Abstract Class representing all FULLY Discrete Conditional PDF's. More...

#include <discreteconditionalpdf.h>

Inheritance diagram for DiscreteConditionalPdf:

Public Member Functions
	DiscreteConditionalPdf (int num_states=1, int num_conditional_arguments=1, int cond_arg_dimensions[]=NULL)
	Constructor. More...

	DiscreteConditionalPdf (const DiscreteConditionalPdf &pdf)
	Copy constructor.

virtual	~DiscreteConditionalPdf ()
	Destructor.

virtual DiscreteConditionalPdf *	Clone () const
	Clone function.

unsigned int	NumStatesGet () const
	Get the number of discrete states.

Probability	ProbabilityGet (const int &input) const
	Get the probability of a certain argument. More...

virtual bool	SampleFrom (Sample< int > &one_sample, int method, void *args) const

virtual bool	SampleFrom (vector< Sample< int > > &list_samples, unsigned int num_samples, int method, void *args) const

void	ProbabilitySet (const double &prob, const int &input, const std::vector< int > &condargs) const
	Set the probability (Typical for discrete Pdf's)

unsigned int	NumConditionalArgumentsGet () const
	Get the Number of conditional arguments. More...

virtual void	NumConditionalArgumentsSet (unsigned int numconditionalarguments)
	Set the Number of conditional arguments. More...

const std::vector< int > &	ConditionalArgumentsGet () const
	Get the whole list of conditional arguments. More...

virtual void	ConditionalArgumentsSet (std::vector< int > ConditionalArguments)
	Set the whole list of conditional arguments. More...

const int &	ConditionalArgumentGet (unsigned int n_argument) const
	Get the n-th argument of the list. More...

virtual void	ConditionalArgumentSet (unsigned int n_argument, const int &argument)
	Set the n-th argument of the list. More...

virtual 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) More...

virtual bool	SampleFrom (Sample< int > &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...

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

virtual int	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...

Protected Member Functions
int	IndexGet (const int &input, const std::vector< int > &condargs) const
	Get the correct index in the row of doubles (double * probability)

Protected Attributes
unsigned int	_num_states
	number of discrete states

double *	_probability_p
	Pointer to the probability values. More...

int *	_cond_arg_dims_p
	"Possible discrete states" of all the conditional arguments

int	_total_dimension
	Total dimension of the likelihoodtable.

std::vector< double >	_probs

std::vector< double >	_valuelist

Detailed Description

Abstract Class representing all FULLY Discrete Conditional PDF's.

This class inherits only from ConditionalPdf (not from DiscretePdf, avoiding a circular class structure

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Todo:: Check if this is the best way to implement this.

Note: that the name of this class could be better chosen. Something like Discrete-DiscreteConditionalPdf would maybe be more clear (???), but quite long...

See Also: ConditionalPdf

Definition at line 53 of file discreteconditionalpdf.h.

Constructor & Destructor Documentation

DiscreteConditionalPdf	(	int	num_states = `1`,
		int	num_conditional_arguments = `1`,
		int	cond_arg_dimensions[] = `NULL`
	)

Constructor.

Precondition: The number of elements of cond_arg_dimensions should be equal to num_conditional_arguments, otherwise -> Segfaults

Parameters

num_states	int representing the number of possible states
num_conditional_arguments	the number of arguments behind the \|
cond_arg_dimensions[]	possible number of states of the different conditional arguments

See Also: ConditionalPdf

Todo:: Get cleaner api and implementation

Member Function Documentation

const int & ConditionalArgumentGet ( unsigned int n_argument ) const

inherited

Get the n-th argument of the list.

Returns: The current value of the n-th conditional argument (starting from 0!)

virtual void ConditionalArgumentSet	(	unsigned int	n_argument,
		const int &	argument
	)

virtualinherited

Set the n-th argument of the list.

Parameters

n_argument	which one of the conditional arguments
argument	value of the n-th argument

const std::vector<int >& ConditionalArgumentsGet ( ) const

inherited

Get the whole list of conditional arguments.

Returns: an STL-vector containing all the current values of the conditional arguments

virtual void ConditionalArgumentsSet ( std::vector< int > ConditionalArguments )

virtualinherited

Set the whole list of conditional arguments.

Parameters

ConditionalArguments

an STL-vector of type

containing the condtional arguments

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const

virtualinherited

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!

unsigned int DimensionGet ( ) const

inherited

Get the dimension of the argument.

Returns: the dimension of the argument

virtual void DimensionSet ( unsigned int dim )

virtualinherited

Set the dimension of the argument.

Parameters

dim	the dimension

virtual int ExpectedValueGet ( ) const

virtualinherited

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?

unsigned int NumConditionalArgumentsGet ( ) const

inherited

Get the Number of conditional arguments.

Returns: the number of conditional arguments

virtual void NumConditionalArgumentsSet ( unsigned int numconditionalarguments )

virtualinherited

Set the Number of conditional arguments.

Parameters

numconditionalarguments the number of conditionalarguments

Bug:: will probably give rise to memory allocation problems if you herit from this class and do not redefine this method.

Probability ProbabilityGet ( const int & 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< int >.

virtual bool SampleFrom	(	vector< Sample< int > > &	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!

Reimplemented in DiscretePdf.

virtual bool SampleFrom	(	Sample< int > &	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!

Reimplemented in DiscretePdf.

Member Data Documentation

double* _probability_p

protected

Pointer to the probability values.

For now we implement this using a simple row of doubles, this should probably become a tensor in the future

Definition at line 63 of file discreteconditionalpdf.h.

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

discreteconditionalpdf.h

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation