Class representing Gaussian (or normal density) More...

#include <gaussian.h>

Inheritance diagram for Gaussian:

Public Member Functions
	Gaussian (const MatrixWrapper::ColumnVector &Mu, const MatrixWrapper::SymmetricMatrix &Sigma)
	Constructor. More...

	Gaussian (int dimension=0)
	constructor with only dimensions or nothing

virtual	~Gaussian ()
	Default Copy Constructor will do. More...

virtual Gaussian *	Clone () const
	Clone function.

virtual Probability	ProbabilityGet (const MatrixWrapper::ColumnVector &input) const
	Get the probability of a certain argument. More...

bool	SampleFrom (vector< Sample< MatrixWrapper::ColumnVector > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const

virtual bool	SampleFrom (Sample< MatrixWrapper::ColumnVector > &one_sample, int method=DEFAULT, void *args=NULL) const

virtual MatrixWrapper::ColumnVector	ExpectedValueGet () const
	Get the expected value E[x] of the pdf. More...

virtual MatrixWrapper::SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. More...

virtual void	DimensionSet (unsigned int dim)
	Set the dimension of the argument. More...

void	ExpectedValueSet (const MatrixWrapper::ColumnVector &mu)
	Set the Expected Value. More...

void	CovarianceSet (const MatrixWrapper::SymmetricMatrix &cov)
	Set the Covariance Matrix. More...

virtual bool	SampleFrom (vector< Sample< MatrixWrapper::ColumnVector > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const
	Draw multiple samples from the Pdf (overloaded) More...

virtual bool	SampleFrom (Sample< MatrixWrapper::ColumnVector > &one_sample, int method=DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf: More...

unsigned int	DimensionGet () const
	Get the dimension of the argument. More...

Friends
std::ostream &	operator<< (std::ostream &os, const Gaussian &g)
	output stream for Gaussian

Detailed Description

Class representing Gaussian (or normal density)

Definition at line 27 of file gaussian.h.

Constructor & Destructor Documentation

Gaussian	(	const MatrixWrapper::ColumnVector &	Mu,
		const MatrixWrapper::SymmetricMatrix &	Sigma
	)

Constructor.

Parameters

Mu	Mean Vector of the Gaussian
Sigma	Covariance Matrix of the Gaussian

virtual ~Gaussian ( )

virtual

Default Copy Constructor will do.

Destructor

Member Function Documentation

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const

virtual

Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.

Get first order statistic (Covariance) of this AnalyticPdf

Returns: The Covariance of the Pdf (a SymmetricMatrix of dim DIMENSION)

Todo:: extend this more general to n-th order statistic

Bug:: Discrete pdfs should not be able to use this!

Reimplemented from Pdf< MatrixWrapper::ColumnVector >.

void CovarianceSet ( const MatrixWrapper::SymmetricMatrix & cov )

Set the Covariance Matrix.

Set the Covariance Matrix

Parameters

cov	The new Covariance matrix

unsigned int DimensionGet ( ) const

inherited

Get the dimension of the argument.

Returns: the dimension of the argument

virtual void DimensionSet ( unsigned int dim )

virtual

Set the dimension of the argument.

Parameters

dim	the dimension

Reimplemented from Pdf< MatrixWrapper::ColumnVector >.

virtual MatrixWrapper::ColumnVector ExpectedValueGet ( ) const

virtual

Get the expected value E[x] of the pdf.

Get low order statistic (Expected Value) of this AnalyticPdf

Returns: The Expected Value of the Pdf (a ColumnVector with DIMENSION rows)

Note: No set functions here! This can be useful for analytic functions, but not for sample based representations!; For certain discrete Pdfs, this function has no meaning, what is the average between yes and no?

Reimplemented from Pdf< MatrixWrapper::ColumnVector >.

void ExpectedValueSet ( const MatrixWrapper::ColumnVector & mu )

Set the Expected Value.

Set the Expected Value

Parameters

mu	The new Expected Value

virtual Probability ProbabilityGet ( const MatrixWrapper::ColumnVector & input ) const

virtual

Get the probability of a certain argument.

Parameters

input T argument of the Pdf

Returns: the probability value of the argument

Reimplemented from Pdf< MatrixWrapper::ColumnVector >.

virtual bool SampleFrom	(	vector< Sample< MatrixWrapper::ColumnVector > > &	list_samples,
		const unsigned int	num_samples,
		int	method = `DEFAULT`,
		void *	args = `NULL`
	)		const

virtualinherited

Draw multiple samples from the Pdf (overloaded)

Parameters

list_samples	list of samples that will contain result of sampling
num_samples	Number of Samples to be drawn (iid)
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer to a struct representing extra sample arguments. "Sample Arguments" can be anything (the number of steps a gibbs-iterator should take, the interval width in MCMC, ... (or nothing), so it is hard to give a meaning to what exactly Sample Arguments should represent...

Todo:: replace the C-call "void * args" by a more object-oriented structure: Perhaps something like virtual Sample * Sample (const int num_samples,class Sampler)

Bug:: Sometimes the compiler doesn't know which method to choose!

virtual bool SampleFrom	(	Sample< MatrixWrapper::ColumnVector > &	one_sample,
		int	method = `DEFAULT`,
		void *	args = `NULL`
	)		const

virtualinherited

Draw 1 sample from the Pdf:

There's no need to create a list for only 1 sample!

Parameters

one_sample	sample that will contain result of sampling
method	Sampling method to be used. Each sampling method is currently represented by a #define statement, eg. #define BOXMULLER 1
args	Pointer to a struct representing extra sample arguments

See Also: SampleFrom()

Bug:: Sometimes the compiler doesn't know which method to choose!

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

gaussian.h

Public Member Functions

Friends

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation