Public Member Functions
virtual double	apply_pairwise_sgd_step (size_t thread_idx, const std::vector< ml_data_entry > &x_positive, const std::vector< ml_data_entry > &x_negative, double step_size)=0

Private Member Functions
virtual void	setup (const v2::ml_data &train_data, const std::map< std::string, flexible_type > &options)

virtual void	setup_iteration (size_t iteration, double step_size)

virtual void	finalize_iteration ()

virtual double	l2_regularization_factor () const

virtual double	max_step_size () const

virtual bool	state_is_numerically_stable () const

virtual void	setup_optimization (size_t random_seed=size_t(-1), bool _in_trial_mode=false)=0

virtual double	calculate_loss (const v2::ml_data &data) const =0

virtual double	reported_loss_value (double accumulative_loss) const =0

virtual std::string	reported_loss_name () const =0

virtual double	current_regularization_penalty () const =0

virtual double	apply_sgd_step (size_t thread_idx, const std::vector< v2::ml_data_entry > &x, double y, double step_size)=0

Detailed Description

The base class for the ranking SGD interfaces. This interface governs all the interactions between the sgd solvers and the model.

To use the ranking sgd solver, implement the following options.

Definition at line 20 of file sgd_ranking_interface.hpp.

Member Function Documentation

virtual double turi::sgd::sgd_ranking_interface::apply_pairwise_sgd_step	(	size_t	thread_idx,
		const std::vector< ml_data_entry > &	x_positive,
		const std::vector< ml_data_entry > &	x_negative,
		double	step_size
	)

pure virtual

Apply two sgd steps to the code to increase the predicted value of x_positive and decrease the predicted value of x_negative.

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