lds::poisson::Fit #
PLDS Fit Type.
#include <lds_poisson_fit.h>
Inherits from lds::Fit
Public Functions #
| Name | |
|---|---|
| Fit() =default | |
| Fit(size_t n_u, size_t n_x, size_t n_y, data_t dt) Constructs a new Fit. |
|
| virtual View | h(Matrix & y, const Matrix & x, size_t t) override output function |
| virtual void | set_R(const Matrix & R) override sets output noise covariance (if any) |
| virtual const Matrix & | R() const override |
Additional inherited members #
Public Functions inherited from lds::Fit
| Name | |
|---|---|
| virtual | ~Fit() =default |
| size_t | n_u() const gets number of inputs |
| size_t | n_x() const gets number of states |
| size_t | n_y() const gets number of outputs |
| data_t | dt() const gets sample period |
| const Matrix & | A() const gets state matrix |
| const Matrix & | B() const gets input matrix |
| const Vector & | g() const gets input gain |
| const Vector & | m() const gets process disturbance |
| const Matrix & | Q() const gets process noise covariance |
| const Vector & | x0() const gets initial state estimate |
| const Matrix & | P0() const gets covariance of initial state estimate |
| const Matrix & | C() const gets output matrix |
| const Vector & | d() const gets output bias |
| void | set_A(const Matrix & A) sets state matrix |
| void | set_B(const Matrix & B) sets input matrix |
| void | set_g(const Vector & g) sets input gain/conversion factor |
| void | set_m(const Vector & m) sets process disturbance |
| void | set_Q(const Matrix & Q) sets process noise covariance |
| void | set_x0(const Vector & x0) sets initial state estimate |
| void | set_P0(const Matrix & P0) sets initial state estimate covariance |
| void | set_C(const Matrix & C) sets output matrix |
| void | set_d(const Vector & d) sets output bias |
| View | f(Matrix & x, const Matrix & u, size_t t) system dynamics function |
| View | f(Matrix & x_pre, const Matrix & x_post, const Matrix & u, size_t t) system dynamics function |
Protected Attributes inherited from lds::Fit
| Name | |
|---|---|
| data_t | dt_ sample period |
| Matrix | A_ state matrix |
| Matrix | B_ input matrix |
| Vector | g_ input gain |
| Vector | m_ process noise mean |
| Matrix | Q_ process noise cov |
| Matrix | C_ output matrix |
| Vector | d_ output bias |
| Matrix | R_ measurement noise |
| Vector | x0_ initial state |
| Matrix | P0_ initial covar |
| size_t | n_u_ number of inputs |
| size_t | n_x_ number of states |
| size_t | n_y_ number of outputs |
Public Function Details #
Fit #
Fit() =default
Fit #
inline Fit(
size_t n_u,
size_t n_x,
size_t n_y,
data_t dt
)
Parameters:
- n_u number of inputs
- n_x number of states
- n_y number of outputs
- dt sample period
h #
inline virtual View h(
Matrix & y,
const Matrix & x,
size_t t
) override
Parameters:
- y output estimate (over time)
- x state estimate (over time)
- t time index
Return: output
Reimplements: lds::Fit::h
set_R #
inline virtual void set_R(
const Matrix & R
) override
Reimplements: lds::Fit::set_R
R #
inline virtual const Matrix & R() const override
Reimplements: lds::Fit::R
Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time