    // Copyright (C) 2010  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_ONE_VS_ONE_TRAiNER_ABSTRACT_Hh_
    #ifdef DLIB_ONE_VS_ONE_TRAiNER_ABSTRACT_Hh_

    #include "one_vs_one_decision_function_abstract.h"
    #include <vector>

    #include "../any/any_trainer_abstract.h"

    namespace dlib
    {

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

        template <
            typename any_trainer,
            typename label_type_ = double
            >
        class one_vs_one_trainer
        {
            /*!
                REQUIREMENTS ON any_trainer
                    must be an instantiation of the dlib::any_trainer template.

                REQUIREMENTS ON label_type_
                    label_type_ must be default constructable, copyable, and comparable using
                    operator < and ==.  It must also be possible to write it to an std::ostream
                    using operator<<.

                WHAT THIS OBJECT REPRESENTS
                    This object is a tool for turning a bunch of binary classifiers
                    into a multiclass classifier.  It does this by training the binary
                    classifiers in a one vs. one fashion.  That is, if you have N possible
                    classes then it trains N*(N-1)/2 binary classifiers which are then used
                    to vote on the identity of a test sample.

                    This object works with any kind of binary classification trainer object
                    capable of being assigned to an any_trainer object.  (e.g. the svm_nu_trainer)
            !*/

        public:

            typedef label_type_ label_type;

            typedef typename any_trainer::sample_type sample_type;
            typedef typename any_trainer::scalar_type scalar_type;
            typedef typename any_trainer::mem_manager_type mem_manager_type;

            typedef one_vs_one_decision_function<one_vs_one_trainer> trained_function_type;

            one_vs_one_trainer (
            );
            /*!
                ensures
                    - This object is properly initialized
                    - This object will not be verbose unless be_verbose() is called.
                    - No binary trainers are associated with *this.  I.e. you have to
                      call set_trainer() before calling train().
                    - #get_num_threads() == 4
            !*/

            void set_trainer (
                const any_trainer& trainer
            );
            /*!
                ensures
                    - sets the trainer used for all pairs of training.  Any previous
                      calls to set_trainer() are overridden by this function.  Even the
                      more specific set_trainer(trainer, l1, l2) form.
            !*/

            void set_trainer (
                const any_trainer& trainer,
                const label_type& l1,
                const label_type& l2
            );
            /*!
                requires
                    - l1 != l2
                ensures
                    - Sets the trainer object used to create a binary classifier to
                      distinguish l1 labeled samples from l2 labeled samples.
            !*/

            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_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.
            !*/

            struct invalid_label : public dlib::error
            {
                /*!
                    This is the exception thrown by the train() function below.
                !*/
                label_type l1, l2;
            };

            trained_function_type train (
                const std::vector<sample_type>& all_samples,
                const std::vector<label_type>& all_labels
            ) const;
            /*!
                requires
                    - is_learning_problem(all_samples, all_labels)
                ensures
                    - trains a bunch of binary classifiers in a one vs one fashion to solve the given
                      multiclass classification problem.
                    - returns a one_vs_one_decision_function F with the following properties:
                        - F contains all the learned binary classifiers and can be used to predict
                          the labels of new samples.
                        - if (new_x is a sample predicted to have a label of L) then
                            - F(new_x) == L
                        - F.get_labels() == select_all_distinct_labels(all_labels)
                        - F.number_of_classes() == select_all_distinct_labels(all_labels).size()
                throws
                    - invalid_label
                      This exception is thrown if there are labels in all_labels which don't have
                      any corresponding trainer object.  This will never happen if set_trainer(trainer)
                      has been called.  However, if only the set_trainer(trainer,l1,l2) form has been
                      used then this exception is thrown if not all necessary label pairs have been
                      given a trainer.

                      invalid_label::l1 and invalid_label::l2 will contain the label pair which is
                      missing a trainer object.  Additionally, the exception will contain an
                      informative error message available via invalid_label::what().
            !*/

        };

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

    }

    #endif // DLIB_ONE_VS_ONE_TRAiNER_ABSTRACT_Hh_