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

#include "projection_hash_abstract.h"
#include "../rand.h"

namespace dlib
{

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

    template <
        typename vector_type
        >
    projection_hash create_random_projection_hash (
        const vector_type& v,
        const int bits,
        dlib::rand& rnd
    );
    /*!
        requires
            - 0 < bits <= 32
            - v.size() > 1
            - vector_type == a std::vector or compatible type containing dlib::matrix 
              objects, each representing a column vector of the same size.
            - for all valid i, j:
                - is_col_vector(v[i]) == true 
                - v[i].size() > 0
                - v[i].size() == v[j].size() 
                - i.e. v contains only column vectors and all the column vectors
                  have the same non-zero length
            - rand_type == a type that implements the dlib/rand/rand_kernel_abstract.h interface
        ensures
            - returns a hash function H such that:
                - H.num_hash_bins() == pow(2,bits)
                - H will be setup so that it hashes the contents of v such that each bin
                  ends up with roughly the same number of elements in it.  This is
                  accomplished by picking random hyperplanes passing though the data.  In
                  particular, each plane normal vector is filled with Gaussian random
                  numbers and we also perform basic centering to ensure the plane passes
                  though the data.
            - This function uses the supplied random number generator, rnd, to drive part
              of its processing.  Therefore, giving different random number generators
              will produce different outputs.
    !*/

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

    template <
        typename vector_type
        >
    projection_hash create_random_projection_hash (
        const vector_type& v,
        const int bits
    );
    /*!
        requires
            - 0 < bits <= 32
            - v.size() > 1
            - vector_type == a std::vector or compatible type containing dlib::matrix 
              objects, each representing a column vector of the same size.
            - for all valid i, j:
                - is_col_vector(v[i]) == true 
                - v[i].size() > 0
                - v[i].size() == v[j].size() 
                - i.e. v contains only column vectors and all the column vectors
                  have the same non-zero length
        ensures
            - returns create_random_projection_hash(v,bits,dlib::rand())
              (i.e. calls the above function with a default initialized random number generator)
    !*/

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

    template <
        typename vector_type
        >
    projection_hash create_max_margin_projection_hash (
        const vector_type& v,
        const int bits,
        const double C,
        dlib::rand& rnd
    );
    /*!
        requires
            - 0 < bits <= 32
            - v.size() > 1
            - vector_type == a std::vector or compatible type containing dlib::matrix 
              objects, each representing a column vector of the same size.
            - for all valid i, j:
                - is_col_vector(v[i]) == true 
                - v[i].size() > 0
                - v[i].size() == v[j].size() 
                - i.e. v contains only column vectors and all the column vectors
                  have the same non-zero length
            - rand_type == a type that implements the dlib/rand/rand_kernel_abstract.h interface
        ensures
            - returns a hash function H such that:
                - H.num_hash_bins() == pow(2,bits)
                - H will be setup so that it hashes the contents of v such that
                  each bin ends up with roughly the same number of elements
                  in it.  This is accomplished using a variation on the random hyperplane
                  generation technique from the paper:
                    Random Maximum Margin Hashing by Alexis Joly and Olivier Buisson
                  In particular, we use the svm_c_linear_dcd_trainer to generate planes.
                  We train it on randomly selected and randomly labeled points from v.
                  The C SVM parameter is set to the given C argument.
            - This function uses the supplied random number generator, rnd, to drive part
              of its processing.  Therefore, giving different random number generators
              will produce different outputs.
    !*/

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

    template <
        typename vector_type
        >
    projection_hash create_max_margin_projection_hash (
        const vector_type& v,
        const int bits,
        const double C = 10
    );
    /*!
        requires
            - 0 < bits <= 32
            - v.size() > 1
            - vector_type == a std::vector or compatible type containing dlib::matrix 
              objects, each representing a column vector of the same size.
            - for all valid i, j:
                - is_col_vector(v[i]) == true 
                - v[i].size() > 0
                - v[i].size() == v[j].size() 
                - i.e. v contains only column vectors and all the column vectors
                  have the same non-zero length
        ensures
            - returns create_max_margin_projection_hash(v,bits,C,dlib::rand())
              (i.e. calls the above function with a default initialized random number generator)
    !*/

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

}

#endif // DLIB_CREATE_RANDOM_PROJECTION_HAsH_ABSTRACT_Hh_


