lds::poisson::FitSSID #

Subspace Identification (SSID) for PLDS.
#include <lds_poisson_fit_ssid.h>

Additional inherited members #

Public Functions inherited from lds::SSID< Fit >

	Name
	SSID() =default Constructs a new SSID Fit type.
	SSID(size_t n_x, size_t n_h, data_t dt, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train, const Vector & d =Vector(1).fill(-kInf)) Constructs a new SSID Fit type.
std::tuple< Fit, Vector >	Run(SSIDWt ssid_wt) Runs fitting by subspace identification (SSID)
std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > >	ReturnData() Returns the I/O data to caller.

Protected Functions inherited from lds::SSID< Fit >

	Name
void	CalcD(data_t t_silence =0.1, data_t thresh_silence =0.001) Using periods of silence in inputs (u), calculates the output \ bias (d)
void	CreateHankelDataMat() Creates the block-hankel I/O data matrix.
void	CalcSVD(SSIDWt wt) performs the singular value decomposition (SVD)
void	Solve(data_t wt_dc) solves for LDS parameters
void	RecomputeExtObs() recompute extended observability matrix from estimates of A, C

Protected Attributes inherited from lds::SSID< Fit >

	Name
UniformMatrixList< kMatFreeDim2 >	u_ input training data
UniformMatrixList< kMatFreeDim2 >	z_ measurement training data
Matrix	D_ block-Hankel I/O data matrix
Fit	fit_ fit
Matrix	g_dc_ I/O gain @ DC.
data_t	dt_ sample period
size_t	n_u_ number of inputs
size_t	n_x_ number of states
size_t	n_y_ number of outputs
size_t	n_h_
size_t	n_trials_ number of input/output data sequences
std::vector< size_t >	n_t_ number of time steps
size_t	n_t_tot_ total number of time steps across trials
Matrix	L_ lower triangle decomp of covariance matrix
Vector	s_ singular values
Matrix	ext_obs_t_ extended observability matrix

Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time