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

    #include "matrix_abstract.h"

    namespace dlib
    {

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

        const matrix_exp conv (
            const matrix_exp& m1,
            const matrix_exp& m2
        );
        /*!
            requires
                - m1 and m2 both contain elements of the same type
            ensures
                - returns a matrix R such that:
                    - R is the convolution of m1 with m2.  In particular, this function is
                      equivalent to performing the following in matlab: R = conv2(m1,m2).
                    - R::type == the same type that was in m1 and m2.
                    - R.nr() == m1.nr()+m2.nr()-1
                    - R.nc() == m1.nc()+m2.nc()-1
        !*/

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

        const matrix_exp xcorr (
            const matrix_exp& m1,
            const matrix_exp& m2
        );
        /*!
            requires
                - m1 and m2 both contain elements of the same type
            ensures
                - returns a matrix R such that:
                    - R is the cross-correlation of m1 with m2.  In particular, this
                      function returns conv(m1,flip(m2)) if the matrices contain real
                      elements and conv(m1,flip(conj(m2))) if they are complex.
                    - R::type == the same type that was in m1 and m2.
                    - R.nr() == m1.nr()+m2.nr()-1
                    - R.nc() == m1.nc()+m2.nc()-1
        !*/

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

        const matrix_exp conv_same (
            const matrix_exp& m1,
            const matrix_exp& m2
        );
        /*!
            requires
                - m1 and m2 both contain elements of the same type
            ensures
                - returns a matrix R such that:
                    - R is the convolution of m1 with m2.  In particular, this function is
                      equivalent to performing the following in matlab: R = conv2(m1,m2,'same').
                      In particular, this means the result will have the same dimensions as m1 and will
                      contain the central part of the full convolution.  Therefore, conv_same(m1,m2) is
                      equivalent to subm(conv(m1,m2), m2.nr()/2, m2.nc()/2, m1.nr(), m1.nc()).
                    - R::type == the same type that was in m1 and m2.
                    - R.nr() == m1.nr()
                    - R.nc() == m1.nc()
        !*/

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

        const matrix_exp xcorr_same (
            const matrix_exp& m1,
            const matrix_exp& m2
        );
        /*!
            requires
                - m1 and m2 both contain elements of the same type
            ensures
                - returns a matrix R such that:
                    - R is the cross-correlation of m1 with m2.  In particular, this
                      function returns conv_same(m1,flip(m2)) if the matrices contain real
                      elements and conv_same(m1,flip(conj(m2))) if they are complex.
                    - R::type == the same type that was in m1 and m2.
                    - R.nr() == m1.nr()
                    - R.nc() == m1.nc()
        !*/

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

        const matrix_exp conv_valid (
            const matrix_exp& m1,
            const matrix_exp& m2
        );
        /*!
            requires
                - m1 and m2 both contain elements of the same type
            ensures
                - returns a matrix R such that:
                    - R is the convolution of m1 with m2.  In particular, this function is
                      equivalent to performing the following in matlab: R = conv2(m1,m2,'valid').
                      In particular, this means only elements of the convolution which don't require
                      zero padding are included in the result.
                    - R::type == the same type that was in m1 and m2.
                    - if (m1 has larger dimensions than m2) then
                        - R.nr() == m1.nr()-m2.nr()+1
                        - R.nc() == m1.nc()-m2.nc()+1
                    - else
                        - R.nr() == 0
                        - R.nc() == 0
        !*/

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

        const matrix_exp xcorr_valid (
            const matrix_exp& m1,
            const matrix_exp& m2
        );
        /*!
            requires
                - m1 and m2 both contain elements of the same type
            ensures
                - returns a matrix R such that:
                    - R is the cross-correlation of m1 with m2.  In particular, this
                      function returns conv_valid(m1,flip(m2)) if the matrices contain real
                      elements and conv_valid(m1,flip(conj(m2))) if they are complex.
                    - R::type == the same type that was in m1 and m2.
                    - if (m1 has larger dimensions than m2) then
                        - R.nr() == m1.nr()-m2.nr()+1
                        - R.nc() == m1.nc()-m2.nc()+1
                    - else
                        - R.nr() == 0
                        - R.nc() == 0
        !*/

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

    }

    #endif // DLIB_MATRIx_CONV_ABSTRACT_H__