    // Copyright (C) 2011  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_STRUCTURAL_SEQUENCE_LABELING_TRAiNER_ABSTRACT_H__
    #ifdef DLIB_STRUCTURAL_SEQUENCE_LABELING_TRAiNER_ABSTRACT_H__

    #include "../algs.h"
    #include "../optimization.h"
    #include "structural_svm_sequence_labeling_problem_abstract.h"
    #include "sequence_labeler_abstract.h"

    namespace dlib
    {

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

        template <
            typename feature_extractor
            >
        class structural_sequence_labeling_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_labeler_abstract.h.

                WHAT THIS OBJECT REPRESENTS
                    This object is a tool for learning to do sequence labeling based
                    on a set of training data.  The training procedure produces a
                    sequence_labeler object which can be used to predict the labels of
                    new data sequences.

                    Note that this is just a convenience wrapper around the
                    structural_svm_sequence_labeling_problem to make it look
                    similar to all the other trainers in dlib.
            !*/

        public:
            typedef typename feature_extractor::sequence_type sample_sequence_type;
            typedef std::vector<unsigned long> labeled_sequence_type;
            typedef sequence_labeler<feature_extractor> trained_function_type;

            structural_sequence_labeling_trainer (
            );
            /*!
                ensures
                    - #get_c() == 100
                    - this object isn't verbose
                    - #get_epsilon() == 0.1
                    - #get_num_threads() == 2
                    - #get_max_cache_size() == 5
                    - #get_feature_extractor() == a default initialized feature_extractor
            !*/

            explicit structural_sequence_labeling_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() == 5
                    - #get_feature_extractor() == fe
            !*/

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

            unsigned long num_labels (
            ) const;
            /*!
                ensures
                    - returns get_feature_extractor().num_labels()
                      (i.e. returns the number of possible output labels for each
                      element of a sequence)
            !*/

            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
            !*/

            const 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 labeling 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_labeler on
                      each training sample, over and over.  To speed this up, it is possible to
                      cache the results of these labeler 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.
            !*/

            double get_loss (
                unsigned long label
            ) const;
            /*!
                requires
                    - label < num_labels()
                ensures
                    - returns the loss incurred when a sequence element with the given
                      label is misclassified.  This value controls how much we care about
                      correctly classifying this type of label.  Larger loss values indicate
                      that we care more strongly than smaller values.
            !*/

            void set_loss (
                unsigned long label,
                double value
            );
            /*!
                requires
                    - label < num_labels()
                    - value >= 0
                ensures
                    - #get_loss(label) == value
            !*/

            const sequence_labeler<feature_extractor> train(
                const std::vector<sample_sequence_type>& x,
                const std::vector<labeled_sequence_type>& y
            ) const;
            /*!
                requires
                    - is_sequence_labeling_problem(x, y) == true
                    - contains_invalid_labeling(get_feature_extractor(), x, y) == false
                    - for all valid i and j: y[i][j] < num_labels()
                ensures
                    - Uses the structural_svm_sequence_labeling_problem to train a
                      sequence_labeler on the given x/y training pairs.  The idea is
                      to learn to predict a y given an input x.
                    - returns a function F with the following properties:
                        - F(new_x) == A sequence of predicted labels for the elements of new_x.
                        - F(new_x).size() == new_x.size()
                        - for all valid i:
                            - F(new_x)[i] == the predicted label of new_x[i]
            !*/

        };

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

    }

    #endif // DLIB_STRUCTURAL_SEQUENCE_LABELING_TRAiNER_ABSTRACT_H__