PPC_KO_NoCV< solver, k_imp, valid_err_ret, cv_strat, cv_err_eval > Class Template Reference

Derived from 'PPC_KO_base' class for computing PPCKO algorithm without cross-validation. More...

#include <PPC_KO_NoCV.hpp>

Inheritance diagram for PPC_KO_NoCV< solver, k_imp, valid_err_ret, cv_strat, cv_err_eval >:

Public Member Functions
template<typename STOR_OBJ>
	PPC_KO_NoCV (STOR_OBJ &&X, double alpha, int k, int number_threads)
	Constructor for no cv version if k is passed as parameter.
template<typename STOR_OBJ>
	PPC_KO_NoCV (STOR_OBJ &&X, double alpha, double threshold_ppc, int number_threads)
	Constructor for no cv version if k is selected through explanatory power criterion.
void	solving ()
	Method to perform PPCKO if no cv is performed.
Public Member Functions inherited from PPC_KO_base< PPC_KO_NoCV< solver, k_imp, valid_err_ret, cv_strat, cv_err_eval >, solver, k_imp, valid_err_ret, cv_strat, cv_err_eval >
	PPC_KO_base (STOR_OBJ &&X, int number_threads)
	Constructor: centers data, evaluate mean function, sample covariance, sample cross-covariance and its square.
std::size_t	m () const
	Getter for the number of evaluation of the curve/surface.
std::size_t	n () const
	Getter for the number of time instants.
KO_Traits::StoringMatrix	X () const
	Getter for fts data matrix (centered)
KO_Traits::StoringArray	means () const
	Getter for the mean function.
KO_Traits::StoringMatrix	Cov () const
	Getter for the covariance operator estimate.
double	trace_cov () const
	Getter for the covariance operator estimate's trace.
KO_Traits::StoringMatrix &	CovReg ()
	Setter for the regularized sample covariance operator.
KO_Traits::StoringMatrix	rho () const
	Getter for the autoregressive operator estimate.
KO_Traits::StoringMatrix	a () const
	Getter for the predictive loadings (PPCs directions)
KO_Traits::StoringMatrix	b () const
	Getter for the predictive factors factor (PPCs weights)
std::vector< double >	explanatory_power () const
	Getter for the cumulative explanatory power of the PPCs.
double	alpha () const
	Getter for the regularization parameter.
double &	alpha ()
	Setter for the regularization parameter.
int	k () const
	Getter for the number of retained PPCs.
int &	k ()
	Setter for the number of retained PPCs.
double	threshold_ppc () const
	Getter for the requested explanatory power of the retained PPCs.
double &	threshold_ppc ()
	Setter for the requested explanatory power of the retained PPCs.
valid_err_variant	ValidErr () const
	Getter for the validation errors.
valid_err_variant &	ValidErr ()
	Setter for the validation errors.
int	number_threads () const
	Getter for the number of threads for OMP.
std::tuple< int, KO_Traits::StoringVector, KO_Traits::StoringMatrix >	PPC_retained ()
	Retaining the the PPCs: pairs eigenvalue-eigenvector and their number.
void	KO_algo ()
	Performing PPCKO algorithm once regularization parameter is selected and k or it is fixed or to be retained through explanatory power. Computes PPCs, direction and weight, their number and their cumulative explanatory power, and the estimate of the autoregressive operator.
KO_Traits::StoringArray	prediction () const
	Performs one-step ahead prediction of the fts. The mean function is added.
std::vector< double >	scores () const
	Computes the scores of the PPCs, defined as scalar product between the direction and the fts at the last instant.
std::vector< std::array< double, 2 > >	sd_scores_dir_wei () const
	Computes the standard deviation of the scores of directions and weights.
void	solve ()
	Method to solve PPCKO according to which cross-validation is performed, if any.

Detailed Description

template<SOLVER solver, K_IMP k_imp, VALID_ERR_RET valid_err_ret, CV_STRAT cv_strat, CV_ERR_EVAL cv_err_eval>
class PPC_KO_NoCV< solver, k_imp, valid_err_ret, cv_strat, cv_err_eval >

Derived from 'PPC_KO_base' class for computing PPCKO algorithm without cross-validation.

Template Parameters

solver	if algorithm solved inverting the regularized covariance or avoiding it through gep (not possible if retaining the number of PPCs with explanatory power criterion)
k_imp	if k is imposed or has to be found through explanatory power criterion
valid_err_ret	if validation error are stored
cv_strat	strategy for splitting training/validation sets
err_eval	how to evaluate the loss between prediction on validation set and validation set

Constructor & Destructor Documentation

PPC_KO_NoCV() [1/2]

template<SOLVER solver, K_IMP k_imp, VALID_ERR_RET valid_err_ret, CV_STRAT cv_strat, CV_ERR_EVAL cv_err_eval>

template<typename STOR_OBJ>

PPC_KO_NoCV< solver, k_imp, valid_err_ret, cv_strat, cv_err_eval >::PPC_KO_NoCV ( STOR_OBJ && X, double alpha, int k, int number_threads )

inline

Constructor for no cv version if k is passed as parameter.

Parameters

X	fts
alpha	regularization parameter
k	number of retained PPCs
number_threads	number of threads for OMP

Universal constructor: move semantic used to optimazing handling big size objects

PPC_KO_NoCV() [2/2]

template<SOLVER solver, K_IMP k_imp, VALID_ERR_RET valid_err_ret, CV_STRAT cv_strat, CV_ERR_EVAL cv_err_eval>

template<typename STOR_OBJ>

PPC_KO_NoCV< solver, k_imp, valid_err_ret, cv_strat, cv_err_eval >::PPC_KO_NoCV ( STOR_OBJ && X, double alpha, double threshold_ppc, int number_threads )

inline

Constructor for no cv version if k is selected through explanatory power criterion.

Parameters

X	fts
alpha	regularization parameter
threshold_ppc	requested explanatory power of the retained PPCs
number_threads	number of threads for OMP

Universal constructor: move semantic used to optimazing handling big size objects

Member Function Documentation

solving()

template<SOLVER solver, K_IMP k_imp, VALID_ERR_RET valid_err_ret, CV_STRAT cv_strat, CV_ERR_EVAL cv_err_eval>

void PPC_KO_NoCV< solver, k_imp, valid_err_ret, cv_strat, cv_err_eval >::solving ( )

inline

Method to perform PPCKO if no cv is performed.

Wraps the .KO_algo() method of the base class since parameters are known

---

The documentation for this class was generated from the following file:

• src/PPC_KO_NoCV.hpp