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ConditionalGaussianAdditiveNoise Class Reference

Abstract Class representing all Conditional Gaussians with additive gaussian noise. More...

#include <conditionalgaussian_additivenoise.h>

Inheritance diagram for ConditionalGaussianAdditiveNoise:
ConditionalGaussian ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector > Pdf< T >

Public Member Functions

 ConditionalGaussianAdditiveNoise (const Gaussian &gaus, int num_conditional_arguments=1)
 Constructor.
 
 ConditionalGaussianAdditiveNoise (int dim=0, int num_conditional_arguments=0)
 Constructor 2, Gaussian not yet known.
 
virtual ~ConditionalGaussianAdditiveNoise ()
 Destructor.
 
virtual MatrixWrapper::SymmetricMatrix CovarianceGet () const
 Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
 
const MatrixWrapper::ColumnVector & AdditiveNoiseMuGet () const
 Get the mean Value of the Additive Gaussian uncertainty.
 
const MatrixWrapper::SymmetricMatrix & AdditiveNoiseSigmaGet () const
 Get the covariance matrix of the Additive Gaussian uncertainty.
 
void AdditiveNoiseMuSet (const MatrixWrapper::ColumnVector &mu)
 Set the mean Value of the Additive Gaussian uncertainty.
 
void AdditiveNoiseSigmaSet (const MatrixWrapper::SymmetricMatrix &sigma)
 Set the covariance of the Additive Gaussian uncertainty.
 
virtual ConditionalGaussianClone () const
 Clone function.
 
virtual Probability ProbabilityGet (const MatrixWrapper::ColumnVector &input) const
 
virtual Probability ProbabilityGet (const T &input) const
 Get the probability of a certain argument.
 
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
 
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)
 
virtual bool SampleFrom (Sample< T > &one_sample, const SampleMthd method=SampleMthd::DEFAULT, void *args=NULL) const
 Draw 1 sample from the Pdf:
 
unsigned int NumConditionalArgumentsGet () const
 Get the Number of conditional arguments.
 
virtual void NumConditionalArgumentsSet (unsigned int numconditionalarguments)
 Set the Number of conditional arguments.
 
const std::vector< MatrixWrapper::ColumnVector > & ConditionalArgumentsGet () const
 Get the whole list of conditional arguments.
 
virtual void ConditionalArgumentsSet (std::vector< MatrixWrapper::ColumnVector > ConditionalArguments)
 Set the whole list of conditional arguments.
 
const MatrixWrapper::ColumnVector & ConditionalArgumentGet (unsigned int n_argument) const
 Get the n-th argument of the list.
 
virtual void ConditionalArgumentSet (unsigned int n_argument, const MatrixWrapper::ColumnVector &argument)
 Set the n-th argument of the list.
 
unsigned int DimensionGet () const
 Get 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.
 

Protected Attributes

MatrixWrapper::ColumnVector _additiveNoise_Mu
 additive noise expected value
 
MatrixWrapper::SymmetricMatrix _additiveNoise_Sigma
 additive noise covariance
 
ColumnVector _diff
 
ColumnVector _Mu
 
Matrix _Low_triangle
 
ColumnVector _samples
 
ColumnVector _SampleValue
 

Detailed Description

Abstract Class representing all Conditional Gaussians with additive gaussian noise.

This class represents all Pdf's of the type

\[ P ( A | B, C, D, ... ) \]

where

\[ \mu_A = f(B,C,D, ...) + mu_{additiveNoise} \]

and

\[ \Sigma_A = \Sigma_{additiveNoise} \]

and

\[ A = N(\mu_A, \Sigma_A) \]

f is not necessarily a analytical function

Definition at line 39 of file conditionalgaussian_additivenoise.h.

Constructor & Destructor Documentation

◆ ConditionalGaussianAdditiveNoise() [1/2]

ConditionalGaussianAdditiveNoise ( const Gaussian gaus,
int  num_conditional_arguments = 1 
)

Constructor.

Parameters
gausGaussian representing the additive uncertainty
num_conditional_argumentsThe number of conditional arguments.

◆ ConditionalGaussianAdditiveNoise() [2/2]

ConditionalGaussianAdditiveNoise ( int  dim = 0,
int  num_conditional_arguments = 0 
)

Constructor 2, Gaussian not yet known.

Parameters
dimDimension of state
num_conditional_argumentsThe number of conditional arguments.

Member Function Documentation

◆ AdditiveNoiseMuGet()

const MatrixWrapper::ColumnVector & AdditiveNoiseMuGet ( ) const

Get the mean Value of the Additive Gaussian uncertainty.

Returns
the mean Value of the Additive Gaussian uncertainty

◆ AdditiveNoiseMuSet()

void AdditiveNoiseMuSet ( const MatrixWrapper::ColumnVector &  mu)

Set the mean Value of the Additive Gaussian uncertainty.

Parameters
muthe mean Value of the Additive Gaussian uncertainty

◆ AdditiveNoiseSigmaGet()

const MatrixWrapper::SymmetricMatrix & AdditiveNoiseSigmaGet ( ) const

Get the covariance matrix of the Additive Gaussian uncertainty.

Returns
the mean Value of the Additive Gaussian uncertainty

◆ AdditiveNoiseSigmaSet()

void AdditiveNoiseSigmaSet ( const MatrixWrapper::SymmetricMatrix &  sigma)

Set the covariance of the Additive Gaussian uncertainty.

Parameters
sigmathe covariance matrix of the Additive Gaussian uncertainty

◆ Clone()

virtual ConditionalGaussian * Clone ( ) const
virtualinherited

◆ ConditionalArgumentGet()

const MatrixWrapper::ColumnVector & ConditionalArgumentGet ( unsigned int  n_argument) const
inherited

Get the n-th argument of the list.

Returns
The current value of the n-th conditional argument (starting from 0!)

Definition at line 97 of file conditionalpdf.h.

◆ ConditionalArgumentSet()

void ConditionalArgumentSet ( unsigned int  n_argument,
const MatrixWrapper::ColumnVector &  argument 
)
virtualinherited

Set the n-th argument of the list.

Parameters
n_argumentwhich one of the conditional arguments
argumentvalue of the n-th argument

Definition at line 104 of file conditionalpdf.h.

◆ ConditionalArgumentsGet()

const std::vector< MatrixWrapper::ColumnVector > & ConditionalArgumentsGet
inherited

Get the whole list of conditional arguments.

Returns
an STL-vector containing all the current values of the conditional arguments

Definition at line 85 of file conditionalpdf.h.

◆ ConditionalArgumentsSet()

void ConditionalArgumentsSet ( std::vector< MatrixWrapper::ColumnVector >  ConditionalArguments)
virtualinherited

Set the whole list of conditional arguments.

Parameters
ConditionalArgumentsan STL-vector of type
T
containing the condtional arguments

Definition at line 91 of file conditionalpdf.h.

◆ CovarianceGet()

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const
virtual

Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.

Get first order statistic (Covariance) of this AnalyticPdf

Returns
The Covariance of the Pdf (a SymmetricMatrix of dim DIMENSION)
Todo:
extend this more general to n-th order statistic
Bug:
Discrete pdfs should not be able to use this!

Reimplemented from Pdf< T >.

◆ DimensionGet()

template<typename T >
unsigned int DimensionGet
inlineinherited

Get the dimension of the argument.

Returns
the dimension of the argument

Definition at line 166 of file pdf.h.

◆ DimensionSet()

template<typename T >
void DimensionSet ( unsigned int  dim)
virtualinherited

Set the dimension of the argument.

Parameters
dimthe dimension

Reimplemented in Gaussian.

Definition at line 172 of file pdf.h.

◆ ExpectedValueGet()

template<typename T >
T ExpectedValueGet
virtualinherited

Get the expected value E[x] of the pdf.

Get low order statistic (Expected Value) of this AnalyticPdf

Returns
The Expected Value of the Pdf (a ColumnVector with DIMENSION rows)
Note
No set functions here! This can be useful for analytic functions, but not for sample based representations!
For certain discrete Pdfs, this function has no meaning, what is the average between yes and no?

Reimplemented in FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, MCPdf< T >, Mixture< T >, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

Definition at line 212 of file pdf.h.

◆ NumConditionalArgumentsGet()

unsigned int NumConditionalArgumentsGet
inlineinherited

Get the Number of conditional arguments.

Returns
the number of conditional arguments

Definition at line 71 of file conditionalpdf.h.

◆ NumConditionalArgumentsSet()

void NumConditionalArgumentsSet ( unsigned int  numconditionalarguments)
inlinevirtualinherited

Set the Number of conditional arguments.

Parameters
numconditionalargumentsthe number of conditionalarguments
Bug:
will probably give rise to memory allocation problems if you herit from this class and do not redefine this method.

Reimplemented in LinearAnalyticConditionalGaussian.

Definition at line 79 of file conditionalpdf.h.

◆ ProbabilityGet()

template<typename T >
Probability ProbabilityGet ( const T &  input) const
virtualinherited

Get the probability of a certain argument.

Parameters
inputT argument of the Pdf
Returns
the probability value of the argument

Reimplemented in DiscretePdf, Gaussian, Uniform, and Mixture< T >.

Definition at line 204 of file pdf.h.

◆ SampleFrom() [1/2]

template<typename T >
bool SampleFrom ( Sample< T > &  one_sample,
const SampleMthd  method = SampleMthd::DEFAULT,
void *  args = NULL 
) const
virtualinherited

Draw 1 sample from the Pdf:

There's no need to create a list for only 1 sample!

Parameters
one_samplesample that will contain result of sampling
methodSampling method to be used. Each sampling method is currently represented by an enum, eg. SampleMthd::BOXMULLER
argsPointer to a struct representing extra sample arguments
See also
SampleFrom()
Bug:
Sometimes the compiler doesn't know which method to choose!

Reimplemented in DiscretePdf, Gaussian, Uniform, MCPdf< T >, and Mixture< T >.

Definition at line 194 of file pdf.h.

◆ SampleFrom() [2/2]

template<typename T >
bool SampleFrom ( vector< Sample< T > > &  list_samples,
const unsigned int  num_samples,
const SampleMthd  method = SampleMthd::DEFAULT,
void *  args = NULL 
) const
virtualinherited

Draw multiple samples from the Pdf (overloaded)

Parameters
list_sampleslist of samples that will contain result of sampling
num_samplesNumber of Samples to be drawn (iid)
methodSampling method to be used. Each sampling method is currently represented by an enum eg. SampleMthd::BOXMULLER
argsPointer 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 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().

Member Data Documentation

◆ _additiveNoise_Mu

MatrixWrapper::ColumnVector _additiveNoise_Mu
protected

additive noise expected value

Definition at line 94 of file conditionalgaussian_additivenoise.h.

◆ _additiveNoise_Sigma

MatrixWrapper::SymmetricMatrix _additiveNoise_Sigma
protected

additive noise covariance

Definition at line 97 of file conditionalgaussian_additivenoise.h.

◆ _diff

ColumnVector _diff
mutableprotectedinherited

Definition at line 67 of file conditionalgaussian.h.

◆ _Low_triangle

Matrix _Low_triangle
mutableprotectedinherited

Definition at line 69 of file conditionalgaussian.h.

◆ _Mu

ColumnVector _Mu
mutableprotectedinherited

Definition at line 68 of file conditionalgaussian.h.

◆ _samples

ColumnVector _samples
mutableprotectedinherited

Definition at line 70 of file conditionalgaussian.h.

◆ _SampleValue

ColumnVector _SampleValue
mutableprotectedinherited

Definition at line 71 of file conditionalgaussian.h.


The documentation for this class was generated from the following file: