Class PDF: Virtual Base class representing Probability Density Functions.
More...
#include <pdf.h>
|
| Pdf (unsigned int dimension=0) |
| Constructor. More...
|
|
virtual | ~Pdf () |
| Destructor.
|
|
virtual Pdf< T > * | Clone () const =0 |
| Pure virtual clone function.
|
|
virtual bool | SampleFrom (vector< Sample< T > > &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< T > &one_sample, const SampleMthd method=SampleMthd::DEFAULT, void *args=NULL) const |
| Draw 1 sample from the Pdf: More...
|
|
virtual Probability | ProbabilityGet (const T &input) const |
| Get the probability of a certain argument. 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 T | 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...
|
|
template<typename T>
class BFL::Pdf< T >
Class PDF: Virtual Base class representing Probability Density Functions.
Definition at line 50 of file pdf.h.
◆ Pdf()
Pdf |
( |
unsigned int |
dimension = 0 | ) |
|
Constructor.
- Parameters
-
dimension | int representing the number of rows of the state |
Definition at line 147 of file pdf.h.
◆ CovarianceGet()
MatrixWrapper::SymmetricMatrix CovarianceGet |
|
virtual |
◆ DimensionGet()
unsigned int DimensionGet |
|
inline |
Get the dimension of the argument.
- Returns
- the dimension of the argument
Definition at line 166 of file pdf.h.
◆ DimensionSet()
void DimensionSet |
( |
unsigned int |
dim | ) |
|
|
virtual |
Set the dimension of the argument.
- Parameters
-
Reimplemented in Gaussian.
Definition at line 172 of file pdf.h.
◆ ExpectedValueGet()
◆ ProbabilityGet()
Get the probability of a certain argument.
- Parameters
-
input | T argument of the Pdf |
- Returns
- the probability value of the argument
Reimplemented in Mixture< T >.
Definition at line 204 of file pdf.h.
◆ SampleFrom() [1/2]
bool SampleFrom |
( |
Sample< T > & |
one_sample, |
|
|
const SampleMthd |
method = SampleMthd::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 an enum, eg. SampleMthd::BOXMULLER |
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 Mixture< T >, and MCPdf< T >.
Definition at line 194 of file pdf.h.
◆ SampleFrom() [2/2]
bool SampleFrom |
( |
vector< Sample< T > > & |
list_samples, |
|
|
const unsigned int |
num_samples, |
|
|
const SampleMthd |
method = SampleMthd::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 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... |
- 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 Mixture< T >, and MCPdf< T >.
Definition at line 179 of file pdf.h.
The documentation for this class was generated from the following file: