Class PDF: Virtual Base class representing Probability Density Functions. More...
#include <mixtureParticleFilter.h>
Public Member Functions | |
virtual Pdf< T > * | Clone () const =0 |
Pure virtual clone function. | |
virtual Pdf< T > * | Clone () const =0 |
Pure virtual clone function. | |
virtual Pdf< T > * | Clone () const =0 |
Pure virtual clone function. | |
virtual Pdf< T > * | Clone () const =0 |
Pure virtual clone function. | |
virtual MatrixWrapper::SymmetricMatrix | CovarianceGet () const |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. | |
virtual MatrixWrapper::SymmetricMatrix | CovarianceGet () const |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. | |
virtual MatrixWrapper::SymmetricMatrix | CovarianceGet () const |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. | |
virtual MatrixWrapper::SymmetricMatrix | CovarianceGet () const |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. | |
unsigned int | DimensionGet () const |
Get the dimension of the argument. | |
unsigned int | DimensionGet () const |
Get the dimension of the argument. | |
unsigned int | DimensionGet () const |
Get the dimension of the argument. | |
unsigned int | DimensionGet () const |
Get the dimension of the argument. | |
virtual void | DimensionSet (unsigned int dim) |
Set the dimension of the argument. | |
virtual void | DimensionSet (unsigned int dim) |
Set the dimension of the argument. | |
virtual void | DimensionSet (unsigned int dim) |
Set the dimension of the argument. | |
virtual void | DimensionSet (unsigned int dim) |
Set the dimension of the argument. | |
virtual T | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
virtual T | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
virtual T | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
virtual T | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
Pdf (unsigned int dimension=0) | |
Constructor. | |
Pdf (unsigned int dimension=0) | |
Constructor. | |
Pdf (unsigned int dimension=0) | |
Constructor. | |
Pdf (unsigned int dimension=0) | |
Constructor. | |
virtual Probability | ProbabilityGet (const T &input) const |
Get the probability of a certain argument. | |
virtual Probability | ProbabilityGet (const T &input) const |
Get the probability of a certain argument. | |
virtual Probability | ProbabilityGet (const T &input) const |
Get the probability of a certain argument. | |
virtual Probability | ProbabilityGet (const T &input) const |
Get the probability of a certain argument. | |
virtual bool | SampleFrom (vector< Sample< T > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const |
Draw multiple samples from the Pdf (overloaded) | |
virtual bool | SampleFrom (vector< Sample< T > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const |
Draw multiple samples from the Pdf (overloaded) | |
virtual bool | SampleFrom (vector< Sample< T > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const |
Draw multiple samples from the Pdf (overloaded) | |
virtual bool | SampleFrom (vector< Sample< T > > &list_samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const |
Draw multiple samples from the Pdf (overloaded) | |
virtual bool | SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const |
Draw 1 sample from the Pdf: | |
virtual bool | SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const |
Draw 1 sample from the Pdf: | |
virtual bool | SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const |
Draw 1 sample from the Pdf: | |
virtual bool | SampleFrom (Sample< T > &one_sample, int method=DEFAULT, void *args=NULL) const |
Draw 1 sample from the Pdf: | |
virtual | ~Pdf () |
Destructor. | |
virtual | ~Pdf () |
Destructor. | |
virtual | ~Pdf () |
Destructor. | |
virtual | ~Pdf () |
Destructor. | |
Private Attributes | |
unsigned int | _dimension |
Dimension of the argument x of P(x | ...). |
Class PDF: Virtual Base class representing Probability Density Functions.
Definition at line 53 of file mixtureParticleFilter.h.
BFL::BFL::Pdf< T >::Pdf | ( | unsigned int | dimension = 0 | ) |
Constructor.
dimension | int representing the number of rows of the state |
Definition at line 150 of file mixtureParticleFilter.h.
BFL::BFL::Pdf< T >::~Pdf | ( | ) | [virtual] |
Destructor.
Definition at line 161 of file mixtureParticleFilter.h.
BFL::BFL::Pdf< T >::Pdf | ( | unsigned int | dimension = 0 | ) |
Constructor.
dimension | int representing the number of rows of the state |
virtual BFL::BFL::Pdf< T >::~Pdf | ( | ) | [virtual] |
Destructor.
BFL::BFL::Pdf< T >::Pdf | ( | unsigned int | dimension = 0 | ) |
Constructor.
dimension | int representing the number of rows of the state |
virtual BFL::BFL::Pdf< T >::~Pdf | ( | ) | [virtual] |
Destructor.
BFL::BFL::Pdf< T >::Pdf | ( | unsigned int | dimension = 0 | ) |
Constructor.
dimension | int representing the number of rows of the state |
virtual BFL::BFL::Pdf< T >::~Pdf | ( | ) | [virtual] |
Destructor.
virtual Pdf<T>* BFL::BFL::Pdf< T >::Clone | ( | ) | const [pure virtual] |
Pure virtual clone function.
Implemented in BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::ConditionalPdf< Var, CondArg >, BFL::ConditionalPdf< int, int >, BFL::ConditionalPdf< MeasVar, StateVar >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, int >, BFL::ConditionalPdf< T, T >, BFL::ConditionalPdf< StateVar, StateVar >, BFL::ConditionalPdf< ColumnVector, ColumnVector >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::LinearAnalyticConditionalGaussian, BFL::Gaussian, BFL::Uniform, and BFL::ConditionalGaussian.
virtual Pdf<T>* BFL::BFL::Pdf< T >::Clone | ( | ) | const [pure virtual] |
Pure virtual clone function.
Implemented in BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::ConditionalPdf< Var, CondArg >, BFL::ConditionalPdf< int, int >, BFL::ConditionalPdf< MeasVar, StateVar >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, int >, BFL::ConditionalPdf< T, T >, BFL::ConditionalPdf< StateVar, StateVar >, BFL::ConditionalPdf< ColumnVector, ColumnVector >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::LinearAnalyticConditionalGaussian, BFL::Gaussian, BFL::Uniform, and BFL::ConditionalGaussian.
virtual Pdf<T>* BFL::BFL::Pdf< T >::Clone | ( | ) | const [pure virtual] |
Pure virtual clone function.
Implemented in BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::ConditionalPdf< Var, CondArg >, BFL::ConditionalPdf< int, int >, BFL::ConditionalPdf< MeasVar, StateVar >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, int >, BFL::ConditionalPdf< T, T >, BFL::ConditionalPdf< StateVar, StateVar >, BFL::ConditionalPdf< ColumnVector, ColumnVector >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::LinearAnalyticConditionalGaussian, BFL::Gaussian, BFL::Uniform, and BFL::ConditionalGaussian.
virtual Pdf<T>* BFL::BFL::Pdf< T >::Clone | ( | ) | const [pure virtual] |
Pure virtual clone function.
Implemented in BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::ConditionalPdf< Var, CondArg >, BFL::ConditionalPdf< int, int >, BFL::ConditionalPdf< MeasVar, StateVar >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, int >, BFL::ConditionalPdf< T, T >, BFL::ConditionalPdf< StateVar, StateVar >, BFL::ConditionalPdf< ColumnVector, ColumnVector >, BFL::ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::LinearAnalyticConditionalGaussian, BFL::Gaussian, BFL::Uniform, and BFL::ConditionalGaussian.
MatrixWrapper::SymmetricMatrix BFL::BFL::Pdf< T >::CovarianceGet | ( | ) | const [virtual] |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented in BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::ConditionalGaussianAdditiveNoise, BFL::AnalyticConditionalGaussianAdditiveNoise, BFL::FilterProposalDensity, and BFL::OptimalImportanceDensity.
Definition at line 225 of file mixtureParticleFilter.h.
virtual MatrixWrapper::SymmetricMatrix BFL::BFL::Pdf< T >::CovarianceGet | ( | ) | const [virtual] |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented in BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::ConditionalGaussianAdditiveNoise, BFL::AnalyticConditionalGaussianAdditiveNoise, BFL::FilterProposalDensity, and BFL::OptimalImportanceDensity.
virtual MatrixWrapper::SymmetricMatrix BFL::BFL::Pdf< T >::CovarianceGet | ( | ) | const [virtual] |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented in BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::ConditionalGaussianAdditiveNoise, BFL::AnalyticConditionalGaussianAdditiveNoise, BFL::FilterProposalDensity, and BFL::OptimalImportanceDensity.
virtual MatrixWrapper::SymmetricMatrix BFL::BFL::Pdf< T >::CovarianceGet | ( | ) | const [virtual] |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented in BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::ConditionalGaussianAdditiveNoise, BFL::AnalyticConditionalGaussianAdditiveNoise, BFL::FilterProposalDensity, and BFL::OptimalImportanceDensity.
unsigned int BFL::BFL::Pdf< T >::DimensionGet | ( | ) | const [inline] |
Get the dimension of the argument.
Definition at line 169 of file mixtureParticleFilter.h.
unsigned int BFL::BFL::Pdf< T >::DimensionGet | ( | ) | const |
Get the dimension of the argument.
unsigned int BFL::BFL::Pdf< T >::DimensionGet | ( | ) | const |
Get the dimension of the argument.
unsigned int BFL::BFL::Pdf< T >::DimensionGet | ( | ) | const |
Get the dimension of the argument.
virtual void BFL::BFL::Pdf< T >::DimensionSet | ( | unsigned int | dim | ) | [virtual] |
void BFL::BFL::Pdf< T >::DimensionSet | ( | unsigned int | dim | ) | [virtual] |
Set the dimension of the argument.
dim | the dimension |
Reimplemented in BFL::Gaussian.
Definition at line 175 of file mixtureParticleFilter.h.
virtual void BFL::BFL::Pdf< T >::DimensionSet | ( | unsigned int | dim | ) | [virtual] |
virtual void BFL::BFL::Pdf< T >::DimensionSet | ( | unsigned int | dim | ) | [virtual] |
T BFL::BFL::Pdf< T >::ExpectedValueGet | ( | ) | const [virtual] |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Reimplemented in BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::LinearAnalyticConditionalGaussian, BFL::FilterProposalDensity, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::OptimalImportanceDensity, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::Mixture< T >, and BFL::Mixture< T >.
Definition at line 215 of file mixtureParticleFilter.h.
virtual T BFL::BFL::Pdf< T >::ExpectedValueGet | ( | ) | const [virtual] |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Reimplemented in BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::LinearAnalyticConditionalGaussian, BFL::FilterProposalDensity, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::OptimalImportanceDensity, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::Mixture< T >, and BFL::Mixture< T >.
virtual T BFL::BFL::Pdf< T >::ExpectedValueGet | ( | ) | const [virtual] |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Reimplemented in BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::LinearAnalyticConditionalGaussian, BFL::FilterProposalDensity, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::OptimalImportanceDensity, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::Mixture< T >, and BFL::Mixture< T >.
virtual T BFL::BFL::Pdf< T >::ExpectedValueGet | ( | ) | const [virtual] |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Reimplemented in BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::NonLinearAnalyticConditionalGaussian_Ginac, BFL::BFL::MCPdf< T >, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::MCPdf< T >, BFL::Gaussian, BFL::LinearAnalyticConditionalGaussian, BFL::FilterProposalDensity, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::NonLinearAnalyticConditionalGaussianMobile, BFL::OptimalImportanceDensity, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::Mixture< T >, and BFL::Mixture< T >.
virtual Probability BFL::BFL::Pdf< T >::ProbabilityGet | ( | const T & | input | ) | const [virtual] |
Get the probability of a certain argument.
input | T argument of the Pdf |
Reimplemented in BFL::DiscreteConditionalPdf, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::Gaussian, BFL::Uniform, BFL::ConditionalGaussian, BFL::ConditionalUniformMeasPdf1d, and BFL::NonlinearMeasurementPdf.
virtual Probability BFL::BFL::Pdf< T >::ProbabilityGet | ( | const T & | input | ) | const [virtual] |
Get the probability of a certain argument.
input | T argument of the Pdf |
Reimplemented in BFL::DiscreteConditionalPdf, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::Gaussian, BFL::Uniform, BFL::ConditionalGaussian, BFL::ConditionalUniformMeasPdf1d, and BFL::NonlinearMeasurementPdf.
Probability BFL::BFL::Pdf< T >::ProbabilityGet | ( | const T & | input | ) | const [virtual] |
Get the probability of a certain argument.
input | T argument of the Pdf |
Reimplemented in BFL::DiscreteConditionalPdf, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::Gaussian, BFL::Uniform, BFL::ConditionalGaussian, BFL::ConditionalUniformMeasPdf1d, and BFL::NonlinearMeasurementPdf.
Definition at line 207 of file mixtureParticleFilter.h.
virtual Probability BFL::BFL::Pdf< T >::ProbabilityGet | ( | const T & | input | ) | const [virtual] |
Get the probability of a certain argument.
input | T argument of the Pdf |
Reimplemented in BFL::DiscreteConditionalPdf, BFL::BFL::Mixture< T >, BFL::Mixture< T >, BFL::BFL::DiscretePdf, BFL::DiscretePdf, BFL::Gaussian, BFL::Uniform, BFL::ConditionalGaussian, BFL::ConditionalUniformMeasPdf1d, and BFL::NonlinearMeasurementPdf.
bool BFL::BFL::Pdf< T >::SampleFrom | ( | vector< Sample< T > > & | list_samples, |
const unsigned int | num_samples, | ||
int | method = DEFAULT , |
||
void * | args = NULL |
||
) | const [virtual] |
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 BFL::BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, and BFL::BFL::DiscretePdf.
Definition at line 182 of file mixtureParticleFilter.h.
virtual bool BFL::BFL::Pdf< T >::SampleFrom | ( | vector< Sample< T > > & | list_samples, |
const unsigned int | num_samples, | ||
int | method = DEFAULT , |
||
void * | args = NULL |
||
) | const [virtual] |
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 BFL::BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, and BFL::BFL::DiscretePdf.
virtual bool BFL::BFL::Pdf< T >::SampleFrom | ( | vector< Sample< T > > & | list_samples, |
const unsigned int | num_samples, | ||
int | method = DEFAULT , |
||
void * | args = NULL |
||
) | const [virtual] |
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 BFL::BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, and BFL::BFL::DiscretePdf.
virtual bool BFL::BFL::Pdf< T >::SampleFrom | ( | vector< Sample< T > > & | list_samples, |
const unsigned int | num_samples, | ||
int | method = DEFAULT , |
||
void * | args = NULL |
||
) | const [virtual] |
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 BFL::BFL::Mixture< T >, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, and BFL::BFL::DiscretePdf.
virtual bool BFL::BFL::Pdf< T >::SampleFrom | ( | Sample< T > & | 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!
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 BFL::BFL::Mixture< T >, BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::DiscretePdf, and BFL::DiscretePdf.
virtual bool BFL::BFL::Pdf< T >::SampleFrom | ( | Sample< T > & | 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!
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 BFL::BFL::Mixture< T >, BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::DiscretePdf, and BFL::DiscretePdf.
virtual bool BFL::BFL::Pdf< T >::SampleFrom | ( | Sample< T > & | 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!
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 BFL::BFL::Mixture< T >, BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::DiscretePdf, and BFL::DiscretePdf.
bool BFL::BFL::Pdf< T >::SampleFrom | ( | Sample< T > & | 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!
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 BFL::BFL::Mixture< T >, BFL::DiscreteConditionalPdf, BFL::BFL::MCPdf< T >, BFL::BFL::MCPdf< T >, BFL::BFL::DiscretePdf, and BFL::DiscretePdf.
Definition at line 197 of file mixtureParticleFilter.h.
unsigned int BFL::BFL::Pdf< T >::_dimension [private] |
Dimension of the argument x of P(x | ...).
Definition at line 145 of file mixtureParticleFilter.h.