learner.h File Reference

#include <iostream>
#include <memory>
#include "memory.h"
#include "multiclass.h"
#include "simple_label.h"
#include "parser.h"
#include "future_compat.h"
#include "example.h"
#include "scope_exit.h"

Classes
struct	VW::LEARNER::learner< T, E >
	Defines the interface for a learning algorithm. More...
struct	VW::LEARNER::func_data
struct	VW::LEARNER::learn_data
struct	VW::LEARNER::sensitivity_data
struct	VW::LEARNER::save_load_data
struct	VW::LEARNER::finish_example_data
struct	VW::LEARNER::learner< T, E >
	Defines the interface for a learning algorithm. More...

Namespaces
	VW
	[Error Generator]
	VW::LEARNER
	Contains the VW::LEARNER::learner object and utilities for interacting with it.

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

Enumerations
enum	prediction_type_t {   prediction_type_t::scalar, prediction_type_t::scalars, prediction_type_t::action_scores, prediction_type_t::pdf,   prediction_type_t::action_probs, prediction_type_t::multiclass, prediction_type_t::multilabels, prediction_type_t::prob,   prediction_type_t::multiclassprobs, prediction_type_t::decision_probs, prediction_type_t::action_pdf_value }

Functions
const char *	to_string (prediction_type_t prediction_type)
func_data	VW::LEARNER::tuple_dbf (void *data, base_learner *base, void(*func)(void *))
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::noop_sl (void *, io_buf &, bool, bool)
void	VW::LEARNER::noop (void *)
float	VW::LEARNER::noop_sensitivity (void *, base_learner &, example &)
float	VW::LEARNER::recur_sensitivity (void *, base_learner &, example &)
void	VW::LEARNER::increment_offset (example &ex, const size_t increment, const size_t i)
void	VW::LEARNER::increment_offset (multi_ex &ec_seq, const size_t increment, const size_t i)
void	VW::LEARNER::decrement_offset (example &ex, const size_t increment, const size_t i)
void	VW::LEARNER::decrement_offset (multi_ex &ec_seq, const size_t increment, const size_t i)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::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)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (free_ptr< T > &dat, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), size_t params_per_weight)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (void(*predict)(T &, L &, E &), size_t params_per_weight)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::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)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (free_ptr< T > &dat, L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), size_t ws)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (free_ptr< T > &dat, L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &))
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_learner (L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &))
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::init_multiclass_learner (free_ptr< T > &dat, L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &), parser *p, size_t ws, prediction_type_t pred_type=prediction_type_t::multiclass)
template<class T , class E , class L >
learner< T, E > &	VW::LEARNER::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, prediction_type_t pred_type=prediction_type_t::multiclass)
template<class T , class E >
base_learner *	VW::LEARNER::make_base (learner< T, E > &base)
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)
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)

Enumeration Type Documentation

enum prediction_type_t

strong

Enumerator
scalar
scalars
action_scores
pdf
action_probs
multiclass
multilabels
prob
multiclassprobs
decision_probs
action_pdf_value

Function Documentation

const char* to_string

(

prediction_type_t

prediction_type

)