DiscretePdf Class Reference

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

#include <discretepdf.h>

Inheritance diagram for DiscretePdf:

Pdf< int >

List of all members.

Public Member Functions

 DiscretePdf (unsigned int num_states=0)
 Constructor (dimension = number of classes) An equal probability is set for all classes.
 DiscretePdf (const DiscretePdf &)
 Copy Constructor.
virtual ~DiscretePdf ()
 Destructor.
virtual DiscretePdfClone () const
 Clone function.
unsigned int NumStatesGet () const
 Get the number of discrete States.
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:.
vector< ProbabilityProbabilitiesGet () const
 Get all probabilities.
bool ProbabilitiesSet (vector< Probability > &values)
 Set all probabilities.
int MostProbableStateGet ()
 Get the index of the most probable state.
unsigned int DimensionGet () const
 Get the dimension of the argument.
virtual void DimensionSet (unsigned int dim)
 Set the dimension of the argument.
virtual int ExpectedValueGet () const
 Get the expected value E[x] of the pdf.
virtual
MatrixWrapper::SymmetricMatrix 
CovarianceGet () const
 Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.

Protected Member Functions

bool NormalizeProbs ()
 Normalize all the probabilities (eg. after setting a probability).
bool CumPDFUpdate ()
 Updates the cumPDF.

Protected Attributes

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.
vector< double > _CumPDF
 STL-vector containing the Cumulative PDF (for efficient sampling).


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 discretepdf.h.


Constructor & Destructor Documentation

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


Member Function Documentation

bool 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)

bool 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 Pdf< int >.

bool 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
See also:
SampleFrom()
Bug:
Sometimes the compiler doesn't know which method to choose!

Reimplemented from Pdf< int >.

bool 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.

unsigned int DimensionGet (  )  const [inherited]

Get the dimension of the argument.

Returns:
the dimension of the argument

virtual void DimensionSet ( unsigned int  dim  )  [virtual, inherited]

Set the dimension of the argument.

Parameters:
dim the dimension

virtual int ExpectedValueGet (  )  const [virtual, inherited]

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?

virtual MatrixWrapper::SymmetricMatrix CovarianceGet (  )  const [virtual, inherited]

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!


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

Generated on Mon Mar 30 05:43:59 2009 for Bayesian Filtering Library by  doxygen 1.5.5