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

    #include "structural_svm_assignment_problem_abstract.h"
    #include "../matrix.h"
    #include <vector>
    #include <iterator>
    #include "structural_svm_problem_threaded.h"

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

    namespace dlib
    {
        template <long n, typename T>
        struct column_matrix_static_resize
        {
            typedef T type;
        };

        template <long n, typename T, long NR, long NC, typename MM, typename L>
        struct column_matrix_static_resize<n, matrix<T,NR,NC,MM,L> >
        {
            typedef matrix<T,NR+n,NC,MM,L> type;
        };

        template <long n, typename T, long NC, typename MM, typename L>
        struct column_matrix_static_resize<n, matrix<T,0,NC,MM,L> >
        {
            typedef matrix<T,0,NC,MM,L> type;
        };

        template <typename T>
        struct add_one_to_static_feat_size
        {
            typedef typename column_matrix_static_resize<1,typename T::feature_vector_type>::type type;
        };

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

        template <
            typename feature_extractor
            >
        class structural_svm_assignment_problem : noncopyable,
            public structural_svm_problem_threaded<matrix<double,0,1>, typename add_one_to_static_feat_size<feature_extractor>::type >
        {
        public:
            typedef matrix<double,0,1> matrix_type;
            typedef typename add_one_to_static_feat_size<feature_extractor>::type feature_vector_type;

            typedef typename feature_extractor::lhs_element lhs_element;
            typedef typename feature_extractor::rhs_element rhs_element;

            typedef std::pair<std::vector<lhs_element>, std::vector<rhs_element> > sample_type;

            typedef std::vector<long> label_type;

            structural_svm_assignment_problem(
                const std::vector<sample_type>& samples_,
                const std::vector<label_type>& labels_,
                const feature_extractor& fe_,
                bool force_assignment_,
                unsigned long num_threads,
                const double loss_per_false_association_,
                const double loss_per_missed_association_
            ) :
                structural_svm_problem_threaded<matrix_type,feature_vector_type>(num_threads),
                samples(samples_),
                labels(labels_),
                fe(fe_),
                force_assignment(force_assignment_),
                loss_per_false_association(loss_per_false_association_),
                loss_per_missed_association(loss_per_missed_association_)
            {
                // make sure requires clause is not broken
    #ifdef ENABLE_ASSERTS
                DLIB_ASSERT(loss_per_false_association > 0 && loss_per_missed_association > 0,
                    "\t structural_svm_assignment_problem::structural_svm_assignment_problem()"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t loss_per_false_association:  " << loss_per_false_association
                    << "\n\t loss_per_missed_association: " << loss_per_missed_association
                    << "\n\t this: " << this
                );
                if (force_assignment)
                {
                    DLIB_ASSERT(is_forced_assignment_problem(samples, labels),
                                "\t structural_svm_assignment_problem::structural_svm_assignment_problem()"
                                << "\n\t invalid inputs were given to this function"
                                << "\n\t is_forced_assignment_problem(samples,labels): " << is_forced_assignment_problem(samples,labels)
                                << "\n\t is_assignment_problem(samples,labels):        " << is_assignment_problem(samples,labels)
                                << "\n\t is_learning_problem(samples,labels):          " << is_learning_problem(samples,labels)
                                << "\n\t this: " << this
                                );
                }
                else
                {
                    DLIB_ASSERT(is_assignment_problem(samples, labels),
                                "\t structural_svm_assignment_problem::structural_svm_assignment_problem()"
                                << "\n\t invalid inputs were given to this function"
                                << "\n\t is_assignment_problem(samples,labels): " << is_assignment_problem(samples,labels)
                                << "\n\t is_learning_problem(samples,labels):   " << is_learning_problem(samples,labels)
                                << "\n\t this: " << this
                                );
                }
    #endif

            }

        private:
            virtual long get_num_dimensions (
            ) const
            {
                return fe.num_features()+1; // +1 for the bias term
            }

            virtual long get_num_samples (
            ) const
            {
                return samples.size();
            }

            template <typename psi_type>
            typename enable_if<is_matrix<psi_type> >::type get_joint_feature_vector (
                const sample_type& sample,
                const label_type& label,
                psi_type& psi
            ) const
            {
                typename feature_extractor::feature_vector_type feats;
                psi.set_size(get_num_dimensions());
                psi = 0;
                for (unsigned long i = 0; i < sample.first.size(); ++i)
                {
                    if (label[i] != -1)
                    {
                        fe.get_features(sample.first[i], sample.second[label[i]], feats);
                        set_rowm(psi,range(0,feats.size()-1)) += feats;
                        psi(get_num_dimensions()-1) += 1;
                    }
                }
            }

            template <typename T>
            void append_to_sparse_vect (
                T& psi,
                const T& vect
            ) const
            {
                std::copy(vect.begin(), vect.end(), std::back_inserter(psi));
            }

            template <typename psi_type>
            typename disable_if<is_matrix<psi_type> >::type get_joint_feature_vector (
                const sample_type& sample,
                const label_type& label,
                psi_type& psi
            ) const
            {
                psi.clear();
                feature_vector_type feats;
                int num_assignments = 0;
                for (unsigned long i = 0; i < sample.first.size(); ++i)
                {
                    if (label[i] != -1)
                    {
                        fe.get_features(sample.first[i], sample.second[label[i]], feats);
                        append_to_sparse_vect(psi, feats);
                        ++num_assignments;
                    }
                }
                psi.push_back(std::make_pair(get_num_dimensions()-1,num_assignments));
            }

            virtual void get_truth_joint_feature_vector (
                long idx,
                feature_vector_type& psi
            ) const
            {
                get_joint_feature_vector(samples[idx], labels[idx], psi);
            }

            virtual void separation_oracle (
                const long idx,
                const matrix_type& current_solution,
                double& loss,
                feature_vector_type& psi
            ) const
            {
                matrix<double> cost;
                unsigned long size;
                if (force_assignment)
                {
                    unsigned long lhs_size = samples[idx].first.size();
                    unsigned long rhs_size = samples[idx].second.size();
                    size = std::max(lhs_size, rhs_size);
                }
                else
                {
                    unsigned long rhs_size = samples[idx].second.size() + samples[idx].first.size();
                    size = rhs_size;
                }
                cost.set_size(size, size);

                typename feature_extractor::feature_vector_type feats;

                // now fill out the cost assignment matrix
                for (long r = 0; r < cost.nr(); ++r)
                {
                    for (long c = 0; c < cost.nc(); ++c)
                    {
                        if (r < (long)samples[idx].first.size())
                        {
                            if (c < (long)samples[idx].second.size())
                            {
                                fe.get_features(samples[idx].first[r], samples[idx].second[c], feats);
                                const double bias = current_solution(current_solution.size()-1);
                                cost(r,c) = dot(colm(current_solution,0,current_solution.size()-1), feats) + bias;

                                // add in the loss since this corresponds to an incorrect prediction.
                                if (c != labels[idx][r])
                                {
                                    cost(r,c) += loss_per_false_association;
                                }
                            }
                            else
                            {
                                if (labels[idx][r] == -1)
                                    cost(r,c) = 0;
                                else
                                    cost(r,c) = loss_per_missed_association;
                            }

                        }
                        else
                        {
                            cost(r,c) = 0;
                        }
                    }
                }

                std::vector<long> assignment;

                if (cost.size() != 0)
                {
                    // max_cost_assignment() only works with integer matrices, so convert from
                    // double to integer.
                    const double scale = (std::numeric_limits<dlib::int64>::max()/1000)/max(abs(cost));
                    matrix<dlib::int64> int_cost = matrix_cast<dlib::int64>(round(cost*scale));
                    assignment = max_cost_assignment(int_cost);
                    assignment.resize(samples[idx].first.size());
                }

                loss = 0;
                // adjust assignment so that non-assignments have a value of -1. Also compute loss.
                for (unsigned long i = 0; i < assignment.size(); ++i)
                {
                    if (assignment[i] >= (long)samples[idx].second.size())
                        assignment[i] = -1;

                    if (assignment[i] != labels[idx][i])
                    {
                        if (assignment[i] == -1)
                            loss += loss_per_missed_association;
                        else
                            loss += loss_per_false_association;
                    }
                }

                get_joint_feature_vector(samples[idx], assignment, psi);
            }

            const std::vector<sample_type>& samples;
            const std::vector<label_type>& labels;
            const feature_extractor& fe;
            bool force_assignment;
            const double loss_per_false_association;
            const double loss_per_missed_association;
        };

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

    }

    #endif // DLIB_STRUCTURAL_SVM_ASSiGNMENT_PROBLEM_Hh_