Class representing a PDF on a discrete variable. More...

#include <mixtureParticleFilter.h>

Inheritance diagram for BFL::BFL::DiscretePdf:

Public Member Functions
virtual DiscretePdf *	Clone () const
	Clone function.
	DiscretePdf (unsigned int num_states=0)
	Constructor (dimension = number of classes) An equal probability is set for all classes.
	DiscretePdf (const DiscretePdf &)
	Copy Constructor.
int	MostProbableStateGet ()
	Get the index of the most probable state.
unsigned int	NumStatesGet () const
	Get the number of discrete States.
vector< Probability >	ProbabilitiesGet () const
	Get all probabilities.
bool	ProbabilitiesSet (vector< Probability > &values)
	Set all probabilities.
Probability	ProbabilityGet (const int &state) const
	Implementation of virtual base class method.
bool	ProbabilitySet (int state, Probability a)
	Function to change/set the probability of a single state.
bool	SampleFrom (vector< Sample< int > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const
	Draw multiple samples from the Pdf (overloaded)
bool	SampleFrom (Sample< int > &one_sample, int method=DEFAULT, void *args=NULL) const
	Draw 1 sample from the Pdf:
virtual	~DiscretePdf ()
	Destructor.
Protected Member Functions
bool	CumPDFUpdate ()
	Updates the cumPDF.
bool	NormalizeProbs ()
	Normalize all the probabilities (eg. after setting a probability)
Protected Attributes
vector< double >	_CumPDF
	STL-vector containing the Cumulative PDF (for efficient sampling)
unsigned int	_num_states
	The number of discrete state.
vector< Probability > *	_Values_p
	Pointer to the discrete PDF-values, the sum of the elements = 1.

Detailed Description

Class representing a PDF on a discrete variable.

This class is an instantation from the template class Pdf, with added methods to get a set the probability of a certain discrete value (methods only relevant for discrete pdfs)

Definition at line 34 of file mixtureParticleFilter.h.

Constructor & Destructor Documentation

BFL::DiscretePdf::DiscretePdf ( unsigned int num_states = 0 )

Constructor (dimension = number of classes) An equal probability is set for all classes.

Parameters:

num_states number of different classes or states

Definition at line 31 of file discretepdf.cpp.

BFL::DiscretePdf::DiscretePdf ( const DiscretePdf & my_dpdf )

Copy Constructor.

Definition at line 47 of file discretepdf.cpp.

BFL::DiscretePdf::~DiscretePdf ( ) [virtual]

Destructor.

Definition at line 59 of file discretepdf.cpp.

Member Function Documentation

DiscretePdf * BFL::DiscretePdf::Clone ( ) const [virtual]

Clone function.

Implements BFL::BFL::Pdf< int >.

Definition at line 69 of file discretepdf.cpp.

bool BFL::DiscretePdf::CumPDFUpdate ( ) [protected]

Updates the cumPDF.

Definition at line 225 of file discretepdf.cpp.

int BFL::DiscretePdf::MostProbableStateGet ( )

Get the index of the most probable state.

Definition at line 250 of file discretepdf.cpp.

bool BFL::DiscretePdf::NormalizeProbs ( ) [protected]

Normalize all the probabilities (eg. after setting a probability)

Definition at line 207 of file discretepdf.cpp.

unsigned int BFL::DiscretePdf::NumStatesGet ( ) const

Get the number of discrete States.

Definition at line 74 of file discretepdf.cpp.

vector< Probability > BFL::DiscretePdf::ProbabilitiesGet ( ) const

Get all probabilities.

Definition at line 112 of file discretepdf.cpp.

bool BFL::DiscretePdf::ProbabilitiesSet ( vector< Probability > & values )

Set all probabilities.

Parameters:

values vector<Probability> containing the new probability values. The sum of the probabilities of this list is not required to be one since the normalization is automatically carried out.

Definition at line 117 of file discretepdf.cpp.

Probability BFL::DiscretePdf::ProbabilityGet ( const int & state ) const [virtual]

Implementation of virtual base class method.

Reimplemented from BFL::BFL::Pdf< int >.

Definition at line 80 of file discretepdf.cpp.

bool BFL::DiscretePdf::ProbabilitySet	(	int	state,
		Probability	a
	)

Function to change/set the probability of a single state.

Changes the probabilities such that AFTER normalization the probability of the state "state" is equal to the probability a

Parameters:

state	number of state of which the probability will be set
a	probability value to which the probability of state "state" will be set (must be <= 1)

Definition at line 86 of file discretepdf.cpp.

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

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!

Reimplemented from BFL::BFL::Pdf< int >.

Definition at line 128 of file discretepdf.cpp.

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

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