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CKLCholeskyInferenceMethod类 参考

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 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

在文件 KLCholeskyInferenceMethod.h73 行定义.

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

Public 成员函数

 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_BACKTRACKING_STRONG_WOLFE, int max_iterations=1000, float64_t delta=0.0, int past=0, float64_t epsilon=1e-5, float64_t min_step=1e-20, float64_t max_step=1e+20, float64_t ftol=1e-4, float64_t wolfe=0.9, float64_t gtol=0.9, float64_t xtol=1e-16, float64_t orthantwise_c=0.0, int orthantwise_start=0, int orthantwise_end=1)
 
virtual SGMatrix< float64_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 SGMatrix< float64_tget_multiclass_E ()
 
virtual CSGObjectshallow_copy () const
 
virtual CSGObjectdeep_copy () const
 
virtual bool is_generic (EPrimitiveType *generic) const
 
template<class T >
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
template<>
void set_generic ()
 
void unset_generic ()
 
virtual void print_serializable (const char *prefix="")
 
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", 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_alpha ()
 
virtual float64_t get_negative_log_marginal_likelihood_helper ()
 
virtual void get_gradient_of_nlml_wrt_parameters (SGVector< float64_t > gradient)
 
virtual bool 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, 0x1 > 
update_init_helper ()
 
virtual
CVariationalGaussianLikelihood
get_variational_likelihood () const
 
virtual void check_variational_likelihood (CLikelihoodModel *mod) const
 
virtual float64_t lbfgs_optimization ()
 
virtual SGVector< float64_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)
 

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
 
SGMatrix< float64_tm_E
 

构造及析构函数说明

default constructor

在文件 KLCholeskyInferenceMethod.cpp55 行定义.

CKLCholeskyInferenceMethod ( 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 KLCholeskyInferenceMethod.cpp60 行定义.

在文件 KLCholeskyInferenceMethod.cpp101 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.cpp1243 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.cpp275 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.cpp57 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.cpp1360 行定义.

CSGObject * deep_copy ( ) const
virtualinherited

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

在文件 SGObject.cpp200 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.cpp1264 行定义.

SGVector< float64_t > get_alpha ( )
virtual

get alpha vector

返回
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

在文件 KLCholeskyInferenceMethod.cpp78 行定义.

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()

在文件 KLInferenceMethod.cpp461 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.cpp221 行定义.

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

参数
thegradient wrt hyperparameter related to cov

实现了 CKLInferenceMethod.

在文件 KLLowerTriangularInferenceMethod.cpp153 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.cpp410 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.cpp427 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.cpp326 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.cpp342 行定义.

SGVector< float64_t > get_diagonal_vector ( )
virtualinherited

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

在文件 KLLowerTriangularInferenceMethod.cpp91 行定义.

virtual CFeatures* get_features ( )
virtualinherited

get features

返回
features

在文件 InferenceMethod.h236 行定义.

SGIO * get_global_io ( )
inherited

get the io object

返回
io object

在文件 SGObject.cpp237 行定义.

Parallel * get_global_parallel ( )
inherited

get the parallel object

返回
parallel object

在文件 SGObject.cpp278 行定义.

Version * get_global_version ( )
inherited

get the version object

返回
version object

在文件 SGObject.cpp291 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.h215 行定义.

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)

返回
gradient of negative log marginal likelihood

实现了 CKLInferenceMethod.

在文件 KLCholeskyInferenceMethod.cpp132 行定义.

virtual EInferenceType get_inference_type ( ) const
virtualinherited

return what type of inference we are

重载 CInferenceMethod .

在文件 KLInferenceMethod.h99 行定义.

virtual CKernel* get_kernel ( )
virtualinherited

get kernel

返回
kernel

在文件 InferenceMethod.h253 行定义.

virtual CLabels* get_labels ( )
virtualinherited

get labels

返回
labels

在文件 InferenceMethod.h287 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 (in log-domain),

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

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

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

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

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

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 covariance matrix is not numerically positive semi-definite.
返回
unbiased estimate of the marginal likelihood function \( p(y|\theta),\) in log-domain.

在文件 InferenceMethod.cpp91 行定义.

virtual CMeanFunction* get_mean ( )
virtualinherited

get mean

返回
mean

在文件 InferenceMethod.h270 行定义.

CLikelihoodModel* get_model ( )
inherited

get likelihood model

返回
likelihood

在文件 InferenceMethod.h304 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.cpp1135 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.cpp1159 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.cpp1172 行定义.

SGMatrix< float64_t > get_multiclass_E ( )
virtualinherited

get the E matrix used for multi classification

返回
the matrix for multi classification

在文件 InferenceMethod.cpp40 行定义.

virtual const char* get_name ( ) const
virtual

returns the name of the inference method

返回
name KLCholeskyInferenceMethod

重载 CKLLowerTriangularInferenceMethod .

在文件 KLCholeskyInferenceMethod.h96 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.cpp318 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.cpp150 行定义.

float64_t get_negative_log_marginal_likelihood_helper ( )
protectedvirtual

the helper function to compute the negative log marginal likelihood

返回
negative log marginal likelihood

实现了 CKLInferenceMethod.

在文件 KLCholeskyInferenceMethod.cpp190 行定义.

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.cpp275 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.cpp239 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.cpp231 行定义.

virtual float64_t get_scale ( ) const
virtualinherited

get kernel scale

返回
kernel scale

在文件 InferenceMethod.h321 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.h225 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.cpp268 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.cpp297 行定义.

float64_t lbfgs_optimization ( )
protectedvirtualinherited

Using L-BFGS to estimate posterior parameters

CKLDualInferenceMethod 重载.

在文件 KLInferenceMethod.cpp381 行定义.

bool 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)

返回
true if precomputed parameters are valid

实现了 CKLInferenceMethod.

在文件 KLCholeskyInferenceMethod.cpp105 行定义.

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.cpp704 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.cpp545 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.cpp374 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 occurs.

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

在文件 SGObject.cpp1062 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 occurs.

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

在文件 SGObject.cpp1057 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.cpp742 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.cpp949 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.cpp889 行定义.

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

在文件 SGObject.cpp263 行定义.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

在文件 SGObject.cpp1111 行定义.

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

prints registered parameters out

参数
prefixprefix for members

在文件 SGObject.cpp309 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.cpp315 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 occurs.

CKernel 重载.

在文件 SGObject.cpp1072 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 occurs.

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.cpp1067 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.cpp189 行定义.

virtual void set_features ( CFeatures feat)
virtualinherited

set features

参数
featfeatures to set

在文件 InferenceMethod.h242 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp42 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp47 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp52 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp57 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp62 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp67 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp72 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp77 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp82 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp87 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp92 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp97 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp102 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp107 行定义.

void set_generic ( )
inherited

在文件 SGObject.cpp112 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.cpp230 行定义.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

参数
parallelparallel object to use

在文件 SGObject.cpp243 行定义.

void set_global_version ( Version version)
inherited

set the version object

参数
versionversion object to use

在文件 SGObject.cpp284 行定义.

virtual void set_kernel ( CKernel kern)
virtualinherited

set kernel

参数
kernkernel to set

在文件 InferenceMethod.h259 行定义.

virtual void set_labels ( CLabels lab)
virtualinherited

set labels

参数
lablabel to set

在文件 InferenceMethod.h293 行定义.

void set_lbfgs_parameters ( int  m = 100,
int  max_linesearch = 1000,
int  linesearch = LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE,
int  max_iterations = 1000,
float64_t  delta = 0.0,
int  past = 0,
float64_t  epsilon = 1e-5,
float64_t  min_step = 1e-20,
float64_t  max_step = 1e+20,
float64_t  ftol = 1e-4,
float64_t  wolfe = 0.9,
float64_t  gtol = 0.9,
float64_t  xtol = 1e-16,
float64_t  orthantwise_c = 0.0,
int  orthantwise_start = 0,
int  orthantwise_end = 1 
)
virtualinherited

在文件 KLInferenceMethod.cpp282 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.cpp183 行定义.

virtual void set_mean ( CMeanFunction m)
virtualinherited

set mean

参数
mmean function to set

在文件 InferenceMethod.h276 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.cpp177 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.cpp67 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.cpp171 行定义.

virtual void set_scale ( float64_t  scale)
virtualinherited

set kernel scale

参数
scalescale to be set

在文件 InferenceMethod.h327 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.cpp194 行定义.

MatrixXd solve_inverse ( Eigen::MatrixXd  A)
protectedinherited

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.cpp126 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.h167 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.h348 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.h157 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.cpp304 行定义.

void update ( )
virtualinherited

update data all matrices

重载 CInferenceMethod .

在文件 KLInferenceMethod.cpp156 行定义.

void update_alpha ( )
protectedvirtual

update alpha vector

实现了 CInferenceMethod.

在文件 KLCholeskyInferenceMethod.cpp213 行定义.

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

实现了 CKLInferenceMethod.

在文件 KLLowerTriangularInferenceMethod.cpp166 行定义.

void update_chol ( )
protectedvirtualinherited

update cholesky matrix

实现了 CInferenceMethod.

在文件 KLLowerTriangularInferenceMethod.cpp173 行定义.

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

实现了 CInferenceMethod.

在文件 KLLowerTriangularInferenceMethod.cpp99 行定义.

void update_init ( )
protectedvirtualinherited

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

重载 CKLInferenceMethod .

在文件 KLLowerTriangularInferenceMethod.cpp106 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.cpp200 行定义.

void update_InvK_Sigma ( )
protectedvirtual

compute inv(corrected_Kernel)*Sigma matrix

实现了 CKLLowerTriangularInferenceMethod.

在文件 KLCholeskyInferenceMethod.cpp311 行定义.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

在文件 SGObject.cpp250 行定义.

void update_Sigma ( )
protectedvirtual

compute posterior Sigma matrix

实现了 CKLLowerTriangularInferenceMethod.

在文件 KLCholeskyInferenceMethod.cpp303 行定义.

void update_train_kernel ( )
protectedvirtualinherited

update train kernel matrix

CFITCInferenceMethod 重载.

在文件 InferenceMethod.cpp291 行定义.

类成员变量说明

SGIO* io
inherited

io

在文件 SGObject.h496 行定义.

SGVector<float64_t> m_alpha
protectedinherited

alpha vector used in process mean calculation

在文件 InferenceMethod.h443 行定义.

float64_t m_delta
protectedinherited

在文件 KLInferenceMethod.h437 行定义.

SGMatrix<float64_t> m_E
protectedinherited

the matrix used for multi classification

在文件 InferenceMethod.h455 行定义.

float64_t m_epsilon
protectedinherited

在文件 KLInferenceMethod.h443 行定义.

float64_t m_exp_factor
protectedinherited

The factor used to exponentially increase noise_factor

在文件 KLInferenceMethod.h294 行定义.

CFeatures* m_features
protectedinherited

features to use

在文件 InferenceMethod.h437 行定义.

float64_t m_ftol
protectedinherited

在文件 KLInferenceMethod.h452 行定义.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

在文件 SGObject.h511 行定义.

float64_t m_gtol
protectedinherited

在文件 KLInferenceMethod.h458 行定义.

uint32_t m_hash
inherited

Hash of parameter values

在文件 SGObject.h517 行定义.

SGMatrix<float64_t> m_InvK_Sigma
protectedinherited

The K^{-1}Sigma matrix

在文件 KLLowerTriangularInferenceMethod.h127 行定义.

CKernel* m_kernel
protectedinherited

covariance function

在文件 InferenceMethod.h428 行定义.

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)

在文件 KLLowerTriangularInferenceMethod.h136 行定义.

SGVector<index_t> m_Kernel_P
protectedinherited

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

在文件 KLLowerTriangularInferenceMethod.h139 行定义.

SGMatrix<float64_t> m_ktrtr
protectedinherited

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

在文件 InferenceMethod.h452 行定义.

SGMatrix<float64_t> m_L
protectedinherited

upper triangular factor of Cholesky decomposition

在文件 InferenceMethod.h446 行定义.

CLabels* m_labels
protectedinherited

labels of features

在文件 InferenceMethod.h440 行定义.

int m_linesearch
protectedinherited

在文件 KLInferenceMethod.h431 行定义.

float64_t m_log_det_Kernel
protectedinherited

The Log-determinant of Kernel

在文件 KLLowerTriangularInferenceMethod.h133 行定义.

int m_m
protectedinherited

在文件 KLInferenceMethod.h425 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.h297 行定义.

int m_max_iterations
protectedinherited

在文件 KLInferenceMethod.h434 行定义.

int m_max_linesearch
protectedinherited

在文件 KLInferenceMethod.h428 行定义.

float64_t m_max_step
protectedinherited

在文件 KLInferenceMethod.h449 行定义.

CMeanFunction* m_mean
protectedinherited

mean function

在文件 InferenceMethod.h431 行定义.

SGVector<float64_t> m_mean_vec
protectedinherited

The mean vector generated from mean function

在文件 KLLowerTriangularInferenceMethod.h130 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.h288 行定义.

float64_t m_min_step
protectedinherited

在文件 KLInferenceMethod.h446 行定义.

CLikelihoodModel* m_model
protectedinherited

likelihood function to use

在文件 InferenceMethod.h434 行定义.

Parameter* m_model_selection_parameters
inherited

model selection parameters

在文件 SGObject.h508 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.h414 行定义.

float64_t m_noise_factor
protectedinherited

The factor used to ensure kernel matrix to be positive definite

在文件 KLInferenceMethod.h291 行定义.

float64_t m_orthantwise_c
protectedinherited

在文件 KLInferenceMethod.h464 行定义.

int m_orthantwise_end
protectedinherited

在文件 KLInferenceMethod.h470 行定义.

int m_orthantwise_start
protectedinherited

在文件 KLInferenceMethod.h467 行定义.

ParameterMap* m_parameter_map
inherited

map for different parameter versions

在文件 SGObject.h514 行定义.

Parameter* m_parameters
inherited

parameters

在文件 SGObject.h505 行定义.

int m_past
protectedinherited

在文件 KLInferenceMethod.h440 行定义.

SGVector<float64_t> m_s2
protectedinherited

variational parameter sigma2 Note that sigma2 = diag(m_Sigma)

在文件 KLInferenceMethod.h422 行定义.

float64_t m_scale
protectedinherited

kernel scale

在文件 InferenceMethod.h449 行定义.

SGMatrix<float64_t> m_Sigma
protectedinherited

covariance matrix of the approximation to the posterior

在文件 KLInferenceMethod.h417 行定义.

float64_t m_wolfe
protectedinherited

在文件 KLInferenceMethod.h455 行定义.

float64_t m_xtol
protectedinherited

在文件 KLInferenceMethod.h461 行定义.

Parallel* parallel
inherited

parallel

在文件 SGObject.h499 行定义.

Version* version
inherited

version

在文件 SGObject.h502 行定义.


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