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CKLLowerTriangularInferenceMethod类 参考abstract

详细描述

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

在文件 KLLowerTriangularInferenceMethod.h70 行定义.

类 CKLLowerTriangularInferenceMethod 继承关系图:
Inheritance graph
[图例]

Public 成员函数

 CKLLowerTriangularInferenceMethod ()
 
 CKLLowerTriangularInferenceMethod (CKernel *kernel, CFeatures *features, CMeanFunction *mean, CLabels *labels, CLikelihoodModel *model)
 
virtual ~CKLLowerTriangularInferenceMethod ()
 
virtual const char * get_name () const
 
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 SGVector< float64_tget_alpha ()=0
 
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 ()
 
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="", 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 属性

SGIOio
 
Parallelparallel
 
Versionversion
 
Parameterm_parameters
 
Parameterm_model_selection_parameters
 
Parameterm_gradient_parameters
 
ParameterMapm_parameter_map
 
uint32_t m_hash
 

Protected 成员函数

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 void update_Sigma ()=0
 
virtual void update_InvK_Sigma ()=0
 
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_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 void lbfgs_precompute ()=0
 
virtual void check_members () const
 
virtual void update_alpha ()=0
 
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)
 

静态 Protected 成员函数

static void * get_derivative_helper (void *p)
 

Protected 属性

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
 

构造及析构函数说明

default constructor

在文件 KLLowerTriangularInferenceMethod.cpp53 行定义.

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

constructor

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

在文件 KLLowerTriangularInferenceMethod.cpp58 行定义.

在文件 KLLowerTriangularInferenceMethod.cpp86 行定义.

成员函数说明

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.

参数
dictdictionary of parameters to be built.

在文件 SGObject.cpp1185 行定义.

void check_members ( ) const
protectedvirtualinherited

check if members of object are valid for inference

CFITCInferenceMethod , 以及 CExactInferenceMethod 重载.

在文件 InferenceMethod.cpp263 行定义.

void check_variational_likelihood ( CLikelihoodModel mod) const
protectedvirtualinherited

check the provided likelihood model supports variational inference

参数
modthe provided likelihood model
返回
whether the provided likelihood model supports variational inference or not

在文件 KLInferenceMethod.cpp56 行定义.

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.

返回
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

在文件 SGObject.cpp1302 行定义.

CSGObject * deep_copy ( ) const
virtualinherited

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

在文件 SGObject.cpp146 行定义.

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.

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

在文件 SGObject.cpp1206 行定义.

virtual SGVector<float64_t> get_alpha ( )
pure virtualinherited

get alpha vector

返回
vector to compute posterior mean of Gaussian Process:

\[ \mu = K\alpha \]

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

CFITCInferenceMethod, CKLCovarianceInferenceMethod, CKLApproxDiagonalInferenceMethod, CKLCholeskyInferenceMethod, CExactInferenceMethod, CKLDualInferenceMethod, CEPInferenceMethod , 以及 CLaplacianInferenceMethod 内被实现.

SGMatrix< float64_t > get_cholesky ( )
virtualinherited

get Cholesky decomposition matrix

返回
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()

实现了 CInferenceMethod.

在文件 KLInferenceMethod.cpp457 行定义.

void * get_derivative_helper ( void *  p)
staticprotectedinherited

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

在文件 InferenceMethod.cpp209 行定义.

float64_t get_derivative_related_cov ( Eigen::MatrixXd  eigen_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

参数
thegradient wrt hyperparameter related to cov

实现了 CKLInferenceMethod.

在文件 KLLowerTriangularInferenceMethod.cpp152 行定义.

SGVector< float64_t > get_derivative_wrt_inference_method ( const TParameter param)
protectedvirtualinherited

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

参数
paramparameter of CInferenceMethod class
返回
derivative of negative log marginal likelihood

实现了 CInferenceMethod.

在文件 KLInferenceMethod.cpp406 行定义.

SGVector< float64_t > get_derivative_wrt_kernel ( const TParameter param)
protectedvirtualinherited

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

参数
paramparameter of given kernel
返回
derivative of negative log marginal likelihood

实现了 CInferenceMethod.

在文件 KLInferenceMethod.cpp423 行定义.

SGVector< float64_t > get_derivative_wrt_likelihood_model ( const TParameter param)
protectedvirtualinherited

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

参数
paramparameter of given likelihood model
返回
derivative of negative log marginal likelihood

实现了 CInferenceMethod.

在文件 KLInferenceMethod.cpp322 行定义.

SGVector< float64_t > get_derivative_wrt_mean ( const TParameter param)
protectedvirtualinherited

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

参数
paramparameter of given mean function
返回
derivative of negative log marginal likelihood

实现了 CInferenceMethod.

在文件 KLInferenceMethod.cpp338 行定义.

SGVector< float64_t > get_diagonal_vector ( )
virtual

get diagonal vector

返回
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

实现了 CInferenceMethod.

在文件 KLLowerTriangularInferenceMethod.cpp90 行定义.

virtual CFeatures* get_features ( )
virtualinherited

get features

返回
features

在文件 InferenceMethod.h245 行定义.

SGIO * get_global_io ( )
inherited

get the io object

返回
io object

在文件 SGObject.cpp183 行定义.

Parallel * get_global_parallel ( )
inherited

get the parallel object

返回
parallel object

在文件 SGObject.cpp224 行定义.

Version * get_global_version ( )
inherited

get the version object

返回
version object

在文件 SGObject.cpp237 行定义.

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

get the gradient

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

实现了 CDifferentiableFunction.

在文件 InferenceMethod.h224 行定义.

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)

返回
gradient of negative log marginal likelihood

CKLCovarianceInferenceMethod, CKLDualInferenceMethod, CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.

virtual EInferenceType get_inference_type ( ) const
virtualinherited

return what type of inference we are

重载 CInferenceMethod .

在文件 KLInferenceMethod.h92 行定义.

virtual CKernel* get_kernel ( )
virtualinherited

get kernel

返回
kernel

在文件 InferenceMethod.h262 行定义.

virtual CLabels* get_labels ( )
virtualinherited

get labels

返回
labels

在文件 InferenceMethod.h296 行定义.

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).

参数
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.
返回
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).

在文件 InferenceMethod.cpp79 行定义.

virtual CMeanFunction* get_mean ( )
virtualinherited

get mean

返回
mean

在文件 InferenceMethod.h279 行定义.

CLikelihoodModel* get_model ( )
inherited

get likelihood model

返回
likelihood

在文件 InferenceMethod.h313 行定义.

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

在文件 SGObject.cpp1077 行定义.

char * get_modsel_param_descr ( const char *  param_name)
inherited

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

参数
param_namename of the parameter
返回
description of the parameter

在文件 SGObject.cpp1101 行定义.

index_t get_modsel_param_index ( const char *  param_name)
inherited

Returns index of model selection parameter with provided index

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

在文件 SGObject.cpp1114 行定义.

virtual const char* get_name ( ) const
virtual

returns the name of the inference method

返回
name KLLowerTriangularInferenceMethod

重载 CKLInferenceMethod .

CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 重载.

在文件 KLLowerTriangularInferenceMethod.h93 行定义.

float64_t get_negative_log_marginal_likelihood ( )
virtualinherited

get negative log marginal likelihood

返回
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.

实现了 CInferenceMethod.

在文件 KLInferenceMethod.cpp314 行定义.

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

get log marginal likelihood gradient

返回
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.

在文件 InferenceMethod.cpp138 行定义.

virtual float64_t get_negative_log_marginal_likelihood_helper ( )
protectedpure virtualinherited

the helper function to compute the negative log marginal likelihood

返回
negative log marginal likelihood

CKLDualInferenceMethod, CKLCovarianceInferenceMethod, CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.

float64_t get_nlml_wrt_parameters ( )
protectedvirtualinherited

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

返回
negative log marginal likelihood

在文件 KLInferenceMethod.cpp271 行定义.

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

返回
covariance matrix

实现了 CInferenceMethod.

在文件 KLInferenceMethod.cpp238 行定义.

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:

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

返回
mean vector

实现了 CInferenceMethod.

在文件 KLInferenceMethod.cpp230 行定义.

virtual float64_t get_scale ( ) const
virtualinherited

get kernel scale

返回
kernel scale

在文件 InferenceMethod.h330 行定义.

virtual SGVector<float64_t> get_value ( )
virtualinherited

get the function value

返回
vector that represents the function value

实现了 CDifferentiableFunction.

在文件 InferenceMethod.h234 行定义.

CVariationalGaussianLikelihood * get_variational_likelihood ( ) const
protectedvirtualinherited

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

在文件 KLInferenceMethod.cpp264 行定义.

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.

参数
genericset to the type of the generic if returning TRUE
返回
TRUE if a class template.

在文件 SGObject.cpp243 行定义.

float64_t lbfgs_optimization ( )
protectedvirtualinherited

Using L-BFGS to estimate posterior parameters

CKLDualInferenceMethod 重载.

在文件 KLInferenceMethod.cpp377 行定义.

virtual void 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)

CKLDualInferenceMethod, CKLCovarianceInferenceMethod, CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.

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)

参数
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
返回
(sorted) array of created TParameter instances with file data

在文件 SGObject.cpp648 行定义.

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

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

在文件 SGObject.cpp489 行定义.

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!

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

在文件 SGObject.cpp320 行定义.

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.

异常
ShogunExceptionWill be thrown if an error occurres.

CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel , 以及 CExponentialKernel 重载.

在文件 SGObject.cpp1004 行定义.

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.

异常
ShogunExceptionWill be thrown if an error occurres.

CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.

在文件 SGObject.cpp999 行定义.

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

参数
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

在文件 SGObject.cpp686 行定义.

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

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

在文件 SGObject.cpp893 行定义.

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)

参数
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

在文件 SGObject.cpp833 行定义.

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

在文件 SGObject.cpp209 行定义.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

在文件 SGObject.cpp1053 行定义.

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

prints registered parameters out

参数
prefixprefix for members

在文件 SGObject.cpp255 行定义.

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

Save this object to file.

参数
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)
返回
TRUE if done, otherwise FALSE

在文件 SGObject.cpp261 行定义.

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.

异常
ShogunExceptionWill be thrown if an error occurres.

CKernel 重载.

在文件 SGObject.cpp1014 行定义.

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.

异常
ShogunExceptionWill be thrown if an error occurres.

CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool > , 以及 CDynamicObjectArray 重载.

在文件 SGObject.cpp1009 行定义.

void set_exp_factor ( float64_t  exp_factor)
virtualinherited

set exp factor to exponentially increase noise factor

参数
exp_factorshould be greater than 1.0 default value is 2

在文件 KLInferenceMethod.cpp188 行定义.

virtual void set_features ( CFeatures feat)
virtualinherited

set features

参数
featfeatures to set

在文件 InferenceMethod.h251 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp38 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp43 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp48 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp53 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp58 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp63 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp68 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp73 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp78 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp83 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp88 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp93 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp98 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp103 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp108 行定义.

void set_generic ( )
inherited

set generic type to T

void set_global_io ( SGIO io)
inherited

set the io object

参数
ioio object to use

在文件 SGObject.cpp176 行定义.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

参数
parallelparallel object to use

在文件 SGObject.cpp189 行定义.

void set_global_version ( Version version)
inherited

set the version object

参数
versionversion object to use

在文件 SGObject.cpp230 行定义.

virtual void set_kernel ( CKernel kern)
virtualinherited

set kernel

参数
kernkernel to set

在文件 InferenceMethod.h268 行定义.

virtual void set_labels ( CLabels lab)
virtualinherited

set labels

参数
lablabel to set

在文件 InferenceMethod.h302 行定义.

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

在文件 KLInferenceMethod.cpp278 行定义.

void set_max_attempt ( index_t  max_attempt)
virtualinherited

set max attempt to ensure Kernel matrix to be positive definite

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

在文件 KLInferenceMethod.cpp182 行定义.

virtual void set_mean ( CMeanFunction m)
virtualinherited

set mean

参数
mmean function to set

在文件 InferenceMethod.h285 行定义.

void set_min_coeff_kernel ( float64_t  min_coeff_kernel)
virtualinherited

set minimum coeefficient of kernel matrix used in LDLT factorization

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

在文件 KLInferenceMethod.cpp176 行定义.

void set_model ( CLikelihoodModel mod)
virtualinherited

set variational likelihood model

参数
modmodel to set

重载 CInferenceMethod .

CKLDualInferenceMethod 重载.

在文件 KLInferenceMethod.cpp66 行定义.

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

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

在文件 KLInferenceMethod.cpp170 行定义.

virtual void set_scale ( float64_t  scale)
virtualinherited

set kernel scale

参数
scalescale to be set

在文件 InferenceMethod.h336 行定义.

CSGObject * shallow_copy ( ) const
virtualinherited

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

CGaussianKernel 重载.

在文件 SGObject.cpp140 行定义.

MatrixXd solve_inverse ( Eigen::MatrixXd  A)
protected

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

参数
Ainput matrix
返回
inv(corrected_Kernel*sq(m_scale))*A:

在文件 KLLowerTriangularInferenceMethod.cpp125 行定义.

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

重载 CInferenceMethod .

在文件 KLInferenceMethod.h160 行定义.

virtual bool supports_multiclass ( ) const
virtualinherited

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

返回
false

在文件 InferenceMethod.h357 行定义.

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

重载 CInferenceMethod .

在文件 KLInferenceMethod.h150 行定义.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

在文件 SGObject.cpp250 行定义.

void update ( )
virtualinherited

update data all matrices

重载 CInferenceMethod .

在文件 KLInferenceMethod.cpp155 行定义.

virtual void update_alpha ( )
protectedpure virtualinherited
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

实现了 CKLInferenceMethod.

在文件 KLLowerTriangularInferenceMethod.cpp165 行定义.

void update_chol ( )
protectedvirtual

update cholesky matrix

实现了 CInferenceMethod.

在文件 KLLowerTriangularInferenceMethod.cpp172 行定义.

void update_deriv ( )
protectedvirtual

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

实现了 CInferenceMethod.

在文件 KLLowerTriangularInferenceMethod.cpp98 行定义.

void update_init ( )
protectedvirtual

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

重载 CKLInferenceMethod .

在文件 KLLowerTriangularInferenceMethod.cpp105 行定义.

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

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

返回
the LDLT factorization of the corrected kernel matrix

在文件 KLInferenceMethod.cpp199 行定义.

virtual void update_InvK_Sigma ( )
protectedpure virtual

compute inv(corrected_Kernel)*Sigma matrix

CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

在文件 SGObject.cpp196 行定义.

virtual void update_Sigma ( )
protectedpure virtual

compute posterior Sigma matrix

CKLApproxDiagonalInferenceMethod , 以及 CKLCholeskyInferenceMethod 内被实现.

void update_train_kernel ( )
protectedvirtualinherited

update train kernel matrix

CFITCInferenceMethod 重载.

在文件 InferenceMethod.cpp279 行定义.

类成员变量说明

SGIO* io
inherited

io

在文件 SGObject.h461 行定义.

SGVector<float64_t> m_alpha
protectedinherited

alpha vector used in process mean calculation

在文件 InferenceMethod.h445 行定义.

float64_t m_delta
protectedinherited

在文件 KLInferenceMethod.h429 行定义.

float64_t m_epsilon
protectedinherited

在文件 KLInferenceMethod.h435 行定义.

float64_t m_exp_factor
protectedinherited

The factor used to exponentially increase noise_factor

在文件 KLInferenceMethod.h287 行定义.

CFeatures* m_features
protectedinherited

features to use

在文件 InferenceMethod.h439 行定义.

float64_t m_ftol
protectedinherited

在文件 KLInferenceMethod.h444 行定义.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

在文件 SGObject.h476 行定义.

float64_t m_gtol
protectedinherited

在文件 KLInferenceMethod.h450 行定义.

uint32_t m_hash
inherited

Hash of parameter values

在文件 SGObject.h482 行定义.

SGMatrix<float64_t> m_InvK_Sigma
protected

The K^{-1}Sigma matrix

在文件 KLLowerTriangularInferenceMethod.h128 行定义.

CKernel* m_kernel
protectedinherited

covariance function

在文件 InferenceMethod.h430 行定义.

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)

在文件 KLLowerTriangularInferenceMethod.h137 行定义.

SGVector<index_t> m_Kernel_P
protected

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

在文件 KLLowerTriangularInferenceMethod.h140 行定义.

SGMatrix<float64_t> m_ktrtr
protectedinherited

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

在文件 InferenceMethod.h454 行定义.

SGMatrix<float64_t> m_L
protectedinherited

upper triangular factor of Cholesky decomposition

在文件 InferenceMethod.h448 行定义.

CLabels* m_labels
protectedinherited

labels of features

在文件 InferenceMethod.h442 行定义.

int m_linesearch
protectedinherited

在文件 KLInferenceMethod.h423 行定义.

float64_t m_log_det_Kernel
protected

The Log-determinant of Kernel

在文件 KLLowerTriangularInferenceMethod.h134 行定义.

int m_m
protectedinherited

在文件 KLInferenceMethod.h417 行定义.

index_t m_max_attempt
protectedinherited

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

在文件 KLInferenceMethod.h290 行定义.

int m_max_iterations
protectedinherited

在文件 KLInferenceMethod.h426 行定义.

int m_max_linesearch
protectedinherited

在文件 KLInferenceMethod.h420 行定义.

float64_t m_max_step
protectedinherited

在文件 KLInferenceMethod.h441 行定义.

CMeanFunction* m_mean
protectedinherited

mean function

在文件 InferenceMethod.h433 行定义.

SGVector<float64_t> m_mean_vec
protected

The mean vector generated from mean function

在文件 KLLowerTriangularInferenceMethod.h131 行定义.

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

在文件 KLInferenceMethod.h281 行定义.

float64_t m_min_step
protectedinherited

在文件 KLInferenceMethod.h438 行定义.

CLikelihoodModel* m_model
protectedinherited

likelihood function to use

在文件 InferenceMethod.h436 行定义.

Parameter* m_model_selection_parameters
inherited

model selection parameters

在文件 SGObject.h473 行定义.

SGVector<float64_t> m_mu
protectedinherited

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

在文件 KLInferenceMethod.h406 行定义.

float64_t m_noise_factor
protectedinherited

The factor used to ensure kernel matrix to be positive definite

在文件 KLInferenceMethod.h284 行定义.

float64_t m_orthantwise_c
protectedinherited

在文件 KLInferenceMethod.h456 行定义.

int m_orthantwise_end
protectedinherited

在文件 KLInferenceMethod.h462 行定义.

int m_orthantwise_start
protectedinherited

在文件 KLInferenceMethod.h459 行定义.

ParameterMap* m_parameter_map
inherited

map for different parameter versions

在文件 SGObject.h479 行定义.

Parameter* m_parameters
inherited

parameters

在文件 SGObject.h470 行定义.

int m_past
protectedinherited

在文件 KLInferenceMethod.h432 行定义.

SGVector<float64_t> m_s2
protectedinherited

variational parameter sigma2 Note that sigma2 = diag(m_Sigma)

在文件 KLInferenceMethod.h414 行定义.

float64_t m_scale
protectedinherited

kernel scale

在文件 InferenceMethod.h451 行定义.

SGMatrix<float64_t> m_Sigma
protectedinherited

covariance matrix of the approximation to the posterior

在文件 KLInferenceMethod.h409 行定义.

float64_t m_wolfe
protectedinherited

在文件 KLInferenceMethod.h447 行定义.

float64_t m_xtol
protectedinherited

在文件 KLInferenceMethod.h453 行定义.

Parallel* parallel
inherited

parallel

在文件 SGObject.h464 行定义.

Version* version
inherited

version

在文件 SGObject.h467 行定义.


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