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

#include <reductions_fwd.h>

Public Types
using	end_fptr_type = void()(vw &, void , void *)

using	finish_fptr_type = void()(void )

Public Member Functions
void	debug_log_message (example &ec, const std::string &msg)

void	debug_log_message (multi_ex &ec, const std::string &msg)

void	learn (E &ec, size_t i=0)
	Will update the model according to the labels and examples supplied. More...

void	predict (E &ec, size_t i=0)
	Make a prediction for the given example. More...

void	multipredict (E &ec, size_t lo, size_t count, polyprediction *pred, bool finalize_predictions)

template<class L >
void	set_predict (void(*u)(T &, L &, E &))

template<class L >
void	set_learn (void(*u)(T &, L &, E &))

template<class L >
void	set_multipredict (void(u)(T &, L &, E &, size_t, size_t, polyprediction , bool))

void	update (E &ec, size_t i=0)

template<class L >
void	set_update (void(*u)(T &data, L &base, E &))

void	set_sensitivity (float(*u)(T &data, base_learner &base, example &))

float	sensitivity (example &ec, size_t i=0)

void	save_load (io_buf &io, const bool read, const bool text)

void	set_save_load (void(*sl)(T &, io_buf &, bool, bool))

void	persist_metrics (metric_sink &metrics)

void	set_persist_metrics (void(*f)(T &, metric_sink &))

void	set_finish (void(*f)(T &))

void	finish ()

void	end_pass ()

void	set_end_pass (void(*f)(T &))

void	end_examples ()

void	set_end_examples (void(*f)(T &))

void	init_driver ()

void	set_init_driver (void(*f)(T &))

void	finish_example (vw &all, E &ec)

void	set_finish_example (void(*f)(vw &all, T &, E &))

void	set_print_example (void(*f)(vw &all, T &, E &))

void	print_example (vw &all, E &ec)

base_learner *	get_learner_by_name_prefix (std::string reduction_name)

Static Public Member Functions
template<class L >
static learner< T, E > &	init_learner (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)

Public Attributes
prediction_type_t	pred_type

size_t	weights

size_t	increment

bool	is_multiline

bool	learn_returns_prediction = false

Friends
template<class FluentBuilderT , class DataT , class ExampleT , class BaseLearnerT >
struct	common_learner_builder

template<class DataT , class ExampleT >
struct	base_learner_builder

template<class DataT , class ExampleT , class BaseLearnerT >
struct	reduction_learner_builder

template<class ExampleT , class BaseLearnerT >
struct	reduction_no_data_learner_builder

Detailed Description

template<class T, class E>
struct VW::LEARNER::learner< T, E >

Defines the interface for a learning algorithm.

Learner is implemented as a struct of pointers, and associated methods. It implements a sort of virtual inheritance through the use of bundling function pointers with the associated objects to call them with. A reduction will recursively call the base given to it, whereas a base learner will not recurse and will simply return the result. Learner is not intended to be inherited from. Instead it is used through composition, and created through the various VW::LEARNER::init_learner overloaded functions that chain to the central factor function VW::LEARNER::learner::init_learner The state of this learner, or reduction, is stored in the learner_data field. A std::shared_pointer<void> is used as this class uses type erasure to allow for an arbitrary reduction to be implemented. It is extremely important that the function pointers given to the class match the expected types of the object. If the learner is constructed using VW::LEARNER::learner::init_learner and assembled before it is transformed into a VW::LEARNER::base_learner with VW::LEARNER::make_base then the usage of the templated functions should ensure types are correct.

Template Parameters

T	Type of the reduction data object stored. This allows this specific reduction to have it's own state.
E	Example type this reduction supports. Must be one of example or multi_ex

Member Typedef Documentation

template<class T, class E>

using VW::LEARNER::learner< T, E >::end_fptr_type = void (*)(vw&, void*, void*)

template<class T, class E>

using VW::LEARNER::learner< T, E >::finish_fptr_type = void (*)(void*)

Member Function Documentation

template<class T, class E>

void VW::LEARNER::learner< T, E >::debug_log_message	(	example &	ec,
		const std::string &	msg
	)

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::debug_log_message	(	multi_ex &	ec,
		const std::string &	msg
	)

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::end_examples ( )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::end_pass ( )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::finish ( )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::finish_example	(	vw &	all,
		E &	ec
	)

inline

template<class T, class E>

base_learner* VW::LEARNER::learner< T, E >::get_learner_by_name_prefix ( std::string reduction_name )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::init_driver ( )

inline

template<class T, class E>

template<class L >

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

inlinestatic

template<class T, class E>

void VW::LEARNER::learner< T, E >::learn	(	E &	ec,
		size_t	i = `0`
	)

inline

Will update the model according to the labels and examples supplied.

Parameters

ec	The example object or multi_ex to be operated on. This object must have a valid label set for every example in the field example::l that corresponds to the type this reduction expects.
i	This is the offset used for the weights in this call. If using multiple regressors/learners you can increment this value for each call.

Returns: While some reductions may fill the example::pred, this is not guaranteed and is undefined behavior if accessed.

template<class T, class E>

void VW::LEARNER::learner< T, E >::multipredict	(	E &	ec,
		size_t	lo,
		size_t	count,
		polyprediction *	pred,
		bool	finalize_predictions
	)

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::persist_metrics ( metric_sink & metrics )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::predict	(	E &	ec,
		size_t	i = `0`
	)

inline

Make a prediction for the given example.

Parameters

ec	The example object or multi_ex to be operated on. This object must have a valid prediction allocated in the field example::pred that corresponds to this reduction type.
i	This is the offset used for the weights in this call. If using multiple regressors/learners you can increment this value for each call.

Returns: The prediction calculated by this reduction be set on example::pred. If E is multi_ex then the prediction is set on the 0th item in the list.

template<class T, class E>

void VW::LEARNER::learner< T, E >::print_example	(	vw &	all,
		E &	ec
	)

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::save_load	(	io_buf &	io,
		const bool	read,
		const bool	text
	)

inline

template<class T, class E>

float VW::LEARNER::learner< T, E >::sensitivity	(	example &	ec,
		size_t	i = `0`
	)

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::set_end_examples ( void(*)(T &) f )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::set_end_pass ( void(*)(T &) f )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::set_finish ( void(*)(T &) f )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::set_finish_example ( void(*)(vw &all, T &, E &) f )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::set_init_driver ( void(*)(T &) f )

inline

template<class T, class E>

template<class L >

void VW::LEARNER::learner< T, E >::set_learn ( void(*)(T &, L &, E &) u )

inline

template<class T, class E>

template<class L >

void VW::LEARNER::learner< T, E >::set_multipredict ( void(*)(T &, L &, E &, size_t, size_t, polyprediction *, bool) u )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::set_persist_metrics ( void(*)(T &, metric_sink &) f )

inline

template<class T, class E>

template<class L >

void VW::LEARNER::learner< T, E >::set_predict ( void(*)(T &, L &, E &) u )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::set_print_example ( void(*)(vw &all, T &, E &) f )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::set_save_load ( void(*)(T &, io_buf &, bool, bool) sl )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::set_sensitivity ( float(*)(T &data, base_learner< T, E > &base, example &) u )

inline

template<class T, class E>

template<class L >

void VW::LEARNER::learner< T, E >::set_update ( void(*)(T &data, L &base, E &) u )

inline

template<class T, class E>

void VW::LEARNER::learner< T, E >::update	(	E &	ec,
		size_t	i = `0`
	)

inline

Friends And Related Function Documentation

template<class T, class E>

template<class DataT , class ExampleT >

friend struct base_learner_builder

friend

template<class T, class E>

template<class FluentBuilderT , class DataT , class ExampleT , class BaseLearnerT >

friend struct common_learner_builder

friend

template<class T, class E>

template<class DataT , class ExampleT , class BaseLearnerT >

friend struct reduction_learner_builder

friend

template<class T, class E>

template<class ExampleT , class BaseLearnerT >

friend struct reduction_no_data_learner_builder

friend

Member Data Documentation

template<class T, class E>

size_t VW::LEARNER::learner< T, E >::increment

template<class T, class E>

bool VW::LEARNER::learner< T, E >::is_multiline

template<class T, class E>

bool VW::LEARNER::learner< T, E >::learn_returns_prediction = false

template<class T, class E>

prediction_type_t VW::LEARNER::learner< T, E >::pred_type

template<class T, class E>

size_t VW::LEARNER::learner< T, E >::weights

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

/__w/vowpal_wabbit/vowpal_wabbit/vowpalwabbit/learner.h

Public Types

Public Member Functions

Static Public Member Functions

Public Attributes

Friends

Detailed Description

template<class T, class E> struct VW::LEARNER::learner< T, E >

Member Typedef Documentation

Member Function Documentation

Friends And Related Function Documentation

Member Data Documentation

template<class T, class E>
struct VW::LEARNER::learner< T, E >