// Copyright (C) 2007  Davis E. King (davis@dlib.net)
// License: Boost Software License   See LICENSE.txt for the full license.
#ifndef DLIB_SVm_NU_TRAINER_H__ 
#define DLIB_SVm_NU_TRAINER_H__

//#include "local/make_label_kernel_matrix.h"

#include "svm_nu_trainer_abstract.h"
#include <cmath>
#include <limits>
#include <sstream>
#include "../matrix.h"
#include "../algs.h"
#include "../serialize.h"

#include "function.h"
#include "kernel.h"
#include "../optimization/optimization_solve_qp2_using_smo.h"

namespace dlib 
{

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

    template <
        typename K 
        >
    class svm_nu_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;

        svm_nu_trainer (
        ) :
            nu(0.1),
            cache_size(200),
            eps(0.001)
        {
        }

        svm_nu_trainer (
            const kernel_type& kernel_, 
            const scalar_type& nu_
        ) :
            kernel_function(kernel_),
            nu(nu_),
            cache_size(200),
            eps(0.001)
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(0 < nu && nu <= 1,
                "\tsvm_nu_trainer::svm_nu_trainer(kernel,nu)"
                << "\n\t invalid inputs were given to this function"
                << "\n\t nu: " << nu 
                );
        }

        void set_cache_size (
            long cache_size_
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(cache_size_ > 0,
                "\tvoid svm_nu_trainer::set_cache_size(cache_size_)"
                << "\n\t invalid inputs were given to this function"
                << "\n\t cache_size: " << cache_size_ 
                );
            cache_size = cache_size_;
        }

        long get_cache_size (
        ) const
        {
            return cache_size;
        }

        void set_epsilon (
            scalar_type eps_
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(eps_ > 0,
                "\tvoid svm_nu_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;
        }

        void set_kernel (
            const kernel_type& k
        )
        {
            kernel_function = k;
        }

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

        void set_nu (
            scalar_type nu_
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(0 < nu_ && nu_ <= 1,
                "\tvoid svm_nu_trainer::set_nu(nu_)"
                << "\n\t invalid inputs were given to this function"
                << "\n\t nu: " << nu_ 
                );
            nu = nu_;
        }

        const scalar_type get_nu (
        ) const
        {
            return nu;
        }

        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
        {
            return do_train(vector_to_matrix(x), vector_to_matrix(y));
        }

        void swap (
            svm_nu_trainer& item
        )
        {
            exchange(kernel_function, item.kernel_function);
            exchange(nu,              item.nu);
            exchange(cache_size,      item.cache_size);
            exchange(eps,             item.eps);
        }

    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
        ) const
        {
            typedef typename K::scalar_type scalar_type;
            typedef typename decision_function<K>::sample_vector_type sample_vector_type;
            typedef typename decision_function<K>::scalar_vector_type scalar_vector_type;

            // make sure requires clause is not broken
            DLIB_ASSERT(is_binary_classification_problem(x,y) == true,
                "\tdecision_function svm_nu_trainer::train(x,y)"
                << "\n\t invalid inputs were given to this function"
                << "\n\t x.nr(): " << x.nr() 
                << "\n\t y.nr(): " << y.nr() 
                << "\n\t x.nc(): " << x.nc() 
                << "\n\t y.nc(): " << y.nc() 
                << "\n\t is_binary_classification_problem(x,y): " << is_binary_classification_problem(x,y)
                );


            scalar_vector_type alpha;

            solve_qp2_using_smo<scalar_vector_type> solver;

            solver(symmetric_matrix_cache<float>((diagm(y)*kernel_matrix(kernel_function,x)*diagm(y)), cache_size), 
            //solver(symmetric_matrix_cache<float>(make_label_kernel_matrix(kernel_matrix(kernel_function,x),y), cache_size), 
                   y, 
                   nu,
                   alpha,
                   eps);

            scalar_type rho, b;
            calculate_rho_and_b(y,alpha,solver.get_gradient(),rho,b);
            alpha = pointwise_multiply(alpha,y)/rho;

            // count the number of support vectors
            const long sv_count = (long)sum(alpha != 0);

            scalar_vector_type sv_alpha;
            sample_vector_type support_vectors;

            // size these column vectors so that they have an entry for each support vector
            sv_alpha.set_size(sv_count);
            support_vectors.set_size(sv_count);

            // load the support vectors and their alpha values into these new column matrices
            long idx = 0;
            for (long i = 0; i < alpha.nr(); ++i)
            {
                if (alpha(i) != 0)
                {
                    sv_alpha(idx) = alpha(i);
                    support_vectors(idx) = x(i);
                    ++idx;
                }
            }

            // now return the decision function
            return decision_function<K> (sv_alpha, b, kernel_function, support_vectors);
        }

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

        template <
            typename scalar_vector_type,
            typename scalar_vector_type2,
            typename scalar_type
            >
        void calculate_rho_and_b(
            const scalar_vector_type2& y,
            const scalar_vector_type& alpha,
            const scalar_vector_type& df,
            scalar_type& rho, 
            scalar_type& b
        ) const
        {
            using namespace std;
            long num_p_free = 0;
            long num_n_free = 0;
            scalar_type sum_p_free = 0;
            scalar_type sum_n_free = 0;

            scalar_type upper_bound_p = -numeric_limits<scalar_type>::infinity();
            scalar_type upper_bound_n = -numeric_limits<scalar_type>::infinity();
            scalar_type lower_bound_p = numeric_limits<scalar_type>::infinity();
            scalar_type lower_bound_n = numeric_limits<scalar_type>::infinity();

            for(long i = 0; i < alpha.nr(); ++i)
            {
                if(y(i) == 1)
                {
                    if(alpha(i) == 1)
                    {
                        if (df(i) > upper_bound_p)
                            upper_bound_p = df(i);
                    }
                    else if(alpha(i) == 0)
                    {
                        if (df(i) < lower_bound_p)
                            lower_bound_p = df(i);
                    }
                    else
                    {
                        ++num_p_free;
                        sum_p_free += df(i);
                    }
                }
                else
                {
                    if(alpha(i) == 1)
                    {
                        if (df(i) > upper_bound_n)
                            upper_bound_n = df(i);
                    }
                    else if(alpha(i) == 0)
                    {
                        if (df(i) < lower_bound_n)
                            lower_bound_n = df(i);
                    }
                    else
                    {
                        ++num_n_free;
                        sum_n_free += df(i);
                    }
                }
            }

            scalar_type r1,r2;
            if(num_p_free > 0)
                r1 = sum_p_free/num_p_free;
            else
                r1 = (upper_bound_p+lower_bound_p)/2;

            if(num_n_free > 0)
                r2 = sum_n_free/num_n_free;
            else
                r2 = (upper_bound_n+lower_bound_n)/2;

            rho = (r1+r2)/2;
            b = (r1-r2)/2/rho;
        }

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

        kernel_type kernel_function;
        scalar_type nu;
        long cache_size;
        scalar_type eps;
    }; // end of class svm_nu_trainer

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

    template <typename K>
    void swap (
        svm_nu_trainer<K>& a,
        svm_nu_trainer<K>& b
    ) { a.swap(b); }

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

}

#endif // DLIB_SVm_NU_TRAINER_H__

