BFL::Gaussian Class Reference

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

#include <gaussian.h>

Inheritance diagram for BFL::Gaussian:

List of all members.

Public Member Functions
virtual Gaussian *	Clone () const
	Clone function.
virtual MatrixWrapper::SymmetricMatrix	CovarianceGet () const
	Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
void	CovarianceSet (const MatrixWrapper::SymmetricMatrix &cov)
	Set the Covariance Matrix.
virtual MatrixWrapper::ColumnVector	ExpectedValueGet () const
	Get the expected value E[x] of the pdf.
void	ExpectedValueSet (const MatrixWrapper::ColumnVector &mu)
	Set the Expected Value.
	Gaussian (const MatrixWrapper::ColumnVector &Mu, const MatrixWrapper::SymmetricMatrix &Sigma)
	Constructor.
	Gaussian (int dimension=0)
	constructor with only dimensions or nothing
virtual Probability	ProbabilityGet (const MatrixWrapper::ColumnVector &input) const
	Get the probability of a certain argument.
bool	SampleFrom (vector< Sample< MatrixWrapper::ColumnVector > > &list_samples, const 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	~Gaussian ()
	Default Copy Constructor will do.
Private Attributes
ColumnVector	_diff
Matrix	_Low_triangle
MatrixWrapper::ColumnVector	_Mu
ColumnVector	_samples
ColumnVector	_sampleValue
MatrixWrapper::SymmetricMatrix	_Sigma
bool	_Sigma_changed
MatrixWrapper::SymmetricMatrix	_Sigma_inverse
double	_sqrt_pow
ColumnVector	_tempColumn
Friends
std::ostream &	operator<< (std::ostream &os, const Gaussian &g)
	output stream for Gaussian

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Detailed Description

Class representing Gaussian (or normal density)

Definition at line 27 of file gaussian.h.

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Constructor & Destructor Documentation

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

Constructor.

Parameters:

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

BFL::Gaussian::Gaussian

(

int

dimension = 0

)

constructor with only dimensions or nothing

Definition at line 46 of file gaussian.cpp.

BFL::Gaussian::~Gaussian

(

)

[virtual]

Default Copy Constructor will do.

Destructor

Definition at line 60 of file gaussian.cpp.

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Member Function Documentation

Gaussian * BFL::Gaussian::Clone

(

)

const [virtual]

Clone function.

Implements BFL::BFL::Pdf< MatrixWrapper::ColumnVector >.

Definition at line 70 of file gaussian.cpp.

SymmetricMatrix BFL::Gaussian::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 BFL::BFL::Pdf< MatrixWrapper::ColumnVector >.

Definition at line 187 of file gaussian.cpp.

void BFL::Gaussian::CovarianceSet

(

const MatrixWrapper::SymmetricMatrix &

cov

)

Set the Covariance Matrix.

Set the Covariance Matrix

Parameters:

cov	The new Covariance matrix

Definition at line 204 of file gaussian.cpp.

ColumnVector BFL::Gaussian::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 BFL::BFL::Pdf< MatrixWrapper::ColumnVector >.

Definition at line 181 of file gaussian.cpp.

void BFL::Gaussian::ExpectedValueSet

(

const MatrixWrapper::ColumnVector &

mu

)

Set the Expected Value.

Set the Expected Value

Parameters:

mu	The new Expected Value

Definition at line 193 of file gaussian.cpp.

Probability BFL::Gaussian::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 BFL::BFL::Pdf< MatrixWrapper::ColumnVector >.

Definition at line 75 of file gaussian.cpp.

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

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

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Friends And Related Function Documentation

std::ostream& operator<<	(	std::ostream &	os,
		const Gaussian &	g
	)		[friend]

output stream for Gaussian

Definition at line 62 of file gaussian.cpp.

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Member Data Documentation

ColumnVector BFL::Gaussian::_diff [mutable, private]

Definition at line 37 of file gaussian.h.

Matrix BFL::Gaussian::_Low_triangle [mutable, private]

Definition at line 42 of file gaussian.h.

MatrixWrapper::ColumnVector BFL::Gaussian::_Mu [private]

Definition at line 30 of file gaussian.h.

ColumnVector BFL::Gaussian::_samples [mutable, private]

Definition at line 40 of file gaussian.h.

ColumnVector BFL::Gaussian::_sampleValue [mutable, private]

Definition at line 41 of file gaussian.h.

MatrixWrapper::SymmetricMatrix BFL::Gaussian::_Sigma [private]

Definition at line 31 of file gaussian.h.

bool BFL::Gaussian::_Sigma_changed [mutable, private]

Definition at line 34 of file gaussian.h.

MatrixWrapper::SymmetricMatrix BFL::Gaussian::_Sigma_inverse [mutable, private]

Definition at line 35 of file gaussian.h.

double BFL::Gaussian::_sqrt_pow [mutable, private]

Definition at line 36 of file gaussian.h.

ColumnVector BFL::Gaussian::_tempColumn [mutable, private]

Definition at line 38 of file gaussian.h.

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

• gaussian.h
• gaussian.cpp