lds::poisson::FitEM #

PLDS E-M Fit Type. More…

#include <lds_poisson_fit_em.h>

Additional inherited members #

Public Functions inherited from lds::EM< Fit >

	Name
	EM() =default Constructs a new EM Fit type.
	EM(size_t n_x, data_t dt, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train) Constructs a new EM Fit type.
	EM(const Fit & fit0, UniformMatrixList< kMatFreeDim2 > && u_train, UniformMatrixList< kMatFreeDim2 > && z_train) Constructs a new EM Fit type.
virtual	~EM() =default
const Fit &	Run(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =true, bool calc_output =true, bool calc_measurement =true, size_t max_iter =100, data_t tol =1e-2) Runs fitting by Expectation(E)-Maximization(M)
std::tuple< UniformMatrixList< kMatFreeDim2 >, UniformMatrixList< kMatFreeDim2 > >	ReturnData() Returns the input/output data to caller.
const std::vector< Matrix > &	x() const gets estimated state (over time)
const std::vector< Matrix > &	y() const gets estimated output (over time)
const Matrix &	sum_E_x_t_x_t() const gets state-input covariance
const Matrix &	sum_E_xu_tm1_xu_tm1() const gets state-input covariance (t-minus-1)
const Matrix &	sum_E_xu_t_xu_tm1() const gets single lag state-input covariance
size_t	n_t_tot() total number of time samples
const Vector &	theta() const gets parameters updated in M step

Protected Functions inherited from lds::EM< Fit >

	Name
void	Expectation(bool force_common_initial =false) Expectation step.
void	Maximization(bool calc_dynamics =true, bool calc_Q =true, bool calc_init =false, bool calc_output =false, bool calc_measurement =false) Maximization step.
void	MaximizeDynamics()
void	MaximizeQ()
void	MaximizeInitial()
void	Smooth(bool force_common_initial) get smoothed estimates
void	Reset() reset to initial conditions
void	InitVars() Initializes the variables.
Vector	UpdateTheta() updates parameter list, theta

Protected Attributes inherited from lds::EM< Fit >

	Name
UniformMatrixList< kMatFreeDim2 >	u_ input training data
UniformMatrixList< kMatFreeDim2 >	z_ measurement training data
std::vector< Matrix >	x_ state estimate
std::vector< Cube >	P_ state estimate cov
std::vector< Cube >	P_t_tm1_ single-lag state covariance
std::vector< Matrix >	y_ output estimate
Matrix	diag_y_
Matrix	sum_E_x_t_x_t_ state covariance (current time)
Matrix	sum_E_xu_tm1_xu_tm1_ state-input covariance (t-minus-1)
Matrix	sum_E_xu_t_xu_tm1_ single lag state-input covariance
Fit	fit_
Vector	theta_
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_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

class lds::poisson::FitEM;

This type is used in the process of fitting PLDS models by expectation-maximization (EM).

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