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CKLCholeskyInferenceMethod Class Reference

Detailed Description

The KL approximation inference method class.

The class is implemented based on the KL method in the Challis's paper, which uses full Cholesky represention. Note that C is not unique according to the definition of C in the paper.

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.

The adapted Matlab code can be found at https://gist.github.com/yorkerlin/bb400ebded2dbe90c58d

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

Definition at line 74 of file KLCholeskyInferenceMethod.h.

Inheritance diagram for CKLCholeskyInferenceMethod:
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Public Member Functions

 CKLCholeskyInferenceMethod ()
 CKLCholeskyInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
virtual ~CKLCholeskyInferenceMethod ()
virtual const char * get_name () const
virtual SGVector< float64_tget_alpha ()
virtual SGVector< float64_tget_diagonal_vector ()
virtual EInferenceType get_inference_type () const
virtual float64_t get_negative_log_marginal_likelihood ()
virtual SGVector< float64_tget_posterior_mean ()
virtual SGMatrix< float64_tget_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_DEFAULT, 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_tget_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_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 float64_t get_scale () const
virtual void set_scale (float64_t scale)
virtual bool supports_multiclass () const
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
void set_global_io (SGIO *io)
SGIOget_global_io ()
void set_global_parallel (Parallel *parallel)
Parallelget_global_parallel ()
void set_global_version (Version *version)
Versionget_global_version ()
SGStringList< char > get_modelsel_names ()
void print_modsel_params ()
char * get_modsel_param_descr (const char *param_name)
index_t get_modsel_param_index (const char *param_name)
void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
virtual void update_parameter_hash ()
virtual bool parameter_hash_changed ()
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
virtual CSGObjectclone ()

Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

Protected Member Functions

virtual void update_alpha ()
virtual float64_t get_negative_log_marginal_likelihood_helper ()
virtual void get_gradient_of_nlml_wrt_parameters (SGVector< float64_t > gradient)
virtual void lbfgs_precompute ()
virtual void update_Sigma ()
virtual void update_InvK_Sigma ()
virtual void update_chol ()
virtual void update_deriv ()
virtual float64_t get_derivative_related_cov (Eigen::MatrixXd eigen_dK)
virtual void update_approx_cov ()
Eigen::MatrixXd solve_inverse (Eigen::MatrixXd A)
virtual void update_init ()
virtual Eigen::LDLT
< Eigen::MatrixXd > 
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_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 float64_t get_nlml_wrt_parameters ()
virtual void check_members () const
virtual void update_train_kernel ()
virtual TParametermigrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)
virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)
virtual void load_serializable_pre () throw (ShogunException)
virtual void load_serializable_post () throw (ShogunException)
virtual void save_serializable_pre () throw (ShogunException)
virtual void save_serializable_post () throw (ShogunException)

Static Protected Member Functions

static void * get_derivative_helper (void *p)

Protected Attributes

SGMatrix< float64_tm_InvK_Sigma
SGVector< float64_tm_mean_vec
float64_t m_log_det_Kernel
SGMatrix< float64_tm_Kernel_LsD
SGVector< index_tm_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_tm_mu
SGMatrix< float64_tm_Sigma
SGVector< float64_tm_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
CKernelm_kernel
CMeanFunctionm_mean
CLikelihoodModelm_model
CFeaturesm_features
CLabelsm_labels
SGVector< float64_tm_alpha
SGMatrix< float64_tm_L
float64_t m_scale
SGMatrix< float64_tm_ktrtr

Constructor & Destructor Documentation

default constructor

Definition at line 54 of file KLCholeskyInferenceMethod.cpp.

CKLCholeskyInferenceMethod ( 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 KLCholeskyInferenceMethod.cpp.

Definition at line 100 of file KLCholeskyInferenceMethod.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 1185 of file SGObject.cpp.

void check_members ( ) const
protectedvirtualinherited

check if members of object are valid for inference

Reimplemented in CFITCInferenceMethod, and CExactInferenceMethod.

Definition at line 263 of file InferenceMethod.cpp.

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 56 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 1302 of file SGObject.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 146 of file SGObject.cpp.

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

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

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

Parameters
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 1206 of file SGObject.cpp.

SGVector< float64_t > get_alpha ( )
virtual

get alpha vector

Returns
vector to compute posterior mean of Gaussian Process:

Note that m_alpha contains not only the alpha vector defined in the reference but also a vector corresponding to the lower triangular of C

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 CInferenceMethod.

Definition at line 77 of file KLCholeskyInferenceMethod.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 CInferenceMethod.

Definition at line 457 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 209 of file InferenceMethod.cpp.

float64_t get_derivative_related_cov ( Eigen::MatrixXd  eigen_dK)
protectedvirtualinherited

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 152 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 406 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 423 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 322 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 338 of file KLInferenceMethod.cpp.

SGVector< float64_t > get_diagonal_vector ( )
virtualinherited

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

Implements CInferenceMethod.

Definition at line 90 of file KLLowerTriangularInferenceMethod.cpp.

virtual CFeatures* get_features ( )
virtualinherited

get features

Returns
features

Definition at line 245 of file InferenceMethod.h.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 183 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 224 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 237 of file SGObject.cpp.

virtual CMap<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 224 of file InferenceMethod.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 CKLInferenceMethod.

Definition at line 128 of file KLCholeskyInferenceMethod.cpp.

virtual EInferenceType get_inference_type ( ) const
virtualinherited

return what type of inference we are

Reimplemented from CInferenceMethod.

Definition at line 92 of file KLInferenceMethod.h.

virtual CKernel* get_kernel ( )
virtualinherited

get kernel

Returns
kernel

Definition at line 262 of file InferenceMethod.h.

virtual CLabels* get_labels ( )
virtualinherited

get labels

Returns
labels

Definition at line 296 of file InferenceMethod.h.

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

Computes an unbiased estimate of the marginal-likelihood,

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

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

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

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

where \( f^{(i)} \) are samples from the posterior approximation \( q(f|y, \theta) \). The resulting estimator has a low variance if \( q(f|y, \theta) \) is a good approximation. It has large variance otherwise (while still being consistent).

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

Definition at line 79 of file InferenceMethod.cpp.

virtual CMeanFunction* get_mean ( )
virtualinherited

get mean

Returns
mean

Definition at line 279 of file InferenceMethod.h.

CLikelihoodModel* get_model ( )
inherited

get likelihood model

Returns
likelihood

Definition at line 313 of file InferenceMethod.h.

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

Definition at line 1077 of file SGObject.cpp.

char * get_modsel_param_descr ( const char *  param_name)
inherited

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

Parameters
param_namename of the parameter
Returns
description of the parameter

Definition at line 1101 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name)
inherited

Returns index of model selection parameter with provided index

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

Definition at line 1114 of file SGObject.cpp.

virtual const char* get_name ( ) const
virtual

returns the name of the inference method

Returns
name KLCholeskyInferenceMethod

Reimplemented from CKLLowerTriangularInferenceMethod.

Definition at line 97 of file KLCholeskyInferenceMethod.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 314 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 138 of file InferenceMethod.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 CKLInferenceMethod.

Definition at line 186 of file KLCholeskyInferenceMethod.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 271 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 238 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 230 of file KLInferenceMethod.cpp.

virtual float64_t get_scale ( ) const
virtualinherited

get kernel scale

Returns
kernel scale

Definition at line 330 of file InferenceMethod.h.

virtual SGVector<float64_t> get_value ( )
virtualinherited

get the function value

Returns
vector that represents the function value

Implements CDifferentiableFunction.

Definition at line 234 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 264 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 243 of file SGObject.cpp.

float64_t lbfgs_optimization ( )
protectedvirtualinherited

Using L-BFGS to estimate posterior parameters

Reimplemented in CKLDualInferenceMethod.

Definition at line 377 of file KLInferenceMethod.cpp.

void lbfgs_precompute ( )
protectedvirtual

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)

Implements CKLInferenceMethod.

Definition at line 104 of file KLCholeskyInferenceMethod.cpp.

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

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

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

Definition at line 648 of file SGObject.cpp.

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

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

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

Definition at line 489 of file SGObject.cpp.

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

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

Parameters
filewhere to load from
prefixprefix for members
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 320 of file SGObject.cpp.

void load_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
ShogunExceptionWill be thrown if an error occurres.

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

Definition at line 1004 of file SGObject.cpp.

void load_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
ShogunExceptionWill be thrown if an error occurres.

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

Definition at line 999 of file SGObject.cpp.

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

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

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

Definition at line 686 of file SGObject.cpp.

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

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

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

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

Definition at line 893 of file SGObject.cpp.

void one_to_one_migration_prepare ( DynArray< TParameter * > *  param_base,
const SGParamInfo target,
TParameter *&  replacement,
TParameter *&  to_migrate,
char *  old_name = NULL 
)
protectedvirtualinherited

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

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

Definition at line 833 of file SGObject.cpp.

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

Definition at line 209 of file SGObject.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1053 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 255 of file SGObject.cpp.

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

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 261 of file SGObject.cpp.

void save_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
ShogunExceptionWill be thrown if an error occurres.

Reimplemented in CKernel.

Definition at line 1014 of file SGObject.cpp.

void save_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

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

Exceptions
ShogunExceptionWill be thrown if an error occurres.

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

Definition at line 1009 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 188 of file KLInferenceMethod.cpp.

virtual void set_features ( CFeatures feat)
virtualinherited

set features

Parameters
featfeatures to set

Definition at line 251 of file InferenceMethod.h.

void set_generic< complex128_t > ( )
inherited

set generic type to T

Definition at line 38 of file SGObject.cpp.

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 176 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 189 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 230 of file SGObject.cpp.

virtual void set_kernel ( CKernel kern)
virtualinherited

set kernel

Parameters
kernkernel to set

Definition at line 268 of file InferenceMethod.h.

virtual void set_labels ( CLabels lab)
virtualinherited

set labels

Parameters
lablabel to set

Definition at line 302 of file InferenceMethod.h.

void set_lbfgs_parameters ( int  m = 100,
int  max_linesearch = 1000,
int  linesearch = LBFGS_LINESEARCH_DEFAULT,
int  max_iterations = 1000,
float64_t  delta = 0.0,
int  past = 0,
float64_t  epsilon = 1e-5,
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 278 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 182 of file KLInferenceMethod.cpp.

virtual void set_mean ( CMeanFunction m)
virtualinherited

set mean

Parameters
mmean function to set

Definition at line 285 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 176 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 66 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 170 of file KLInferenceMethod.cpp.

virtual void set_scale ( float64_t  scale)
virtualinherited

set kernel scale

Parameters
scalescale to be set

Definition at line 336 of file InferenceMethod.h.

CSGObject * shallow_copy ( ) const
virtualinherited

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

Reimplemented in CGaussianKernel.

Definition at line 140 of file SGObject.cpp.

MatrixXd solve_inverse ( Eigen::MatrixXd  A)
protectedinherited

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

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

Definition at line 125 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 160 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 357 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 150 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 250 of file SGObject.cpp.

void update ( )
virtualinherited

update data all matrices

Reimplemented from CInferenceMethod.

Definition at line 155 of file KLInferenceMethod.cpp.

void update_alpha ( )
protectedvirtual

update alpha vector

Implements CInferenceMethod.

Definition at line 209 of file KLCholeskyInferenceMethod.cpp.

void update_approx_cov ( )
protectedvirtualinherited

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 165 of file KLLowerTriangularInferenceMethod.cpp.

void update_chol ( )
protectedvirtualinherited

update cholesky matrix

Implements CInferenceMethod.

Definition at line 172 of file KLLowerTriangularInferenceMethod.cpp.

void update_deriv ( )
protectedvirtualinherited

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

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

Implements CInferenceMethod.

Definition at line 98 of file KLLowerTriangularInferenceMethod.cpp.

void update_init ( )
protectedvirtualinherited

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

Reimplemented from CKLInferenceMethod.

Definition at line 105 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 199 of file KLInferenceMethod.cpp.

void update_InvK_Sigma ( )
protectedvirtual

compute inv(corrected_Kernel)*Sigma matrix

Implements CKLLowerTriangularInferenceMethod.

Definition at line 307 of file KLCholeskyInferenceMethod.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 196 of file SGObject.cpp.

void update_Sigma ( )
protectedvirtual

compute posterior Sigma matrix

Implements CKLLowerTriangularInferenceMethod.

Definition at line 299 of file KLCholeskyInferenceMethod.cpp.

void update_train_kernel ( )
protectedvirtualinherited

update train kernel matrix

Reimplemented in CFITCInferenceMethod.

Definition at line 279 of file InferenceMethod.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 461 of file SGObject.h.

SGVector<float64_t> m_alpha
protectedinherited

alpha vector used in process mean calculation

Definition at line 445 of file InferenceMethod.h.

float64_t m_delta
protectedinherited

Definition at line 429 of file KLInferenceMethod.h.

float64_t m_epsilon
protectedinherited

Definition at line 435 of file KLInferenceMethod.h.

float64_t m_exp_factor
protectedinherited

The factor used to exponentially increase noise_factor

Definition at line 287 of file KLInferenceMethod.h.

CFeatures* m_features
protectedinherited

features to use

Definition at line 439 of file InferenceMethod.h.

float64_t m_ftol
protectedinherited

Definition at line 444 of file KLInferenceMethod.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 476 of file SGObject.h.

float64_t m_gtol
protectedinherited

Definition at line 450 of file KLInferenceMethod.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 482 of file SGObject.h.

SGMatrix<float64_t> m_InvK_Sigma
protectedinherited

The K^{-1}Sigma matrix

Definition at line 128 of file KLLowerTriangularInferenceMethod.h.

CKernel* m_kernel
protectedinherited

covariance function

Definition at line 430 of file InferenceMethod.h.

SGMatrix<float64_t> m_Kernel_LsD
protectedinherited

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
protectedinherited

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 454 of file InferenceMethod.h.

SGMatrix<float64_t> m_L
protectedinherited

upper triangular factor of Cholesky decomposition

Definition at line 448 of file InferenceMethod.h.

CLabels* m_labels
protectedinherited

labels of features

Definition at line 442 of file InferenceMethod.h.

int m_linesearch
protectedinherited

Definition at line 423 of file KLInferenceMethod.h.

float64_t m_log_det_Kernel
protectedinherited

The Log-determinant of Kernel

Definition at line 134 of file KLLowerTriangularInferenceMethod.h.

int m_m
protectedinherited

Definition at line 417 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 290 of file KLInferenceMethod.h.

int m_max_iterations
protectedinherited

Definition at line 426 of file KLInferenceMethod.h.

int m_max_linesearch
protectedinherited

Definition at line 420 of file KLInferenceMethod.h.

float64_t m_max_step
protectedinherited

Definition at line 441 of file KLInferenceMethod.h.

CMeanFunction* m_mean
protectedinherited

mean function

Definition at line 433 of file InferenceMethod.h.

SGVector<float64_t> m_mean_vec
protectedinherited

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 281 of file KLInferenceMethod.h.

float64_t m_min_step
protectedinherited

Definition at line 438 of file KLInferenceMethod.h.

CLikelihoodModel* m_model
protectedinherited

likelihood function to use

Definition at line 436 of file InferenceMethod.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 473 of file SGObject.h.

SGVector<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 406 of file KLInferenceMethod.h.

float64_t m_noise_factor
protectedinherited

The factor used to ensure kernel matrix to be positive definite

Definition at line 284 of file KLInferenceMethod.h.

float64_t m_orthantwise_c
protectedinherited

Definition at line 456 of file KLInferenceMethod.h.

int m_orthantwise_end
protectedinherited

Definition at line 462 of file KLInferenceMethod.h.

int m_orthantwise_start
protectedinherited

Definition at line 459 of file KLInferenceMethod.h.

ParameterMap* m_parameter_map
inherited

map for different parameter versions

Definition at line 479 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 470 of file SGObject.h.

int m_past
protectedinherited

Definition at line 432 of file KLInferenceMethod.h.

SGVector<float64_t> m_s2
protectedinherited

variational parameter sigma2 Note that sigma2 = diag(m_Sigma)

Definition at line 414 of file KLInferenceMethod.h.

float64_t m_scale
protectedinherited

kernel scale

Definition at line 451 of file InferenceMethod.h.

SGMatrix<float64_t> m_Sigma
protectedinherited

covariance matrix of the approximation to the posterior

Definition at line 409 of file KLInferenceMethod.h.

float64_t m_wolfe
protectedinherited

Definition at line 447 of file KLInferenceMethod.h.

float64_t m_xtol
protectedinherited

Definition at line 453 of file KLInferenceMethod.h.

Parallel* parallel
inherited

parallel

Definition at line 464 of file SGObject.h.

Version* version
inherited

version

Definition at line 467 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation