LEARNER Namespace Reference

Classes
class	custom_examples_queue
struct	finish_example_data
struct	func_data
struct	learn_data
struct	learner
class	multi_example_handler
class	multi_instance_context
class	ready_examples_queue
struct	save_load_data
struct	sensitivity_data
class	single_example_handler
class	single_instance_context

Typedefs
using	base_learner = learner< char, char >
using	single_learner = learner< char, example >
using	multi_learner = learner< char, multi_ex >

Functions
void	learn_ex (example &ec, vw &all)
void	learn_multi_ex (multi_ex &ec_seq, vw &all)
void	end_pass (example &ec, vw &all)
void	save (example &ec, vw &all)
bool	example_is_newline_not_header (example &ec, vw &all)
bool	is_save_cmd (example *ec)
void	drain_examples (vw &all)
template<typename queue_type , typename handler_type >
void	process_examples (queue_type &examples, handler_type &handler)
template<typename context_type >
void	generic_driver (ready_examples_queue &examples, context_type &context)
void	generic_driver (vw &all)
void	generic_driver (const std::vector< vw *> &all)
template<typename handler_type >
void	generic_driver_onethread (vw &all)
float	recur_sensitivity (void *, base_learner &base, example &ec)
func_data	tuple_dbf (void *data, base_learner *base, void(*func)(void *))
void	noop_sl (void *, io_buf &, bool, bool)
void	noop (void *)
float	noop_sensitivity (void *, base_learner &, example &)
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::prediction_type_t pred_type)
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)
template<class T , class E , class L >
learner< T, E > &	init_learner (void(*predict)(T &, L &, E &), size_t params_per_weight)
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::prediction_type_t pred_type)
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)
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 &))
template<class T , class E , class L >
learner< T, E > &	init_learner (L *base, void(*learn)(T &, L &, E &), void(*predict)(T &, L &, E &))
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, prediction_type::prediction_type_t pred_type=prediction_type::multiclass)
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, prediction_type::prediction_type_t pred_type=prediction_type::multiclass)
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)

Typedef Documentation

base_learner

typedef learner< char, char > LEARNER::base_learner

Definition at line 15 of file conditional_contextual_bandit.h.

multi_learner

typedef learner< char, multi_ex > LEARNER::multi_learner

Definition at line 41 of file learner.h.

single_learner

typedef learner< char, example > LEARNER::single_learner

Definition at line 40 of file learner.h.

Function Documentation

as_multiline()

template<class T , class E >

multi_learner* LEARNER::as_multiline

(

learner< T, E > *

l

)

Definition at line 468 of file learner.h.

References LEARNER::learner< T, E >::is_multiline, and THROW.

Referenced by cb_adf_setup(), cb_sample_setup(), cbify_setup(), cbifyldf_setup(), CCB::ccb_explore_adf_setup(), explore_eval_setup(), vw::finish_example(), Search::generate_training_example(), vw::learn(), learn_multi_ex(), learn_sup_adf(), vw::predict(), VW::cb_explore_adf::softmax::setup(), VW::cb_explore_adf::greedy::setup(), VW::cb_explore_adf::first::setup(), VW::cb_explore_adf::bag::setup(), VW::cb_explore_adf::cover::setup(), VW::cb_explore_adf::regcb::setup(), VW::shared_feature_merger::shared_feature_merger_setup(), Search::single_prediction_LDF(), and warm_cb_setup().

{
  if (l->is_multiline)  // Tried to use a singleline reduction as a multiline reduction
    return (multi_learner*)(l);
  THROW("Tried to use a singleline reduction as a multiline reduction");
}

as_singleline()

template<class T , class E >

single_learner* LEARNER::as_singleline

(

learner< T, E > *

l

)

Definition at line 476 of file learner.h.

References LEARNER::learner< T, E >::is_multiline, and THROW.

Referenced by active_cover_setup(), active_setup(), audit_regressor_setup(), autolink_setup(), baseline_setup(), binary_setup(), boosting_setup(), bs_setup(), cb_algs_setup(), cb_explore_setup(), cbify_setup(), classweight_setup(), confidence_setup(), cs_active_setup(), CSOAA::csldf_setup(), CSOAA::csoaa_setup(), ect_setup(), ExpReplay::expreplay_setup(), vw::finish_example(), Search::generate_training_example(), interact_setup(), vw::learn(), learn_ex(), log_multi_setup(), lrq_setup(), lrqfa_setup(), marginal_setup(), memory_tree_setup(), mf_setup(), multilabel_oaa_setup(), mwt_setup(), nn_setup(), oaa_setup(), vw::predict(), recall_tree_setup(), scorer_setup(), sensitivity(), Search::single_prediction_notLDF(), stagewise_poly_setup(), topk_setup(), VW_Predict(), and VW_PredictCostSensitive().

{
  if (!l->is_multiline)  // Tried to use a multiline reduction as a singleline reduction
    return (single_learner*)(l);
  THROW("Tried to use a multiline reduction as a singleline reduction");
}

decrement_offset() [1/2]

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

inline

Definition at line 120 of file learner.h.

References example_predict::ft_offset.

{
  assert(ex.ft_offset >= increment * i);
  ex.ft_offset -= static_cast<uint32_t>(increment * i);
}

decrement_offset() [2/2]

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

inline

Definition at line 126 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::learn(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::multipredict(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::predict(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::sensitivity(), and LEARNER::learner< CB_EXPLORE::cb_explore, example >::update().

{
  for (auto ec : ec_seq)
  {
    assert(ec->ft_offset >= increment * i);
    ec->ft_offset -= static_cast<uint32_t>(increment * i);
  }
}

drain_examples()

void LEARNER::drain_examples

(

vw &

all

)

Definition at line 89 of file learner.cc.

References vw::early_terminate, LEARNER::learner< T, E >::end_examples(), VW::finish_example(), VW::get_example(), vw::l, and vw::p.

Referenced by generic_driver().

{
  if (all.early_terminate)
  {  // drain any extra examples from parser.
    example* ec = nullptr;
    while ((ec = VW::get_example(all.p)) != nullptr) VW::finish_example(all, *ec);
  }
  all.l->end_examples();
}

end_pass()

void LEARNER::end_pass	(	example &	ec,
		vw &	all
	)

Definition at line 44 of file learner.cc.

References vw::current_pass, LEARNER::learner< T, E >::end_pass(), VW::finish_example(), and vw::l.

Referenced by bfgs_setup(), CSOAA::csldf_setup(), ftrl_setup(), gd_mf_setup(), memory_tree_setup(), nn_setup(), GD::setup(), Search::setup(), and stagewise_poly_setup().

{
  all.current_pass++;
  all.l->end_pass();

  VW::finish_example(all, ec);
}

example_is_newline_not_header()

bool LEARNER::example_is_newline_not_header ( example &  ec, vw &  all  )

inline

Definition at line 68 of file learner.cc.

References label_type::ccb, CB::ec_is_example_header(), CCB::ec_is_example_header(), example_is_newline(), and vw::label_type.

Referenced by LEARNER::multi_example_handler< context_type >::complete_multi_ex(), EXPLORE_EVAL::output_example(), CB_ADF::output_example(), and CB_ADF::output_rank_example().

{
  // If we are using CCB, test against CCB implementation otherwise fallback to previous behavior.
  bool is_header = false;
  if (all.label_type == label_type::ccb)
  {
    is_header = CCB::ec_is_example_header(ec);
  }
  else
  {
    is_header = CB::ec_is_example_header(ec);
  }

  return example_is_newline(ec) && !is_header;
}

generic_driver() [1/3]

template<typename context_type >

void LEARNER::generic_driver	(	ready_examples_queue &	examples,
		context_type &	context
	)

Definition at line 253 of file learner.cc.

References drain_examples(), and process_examples().

Referenced by main(), and VW_Finish_Passes().

{
  if (context.get_master().l->is_multiline)
  {
    using handler_type = multi_example_handler<context_type>;
    handler_type handler(context);
    process_examples(examples, handler);
  }
  else
  {
    using handler_type = single_example_handler<context_type>;
    handler_type handler(context);
    process_examples(examples, handler);
  }
  drain_examples(context.get_master());
}

generic_driver() [2/3]

void LEARNER::generic_driver

(

vw &

all

)

Definition at line 270 of file learner.cc.

References generic_driver().

{
  single_instance_context context(all);
  ready_examples_queue examples(all);
  generic_driver(examples, context);
}

generic_driver() [3/3]

void LEARNER::generic_driver

(

const std::vector< vw *> &

all

)

Definition at line 277 of file learner.cc.

References LEARNER::multi_instance_context::get_master().

Referenced by generic_driver().

{
  multi_instance_context context(all);
  ready_examples_queue examples(context.get_master());
  generic_driver(examples, context);
}

generic_driver_onethread()

template<typename handler_type >

void LEARNER::generic_driver_onethread

(

vw &

all

)

Definition at line 285 of file learner.cc.

References LEARNER::learner< T, E >::end_examples(), parser::end_parsed_examples, LEARNER::learner< T, E >::is_multiline, vw::l, vw::p, parse_dispatch(), and process_examples().

Referenced by main().

{
  single_instance_context context(all);
  handler_type handler(context);
  auto multi_ex_fptr = [&handler](vw& all, v_array<example*> examples) {
    all.p->end_parsed_examples += examples.size();  // divergence: lock & signal
    custom_examples_queue examples_queue(examples);
    process_examples(examples_queue, handler);
  };
  parse_dispatch(all, multi_ex_fptr);
  all.l->end_examples();
}

increment_offset() [1/2]

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

inline

Definition at line 110 of file learner.h.

References example_predict::ft_offset.

{
  ex.ft_offset += static_cast<uint32_t>(increment * i);
}

increment_offset() [2/2]

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

inline

Definition at line 115 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::learn(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::multipredict(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::predict(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::sensitivity(), and LEARNER::learner< CB_EXPLORE::cb_explore, example >::update().

{
  for (auto ec : ec_seq) ec->ft_offset += static_cast<uint32_t>(increment * i);
}

init_cost_sensitive_learner()

template<class T , class E , class L >

learner<T, E>& LEARNER::init_cost_sensitive_learner	(	free_ptr< T > &	dat,
		L *	base,
		void(*)(T &, L &, E &)	learn,
		void(*)(T &, L &, E &)	predict,
		parser *	p,
		size_t	ws,
		prediction_type::prediction_type_t	pred_type = prediction_type::multiclass
	)

Definition at line 450 of file learner.h.

References COST_SENSITIVE::cs_label, COST_SENSITIVE::finish_example(), init_learner(), learn(), parser::lp, predict(), and LEARNER::learner< T, E >::set_finish_example().

Referenced by cbify_setup(), and warm_cb_setup().

{
  learner<T, E>& l = learner<T, E>::init_learner(dat.get(), base, learn, predict, ws, pred_type);
  dat.release();
  l.set_finish_example(COST_SENSITIVE::finish_example);
  p->lp = COST_SENSITIVE::cs_label;
  return l;
}

init_learner() [1/7]

template<class T , class E , class L >

learner<T, E>& LEARNER::init_learner	(	free_ptr< T > &	dat,
		L *	base,
		void(*)(T &, L &, E &)	learn,
		void(*)(T &, L &, E &)	predict,
		size_t	ws,
		prediction_type::prediction_type_t	pred_type
	)

Definition at line 369 of file learner.h.

References init_learner(), learn(), and predict().

Referenced by active_cover_setup(), active_setup(), audit_regressor_setup(), autolink_setup(), baseline_setup(), bfgs_setup(), binary_setup(), bs_setup(), cb_adf_setup(), cb_algs_setup(), cb_explore_setup(), cb_sample_setup(), cbifyldf_setup(), CCB::ccb_explore_adf_setup(), confidence_setup(), cs_active_setup(), CSOAA::csldf_setup(), CSOAA::csoaa_setup(), explore_eval_setup(), ExpReplay::expreplay_setup(), ftrl_setup(), gd_mf_setup(), interact_setup(), kernel_svm_setup(), lda_setup(), lrq_setup(), lrqfa_setup(), marginal_setup(), memory_tree_setup(), mf_setup(), multilabel_oaa_setup(), mwt_setup(), nn_setup(), noop_setup(), OjaNewton_setup(), print_setup(), scorer_setup(), sender_setup(), VW::cb_explore_adf::softmax::setup(), VW::cb_explore_adf::greedy::setup(), VW::cb_explore_adf::first::setup(), VW::cb_explore_adf::bag::setup(), VW::cb_explore_adf::cover::setup(), VW::cb_explore_adf::regcb::setup(), GD::setup(), Search::setup(), VW::shared_feature_merger::shared_feature_merger_setup(), stagewise_poly_setup(), svrg_setup(), and topk_setup().

{
  auto ret = &learner<T, E>::init_learner(dat.get(), base, learn, predict, ws, pred_type);

  dat.release();
  return *ret;
}

init_learner() [2/7]

template<class T , class E , class L >

learner<T, E>& LEARNER::init_learner	(	free_ptr< T > &	dat,
		void(*)(T &, L &, E &)	learn,
		void(*)(T &, L &, E &)	predict,
		size_t	params_per_weight
	)

Definition at line 380 of file learner.h.

References init_learner(), learn(), predict(), and prediction_type::scalar.

{
  auto ret =
      &learner<T, E>::init_learner(dat.get(), (L*)nullptr, learn, predict, params_per_weight, prediction_type::scalar);

  dat.release();
  return *ret;
}

init_learner() [3/7]

template<class T , class E , class L >

learner<T, E>& LEARNER::init_learner	(	void(*)(T &, L &, E &)	predict,
		size_t	params_per_weight
	)

Definition at line 392 of file learner.h.

References init_learner(), predict(), and prediction_type::scalar.

{
  return learner<T, E>::init_learner(
      nullptr, (L*)nullptr, predict, predict, params_per_weight, prediction_type::scalar);
}

init_learner() [4/7]

template<class T , class E , class L >

learner<T, E>& LEARNER::init_learner	(	free_ptr< T > &	dat,
		void(*)(T &, L &, E &)	learn,
		void(*)(T &, L &, E &)	predict,
		size_t	params_per_weight,
		prediction_type::prediction_type_t	pred_type
	)

Definition at line 399 of file learner.h.

References init_learner(), learn(), and predict().

{
  auto ret = &learner<T, E>::init_learner(dat.get(), (L*)nullptr, learn, predict, params_per_weight, pred_type);
  dat.release();
  return *ret;
}

init_learner() [5/7]

template<class T , class E , class L >

learner<T, E>& LEARNER::init_learner	(	free_ptr< T > &	dat,
		L *	base,
		void(*)(T &, L &, E &)	learn,
		void(*)(T &, L &, E &)	predict,
		size_t	ws
	)

Definition at line 409 of file learner.h.

References init_learner(), learn(), and predict().

{
  auto ret = &learner<T, E>::init_learner(dat.get(), base, learn, predict, ws, base->pred_type);

  dat.release();
  return *ret;
}

init_learner() [6/7]

template<class T , class E , class L >

learner<T, E>& LEARNER::init_learner	(	free_ptr< T > &	dat,
		L *	base,
		void(*)(T &, L &, E &)	learn,
		void(*)(T &, L &, E &)	predict
	)

Definition at line 420 of file learner.h.

References init_learner(), learn(), and predict().

{
  auto ret = &learner<T, E>::init_learner(dat.get(), base, learn, predict, 1, base->pred_type);

  dat.release();
  return *ret;
}

init_learner() [7/7]

template<class T , class E , class L >

learner<T, E>& LEARNER::init_learner	(	L *	base,
		void(*)(T &, L &, E &)	learn,
		void(*)(T &, L &, E &)	predict
	)

Definition at line 430 of file learner.h.

References learn(), and predict().

Referenced by init_cost_sensitive_learner(), init_learner(), and init_multiclass_learner().

{
  return learner<T, E>::init_learner(nullptr, base, learn, predict, 1, base->pred_type);
}

init_multiclass_learner()

template<class T , class E , class L >

learner<T, E>& LEARNER::init_multiclass_learner	(	free_ptr< T > &	dat,
		L *	base,
		void(*)(T &, L &, E &)	learn,
		void(*)(T &, L &, E &)	predict,
		parser *	p,
		size_t	ws,
		prediction_type::prediction_type_t	pred_type = prediction_type::multiclass
	)

Definition at line 437 of file learner.h.

References init_learner(), learn(), parser::lp, MULTICLASS::mc_label, predict(), and LEARNER::learner< T, E >::set_finish_example().

Referenced by cbify_setup(), ect_setup(), log_multi_setup(), memory_tree_setup(), oaa_setup(), recall_tree_setup(), and warm_cb_setup().

{
  learner<T, E>& l = learner<T, E>::init_learner(dat.get(), base, learn, predict, ws, pred_type);

  dat.release();
  l.set_finish_example(MULTICLASS::finish_example<T>);
  p->lp = MULTICLASS::mc_label;
  return l;
}

is_save_cmd()

bool LEARNER::is_save_cmd ( example *  ec )

inline

Definition at line 84 of file learner.cc.

References v_array< T >::begin(), v_array< T >::size(), and example::tag.

Referenced by LEARNER::single_example_handler< context_type >::on_example(), and LEARNER::multi_example_handler< context_type >::try_complete_multi_ex().

{
  return (ec->tag.size() >= 4) && (0 == strncmp((const char*)ec->tag.begin(), "save", 4));
}

learn_ex()

void LEARNER::learn_ex	(	example &	ec,
		vw &	all
	)

Definition at line 32 of file learner.cc.

References as_singleline(), LEARNER::learner< T, E >::finish_example(), vw::l, and vw::learn().

{
  all.learn(ec);
  as_singleline(all.l)->finish_example(all, ec);
}

learn_multi_ex()

void LEARNER::learn_multi_ex	(	multi_ex &	ec_seq,
		vw &	all
	)

Definition at line 38 of file learner.cc.

References as_multiline(), LEARNER::learner< T, E >::finish_example(), vw::l, and vw::learn().

{
  all.learn(ec_seq);
  as_multiline(all.l)->finish_example(all, ec_seq);
}

make_base()

template<class T , class E >

base_learner* LEARNER::make_base

(

learner< T, E > &

base

)

Definition at line 462 of file learner.h.

Referenced by active_cover_setup(), active_setup(), autolink_setup(), baseline_setup(), bfgs_setup(), binary_setup(), boosting_setup(), bs_setup(), cb_adf_setup(), cb_algs_setup(), cb_explore_setup(), cb_sample_setup(), cbify_setup(), cbifyldf_setup(), CCB::ccb_explore_adf_setup(), classweight_setup(), confidence_setup(), cs_active_setup(), CSOAA::csldf_setup(), CSOAA::csoaa_setup(), ect_setup(), explore_eval_setup(), ExpReplay::expreplay_setup(), ftrl_setup(), gd_mf_setup(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner(), interact_setup(), kernel_svm_setup(), lda_setup(), log_multi_setup(), lrq_setup(), lrqfa_setup(), marginal_setup(), memory_tree_setup(), mf_setup(), multilabel_oaa_setup(), mwt_setup(), nn_setup(), noop_setup(), oaa_setup(), OjaNewton_setup(), print_setup(), recall_tree_setup(), scorer_setup(), sender_setup(), VW::cb_explore_adf::softmax::setup(), VW::cb_explore_adf::greedy::setup(), VW::cb_explore_adf::first::setup(), VW::cb_explore_adf::bag::setup(), VW::cb_explore_adf::cover::setup(), VW::cb_explore_adf::regcb::setup(), GD::setup(), Search::setup(), VW::shared_feature_merger::shared_feature_merger_setup(), stagewise_poly_setup(), svrg_setup(), topk_setup(), and warm_cb_setup().

{
  return (base_learner*)(&base);
}

multiline_learn_or_predict()

template<bool is_learn>

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

Definition at line 484 of file learner.h.

References LEARNER::learner< T, E >::learn(), and LEARNER::learner< T, E >::predict().

{
  std::vector<uint64_t> saved_offsets;
  for (auto ec : examples)
  {
    saved_offsets.push_back(ec->ft_offset);
    ec->ft_offset = offset;
  }

  if (is_learn)
    base.learn(examples, id);
  else
    base.predict(examples, id);

  for (size_t i = 0; i < examples.size(); i++) examples[i]->ft_offset = saved_offsets[i];
}

noop()

void LEARNER::noop ( void *  )

inline

Definition at line 102 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner(), and noop_setup().

{}

noop_sensitivity()

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

inline

Definition at line 103 of file learner.h.

References recur_sensitivity().

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

{
  std::cout << std::endl;
  return 0.;
}

noop_sl()

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

inline

Definition at line 101 of file learner.h.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::init_learner().

{}

process_examples()

template<typename queue_type , typename handler_type >

void LEARNER::process_examples	(	queue_type &	examples,
		handler_type &	handler
	)

Definition at line 245 of file learner.cc.

Referenced by generic_driver(), and generic_driver_onethread().

{
  example* ec;

  while ((ec = examples.pop()) != nullptr) handler.on_example(ec);
}

recur_sensitivity()

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

Definition at line 306 of file learner.cc.

References LEARNER::learner< T, E >::sensitivity().

Referenced by noop_sensitivity().

{ return base.sensitivity(ec); }

save()

void LEARNER::save	(	example &	ec,
		vw &	all
	)

Definition at line 52 of file learner.cc.

References v_array< T >::begin(), vw::final_regressor_name, VW::finish_example(), vw::quiet, save_predictor(), example::tag, and vw::trace_message.

{
  // save state command
  std::string final_regressor_name = all.final_regressor_name;

  if ((ec.tag).size() >= 6 && (ec.tag)[4] == '_')
    final_regressor_name = std::string(ec.tag.begin() + 5, (ec.tag).size() - 5);

  if (!all.quiet)
    all.trace_message << "saving regressor to " << final_regressor_name << std::endl;
  save_predictor(all, final_regressor_name, 0);

  VW::finish_example(all, ec);
}

tuple_dbf()

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

inline

Definition at line 51 of file learner.h.

References LEARNER::func_data::base, LEARNER::func_data::data, and LEARNER::func_data::func.

Referenced by LEARNER::learner< CB_EXPLORE::cb_explore, example >::set_end_examples(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::set_end_pass(), LEARNER::learner< CB_EXPLORE::cb_explore, example >::set_finish(), and LEARNER::learner< CB_EXPLORE::cb_explore, example >::set_init_driver().

{
  func_data foo;
  foo.data = data;
  foo.base = base;
  foo.func = func;
  return foo;
}