    // Copyright (C) 2013  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_STRUCTURAL_SEQUENCE_sEGMENTATION_TRAINER_ABSTRACT_Hh_
    #ifdef DLIB_STRUCTURAL_SEQUENCE_sEGMENTATION_TRAINER_ABSTRACT_Hh_

    #include "sequence_segmenter_abstract.h"

    namespace dlib
    {

    // ----------------------------------------------------------------------------------------

        template <
            typename feature_extractor
            >
        class structural_sequence_segmentation_trainer
        {
            /*!
                REQUIREMENTS ON feature_extractor
                    It must be an object that implements an interface compatible with
                    the example_feature_extractor defined in dlib/svm/sequence_segmenter_abstract.h.

                WHAT THIS OBJECT REPRESENTS
                    This object is a tool for learning to do sequence segmentation based on a
                    set of training data.  The training procedure produces a sequence_segmenter
                    object which can be used to identify the sub-segments of new data
                    sequences.

                    This object internally uses the structural_sequence_labeling_trainer to
                    solve the learning problem.
            !*/

        public:

            typedef typename feature_extractor::sequence_type sample_sequence_type;
            typedef std::vector<std::pair<unsigned long, unsigned long> > segmented_sequence_type;

            typedef sequence_segmenter<feature_extractor> trained_function_type;

            structural_sequence_segmentation_trainer (
            );
            /*!
                ensures
                    - #get_c() == 100
                    - this object isn't verbose
                    - #get_epsilon() == 0.1
                    - #get_num_threads() == 2
                    - #get_max_cache_size() == 40
                    - #get_feature_extractor() == a default initialized feature_extractor
                    - #get_loss_per_missed_segment() == 1
                    - #get_loss_per_false_alarm() == 1
            !*/

            explicit structural_sequence_segmentation_trainer (
                const feature_extractor& fe
            );
            /*!
                ensures
                    - #get_c() == 100
                    - this object isn't verbose
                    - #get_epsilon() == 0.1
                    - #get_num_threads() == 2
                    - #get_max_cache_size() == 40
                    - #get_feature_extractor() == fe
                    - #get_loss_per_missed_segment() == 1
                    - #get_loss_per_false_alarm() == 1
            !*/

            const feature_extractor& get_feature_extractor (
            ) const;
            /*!
                ensures
                    - returns the feature extractor used by this object
            !*/

            void set_num_threads (
                unsigned long num
            );
            /*!
                ensures
                    - #get_num_threads() == num
            !*/

            unsigned long get_num_threads (
            ) const;
            /*!
                ensures
                    - returns the number of threads used during training.  You should
                      usually set this equal to the number of processing cores on your
                      machine.
            !*/

            void set_epsilon (
                double eps_
            );
            /*!
                requires
                    - eps > 0
                ensures
                    - #get_epsilon() == eps
            !*/

            double get_epsilon (
            ) const;
            /*!
                ensures
                    - returns the error epsilon that determines when training should stop.
                      Smaller values may result in a more accurate solution but take longer
                      to train.  You can think of this epsilon value as saying "solve the
                      optimization problem until the average number of segmentation mistakes
                      per training sample is within epsilon of its optimal value".
            !*/

            void set_max_cache_size (
                unsigned long max_size
            );
            /*!
                ensures
                    - #get_max_cache_size() == max_size
            !*/

            unsigned long get_max_cache_size (
            ) const;
            /*!
                ensures
                    - During training, this object basically runs the sequence_segmenter on
                      each training sample, over and over.  To speed this up, it is possible to
                      cache the results of these segmenter invocations.  This function returns
                      the number of cache elements per training sample kept in the cache.  Note
                      that a value of 0 means caching is not used at all.
            !*/

            void be_verbose (
            );
            /*!
                ensures
                    - This object will print status messages to standard out so that a user can
                      observe the progress of the algorithm.
            !*/

            void be_quiet (
            );
            /*!
                ensures
                    - this object will not print anything to standard out
            !*/

            void set_oca (
                const oca& item
            );
            /*!
                ensures
                    - #get_oca() == item
            !*/

            const oca get_oca (
            ) const;
            /*!
                ensures
                    - returns a copy of the optimizer used to solve the structural SVM problem.
            !*/

            void set_c (
                double C
            );
            /*!
                requires
                    - C > 0
                ensures
                    - #get_c() = C
            !*/

            double get_c (
            ) const;
            /*!
                ensures
                    - returns the SVM regularization parameter.  It is the parameter that
                      determines the trade-off between trying to fit the training data (i.e.
                      minimize the loss) or allowing more errors but hopefully improving the
                      generalization of the resulting sequence labeler.  Larger values
                      encourage exact fitting while smaller values of C may encourage better
                      generalization.
            !*/

            void set_loss_per_missed_segment (
                double loss
            );
            /*!
                requires
                    - loss >= 0
                ensures
                    - #get_loss_per_missed_segment() == loss
            !*/

            double get_loss_per_missed_segment (
            ) const;
            /*!
                ensures
                    - returns the amount of loss incurred for failing to detect a segment.  The
                      larger the loss the more important it is to detect all the segments.
            !*/

            void set_loss_per_false_alarm (
                double loss
            );
            /*!
                requires
                    - loss >= 0
                ensures
                    - #get_loss_per_false_alarm() == loss
            !*/

            double get_loss_per_false_alarm (
            ) const;
            /*!
                ensures
                    - returns the amount of loss incurred for outputting a false detection. The
                      larger the loss the more important it is to avoid outputting false
                      detections.
            !*/

            const sequence_segmenter<feature_extractor> train(
                const std::vector<sample_sequence_type>& x,
                const std::vector<segmented_sequence_type>& y
            ) const;
            /*!
                requires
                    - is_sequence_segmentation_problem(x, y) == true
                ensures
                    - Uses the given training data to learn to do sequence segmentation.  That
                      is, this function will try to find a sequence_segmenter capable of
                      predicting y[i] when given x[i] as input.  Moreover, it should also be
                      capable of predicting the segmentation of new input sequences.  Or in
                      other words, the learned sequence_segmenter should also generalize to new
                      data outside the training dataset.
            !*/

        };

    // ----------------------------------------------------------------------------------------

    }

    #endif // DLIB_STRUCTURAL_SEQUENCE_sEGMENTATION_TRAINER_ABSTRACT_Hh_