Bayesian Filtering Library
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Class representing a PDF on a discrete variable. More...
#include <discretepdf.h>
Public Member Functions | |
DiscretePdf (unsigned int num_states=0) | |
Constructor (dimension = number of classes) An equal probability is set for all classes. More... | |
DiscretePdf (const DiscretePdf &) | |
Copy Constructor. | |
virtual | ~DiscretePdf () |
Destructor. | |
virtual DiscretePdf * | Clone () 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. More... | |
bool | SampleFrom (vector< Sample< int > > &list_samples, const unsigned int num_samples, const SampleMthd method=SampleMthd::DEFAULT, void *args=NULL) const |
bool | SampleFrom (Sample< int > &one_sample, const SampleMthd method=SampleMthd::DEFAULT, void *args=NULL) const |
vector< Probability > | ProbabilitiesGet () const |
Get all probabilities. | |
bool | ProbabilitiesSet (vector< Probability > &values) |
Set all probabilities. More... | |
int | MostProbableStateGet () |
Get the index of the most probable state. | |
virtual bool | SampleFrom (vector< Sample< int > > &list_samples, const unsigned int num_samples, const SampleMthd method=SampleMthd::DEFAULT, void *args=NULL) const |
Draw multiple samples from the Pdf (overloaded) More... | |
virtual bool | SampleFrom (Sample< int > &one_sample, const SampleMthd method=SampleMthd::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 | |
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) | |
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.
DiscretePdf | ( | unsigned int | num_states = 0 | ) |
Constructor (dimension = number of classes) An equal probability is set for all classes.
num_states | number of different classes or states |
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virtualinherited |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Definition at line 222 of file mixtureParticleFilter.h.
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inlineinherited |
Get the dimension of the argument.
Definition at line 166 of file mixtureParticleFilter.h.
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virtualinherited |
Set the dimension of the argument.
dim | the dimension |
Definition at line 172 of file mixtureParticleFilter.h.
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virtualinherited |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Definition at line 212 of file mixtureParticleFilter.h.
bool ProbabilitiesSet | ( | vector< Probability > & | values | ) |
Set all probabilities.
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. |
bool ProbabilitySet | ( | int | state, |
Probability | a | ||
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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
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) |
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virtualinherited |
Draw multiple samples from the Pdf (overloaded)
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 an enum eg. SampleMthd::BOXMULLER |
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... |
Reimplemented in DiscretePdf.
Definition at line 179 of file mixtureParticleFilter.h.
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virtualinherited |
Draw 1 sample from the Pdf:
There's no need to create a list for only 1 sample!
one_sample | sample that will contain result of sampling |
method | Sampling method to be used. Each sampling method is currently represented by an enum, eg. SampleMthd::BOXMULLER |
args | Pointer to a struct representing extra sample arguments |
Reimplemented in DiscretePdf.
Definition at line 194 of file mixtureParticleFilter.h.