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CSoftMaxLikelihood类 参考

详细描述

Class that models Soft-Max likelihood.

softmax_i(f)={{f_i}}{{f_i}}

Code adapted from https://gist.github.com/yorkerlin/8a36e8f9b298aa0246a4 and GPstuff - Gaussian process models for Bayesian analysis http://becs.aalto.fi/en/research/bayes/gpstuff/

The reference pseudo code is the algorithm 3.4 of the GPML textbook

The implementation of predictive statistics is based on the mc sampler. The basic idea of the sampler is that first generating samples from the posterior Gaussian distribution given by mu and s2 and then using the samplers to estimate the predictive marginal distribution.

在文件 SoftMaxLikelihood.h79 行定义.

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

Public 成员函数

 CSoftMaxLikelihood ()
 
virtual ~CSoftMaxLikelihood ()
 
virtual const char * get_name () const
 
virtual SGVector< float64_tget_predictive_means (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const
 
virtual SGVector< float64_tget_predictive_variances (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const
 
virtual SGVector< float64_tget_predictive_log_probabilities (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const
 
virtual SGVector< float64_tget_log_probability_f (const CLabels *lab, SGVector< float64_t > func) const
 
virtual SGVector< float64_tget_log_probability_derivative_f (const CLabels *lab, SGVector< float64_t > func, index_t i) const
 
virtual SGVector< float64_tget_log_zeroth_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const
 
virtual float64_t get_first_moment (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const
 
virtual float64_t get_second_moment (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const
 
virtual bool supports_multiclass () const
 
virtual void set_num_samples (index_t num_samples)
 
virtual SGVector< float64_tget_predictive_log_probabilities (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL)
 
virtual ELikelihoodModelType get_model_type () const
 
virtual SGVector< float64_tget_log_probability_fmatrix (const CLabels *lab, SGMatrix< float64_t > F) const
 
virtual SGVector< float64_tget_first_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
 
virtual SGVector< float64_tget_second_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
 
virtual SGVector< float64_tget_third_derivative (const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
 
virtual SGVector< float64_tget_first_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const
 
virtual SGVector< float64_tget_second_moments (SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const
 
virtual bool supports_regression () const
 
virtual bool supports_binary () 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 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)
 

构造及析构函数说明

default constructor

在文件 SoftMaxLikelihood.cpp50 行定义.

~CSoftMaxLikelihood ( )
virtual

destructor

在文件 SoftMaxLikelihood.cpp55 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.

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.

virtual SGVector<float64_t> get_first_derivative ( const CLabels lab,
SGVector< float64_t func,
const TParameter param 
) const
virtualinherited

get derivative of log likelihood \(log(p(y|f))\) with respect to given parameter

参数
lablabels used
funcfunction location
paramparameter
返回
derivative

CVariationalLikelihood, CStudentsTLikelihood , 以及 CGaussianLikelihood 重载.

在文件 LikelihoodModel.h171 行定义.

virtual float64_t get_first_moment ( SGVector< float64_t mu,
SGVector< float64_t s2,
const CLabels lab,
index_t  i 
) const
virtual

returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).

NOTE: NOT IMPLEMENTED

参数
mumean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
s2variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
lablabels \(y_i\)
iindex i
返回
first moment of \(q(f_i)\)

实现了 CLikelihoodModel.

在文件 SoftMaxLikelihood.h225 行定义.

SGVector< float64_t > get_first_moments ( SGVector< float64_t mu,
SGVector< float64_t s2,
const CLabels lab 
) const
virtualinherited

returns the first moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) for each \(f_i\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).

Wrapper method which calls get_first_moment multiple times.

参数
mumean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
s2variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
lablabels \(y_i\)
返回
the first moment of \(q(f_i)\) for each \(f_i\)

在文件 LikelihoodModel.cpp52 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.

SGVector< float64_t > get_log_probability_derivative_f ( const CLabels lab,
SGVector< float64_t func,
index_t  i 
) const
virtual

get derivative of log likelihood \(log(p(y|f))\) with respect to location function \(f\)

参数
lablabels \(y_i\), an integer between 1 and C (ie. num of classes)
funcfunction location
iindex, choices are 1, 2, and 3 for first, second, and third derivatives respectively
返回
derivative

实现了 CLikelihoodModel.

在文件 SoftMaxLikelihood.cpp103 行定义.

SGVector< float64_t > get_log_probability_f ( const CLabels lab,
SGVector< float64_t func 
) const
virtual

returns the logarithm of the point-wise likelihood \(log(p(y_i|f_i))\) for each label \(y_i\), an integer between 1 and C (ie. number of classes).

One can evaluate log-likelihood like: \(log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\)

参数
lablabels \(y_i\), an integer between 1 and C (ie. num of classes)
funcvalues of the function \(f_i\)
返回
logarithm of the point-wise likelihood

实现了 CLikelihoodModel.

在文件 SoftMaxLikelihood.cpp67 行定义.

SGVector< float64_t > get_log_probability_fmatrix ( const CLabels lab,
SGMatrix< float64_t F 
) const
virtualinherited

Returns the log-likelihood \(log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\) for each of the provided functions \( f \) in the given matrix.

Wrapper method which calls get_log_probability_f multiple times.

参数
lablabels \(y_i\)
Fvalues of the function \(f_i\) where each column of the matrix is one function \( f \).
返回
log-likelihood for every provided function

在文件 LikelihoodModel.cpp31 行定义.

virtual SGVector<float64_t> get_log_zeroth_moments ( SGVector< float64_t mu,
SGVector< float64_t s2,
const CLabels lab 
) const
virtual

returns the zeroth moment of a given (unnormalized) probability distribution:

NOTE: NOT IMPLEMENTED

参数
mumean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
s2variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
lablabels \(y_i\)
返回
log zeroth moment \(log(Z_i)\)

实现了 CLikelihoodModel.

在文件 SoftMaxLikelihood.h204 行定义.

virtual ELikelihoodModelType get_model_type ( ) const
virtualinherited

get model type

返回
model type NONE

CStudentsTLikelihood, CGaussianLikelihood, CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 重载.

在文件 LikelihoodModel.h118 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.

virtual const char* get_name ( ) const
virtual

returns the name of the likelihood model

返回
name SoftMaxLikelihood

实现了 CSGObject.

在文件 SoftMaxLikelihood.h92 行定义.

SGVector< float64_t > get_predictive_log_probabilities ( SGVector< float64_t mu,
SGVector< float64_t s2,
const CLabels lab = NULL 
)
virtualinherited

returns the logarithm of the predictive density of \(y_*\):

\[ log(p(y_*|X,y,x_*)) = log\left(\int p(y_*|f_*) p(f_*|X,y,x_*) df_*\right) \]

which approximately equals to

\[ log\left(\int p(y_*|f_*) \mathcal{N}(f_*|\mu,\sigma^2) df_*\right) \]

where normal distribution \(\mathcal{N}(\mu,\sigma^2)\) is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\).

NOTE: if lab equals to NULL, then each \(y_*\) equals to one.

参数
muposterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
s2posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
lablabels \(y_*\)
返回
\(log(p(y_*|X, y, x*))\) for each label \(y_*\)

在文件 LikelihoodModel.cpp25 行定义.

SGVector< float64_t > get_predictive_log_probabilities ( SGVector< float64_t mu,
SGVector< float64_t s2,
const CLabels lab = NULL 
) const
virtual

returns the logarithm of the predictive density of \(y_*\): The implementation is based on a simple Monte Carlo sampler from the pseudo code.

\[ log(p(y_*|X,y,x_*)) = log\left(\int p(y_*|f_*) p(f_*|X,y,x_*) df_*\right) \]

which approximately equals to

\[ log\left(\int p(y_*|f_*) \mathcal{N}(f_*|\mu,\sigma^2) df_*\right) \]

where normal distribution \(\mathcal{N}(\mu,\sigma^2)\) is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\).

NOTE: if lab equals to NULL, then each \(y_*\) equals to one.

参数
muposterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
s2posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
lablabels \(y_*\)

Note that the log_probability vector should be a column-marjor linearized C-by-n matrix, where C is the number of classes and n is the number of samplers

返回
\(log(p(y_*|X, y, x*))\) for each label \(y_*\) (based on 0 and 1 bernoulli-encoding)

在文件 SoftMaxLikelihood.cpp303 行定义.

SGVector< float64_t > get_predictive_means ( SGVector< float64_t mu,
SGVector< float64_t s2,
const CLabels lab = NULL 
) const
virtual

returns mean of the predictive marginal \(p(y_*|X,y,x_*)\) The implementation is based on a simple Monte Carlo sampler from the pseudo code.

参数
muposterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
s2posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
lablabels \(y_*\)

Note that the mean vector should be a column-marjor linearized C-by-n matrix, where C is the number of classes and n is the number of samplers

返回
final means (based on 0 and 1 bernoulli-encoding) evaluated by likelihood function

实现了 CLikelihoodModel.

在文件 SoftMaxLikelihood.cpp335 行定义.

SGVector< float64_t > get_predictive_variances ( SGVector< float64_t mu,
SGVector< float64_t s2,
const CLabels lab = NULL 
) const
virtual

returns variance of the predictive marginal \(p(y_*|X,y,x_*)\) The implementation is based on a simple Monte Carlo sampler from the pseudo code.

参数
muposterior mean of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
s2posterior variance of a Gaussian distribution \(\mathcal{N}(\mu,\sigma^2)\), which is an approximation to the posterior marginal \(p(f_*|X,y,x_*)\)
lablabels \(y_*\)

Note that the variance vector should be a column-marjor linearized C-by-n matrix, where C is the number of classes and n is the number of samplers

返回
final variances (based on 0 and 1 bernoulli-encoding) evaluated by likelihood function

实现了 CLikelihoodModel.

在文件 SoftMaxLikelihood.cpp342 行定义.

virtual SGVector<float64_t> get_second_derivative ( const CLabels lab,
SGVector< float64_t func,
const TParameter param 
) const
virtualinherited

get derivative of the first derivative of log likelihood with respect to function location, i.e. \(\frac{\partial log(p(y|f))}{\partial f}\) with respect to given parameter

参数
lablabels used
funcfunction location
paramparameter
返回
derivative

CVariationalLikelihood, CStudentsTLikelihood , 以及 CGaussianLikelihood 重载.

在文件 LikelihoodModel.h189 行定义.

virtual float64_t get_second_moment ( SGVector< float64_t mu,
SGVector< float64_t s2,
const CLabels lab,
index_t  i 
) const
virtual

returns the second moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).

NOTE: NOT IMPLEMENTED

参数
mumean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
s2variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
lablabels \(y_i\)
iindex i
返回
the second moment of \(q(f_i)\)

实现了 CLikelihoodModel.

在文件 SoftMaxLikelihood.h246 行定义.

SGVector< float64_t > get_second_moments ( SGVector< float64_t mu,
SGVector< float64_t s2,
const CLabels lab 
) const
virtualinherited

returns the second moment of a given (unnormalized) probability distribution \(q(f_i) = Z_i^-1 p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\) for each \(f_i\), where \( Z_i=\int p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\).

Wrapper method which calls get_second_moment multiple times.

参数
mumean of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
s2variance of the \(\mathcal{N}(f_i|\mu,\sigma^2)\)
lablabels \(y_i\)
返回
the second moment of \(q(f_i)\) for each \(f_i\)

在文件 LikelihoodModel.cpp69 行定义.

virtual SGVector<float64_t> get_third_derivative ( const CLabels lab,
SGVector< float64_t func,
const TParameter param 
) const
virtualinherited

get derivative of the second derivative of log likelihood with respect to function location, i.e. \(\frac{\partial^{2} log(p(y|f))}{\partial f^{2}}\) with respect to given parameter

参数
lablabels used
funcfunction location
paramparameter
返回
derivative

CVariationalLikelihood, CStudentsTLikelihood , 以及 CGaussianLikelihood 重载.

在文件 LikelihoodModel.h206 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.

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_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.

void set_num_samples ( index_t  num_samples)
virtual

set the num_samples used in the mc sampler

参数
num_samplesnumber of samples to be generated

在文件 SoftMaxLikelihood.cpp224 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.

virtual bool supports_binary ( ) const
virtualinherited

return whether likelihood function supports binary classification

返回
false

CVariationalLikelihood, CProbitLikelihood , 以及 CLogitLikelihood 重载.

在文件 LikelihoodModel.h308 行定义.

virtual bool supports_multiclass ( ) const
virtual

return whether likelihood function supports multiclass classification

返回
true

重载 CLikelihoodModel .

在文件 SoftMaxLikelihood.h257 行定义.

virtual bool supports_regression ( ) const
virtualinherited

return whether likelihood function supports regression

返回
false

CStudentsTLikelihood, CGaussianLikelihood , 以及 CVariationalLikelihood 重载.

在文件 LikelihoodModel.h302 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_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

在文件 SGObject.cpp250 行定义.

类成员变量说明

SGIO* io
inherited

io

在文件 SGObject.h496 行定义.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

在文件 SGObject.h511 行定义.

uint32_t m_hash
inherited

Hash of parameter values

在文件 SGObject.h517 行定义.

Parameter* m_model_selection_parameters
inherited

model selection parameters

在文件 SGObject.h508 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.

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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