    // Copyright (C) 2012  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_STRUCTURAL_GRAPH_LABELING_tRAINER_ABSTRACT_H__
    #ifdef DLIB_STRUCTURAL_GRAPH_LABELING_tRAINER_ABSTRACT_H__

    #include "../algs.h"
    #include "../optimization.h"
    #include "structural_svm_graph_labeling_problem_abstract.h"
    #include "../graph_cuts/graph_labeler_abstract.h"

    namespace dlib
    {

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

        template <
            typename vector_type
            >
        class structural_graph_labeling_trainer
        {
            /*!
                REQUIREMENTS ON vector_type
                    - vector_type is a dlib::matrix capable of representing column
                      vectors or it is a sparse vector type as defined in dlib/svm/sparse_vector_abstract.h.

                WHAT THIS OBJECT REPRESENTS
                    This object is a tool for learning to solve a graph labeling problem based
                    on a training dataset of example labeled graphs.  The training procedure
                    produces a graph_labeler object which can be used to predict the labelings
                    of new graphs.

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

        public:
            typedef std::vector<bool> label_type;
            typedef graph_labeler<vector_type> trained_function_type;

            structural_graph_labeling_trainer (
            );
            /*!
                ensures
                    - #get_c() == 10
                    - this object isn't verbose
                    - #get_epsilon() == 0.1
                    - #get_num_threads() == 2
                    - #get_max_cache_size() == 5
                    - #get_loss_on_positive_class() == 1.0
                    - #get_loss_on_negative_class() == 1.0
            !*/

            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 labeling mistakes per
                      example graph 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 graph_labeler on each
                      training sample, over and over.  To speed this up, it is possible to
                      cache the results of these 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 graph_labeler.  Larger
                      values encourage exact fitting while smaller values of C may encourage
                      better generalization.
            !*/

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

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

            double get_loss_on_positive_class (
            ) const;
            /*!
                ensures
                    - returns the loss incurred when a graph node which is supposed to have
                      a label of true gets misclassified.  This value controls how much we care
                      about correctly classifying nodes which should be labeled as true.  Larger
                      loss values indicate that we care more strongly than smaller values.
            !*/

            double get_loss_on_negative_class (
            ) const;
            /*!
                ensures
                    - returns the loss incurred when a graph node which is supposed to have
                      a label of false gets misclassified.  This value controls how much we care
                      about correctly classifying nodes which should be labeled as false.  Larger
                      loss values indicate that we care more strongly than smaller values.
            !*/

            template <
                typename graph_type
                >
            const graph_labeler<vector_type> train (
                const dlib::array<graph_type>& samples,
                const std::vector<label_type>& labels
            ) const;
            /*!
                requires
                    - is_graph_labeling_problem(samples,labels) == true
                ensures
                    - Uses the structural_svm_graph_labeling_problem to train a graph_labeler
                      on the given samples/labels training pairs.  The idea is to learn to
                      predict a label given an input sample.
                    - The values of get_loss_on_positive_class() and get_loss_on_negative_class()
                      are used to determine how to value mistakes on each node during training.
                    - returns a function F with the following properties:
                        - F(new_sample) == The predicted labels for the nodes in the graph
                          new_sample.
            !*/

            template <
                typename graph_type
                >
            const graph_labeler<vector_type> train (
                const dlib::array<graph_type>& samples,
                const std::vector<label_type>& labels,
                const std::vector<std::vector<double> >& losses
            ) const;
            /*!
                requires
                    - is_graph_labeling_problem(samples,labels) == true
                    - if (losses.size() != 0) then
                        - sizes_match(labels, losses) == true
                        - all_values_are_nonnegative(losses) == true
                ensures
                    - Uses the structural_svm_graph_labeling_problem to train a graph_labeler
                      on the given samples/labels training pairs.  The idea is to learn to
                      predict a label given an input sample.
                    - returns a function F with the following properties:
                        - F(new_sample) == The predicted labels for the nodes in the graph
                          new_sample.
                    - if (losses.size() == 0) then
                        - The values of get_loss_on_positive_class() and get_loss_on_negative_class()
                          are used to determine how to value mistakes on each node during training.
                        - The losses argument is effectively ignored if its size is zero.
                    - else
                        - Each node in the training data has its own loss value defined by the
                          corresponding entry of losses.  In particular, this means that the
                          node with label labels[i][j] incurs a loss of losses[i][j] if it is
                          incorrectly labeled.
                        - The get_loss_on_positive_class() and get_loss_on_negative_class()
                          parameters are ignored.  Only losses is used in this case.
            !*/
        };

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

    }

    #endif // DLIB_STRUCTURAL_GRAPH_LABELING_tRAINER_ABSTRACT_H__