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

    #include "structural_svm_problem_abstract.h"
    #include "../algs.h"
    #include <vector>
    #include "../optimization/optimization_oca.h"
    #include "../matrix.h"
    #include "sparse_vector.h"
    #include <iostream>

    namespace dlib
    {

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

        template <
            typename structural_svm_problem
            >
        class cache_element_structural_svm
        {
        public:

            cache_element_structural_svm (
            ) : prob(0), sample_idx(0), last_true_risk_computed(std::numeric_limits<double>::infinity()) {}

            typedef typename structural_svm_problem::scalar_type scalar_type;
            typedef typename structural_svm_problem::matrix_type matrix_type;
            typedef typename structural_svm_problem::feature_vector_type feature_vector_type;

            void init (
                const structural_svm_problem* prob_,
                const long idx
            )
            /*!
                ensures
                    - This object will be a cache for the idx-th sample in the given
                      structural_svm_problem.
            !*/
            {
                prob = prob_;
                sample_idx = idx;

                loss.clear();
                psi.clear();
                lru_count.clear();

                if (prob->get_max_cache_size() != 0)
                {
                    prob->get_truth_joint_feature_vector(idx, true_psi);
                    compact_sparse_vector(true_psi);
                }
            }

            void get_truth_joint_feature_vector_cached (
                feature_vector_type& psi
            ) const
            {
                if (prob->get_max_cache_size() != 0)
                    psi = true_psi;
                else
                    prob->get_truth_joint_feature_vector(sample_idx, psi);
            }

            void separation_oracle_cached (
                const bool skip_cache,
                const scalar_type& saved_current_risk_gap,
                const matrix_type& current_solution,
                scalar_type& out_loss,
                feature_vector_type& out_psi
            ) const
            {
                const bool cache_enabled = prob->get_max_cache_size() != 0;

                // Don't waste time computing this if the cache isn't going to be used.
                const scalar_type dot_true_psi = cache_enabled ? dot(true_psi, current_solution) : 0;

                if (!skip_cache && cache_enabled)
                {
                    scalar_type best_risk = -std::numeric_limits<scalar_type>::infinity();
                    unsigned long best_idx = 0;

                    // figure out which element in the cache is the best (i.e. has the biggest risk)
                    long max_lru_count = 0;
                    for (unsigned long i = 0; i < loss.size(); ++i)
                    {
                        const scalar_type risk = loss[i] + dot(psi[i], current_solution) - dot_true_psi;
                        if (risk > best_risk)
                        {
                            best_risk = risk;
                            out_loss = loss[i];
                            best_idx = i;
                        }
                        if (lru_count[i] > max_lru_count)
                            max_lru_count = lru_count[i];
                    }

                    // Check if the best psi vector in the cache is still good enough to use as
                    // a proxy for the true separation oracle.  If the risk value has dropped
                    // by enough to get into the stopping condition then the best psi isn't
                    // good enough.
                    if (best_risk + saved_current_risk_gap-prob->get_epsilon() > last_true_risk_computed)
                    {
                        out_psi = psi[best_idx];
                        lru_count[best_idx] = max_lru_count + 1;
                        return;
                    }
                }

                prob->separation_oracle(sample_idx, current_solution, out_loss, out_psi);

                if (!cache_enabled)
                    return;

                compact_sparse_vector(out_psi);

                last_true_risk_computed = out_loss + dot(out_psi, current_solution) - dot_true_psi;

                // if the cache is full
                if (loss.size() >= prob->get_max_cache_size())
                {
                    // find least recently used cache entry for idx-th sample
                    const long i       = index_of_min(mat(lru_count));

                    // save our new data in the cache
                    loss[i] = out_loss;
                    psi[i]  = out_psi;

                    const long max_use = max(mat(lru_count));
                    // Make sure this new cache entry has the best lru count since we have used
                    // it most recently.
                    lru_count[i] = max_use + 1;
                }
                else
                {
                    loss.push_back(out_loss);
                    psi.push_back(out_psi);
                    long max_use = 1;
                    if (lru_count.size() != 0)
                        max_use = max(mat(lru_count)) + 1;
                    lru_count.push_back(max_use);
                }
            }

            // Do nothing if T isn't actually a sparse vector
            template <typename T> void compact_sparse_vector( T& ) const { }

            template <
                typename T,
                typename U,
                typename alloc
                >
            void compact_sparse_vector (
                std::vector<std::pair<T,U>,alloc>& vect
            ) const
            {
                // If the sparse vector has more entires than dimensions then it must have some
                // duplicate elements.  So compact them using make_sparse_vector_inplace().
                if (vect.size() > (unsigned long)prob->get_num_dimensions())
                {
                    make_sparse_vector_inplace(vect);
                    // make sure the vector doesn't use more RAM than is necessary
                    std::vector<std::pair<T,U>,alloc>(vect).swap(vect);
                }
            }

            const structural_svm_problem* prob;

            long sample_idx;

            mutable feature_vector_type true_psi;
            mutable std::vector<scalar_type> loss;
            mutable std::vector<feature_vector_type> psi;
            mutable std::vector<long> lru_count;
            mutable double last_true_risk_computed;
        };

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

        template <
            typename matrix_type_,
            typename feature_vector_type_ = matrix_type_
            >
        class structural_svm_problem : public oca_problem<matrix_type_>
        {
        public:
            /*!
                CONVENTION
                    - C == get_c()
                    - eps == get_epsilon()
                    - if (skip_cache) then
                        - we won't use the oracle cache when we need to evaluate the separation
                          oracle. Instead, we will directly call the user supplied separation_oracle().

                    - get_max_cache_size() == max_cache_size

                    - if (cache.size() != 0) then
                        - cache.size() == get_num_samples()
                        - for all i: cache[i] == the cached results of calls to separation_oracle()
                          for the i-th sample.
            !*/

            typedef matrix_type_ matrix_type;
            typedef typename matrix_type::type scalar_type;
            typedef feature_vector_type_ feature_vector_type;

            structural_svm_problem (
            ) :
                saved_current_risk_gap(0),
                eps(0.001),
                verbose(false),
                skip_cache(true),
                max_cache_size(5),
                C(1)
            {}

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

                eps = eps_;
            }

            const scalar_type 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;
            }

            scalar_type get_c (
            ) const { return C; }

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

                C = C_;
            }

            virtual long get_num_dimensions (
            ) const = 0;

            virtual long get_num_samples (
            ) const = 0;

            virtual void get_truth_joint_feature_vector (
                long idx,
                feature_vector_type& psi
            ) const = 0;

            virtual void separation_oracle (
                const long idx,
                const matrix_type& current_solution,
                scalar_type& loss,
                feature_vector_type& psi
            ) const = 0;

        private:

            virtual bool risk_has_lower_bound (
                scalar_type& lower_bound
            ) const
            {
                lower_bound = 0;
                return true;
            }

            virtual bool optimization_status (
                scalar_type current_objective_value,
                scalar_type current_error_gap,
                scalar_type current_risk_value,
                scalar_type current_risk_gap,
                unsigned long num_cutting_planes,
                unsigned long num_iterations
            ) const
            {
                if (verbose)
                {
                    using namespace std;
                    cout << "objective:     " << current_objective_value << endl;
                    cout << "objective gap: " << current_error_gap << endl;
                    cout << "risk:          " << current_risk_value << endl;
                    cout << "risk gap:      " << current_risk_gap << endl;
                    cout << "num planes:    " << num_cutting_planes << endl;
                    cout << "iter:          " << num_iterations << endl;
                    cout << endl;
                }

                saved_current_risk_gap = std::max<scalar_type>(current_risk_value - current_risk_gap, 0);
                saved_current_risk_gap = current_risk_gap;

                bool should_stop = false;

                if (current_risk_gap < eps)
                    should_stop = true;

                if (should_stop && !skip_cache && max_cache_size != 0)
                {
                    // Instead of stopping we shouldn't use the cache on the next iteration.  This way
                    // we can be sure to have the best solution rather than assuming the cache is up-to-date
                    // enough.
                    should_stop = false;
                    skip_cache = true;
                }
                else
                {
                    skip_cache = false;
                }

                return should_stop;
            }

            virtual void get_risk (
                matrix_type& w,
                scalar_type& risk,
                matrix_type& subgradient
            ) const
            {
                feature_vector_type ftemp;
                const unsigned long num = get_num_samples();

                // initialize the cache and compute psi_true.
                if (cache.size() == 0)
                {
                    cache.resize(get_num_samples());
                    for (unsigned long i = 0; i < cache.size(); ++i)
                        cache[i].init(this,i);

                    psi_true.set_size(w.size(),1);
                    psi_true = 0;

                    for (unsigned long i = 0; i < num; ++i)
                    {
                        cache[i].get_truth_joint_feature_vector_cached(ftemp);

                        subtract_from(psi_true, ftemp);
                    }
                }

                subgradient = psi_true;
                scalar_type total_loss = 0;
                call_separation_oracle_on_all_samples(w,subgradient,total_loss);

                subgradient /= num;
                total_loss /= num;
                risk = total_loss + dot(subgradient,w);
            }

            virtual void call_separation_oracle_on_all_samples (
                const matrix_type& w,
                matrix_type& subgradient,
                scalar_type& total_loss
            ) const
            {
                feature_vector_type ftemp;
                const unsigned long num = get_num_samples();
                for (unsigned long i = 0; i < num; ++i)
                {
                    scalar_type loss;
                    separation_oracle_cached(i, w, loss, ftemp);
                    total_loss += loss;
                    add_to(subgradient, ftemp);
                }
            }

        protected:
            void separation_oracle_cached (
                const long idx,
                const matrix_type& current_solution,
                scalar_type& loss,
                feature_vector_type& psi
            ) const
            {
                cache[idx].separation_oracle_cached(skip_cache,
                                                    saved_current_risk_gap,
                                                    current_solution,
                                                    loss,
                                                    psi);
            }
        private:

            mutable scalar_type saved_current_risk_gap;
            mutable matrix_type psi_true;
            scalar_type eps;
            mutable bool verbose;

            mutable std::vector<cache_element_structural_svm<structural_svm_problem> > cache;
            mutable bool skip_cache;
            unsigned long max_cache_size;

            scalar_type C;
        };

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

    }

    #endif // DLIB_STRUCTURAL_SVM_PRObLEM_H__