    // Copyright (C) 2010  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_SORT_BASIS_VECTORs_ABSTRACT_Hh_
    #ifdef DLIB_SORT_BASIS_VECTORs_ABSTRACT_Hh_

    #include <vector>

    #include "../matrix.h"
    #include "../statistics.h"

    namespace dlib
    {

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

        template <
            typename kernel_type,
            typename vect1_type,
            typename vect2_type,
            typename vect3_type
            >
        const std::vector<typename kernel_type::sample_type> sort_basis_vectors (
            const kernel_type& kern,
            const vect1_type& samples,
            const vect2_type& labels,
            const vect3_type& basis,
            double eps = 0.99
        );
        /*!
            requires
                - is_binary_classification_problem(samples, labels)
                - 0 < eps <= 1
                - basis.size() > 0
                - kernel_type is a kernel function object as defined in dlib/svm/kernel_abstract.h
                  It must be capable of operating on the elements of samples and basis.
                - vect1_type == a matrix or something convertible to a matrix via mat()
                - vect2_type == a matrix or something convertible to a matrix via mat()
                - vect3_type == a matrix or something convertible to a matrix via mat()
            ensures
                - A kernel based learning method ultimately needs to select a set of basis functions
                  represented by a particular choice of kernel and a set of basis vectors.
                  sort_basis_vectors() attempts to order the elements of basis so that elements which are
                  most useful in solving the binary classification problem defined by samples and
                  labels come first.
                - In particular, this function returns a std::vector, SB, of sorted basis vectors such that:
                    - 0 < SB.size() <= basis.size()
                    - SB will contain elements from basis but they will have been sorted so that
                      the most useful elements come first (i.e. SB[0] is the most important).
                    - eps notionally controls how big SB will be.  Bigger eps corresponds to a
                      bigger basis.  You can think of it like asking for eps percent of the
                      discriminating power from the input basis.
        !*/

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

    }

    #endif // DLIB_SORT_BASIS_VECTORs_ABSTRACT_Hh_