MRPT  1.9.9
transform_gaussian.h File Reference
#include <mrpt/math/CMatrixDynamic.h>
#include <mrpt/math/CMatrixFixed.h>
#include <mrpt/math/data_utils.h>
#include <mrpt/math/math_frwds.h>
#include <mrpt/math/num_jacobian.h>
#include <mrpt/math/ops_matrices.h>
#include <mrpt/random.h>
#include <functional>
#include <vector>
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Namespaces

 mrpt::math
 This base provides a set of functions for maths stuff.
 

Functions

template<class VECTORLIKE1 , class MATLIKE1 , class USERPARAM , class VECTORLIKE2 , class VECTORLIKE3 , class MATLIKE2 >
void mrpt::math::transform_gaussian_unscented (const VECTORLIKE1 &x_mean, const MATLIKE1 &x_cov, void(*functor)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y), const USERPARAM &fixed_param, VECTORLIKE2 &y_mean, MATLIKE2 &y_cov, const bool *elem_do_wrap2pi=nullptr, const double alpha=1e-3, const double K=0, const double beta=2.0)
 Scaled unscented transformation (SUT) for estimating the Gaussian distribution of a variable Y=f(X) for an arbitrary function f() provided by the user. More...
 
template<class VECTORLIKE1 , class MATLIKE1 , class USERPARAM , class VECTORLIKE2 , class VECTORLIKE3 , class MATLIKE2 >
void mrpt::math::transform_gaussian_montecarlo (const VECTORLIKE1 &x_mean, const MATLIKE1 &x_cov, void(*functor)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y), const USERPARAM &fixed_param, VECTORLIKE2 &y_mean, MATLIKE2 &y_cov, const size_t num_samples=1000, std::vector< VECTORLIKE3 > *out_samples_y=nullptr)
 Simple Montecarlo-base estimation of the Gaussian distribution of a variable Y=f(X) for an arbitrary function f() provided by the user. More...
 
template<class VECTORLIKE1 , class MATLIKE1 , class USERPARAM , class VECTORLIKE2 , class VECTORLIKE3 , class MATLIKE2 >
void mrpt::math::transform_gaussian_linear (const VECTORLIKE1 &x_mean, const MATLIKE1 &x_cov, void(*functor)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y), const USERPARAM &fixed_param, VECTORLIKE2 &y_mean, MATLIKE2 &y_cov, const VECTORLIKE1 &x_increments)
 First order uncertainty propagation estimator of the Gaussian distribution of a variable Y=f(X) for an arbitrary function f() provided by the user. More...
 



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