Contains the VW::LEARNER::learner object and utilities for interacting with it. More...

Classes
struct	finish_example_data

struct	func_data

struct	learn_data

struct	learner
	Defines the interface for a learning algorithm. More...

struct	save_load_data

struct	sensitivity_data

Typedefs
using	base_learner = learner< char, char >
	Used to type erase the object and pass around common type. More...

using	single_learner = learner< char, example >
	Used for reductions that process single example objects at at time. It type erases the specific reduction object type. More...

using	multi_learner = learner< char, multi_ex >
	Used for multiline examples where there are several example objects required to describe the overall example. It type erases the specific reduction object type. More...

Functions
func_data	tuple_dbf (void data, base_learner base, void(func)(void ))

void	generic_driver (vw &all)

void	generic_driver (const std::vector< vw * > &alls)

void	generic_driver_onethread (vw &all)

void	noop_sl (void *, io_buf &, bool, bool)

void	noop (void *)

float	noop_sensitivity (void *, base_learner &, example &)

float	recur_sensitivity (void *, base_learner &, example &)

void	debug_increment_depth (example &ex)

void	debug_increment_depth (multi_ex &ec_seq)

void	debug_decrement_depth (example &ex)

void	debug_decrement_depth (multi_ex &ec_seq)

void	increment_offset (example &ex, const size_t increment, const size_t i)

void	increment_offset (multi_ex &ec_seq, const size_t increment, const size_t i)

void	decrement_offset (example &ex, const size_t increment, const size_t i)

void	decrement_offset (multi_ex &ec_seq, const size_t increment, const size_t i)

template<class T , class E , class L >
learner< T, E > &	init_learner (free_ptr< T > &dat, L base, void(learn)(T &, L &, E &), void(*predict)(T &, L &, E &), size_t ws, prediction_type_t pred_type, const std::string &name, bool learn_returns_prediction=false)

template<class T , class E , class L >
learner< T, E > &	init_learner (free_ptr< T > &dat, void(learn)(T &, L &, E &), void(predict)(T &, L &, E &), size_t params_per_weight, const std::string &name, bool learn_returns_prediction=false)

template<class T , class E , class L >
learner< T, E > &	init_learner (void(*predict)(T &, L &, E &), size_t params_per_weight, const std::string &name)

template<class T , class E , class L >
learner< T, E > &	init_learner (free_ptr< T > &dat, void(learn)(T &, L &, E &), void(predict)(T &, L &, E &), size_t params_per_weight, prediction_type_t pred_type, const std::string &name, bool learn_returns_prediction=false)

template<class T , class E , class L >
learner< T, E > &	init_learner (free_ptr< T > &dat, L base, void(learn)(T &, L &, E &), void(*predict)(T &, L &, E &), size_t ws, const std::string &name, bool learn_returns_prediction=false)

template<class T , class E , class L >
learner< T, E > &	init_learner (free_ptr< T > &dat, L base, void(learn)(T &, L &, E &), void(*predict)(T &, L &, E &), const std::string &name, bool learn_returns_prediction=false)

template<class T , class E , class L >
learner< T, E > &	init_learner (L base, void(learn)(T &, L &, E &), void(*predict)(T &, L &, E &), const std::string &name, bool learn_returns_prediction=false)

template<class T , class E , class L >
learner< T, E > &	init_multiclass_learner (free_ptr< T > &dat, L base, void(learn)(T &, L &, E &), void(predict)(T &, L &, E &), parser p, size_t ws, const std::string &name, prediction_type_t pred_type=prediction_type_t::multiclass, bool learn_returns_prediction=false)

template<class T , class E , class L >
learner< T, E > &	init_cost_sensitive_learner (free_ptr< T > &dat, L base, void(learn)(T &, L &, E &), void(predict)(T &, L &, E &), parser p, size_t ws, const std::string &name, prediction_type_t pred_type=prediction_type_t::multiclass, bool learn_returns_prediction=false)

template<class T , class E >
base_learner *	make_base (learner< T, E > &base)

template<class T , class E >
multi_learner *	as_multiline (learner< T, E > *l)

template<class T , class E >
single_learner *	as_singleline (learner< T, E > *l)

template<bool is_learn>
void	multiline_learn_or_predict (multi_learner &base, multi_ex &examples, const uint64_t offset, const uint32_t id=0)

Detailed Description

Contains the VW::LEARNER::learner object and utilities for interacting with it.

Typedef Documentation

typedef learner< char, char > VW::LEARNER::base_learner

Used to type erase the object and pass around common type.

typedef learner< char, multi_ex > VW::LEARNER::multi_learner

Used for multiline examples where there are several example objects required to describe the overall example. It type erases the specific reduction object type.

typedef learner< char, example > VW::LEARNER::single_learner

Used for reductions that process single example objects at at time. It type erases the specific reduction object type.

Function Documentation

template<class T , class E >

multi_learner* VW::LEARNER::as_multiline ( learner< T, E > * l )

template<class T , class E >

single_learner* VW::LEARNER::as_singleline ( learner< T, E > * l )

void VW::LEARNER::debug_decrement_depth ( example & ex )

inline

void VW::LEARNER::debug_decrement_depth ( multi_ex & ec_seq )

inline

void VW::LEARNER::debug_increment_depth ( example & ex )

inline

void VW::LEARNER::debug_increment_depth ( multi_ex & ec_seq )

inline

void VW::LEARNER::decrement_offset	(	example &	ex,
		const size_t	increment,
		const size_t	i
	)

inline

void VW::LEARNER::decrement_offset	(	multi_ex &	ec_seq,
		const size_t	increment,
		const size_t	i
	)

inline

void VW::LEARNER::generic_driver ( vw & all )

void VW::LEARNER::generic_driver ( const std::vector< vw * > & alls )

void VW::LEARNER::generic_driver_onethread ( vw & all )

void VW::LEARNER::increment_offset	(	example &	ex,
		const size_t	increment,
		const size_t	i
	)

inline

void VW::LEARNER::increment_offset	(	multi_ex &	ec_seq,
		const size_t	increment,
		const size_t	i
	)

inline