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Uniform Class Reference

Class representing uniform density. More...

#include <uniform.h>

Inheritance diagram for Uniform:
Pdf< MatrixWrapper::ColumnVector >

List of all members.

Public Member Functions

 Uniform (const MatrixWrapper::ColumnVector &Center, const MatrixWrapper::ColumnVector &Width)
 Constructor.
 Uniform (int dimension=0)
 constructor with only dimensions or nothing
virtual ~Uniform ()
 Default Copy Constructor will do.
virtual UniformClone () const
 Clone function.
virtual Probability ProbabilityGet (const MatrixWrapper::ColumnVector &input) const
 Get the probability of a certain argument.
bool SampleFrom (vector< Sample< MatrixWrapper::ColumnVector > > &list_samples, const int num_samples, int method=DEFAULT, void *args=NULL) const
virtual bool SampleFrom (Sample< MatrixWrapper::ColumnVector > &one_sample, int method=DEFAULT, void *args=NULL) const
virtual MatrixWrapper::ColumnVector CenterGet () const
 Get the center of the uniform.
virtual MatrixWrapper::ColumnVector WidthGet () const
 Get the Width of the uniform distribution.
void UniformSet (const MatrixWrapper::ColumnVector &center, const MatrixWrapper::ColumnVector &width)
 Set the center and width of the uniform.
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)
virtual bool SampleFrom (Sample< MatrixWrapper::ColumnVector > &one_sample, int method=DEFAULT, void *args=NULL) const
 Draw 1 sample from the 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 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.
virtual MatrixWrapper::ColumnVector ExpectedValueGet () const
 Get the expected value E[x] of the 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.
virtual
MatrixWrapper::SymmetricMatrix 
CovarianceGet () const
 Get the Covariance Matrix E[(x - E[x])^2] of the Analytic pdf.

Friends

std::ostream & operator<< (std::ostream &os, const Uniform &u)
 output stream for Uniform distribution

Detailed Description

Class representing uniform density.

Definition at line 26 of file uniform.h.


Constructor & Destructor Documentation

Uniform ( const MatrixWrapper::ColumnVector &  Center,
const MatrixWrapper::ColumnVector &  Width 
)

Constructor.

Parameters:
Centercenter of the uniform distribution
Widthwidth of the uniform distribution
virtual ~Uniform ( ) [virtual]

Default Copy Constructor will do.

Destructor


Member Function Documentation

virtual MatrixWrapper::ColumnVector CenterGet ( ) const [virtual]

Get the center of the uniform.

Get the center of the uniform

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const [virtual, inherited]

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 in AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const [virtual, inherited]

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 in AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const [virtual, inherited]

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 in AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::SymmetricMatrix CovarianceGet ( ) const [virtual, inherited]

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 in AnalyticConditionalGaussianAdditiveNoise, ConditionalGaussianAdditiveNoise, FilterProposalDensity, Gaussian, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

unsigned int DimensionGet ( ) const [inherited]

Get the dimension of the argument.

Returns:
the dimension of the argument
unsigned int DimensionGet ( ) const [inherited]

Get the dimension of the argument.

Returns:
the dimension of the argument
unsigned int DimensionGet ( ) const [inherited]

Get the dimension of the argument.

Returns:
the dimension of the argument
unsigned int DimensionGet ( ) const [inherited]

Get the dimension of the argument.

Returns:
the dimension of the argument
virtual void DimensionSet ( unsigned int  dim) [virtual, inherited]

Set the dimension of the argument.

Parameters:
dimthe dimension
virtual void DimensionSet ( unsigned int  dim) [virtual, inherited]

Set the dimension of the argument.

Parameters:
dimthe dimension
virtual void DimensionSet ( unsigned int  dim) [virtual, inherited]

Set the dimension of the argument.

Parameters:
dimthe dimension
virtual void DimensionSet ( unsigned int  dim) [virtual, inherited]

Set the dimension of the argument.

Parameters:
dimthe dimension
virtual MatrixWrapper::ColumnVector ExpectedValueGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::ColumnVector ExpectedValueGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::ColumnVector ExpectedValueGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual MatrixWrapper::ColumnVector ExpectedValueGet ( ) const [virtual, inherited]

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, NonLinearAnalyticConditionalGaussian_Ginac, and OptimalImportanceDensity.

virtual Probability ProbabilityGet ( const MatrixWrapper::ColumnVector &  input) const [virtual]

Get the probability of a certain argument.

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

Reimplemented from Pdf< MatrixWrapper::ColumnVector >.

virtual bool SampleFrom ( Sample< MatrixWrapper::ColumnVector > &  one_sample,
int  method = DEFAULT,
void *  args = NULL 
) const [virtual, inherited]

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 a #define statement, eg. #define BOXMULLER 1
argsPointer to a struct representing extra sample arguments
See also:
SampleFrom()
Bug:
Sometimes the compiler doesn't know which method to choose!
virtual bool SampleFrom ( vector< Sample< MatrixWrapper::ColumnVector > > &  list_samples,
const unsigned int  num_samples,
int  method = DEFAULT,
void *  args = NULL 
) const [virtual, inherited]

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 a #define statement, eg. #define BOXMULLER 1
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!
void UniformSet ( const MatrixWrapper::ColumnVector &  center,
const MatrixWrapper::ColumnVector &  width 
)

Set the center and width of the uniform.

Set the center and width of the uniform

Parameters:
centerThe new center of uniform distribution
widthThe new width of the uniform distribution
virtual MatrixWrapper::ColumnVector WidthGet ( ) const [virtual]

Get the Width of the uniform distribution.

Get the Width of the uniform distribution


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