    // Copyright (C) 2012  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_SVm_C_LINEAR_DCD_TRAINER_Hh_
    #define DLIB_SVm_C_LINEAR_DCD_TRAINER_Hh_

    #include "svm_c_linear_dcd_trainer_abstract.h"
    #include <cmath>
    #include <limits>
    #include "../matrix.h"
    #include "../algs.h"
    #include "../rand.h"

    #include "function.h"
    #include "kernel.h"

    namespace dlib
    {

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

        template <
            typename K
            >
        class svm_c_linear_dcd_trainer
        {
        public:
            typedef K kernel_type;
            typedef typename kernel_type::scalar_type scalar_type;
            typedef typename kernel_type::sample_type sample_type;
            typedef typename kernel_type::mem_manager_type mem_manager_type;
            typedef decision_function<kernel_type> trained_function_type;
            typedef typename decision_function<K>::sample_vector_type sample_vector_type;
            typedef typename decision_function<K>::scalar_vector_type scalar_vector_type;

            // You are getting a compiler error on this line because you supplied a non-linear
            // kernel to the svm_c_linear_dcd_trainer object.  You have to use one of the
            // linear kernels with this trainer.
            COMPILE_TIME_ASSERT((is_same_type<K, linear_kernel<sample_type> >::value ||
                                 is_same_type<K, sparse_linear_kernel<sample_type> >::value ));

            svm_c_linear_dcd_trainer (
            ) :
                Cpos(1),
                Cneg(1),
                eps(0.1),
                max_iterations(10000),
                verbose(false),
                have_bias(true),
                last_weight_1(false),
                do_shrinking(true)
            {
            }

            explicit svm_c_linear_dcd_trainer (
                const scalar_type& C_
            ) :
                Cpos(C_),
                Cneg(C_),
                eps(0.1),
                max_iterations(10000),
                verbose(false),
                have_bias(true),
                last_weight_1(false),
                do_shrinking(true)
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(0 < C_,
                    "\tsvm_c_trainer::svm_c_linear_dcd_trainer(kernel,C)"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t C_: " << C_
                    );
            }

            bool includes_bias (
            ) const
            {
                return have_bias;
            }

            void include_bias (
                bool should_have_bias
            )
            {
                have_bias = should_have_bias;
            }

            bool forces_last_weight_to_1 (
            ) const
            {
                return last_weight_1;
            }

            void force_last_weight_to_1 (
                bool should_last_weight_be_1
            )
            {
                last_weight_1 = should_last_weight_be_1;
            }

            bool shrinking_enabled (
            ) const { return do_shrinking; }

            void enable_shrinking (
                bool enabled
            ) { do_shrinking = enabled; }

            void be_verbose (
            )
            {
                verbose = true;
            }

            void be_quiet (
            )
            {
                verbose = false;
            }

            void set_epsilon (
                scalar_type eps_
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(eps_ > 0,
                    "\tvoid svm_c_linear_dcd_trainer::set_epsilon(eps_)"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t eps_: " << eps_
                    );
                eps = eps_;
            }

            const scalar_type get_epsilon (
            ) const
            {
                return eps;
            }

            const kernel_type& get_kernel (
            ) const
            {
                return kernel_type();
            }

            unsigned long get_max_iterations (
            ) const { return max_iterations; }

            void set_max_iterations (
                unsigned long max_iter
            )
            {
                max_iterations = max_iter;
            }

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

                Cpos = C;
                Cneg = C;
            }

            const scalar_type get_c_class1 (
            ) const
            {
                return Cpos;
            }

            const scalar_type get_c_class2 (
            ) const
            {
                return Cneg;
            }

            void set_c_class1 (
                scalar_type C
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(C > 0,
                    "\t void svm_c_linear_dcd_trainer::set_c_class1()"
                    << "\n\t C must be greater than 0"
                    << "\n\t C:    " << C
                    << "\n\t this: " << this
                    );

                Cpos = C;
            }

            void set_c_class2 (
                scalar_type C
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(C > 0,
                    "\t void svm_c_linear_dcd_trainer::set_c_class2()"
                    << "\n\t C must be greater than 0"
                    << "\n\t C:    " << C
                    << "\n\t this: " << this
                    );

                Cneg = C;
            }

            class optimizer_state
            {
                friend class svm_c_linear_dcd_trainer;

            public:
                optimizer_state() : did_init(false) {}

            private:

                template <
                    typename in_sample_vector_type
                    >
                void init(
                    const in_sample_vector_type& x,
                    bool have_bias_,
                    bool last_weight_1_
                )
                {
                    const long new_dims = max_index_plus_one(x);
                    long new_idx = 0;

                    if (did_init)
                    {
                        DLIB_CASSERT(have_bias_ == have_bias &&
                                     last_weight_1_ == last_weight_1,
                                    "\t decision_function svm_c_linear_dcd_trainer::train(x,y,state)"
                                    << "\n\t The given state object is invalid because the previous trainer was configured differently."
                                    << "\n\t have_bias_:     " << have_bias_
                                    << "\n\t have_bias:      " << have_bias
                                    << "\n\t last_weight_1_: " << last_weight_1_
                                    << "\n\t last_weight_1:  " << last_weight_1
                                     );

                        DLIB_CASSERT( new_dims >= dims,
                                    "\t decision_function svm_c_linear_dcd_trainer::train(x,y,state)"
                                    << "\n\t The given state object is invalid because the training data dimensions have shrunk."
                                    << "\n\t new_dims:  " << new_dims
                                    << "\n\t dims:      " << dims
                            );

                        DLIB_CASSERT( x.size() >= static_cast<long>(alpha.size()),
                                    "\t decision_function svm_c_linear_dcd_trainer::train(x,y,state)"
                                    << "\n\t The given state object is invalid because the training data has fewer samples than previously."
                                    << "\n\t x.size():     " << x.size()
                                    << "\n\t alpha.size(): " << alpha.size()
                            );

                        // make sure we amortize the cost of growing the alpha vector.
                        if (alpha.capacity() < static_cast<unsigned long>(x.size()))
                            alpha.reserve(x.size()*2);

                        new_idx = alpha.size();

                        // Make sure alpha has the same length as x.  So pad with extra zeros if
                        // necessary to make this happen.
                        alpha.resize(x.size(),0);

                        if (new_dims != dims)
                        {
                            // The only valid way the dimensions can be different here is if
                            // you are using a sparse vector type.  This is because we might
                            // have had training samples which just happened to not include all
                            // the features previously.  Therefore, max_index_plus_one() would
                            // have given too low of a result.  But for dense vectors it is
                            // definitely a user error if the dimensions don't match.

                            DLIB_CASSERT(is_matrix<sample_type>::value == false,
                                    "\t decision_function svm_c_linear_dcd_trainer::train(x,y,state)"
                                    << "\n\t The given state object is invalid because the training data dimensions have changed."
                                    << "\n\t new_dims:  " << new_dims
                                    << "\n\t dims:      " << dims
                                );

                            // extend w by the right number of elements
                            if (have_bias && !last_weight_1)
                            {
                                // Splice some zeros into the w vector so it will have the
                                // right length.  Here we are being careful to move the bias
                                // weight to the end of the resulting vector.
                                w = join_cols(join_cols(
                                        colm(w,0,dims),
                                        zeros_matrix<scalar_type>(new_dims-dims,1)),
                                        uniform_matrix<scalar_type>(1,1,w(dims))
                                        );
                            }
                            else
                            {
                                // Just concatenate the right number of zeros.
                                w = join_cols(w, zeros_matrix<scalar_type>(new_dims-dims,1));
                            }
                            dims = new_dims;
                        }

                    }
                    else
                    {
                        did_init = true;
                        have_bias = have_bias_;
                        last_weight_1 = last_weight_1_;
                        dims = new_dims;

                        alpha.resize(x.size());

                        index.reserve(x.size());
                        Q.reserve(x.size());

                        if (have_bias && !last_weight_1)
                            w.set_size(dims+1);
                        else
                            w.set_size(dims);

                        w = 0;
                    }

                    for (long i = new_idx; i < x.size(); ++i)
                    {
                        Q.push_back(length_squared(x(i)));

                        if (have_bias && !last_weight_1)
                        {
                            index.push_back(i);
                            Q.back() += 1;
                        }
                        else if (Q.back() != 0)
                        {
                            index.push_back(i);
                        }
                    }

                    if (last_weight_1)
                        w(dims-1) = 1;
                }

                template <typename T>
                typename enable_if<is_matrix<T>,scalar_type>::type length_squared (const T& x) const
                {
                    if (!last_weight_1)
                    {
                        return dlib::dot(x,x);
                    }
                    else
                    {
                        // skip the last dimension
                        return dlib::dot(colm(x,0,x.size()-1),
                                         colm(x,0,x.size()-1));
                    }

                }

                template <typename T>
                typename disable_if<is_matrix<T>,scalar_type>::type length_squared (const T& x) const
                {
                    if (!last_weight_1)
                    {
                        return dlib::dot(x,x);
                    }
                    else
                    {
                        scalar_type temp = 0;
                        typename T::const_iterator i;
                        for (i = x.begin(); i != x.end(); ++i)
                        {
                            // skip the last dimension
                            if (static_cast<long>(i->first) < dims-1)
                                temp += i->second*i->second;
                        }
                        return temp;
                    }
                }

                bool did_init;
                bool have_bias;
                bool last_weight_1;
                std::vector<scalar_type> alpha;
                scalar_vector_type w;
                std::vector<scalar_type> Q;
                std::vector<long> index;
                long dims;
                dlib::rand rnd;

            public:
                friend void serialize(const optimizer_state& item, std::ostream& out)
                {
                    const int version = 1;
                    dlib::serialize(version, out);
                    dlib::serialize(item.did_init, out);
                    dlib::serialize(item.have_bias, out);
                    dlib::serialize(item.last_weight_1, out);
                    dlib::serialize(item.alpha, out);
                    dlib::serialize(item.w, out);
                    dlib::serialize(item.Q, out);
                    dlib::serialize(item.index, out);
                    dlib::serialize(item.dims, out);
                    dlib::serialize(item.rnd, out);
                }

                friend void deserialize(optimizer_state& item, std::istream& in)
                {
                    int version = 0;
                    dlib::deserialize(version, in);
                    if (version != 1)
                    {
                        throw dlib::serialization_error(
                            "Error while deserializing dlib::svm_c_linear_dcd_trainer::optimizer_state, unexpected version."
                            );
                    }

                    dlib::deserialize(item.did_init, in);
                    dlib::deserialize(item.have_bias, in);
                    dlib::deserialize(item.last_weight_1, in);
                    dlib::deserialize(item.alpha, in);
                    dlib::deserialize(item.w, in);
                    dlib::deserialize(item.Q, in);
                    dlib::deserialize(item.index, in);
                    dlib::deserialize(item.dims, in);
                    dlib::deserialize(item.rnd, in);
                }

            };

            template <
                typename in_sample_vector_type,
                typename in_scalar_vector_type
                >
            const decision_function<kernel_type> train (
                const in_sample_vector_type& x,
                const in_scalar_vector_type& y
            ) const
            {
                optimizer_state state;
                return do_train(mat(x), mat(y), state);
            }

            template <
                typename in_sample_vector_type,
                typename in_scalar_vector_type
                >
            const decision_function<kernel_type> train (
                const in_sample_vector_type& x,
                const in_scalar_vector_type& y,
                optimizer_state& state
            ) const
            {
                return do_train(mat(x), mat(y), state);
            }

        private:

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

            template <
                typename in_sample_vector_type,
                typename in_scalar_vector_type
                >
            const decision_function<kernel_type> do_train (
                const in_sample_vector_type& x,
                const in_scalar_vector_type& y,
                optimizer_state& state
            ) const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(is_learning_problem(x,y) == true,
                    "\t decision_function svm_c_linear_dcd_trainer::train(x,y)"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t x.size(): " << x.size()
                    << "\n\t y.size(): " << y.size()
                    << "\n\t is_learning_problem(x,y): " << is_learning_problem(x,y)
                    );
    #ifdef ENABLE_ASSERTS
                for (long i = 0; i < x.size(); ++i)
                {
                    DLIB_ASSERT(y(i) == +1 || y(i) == -1,
                        "\t decision_function svm_c_linear_dcd_trainer::train(x,y)"
                        << "\n\t invalid inputs were given to this function"
                        << "\n\t y("<<i<<"): " << y(i)
                    );
                }
    #endif

                state.init(x,have_bias,last_weight_1);

                std::vector<scalar_type>& alpha = state.alpha;
                scalar_vector_type& w = state.w;
                std::vector<long>& index = state.index;
                const long dims = state.dims;

                unsigned long active_size = index.size();

                scalar_type PG_max_prev = std::numeric_limits<scalar_type>::infinity();
                scalar_type PG_min_prev = -std::numeric_limits<scalar_type>::infinity();

                // main loop
                for (unsigned long iter = 0; iter < max_iterations; ++iter)
                {
                    scalar_type PG_max = -std::numeric_limits<scalar_type>::infinity();
                    scalar_type PG_min = std::numeric_limits<scalar_type>::infinity();

                    // randomly shuffle the indices
                    for (unsigned long i = 0; i < active_size; ++i)
                    {
                        // pick a random index >= i
                        const long j = i + state.rnd.get_random_32bit_number()%(active_size-i);
                        std::swap(index[i], index[j]);
                    }

                    // for all the active training samples
                    for (unsigned long ii = 0; ii < active_size; ++ii)
                    {
                        const long i = index[ii];

                        const scalar_type G = y(i)*dot(w, x(i)) - 1;
                        const scalar_type C = (y(i) > 0) ? Cpos : Cneg;

                        scalar_type PG = 0;
                        if (alpha[i] == 0)
                        {
                            if (G > PG_max_prev)
                            {
                                // shrink the active set of training examples
                                --active_size;
                                std::swap(index[ii], index[active_size]);
                                --ii;
                                continue;
                            }

                            if (G < 0)
                                PG = G;
                        }
                        else if (alpha[i] == C)
                        {
                            if (G < PG_min_prev)
                            {
                                // shrink the active set of training examples
                                --active_size;
                                std::swap(index[ii], index[active_size]);
                                --ii;
                                continue;
                            }

                            if (G > 0)
                                PG = G;
                        }
                        else
                        {
                            PG = G;
                        }

                        if (PG > PG_max)
                            PG_max = PG;
                        if (PG < PG_min)
                            PG_min = PG;

                        // if PG != 0
                        if (std::abs(PG) > 1e-12)
                        {
                            const scalar_type alpha_old = alpha[i];
                            alpha[i] = std::min(std::max(alpha[i] - G/state.Q[i], (scalar_type)0.0), C);
                            const scalar_type delta = (alpha[i]-alpha_old)*y(i);
                            add_to(w, x(i), delta);
                            if (have_bias && !last_weight_1)
                                w(w.size()-1) -= delta;

                            if (last_weight_1)
                                w(dims-1) = 1;
                        }

                    }

                    if (verbose)
                    {
                        using namespace std;
                        cout << "gap:         " << PG_max - PG_min << endl;
                        cout << "active_size: " << active_size << endl;
                        cout << "iter:        " << iter << endl;
                        cout << endl;
                    }

                    if (PG_max - PG_min <= eps)
                    {
                        // stop if we are within eps tolerance and the last iteration
                        // was over all the samples
                        if (active_size == index.size())
                            break;

                        // Turn of shrinking on the next iteration.  We will stop if the
                        // tolerance is still <= eps when shrinking is off.
                        active_size = index.size();
                        PG_max_prev = std::numeric_limits<scalar_type>::infinity();
                        PG_min_prev = -std::numeric_limits<scalar_type>::infinity();
                    }
                    else if (do_shrinking)
                    {
                        PG_max_prev = PG_max;
                        PG_min_prev = PG_min;
                        if (PG_max_prev <= 0)
                            PG_max_prev = std::numeric_limits<scalar_type>::infinity();
                        if (PG_min_prev >= 0)
                            PG_min_prev = -std::numeric_limits<scalar_type>::infinity();
                    }

                } // end of main optimization loop

                // put the solution into a decision function and then return it
                decision_function<kernel_type> df;
                if (have_bias && !last_weight_1)
                    df.b = w(w.size()-1);
                else
                    df.b = 0;

                df.basis_vectors.set_size(1);
                // Copy the plane normal into the output basis vector.  The output vector might
                // be a sparse vector container so we need to use this special kind of copy to
                // handle that case.
                assign(df.basis_vectors(0), colm(w, 0, dims));
                df.alpha.set_size(1);
                df.alpha(0) = 1;

                return df;
            }

            scalar_type dot (
                const scalar_vector_type& w,
                const sample_type& sample
            ) const
            {
                if (have_bias && !last_weight_1)
                {
                    const long w_size_m1 = w.size()-1;
                    return dlib::dot(colm(w,0,w_size_m1), sample) - w(w_size_m1);
                }
                else
                {
                    return dlib::dot(w, sample);
                }
            }

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

            scalar_type Cpos;
            scalar_type Cneg;
            scalar_type eps;
            unsigned long max_iterations;
            bool verbose;
            bool have_bias; // having a bias means we pretend all x vectors have an extra element which is always -1.
            bool last_weight_1;
            bool do_shrinking;

        }; // end of class svm_c_linear_dcd_trainer

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

    }

    #endif // DLIB_SVm_C_LINEAR_DCD_TRAINER_Hh_