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CGaussian Class Reference

Detailed Description

Gaussian distribution interface.

Takes as input a mean vector and covariance matrix. Also possible to train from data. Likelihood is computed using the Gaussian PDF \((2\pi)^{-\frac{k}{2}}|\Sigma|^{-\frac{1}{2}}e^{-\frac{1}{2}(x-\mu)'\Sigma^{-1}(x-\mu)}\) The actual computations depend on the type of covariance used.

Definition at line 49 of file Gaussian.h.

Inheritance diagram for CGaussian:
[legend]

Public Member Functions

 CGaussian ()
 
 CGaussian (const SGVector< float64_t > mean, SGMatrix< float64_t > cov, ECovType cov_type=FULL)
 
virtual ~CGaussian ()
 
void init ()
 
virtual bool train (CFeatures *data=NULL)
 
virtual int32_t get_num_model_parameters ()
 
virtual float64_t get_log_model_parameter (int32_t num_param)
 
virtual float64_t get_log_derivative (int32_t num_param, int32_t num_example)
 
virtual float64_t get_log_likelihood_example (int32_t num_example)
 
virtual float64_t update_params_em (float64_t *alpha_k, int32_t len)
 
virtual float64_t compute_PDF (SGVector< float64_t > point)
 
virtual float64_t compute_log_PDF (SGVector< float64_t > point)
 
virtual SGVector< float64_t > get_mean ()
 
virtual void set_mean (const SGVector< float64_t > mean)
 
virtual SGMatrix< float64_t > get_cov ()
 
virtual void set_cov (SGMatrix< float64_t > cov)
 
ECovType get_cov_type ()
 
void set_cov_type (ECovType cov_type)
 
SGVector< float64_t > get_d ()
 
void set_d (const SGVector< float64_t > d)
 
SGMatrix< float64_t > get_u ()
 
void set_u (SGMatrix< float64_t > u)
 
SGVector< float64_t > sample ()
 
virtual const char * get_name () const
 
virtual int32_t get_num_relevant_model_parameters ()
 
virtual float64_t get_log_likelihood_sample ()
 
virtual SGVector< float64_t > get_log_likelihood ()
 
virtual float64_t get_model_parameter (int32_t num_param)
 
virtual float64_t get_derivative (int32_t num_param, int32_t num_example)
 
virtual float64_t get_likelihood_example (int32_t num_example)
 
virtual SGVector< float64_t > get_likelihood_for_all_examples ()
 
virtual void set_features (CFeatures *f)
 
virtual CFeatures * get_features ()
 
virtual void set_pseudo_count (float64_t pseudo)
 
virtual float64_t get_pseudo_count ()
 
virtual CSGObject * shallow_copy () const
 
virtual CSGObject * deep_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)
 
SGIO * get_global_io ()
 
void set_global_parallel (Parallel *parallel)
 
Parallel * get_global_parallel ()
 
void set_global_version (Version *version)
 
Version * get_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 >
T get (const Tag< T > &_tag) const
 
template<typename T , typename U = void>
T 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 CSGObject * clone ()
 

Static Public Member Functions

static CGaussian * obtain_from_generic (CDistribution *distribution)
 
static CDistribution * obtain_from_generic (CSGObject *object)
 

Public Attributes

SGIO * io
 
Parallel * parallel
 
Version * version
 
Parameter * m_parameters
 
Parameter * m_model_selection_parameters
 
Parameter * m_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)
 

Protected Attributes

float64_t m_constant
 
SGVector< float64_t > m_d
 
SGMatrix< float64_t > m_u
 
SGVector< float64_t > m_mean
 
ECovType m_cov_type
 
CFeatures * features
 
float64_t pseudo_count
 

Constructor & Destructor Documentation

CGaussian ( )

default constructor

Definition at line 22 of file Gaussian.cpp.

CGaussian ( const SGVector< float64_t >  mean,
SGMatrix< float64_t >  cov,
ECovType  cov_type = FULL 
)

constructor

Parameters
meanmean of the Gaussian
covcovariance of the Gaussian
cov_typecovariance type (full, diagonal or shperical)

Definition at line 27 of file Gaussian.cpp.

~CGaussian ( )
virtual

Definition at line 62 of file Gaussian.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.

float64_t compute_log_PDF ( SGVector< float64_t >  point)
virtual

compute log PDF

Parameters
pointpoint for which to compute the log PDF
Returns
computed log PDF

Definition at line 222 of file Gaussian.cpp.

virtual float64_t compute_PDF ( SGVector< float64_t >  point)
virtual

compute PDF

Parameters
pointpoint for which to compute the PDF
Returns
computed PDF

Definition at line 119 of file Gaussian.h.

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.

SGMatrix< float64_t > get_cov ( )
virtual

get covariance

Returns
cov covariance, memory needs to be freed by user

Definition at line 288 of file Gaussian.cpp.

ECovType get_cov_type ( )

get covariance type

Returns
covariance type

Definition at line 161 of file Gaussian.h.

SGVector<float64_t> get_d ( )

get diagonal

Returns
diagonal

Definition at line 181 of file Gaussian.h.

virtual float64_t get_derivative ( int32_t  num_param,
int32_t  num_example 
)
virtualinherited

get partial derivative of likelihood function

Parameters
num_parampartial derivative against which param
num_examplewhich example
Returns
derivative of likelihood function

Definition at line 134 of file Distribution.h.

virtual CFeatures* get_features ( )
virtualinherited

get feature vectors

Returns
feature vectors

Definition at line 171 of file Distribution.h.

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.

virtual float64_t get_likelihood_example ( int32_t  num_example)
virtualinherited

compute likelihood for example

Parameters
num_examplewhich example
Returns
likelihood for example

Reimplemented in CGMM, and CLinearHMM.

Definition at line 145 of file Distribution.h.

SGVector< float64_t > get_likelihood_for_all_examples ( )
virtualinherited

compute likelihood for all vectors in sample

Returns
likelihood vector for all examples

Definition at line 65 of file Distribution.cpp.

float64_t get_log_derivative ( int32_t  num_param,
int32_t  num_example 
)
virtual

get partial derivative of likelihood function (logarithmic)

Parameters
num_paramderivative against which param
num_examplewhich example
Returns
derivative of likelihood (logarithmic)

Implements CDistribution.

Definition at line 104 of file Gaussian.cpp.

SGVector< float64_t > get_log_likelihood ( )
virtualinherited

compute log likelihood for each example

Returns
log likelihood vector

Definition at line 39 of file Distribution.cpp.

float64_t get_log_likelihood_example ( int32_t  num_example)
virtual

compute log likelihood for example

abstract base method

Parameters
num_examplewhich example
Returns
log likelihood for example

Implements CDistribution.

Definition at line 110 of file Gaussian.cpp.

float64_t get_log_likelihood_sample ( )
virtualinherited

compute log likelihood for whole sample

Returns
log likelihood for whole sample

Definition at line 28 of file Distribution.cpp.

float64_t get_log_model_parameter ( int32_t  num_param)
virtual

get model parameter (logarithmic)

Returns
model parameter (logarithmic) if num_param < m_dim returns an element from the mean, else return an element from the covariance

Implements CDistribution.

Definition at line 98 of file Gaussian.cpp.

SGVector< float64_t > get_mean ( )
virtual

get mean

Returns
mean

Definition at line 261 of file Gaussian.cpp.

virtual float64_t get_model_parameter ( int32_t  num_param)
virtualinherited

get model parameter

Parameters
num_paramwhich param
Returns
model parameter

Definition at line 123 of file Distribution.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
object name

Implements CSGObject.

Definition at line 223 of file Gaussian.h.

int32_t get_num_model_parameters ( )
virtual

get number of parameters in model

Returns
number of parameters in model

Implements CDistribution.

Definition at line 84 of file Gaussian.cpp.

int32_t get_num_relevant_model_parameters ( )
virtualinherited

get number of parameters in model that are relevant, i.e. > ALMOST_NEG_INFTY

Returns
number of relevant model parameters

Definition at line 52 of file Distribution.cpp.

virtual float64_t get_pseudo_count ( )
virtualinherited

get pseudo count

Returns
pseudo count

Definition at line 187 of file Distribution.h.

SGMatrix<float64_t> get_u ( )

get unitary matrix

Returns
unitary matrix

Definition at line 196 of file Gaussian.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.

void init ( )

Compute the constant part

Definition at line 46 of file Gaussian.cpp.

bool is_generic ( EPrimitiveType *  generic) const
virtualinherited

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

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

Definition at line 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.

CDistribution * obtain_from_generic ( CSGObject *  object)
staticinherited

obtain from generic

Parameters
objectgeneric object
Returns
Distribution object

Definition at line 85 of file Distribution.cpp.

CGaussian * obtain_from_generic ( CDistribution *  distribution)
static
Parameters
distributionis casted to CGaussian, NULL if not possible Note that the object is SG_REF'ed
Returns
casted CGaussian object

Definition at line 413 of file Gaussian.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.

SGVector< float64_t > sample ( )

sample from distribution

Returns
sample

Definition at line 362 of file Gaussian.cpp.

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_cov ( SGMatrix< float64_t >  cov)
virtual

set covariance

Doesn't store the covariance, but decomposes, thus the covariance can be freed after exit without harming the object

Parameters
covnew covariance

Definition at line 274 of file Gaussian.cpp.

void set_cov_type ( ECovType  cov_type)

set covariance type

Will only take effect after covariance is changed

Parameters
cov_typenew covariance type

Definition at line 172 of file Gaussian.h.

void set_d ( const SGVector< float64_t >  d)

set diagonal

Parameters
dnew diagonal

Definition at line 282 of file Gaussian.cpp.

virtual void set_features ( CFeatures *  f)
virtualinherited

set feature vectors

Parameters
fnew feature vectors

Definition at line 160 of file Distribution.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_mean ( const SGVector< float64_t >  mean)
virtual

set mean

Parameters
meannew mean

Definition at line 266 of file Gaussian.cpp.

virtual void set_pseudo_count ( float64_t  pseudo)
virtualinherited

set pseudo count

Parameters
pseudonew pseudo count

Definition at line 181 of file Distribution.h.

void set_u ( SGMatrix< float64_t >  u)

set unitary matrix

Parameters
unew unitary matrix

Definition at line 205 of file Gaussian.h.

CSGObject * shallow_copy ( ) const
virtualinherited

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

Reimplemented in CGaussianKernel.

Definition at line 225 of file SGObject.cpp.

bool train ( CFeatures *  data = NULL)
virtual

learn distribution

Parameters
datatraining data
Returns
whether training was successful

Implements CDistribution.

Definition at line 66 of file Gaussian.cpp.

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.

float64_t update_params_em ( float64_t *  alpha_k,
int32_t  len 
)
virtual

update parameters in the em maximization step for mixture model of which this distribution is a part

Parameters
alpha_k"belongingness" values of various data points
lenlength of alpha_k array
Returns
sum of values in alpha_k

Reimplemented from CDistribution.

Definition at line 118 of file Gaussian.cpp.

Member Data Documentation

CFeatures* features
protectedinherited

feature vectors

Definition at line 209 of file Distribution.h.

SGIO* io
inherited

io

Definition at line 537 of file SGObject.h.

float64_t m_constant
protected

constant part

Definition at line 237 of file Gaussian.h.

ECovType m_cov_type
protected

covariance type

Definition at line 245 of file Gaussian.h.

SGVector<float64_t> m_d
protected

diagonal

Definition at line 239 of file Gaussian.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.

SGVector<float64_t> m_mean
protected

mean

Definition at line 243 of file Gaussian.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.

SGMatrix<float64_t> m_u
protected

unitary matrix

Definition at line 241 of file Gaussian.h.

Parallel* parallel
inherited

parallel

Definition at line 540 of file SGObject.h.

float64_t pseudo_count
protectedinherited

pseudo count

Definition at line 211 of file Distribution.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