SHOGUN  6.1.3
List of all members | Public Types | Public Member Functions | Static Public Member Functions | Public Attributes | Protected Member Functions | Protected Attributes
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 47 of file Gaussian.h.

Inheritance diagram for CGaussian:
[legend]

Public Types

typedef rxcpp::subjects::subject< ObservedValueSGSubject
 
typedef rxcpp::observable< ObservedValue, rxcpp::dynamic_observable< ObservedValue > > SGObservable
 
typedef rxcpp::subscriber< ObservedValue, rxcpp::observer< ObservedValue, void, void, void, void > > SGSubscriber
 

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_tget_mean ()
 
virtual void set_mean (const SGVector< float64_t > mean)
 
virtual SGMatrix< float64_tget_cov ()
 
virtual void set_cov (SGMatrix< float64_t > cov)
 
ECovType get_cov_type ()
 
void set_cov_type (ECovType cov_type)
 
SGVector< float64_tget_d ()
 
void set_d (const SGVector< float64_t > d)
 
SGMatrix< float64_tget_u ()
 
void set_u (SGMatrix< float64_t > u)
 
SGVector< float64_tsample ()
 
virtual const char * get_name () const
 
virtual int32_t get_num_relevant_model_parameters ()
 
virtual float64_t get_log_likelihood_sample ()
 
virtual SGVector< float64_tget_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_tget_likelihood_for_all_examples ()
 
virtual void set_features (CFeatures *f)
 
virtual CFeaturesget_features ()
 
virtual void set_pseudo_count (float64_t pseudo)
 
virtual float64_t get_pseudo_count ()
 
int32_t ref ()
 
int32_t ref_count ()
 
int32_t unref ()
 
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
 
SGObservableget_parameters_observable ()
 
void subscribe_to_parameters (ParameterObserverInterface *obs)
 
void list_observable_parameters ()
 
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 ()
 

Static Public Member Functions

static CGaussianobtain_from_generic (CDistribution *distribution)
 
static CDistributionobtain_from_generic (CSGObject *object)
 

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)
 
bool clone_parameters (CSGObject *other)
 
void observe (const ObservedValue value)
 
void register_observable_param (const std::string &name, const SG_OBS_VALUE_TYPE type, const std::string &description)
 

Protected Attributes

float64_t m_constant
 
SGVector< float64_tm_d
 
SGMatrix< float64_tm_u
 
SGVector< float64_tm_mean
 
ECovType m_cov_type
 
CFeaturesfeatures
 
float64_t pseudo_count
 

Member Typedef Documentation

◆ SGObservable

Definition at line 130 of file SGObject.h.

◆ SGSubject

Definition at line 127 of file SGObject.h.

◆ SGSubscriber

typedef rxcpp::subscriber< ObservedValue, rxcpp::observer<ObservedValue, void, void, void, void> > SGSubscriber
inherited

Definition at line 133 of file SGObject.h.

Constructor & Destructor Documentation

◆ CGaussian() [1/2]

CGaussian ( )

default constructor

Definition at line 23 of file Gaussian.cpp.

◆ CGaussian() [2/2]

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 28 of file Gaussian.cpp.

◆ ~CGaussian()

~CGaussian ( )
virtual

Definition at line 64 of file Gaussian.cpp.

Member Function Documentation

◆ build_gradient_parameter_dictionary()

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 635 of file SGObject.cpp.

◆ clone()

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

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

Definition at line 734 of file SGObject.cpp.

◆ clone_parameters()

bool clone_parameters ( CSGObject other)
protectedinherited

Definition at line 759 of file SGObject.cpp.

◆ compute_log_PDF()

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 237 of file Gaussian.cpp.

◆ compute_PDF()

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 117 of file Gaussian.h.

◆ deep_copy()

CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 232 of file SGObject.cpp.

◆ equals()

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 656 of file SGObject.cpp.

◆ get() [1/2]

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 381 of file SGObject.h.

◆ get() [2/2]

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 404 of file SGObject.h.

◆ get_cov()

SGMatrix< float64_t > get_cov ( )
virtual

get covariance

Returns
cov covariance, memory needs to be freed by user

Definition at line 303 of file Gaussian.cpp.

◆ get_cov_type()

ECovType get_cov_type ( )

get covariance type

Returns
covariance type

Definition at line 159 of file Gaussian.h.

◆ get_d()

SGVector<float64_t> get_d ( )

get diagonal

Returns
diagonal

Definition at line 179 of file Gaussian.h.

◆ get_derivative()

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.

◆ get_features()

virtual CFeatures* get_features ( )
virtualinherited

get feature vectors

Returns
feature vectors

Definition at line 171 of file Distribution.h.

◆ get_global_io()

SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 269 of file SGObject.cpp.

◆ get_global_parallel()

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 311 of file SGObject.cpp.

◆ get_global_version()

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 324 of file SGObject.cpp.

◆ get_likelihood_example()

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.

◆ get_likelihood_for_all_examples()

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.

◆ get_log_derivative()

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 106 of file Gaussian.cpp.

◆ get_log_likelihood()

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.

◆ get_log_likelihood_example()

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 112 of file Gaussian.cpp.

◆ get_log_likelihood_sample()

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.

◆ get_log_model_parameter()

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 100 of file Gaussian.cpp.

◆ get_mean()

SGVector< float64_t > get_mean ( )
virtual

get mean

Returns
mean

Definition at line 276 of file Gaussian.cpp.

◆ get_model_parameter()

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.

◆ get_modelsel_names()

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

Definition at line 536 of file SGObject.cpp.

◆ get_modsel_param_descr()

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 560 of file SGObject.cpp.

◆ get_modsel_param_index()

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 573 of file SGObject.cpp.

◆ get_name()

virtual const char* get_name ( ) const
virtual
Returns
object name

Implements CSGObject.

Definition at line 221 of file Gaussian.h.

◆ get_num_model_parameters()

int32_t get_num_model_parameters ( )
virtual

get number of parameters in model

Returns
number of parameters in model

Implements CDistribution.

Definition at line 86 of file Gaussian.cpp.

◆ get_num_relevant_model_parameters()

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.

◆ get_parameters_observable()

SGObservable* get_parameters_observable ( )
inherited

Get parameters observable

Returns
RxCpp observable

Definition at line 415 of file SGObject.h.

◆ get_pseudo_count()

virtual float64_t get_pseudo_count ( )
virtualinherited

get pseudo count

Returns
pseudo count

Definition at line 187 of file Distribution.h.

◆ get_u()

SGMatrix<float64_t> get_u ( )

get unitary matrix

Returns
unitary matrix

Definition at line 194 of file Gaussian.h.

◆ has() [1/3]

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 304 of file SGObject.h.

◆ has() [2/3]

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 315 of file SGObject.h.

◆ has() [3/3]

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 326 of file SGObject.h.

◆ init()

void init ( )

Compute the constant part

Definition at line 48 of file Gaussian.cpp.

◆ is_generic()

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 330 of file SGObject.cpp.

◆ list_observable_parameters()

void list_observable_parameters ( )
inherited

Print to stdout a list of observable parameters

Definition at line 878 of file SGObject.cpp.

◆ load_serializable()

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 403 of file SGObject.cpp.

◆ load_serializable_post()

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 460 of file SGObject.cpp.

◆ load_serializable_pre()

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 455 of file SGObject.cpp.

◆ observe()

void observe ( const ObservedValue  value)
protectedinherited

Observe a parameter value and emit them to observer.

Parameters
valueObserved parameter's value

Definition at line 828 of file SGObject.cpp.

◆ obtain_from_generic() [1/2]

CDistribution * obtain_from_generic ( CSGObject object)
staticinherited

obtain from generic

Parameters
objectgeneric object
Returns
Distribution object

Definition at line 85 of file Distribution.cpp.

◆ obtain_from_generic() [2/2]

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 449 of file Gaussian.cpp.

◆ parameter_hash_changed()

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

Definition at line 296 of file SGObject.cpp.

◆ print_modsel_params()

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 512 of file SGObject.cpp.

◆ print_serializable()

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

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 342 of file SGObject.cpp.

◆ ref()

int32_t ref ( )
inherited

increase reference counter

Returns
reference count

Definition at line 186 of file SGObject.cpp.

◆ ref_count()

int32_t ref_count ( )
inherited

display reference counter

Returns
reference count

Definition at line 193 of file SGObject.cpp.

◆ register_observable_param()

void register_observable_param ( const std::string &  name,
const SG_OBS_VALUE_TYPE  type,
const std::string &  description 
)
protectedinherited

Register which params this object can emit.

Parameters
namethe param name
typethe param type
descriptiona user oriented description

Definition at line 871 of file SGObject.cpp.

◆ register_param() [1/2]

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 472 of file SGObject.h.

◆ register_param() [2/2]

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 485 of file SGObject.h.

◆ sample()

SGVector< float64_t > sample ( )

sample from distribution

Returns
sample

Definition at line 391 of file Gaussian.cpp.

◆ save_serializable()

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 348 of file SGObject.cpp.

◆ save_serializable_post()

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 470 of file SGObject.cpp.

◆ save_serializable_pre()

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 465 of file SGObject.cpp.

◆ set() [1/2]

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 342 of file SGObject.h.

◆ set() [2/2]

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 368 of file SGObject.h.

◆ set_cov()

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 289 of file Gaussian.cpp.

◆ set_cov_type()

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 170 of file Gaussian.h.

◆ set_d()

void set_d ( const SGVector< float64_t d)

set diagonal

Parameters
dnew diagonal

Definition at line 297 of file Gaussian.cpp.

◆ set_features()

virtual void set_features ( CFeatures f)
virtualinherited

set feature vectors

Parameters
fnew feature vectors

Definition at line 160 of file Distribution.h.

◆ set_generic() [1/16]

void set_generic ( )
inherited

Definition at line 73 of file SGObject.cpp.

◆ set_generic() [2/16]

void set_generic ( )
inherited

Definition at line 78 of file SGObject.cpp.

◆ set_generic() [3/16]

void set_generic ( )
inherited

Definition at line 83 of file SGObject.cpp.

◆ set_generic() [4/16]

void set_generic ( )
inherited

Definition at line 88 of file SGObject.cpp.

◆ set_generic() [5/16]

void set_generic ( )
inherited

Definition at line 93 of file SGObject.cpp.

◆ set_generic() [6/16]

void set_generic ( )
inherited

Definition at line 98 of file SGObject.cpp.

◆ set_generic() [7/16]

void set_generic ( )
inherited

Definition at line 103 of file SGObject.cpp.

◆ set_generic() [8/16]

void set_generic ( )
inherited

Definition at line 108 of file SGObject.cpp.

◆ set_generic() [9/16]

void set_generic ( )
inherited

Definition at line 113 of file SGObject.cpp.

◆ set_generic() [10/16]

void set_generic ( )
inherited

Definition at line 118 of file SGObject.cpp.

◆ set_generic() [11/16]

void set_generic ( )
inherited

Definition at line 123 of file SGObject.cpp.

◆ set_generic() [12/16]

void set_generic ( )
inherited

Definition at line 128 of file SGObject.cpp.

◆ set_generic() [13/16]

void set_generic ( )
inherited

Definition at line 133 of file SGObject.cpp.

◆ set_generic() [14/16]

void set_generic ( )
inherited

Definition at line 138 of file SGObject.cpp.

◆ set_generic() [15/16]

void set_generic ( )
inherited

Definition at line 143 of file SGObject.cpp.

◆ set_generic() [16/16]

void set_generic ( )
inherited

set generic type to T

◆ set_global_io()

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 262 of file SGObject.cpp.

◆ set_global_parallel()

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 275 of file SGObject.cpp.

◆ set_global_version()

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 317 of file SGObject.cpp.

◆ set_mean()

void set_mean ( const SGVector< float64_t mean)
virtual

set mean

Parameters
meannew mean

Definition at line 281 of file Gaussian.cpp.

◆ set_pseudo_count()

virtual void set_pseudo_count ( float64_t  pseudo)
virtualinherited

set pseudo count

Parameters
pseudonew pseudo count

Definition at line 181 of file Distribution.h.

◆ set_u()

void set_u ( SGMatrix< float64_t u)

set unitary matrix

Parameters
unew unitary matrix

Definition at line 203 of file Gaussian.h.

◆ shallow_copy()

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 226 of file SGObject.cpp.

◆ subscribe_to_parameters()

void subscribe_to_parameters ( ParameterObserverInterface obs)
inherited

Subscribe a parameter observer to watch over params

Definition at line 811 of file SGObject.cpp.

◆ train()

bool train ( CFeatures data = NULL)
virtual

learn distribution

Parameters
datatraining data
Returns
whether training was successful

Implements CDistribution.

Definition at line 68 of file Gaussian.cpp.

◆ unref()

int32_t unref ( )
inherited

decrement reference counter and deallocate object if refcount is zero before or after decrementing it

Returns
reference count

Definition at line 200 of file SGObject.cpp.

◆ unset_generic()

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 337 of file SGObject.cpp.

◆ update_parameter_hash()

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 282 of file SGObject.cpp.

◆ update_params_em()

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 120 of file Gaussian.cpp.

Member Data Documentation

◆ features

CFeatures* features
protectedinherited

feature vectors

Definition at line 209 of file Distribution.h.

◆ io

SGIO* io
inherited

io

Definition at line 600 of file SGObject.h.

◆ m_constant

float64_t m_constant
protected

constant part

Definition at line 235 of file Gaussian.h.

◆ m_cov_type

ECovType m_cov_type
protected

covariance type

Definition at line 243 of file Gaussian.h.

◆ m_d

SGVector<float64_t> m_d
protected

diagonal

Definition at line 237 of file Gaussian.h.

◆ m_gradient_parameters

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 615 of file SGObject.h.

◆ m_hash

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 618 of file SGObject.h.

◆ m_mean

SGVector<float64_t> m_mean
protected

mean

Definition at line 241 of file Gaussian.h.

◆ m_model_selection_parameters

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 612 of file SGObject.h.

◆ m_parameters

Parameter* m_parameters
inherited

parameters

Definition at line 609 of file SGObject.h.

◆ m_u

SGMatrix<float64_t> m_u
protected

unitary matrix

Definition at line 239 of file Gaussian.h.

◆ parallel

Parallel* parallel
inherited

parallel

Definition at line 603 of file SGObject.h.

◆ pseudo_count

float64_t pseudo_count
protectedinherited

pseudo count

Definition at line 211 of file Distribution.h.

◆ version

Version* version
inherited

version

Definition at line 606 of file SGObject.h.


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

SHOGUN Machine Learning Toolbox - Documentation