lds::poisson::Fit

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