lds::SSID #

Inherited by lds::gaussian::FitSSID, lds::poisson::FitSSID

Public Functions #

	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 #

	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.
virtual void	DecomposeData() =0 Decompose data to lower-triangular matrix (used in Solve)
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 #

	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

Detailed Description #

template <typename Fit >
class lds::SSID;

Public Function Details #

SSID #

SSID() =default

SSID #

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)
)

Parameters:

n_x number of states
n_h size of block-hankel data matrix
dt sample period
u_train input training data
z_train measurement training data
d output bias

Run #

std::tuple< Fit, Vector > Run(
    SSIDWt ssid_wt
)

Parameters:

ssid_wt weight for singular value decomp

Return: tuple (Fit, singular values)

ReturnData #

inline std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > > ReturnData()

Return: tuple(input data, output data)

Protected Function Details #

CalcD #

void CalcD(
    data_t t_silence =0.1,
    data_t thresh_silence =0.001
)

Parameters:

t_silence threshold on period of time that qualifies as “silence”
thresh_silence threshold on input amplitude u that qualifies as “silence”

CreateHankelDataMat #

void CreateHankelDataMat()

Creates the block-hankel I/O data matrix. Also calculates I/O gain @ DC.

DecomposeData #

virtual void DecomposeData() =0

Reimplemented by: lds::gaussian::FitSSID::DecomposeData, lds::poisson::FitSSID::DecomposeData

CalcSVD #

void CalcSVD(
    SSIDWt wt
)

Parameters:

ssid_wt weight for SVD

Solve #

void Solve(
    data_t wt_dc
)

Parameters:

wt_dc weight placed on getting correct DC I/O gain

RecomputeExtObs #

void RecomputeExtObs()

Protected Attribute Details #

u_ #

UniformMatrixList< kMatFreeDim2 > u_;

z_ #

UniformMatrixList< kMatFreeDim2 > z_;

D_ #

Matrix D_;

fit_ #

Fit fit_;

g_dc_ #

Matrix g_dc_;

dt_ #

data_t dt_ {};

n_u_ #

size_t n_u_ {};

n_x_ #

size_t n_x_ {};

n_y_ #

size_t n_y_ {};

n_h_ #

size_t n_h_ {};

n_trials_ #

size_t n_trials_ {};

n_t_ #

std::vector< size_t > n_t_;

n_t_tot_ #

size_t n_t_tot_ {};

L_ #

Matrix L_;

s_ #

Vector s_;

ext_obs_t_ #

Matrix ext_obs_t_;

Updated on 3 April 2025 at 13:48:29 EDT