SHOGUN  4.1.0
CGaussianDistribution Class Reference

## Detailed Description

Dense version of the well-known Gaussian probability distribution, defined as

$\mathcal{N}_x(\mu,\Sigma)= \frac{1}{\sqrt{|2\pi\Sigma|}} \exp\left(-\frac{1}{2}(x-\mu)^T\Sigma^{-1}(x-\mu)\right)$

.

The implementation represents the covariance matrix $$\Sigma$$, as Cholesky factorisation, such that the covariance can be computed as $$\Sigma=LL^T$$.

Definition at line 61 of file GaussianDistribution.h.

Inheritance diagram for CGaussianDistribution:
[legend]

## Public Member Functions

CGaussianDistribution ()

CGaussianDistribution (SGVector< float64_t > mean, SGMatrix< float64_t > cov, bool cov_is_factor=false)

virtual ~CGaussianDistribution ()

virtual SGMatrix< float64_tsample (int32_t num_samples, SGMatrix< float64_t > pre_samples=SGMatrix< float64_t >()) const

virtual SGVector< float64_tlog_pdf_multiple (SGMatrix< float64_t > samples) const

virtual const char * get_name () const

virtual SGVector< float64_tsample () const

virtual float64_t log_pdf (SGVector< float64_t > sample_vec) 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)

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 float64_t univariate_log_pdf (float64_t sample, float64_t mu=0.0, float64_t sigma2=1.0)

## Public Attributes

SGIOio

Parallelparallel

Versionversion

Parameterm_parameters

Parameterm_model_selection_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)

## Protected Attributes

SGVector< float64_tm_mean

SGMatrix< float64_tm_L

int32_t m_dimension

## Constructor & Destructor Documentation

 CGaussianDistribution ( )

Default constructor

Definition at line 20 of file GaussianDistribution.cpp.

 CGaussianDistribution ( SGVector< float64_t > mean, SGMatrix< float64_t > cov, bool cov_is_factor = false )

Constructor for which takes Gaussian mean and its covariance matrix. It is also possible to pass a precomputed matrix factor of the specified form. In this case, the factorization is not explicitly computed.

Parameters
 mean mean of the Gaussian cov covariance of the Gaussian, or covariance factor cov_is_factor whether cov is a factor of the covariance or not (default is false). If false, the factorization is explicitly computed

Definition at line 25 of file GaussianDistribution.cpp.

 ~CGaussianDistribution ( )
virtual

Destructor

Definition at line 65 of file GaussianDistribution.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
 dict dictionary of parameters to be built.

Definition at line 597 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 714 of file SGObject.cpp.

 CSGObject * deep_copy ( ) const
virtualinherited

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

Definition at line 198 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
 other object to compare with accuracy accuracy to use for comparison (optional) tolerant allows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 618 of file SGObject.cpp.

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 235 of file SGObject.cpp.

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 277 of file SGObject.cpp.

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 290 of file SGObject.cpp.

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

Definition at line 498 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_name name of the parameter
Returns
description of the parameter

Definition at line 522 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_name name of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 535 of file SGObject.cpp.

 virtual const char* get_name ( ) const
virtual
Returns
name of the SGSerializable

Implements CProbabilityDistribution.

Definition at line 112 of file GaussianDistribution.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
 generic set to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 296 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
 file where to load from prefix prefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 369 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
 ShogunException will be thrown if an error occurs.

Definition at line 426 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
 ShogunException will be thrown if an error occurs.

Definition at line 421 of file SGObject.cpp.

 float64_t log_pdf ( SGVector< float64_t > sample_vec ) const
virtualinherited

Computes the log-pdf for a single provided sample. Wrapper method which calls log_pdf_multiple

Parameters
 sample_vec sample_vec to compute log-pdf for
Returns
log-pdf of the given sample

Definition at line 61 of file ProbabilityDistribution.cpp.

 SGVector< float64_t > log_pdf_multiple ( SGMatrix< float64_t > samples ) const
virtual

Computes the log-pdf for all provided samples. That is

$\log(\mathcal{N}_x(\mu,\Sigma))= - \frac{d}{2} \log(2\pi) -\frac{1}{2}\log(\det(\Sigma)) -\frac{1}{2}(x-\mu)^T\Sigma^{-1}(x-\mu),$

where $$d$$ is the dimension of the Gaussian. The method to compute the log-determinant is based on the factorization of the covariance matrix.

Parameters
 samples samples to compute log-pdf of (column vectors)
Returns
vector with log-pdfs of given samples

Reimplemented from CProbabilityDistribution.

Definition at line 111 of file GaussianDistribution.cpp.

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

Definition at line 262 of file SGObject.cpp.

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 474 of file SGObject.cpp.

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

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 308 of file SGObject.cpp.

 SGVector< float64_t > sample ( ) const
virtualinherited

Samples from the distribution once. Wrapper method. No pre-sample passing is possible with this method.

Returns
vector with single sample

Definition at line 46 of file ProbabilityDistribution.cpp.

 SGMatrix< float64_t > sample ( int32_t num_samples, SGMatrix< float64_t > pre_samples = SGMatrix() ) const
virtual

Samples from the distribution multiple times

Parameters
 num_samples number of samples to generate pre_samples a matrix of standard normal samples that might be used for sampling the Gaussian. Ignored by default. If passed, the pre-samples will be modified.
Returns
matrix with samples (column vectors)

Reimplemented from CProbabilityDistribution.

Definition at line 70 of file GaussianDistribution.cpp.

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

Save this object to file.

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

Definition at line 314 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
 ShogunException will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 436 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
 ShogunException will be thrown if an error occurs.

Definition at line 431 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 41 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 46 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 51 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 56 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 61 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 66 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 71 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 76 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 81 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 86 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 91 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 96 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 101 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 106 of file SGObject.cpp.

 void set_generic ( )
inherited

Definition at line 111 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
 io io object to use

Definition at line 228 of file SGObject.cpp.

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 241 of file SGObject.cpp.

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 283 of file SGObject.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 192 of file SGObject.cpp.

 static float64_t univariate_log_pdf ( float64_t sample, float64_t mu = 0.0, float64_t sigma2 = 1.0 )
static

Computes the univariate pdf for one given sample.

Parameters
 sample is a given sample mu is the mean of univariate Normal distribution (default value is 0.0) sigma2 is the variance of univariate Normal distribution (default value is 1.0)
Returns
the pdf of the distribution given the sample

Definition at line 125 of file GaussianDistribution.h.

 void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 303 of file SGObject.cpp.

 void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 248 of file SGObject.cpp.

## Member Data Documentation

 SGIO* io
inherited

io

Definition at line 369 of file SGObject.h.

 int32_t m_dimension
protectedinherited

Dimension of the distribution

Definition at line 82 of file ProbabilityDistribution.h.

inherited

parameters wrt which we can compute gradients

Definition at line 384 of file SGObject.h.

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 387 of file SGObject.h.

 SGMatrix m_L
protected

Lower factor of covariance matrix (depends on factorization type). Covariance (approximation) is given by $$\Sigma=LL^T$$

Definition at line 142 of file GaussianDistribution.h.

 SGVector m_mean
protected

Mean

Definition at line 138 of file GaussianDistribution.h.

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 381 of file SGObject.h.

 Parameter* m_parameters
inherited

parameters

Definition at line 378 of file SGObject.h.

 Parallel* parallel
inherited

parallel

Definition at line 372 of file SGObject.h.

 Version* version
inherited

version

Definition at line 375 of file SGObject.h.

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

SHOGUN Machine Learning Toolbox - Documentation