CV_base< D, cv_strat, err_eval, k_imp, valid_err_ret > Class Template Reference

Template class for performing cross-validation. More...

#include <CV.hpp>

Public Member Functions
template<typename STOR_OBJ, typename STRATEGY>
	CV_base (STOR_OBJ &&Data, STRATEGY &&strategy, int number_threads)
	Constructor for the class.
KO_Traits::StoringMatrix	Data () const
	Getter for the data matrix.
cv_strategy< cv_strat >	strategy () const
	Getter for the training/validation set splitting.
int	number_threads () const
	Getter for the number of threads for OMP.
double	err_valid_set_eval (const KO_Traits::StoringVector &pred, const KO_Traits::StoringVector &valid, int number_threads) const
	Evaluation of the loss between prediction on validation set and validation set: estimate of L2 norm loss. Tag-dispacther.

Detailed Description

template<class D, CV_STRAT cv_strat, CV_ERR_EVAL err_eval, K_IMP k_imp, VALID_ERR_RET valid_err_ret>
class CV_base< D, cv_strat, err_eval, k_imp, valid_err_ret >

Template class for performing cross-validation.

Template Parameters

D	type of the derived class (for static polymorphism thorugh CRTP): 'CV_alpha' 'CV_k' 'CV_alpha_k'
cv_strat	strategy for splitting training/validation sets
err_eval	how to evaluate the loss between prediction on validation set and validation set
k_imp	if k is imposed or has to be found through explanatory power criterion
valid_err_ret	if validation error are stored

It is the base class. Polymorphism is known at compile time thanks to Curiously Recursive Template Pattern (CRTP)

Constructor & Destructor Documentation

CV_base()

template<class D, CV_STRAT cv_strat, CV_ERR_EVAL err_eval, K_IMP k_imp, VALID_ERR_RET valid_err_ret>

template<typename STOR_OBJ, typename STRATEGY>

CV_base< D, cv_strat, err_eval, k_imp, valid_err_ret >::CV_base ( STOR_OBJ && Data, STRATEGY && strategy, int number_threads )

inline

Constructor for the class.

Parameters

Data	fts matrix
strategy	strategy for splitting training/validation sets
number_threads	number of threads for OMP

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

Member Function Documentation

Data()

template<class D, CV_STRAT cv_strat, CV_ERR_EVAL err_eval, K_IMP k_imp, VALID_ERR_RET valid_err_ret>

KO_Traits::StoringMatrix CV_base< D, cv_strat, err_eval, k_imp, valid_err_ret >::Data ( ) const

inline

Getter for the data matrix.

Returns

the private m_Data

err_valid_set_eval()

template<class D, CV_STRAT cv_strat, CV_ERR_EVAL err_eval, K_IMP k_imp, VALID_ERR_RET valid_err_ret>

double CV_base< D, cv_strat, err_eval, k_imp, valid_err_ret >::err_valid_set_eval ( const KO_Traits::StoringVector & pred, const KO_Traits::StoringVector & valid, int number_threads ) const

inline

Evaluation of the loss between prediction on validation set and validation set: estimate of L2 norm loss. Tag-dispacther.

Parameters

pred	prediction on validation set
valid	validation set
number_threads	number of threads for multi-threading

Returns

the error between prediction on validation set and validation set

number_threads()

template<class D, CV_STRAT cv_strat, CV_ERR_EVAL err_eval, K_IMP k_imp, VALID_ERR_RET valid_err_ret>

int CV_base< D, cv_strat, err_eval, k_imp, valid_err_ret >::number_threads ( ) const

inline

Getter for the number of threads for OMP.

Returns

the private m_number_threads

strategy()

template<class D, CV_STRAT cv_strat, CV_ERR_EVAL err_eval, K_IMP k_imp, VALID_ERR_RET valid_err_ret>

cv_strategy< cv_strat > CV_base< D, cv_strat, err_eval, k_imp, valid_err_ret >::strategy ( ) const

inline

Getter for the training/validation set splitting.

Returns

the private m_strategy

---

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

• src/CV.hpp
• src/CV_err_valid_eval.hpp