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CSoftMaxLikelihood Class Reference

Detailed Description

Class that models Soft-Max likelihood.

\( \text{softmax}_i(f)=\frac{\exp{f_i}}{\sum\exp{f_i}} \)

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.

Definition at line 79 of file SoftMaxLikelihood.h.

Inheritance diagram for CSoftMaxLikelihood:
[legend]

Public Member Functions

 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)
 
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 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="")
 
virtual bool load_serializable (CSerializableFile *file, const char *prefix="")
 
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)
 
bool has (const std::string &name) const
 
template<typename T >
bool has (const Tag< T > &tag) const
 
template<typename T , typename U = void>
bool has (const std::string &name) const
 
template<typename T >
void set (const Tag< T > &_tag, const T &value)
 
template<typename T , typename U = void>
void set (const std::string &name, const T &value)
 
template<typename T >
get (const Tag< T > &_tag) const
 
template<typename T , typename U = void>
get (const std::string &name) const
 
virtual void update_parameter_hash ()
 
virtual bool parameter_hash_changed ()
 
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
 
virtual CSGObjectclone ()
 

Public Attributes

SGIOio
 
Parallelparallel
 
Versionversion
 
Parameterm_parameters
 
Parameterm_model_selection_parameters
 
Parameterm_gradient_parameters
 
uint32_t m_hash
 

Protected Member Functions

virtual void load_serializable_pre () throw (ShogunException)
 
virtual void load_serializable_post () throw (ShogunException)
 
virtual void save_serializable_pre () throw (ShogunException)
 
virtual void save_serializable_post () throw (ShogunException)
 
template<typename T >
void register_param (Tag< T > &_tag, const T &value)
 
template<typename T >
void register_param (const std::string &name, const T &value)
 

Constructor & Destructor Documentation

default constructor

Definition at line 50 of file SoftMaxLikelihood.cpp.

~CSoftMaxLikelihood ( )
virtual

destructor

Definition at line 55 of file SoftMaxLikelihood.cpp.

Member Function Documentation

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

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

Parameters
dictdictionary of parameters to be built.

Definition at line 630 of file SGObject.cpp.

CSGObject * clone ( )
virtualinherited

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

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

Definition at line 747 of file SGObject.cpp.

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 231 of file SGObject.cpp.

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

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

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

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

Definition at line 651 of file SGObject.cpp.

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

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

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

Definition at line 367 of file SGObject.h.

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

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

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

Definition at line 388 of file SGObject.h.

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

Parameters
lablabels used
funcfunction location
paramparameter
Returns
derivative

Reimplemented in CVariationalLikelihood, CStudentsTLikelihood, and CGaussianLikelihood.

Definition at line 192 of file LikelihoodModel.h.

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

Parameters
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
Returns
first moment of \(q(f_i)\)

Implements CLikelihoodModel.

Definition at line 225 of file SoftMaxLikelihood.h.

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.

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

Definition at line 72 of file LikelihoodModel.cpp.

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 268 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 310 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 323 of file SGObject.cpp.

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 \(f\)

Parameters
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
Returns
derivative

Implements CLikelihoodModel.

Definition at line 103 of file SoftMaxLikelihood.cpp.

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: \(log(p(y|f)) = \sum_{i=1}^{n} log(p(y_i|f_i))\)

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

Implements CLikelihoodModel.

Definition at line 67 of file SoftMaxLikelihood.cpp.

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.

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

Definition at line 51 of file LikelihoodModel.cpp.

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

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

Implements CLikelihoodModel.

Definition at line 204 of file SoftMaxLikelihood.h.

virtual ELikelihoodModelType get_model_type ( ) const
virtualinherited

get model type

Returns
model type NONE

Reimplemented in CStudentsTLikelihood, CGaussianLikelihood, CVariationalLikelihood, CProbitLikelihood, and CLogitLikelihood.

Definition at line 139 of file LikelihoodModel.h.

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

Definition at line 531 of file SGObject.cpp.

char * get_modsel_param_descr ( const char *  param_name)
inherited

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

Parameters
param_namename of the parameter
Returns
description of the parameter

Definition at line 555 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name)
inherited

Returns index of model selection parameter with provided index

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

Definition at line 568 of file SGObject.cpp.

virtual const char* get_name ( ) const
virtual

returns the name of the likelihood model

Returns
name SoftMaxLikelihood

Implements CSGObject.

Definition at line 92 of file SoftMaxLikelihood.h.

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

Parameters
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

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

Reimplemented from CLikelihoodModel.

Definition at line 303 of file SoftMaxLikelihood.cpp.

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.

Parameters
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

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

Implements CLikelihoodModel.

Definition at line 335 of file SoftMaxLikelihood.cpp.

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.

Parameters
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

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

Implements CLikelihoodModel.

Definition at line 342 of file SoftMaxLikelihood.cpp.

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

Parameters
lablabels used
funcfunction location
paramparameter
Returns
derivative

Reimplemented in CVariationalLikelihood, CStudentsTLikelihood, and CGaussianLikelihood.

Definition at line 210 of file LikelihoodModel.h.

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

Parameters
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
Returns
the second moment of \(q(f_i)\)

Implements CLikelihoodModel.

Definition at line 246 of file SoftMaxLikelihood.h.

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.

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

Definition at line 89 of file LikelihoodModel.cpp.

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

Parameters
lablabels used
funcfunction location
paramparameter
Returns
derivative

Reimplemented in CVariationalLikelihood, CStudentsTLikelihood, and CGaussianLikelihood.

Definition at line 227 of file LikelihoodModel.h.

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

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

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

Definition at line 289 of file SGObject.h.

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

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

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

Definition at line 301 of file SGObject.h.

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

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

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

Definition at line 312 of file SGObject.h.

bool is_generic ( EPrimitiveType *  generic) const
virtualinherited

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

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

Definition at line 329 of file SGObject.cpp.

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

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

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

Definition at line 402 of file SGObject.cpp.

void load_serializable_post ( )
throw (ShogunException
)
protectedvirtualinherited

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

Exceptions
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 459 of file SGObject.cpp.

void load_serializable_pre ( )
throw (ShogunException
)
protectedvirtualinherited

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

Exceptions
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 454 of file SGObject.cpp.

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

Definition at line 295 of file SGObject.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 507 of file SGObject.cpp.

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 341 of file SGObject.cpp.

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

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

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 439 of file SGObject.h.

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

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

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

Definition at line 452 of file SGObject.h.

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

Save this object to file.

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

Definition at line 347 of file SGObject.cpp.

void save_serializable_post ( )
throw (ShogunException
)
protectedvirtualinherited

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

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 469 of file SGObject.cpp.

void save_serializable_pre ( )
throw (ShogunException
)
protectedvirtualinherited

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

Exceptions
ShogunExceptionwill be thrown if an error occurs.

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

Definition at line 464 of file SGObject.cpp.

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

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

Parameters
_tagname and type information of parameter
valuevalue of the parameter

Definition at line 328 of file SGObject.h.

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

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

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

Definition at line 354 of file SGObject.h.

void set_generic ( )
inherited

Definition at line 74 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 79 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 84 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 89 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 94 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 99 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 104 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 109 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 114 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 119 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 124 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 129 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 134 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 139 of file SGObject.cpp.

void set_generic ( )
inherited

Definition at line 144 of file SGObject.cpp.

void set_generic ( )
inherited

set generic type to T

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 261 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 274 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 316 of file SGObject.cpp.

void set_num_samples ( index_t  num_samples)
virtual

set the num_samples used in the mc sampler

Parameters
num_samplesnumber of samples to be generated

Definition at line 224 of file SoftMaxLikelihood.cpp.

CSGObject * shallow_copy ( ) const
virtualinherited

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

Reimplemented in CGaussianKernel.

Definition at line 225 of file SGObject.cpp.

virtual bool supports_binary ( ) const
virtualinherited

return whether likelihood function supports binary classification

Returns
false

Reimplemented in CVariationalLikelihood, CProbitLikelihood, and CLogitLikelihood.

Definition at line 329 of file LikelihoodModel.h.

virtual bool supports_multiclass ( ) const
virtual

return whether likelihood function supports multiclass classification

Returns
true

Reimplemented from CLikelihoodModel.

Definition at line 257 of file SoftMaxLikelihood.h.

virtual bool supports_regression ( ) const
virtualinherited

return whether likelihood function supports regression

Returns
false

Reimplemented in CStudentsTLikelihood, CGaussianLikelihood, and CVariationalLikelihood.

Definition at line 323 of file LikelihoodModel.h.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 336 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 281 of file SGObject.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 552 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 555 of file SGObject.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 549 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 546 of file SGObject.h.

Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

Version* version
inherited

version

Definition at line 543 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation