Optimal importance density for Nonlinear Gaussian SS Models.
More...
#include <optimal_importance_density.h>
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| OptimalImportanceDensity (AnalyticConditionalGaussian *SystemPdf, LinearAnalyticConditionalGaussian *MeasPdf) |
| Constructor. More...
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virtual | ~OptimalImportanceDensity () |
| Destructor.
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virtual ColumnVector | ExpectedValueGet () const |
| Get the expected value E[x] of the pdf. More...
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virtual SymmetricMatrix | CovarianceGet () const |
| Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf. More...
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virtual Matrix | dfGet (int i) const |
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virtual MatrixWrapper::Matrix | dfGet (unsigned int i) const |
| returns derivative from function to n-th conditional variable More...
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virtual ConditionalGaussian * | Clone () const |
| Clone function.
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virtual Probability | ProbabilityGet (const MatrixWrapper::ColumnVector &input) const |
| Get the probability of a certain argument. More...
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virtual bool | SampleFrom (Sample< MatrixWrapper::ColumnVector > &sample, int method=DEFAULT, void *args=NULL) const |
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virtual bool | SampleFrom (std::vector< Sample< MatrixWrapper::ColumnVector > > &samples, const unsigned int num_samples, int method=DEFAULT, void *args=NULL) const |
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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...
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virtual bool | SampleFrom (Sample< MatrixWrapper::ColumnVector > &one_sample, int method=DEFAULT, void *args=NULL) const |
| Draw 1 sample from the Pdf: More...
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unsigned int | NumConditionalArgumentsGet () const |
| Get the Number of conditional arguments. More...
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virtual void | NumConditionalArgumentsSet (unsigned int numconditionalarguments) |
| Set the Number of conditional arguments. More...
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const std::vector< MatrixWrapper::ColumnVector > & | ConditionalArgumentsGet () const |
| Get the whole list of conditional arguments. More...
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virtual void | ConditionalArgumentsSet (std::vector< MatrixWrapper::ColumnVector > ConditionalArguments) |
| Set the whole list of conditional arguments. More...
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const MatrixWrapper::ColumnVector & | ConditionalArgumentGet (unsigned int n_argument) const |
| Get the n-th argument of the list. More...
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virtual void | ConditionalArgumentSet (unsigned int n_argument, const MatrixWrapper::ColumnVector &argument) |
| Set the n-th argument of the list. More...
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unsigned int | DimensionGet () const |
| Get the dimension of the argument. More...
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virtual void | DimensionSet (unsigned int dim) |
| Set the dimension of the argument. More...
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ColumnVector | _diff |
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ColumnVector | _Mu |
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Matrix | _Low_triangle |
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ColumnVector | _samples |
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ColumnVector | _SampleValue |
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Optimal importance density for Nonlinear Gaussian SS Models.
Describes the optimal importance density for all systems of the form
This means all systems with a system equation that uses a AnalyticConditionalGaussian Class and a measurement equation that uses a LinearAnalyticConditionalGaussian class
Definition at line 37 of file optimal_importance_density.h.
Get the n-th argument of the list.
- Returns
- The current value of the n-th conditional argument (starting from 0!)
Set the n-th argument of the list.
- Parameters
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n_argument | which one of the conditional arguments |
argument | value of the n-th argument |
Get the whole list of conditional arguments.
- Returns
- an STL-vector containing all the current values of the conditional arguments
Set the whole list of conditional arguments.
- Parameters
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ConditionalArguments | an STL-vector of type T containing the condtional arguments |
virtual SymmetricMatrix CovarianceGet |
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const |
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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
- 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< MatrixWrapper::ColumnVector >.
unsigned int DimensionGet |
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const |
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inherited |
Get the dimension of the argument.
- Returns
- the dimension of the argument
virtual void DimensionSet |
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unsigned int |
dim | ) |
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virtualinherited |
Set the dimension of the argument.
- Parameters
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Reimplemented in Gaussian.
virtual ColumnVector ExpectedValueGet |
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const |
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virtual |
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!
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For certain discrete Pdfs, this function has no meaning, what is the average between yes and no?
Reimplemented from Pdf< MatrixWrapper::ColumnVector >.
unsigned int NumConditionalArgumentsGet |
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const |
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inherited |
Get the Number of conditional arguments.
- Returns
- the number of conditional arguments
virtual void NumConditionalArgumentsSet |
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unsigned int |
numconditionalarguments | ) |
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virtualinherited |
Set the Number of conditional arguments.
- Parameters
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numconditionalarguments | the 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.
virtual bool SampleFrom |
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vector< Sample< MatrixWrapper::ColumnVector > > & |
list_samples, |
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const unsigned int |
num_samples, |
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int |
method = DEFAULT , |
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void * |
args = NULL |
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virtualinherited |
Draw multiple samples from the Pdf (overloaded)
- Parameters
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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... |
- 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!
Draw 1 sample from the Pdf:
There's no need to create a list for only 1 sample!
- Parameters
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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 |
- See also
- SampleFrom()
- Bug:
- Sometimes the compiler doesn't know which method to choose!
The documentation for this class was generated from the following file: