    // Copyright (C) 2014  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_STRUCTURAL_TRACK_ASSOCIATION_TRAnER_H__
    #define DLIB_STRUCTURAL_TRACK_ASSOCIATION_TRAnER_H__

    #include "structural_track_association_trainer_abstract.h"
    #include "../algs.h"
    #include "svm.h"
    #include <utility>
    #include "track_association_function.h"
    #include "structural_assignment_trainer.h"
    #include <map>

    namespace dlib
    {

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

        namespace impl
        {
            template <
                typename detection_type,
                typename label_type
                >
            std::vector<detection_type> get_unlabeled_dets (
                const std::vector<labeled_detection<detection_type,label_type> >& dets
            )
            {
                std::vector<detection_type> temp;
                temp.reserve(dets.size());
                for (unsigned long i = 0; i < dets.size(); ++i)
                    temp.push_back(dets[i].det);
                return temp;
            }

        }

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

        class structural_track_association_trainer
        {
        public:

            structural_track_association_trainer (
            )
            {
                set_defaults();
            }

            void set_num_threads (
                unsigned long num
            )
            {
                num_threads = num;
            }

            unsigned long get_num_threads (
            ) const
            {
                return num_threads;
            }

            void set_epsilon (
                double eps_
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(eps_ > 0,
                    "\t void structural_track_association_trainer::set_epsilon()"
                    << "\n\t eps_ must be greater than 0"
                    << "\n\t eps_: " << eps_
                    << "\n\t this: " << this
                    );

                eps = eps_;
            }

            double get_epsilon (
            ) const { return eps; }

            void set_max_cache_size (
                unsigned long max_size
            )
            {
                max_cache_size = max_size;
            }

            unsigned long get_max_cache_size (
            ) const
            {
                return max_cache_size;
            }

            void be_verbose (
            )
            {
                verbose = true;
            }

            void be_quiet (
            )
            {
                verbose = false;
            }

            void set_oca (
                const oca& item
            )
            {
                solver = item;
            }

            const oca get_oca (
            ) const
            {
                return solver;
            }

            void set_c (
                double C_
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(C_ > 0,
                    "\t void structural_track_association_trainer::set_c()"
                    << "\n\t C_ must be greater than 0"
                    << "\n\t C_:    " << C_
                    << "\n\t this: " << this
                    );

                C = C_;
            }

            double get_c (
            ) const
            {
                return C;
            }

            bool learns_nonnegative_weights (
            ) const { return learn_nonnegative_weights; }

            void set_learns_nonnegative_weights (
                bool value
            )
            {
                learn_nonnegative_weights = value;
            }

            template <
                typename detection_type,
                typename label_type
                >
            const track_association_function<detection_type> train (
                const std::vector<std::vector<std::vector<labeled_detection<detection_type,label_type> > > >& samples
            ) const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(is_track_association_problem(samples),
                            "\t track_association_function structural_track_association_trainer::train()"
                            << "\n\t invalid inputs were given to this function"
                            << "\n\t is_track_association_problem(samples): " << is_track_association_problem(samples)
                );

                typedef typename detection_type::track_type track_type;

                const unsigned long num_dims = find_num_dims(samples);

                feature_extractor_track_association<detection_type> fe(num_dims, learn_nonnegative_weights?num_dims:0);
                structural_assignment_trainer<feature_extractor_track_association<detection_type> > trainer(fe);

                if (verbose)
                    trainer.be_verbose();

                trainer.set_c(C);
                trainer.set_epsilon(eps);
                trainer.set_max_cache_size(max_cache_size);
                trainer.set_num_threads(num_threads);
                trainer.set_oca(solver);

                std::vector<std::pair<std::vector<detection_type>, std::vector<track_type> > > assignment_samples;
                std::vector<std::vector<long> > labels;
                for (unsigned long i = 0; i < samples.size(); ++i)
                    convert_dets_to_association_sets(samples[i], assignment_samples, labels);

                return track_association_function<detection_type>(trainer.train(assignment_samples, labels));
            }

            template <
                typename detection_type,
                typename label_type
                >
            const track_association_function<detection_type> train (
                const std::vector<std::vector<labeled_detection<detection_type,label_type> > >& sample
            ) const
            {
                std::vector<std::vector<std::vector<labeled_detection<detection_type,label_type> > > > samples;
                samples.push_back(sample);
                return train(samples);
            }

        private:

            template <
                typename detection_type,
                typename label_type
                >
            static unsigned long find_num_dims (
                const std::vector<std::vector<std::vector<labeled_detection<detection_type,label_type> > > >& samples
            )
            {
                typedef typename detection_type::track_type track_type;
                // find a detection_type object so we can call get_similarity_features() and
                // find out how big the feature vectors are.

                // for all detection histories
                for (unsigned long i = 0; i < samples.size(); ++i)
                {
                    // for all time instances in the detection history
                    for (unsigned j = 0; j < samples[i].size(); ++j)
                    {
                        if (samples[i][j].size() > 0)
                        {
                            track_type new_track;
                            new_track.update_track(samples[i][j][0].det);
                            typename track_type::feature_vector_type feats;
                            new_track.get_similarity_features(samples[i][j][0].det, feats);
                            return feats.size();
                        }
                    }
                }

                DLIB_CASSERT(false,
                    "No detection objects were given in the call to dlib::structural_track_association_trainer::train()");
            }

            template <
                typename detections_at_single_time_step,
                typename detection_type,
                typename track_type
                >
            static void convert_dets_to_association_sets (
                const std::vector<detections_at_single_time_step>& det_history,
                std::vector<std::pair<std::vector<detection_type>, std::vector<track_type> > >& data,
                std::vector<std::vector<long> >& labels
            )
            {
                if (det_history.size() < 1)
                    return;

                typedef typename detections_at_single_time_step::value_type::label_type label_type;
                std::vector<track_type> tracks;
                // track_labels maps from detection labels to the index in tracks.  So track
                // with detection label X is at tracks[track_labels[X]].
                std::map<label_type,unsigned long> track_labels;
                add_dets_to_tracks(tracks, track_labels, det_history[0]);

                using namespace impl;
                for (unsigned long i = 1; i < det_history.size(); ++i)
                {
                    data.push_back(std::make_pair(get_unlabeled_dets(det_history[i]), tracks));
                    labels.push_back(get_association_labels(det_history[i], track_labels));
                    add_dets_to_tracks(tracks, track_labels, det_history[i]);
                }
            }

            template <
                typename labeled_detection,
                typename label_type
                >
            static std::vector<long> get_association_labels(
                const std::vector<labeled_detection>& dets,
                const std::map<label_type,unsigned long>& track_labels
            )
            {
                std::vector<long> assoc(dets.size(),-1);
                // find out which detections associate to what tracks
                for (unsigned long i = 0; i < dets.size(); ++i)
                {
                    typename std::map<label_type,unsigned long>::const_iterator j;
                    j = track_labels.find(dets[i].label);
                    // If this detection matches one of the tracks then record which track it
                    // matched with.
                    if (j != track_labels.end())
                        assoc[i] = j->second;
                }
                return assoc;
            }

            template <
                typename track_type,
                typename label_type,
                typename labeled_detection
                >
            static void add_dets_to_tracks (
                std::vector<track_type>& tracks,
                std::map<label_type,unsigned long>& track_labels,
                const std::vector<labeled_detection>& dets
            )
            {
                std::vector<bool> updated_track(tracks.size(), false);

                // first assign the dets to the tracks
                for (unsigned long i = 0; i < dets.size(); ++i)
                {
                    const label_type& label = dets[i].label;
                    if (track_labels.count(label))
                    {
                        const unsigned long track_idx = track_labels[label];
                        tracks[track_idx].update_track(dets[i].det);
                        updated_track[track_idx] = true;
                    }
                    else
                    {
                        // this detection creates a new track
                        track_type new_track;
                        new_track.update_track(dets[i].det);
                        tracks.push_back(new_track);
                        track_labels[label] = tracks.size()-1;
                    }

                }

                // Now propagate all the tracks that didn't get any detections.
                for (unsigned long i = 0; i < updated_track.size(); ++i)
                {
                    if (!updated_track[i])
                        tracks[i].propagate_track();
                }
            }

            double C;
            oca solver;
            double eps;
            bool verbose;
            unsigned long num_threads;
            unsigned long max_cache_size;
            bool learn_nonnegative_weights;

            void set_defaults ()
            {
                C = 100;
                verbose = false;
                eps = 0.001;
                num_threads = 2;
                max_cache_size = 5;
                learn_nonnegative_weights = false;
            }
        };

    }

    #endif // DLIB_STRUCTURAL_TRACK_ASSOCIATION_TRAnER_H__