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

Classes
struct	base_learner_builder

struct	common_learner_builder

struct	finish_example_data

struct	func_data

struct	learn_data

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

struct	reduction_learner_builder

struct	reduction_no_data_learner_builder

struct	save_load_data

struct	save_metric_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::workspace &all)

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

void	generic_driver_onethread (VW::workspace &all)

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

void	noop_persist_metrics (void *, metric_sink &)

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)

bool	ec_is_example_header (example const &ec, label_type_t label_type)

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)

float	noop_sensitivity_base (void *, example &)

template<class DataT , class ExampleT , class BaseLearnerT >
VW_WARNING_STATE_POP reduction_learner_builder< DataT, ExampleT, BaseLearnerT >	make_reduction_learner (std::unique_ptr< DataT > &&data, BaseLearnerT base, void(learn_fn)(DataT &, BaseLearnerT &, ExampleT &), void(*predict_fn)(DataT &, BaseLearnerT &, ExampleT &), const std::string &name)

template<class ExampleT , class BaseLearnerT >
reduction_no_data_learner_builder< ExampleT, BaseLearnerT >	make_no_data_reduction_learner (BaseLearnerT base, void(learn_fn)(char &, BaseLearnerT &, ExampleT &), void(*predict_fn)(char &, BaseLearnerT &, ExampleT &), const std::string &name)

template<class DataT , class ExampleT >
base_learner_builder< DataT, ExampleT >	make_base_learner (std::unique_ptr< DataT > &&data, void(learn_fn)(DataT &, base_learner &, ExampleT &), void(predict_fn)(DataT &, base_learner &, ExampleT &), const std::string &name, prediction_type_t out_pred_type, label_type_t in_label_type)

template<class ExampleT >
base_learner_builder< char, ExampleT >	make_no_data_base_learner (void(learn_fn)(char &, base_learner &, ExampleT &), void(predict_fn)(char &, base_learner &, ExampleT &), const std::string &name, prediction_type_t out_pred_type, label_type_t in_label_type)

Detailed Description

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

Typedef Documentation

◆ base_learner

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

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

◆ multi_learner

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.

◆ single_learner

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

◆ as_multiline()

template<class T , class E >

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

◆ as_singleline()

template<class T , class E >

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

◆ debug_decrement_depth() [1/2]

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

inline

◆ debug_decrement_depth() [2/2]

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

inline

◆ debug_increment_depth() [1/2]

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

inline

◆ debug_increment_depth() [2/2]

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

inline

◆ decrement_offset() [1/2]

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

inline

◆ decrement_offset() [2/2]

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

inline

◆ ec_is_example_header()

bool VW::LEARNER::ec_is_example_header	(	example const &	ec,
		label_type_t	label_type
	)

inline

◆ generic_driver() [1/2]

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

◆ generic_driver() [2/2]

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

◆ generic_driver_onethread()

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

◆ increment_offset() [1/2]

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

inline

◆ increment_offset() [2/2]

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

inline

◆ make_base()

template<class T , class E >

base_learner* VW::LEARNER::make_base ( learner< T, E > & base )

◆ make_base_learner()

template<class DataT , class ExampleT >

base_learner_builder<DataT, ExampleT> VW::LEARNER::make_base_learner	(	std::unique_ptr< DataT > &&	data,
		void(*)(DataT &, base_learner &, ExampleT &)	learn_fn,
		void(*)(DataT &, base_learner &, ExampleT &)	predict_fn,
		const std::string &	name,
		prediction_type_t	out_pred_type,
		label_type_t	in_label_type
	)

◆ make_no_data_base_learner()

template<class ExampleT >

base_learner_builder<char, ExampleT> VW::LEARNER::make_no_data_base_learner	(	void(*)(char &, base_learner &, ExampleT &)	learn_fn,
		void(*)(char &, base_learner &, ExampleT &)	predict_fn,
		const std::string &	name,
		prediction_type_t	out_pred_type,
		label_type_t	in_label_type
	)

◆ make_no_data_reduction_learner()

template<class ExampleT , class BaseLearnerT >

reduction_no_data_learner_builder<ExampleT, BaseLearnerT> VW::LEARNER::make_no_data_reduction_learner	(	BaseLearnerT *	base,
		void(*)(char &, BaseLearnerT &, ExampleT &)	learn_fn,
		void(*)(char &, BaseLearnerT &, ExampleT &)	predict_fn,
		const std::string &	name
	)

◆ make_reduction_learner()

template<class DataT , class ExampleT , class BaseLearnerT >

VW_WARNING_STATE_POP reduction_learner_builder<DataT, ExampleT, BaseLearnerT> VW::LEARNER::make_reduction_learner	(	std::unique_ptr< DataT > &&	data,
		BaseLearnerT *	base,
		void(*)(DataT &, BaseLearnerT &, ExampleT &)	learn_fn,
		void(*)(DataT &, BaseLearnerT &, ExampleT &)	predict_fn,
		const std::string &	name
	)

◆ multiline_learn_or_predict()

template<bool is_learn>

void VW::LEARNER::multiline_learn_or_predict	(	multi_learner &	base,
		multi_ex &	examples,
		const uint64_t	offset,
		const uint32_t	id = `0`
	)

◆ noop()

void VW::LEARNER::noop ( void * )

inline

◆ noop_persist_metrics()

void VW::LEARNER::noop_persist_metrics	(	void *	,
		metric_sink &
	)

inline

◆ noop_save_load()

void VW::LEARNER::noop_save_load	(	void *	,
		io_buf &	,
		bool	,
		bool
	)

inline

◆ noop_sensitivity()

float VW::LEARNER::noop_sensitivity	(	void *	,
		base_learner &	,
		example &
	)

inline

◆ noop_sensitivity_base()

float VW::LEARNER::noop_sensitivity_base	(	void *	,
		example &
	)

inline

◆ recur_sensitivity()

float VW::LEARNER::recur_sensitivity	(	void *	,
		base_learner &	,
		example &
	)

◆ tuple_dbf()

func_data VW::LEARNER::tuple_dbf	(	void *	data,
		base_learner *	base,
		void()(void )	func
	)

inline

Classes

Typedefs

Functions

Detailed Description

Typedef Documentation

◆ base_learner

◆ multi_learner

◆ single_learner

Function Documentation

◆ as_multiline()

◆ as_singleline()

◆ debug_decrement_depth() [1/2]

◆ debug_decrement_depth() [2/2]

◆ debug_increment_depth() [1/2]

◆ debug_increment_depth() [2/2]

◆ decrement_offset() [1/2]

◆ decrement_offset() [2/2]

◆ ec_is_example_header()

◆ generic_driver() [1/2]

◆ generic_driver() [2/2]

◆ generic_driver_onethread()

◆ increment_offset() [1/2]

◆ increment_offset() [2/2]

◆ make_base()

◆ make_base_learner()

◆ make_no_data_base_learner()

◆ make_no_data_reduction_learner()

◆ make_reduction_learner()

◆ multiline_learn_or_predict()

◆ noop()

◆ noop_persist_metrics()

◆ noop_save_load()

◆ noop_sensitivity()

◆ noop_sensitivity_base()

◆ recur_sensitivity()

◆ tuple_dbf()