInference.

Definition at line 412 of file KLInference.cpp.

void * get_derivative_helper ( void *  p)
staticprotectedinherited

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

Definition at line 268 of file Inference.cpp.

float64_t get_derivative_related_cov ( 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
dKthe gradient wrt hyperparameter related to cov

Implements CKLInference.

Definition at line 247 of file KLCovarianceInferenceMethod.cpp.

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

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

Parameters
paramparameter of CInference class
Returns
derivative of negative log marginal likelihood

Implements CInference.

Definition at line 372 of file KLInference.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 CInference.

Definition at line 388 of file KLInference.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 CInference.

Definition at line 297 of file KLInference.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 CInference.

Definition at line 313 of file KLInference.cpp.

SGVector< float64_t > get_diagonal_vector ( )
virtual

get diagonal vector

Returns
diagonal of matrix used to calculate posterior covariance matrix:

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

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

Implements CInference.

Definition at line 336 of file KLCovarianceInferenceMethod.cpp.

virtual CFeatures* get_features ( )
virtualinherited

get features

Returns
features

Definition at line 266 of file Inference.h.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 268 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 310 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 323 of file SGObject.cpp.

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

get the gradient

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

Implements CDifferentiableFunction.

Definition at line 245 of file Inference.h.

void get_gradient_of_nlml_wrt_parameters ( SGVector< float64_t >  gradient)
protectedvirtual

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

Returns
gradient of negative log marginal likelihood

Implements CKLInference.

Definition at line 169 of file KLCovarianceInferenceMethod.cpp.

virtual EInferenceType get_inference_type ( ) const
virtual

return what type of inference we are

Returns
inference type KL_COVARIANCE

Reimplemented from CKLInference.

Definition at line 101 of file KLCovarianceInferenceMethod.h.

virtual CKernel* get_kernel ( )
virtualinherited

get kernel

Returns
kernel

Definition at line 283 of file Inference.h.

virtual CLabels* get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 317 of file Inference.h.

float64_t get_marginal_likelihood_estimate ( int32_t  num_importance_samples = 1,
float64_t  ridge_size = 1e-15 
)
inherited

Computes an unbiased estimate of the marginal-likelihood (in log-domain),

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

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

This is done via a Gaussian approximation to the posterior \(q(f|y, \theta)\approx p(f|y, \theta)\), which is computed by the underlying CInference instance (if implemented, otherwise error), and then using an importance sample estimator

\[ p(y|\theta)=\int p(y|f)p(f|\theta)df =\int p(y|f)\frac{p(f|\theta)}{q(f|y, \theta)}q(f|y, \theta)df \approx\frac{1}{n}\sum_{i=1}^n p(y|f^{(i)})\frac{p(f^{(i)}|\theta)} {q(f^{(i)}|y, \theta)}, \]

where \( f^{(i)} \) are samples from the posterior approximation \( q(f|y, \theta) \). The resulting estimator has a low variance if \( q(f|y, \theta) \) is a good approximation. It has large variance otherwise (while still being consistent). Storing all number of log-domain ensures numerical stability.

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

Definition at line 139 of file Inference.cpp.

virtual CMeanFunction* get_mean ( )
virtualinherited

get mean

Returns
mean

Definition at line 300 of file Inference.h.

CLikelihoodModel* get_model ( )
inherited

get likelihood model

Returns
likelihood

Definition at line 334 of file Inference.h.

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

Definition at line 531 of file SGObject.cpp.

char * get_modsel_param_descr ( const char *  param_name)
inherited

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

Parameters
param_namename of the parameter
Returns
description of the parameter

Definition at line 555 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name)
inherited

Returns index of model selection parameter with provided index

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

Definition at line 568 of file SGObject.cpp.

SGMatrix< float64_t > get_multiclass_E ( )
virtualinherited

get the E matrix used for multi classification

Returns
the matrix for multi classification

Definition at line 71 of file Inference.cpp.

virtual const char* get_name ( ) const
virtual

returns the name of the inference method

Returns
name KLCovarianceInferenceMethod

Reimplemented from CKLInference.

Definition at line 95 of file KLCovarianceInferenceMethod.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 CInference.

Definition at line 289 of file KLInference.cpp.

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

get log marginal likelihood gradient

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

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

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

Definition at line 198 of file Inference.cpp.

float64_t get_negative_log_marginal_likelihood_helper ( )
protectedvirtual

the helper function to compute the negative log marginal likelihood

Returns
negative log marginal likelihood

Implements CKLInference.

Definition at line 220 of file KLCovarianceInferenceMethod.cpp.

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 282 of file KLInference.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 CInference.

Definition at line 268 of file KLInference.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 CInference.

Definition at line 261 of file KLInference.cpp.

float64_t get_scale ( ) const
virtualinherited

get kernel scale

Returns
kernel scale

Definition at line 60 of file Inference.cpp.

virtual SGVector<float64_t> get_value ( )
virtualinherited

get the function value

Returns
vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 255 of file Inference.h.

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 275 of file KLInference.cpp.

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

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

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name

Definition at line 289 of file SGObject.h.

bool has ( const Tag< T > &  tag) const
inherited

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

Parameters
tagtag of the parameter containing name and type information
Returns
true if the parameter exists with the input tag

Definition at line 301 of file SGObject.h.

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

Checks if a type exists for a class parameter identified by a name.

Parameters
namename of the parameter
Returns
true if the parameter exists with the input name and type

Definition at line 312 of file SGObject.h.

bool is_generic ( EPrimitiveType *  generic) const
virtualinherited

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

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

Definition at line 329 of file SGObject.cpp.

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

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

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

Definition at line 402 of file SGObject.cpp.

void load_serializable_post ( )
throw (ShogunException
)
protectedvirtualinherited

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

Exceptions
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 459 of file SGObject.cpp.

void load_serializable_pre ( )
throw (ShogunException
)
protectedvirtualinherited

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

Exceptions
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 454 of file SGObject.cpp.

CKLCovarianceInferenceMethod * obtain_from_generic ( CInference *  inference)
static

helper method used to specialize a base class instance

Parameters
inferenceinference method
Returns
casted CKLCovarianceInferenceMethod object

Definition at line 116 of file KLCovarianceInferenceMethod.cpp.

float64_t optimization ( )
protectedvirtualinherited

Using an optimizer to estimate posterior parameters

Reimplemented in CKLDualInferenceMethod.

Definition at line 342 of file KLInference.cpp.

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

Definition at line 295 of file SGObject.cpp.

bool precompute ( )
protectedvirtual

pre-compute the information for 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

Implements CKLInference.

Definition at line 129 of file KLCovarianceInferenceMethod.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 507 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 341 of file SGObject.cpp.

void register_minimizer ( Minimizer *  minimizer)
virtualinherited

Set a minimizer

Parameters
minimizerminimizer used in inference method

Reimplemented from CInference.

Reimplemented in CKLDualInferenceMethod.

Definition at line 363 of file KLInference.cpp.

void register_param ( Tag< T > &  _tag,
const T &  value 
)
protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 439 of file SGObject.h.

void register_param ( const std::string &  name,
const T &  value 
)
protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 452 of file SGObject.h.

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

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 347 of file SGObject.cpp.

void save_serializable_post ( )
throw (ShogunException
)
protectedvirtualinherited

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

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 469 of file SGObject.cpp.

void save_serializable_pre ( )
throw (ShogunException
)
protectedvirtualinherited

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

Exceptions
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 464 of file SGObject.cpp.

void set ( const Tag< T > &  _tag,
const T &  value 
)
inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 328 of file SGObject.h.

void set ( const std::string &  name,
const T &  value 
)
inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
namename of the parameter
valuevalue of the parameter along with type information

Definition at line 354 of file SGObject.h.

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 218 of file KLInference.cpp.

virtual void set_features ( CFeatures *  feat)
virtualinherited

set features

Parameters
featfeatures to set

Definition at line 272 of file Inference.h.

void set_generic ( )
inherited

Definition at line 74 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 79 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 84 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 89 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 94 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 99 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 104 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 109 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 114 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 119 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 124 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 129 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 134 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 139 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 144 of file SGObject.cpp.

void set_generic ( )
inherited

set generic type to T

void set_global_io ( SGIO *  io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel *  parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version *  version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 316 of file SGObject.cpp.

virtual void set_kernel ( CKernel *  kern)
virtualinherited

set kernel

Parameters
kernkernel to set

Reimplemented in CSingleSparseInference.

Definition at line 289 of file Inference.h.

virtual void set_labels ( CLabels *  lab)
virtualinherited

set labels

Parameters
lablabel to set

Definition at line 323 of file Inference.h.

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 212 of file KLInference.cpp.

virtual void set_mean ( CMeanFunction *  m)
virtualinherited

set mean

Parameters
mmean function to set

Definition at line 306 of file Inference.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 206 of file KLInference.cpp.

void set_model ( CLikelihoodModel *  mod)
virtualinherited

set variational likelihood model

Parameters
modmodel to set

Reimplemented from CInference.

Reimplemented in CKLDualInferenceMethod.

Definition at line 133 of file KLInference.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 200 of file KLInference.cpp.

void set_scale ( float64_t  scale)
virtualinherited

set kernel scale

Parameters
scalescale to be set

Definition at line 65 of file Inference.cpp.

CSGObject * shallow_copy ( ) const
virtualinherited

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

Reimplemented in CGaussianKernel.

Definition at line 225 of file SGObject.cpp.

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

Reimplemented from CInference.

Definition at line 165 of file KLInference.h.

virtual bool supports_multiclass ( ) const
virtualinherited

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

Returns
false

Reimplemented in CMultiLaplaceInferenceMethod.

Definition at line 378 of file Inference.h.

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

Reimplemented from CInference.

Definition at line 155 of file KLInference.h.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 336 of file SGObject.cpp.

void update ( )
virtualinherited

update all matrices except gradients

Reimplemented from CInference.

Definition at line 186 of file KLInference.cpp.

void update_alpha ( )
protectedvirtual

update alpha matrix

Implements CInference.

Definition at line 272 of file KLCovarianceInferenceMethod.cpp.

void update_approx_cov ( )
protectedvirtual

update covariance matrix of the approximation to the posterior

The variational co-variational matrix, which is automatically computed when update_alpha is called, is an approximated posterior co-variance matrix Therefore, this function body is empty

Implements CKLInference.

Definition at line 358 of file KLCovarianceInferenceMethod.cpp.

void update_chol ( )
protectedvirtual

update cholesky matrix

L is automatically updated when update_alpha is called Therefore, this function body is empty

Implements CInference.

Definition at line 351 of file KLCovarianceInferenceMethod.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 CInference.

Definition at line 344 of file KLCovarianceInferenceMethod.cpp.

void update_init ( )
protectedvirtualinherited

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

Reimplemented in CKLLowerTriangularInference.

Definition at line 224 of file KLInference.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 229 of file KLInference.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

void update_train_kernel ( )
protectedvirtualinherited

update train kernel matrix

Reimplemented in CSparseInference.

Definition at line 337 of file Inference.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

SGVector<float64_t> m_alpha
protectedinherited

alpha vector used in process mean calculation

Definition at line 484 of file Inference.h.

SGMatrix<float64_t> m_E
protectedinherited

the matrix used for multi classification

Definition at line 496 of file Inference.h.

float64_t m_exp_factor
protectedinherited

The factor used to exponentially increase noise_factor

Definition at line 247 of file KLInference.h.

CFeatures* m_features
protectedinherited

features to use

Definition at line 478 of file Inference.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

bool m_gradient_update
protectedinherited

Whether gradients are updated

Definition at line 499 of file Inference.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

CKernel* m_kernel
protectedinherited

covariance function

Definition at line 469 of file Inference.h.

SGMatrix<float64_t> m_ktrtr
protectedinherited

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

Definition at line 493 of file Inference.h.

SGMatrix<float64_t> m_L
protectedinherited

upper triangular factor of Cholesky decomposition

Definition at line 487 of file Inference.h.

CLabels* m_labels
protectedinherited

labels of features

Definition at line 481 of file Inference.h.

float64_t m_log_scale
protectedinherited

kernel scale

Definition at line 490 of file Inference.h.

index_t m_max_attempt
protectedinherited

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

Definition at line 250 of file KLInference.h.

CMeanFunction* m_mean
protectedinherited

mean function

Definition at line 472 of file Inference.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 241 of file KLInference.h.

Minimizer* m_minimizer
protectedinherited

minimizer

Definition at line 466 of file Inference.h.

CLikelihoodModel* m_model
protectedinherited

likelihood function to use

Definition at line 475 of file Inference.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 549 of file SGObject.h.

SGVector<float64_t> m_mu
protectedinherited

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

Definition at line 367 of file KLInference.h.

float64_t m_noise_factor
protectedinherited

The factor used to ensure kernel matrix to be positive definite

Definition at line 244 of file KLInference.h.

Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.h.

SGVector<float64_t> m_s2
protectedinherited

variational parameter sigma2 Note that sigma2 = diag(m_Sigma)

Definition at line 375 of file KLInference.h.

SGMatrix<float64_t> m_Sigma
protectedinherited

covariance matrix of the approximation to the posterior

Definition at line 370 of file KLInference.h.

Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

Version* version
inherited

version

Definition at line 543 of file SGObject.h.

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