Bayesian Filtering Library
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Abstract Class representing all Conditional Gaussians with additive gaussian noise. More...
#include <conditionalgaussian_additivenoise.h>
Public Member Functions | |
ConditionalGaussianAdditiveNoise (const Gaussian &gaus, int num_conditional_arguments=1) | |
Constructor. More... | |
ConditionalGaussianAdditiveNoise (int dim=0, int num_conditional_arguments=0) | |
Constructor 2, Gaussian not yet known. More... | |
virtual | ~ConditionalGaussianAdditiveNoise () |
Destructor. | |
virtual MatrixWrapper::SymmetricMatrix | CovarianceGet () const |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. More... | |
const MatrixWrapper::ColumnVector & | AdditiveNoiseMuGet () const |
Get the mean Value of the Additive Gaussian uncertainty. More... | |
const MatrixWrapper::SymmetricMatrix & | AdditiveNoiseSigmaGet () const |
Get the covariance matrix of the Additive Gaussian uncertainty. More... | |
void | AdditiveNoiseMuSet (const MatrixWrapper::ColumnVector &mu) |
Set the mean Value of the Additive Gaussian uncertainty. More... | |
void | AdditiveNoiseSigmaSet (const MatrixWrapper::SymmetricMatrix &sigma) |
Set the covariance of the Additive Gaussian uncertainty. More... | |
virtual ConditionalGaussian * | Clone () const |
Clone function. | |
virtual Probability | ProbabilityGet (const MatrixWrapper::ColumnVector &input) const |
Get the probability of a certain argument. More... | |
virtual bool | SampleFrom (Sample< MatrixWrapper::ColumnVector > &sample, int method=DEFAULT, void *args=NULL) const |
virtual bool | SampleFrom (std::vector< Sample< MatrixWrapper::ColumnVector > > &samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const |
virtual bool | SampleFrom (vector< Sample< MatrixWrapper::ColumnVector > > &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< MatrixWrapper::ColumnVector > &one_sample, int method=DEFAULT, void *args=NULL) const |
Draw 1 sample from the Pdf: More... | |
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... | |
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 MatrixWrapper::ColumnVector | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. More... | |
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 |
Abstract Class representing all Conditional Gaussians with additive gaussian noise.
This class represents all Pdf's of the type
where
and
and
f is not necessarily a analytical function
Definition at line 39 of file conditionalgaussian_additivenoise.h.
ConditionalGaussianAdditiveNoise | ( | const Gaussian & | gaus, |
int | num_conditional_arguments = 1 |
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Constructor.
gaus | Gaussian representing the additive uncertainty |
num_conditional_arguments | The number of conditional arguments. |
ConditionalGaussianAdditiveNoise | ( | int | dim = 0 , |
int | num_conditional_arguments = 0 |
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) |
Constructor 2, Gaussian not yet known.
dim | Dimension of state |
num_conditional_arguments | The number of conditional arguments. |
const MatrixWrapper::ColumnVector& AdditiveNoiseMuGet | ( | ) | const |
void AdditiveNoiseMuSet | ( | const MatrixWrapper::ColumnVector & | mu | ) |
const MatrixWrapper::SymmetricMatrix& AdditiveNoiseSigmaGet | ( | ) | const |
void AdditiveNoiseSigmaSet | ( | const MatrixWrapper::SymmetricMatrix & | sigma | ) |
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inherited |
Get the n-th argument of the list.
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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 |
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inherited |
Get the whole list of conditional arguments.
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virtualinherited |
Set the whole list of conditional arguments.
ConditionalArguments | an STL-vector of type Tcontaining the condtional arguments |
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virtual |
Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented from Pdf< MatrixWrapper::ColumnVector >.
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inherited |
Get the dimension of the argument.
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virtualinherited |
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virtualinherited |
Get the expected value E[x] of the pdf.
Get low order statistic (Expected Value) of this AnalyticPdf
Reimplemented in NonLinearAnalyticConditionalGaussian_Ginac, Gaussian, LinearAnalyticConditionalGaussian, FilterProposalDensity, and OptimalImportanceDensity.
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inherited |
Get the Number of conditional arguments.
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virtualinherited |
Set the Number of conditional arguments.
numconditionalarguments | the number of conditionalarguments |
Reimplemented in LinearAnalyticConditionalGaussian.
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virtualinherited |
Get the probability of a certain argument.
input | T argument of the Pdf |
Reimplemented from Pdf< MatrixWrapper::ColumnVector >.
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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... |
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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 |