Bayesian Filtering Library Generated from SVN r
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Abstract Class representing all full Analytical Conditional gaussians with Additive Gaussian Noise. More...
#include <analyticconditionalgaussian_additivenoise.h>
Public Member Functions | |
AnalyticConditionalGaussianAdditiveNoise (const Gaussian &gaus, int num_conditional_arguments=1) | |
Constructor. | |
AnalyticConditionalGaussianAdditiveNoise (int dim=0, int num_conditional_arguments=0) | |
Constructor 2, Gaussian not yet known. | |
virtual | ~AnalyticConditionalGaussianAdditiveNoise () |
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 MatrixWrapper::Matrix | dfGet (unsigned int i) const |
returns derivative from function to n-th conditional variable | |
virtual ConditionalGaussian * | Clone () const |
Clone function. | |
virtual Probability | ProbabilityGet (const MatrixWrapper::ColumnVector &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< MatrixWrapper::ColumnVector > > &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< MatrixWrapper::ColumnVector > &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. | |
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 MatrixWrapper::ColumnVector | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
virtual MatrixWrapper::ColumnVector | ExpectedValueGet () const |
Get the expected value E[x] of the pdf. | |
virtual MatrixWrapper::ColumnVector | 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 |
Abstract Class representing all full Analytical Conditional gaussians with Additive Gaussian Noise.
This class represents all Pdf's of the type
where
and
and
Definition at line 37 of file analyticconditionalgaussian_additivenoise.h.
AnalyticConditionalGaussianAdditiveNoise | ( | const Gaussian & | gaus, |
int | num_conditional_arguments = 1 ) |
Constructor.
gaus | Gaussian representing the additive uncertainty |
num_conditional_arguments | The number of conditional arguments. |
AnalyticConditionalGaussianAdditiveNoise | ( | int | dim = 0, |
int | num_conditional_arguments = 0 ) |
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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virtualinherited |
Clone function.
Reimplemented from ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector >.
Reimplemented in LinearAnalyticConditionalGaussian.
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inherited |
Get the n-th argument of the list.
Definition at line 97 of file conditionalpdf.h.
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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 |
Definition at line 104 of file conditionalpdf.h.
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inherited |
Get the whole list of conditional arguments.
Definition at line 85 of file conditionalpdf.h.
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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.
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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
Get first order statistic (Covariance) of this AnalyticPdf
Reimplemented from Pdf< MatrixWrapper::ColumnVector >.
Reimplemented in NonLinearAnalyticConditionalGaussian_Ginac.
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virtualinherited |
returns derivative from function to n-th conditional variable
i | Number of the conditional variable to use for partial derivation |
Reimplemented in FilterProposalDensity, LinearAnalyticConditionalGaussian, and NonLinearAnalyticConditionalGaussian_Ginac.
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inlineinherited |
Get the dimension of the argument.
Definition at line 113 of file mixtureParticleFilter.h.
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inherited |
Get the dimension of the argument.
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inherited |
Get the dimension of the argument.
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virtualinherited |
Set the dimension of the argument.
dim | the dimension |
Reimplemented in Gaussian.
Definition at line 118 of file mixtureParticleFilter.h.
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virtualinherited |
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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 FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.
Definition at line 129 of file mixtureParticleFilter.h.
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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 FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.
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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 FilterProposalDensity, Gaussian, LinearAnalyticConditionalGaussian, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.
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inlineinherited |
Get the Number of conditional arguments.
Definition at line 71 of file conditionalpdf.h.
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inlinevirtualinherited |
Set the Number of conditional arguments.
numconditionalarguments | the number of conditionalarguments |
Reimplemented in LinearAnalyticConditionalGaussian.
Definition at line 79 of file conditionalpdf.h.
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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 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 |
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 |
Definition at line 100 of file mixtureParticleFilter.h.
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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 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... |
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... |
Definition at line 84 of file mixtureParticleFilter.h.
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protected |
additive noise expected value
Definition at line 92 of file analyticconditionalgaussian_additivenoise.h.
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protected |
additive noise covariance
Definition at line 95 of file analyticconditionalgaussian_additivenoise.h.
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mutableprotectedinherited |
Definition at line 67 of file conditionalgaussian.h.
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mutableprotectedinherited |
Definition at line 69 of file conditionalgaussian.h.
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mutableprotectedinherited |
Definition at line 68 of file conditionalgaussian.h.
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mutableprotectedinherited |
Definition at line 70 of file conditionalgaussian.h.
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mutableprotectedinherited |
Definition at line 71 of file conditionalgaussian.h.