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

    #include "rls_filter_abstract.h"
    #include "../svm/rls.h"
    #include <vector>
    #include "../matrix.h"
    #include "../sliding_buffer.h"

    namespace dlib
    {

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

        class rls_filter
        {
            /*!
                CONVENTION
                    - data.size() == the number of variables in a measurement
                    - data[i].size() == data[j].size() for all i and j.
                    - data[i].size() == get_window_size()
                    - data[i][0] == most recent measurement of i-th variable given to update.
                    - data[i].back() == oldest measurement of i-th variable given to update
                      (or zero if we haven't seen this much data yet).

                    - if (count <= 2) then
                        - count == number of times update(z) has been called
            !*/
        public:

            rls_filter()
            {
                size = 5;
                count = 0;
                filter = rls(0.8, 100);
            }

            explicit rls_filter (
                unsigned long size_,
                double forget_factor = 0.8,
                double C = 100
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(0 < forget_factor && forget_factor <= 1 &&
                            0 < C && size_ >= 2,
                    "\t rls_filter::rls_filter()"
                    << "\n\t invalid arguments were given to this function"
                    << "\n\t forget_factor: " << forget_factor
                    << "\n\t C:     " << C
                    << "\n\t size_: " << size_
                    << "\n\t this: " << this
                    );

                size = size_;
                count = 0;
                filter = rls(forget_factor, C);
            }

            double get_c(
            ) const
            {
                return filter.get_c();
            }

            double get_forget_factor(
            ) const
            {
                return filter.get_forget_factor();
            }

            unsigned long get_window_size (
            ) const
            {
                return size;
            }

            void update (
            )
            {
                if (filter.get_w().size() == 0)
                    return;

                for (unsigned long i = 0; i < data.size(); ++i)
                {
                    // Put old predicted value into the circular buffer as if it was
                    // the measurement we just observed.  But don't update the rls filter.
                    data[i].push_front(next(i));
                }

                // predict next state
                for (long i = 0; i < next.size(); ++i)
                    next(i) = filter(mat(data[i]));
            }

            template <typename EXP>
            void update (
                const matrix_exp<EXP>& z
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(is_col_vector(z) == true &&
                            z.size() != 0 &&
                            (get_predicted_next_state().size()==0 || z.size()==get_predicted_next_state().size()),
                    "\t void rls_filter::update(z)"
                    << "\n\t invalid arguments were given to this function"
                    << "\n\t is_col_vector(z): " << is_col_vector(z)
                    << "\n\t z.size():         " << z.size()
                    << "\n\t get_predicted_next_state().size(): " << get_predicted_next_state().size()
                    << "\n\t this: " << this
                    );

                // initialize data if necessary
                if (data.size() == 0)
                {
                    data.resize(z.size());
                    for (long i = 0; i < z.size(); ++i)
                        data[i].assign(size, 0);
                }

                for (unsigned long i = 0; i < data.size(); ++i)
                {
                    // Once there is some stuff in the circular buffer, start
                    // showing it to the rls filter so it can do its thing.
                    if (count >= 2)
                    {
                        filter.train(mat(data[i]), z(i));
                    }

                    // keep track of the measurements in our circular buffer
                    data[i].push_front(z(i));
                }

                // Don't bother with the filter until we have seen two samples
                if (count >= 2)
                {
                    // predict next state
                    for (long i = 0; i < z.size(); ++i)
                        next(i) = filter(mat(data[i]));
                }
                else
                {
                    // Use current measurement as the next state prediction
                    // since we don't know any better at this point.
                    ++count;
                    next = matrix_cast<double>(z);
                }
            }

            const matrix<double,0,1>& get_predicted_next_state(
            ) const
            {
                return next;
            }

            friend inline void serialize(const rls_filter& item, std::ostream& out)
            {
                int version = 1;
                serialize(version, out);
                serialize(item.count, out);
                serialize(item.size, out);
                serialize(item.filter, out);
                serialize(item.next, out);
                serialize(item.data, out);
            }

            friend inline void deserialize(rls_filter& item, std::istream& in)
            {
                int version = 0;
                deserialize(version, in);
                if (version != 1)
                    throw dlib::serialization_error("Unknown version number found while deserializing rls_filter object.");

                deserialize(item.count, in);
                deserialize(item.size, in);
                deserialize(item.filter, in);
                deserialize(item.next, in);
                deserialize(item.data, in);
            }

        private:

            unsigned long count;
            unsigned long size;
            rls filter;
            matrix<double,0,1> next;
            std::vector<circular_buffer<double> > data;
        };

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

    }

    #endif // DLIB_RLS_FiLTER_Hh_