Bayesian Filtering Library Generated from SVN r
|
Abstract Class representing all Conditional gaussians. More...
#include <conditionalgaussian.h>
Public Member Functions | |
ConditionalGaussian (int dim=0, int num_conditional_arguments=0) | |
Constructor. More... | |
virtual | ~ConditionalGaussian () |
Destructor. | |
virtual ConditionalGaussian * | Clone () const |
Clone function. More... | |
virtual Probability | ProbabilityGet (const MatrixWrapper::ColumnVector &input) const |
virtual bool | SampleFrom (Sample< MatrixWrapper::ColumnVector > &sample, const SampleMthd method=SampleMthd::DEFAULT, void *args=NULL) const |
virtual bool | SampleFrom (std::vector< Sample< MatrixWrapper::ColumnVector > > &samples, const unsigned int num_samples, const SampleMthd method=SampleMthd::DEFAULT, void *args=NULL) const |
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< MatrixWrapper::ColumnVector > & | ConditionalArgumentsGet () const |
Get the whole list of conditional arguments. More... | |
virtual void | ConditionalArgumentsSet (std::vector< MatrixWrapper::ColumnVector > ConditionalArguments) |
Set the whole list of conditional arguments. More... | |
const MatrixWrapper::ColumnVector & | ConditionalArgumentGet (unsigned int n_argument) const |
Get the n-th argument of the list. More... | |
virtual void | ConditionalArgumentSet (unsigned int n_argument, const MatrixWrapper::ColumnVector &argument) |
Set the n-th argument of the list. More... | |
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... | |
Protected Attributes | |
ColumnVector | _diff |
ColumnVector | _Mu |
Matrix | _Low_triangle |
ColumnVector | _samples |
ColumnVector | _SampleValue |
Abstract Class representing all Conditional gaussians.
This class inherits only from ConditionalPdf<ColumnVector, ColumnVector>.
So this class represents all Pdf's of the type
where
and
and
f and g are not necessarily analytical functions
Definition at line 40 of file conditionalgaussian.h.
ConditionalGaussian | ( | int | dim = 0 , |
int | num_conditional_arguments = 0 |
||
) |
Constructor.
dim | Dimension of state |
num_conditional_arguments | The number of conditional arguments. |
|
virtual |
Clone function.
Reimplemented from ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >.
Reimplemented in LinearAnalyticConditionalGaussian.
|
inherited |
Get the n-th argument of the list.
Definition at line 97 of file conditionalpdf.h.
|
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 104 of file conditionalpdf.h.
|
inherited |
Get the whole list of conditional arguments.
Definition at line 85 of file conditionalpdf.h.
|
virtualinherited |
Set the whole list of conditional arguments.
ConditionalArguments | an STL-vector of type Tcontaining the condtional arguments |
Definition at line 91 of file conditionalpdf.h.
|
virtualinherited |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented in AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.
|
inlineinherited |
|
virtualinherited |
|
virtualinherited |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Reimplemented in FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.
|
inlineinherited |
Get the Number of conditional arguments.
Definition at line 71 of file conditionalpdf.h.
|
inlinevirtualinherited |
Set the Number of conditional arguments.
numconditionalarguments | the number of conditionalarguments |
Reimplemented in LinearAnalyticConditionalGaussian.
Definition at line 79 of file conditionalpdf.h.
|
virtualinherited |
Get the probability of a certain argument.
input | T argument of the Pdf |
Reimplemented in DiscretePdf, Gaussian, Uniform, and Mixture< T >.
|
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, Gaussian, Uniform, MCPdf< T >, and Mixture< T >.
|
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, Gaussian, Uniform, MCPdf< T >, and Mixture< T >.
Definition at line 179 of file pdf.h.
Referenced by MCPdf< T >::SampleFrom(), and Mixture< T >::SampleFrom().
|
mutableprotected |
Definition at line 67 of file conditionalgaussian.h.
|
mutableprotected |
Definition at line 69 of file conditionalgaussian.h.
|
mutableprotected |
Definition at line 68 of file conditionalgaussian.h.
|
mutableprotected |
Definition at line 70 of file conditionalgaussian.h.
|
mutableprotected |
Definition at line 71 of file conditionalgaussian.h.