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

    #include "one_vs_one_trainer_abstract.h"

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

    #include "../unordered_pair.h"
    #include "multiclass_tools.h"

    #include <sstream>
    #include <iostream>

    #include "../any.h"
    #include <map>
    #include <set>

    namespace dlib
    {

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

        template <
            typename any_trainer,
            typename label_type_ = double
            >
        class one_vs_one_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 (
            ) :
                verbose(false)
            {}

            void set_trainer (
                const any_trainer& trainer
            )
            {
                default_trainer = trainer;
                trainers.clear();
            }

            void set_trainer (
                const any_trainer& trainer,
                const label_type& l1,
                const label_type& l2
            )
            {
                trainers[make_unordered_pair(l1,l2)] = trainer;
            }

            void be_verbose (
            )
            {
                verbose = true;
            }

            void be_quiet (
            )
            {
                verbose = false;
            }

            struct invalid_label : public dlib::error
            {
                invalid_label(const std::string& msg, const label_type& l1_, const label_type& l2_
                    ) : dlib::error(msg), l1(l1_), l2(l2_) {};

                virtual ~invalid_label(
                ) throw() {}

                label_type l1, l2;
            };

            trained_function_type train (
                const std::vector<sample_type>& all_samples,
                const std::vector<label_type>& all_labels
            ) const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(is_learning_problem(all_samples,all_labels),
                    "\t trained_function_type one_vs_one_trainer::train(all_samples,all_labels)"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t all_samples.size():     " << all_samples.size()
                    << "\n\t all_labels.size():      " << all_labels.size()
                    );

                const std::vector<label_type> distinct_labels = select_all_distinct_labels(all_labels);

                std::vector<sample_type> samples;
                std::vector<scalar_type> labels;

                typename trained_function_type::binary_function_table dfs;

                for (unsigned long i = 0; i < distinct_labels.size(); ++i)
                {
                    for (unsigned long j = i+1; j < distinct_labels.size(); ++j)
                    {
                        samples.clear();
                        labels.clear();

                        const unordered_pair<label_type> p(distinct_labels[i], distinct_labels[j]);

                        // pick out the samples corresponding to these two classes
                        for (unsigned long k = 0; k < all_samples.size(); ++k)
                        {
                            if (all_labels[k] == p.first)
                            {
                                samples.push_back(all_samples[k]);
                                labels.push_back(+1);
                            }
                            else if (all_labels[k] == p.second)
                            {
                                samples.push_back(all_samples[k]);
                                labels.push_back(-1);
                            }
                        }

                        if (verbose)
                        {
                            std::cout << "Training classifier for " << p.first << " vs. " << p.second << std::endl;
                        }

                        // now train a binary classifier using the samples we selected
                        const typename binary_function_table::const_iterator itr = trainers.find(p);

                        if (itr != trainers.end())
                        {
                            dfs[p] = itr->second.train(samples, labels);
                        }
                        else if (default_trainer.is_empty() == false)
                        {
                            dfs[p] = default_trainer.train(samples, labels);
                        }
                        else
                        {
                            std::ostringstream sout;
                            sout << "In one_vs_one_trainer, no trainer registered for the (" << p.first << ", " << p.second << ") label pair.";
                            throw invalid_label(sout.str(), p.first, p.second);
                        }
                    }
                }

                return trained_function_type(dfs);
            }

        private:

            any_trainer default_trainer;

            typedef std::map<unordered_pair<label_type>, any_trainer> binary_function_table;
            binary_function_table trainers;

            bool verbose;

        };

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

    }

    #endif // DLIB_ONE_VS_ONE_TRAiNER_H__