Typedefs
using	void_func = std::function< void(void)>

using	example_func = std::function< void(polymorphic_ex ex)>

using	multipredict_func = std::function< void(polymorphic_ex ex, size_t count, size_t step, polyprediction *pred, bool finalize_predictions)>

using	sensitivity_func = std::function< float(example &ex)>

using	save_load_func = std::function< void(io_buf &, bool read, bool text)>

using	pre_save_load_func = std::function< void(VW::workspace &all)>

using	save_metric_func = std::function< void(metric_sink &metrics)>

using	finish_example_func = std::function< void(VW::workspace &, polymorphic_ex ex)>

using	update_stats_func = std::function< void(const VW::workspace &, VW::shared_data &sd, const polymorphic_ex ex, VW::io::logger &logger)>

using	output_example_prediction_func = std::function< void(VW::workspace &, const polymorphic_ex ex, VW::io::logger &logger)>

using	print_update_func = std::function< void(VW::workspace &, VW::shared_data &sd, const polymorphic_ex ex, VW::io::logger &logger)>

using	cleanup_example_func = std::function< void(polymorphic_ex ex)>

using	merge_func = std::function< void(const std::vector< float > &per_model_weighting, const std::vector< const void * > &all_data, void *output_data)>

using	merge_with_all_func = std::function< void(const std::vector< float > &per_model_weighting, const std::vector< const VW::workspace * > &all_workspaces, const std::vector< const void * > &all_data, VW::workspace &output_workspace, void *output_data)>

using	add_subtract_func = std::function< void(const void data1, const void data2, void *data_out)>

using	add_subtract_with_all_func = std::function< void(const VW::workspace &ws1, const void data1, const VW::workspace &ws2, const void data2, VW::workspace &ws_out, void *data_out)>

Functions
void	debug_increment_depth (polymorphic_ex ex)

void	debug_decrement_depth (polymorphic_ex ex)

void	increment_offset (polymorphic_ex ex, const size_t feature_width_below, const size_t i)

void	decrement_offset (polymorphic_ex ex, const size_t feature_width_below, const size_t i)

void	learner_build_diagnostic (VW::string_view this_name, VW::string_view base_name, prediction_type_t in_pred_type, prediction_type_t base_out_pred_type, label_type_t out_label_type, label_type_t base_in_label_type, details::merge_func merge_f, details::merge_with_all_func merge_with_all_f)

float	recur_sensitivity (learner &base, example &ec)

template<typename DataT >
float	recur_sensitivity (DataT &, learner &base, example &ec)

Typedef Documentation

◆ add_subtract_func

using VW::LEARNER::details::add_subtract_func = typedef std::function<void(const void* data1, const void* data2, void* data_out)>

◆ add_subtract_with_all_func

using VW::LEARNER::details::add_subtract_with_all_func = typedef std::function<void(const VW::workspace& ws1, const void* data1, const VW::workspace& ws2, const void* data2, VW::workspace& ws_out, void* data_out)>

◆ cleanup_example_func

using VW::LEARNER::details::cleanup_example_func = typedef std::function<void(polymorphic_ex ex)>

◆ example_func

using VW::LEARNER::details::example_func = typedef std::function<void(polymorphic_ex ex)>

◆ finish_example_func

using VW::LEARNER::details::finish_example_func = typedef std::function<void(VW::workspace&, polymorphic_ex ex)>

◆ merge_func

using VW::LEARNER::details::merge_func = typedef std::function<void( const std::vector<float>& per_model_weighting, const std::vector<const void*>& all_data, void* output_data)>

◆ merge_with_all_func

using VW::LEARNER::details::merge_with_all_func = typedef std::function<void(const std::vector<float>& per_model_weighting, const std::vector<const VW::workspace*>& all_workspaces, const std::vector<const void*>& all_data, VW::workspace& output_workspace, void* output_data)>

◆ multipredict_func

using VW::LEARNER::details::multipredict_func = typedef std::function<void(polymorphic_ex ex, size_t count, size_t step, polyprediction* pred, bool finalize_predictions)>

◆ output_example_prediction_func

using VW::LEARNER::details::output_example_prediction_func = typedef std::function<void(VW::workspace&, const polymorphic_ex ex, VW::io::logger& logger)>

◆ pre_save_load_func

using VW::LEARNER::details::pre_save_load_func = typedef std::function<void(VW::workspace& all)>

◆ print_update_func

using VW::LEARNER::details::print_update_func = typedef std::function<void(VW::workspace&, VW::shared_data& sd, const polymorphic_ex ex, VW::io::logger& logger)>

◆ save_load_func

using VW::LEARNER::details::save_load_func = typedef std::function<void(io_buf&, bool read, bool text)>

◆ save_metric_func

using VW::LEARNER::details::save_metric_func = typedef std::function<void(metric_sink& metrics)>

◆ sensitivity_func

using VW::LEARNER::details::sensitivity_func = typedef std::function<float(example& ex)>

◆ update_stats_func

using VW::LEARNER::details::update_stats_func = typedef std::function<void(const VW::workspace&, VW::shared_data& sd, const polymorphic_ex ex, VW::io::logger& logger)>

◆ void_func

using VW::LEARNER::details::void_func = typedef std::function<void(void)>

Function Documentation

◆ debug_decrement_depth()

void VW::LEARNER::details::debug_decrement_depth ( polymorphic_ex ex )

◆ debug_increment_depth()

void VW::LEARNER::details::debug_increment_depth ( polymorphic_ex ex )

◆ decrement_offset()

void VW::LEARNER::details::decrement_offset	(	polymorphic_ex	ex,
		const size_t	feature_width_below,
		const size_t	i
	)

◆ increment_offset()

void VW::LEARNER::details::increment_offset	(	polymorphic_ex	ex,
		const size_t	feature_width_below,
		const size_t	i
	)

◆ learner_build_diagnostic()

void VW::LEARNER::details::learner_build_diagnostic	(	VW::string_view	this_name,
		VW::string_view	base_name,
		prediction_type_t	in_pred_type,
		prediction_type_t	base_out_pred_type,
		label_type_t	out_label_type,
		label_type_t	base_in_label_type,
		details::merge_func	merge_f,
		details::merge_with_all_func	merge_with_all_f
	)

◆ recur_sensitivity() [1/2]

template<typename DataT >

float VW::LEARNER::details::recur_sensitivity	(	DataT &	,
		learner &	base,
		example &	ec
	)

inline

◆ recur_sensitivity() [2/2]

float VW::LEARNER::details::recur_sensitivity	(	learner &	base,
		example &	ec
	)