Bayesian Filtering Library
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Abstract Class representing all FULLY Discrete Conditional PDF's. More...
#include <discreteconditionalpdf.h>
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 |
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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Definition at line 53 of file discreteconditionalpdf.h.
DiscreteConditionalPdf | ( | int | num_states = 1 , |
int | num_conditional_arguments = 1 , |
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int | cond_arg_dimensions[] = NULL |
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Constructor.
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 |
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inherited |
Get the n-th argument of the list.
Definition at line 165 of file conditionalpdf.h.
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virtualinherited |
Set the n-th argument of the list.
n_argument | which one of the conditional arguments |
argument | value of the n-th argument |
Definition at line 173 of file conditionalpdf.h.
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inherited |
Get the whole list of conditional arguments.
Definition at line 152 of file conditionalpdf.h.
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virtualinherited |
Set the whole list of conditional arguments.
ConditionalArguments | an STL-vector of type Tcontaining the condtional arguments |
Definition at line 158 of file conditionalpdf.h.
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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 225 of file mixtureParticleFilter.h.
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inlineinherited |
Get the dimension of the argument.
Definition at line 169 of file mixtureParticleFilter.h.
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virtualinherited |
Set the dimension of the argument.
dim | the dimension |
Definition at line 175 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 215 of file mixtureParticleFilter.h.
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inlineinherited |
Get the Number of conditional arguments.
Definition at line 135 of file conditionalpdf.h.
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inlinevirtualinherited |
Set the Number of conditional arguments.
numconditionalarguments | the number of conditionalarguments |
Definition at line 141 of file conditionalpdf.h.
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virtual |
Get the probability of a certain argument.
input | T argument of the Pdf |
Reimplemented from Pdf< int >.
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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 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... |
Reimplemented in DiscretePdf.
Definition at line 182 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 a #define statement, eg. #define BOXMULLER 1 |
args | Pointer to a struct representing extra sample arguments |
Reimplemented in DiscretePdf.
Definition at line 197 of file mixtureParticleFilter.h.
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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.